{"paper_id":"b3dad9c8-8801-416e-b2d8-bf771a20ee72","body_text":"Targeting RhoA activity rejuvenates aged hematopoietic stem cells | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Targeting RhoA activity rejuvenates aged hematopoietic stem cells Maria Carolina Florian, Eva Mejia-Ramirez, Pablo Iañez Picazo, and 14 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6333603/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 24 Nov, 2025 Read the published version in Nature Aging → Version 1 posted You are reading this latest preprint version Abstract Biomechanical alterations contribute to the decreased regenerative capacity of hematopoietic stem cells (HSCs) upon aging. RhoA is a key regulator of mechano-signaling but its role for mechanotransduction in stem cell aging has not been investigated yet. Here, we show that murine HSCs respond to increased nuclear envelope (NE) tension by inducing NE translocation of P-cPLA2, which cell intrinsically activates RhoA. Interestingly, aged HSCs experience physiologically higher intrinsic NE tension, associated with increased NE P-cPLA2 and RhoA activity. Reducing RhoA activity lowers NE tension in aged HSCs. Feature image analysis of HSC nuclei reveals that chromatin remodeling is associated to RhoA inhibition, which includes the restoration of youthful levels of the heterochromatin marker H3K9me2 and a decrease in chromatin accessibility and transcription at retrotransposons. Eventually, we demonstrate that RhoA inhibition upregulates Klf4 expression and transcriptional activity, improving aged HSCs regenerative capacity and lympho/myeloid skewing in vivo . Overall, our data support that an intrinsic mechano-signaling axis dependent on RhoA can be pharmacologically targeted to rejuvenate stem cell function upon aging. Biological sciences/Stem cells/Ageing Biological sciences/Stem cells/Adult stem cells Biological sciences/Stem cells/Epigenetic memory Aging hematopoietic stem cells RhoA regeneration hematopoiesis nuclear envelope cell confinement chromatin accessibility retrotransposones LINE-1 LTR Klf4 Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 INTRODUCTION Aging is characterized by the decline in tissue function and is the primary risk factor for major diseases. In particular, aged HSC functional decline critically impacts not only on their ability to regenerate the hematopoietic system and to support lymphoid cell production over time 1 – 4 but also directly contributes to major aging-related diseases 5 , 6 . Aging is a complex, multifaceted process that is accompanied by biomechanical changes affecting tissues and organs and also cells and subcellular organelles 7 – 12 . Among others, these biomechanical changes include alterations in NE tension and aging correlates with progressive changes in the nucleus mechanical integrity and impaired mechanotransduction 11 , 12 . However, how to possibly target changes in nuclear mechano-signaling to explain and prevent aging of somatic stem cells is still largely under-investigated. In addition, epigenetic alterations are considered one of the primary hallmarks of aging 13 and despite the large amount of data demonstrating the occurrence of an epigenetic drift upon stem cell aging and disease, there is a lack of knowledge on molecular mechanisms to explain this epigenetic drift and whether it possibly associates to mechanical alterations of the nuclear and chromatin architecture. Mechanical forces trigger multiple signaling pathways that converge in the activation of RhoA 14 , which is a small RhoGTPase that can cycle between an active (RhoA-GTP) and an inactive (RhoA-GDP) status. RhoA is a key regulator of mechanotransduction 15 regardless of whether the activating mechanical stimulus is cell extrinsic, as occurs in cells responding to alterations of substrate stiffness 16 , or cell intrinsic, like for example when the cell nucleus acts as a mechanosensor of genomic changes 17 – 19 . So far, in HSCs RhoA has been shown to be important for cytokinesis 20 . In vivo , knocking out RhoA in bone marrow cells induces a dramatic phenotype, characterized by a multilineage hematopoietic failure due to programmed necrosis of hematopoietic progenitors, while HSCs retain long-term engraftment potential but fail to produce hematopoietic progenitors and lineage-defined blood cells 20 . Here, we investigate the role of RhoA in nuclear mechanotransduction in HSCs and its involvement in preserving nuclear architecture and stem cell function upon aging. Our data reveal that RhoA activity increases upon increased NE tension in HSCs, which is intrinsically altered upon aging and can be targeted to improve in vivo function of aged blood stem cells. RESULTS RhoA is necessary for HSCs to survive under increased NE tension. The nuclear membrane is able to stretch under various pathophysiological conditions involving nuclear lamina weakening, which include aging and laminopathies 21 , and stretching of the nucleus is thought to be a fundamental mechanism engaging nuclear mechnotransduction 21 , 22 . Therefore, we wondered if nuclear stretching might be engaging RhoA mechano-signaling pathways in HSCs. HSCs are notoriously small sized non adherent cells with an average cell diameter of 10 µm and a high nuclear/cytosol ratio. To induce nuclear stretching in HSCs, we took advantage of a previously established cell confinement device 18 , 23 ( Figure S1 A ). We isolated HSCs (sorted as Cd11b − , Ter119 − , Cd8 − , Cd5 − , B220 − , Gr1 − , c-kit + , Sca1 + , Flt3 − , CD34 − ) from the bone marrow (BM) of young adult (8 to 20 weeks old) C57Bl6 mice and we seeded them on fibronectin-coated coverslips followed by confinement with non-adhesive coverslips functionalized with pillars of 8, 5 and 3 µm height for 2 hours (Fig. 1 A and Figure S1 A-B ). Upon confinement, HSCs were fixed and stained with DAPI (4′,6-diamidino-2-phenylindole) to image and measure the nuclear stretching, and with an anti-RhoA-GTP antibody for quantifying the activation levels of RhoA. By 3D single-cell confocal microscopy, we observed that cell confinement produced a reduction of the nuclear height proportional to the applied level of confinement (Fig. 1 B). The maximum nuclear diameter increased significantly already under 8 µm confinement, and it reached its maximum at almost 10 µm when HSCs were confined at 5 µm, showing a significant inverse correlation with nuclear height (Fig. 1 B). We observed the formation of nucleoplasm containing blebs, which was progressive with confinement and particularly prominent when HSCs were confined at 5µm (Fig. 1 C). This can be quantified looking at the Excess of Perimeter (EOP) of the largest 2D slide 24 and at the major axis length, which significantly increased in HSCs under the 5 µm confinement conditions ( Figure S1 C-D ). Interestingly, nuclear stretching was paralleled by a progressive alteration of the DAPI intensity profile of the nucleus and by a sharp increase in the activation levels of RhoA, which was mildly but directly correlated with the increase in nuclear diameter (Fig. 1 C and Figure S1 E ). Indeed, RhoA-GTP levels increased significantly in direct correlation to the level of confinement at 8 µm and 5 µm (Fig. 1 C). When the nuclear height was reduced to 3 µm, RhoA-GTP levels decreased dramatically. In this condition of tight confinement, HSCs displayed severe nuclear rupture, which suggests cell distress and the inability to react against such a reduction of nuclear height, as evidenced by the dramatic reduction of the nuclear volume and DAPI intensity profile, the appearance of nuclear blebs and the extreme standard deviation variability of the EOP (Fig. 1 C and Figure S1 C-E and Video S1-4 ). This data is consistent with a threshold of up to 70% compression of the nuclear height for HSC survival and consequent RhoA activation before irreversible nuclear lamina rupture, in agreement with what was described for other cells 24 . To investigate whether RhoA is necessary for the response to nuclear deformation in HSCs, we isolated HSCs from CreERt2 X RhoA flox/flox mice, in which the activity of the Cre recombinase to knock-out RhoA can be induced in vitro by overnight treatment with 4-hydroxy tamoxifen ( Figure S1 F; hereafter referred to as RhoA knock-out (KO) HSCs). To note, after 12–16 hours from induction of the Cre-recombinase, levels of RhoA-GTP were clearly reduced ( Figure S1 G ). Next, we confined the cells at 5 µm, according to the protocol described above to image nuclei and quantify the impact of RhoA knock-out. Surprisingly, in RhoAKO HSCs, the nuclei were completely broken, indicating that RhoAKO HSCs were unable to resist the 5 µm confinement (Fig. 1 D). Since our experiments were performed in vitro on sorted non adherent HSCs, we reasoned that RhoA activation might be intrinsically induced by the mechanical tension at the nuclear envelope (NE). To investigate this hypothesis, we decided to quantify the phosphorylated form of the nuclear protein cPLA2 (P-cPLA2), which translocates to the NE due to a physical process mediated by tension at the NE 21 , 22 , 25 , 26 . NE P-cPLA2 catalyzes the hydrolysis of phospholipids releasing arachidonic acid (AA) 18 , 22 , 23 , 27 , 28 , which is a well-established activator of RhoA 29 , 30 . We stained P-cPLA2 and quantified its NE localization, which increased when HSCs are under confinement, consistent with the increased RhoA activation ( Figure S1 H ). To further investigate whether intrinsic RhoA activation is induced by NE tension, we treated freshly sorted HSCs with sodium butyrate (NaB), a histone deacetylase inhibitor known to increase levels of histone acetylation and induce chromatin decompaction (intrinsically increasing NE tension) in different cells and also in HSCs 31 – 33 . RhoA activity levels were sharply upregulated in HSCs treated with 5mM NaB, in parallel with the increase in NE tension, as shown by the increase in the nuclear diameter and P-cPLA2 translocation to the NE (Fig. 1 E-G and Video S5-6 ) To further support that P-cPLA2 translocation to the NE can be triggered by changes in NE tension 26 , we used hypotonic shock which was previously used to increase nuclear volume 22 , 25 , 34 and NE tension and we quantified NE P-cPLA2 and RhoA activation. Consistently, the data shows an increase in NE P-cPLA2 and RhoA-GTP levels in HSCs after hypotonic osmotic shock ( Figure S1 I ). Overall, the data demonstrates that intrinsic changes in the NE tension activate RhoA in HSCs. However, RhoA activity might also be triggered by the extracellular matrix (niche or extrinsic activation). To explore this possibility, we focused on tissue stiffness, a well-described mechanical stimulus inducing RhoA activity in different cells and tissues 35 , 36 . Since in adult mammals HSCs reside in the BM, we first measured the stiffness of this compartment, which is a semi-solid tissue with viscoelastic properties and a quite heterogeneous mechanical behavior 37 . To this end, we used a Nanoindenter device equipped with a small and sensitive cantilever tip. We designed a matrix of several points to cover the whole area of the femoral BM tissue and obtain a map of the stiffness of the murine BM cavity ( Figure S1 J ). In agreement with previous observations 38 , within the BM we detected areas with different levels of stiffness, ranging from 0.5 to 10kPa (on average 4kPa) in the inner marrow to a range of 5 to 50 kPa (on average 12kPa) at the endosteum ( Figure S1 K ). Based on these results, we prepared polyacrylamide hydrogels functionalized with fibronectin (FN) to mimic in vitro the lowest (at the inner marrow; ~1kPa) and the highest (endosteum; ~40kPa) stiffness values that we detected in our murine BM samples. We then isolated HSCs and cultured them overnight on the functionalized hydrogels with different stiffness values. HSCs were then recovered and stained for RhoA-GTP and DAPI and used for a colony-forming unit (CFU) assay ( Figure S1 L ). Interestingly, RhoA-GTP levels did not changed in HSCs cultured on the stiff (40kPa) hydrogels compared to those cultured on the 1kPa hydrogels, contrary to what expected for RhoA being activated by an increased stiffness of the substrate 39 (Fig. 1 H). As for the CFU assay, we did not detect differences in colony number, but the number of c-kit + cells (hematopoietic progenitors) and the total cell number were reduced in the 40kPa-stiff hydrogels with no differences in the number of myeloid cells (Gr1 + and Mac1 + cells) (Fig. 1 I-K and Figure S1 M ). Intriguingly, RhoAKO HSCs behaved as their wild-type control for all the measured parameters (Fig. 1 I-K and Figure S1 M ). Therefore, our data reveals that in HSCs RhoA is dispensable in the response to changes in extracellular stiffness, while RhoA is necessary to survive intrinsic changes in NE tension. In HSCs RhoA activity sharply increases after NE tension raise induced by confinement, osmotic shock and chromatin decompaction after NaB treatment (Fig. 1 L). RhoA activity is increased in aged HSCs Aging alters the biomechanical properties of tissues and cells 40 . Since increased tissue stiffening upon aging has been reported to induce an increase in RhoA activity levels for example in the hair follicle 41 , we wondered if RhoA activity was altered in aged haematopoietic stem and progenitor cells (HSPCs) and whether it was correlated with increased stiffness of the aged BM 42 . We profiled the stiffness of the aged BM by performing indentation experiments using the same NanoIndenter device as reported above ( Figure S1 J ). We collected several measurements alongside a matrix to cover the overall BM cavity of femurs and tibiae comparing in parallel young (8 to 20 weeks old) and aged C57Bl6 (> 80 weeks old) mice. Strikingly, the indentation measurements showed an overall decreased bone marrow stiffness in aged samples compared to young, resulting in a homogenously low stiffness along the transversal section of the bone marrow ( Figure S2 A-B ). To investigate whether the decreased BM stiffness was associated to changes in RhoA activity, we performed western blot and pull-down assays on HSPCs isolated from young and aged mice. We detected a significant upregulation of RhoA activity levels in aged BM cells, that could be targeted and significantly reduced by treatment with a selective RhoA inhibitor (Rhosin, here referred to as Ri) 43 ( Figure S2 C ). Further, we measured RhoA-GTP levels in sorted HSCs by immunofluorescence. To this end, HSCs were harvested from aged mice and incubated with or without Ri. Young HSCs were sorted alongside as control (Fig. 2 A and Figure S2 D ). Aged HSCs showed a dramatic upregulation of RhoA activity and treatment with 100µM Ri significantly decreased levels of RhoA-GTP in aged HSCs (Fig. 2 B), consistent with the western blot/pulldown results on HSPCs ( Figure S2 C ). Therefore, in agreement with our conclusions based on the hydrogel experiments (Fig. 1 I-K), RhoA activity appears not to be associated to the extracellular stiffness and, while the aged BM stiffness decreases, RhoA activity increases in HSCs upon aging, supporting an intrinsic activation of RhoA in HSCs upon aging. Since we reported above that changes in NE tension can intrinsically activate RhoA in HSCs, we hypothesized that RhoA-GTP levels are higher in aged HSCs because their nucleus might be experiencing higher nuclear tension. Consistent with this hypothesis, we detected a significant increase of NE P-cPLA2 in aged stem cells (Fig. 2 C and Figure S2 E ). By treatment with the cPLA2 inhibitor (AACOCF3), NE translocation of P-cPLA2 is clearly reduced across all measurement metrics (Fig. 2 C and Figure S2 E ). Importantly, levels of RhoA-GTP in aged cells were sharply reduced to levels similar to young HSCs after treatment with AACOCF3, supporting that changes in NE tension are necessary to activate RhoA in aged HSCs (Fig. 2 D). To further investigate the increased NE tension in aged stem cells, we performed additional experiments to measure the wrinkling of the NE, a structural feature of nuclear architecture that has previously been used as a measure of NE tension 22 , 25 , 26 , 44 , 45 . LaminB staining clearly shows increased NE circularity and reduced NE excess folding in aged HSCs compared to young (Fig. 2 E-F). This data strongly supports the results based on NE P-cPLA2 increase in aged stem cells and therefore that the NE tension in aged HSCs is higher compared to young stem cells (Fig. 2 E-F). Strikingly, RhoA inhibition sharply decreases NE circularity and NE excess folding in aged HSCs (Fig. 2 E-F). To corroborate further these observations, we quantified also the nuclear import of the mechanosensitive transcription factor TAZ, which is known to accumulate in the nucleus with increasing NE tension 46 , 47 . In agreement, we measured increased nuclear translocation of TAZ after 8µm and 5µm HSC confinement ( Figure S2 F ). As reported also previously 48 , we quantified higher level of nuclear TAZ in aged HSCs compared to young, which is dependent on RhoA activity ( Figure S2 F ). Altogether, the results support the functional connection between changes in NE tension and RhoA activity as a mechano-sensitive regulator of HSC ageing (Fig. 2 G). RhoA inhibition restores DAPI-Intense Regions in aged HSCs To explore if changes in NE tension impact on chromatin of aged HSCs, we developed a computational approach leveraging image analysis algorithms to extract morphometric and fluorescence intensity features from 3D-confocal images of HSC nuclei stained with DAPI, which is a photostable fluorescent DNA dye and its fluorescence intensity has been used in several applications to quantify DNA amount and chromatin condensation in intact nuclei 49 , 50 . First, consistent with increased NE tension, the results show that aged HSCs display a significant increase in nuclear volume, nuclear diameter, surface area, perimeter of the largest Z slide and DAPI-Intense Regions (DIRs) volume, among other related features, compared to young HSCs (Fig. 3 A). To note, HSC confinement (8µm) induced a similar larger increase in the same morphometric features, supporting that these alterations of nuclear volume, size and shape in aged HSCs are compatible with increased NE tension (Fig. 3 A) 19 , 25 , 26 , 51 . Next, since RhoA inhibition sharply decreases NE circularity and NE excess folding in aged HSCs (Fig. 2 E-F), we asked whether decreasing RhoA activity might feedback to the nucleus inducing any change in morphometric and fluorescence intensity features of aged HSC nuclei. Surprisingly, the data revealed differences mainly in the pattern of DAPI intensity, that we extracted by quantifying fluorescence intensities along 3D iso-distant intervals from the nuclear border ( Figure S3A and Fig. 3 B ) . The pattern of DAPI intensities showed lower values near the nuclear border for aged HSCs compared to young and in aged HSCs DIRs localize relatively far from the nuclear border towards the central part of the nucleus (Fig. 3 C). Most aged + Ri HSCs nuclei displayed a large proportion of DIRs near the NE, similar to young HSCs (Fig. 3 C). In addition, other DAPI intensity features were different between young and aged HSCs and were restored to a youthful level after RhoA inhibition, namely DIRs height, DIRs major axis length, DIR distance to the border and number of DIRs ( Figure S3B ). Next, as we calculated multiple morphometric and intensity features, we proceeded to conduct dimensionality reduction analyses on our feature set to explore similarity patterns within our HSC nucleus images ( Figure S3C-D ). First, we performed feature engineering and clustering analysis on all available nucleus images to uncover potential mechanisms underlying nuclear remodeling in young, aged and aged Ri-treated HSCs. The extracted imaging features are categorized into three groups: ( i ) whole nucleus level features, ( ii ) DIRs level features, and ( iii ) features computed from the largest 2D z slide in the xy plane. A comprehensive summary of these features is provided in Table S1 . To address the high dimensionality and notable correlation of our feature space, we employed the non-linear dimensionality reduction technique Uniform Manifold Approximation and Projection 52 (UMAP). Feature selection involved identifying statistically significant features (Mann Whitney U-test p-value < 0.05) among young and aged HSCs, and among aged and aged Ri-treated HSCs ( Figure S3C-D ). These significant features were then combined and filtered to remove those exhibiting high Pearson correlation coefficient (absolute value of correlation > 80%), resulting in a final set of 20 features ( Figure S3E ). The UMAP revealed that young, aged and aged + Ri HSCs exhibit overlapping yet distinct distributions, indicating underlying differences in their chromatin properties (Fig. 3 D). By grouping the nucleus data points using the K-Means clustering algorithm on the original feature space, we identified four distinct clusters that differ in morphometrics and intensity characteristics (Fig. 3 E and Figure S3F-G ). Analysis of individual features’ impact over the UMAP representation revealed that morphometric-related features polarize the embedding vertically, whereas intensity and DIRs-related features polarize the embedding horizontally (Fig. 3 E and Figure S3H ). Subsequently, by assessing the most representative features and biological populations, we annotated the clusters as Low Size (LS), High Size (HS), Border Intensity (BI) and Central Intensity (CI) (Fig. 3 E). Feature importance for each cluster was evaluated by measuring feature statistical significance and fold change per cluster ( Figure S3I ). Cluster BI includes mostly young and aged + Ri HSCs exhibiting high intensity near the NE and lower intensity in the nuclear center, along with an increased number of small DIRs closer to the border, which tend to be less spherical compared to nuclei in other clusters (Fig. 3 E-G and Figure S3H-I ). Cluster CI predominantly consists of aged nuclei, with a reduced number of aged Ri-treated nuclei. These nuclei present with larger DIRs located farther from the NE and are notably spherical, with decreased intensity near the nuclear border (Fig. 3 E-G and Figure S3H-I ). Cluster LS mainly contains young nuclei characterized by smaller size and lower mean intensity of DIRs, with surprisingly not much accentuated intensity values near the border (Fig. 3 E-G and Figure S3H-I ). Cluster HS contains a mix of biological conditions, with nuclei characterized by larger size, DIRs positioned away from the nuclear border, and a decreased aspect ratio indicating these nuclei are wider than they are tall (Fig. 3 E-G and Figure S3H-I ). By plotting feature trajectories across clusters, morphometric features progressively decrease or increase with decreasing frequencies of young HSCs within the clusters spanning from LS to HS clusters (Fig. 3 F). Interestingly, intensity features, and DIR-related features progressively decrease or increase with increasing frequencies of aged + Ri HSCs from CI to BI clusters (Fig. 3 G). Therefore, aged Ri-treated nuclei appear to share some morphometric similarities with aged nuclei yet exhibit intensity and DIR-related characteristics notably similar to those of young HSCs. Overall, our computational approach suggests that DAPI imaging features elucidate chromatin differences in stem cells revealing a Ri-associated nuclear remodeling, which is mainly linked to changes in DAPI intensity and DIR volume, number and localization. RhoA inhibition restores H3K9me2 at heterochromatin Alterations in the mechanobiology of the cell have been associated with aging-dependent changes in chromatin architecture 40 , 53 and several epigenetic alterations characterize intrinsic HSC aging 54 – 56 , among which it has been previously reported a general loss of heterochromatin 3 , 57 , 58 . Intrigued by the observation that in aged HSCs treated with Ri some nuclear DAPI intensity and DIR-related features were significantly reverted to the level found in young HSCs (Fig. 3 C and Figure S3B ) and are associated to the nuclear remodeling induced by Ri (Fig. 3 G), we focused on heterochromatin because especially DIRs are related to the most condensed portion of chromatin. Therefore, we analyzed the levels and distribution of H3K9me2, a known heterochromatin histone mark which is altered in aged HSCs 3 , 33 . By 3D-IF analyses, we measure a significant increase of H3K9me2 levels in aged + Ri HSCs compared to aged controls, together with a partial re-localization at the nuclear border like in young HSCs (Fig. 4 A). Interestingly, treatment with UNC0631, a selective inhibitor of the histone methyltransferase G9a specific to H3K9me2 59 , blunts completely the effect of Ri on H3K9me2 in aged HSCs (Fig. 4 A). This data suggests the requirement of G9a to increase levels of H3K9me2 in aged + Ri HSCs. Moreover, it reveals that aged HSCs are not affected by UNC0631 treatment alone, most likely because of the very low expression of G9a in these cells in basal conditions, which is partially rescued by Ri treatment ( Figure S4A ). To causally explain the role of decreased H3K9me2 in HSCs, we transduced young hematopoietic stem and progenitor cells (HSPCs or LSKs, gated as Lin − c-Kit + Sca-1 + ) with a retroviral vector, codifying for a histone variant in which the lysine of H3K9 is replaced by an arginine (H3R9) (Fig. 4 B and Figure S4B ). The arginine in position 9 on H3 cannot be methylated, enforcing H3K9me reduction in HSCs. We functionally validated the strategy and the H3R9 incorporation by transplanting transduced LSKs into lethally irradiated recipient mice. We measured a significant reduction of H3K9me in H3R9 myeloid progenitors (MPs) compared to control vector transduced H3K9 MPs (Fig. 4 C). Young H3R9 HSCs isolated from transplanted mice present with higher RhoA-GTP levels and increased nuclear stretching compared to controls (Fig. 4 D). Moreover, H3R9 HSCs show a clear premature-aging phenotype upon transplantations in vivo , characterized by expansion of LSK and granulocyte-monocyte progenitors (GMP), reduced BM and peripheral blood (PB) regeneration, reduced B-lymphopoiesis and myeloid skewing (Fig. 4 E-G). Altogether, this data demonstrates that reduced methylation of H3K9 causes nuclear stretching, increasing RhoA activation and driving aging of HSCs. Importantly, RhoA inhibition restores H3K9 methylation levels in aged HSCs. RhoA regulates chromatin accessibility at retrotransposons in aged HSCs Previously, it has been proposed that the cell nucleus can directly respond to mechanical stress by inducing chromatin remodeling, altering polymerase and transcription factor accessibility and activity 60 – 62 , while increased chromatin accessibility as measured by ATAC-seq has been already characterized as an epigenetic alteration intrinsic to HSC aging 63 , 64 . Intrigued by possibility that RhoA inhibition might underscore a link between increased NE tension and increased chromatin accessibility in aged HSCs, we investigated further the chromatin remodeling associated with Ri treatment by performing ATAC-seq profiling of sorted young, aged, and aged + Ri HSCs (Fig. 5 A). Peak-calling identified a total of 57,289 accessible regions consistent between samples, most of them located in introns, intergenic regions, and gene promoters ( Figure S5A-B and Table S2 ). In agreement with previously published data 63 , 64 , we detected an increase in open differentially accessible regions (DARs) with aging (2713 DARs open and 1103 DARs closed in aged compared to young HSCs; ~8% FPR; Fig. 5 B and Table S2 ). After applying the Ri treatment to aged HSCs, 743 chromatin regions opened and 355 closed (~ 8% FPR; Fig. 5 B and Table S2 ). Overall, 144 DARs were detected in both comparisons (aged vs young and aged + Ri vs aged), with 85.42% of them showing accessibility levels after the Ri treatment changing in the direction of the young levels (Fig. 5 C and Figure S5C and Table S2 ). Among the regions opening in aged HSCs treated with Ri, Gene ontology (GO) enrichment analysis revealed pathways related to cell migration, morphogenesis, adhesion, and chemotaxis ( Figure S5D and Table S2 ). Interestingly, no GOs were significantly enriched among the DARs closing in aged HSCs + Ri and a high percentage of these closing DARs were located at retrotransposons (Fig. 5 D lower right panel ), especially Long Terminal Repeats (LTRs) and Long INnterspersed Elements (LINE), like ERVL-MaLR, ERVK, ERV1, and L1 families (2-fold/~1 log2FC higher percentage compared to the percentage in consensus peaks; FDR = 0.0018 for LTRs and FDR = 0.014 for LINEs in a one-proportion z-test; Fig. 5 D-E and Table S2 ) 65 . Notably, in aged HSCs we observe an opening of chromatin at retrotransposons, with a 1.65-fold (0.72 log2FC) and a 1.25-fold (0.33 log2FC) increase in the percentage of open DARs localizing in LTRs and LINEs, respectively (FDR = 3.2x10 − 13 for LTRs and FDR = 0.04998 for LINEs in a one-proportion z-test; Fig. 5 D-E and Table S2 ). Some of these DARs at LTRs and LINEs were overlapping with enhancers described previously 66 , while others were located in intronic and intergenic regions (Fig. 5 E and Table S2 ). Interestingly, retrotransposons and in particular LINE-1 have been suggested to directly contribute to aging of somatic cells and aging-related diseases 65 , 67 – 69 . To explore if the increase in chromatin accessibility at retrotransposons upon HSC aging is linked to increased NE tension, we performed ATAC-seq of young HSCs under 5µm confinement and compared their chromatin accessibility profiles to those of unconfined HSCs sorted in parallel from the same mice (Fig. 5 F). As a reference for DAR identification, we used the 42,632 consensus peaks identified between young and aged HSCs samples ( Table S3 ). Data show 443 DARs opening and only 25 DARs closing in young HSCs under confinement (~ 6.6% FPR; Fig. 5 G-H and Figure S5E and Table S3 ). Strikingly, while the DARs that are closing in confined HSCs are located at promoter-TSS and 5’UTR (1.67 log2FC over consensus peaks; FDR = 4.1x10 − 6 in a one-proportion z-test; Fig. 5 I lower panel) , the DARs that are opening are mainly at LTRs and LINEs (0.75 and 1.18 log2FC over consensus peaks, respectively; FDR = 0.008 for LTRs and FDR = 0.003 for LINEs in a one-proportion z-test; Fig. 5 I upper panel ). Overall, the results support that the increased accessibility at REs observed in the aged HSCs and reverted (closed) by Ri treatment are located at LTR and LINE, which are the same type of genomic regions that are open by increasing NE tension by mechanical confinement. Next, we investigated transcriptional changes by bulk RNA-seq analysis of young, aged and aged + Ri HSCs. We identified 38 genes upregulated and 27 downregulated in aged HSCs after treatment with Ri (~ 5% FPR; Figure S5F-H and Table S4 ). Consistently with the ATAC-seq data, after Ri treatment we detected a downregulation in the transcription of several retrotransposons subfamilies that are upregulated in aged HSCs, mainly LTRs but also some LINEs and DNA transposon subfamilies, like L1 (L1ME1 and L1ME3A), ERV1 (MER110-int) and hAT-Blackjack (MER63C) (~ 8% FPR; Fig. 5 J and Figure S5I and Table S4 ). Gene Set Enrichment Analysis (GSEA) revealed enrichment for several GOs related to inflammation, innate immune response activation and interferon response among the genes downregulated after Ri treatment, again consistent with a downregulation of retrotransposons 65 , 68 , 70 (FDR < 0.05; Fig. 5 K-L and Figure S5J and Table S4 ). Using GSEA, we also detected a decrease in the Interferome.org gene signature 71 and of interferon-stimulated genes (ISG) 72 in aged + Ri HSCs (Fig. 5 M-N and Figure S5K-L ). Moreover, we measured a significantly negative enrichment score in aged + Ri vs aged samples for the HSC aging signature defined by Svendsen et al. 73 , while the same signature was clearly positively enriched in the aged samples compared to the young ones (Fig. 5 O and Figure S5M ). In summary, by ATAC-seq profiling, we detect changes in chromatin accessibility in aged Ri-treated HSCs that revealed reduction of open regions at LTRs and LINE, belonging mainly to ERVL-MaLR, ERVK, ERV1 and L1 families. ATAC-seq profiling of young 5µm-confined HSCs supports that the increased accessibility at retrotransposones observed in the aged HSCs and reverted by Ri treatment could be a consequence of increased nuclear stretching. Consistently, by bulk RNA-seq profiling of aged + Ri HSCs we detected a reduction in the transcription of LTRs and LINEs and a downregulation of the immune response, inflammatory response, interferon response and aging gene signatures compared to aged control HSCs. Inhibiting RhoA activity improves function of aged HSCs Next, to further characterize the transcriptional changes in aged HSCs after Ri treatment, we focused on the few upregulated genes ( Figure S5G-H Table S4 ) and, interestingly, we noticed three transcription factors (TF) belonging to the same family: Klf4 , Klf6 and Klf2 (Fig. 6 A and Table S4 ). Noteworthy, the upregulation of Klfs is consistent with the opening of Klf4 motifs detected by ATAC-seq TF-binding motif analysis, which revealed enrichment in motifs for AP-1 and Klf4 (Fig. 6 B and Table S2 ). In hematopoietic cells, AP-1 can interact with chromatin remodelers to assist in the binding of other TFs 74 and Klf4 has been previously shown to be important in cell reprogramming, blood formation and mechanosensing 75 – 80 . In addition, we identified an open DAR in aged + Ri HSCs in correspondence with a recently annotated Klf4 enhancer 81 (Fig. 6 C) and we measured a clear increase in Klf4 protein in the nucleus of aged stem cells after Ri treatment ( Figure S6A ). Next, we analyzed the expression level of the genes targeted by a higher accessibility of Klf4-binding motifs and most of them increased their expression after Ri ( Figure S6B and Table S2 ). GO enrichment analysis of these genes revealed enrichment for morphogenesis, differentiation, and actin polymerization (FDR < 0.05; Fig. 6 D and Table S4 ). Notably, actin polymerization (filamentous actin or F-actin) has been reported to restrict nuclear stretching 21 , 22 . Consistently, while in aged HSCs F-actin is decreased compared to young, F-actin levels increase upon Ri treatment similarly to young HSCs (Fig. 6 E). To gain further insights into the transcriptional rewiring of aged HSCs treated with Ri, we performed scRNA-seq of young, aged, and aged Ri-treated LSK cells. We obtained a total of 60,648 cells expressing 15,049 genes. Clustering of the integrated UMAP identified 11 clusters (Fig. 6 F) that were annotated as based on known marker gene expression and enrichment for several previously identified HSC and LSK signatures 82 – 84 (Fig. 6 C-F and Table S5) . Compositional analysis of cell clusters showed the expected increase in the percentage of HSCs in all aged samples compared to the young ones ( Figure S6G and Table S5 ). It also revealed an increase in the percentage of MPP3 with aging and its decrease with the Ri treatment ( Figure S6G and Table S5 ). Interestingly, the scRNA-seq dataset showed that the increase of Klf4 expression in aged + Ri LSKs is more prominent in the HSC and the MPP1 clusters (Fig. 6 G). In detail, 43.58% of aged + Ri HSCs are expressing Klf4, while only 2.46% and 2.80% of young and aged HSCs, respectively, express this TF. Differential gene expression analysis between the three conditions in the HSC cluster confirms the upregulation of several genes of the Klf family after Ri treatment (|log2FC| > 1 and FDR < 0.05; Figure S6H and Table S5 ). To further analyze the activity of the different TFs and identify the network of regulated genes, we calculated TF activity scores in each single HSC using SCENIC and generated a UMAP based on TF activity scores (Fig. 6 H). Interestingly, the percentage of aged + Ri HSCs with active Klf4 is 89.52%, while it is negligible in young and aged HSCs (1.89% and 0.69% respectively) (Fig. 6 I and Figure S6I ). Differential analysis of the activity scores in between conditions confirms the increased activity of several Klf TFs, as well as other TFs (|log2FC| > 0.5 and FDR < 0.05; Fig. 6 J). Since Klf4 is known for its role in cell reprogramming and dedifferentiation, we wondered if aged + Ri HSCs show a more dedifferentiated transcriptional state. Interestingly, GSEA reveals an enrichment of the hemogenic precursor signature defined by Pereira et al. 85 in the aged + Ri HSCs compared to the aged controls (Fig. 6 K), supporting that Ri treatment might induce a partial transcriptional reprogramming of aged HSCs. Since the scRNA-seq analysis indicates a partial reprogramming suggestive at the transcriptomic level of a possible functional improvement, we decided to assess the regenerative capacity of aged HSCs after Ri treatment in vivo . Previously, we correlated changes in cell epigenetic polarity of H4K16ac to function of HSCs 33 , 86 – 88 . 3D-IF staining of young, aged and aged + Ri HSCs clearly showed that Ri treatment restores H4K16ac polarity in aged stem cells ( Figure S7A ). Next, we tested in a non-competitive transplantation assay into young immunocompromised and Kit W−41J mutant mice (NBSGW) the regenerative capacity of aged Ri treated HSCs compared to young and aged control stem cells. We transplanted 200 aged HSCs treated overnight ex vivo with 100µM Ri, alongside control recipient mice transplanted with solvent treated young or aged HSC (Fig. 7 A). Donor mice were genetically tagged by constitutive expression in Rosa26 locus of a bright and stable red-fluorescent protein (tdRFP) 89 and we measured donor-derived contribution in peripheral blood (PB) by detecting RFP + cells at several time-points after transplantation. Notably, aged Ri-treated HSCs engrafted at the endpoint similarly to young HSCs, showing a significant increase in their peripheral blood (PB) regenerative capacity compared to aged control HSCs (Fig. 7 B-C). Remarkably, Ri treatment also significantly increased the B cell lymphoid differentiation potential of aged HSCs and decreased the contribution to the myeloid lineage 18 weeks after transplantation (Fig. 7 B-D). Ri treatment did not change the differentiation to the T cell compartment (Fig. 7 B-D). Engraftment in BM and HSC compartments was not significantly different in Ri treated recipients compared with either young or aged control recipients, showing a trend for both parameters to resemble young donor HSCs ( Figure S7B-C ). Overall, we conclude that inactivation of RhoA in aged HSCs functionally improves the regenerative capacity of old stem cells and their myeloid/B-lymphoid skewing in vivo . DISCUSSION The regenerative potential of HSCs declines upon aging 90 . Moreover, aged HSCs are skewed toward myeloid differentiation, which contributes to immunosenescence and to the increased incidence of hematopoietic disorders in the elderly 1 – 4 . Previously, intrinsic epigenetic alterations have been associated with HSC aging, such as for example increased chromatin accessibility 3 , 33 , 54 – 56 . We also described that some of these epigenetic alterations depend on a reduction of LaminA/C expression in aged HSCs 33 , 63 , which is suggestive of a possible impairment of the mechanical properties of the nucleus. Supporting a novel emerging perspective that focuses on nuclear mechanoregulation 16 , 18 , 47 , 91 , 92 , here we investigate RhoA, a small GTPase critical for HSC cytokinesis and differentiation 20 that has been involved also in mechanotransduction in different cell types 16 . Our data shows that in HSCs RhoA is activated by increasing NE tension under cell confinement, by chromatin decompaction after NaB treatment, by nuclear swelling after hypotonic osmotic shock and after reduction of H3K9 methylation levels. To note, we identify the loss of methylation of H3K9 as a likely cause not only of RhoA over-activation and increased nuclear stretching in aged HSCs, but also of many phenotypes associated with functional aging of HSCs (reduced regenerative capacity, expansion of the primitive GMPs and LSKs populations and myeloid/lymphoid skewing in BM). Interestingly, G9a activity is required for Ri restoration of H3K9me2 levels in aged HSCs. Further work is necessary to clarify mechanistically how RhoA activity downregulation affects the activity of this histone methyltransferase. To note, the data reveals also that RhoA is necessary to survive cell confinement, which intrinsically induces RhoA activation. Differently from other cell types 39 , 41 , we show that in HSCs RhoA is not involved in transducing changes in extracellular stiffness, since RhoA is dispensable in the response to changes induced by culturing HSC on hydrogel with high stiffness. Furthermore, our results reveal that NE tension is physiologically increased upon HSC aging and that NE tension is necessary to activate RhoA in aged HSCs. RhoA activity in aged HSCs can be targeted by a specific small molecule inhibitor 43 , 93 , which restores H3K9me2 levels and recovers the phenotypes of young HSC nuclei such as DAPI intensity, DIR volume, number and localization. This is further supported by our machine learning approach, which demonstrates that DAPI-imaging morphometric and intensity features can be used to explain differences between HSCs and to inform on the age and fitness of the stem cells. Importantly, by ATAC-seq and RNA-seq we detect a downregulation in chromatin accessibility and transcription at LTRs and LINE and a decrease in inflammation, immune response, interferon responsive genes and aging signatures in aged Ri-treated HSCs. Moreover, after treatment of aged HSCs with Ri we measure an increased transcription of Klf4, an opening of Klf4-binding motifs and an increased activity of Klf4 TF. Genes targeted by Klf4 are enriched for pathways related to actin polymerization and the increased levels of F-actin are likely acting to restrict nuclear stretching in aged Ri-treated HSCs. Moreover, Klf4 targets also genes enriched for a signature of hemogenic precursors, compatible with a partial reprogramming of aged HSCs, supporting the functional improvement in the regenerative capacity and myeloid/lymphoid skewing of aged + Ri stem cells in transplantation assays (Fig. 7 E). So far, several reports associated the aging process of different cell types with an epigenetic drift involving loos of heterochromatin and H3K9me and a dis-regulation of normally silenced and closed retrotransposons 65 , 67 , 68 , 94 , 95 . Here we report that an intrinsic nuclear mechanosignalling pathway dependent on RhoA can be pharmacologically targeted to revert these drifted epigenetic features, improving function of aged somatic stem cells (Fig. 7 E). To note, overactivation of RhoA has been previously reported to be associated with functional impairment of human HSCs, supporting possible translations of our findings 96 . Altogether our data sheds light on a new perspective of intrinsic nuclear mechanotransduction to control the aging-related epigenetic drift of somatic stem cells as a potential target for improving tissue homeostasis over time. METHODS Reagents A list of reagents, chemicals, commercial kits and antibodies is provided as Source Data file . Mice Young and aged C57BL/6 mice were obtained from the internal divisional stock (derived from mice obtained from The Jackson Laboratory). Young and aged acRFP C57BL/6 mice were obtained from the internal divisional stock (originally kindly donated by Prof. Fehling, Ulm University and previously described 89 ). The NBSGW mice were obtained from the internal divisional stock (derived from mice obtained from The Jackson Laboratory, JAXStock No.026622) and were maintained as homozygotes. RhoAflox mice were described previously 20 and crossed with CreErt2 mice (JAXStock No.008463). All mice were housed in the animal barrier facility under pathogen-free conditions at the Biomedical Research Institute of Bellvitge (IDIBELL). Throughout the manuscript, young mice are between 10 and 20 weeks old and aged mice are at least 80 weeks old. C57BL/6 mice were randomized for sex. For the transplantation study NBSGW mice were randomized for sex and equal number of male and female mice were used across samples. Mice that failed to recover from blood sampling and mice that died due to laboratory errors were excluded. Mice that needed to be euthanized because they were scored as “weak and about to die” according to our approved animal license protocol for evaluating mouse health status remained part of the dataset. Allocation to control or treated group was done randomly (4-5 mice each experimental group in four different experimental batches). All animals were maintained according to the recommendations of the European Convention for the Protection of Vertebrate Animals used for Experimental and other Scientific Purposes (ETS 123). Animals were housed in groups of up to 4 animals per cage in Macrolon Type II (long) cages with bedding and paper nesting material. The animals had access to food (V1124-3, ssniff®) and water ad libitum . Animals were kept at a day/night rhythm of 12/12 hours throughout the entire experiment. Ethical compliance for mouse experiments All mouse experiments were performed in compliance with the ethical regulations according to the Spanish Law for Animal Protection and Welfare Code and were previously approved in the project AR18008/10399 by IDIBELL’s Ethical Committee for Animal Experimentation (CEEA-IDIBELL) as well as by Generalitat of Catalunya. HSC transplantation in NBSGW For HSC transplantation, young and aged acRFP C57BL/6 were used as donors. Two-hundred HSCs were sorted into 72-well Terasaki multi-well plates (Merck, M5812) and cultured for 16 h in HBSS + 10% FBS + 1% Penicylin/Streptomycin (P/S) with or without Rhosin (RhoA inhibitor or Ri) 43 at 100µM in a water-jacketed incubator at 37 °C, 5% CO 2 , 3% O 2 . Cells were transplanted via retro-orbital vein injection. Peripheral blood chimerism was determined by FACS analysis every 6 weeks up to 18 weeks after transplants. The transplantation experiment was performed four times with a cohort of four recipient mice per group each transplant. In general, transplanted mice were regarded as engrafted when peripheral blood chimerism was higher or equal to 0.2% and contribution was detected in all lineages. Retroviral vector construction and viral production H3K9 and the mutant form H3R9 sequences were kindly provided by the laboratory of Prof. Clemens Schmitt 97 . They are based on the H3.1 histone isoform with the K9 mutated to R9. Sequences were subcloned into the retroviral backbone pMSCVII (pMSCVII was a gift from Maki Nakayama (Addgene plasmid # 162750; http://n2t.net/addgene:162750 ; RRID:Addgene_162750) adding by PCR a P2A site and mCherry as a reporter. Resultant vectors were transfected into Phoenix-ECO using Lipofectamine 2000 (Thermo-Fisher) following manufacturer instructions. Supernatant containing retroviral particles was collected at 48h and 72h post-transfection and kept at 4ºC until concentration 72h post-transfection. We used Millipore Centricon Plus-70, Ultracel-PL Membrane 100kDa (UFC710008 Millipore) for viral concentration. Concentrated retroviral particles were kept at -80ºC prior to transduction. Retroviral particles were titrated in dilutions ranging from 1/10 to 1/500000 in NIH-3T3 cells (mouse embryonic fibroblasts). The titration was analyzed 48h later assessing mCherry-frequencies by flow cytometry (Cyto Flex SRT, MoFlo Cell Sorter). Viral infectious units (VIU) were calculated based on the initial cell-input, dilution, mCherry-frequency and volume of retroviral supernatant. The resulting values were plotted and the average, representing the transducing units (TU) was calculated from the linear portions of the graph. Flow cytometry and cell sorting PB and BM cell immunostaining was performed according to standard procedures and samples were analysed by Beckman Coulter Gallios Analyzer (Beckman Coulter). RFP signal was used to distinguish donor from recipient cells. For PB and BM lineage analysis the antibodies used were all from eBioscience: anti-CD3ε (clone 145-2C11), anti-B220 (clone RA3-6B2), anti-Mac-1 (clone M1/70) and anti-Gr-1 (clone RC57BL/6-8C5). Lineage FACS analysis data are plotted as the percentage of B220 + , CD3 + and myeloid (Mac-1 + and Gr-1 + Mac-1 + ) cells among donor-derived RFP + cells in case of a transplantation experiment or among total white blood cells within the bone marrow. As for early haematopoiesis analysis, mononuclear cells were isolated by low-density centrifugation (Histopaque 1083, Sigma) and stained with a cocktail of biotinylated lineage antibodies. Biotinylated antibodies used for lineage staining were all rat anti-mouse antibodies: anti-CD11b (clone M1/70), anti-B220 (clone RA3-6B2), anti-CD5 (clone 53-7.3) anti-Gr-1 (clone RB6-8C5), anti-Ter119 and anti-CD8a (clone 53-6.7) (all from eBioscience). After lineage depletion by magnetic separation (Dynabeads, Invitrogen), cells were stained with anti-Sca-1 (clone D7) (eBioscience), anti-c-Kit (clone 2B8) (eBioscience), anti-CD34 (clone RAM34) (eBioscience), anti-Flk-2 (clone A2F10) (eBioscience) and streptavidin (eBioscience). Early haematopoiesis FACS analysis data were plotted as percentage of long-term haematopoietic stem cells (HSCs, gated as LSK CD34 −/low Flk2 − ), short-term haematopoietic stem cells (ST-HSCs, gated as LSK CD34 + Flk2 − ) and lymphoid-primed multipotent progenitors (LMPPs, gated as LSK CD34 + Flk2 + ) 98 distributed among donor-derived LSKs (Lin neg c-Kit + Sca-1 + cells). To isolate HSCs, lineage depletion was performed to enrich for lineage-negative cells. Lineage-negative enriched cells were then stained as mentioned above and sorted using Beckman Coulter High Speed Cell Sorter Moflo-XDP (Beckman Coulter) and CytoFLEX SRT Cell Sorter (Beckman Coulter). For analysis of haematopoietic progenitors in the experiment of H3K9 and H3R9 transduction followed by transplantation, same procedure for BM analysis was carried out except for the staining after lineage magnetic depletion. Cells were stained with anti-IL7Ra (clone A7R34), anti-c-Kit (clone 2B8), anti-Sca1 (clone D7), anti-CD16/32 (clone 2.4G2) and anti CD34 (clone RAM34). Flow cytometry analysis data was plotted as percentage of MP (lin - , mCherry + , Il7Ra - , c-Kit + and Sca1 - ), MEP (lin - , mCherry + , Il7Ra - , c-Kit + , Sca1 - , CD16/32 - , CD34 - ), CMP (lin - , mCherry + , Il7Ra - , c-Kit + , Sca1 - , CD16/32 - CD34 + ), GMP (lin - , mCherry + , Il7Ra - , c-Kit + , Sca1 - , CD16/32 + CD34 + ) and CLP (lin - , mCherry + , Il7Ra + , c-Kit low , Sca1 low ). LSK retroviral transduction and competitive transplantation in lethally irradiated mice. LSK (Lin neg c-Kit + Sca-1 + ) cells were sorted in growth medium (IMDM 10%FBS 1%P/S medium with cytokines mSCF, mTPO and G-CSF at 1µg/ml). They were seeded on fibronectin functionalized wells (50ng/ml) in 96 well plate, 10K cells per 50µl of medium and maintained in normoxia at 37ºC and 5%CO 2 for 20h. Then, medium was changed to growth medium containing polybrene (1/1000, Sigma-Aldrich) and 30MOI of the retroviral vector. Incubate the cells with the retroviral particles for 6-8h. Change medium to normal growth medium and incubate over night at 37ºC, normoxia and 5%CO 2 . Cells were lifted with trypsin 0.05% for 5 minutes, quenched with medium with no cytokines, washed and recovered. A fraction of the cells was saved for transduction efficiency analysis and the rest was used for transplantation in lethally irradiated (9Gy) C57Bl6 mice. Cells for transplantation were mixed with BM competitor cells from non-irradiated C57Bl6 in a ratio 1/15 (20000 transduced LSK and 300000 competitor cells) in cold PBS. Immunofluorescence staining and confocal images acquisition Freshly sorted HSCs were seeded on fibronectin-coated glass coverslips. For polarity staining, HSCs were incubated for 12–16h in HBSS + 10% FBS + 1% P/S and when indicated treated with 100 µM Rhosin 43 , 5mM NaB, 20 µM cPLA2 inhibitor (AACOCF3 23 ), hypotonic medium or left untreated. After incubation at 37 °C, 5% CO 2 , 3% O 2 in growth factor-free medium, cells were fixed with BD Cytofix fixation buffer (BD Biosciences). After fixation cells were gently washed with PBS, permeabilized with 0.2% Triton X-100 (Sigma) in PBS for 20 min and blocked with 10% donkey serum (Sigma) for 30 min. Primary and secondary antibody incubations were performed for 1 h at room temperature. Cells were stained with a DAPI dilution 1:500 in PBS of DAPI 1µg/µl (Thermo, ref) for 10 minutes at room temperature and washed twice with PBS. Coverslips were mounted with ProLong Gold Antifade reagent without DAPI (Invitrogen, Molecular Probes). A list of antibodies used for immunofluorescence staining is provided in Supplementary Data. Samples were imaged with an AxioObserver Z1 microscope (Zeiss) equipped with a ×63 PH objective. Alternatively, samples were analysed with an LSM880 confocal microscope (Zeiss) equipped with a ×63 objective. Z -stacks were obtained by automatically scanning along the z axis of the cell with a confocal microscope and acquiring a picture of the in-focus plane every 0.2-0.4 μm. Immunofluorescence Image Analysis and Rendering Samples for immunofluorescence quantification analysis were rigorously sorted, stained and imaged in parallel within the same experiment to minimize any possible technical variability. Image acquisition at the confocal has been carried out consistently in between experiments regarding laser power, z-stack size and gain master. Antibody specificities have been validated using a knockout model in the case of RhoAGTP or using a control sample only with the secondary antibody in the staining protocol for the rest of the stainings. Quantification of protein signals has been done using Volocity Software 6.5 (Quorum technologies) using the “volume by intensity” tool, which sets a threshold for the positive signal against the negative. Positive signal threshold is set for each experiment by using RhoAKO for RhoAGTP and with the secondary antibody for the rest of the stainings. Morphometric measurements were done using Volocity Software 6.5 or by our computational vision approach. Quantifications of DAPI volume were done with the “volume” tool and Nuclear Height Average (NHA) and diameter was done using the tool “line”. Immunofluorescence images 3D reconstruction and rendering have been performed using Imaris 9.5.0 (Oxford Instruments) using the “surface” tool keeping the threshold constant for the signal in between experiments and conditions. As for polarity scoring, the localization of each single stained protein was considered polarized when a clear asymmetric distribution was visible by drawing a line across the middle of the cell. A total of 50 to 100 HSCs were singularly analysed per sample. Data are plotted as percentage of the total number of cells scored per sample. 3D nuclear DAPI images preprocessing HSCs nucleus microscopy images were exported as Carl Zeiss CZI files for downstream analysis and processed using Python programming language. Images acquired with a different microscope to the ones specified above and images belonging to experiments in which more than 30 days passed between nucleus staining and image data acquisition were excluded from these analyses. As a first quality control, images displaying total pixel intensities higher than 4×108 were labeled as overexposed to DAPI and discarded. Similarly, images with increased Gaussian noise (estimated noise standard deviation > 8) were labeled as noisy and also excluded from further analyses. The remaining images were then corrected using Chambolle’s Total Variation denoising method 99 . Since image acquisition measurements were dynamically adjusted to the size of the nucleus, each 3D image was resized to achieve a uniform resolution of 10 pixels per micrometer in all dimensions through isotropic interpolation that accounted for variations in the number of z-stacks obtained. For each image, a nucleus binary mask was extracted using the Otsu segmentation method 100 , after smoothing with a Gaussian filter and allowing for hysteresis to preserve nuclear border with higher confidence Potential holes in the binary mask due to low intensity areas within the nucleus were filled and marked as part of the segmentation. Both the intensity image and respective nucleus mask were centered in the container array grid by trimming the background and padding the image borders symmetrically. Marginal intensities outside the nucleus mask were removed. To mitigate batch effects associated with technical variations in the fluorescence signal, we standardized the pixel intensity distribution within each nucleus mask using Z-score normalization to enhance comparability among conditions in downstream analysis. Intensity by distance to nuclear border analyses Intensity by distance analyses were performed at iso-distance intervals of 0.1µm from the boundary of the nucleus segmentation, using a distance-transformed mask. A distance-transformed mask is a mask in which each pixel value represents the shortest geodesic distance to the nearest mask boundary. The average intensity value of all pixels within a 3D band with a thickness of 0.15µm was reported at each measurement interval. Young, aged and aged+Ri conditions were measured up to a distance of 1.6µm from the nuclear border. Boxplots of DAPI intensity by discrete distance ranges are computed taking into account the average intensity of all pixels within the specified distance boundaries for each interval. Multivariate feature analyses Most morphometric and intensity features were measured with the scikit-image Python library 101 regionprops_table() function at the nuclear mask level, DIR level, and on the largest 2D slide in the XY plane from each 3D image, comprising a total of 39 features (Table S1). The width, length, and height of each nucleus were obtained from its bounding box. Height deviation was calculated as the average standard deviation of height in the X dimension for each YZ slide. The aspect ratio was defined as the ratio of height to length. The surface area was calculated using the Marching Cubes algorithm after smoothing the nucleus mask with a Gaussian filter. The intensity ratio is computed as the ratio of average intensity within the 1 - 1.5 µm distance interval to the 0 - 0.5 µm distance interval from the nuclear border. The Excess of Perimeter (EOP) was computed as the proportion of the difference of the nuclear perimeter compared to the perimeter of an ellipse with the same major and minor axis length as the nucleus mask. Detailed information about each of these features is shown in Table S1. DIRs were segmented using the Watershed method on the thresholded images. The intensity standardization of the images allowed for the choice of an absolute thresholding value for all nuclei. We set this parameter to the quantile 80% of the intensity distribution for all young nuclei. Individual DIRs were labelled and segmented with Watershed by identifying the intensity peaks as the local maxima in the Euclidean distance transform of the thresholded images. From the total of segmented DIRs, we filtered out those displaying a voxel volume smaller than 0.2µm. Morphometric and intensity features were measured on the resulting DIRs segmentation mask in the same manner as with the nuclear mask. DIRs distance to border was computed as the average of the distances for each voxel within each DIR to the nuclear mask border. After the calculation of these measurements, a second quality control is conducted to eliminate images that produced artifacts in the nuclear and DIR segmentation. The images were filtered out if they produced nuclear masks with a voxel volume smaller than 40µm 3 , an EOP larger than 0.25 or DIRs volume larger than 5µm3, which mostly belonged to confined nuclei that were damaged during the experimental setup and failed nuclear mask segmentations. We proceeded with 177 young nuclei, 164 aged nuclei, 144 aged+Ri nuclei. Feature selection was performed on the original set of features to maximize the separation of samples according to our biological conditions of interest and minimize the information redundancy. First, statistically significant features which differ among young vs. aged and among aged vs. aged Ri-treated were found (Mann-Whitney U-test, p-value < 0.05). Later, features that exhibited absolute correlation values higher than 0.8 were discarded and the resulting sets from each pairwise comparison were merged to form a combined set of 20 features. These features were standardized and used to train the UMAP 52 model on young, aged and aged Ri-treated nuclei. Clustering was conducted in the original multidimensional parameter space using the K-means algorithm 102 with k=4 and depicted in the UMAP embedding. This value of k yielded the best silhouette scores for all clusters (Figure S3F). The resulting clusters revealed biologically relevant groups combining images from the young, aged, and aged+Ri conditions. Volcano plots showing feature importance for each of the identified clusters were generated by calculating the statistical significance (Mann-Whitney U-test) and log2 fold change of the average of each feature between the cluster and the average of the remaining clusters. Bone samples preparation for NanoIndenter Samples were obtained from young and aged mice. Mice were perfused with PFA 4% in PBS, bones were collected as shown in 103 and embedded in liquid OCT (CellPath, KMA-0100-00A) and frozen at -80°C. OCT is a cryo-embedding matrix, designed for cryostat sectioning at -10°C or below. For these experiments, humeri and femurs were used as they show an optimal ratio between rigidity of the bone walls and internal surface area. Samples for Chiaro NanoIndenter analysis were prepared by opening the bone structure and exposing the internal area of the bone marrow by gradually cutting transversally with a cryostat, keeping the sample at -20°C to avoid OCT thawing and exposing the bone marrow inner surface. The open bones were transferred to a 4-well Ibidi µ-slide (Ibidi, 80427) and partially embedded in agarose gel 4% (Condalab, 8010). The inner surface was covered with PBS to avoid tissue drying. Measurements with Chiaro NanoIndenter For measuring stiffness of the tissue, Chiaro NanoIndenter was employed, and the data were elaborated through Piuma software (Optics11Life). 0.5 N/m - 25,6 µm-diameter probes (Optics11Life) were used for these experiments. The probes were calibrated through the Piuma software automated procedure, using a glass Petri dish filled with PBS. After the calibration, the Petri dish was replaced with the Ibidi µ-slide containing the samples of interest. The measurement was performed programming a 30-points matrix, a group of sequential measurements covering the whole diameter of the bone marrow. The points were distant in width (D y ) 350 µm for the humerus and 450 µm for the femur; in length instead the distance (D x ) was 150 µm for both humerus and femur. At least 3 matrixes per sample were performed, two on the peri-epiphyseal part and one on the diaphyseal part. The effective Young’s modulus was derived from force vs. indentation curves, using a Hertzian model. The stiffness of the bone marrow was measured and calculated in kPa (kiloPascals) units. Protein Sample preparation Bone marrow cells for western blot were isolated by flushing bones from young and aged mice into HBSS (Gibco, 24020117), complemented with FBS 10% and P/S 1%. Cells were then centrifuged at 1500 RPM for 5 minutes at RT, resuspended in 3 ml HBSS per mouse and filtered through a 70 µm cell strainer. For each mouse, 27 ml of 1X Red Blood Lysis Buffer (Biolegend, 420301) were added and incubated for 5 minutes at RT in the dark. At the end of the incubation, PBS was added to the top of the tube, to stop the lysis reaction and dilute the buffer. Cells were then centrifuged at 1500 RPM for 5 minutes at 4°C, washed twice and resuspended in PBS. After cell count, cells were pelleted, washed with PBS and lysed with RIPA buffer (Pierce™ RIPA Buffer, ThermoFisher, 89900) complemented with protease inhibitor cocktail (cOmplete, Sigma Aldrich, 11697498001), for 15 minutes on ice, flicking the tube several times during the incubation. The cells were then centrifuged at 14,000 RPM for 15 minutes at 4°C; the supernatant containing the protein lysate was stored at -80ºC. RhoA-GTP bound Pull Down To isolate the active isoform of RhoA, GTP-bound RhoA, Active RhoA Detection Kit (Cell Signaling, 8820) was used following manufacturer instructions. Western Blotting Acrylamide gel was prepared, run and transferred in nitrocellulose membranes following the indications of the kit manufacturer (TGX™ FastCast™ Acrylamide Kit 12%, BioRad, 1610185). The transferred membrane was cut as needed and washed twice in TBS-T (Tris-buffered saline (TBS) 1x Tween20 0.1%) for 15 minutes each time, then blocked with low-fat milk 5% in TBS-T on a rocker for 1 hour at RT. Anti-RhoA (rabbit monoclonal, Cell Signaling, 2117) and anti-actin (mouse monoclonal, Sigma Aldrich, A2228-100UL) primary antibodies were diluted 1:100 and 1:2000 respectively in milk 5% in TBS-T and incubated in a cold room with the samples at 4°C overnight. After incubation, the membranes were washed twice in TBS-T for 15 minutes each, and incubated with HPR-goat anti-rabbit (Biorad, 1706515) and anti-mouse (Biorad, secondary antibodies (BioRad), diluted in milk 5% in TBS-T, at a concentration of 1:2000 and 1:5000 respectively, for 1 hour at RT. The membranes were then washed twice in TBS-T for 15 minutes. ECL Prime Western Blotting Detection Reagents (GE Healthcare, 28980926) was used for developing, by pipetting 500 µl of reagent A and B on the membrane and incubating it for 5 minutes in the dark. The antibodies were detected by UV reveal in BioRad’s ChemiDoc and analyzed using Image Lab 6.1 software (BioRad). Confinement assay The confinement device used in this protocol was adapted to a single well plate from a previously described method 25,104 . Briefly, the confinement device was made by a magnetic container, two metallic rings, a polymeric ring and a closing ring ( Figure S1A ). The compression is mediated by a pillar coverslip, a polymeric piston and the magnetic lid of the device that exerts the confinement pressure. 3,000 HSCs were seeded in a volume of 40 µl HBSS 10% FBS 1% P/S onto a 35-mm glass coverslip. The coverslip was previously functionalized with fibronectin: 40 µl of fibronectin (50 µg/ml) were applied upon the surface of the coverslip for 2 hours at RT, blocked with the same volume of BSA 2% for 1h at 37°C and washed with PBS Ca 2+ /Mg 2+ . The coverslip with cells was mounted in the confinement device and compressed with pillars of 3, 5 and 8 µm for 2 hours at 37°C in a hypoxic incubator (5% CO 2 , 3% O 2 ). Cells were then fixed directly in the confinement device with 1 ml of PFA 4.21% (BD Cytofix, Thermo Fisher, 15817828) at 4°C for 15’. The sample on the coverslip was then removed from the confiner and stocked at 4°C in PBS for a maximum of 2 weeks before staining. Hypotonic shock assay Briefly, HSCs were cultured on fibronectin functionalized coverslips in 30 µl of isotonic medium (HBSS 10% FBS 1% P/S) for 12h after sorting in a hypoxic incubator (5% CO 2 , 3% O 2 ). Then, the medium was removed almost completely carefully and replaced by fresh isotonic medium or by hypotonic medium. Hypotonic medium consists on a 0.75X dilution of isotonic medium with sterile distillated water. HSCs were incubated for 2h in the same hypoxic conditions and then fixed as explained above. Protocol of staining was performed as usual. Nuclear Wrinkling Analysis To analyze nuclear wrinkling in LaminB stained HSCs, we developed an automatic image analysis pipeline in Matlab (Mathworks, R2024b). Fluorescence images of LaminB were acquired in a LSM880 confocal microscope (Zeiss) commented in section above “ Immunofluorescence staining and confocal images acquisition” For each dataset, the equatorial maximum cross-section section was identified from Z-slices and 3 slices were selected above and below, covering approximately 50 percent of the nucleus surface with optimal resolution of surface wrinkles in the lateral X-Y dimension. Selected image slices were then subjected to binning (factor n=3) using a bilinear interpolation to reduce noise and facilitate subsequent processing steps, effectively preserving key structural information. A threshold intensity of 25 counts was applied to create a binary mask and to segment the regions of interest representing the nuclear envelope. A Gaussian blur with a sigma of 3 pixels was applied to the binary mask to smooth the segmented regions and facilitate the subsequent skeletonization of nucleus envelope features based on a morphological thinning algorithm. The skeletonization process enabled to extract the structural features of the segmented Lamin signal, highlighting the wrinkles of the nuclear envelope. To remove structural artefacts outside the nuclear region of interest, the skeletonized image was filled to generate an inner region (representing the inside of the nuclear wrinkles) and an outer region (around the nucleus periphery) by applying an inverted mask to the filled skeleton. This allowed to separate the nuclear envelope from the surrounding structures and detect the points representing nuclear invaginations. The coordinates of the nuclear surface and inner invaginations represent the total nuclear periphery and were extracted for further quantitative metrics of nuclear wrinkles. We derived the nuclear circularity (𝐶 = 4𝜋𝐴⁄𝑃 2 ) from the total perimeter 𝑃 and area 𝐴 of nuclear cross-sections from multiple Z-slices for each cell. The circularity provides a quantitative measure of nuclear envelope deformations, with values close to 1 indicating a more circular shape and values < 1 a highly deformed and wrinkled nuclear surface. In addition, we derived the excess folding parameter (𝐸 = 1 − 𝑝⁄𝑃) as the ratio of the boundary outline of the nucleus 𝑝 to the total perimeter 𝑃 of nuclear cross-sections, with values close to 0 resembling a circular shape and values closer to 1 indicating increased nuclear wrinkling. Statistical data analysis was performed in Prism (Version 10.2.3.) using a Kruskal-Wallis test for multiple comparisons. Bulk ATAC-seq of HSCs HSCs were isolated from young and aged animals via FACS sorting. Between 2,000 and 5,000 cells were isolated for each library and we prepared 3-5 biological replicates for each sample arm. Cells were cultured overnight without growth factors at 3% O2 and washed twice with PBS before processing. Aged cells from same animal were used as aged control sample and aged treated with Ri sample. Young cells from same animal were used as young control sample and young confined under 5μm sample. Cells were subjected to fragmentation of open chromatin regions using Tn5 transposase (Illumina), followed by a pre-amplification step, library preparation and subsequent paired-end sequencing. For the pre-amplification, NEBNext Ultra II Q5 Master Mix was used with Primer 1: 5’GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAG3’ and Primer 2: 5’TCGTCGGCAGCGTCAGATGTGTATAAGAGACAG3’. For dual-indexing, 10 μL of the pre-amplified ATAC reaction was used as input for Nextera XT index kit (Illumina). The generated libraries were quantified using an Agilent Bioanalyzer and a qPCR kit (New England Biolabs), pooled and subjected to next-generation sequencing in a NextSeq550 or Illumina HiSeq 2000 for paired-end 150 bp or 250 bp sequencing condition. Initial quality control was performed with FastQC v0.11.5. The ENCODE ATAC-seq pipeline v2.1.3 was used to process the FASTQ files, including adapter trimming, alignment to the reference genome (mm10), filtering and peak calling, and using the parameters \"atac.pipeline_type\" : \"atac\", \"atac.align_only\" : false, \"atac.true_rep_only\" : false, \"atac.paired_end\" : true, \"atac.auto_detect_adapter\" : true, \"atac.multimapping\" : 20, \"atac.mapq_thresh\" : 20. Samtools v1.14 105 was used to index BAM files. ATACseqQC v1.20.2 106 , BSgenome v1.64.0, Bsgenome.Mmusculus.UCSC.mm10 v1.4.3 and TxDb.Mmusculus.UCSC.mm10.knownGene v3.10.0 in R v4.2.0 and Bioconductor v3.15.2 were used for quality control and Tn5 shifting. Deeptools v3.5.1 107 was used to transform BAM files to normalized BigWig files (with parameters --effectiveGenomeSize 2407883318 --normalizeUsing RPKM --exactScaling --binSize 50 –extendReads) to be visualized in the Integrative Genomics Viewer (IGV) 108 web app and to check for read enrichment in transcription start sites (TSS). A consensus set of peaks was defined by taking those peaks detected in at least 6 samples. This threshold was determined using Monte Carlo simulation. Briefly, a binary matrix was generated including all detected peaks as rows and all samples as columns, with 1 for presence of the peaks and 0 for absence. The binary matrix was randomized 1,000 times and the number of peaks detected in all possible minimum number of samples was calculated for each randomized matrix. The chosen minimum number of samples was the one where the mean number of consensus peaks in the simulated data was ≈10% of the number of consensus peaks in the empirical data (false positive rate, FPR). The final 57,289 consensus peaks were resized to have a width of 700 bp. In the case of the young 5μm confined analysis, 42,632 consensus peaks were defined following the same strategy but using only the young and aged control samples. Peaks were annotated using annotatePeaks function from Homer v4.11.1 109 and a donut chart was plotted using ggplot2 v3.3.6, RColorBrewer v1.1-3 and ggrepel v0.9.1. In R v4.2.0 and Bioconductor v3.15.2, the featureCounts function from Rsubread v2.10.5 110 was used to count the number of reads in consensus peaks with parameters largestOverlap = TRUE, isPairedEnd = TRUE, countReadPairs = TRUE, requireBothEndsMapped = TRUE, checkFragLength = TRUE, minFragLength = 0, maxFragLength = 2000. The count matrix was transformed and normalized using the voom function from limma v3.52.1 111 and the quantile normalization method. Batch effects were removed with the removeBatchEffect function only for visualization purposes. Principal component analysis (PCA) was performed using the PCA function from FactoMineR v2.6 112 and the top 10,000 most variable peaks. limma was used to perform differential accessibility (DA) analysis between conditions, adding the batch as a covariate. The p-value threshold to determine the significance of the DA was defined using Monte Carlo simulation. Briefly, the normalized count matrix was randomized 1,000 times and the number of DARs at different p-value thresholds was calculated for each randomized matrix. The chosen p-value threshold was the one where the number of DARs in the simulated data was ≈6-8% of the number of DARs in the empirical data (FPR), a p-value of 0.005 in the case of the Ri analysis and 0.001 in the case of the young 5μm confined analysis. Volcano plots were plotted using ggplot2, ggrepel and patchwork v1.1.1. Venn diagram was plotted using VennDiagram v1.7.3 113 . Heatmap was plotted using pheatmap v1.0.12. GO enrichment analysis of genes close to DARs (considered close if the distance of the DAR to the TSS of the gene was less than 100 kb upstream or 25 kb downstream) was performed using clusterProfiler v4.4.1 114 , AnnotationDbi v1.58.0 and org.Mm.eg.db v3.15.0 with the function enrichGO and parameters ont = \"BP\", pvalueCutoff = 0.1, pAdjustMethod = \"BH\", minGSSize = 10, maxGSSize = 500, readable = TRUE. Selected GOs were plotted in radar plots using the function radarchart from fmsb v0.7.4. Barplots representing log2FCs in the proportions of genomic region types among the DARs from the different comparisons compared to the proportions in the consensus peaks were plotted using ggplot2, RColorBrewer and patchwork. Fisher’s exact tests were performed to compare the proportions in DARs vs the proportions in consensus peaks. One-proportion z-tests were used to compare the proportion of a specific genomic region (i.e. intron) among the DARs vs among the consensus peaks. TF-binding motifs in the different sets of DARs were found using the findMotifsGenome function from Homer, using the consensus peaks as background. In R v4.3.0 and Bioconductor v3.17, ATACseqTFEA v1.2.0 was used to obtain the coordinates of the Klf4 motif (Jaspar MA0039.4) across the DARs opening with Ri, which were later used to determine the Klf4-targeted genes (<100kb upstream or <25kb downstream). Bulk RNA-seq of HSCs RNA-seq libraries were generated from 2,000 pooled young and aged HSCs (n=3 biological repeats per condition). Cells were cultured overnight without growth factors at 3% O2 and washed twice with PBS before processing. Aged cells from same animal were used as control sample and aged treated with Ri sample. SMART-Seq® v4 Ultra® Low Input RNA Kit for Sequencing manufacturer’s protocol was strictly followed. We quantified the cDNA quality and quantity using an Agilent Bioanalyzer. For the library preparation, 150 pg of cDNA were used per sample using the NexteraXT index kit. We performed quality control of our libraries using an Agilent Bioanalyzer and quantifying with qPCR kit (New England Biolabs). Libraries were then pooled to be subjected to next generation sequencing in a NextSeq550 for paired-end 150 bp sequencing condition. After performing quality control with FastQC v0.11.5, adapters were removed from the FASTQ files using Cutadapt v1.18 115 with parameters -m 20 -O 6 -q 20. Reads were mapped to the reference genome mm10 (Mus Musculus GRCm38, Ensembl 102 Nov 2020) using STAR v2.7.0 116 and BAM files were sorted and indexed using Samtools v1.14 105 . Library complexity was estimated using Picard v2.26.7. Deeptools v3.5.1 107 was used to transform BAM files to normalized BigWig files (with parameters --effectiveGenomeSize 2407883318 --normalizeUsing CPM --exactScaling --binSize 50) to be visualized in IGV 108 web app and to check for read enrichment in exons. In R v4.2.0 and Bioconductor v3.15.2, the featureCounts function from Rsubread v2.10.5 110 was used to count the number of reads in genes for each sample, using Ensembl GTF annotation for GRCm38 102 version, filtered for protein coding genes and with parameters GTF.featureType = \"exon\", GTF.attrType = \"gene_name\", useMetaFeatures = TRUE, isPairedEnd = TRUE, countReadPairs = TRUE, requireBothEndsMapped = TRUE, countMultiMappingReads = TRUE, fraction = TRUE. The function filterByExpr from edgeR v3.38.1 117 was used to keep only those genes with more than 20 counts in at least three samples and a minimum total count of 60. The count matrix was transformed and normalized using the voom function from limma v3.52.1 111 and the quantile normalization method. 112 limma was used to perform differential expression (DE) analysis between the sequencing batches and 24 genes were removed from the analysis for showing significant batch effect (with <5% FPR; p-value < 0.0001). The DE analysis between conditions was also performed with limma. The p-value threshold to determine the significance of the DE was defined using Monte Carlo simulation as for the DA analysis in ATAC-seq. In this case, the chosen p-value threshold was the one where the FPR was ≈5%, a p-value of 0.001. Volcano plots were plotted using ggplot2 v3.3.6, ggrepel v0.9.1 and patchwork v1.1.1. Venn diagram was plotted using VennDiagram v1.7.3 113 . GSEA was performed using clusterProfiler v4.4.1 114 , AnnotationDbi v1.58.0 and org.Mm.eg.db v3.15.0 with the function gseGO and parameters ont = \"BP\", pvalueCutoff = 0.05, pAdjustMethod = \"BH\", minGSSize = 10, maxGSSize = 500, seed = TRUE, eps = 0. Selected GOs were plotted in radar plots using the function radarchart from fmsb v0.7.4 and the enrichment curve for “acute inflammatory response” was plotted using the gseaplot2 function of enrichplot v1.16.1. GSEA for the Interferome.org 71 (filtering by Species: Mus musculus; System: Haemopoietic/Immune; Cell: HSC or haematopoietic stem cells; and FC: 2), interferon-stimulated 72 and aging 73 signatures was performed with the function GSEA of clusterProfiler and parameters minGSSize = 1, maxGSSize = Inf, pvalueCutoff = Inf, pAdjustMethod = \"BH\", seed = TRUE and plotted using the gseaplot2 function of enrichplot. To analyse REs, the STAR alignment was repeated with parameters --winAnchorMultimapNmax 200 --outFilterMultimapNmax 100 to allow for more multimapping. The function TEcount from TEtranscripts v2.2.1 118 was used to count the reads mapping to RE subfamilies, using the annotation downloaded from Hammell’s lab website for GRCm38 Ensembl rmsk. The resulting count matrix was processed as previously described for the genes but keeping only those RE subfamilies with more than 10 counts in at least three samples and a minimum total count of 30. No RE subfamilies were significantly affected by the sequencing batch. The DE significance p-value threshold used was 0.005 (FPR ~8%). Venn diagram was plotted using VennDiagram. Heatmap was plotted using ComplexHeatmap v2.12.1 119 . Boxplot was plotted using ggplot2. The t-statistics for the Klf4-targeted genes were plotted with ggplot2 and ggrepel. GO enrichment of upregulated Klf4-targeted genes was performed using clusterProfiler, AnnotationDbi and org.Mm.eg.db with the function enrichGO and parameters ont = \"BP\", pvalueCutoff = 0.05, pAdjustMethod = \"BH\", minGSSize = 10, maxGSSize = 500, readable = TRUE. Selected GOs were plotted in a radar plot using the function radarchart from fmsb. Single-cell RNA seq LSK cells were sorted as explained previously from young and aged mice (n = 3 for each). Cells were incubated 16h in IMDM 10%FBS 1%P/S in the presence or absence of Ri 100µM and then profiled by using standard 10x Genomics protocols for single cell sequencing. After performing the quality control of the FASTQ files with FastQC v0.11.5, reads were aligned to the reference genome (GRCm38/mm10, annotation Ensembl 98), filtered, and counted using Cell Ranger software v7.2.0 (10x Genomics). Count matrices were pre-processed using R v4.2.0 and Seurat package v4.1.1 120 . In an initial filtering step, genes expressed in <10 cells and cells expressing <10 genes were discarded. Cells with >6% of mitochondrial RNA, <1,500 genes, >7,000 genes, <1,500 UMI counts, or >40,000 UMI counts were also discarded. We further discarded genes with less than 400 UMI counts (decided based on the distribution of the total counts per gene). We obtained a total of 60,648 cells and 15,049 genes to continue with the analysis. After log-normalizing the data, the genes defined by Kowalczyk et al. 121 associated to G0, early G1, late G1, S and G2/M cell cycle phases were used to score each cell for the average expression of each set of genes using the AddModuleScore function of Seurat. A cell cycle phase was assigned to each cell according to the highest score. Seurat’s SCTransform function was used to normalize the gene counts for each condition and sequencing batch separately, regressing out the effect of the number of genes, the number of UMI counts, the percentage of mitochondrial RNA, and the scores for the cell cycle phases in every cell and returning 3000 variable genes for each condition. Then, integration was performed following Seurat’s integration workflow, using 3000 integration features and canonical correlation analysis with 30 dimensions. The integrated dataset, 50 principal components (PCs), and 500 epochs were used to generate the UMAP. Clustering was performed with Seurat’s FindNeighbors and FindClusters functions, using 50 PCs, Louvain algorithm, and a resolution of 0.2, chosen after evaluating several resolutions with the clustree package v0.4.4 122 . The resulting clusters were annotated based on their markers (obtained after running the FindAllMarkers function on the log-normalized data with parameters test.use = 'wilcox', logfc.threshold = 0.25, min.pct = 0.1, only.pos = TRUE, return.thresh = 0.05), plotting the expression of genes known to be expressed in HSPCs, and projecting on our data different gene signatures defined in previous studies (like the MolO signature defined by Wilson et al. 82 , the low/high-output HSC signature defined by Rodriguez-Fraticelli et al. 83 , and the dormant/active HSC signature defined by Cabezas-Wallscheid et al. 84 ) using the AddModuleScore function of Seurat. Plots were generated using Seurat’s plotting functions and ggplot2 v3.3.6. scCODA 123 v0.1.9 was used to perform compositional analysis, using the cluster CyclingCells_2 as the reference (as automatically selected by the tool), and running the model 10 times to account for the randomness introduced by the MCMC sampling. Differences were considered statistically credible if at least 7 of the runs showed an effect. In the HSC cluster, differential expression analysis between the three conditions was performed using muscat 124 v1.10.1 (Bioconductor v3.15.2) and the muscat_analysis function of the muscatWrapper v1.0.0 R package on the log-normalized data with parameter de_method_oi = \"limma-voom\" and regressing out the effect of the sequencing batch. Significance was considered if |log2FC| > 1 and FDR < 0.05. MAplots were plotted using ggplot2 and ggrepel v0.9.1. To perform the GSEA of the signature for hemogenic precursors defined by Pereira et al. 85 , we first ordered the genes by their -log10(p-value) * sign(log2FC). Then the GSEA function from clusterProfiler v4.4.1 114 was used to determine the enrichment of the signature with the parameters minGSSize = 1, maxGSSize = Inf, pvalueCutoff = Inf, pAdjustMethod = \"BH\", seed = TRUE, eps = 0. The p-values were adjusted across the three pairwise comparisons between the conditions. The enrichment plot was generated using the gseaplot2 function of the package enrichplot v1.16.1. SCENIC v1.3.1 125 was used to calculate regulons (TF + target genes) activity per cell in the HSC cluster. These 225 regulon activity scores were used to integrate the data by condition and sequencing batch following Seurat’s integration workflow, using all regulons as integration features and canonical correlation analysis with 30 dimensions. The integrated data was scaled and centered using the ScaleData function of Seurat and the effect of the number of genes, the number of UMI counts, the percentage of mitochondrial RNA, and the scores for the cell cycle phases in every cell was regressed out. The UMAP was generated using 25 PCs and 500 epochs. The activity scores of interesting regulons were binarized based on the distribution of their values (AUC = 0.128, 0.007 and 0.01 for Klf4, Ctcf and Hoxa10, respectively). Differential regulon activity analysis between conditions was performed using muscat 124 and the muscat_analysis function of the muscatWrapper R package on the AUC values with parameter de_method_oi = \"limma-voom\" and regressing out the effect of the sequencing batch. Significance was considered if |log2FC| > 0.5 and FDR < 0.05. The activity scores of the significantly different regulons were plotted in a heatmap using ComplexHeatmap v2.12.1 119 . Declarations Data availability statement The source data underlying Figure 1-6 and Figure S1-6 is provided as a Source Data file . The source code for the microscopy image analyses showed on Figure 3 and Figure S3 is available at HYPERLINK \"https://github.com/biomedical-data-science/hsc_rhoa\". Sequencing data underlining Figure 4-5 and Figure S4-5 and Table S2-5 is deposited together with codes under the repository DOI https://doi.org/10.34810/data697. ATAC-seq, RNA-seq and scRNA-seq data are deposited at GEO (accession number GSE233989). Dilutions and catalogue numbers of all commercial antibodies are provided in the Source Data file . Acknowledgments & Funding Sources We thank support from Dr. Laia Traveset Martinez, IDIBELL Innovation Unit. We acknowledge support from Dr. Mercè Marti Gaudes, head of Technical Facilities at IDIBELL together with José Andres Vaquero (IDIBELL FACS and Flow cytometry SCT), Antoni Ventura (IDIBELL Mouse Facility SCT) and Joan Repulles and Saioa Mendizuri (IDIBELL Bioimaging SCT). We thank Esther Castaño, Beatriz Barroso and Benjamin Torrejon (CCiT-UB, Bellvitge). We thank Giulia Lunazzi and the National Center for Genomic Analysis (CNAG, Barcelona) for the support with scRNA-seq experiments. We thank CERCA Program/Generalitat de Catalunya for institutional support. We thank Conxi Lazaro (LCAM laboratory, ICO-HUB) for supporting sequencing experiments. We acknowledge the funding sources: European Research Council (ERC) grant 101002453 (MCF), Spanish Ministry of Science, Innovation and University grants RYC2018-025979-I (MCF) and PGC2018-102049-B-I00 (MCF) and INPhINIT Incoming fellowship from “la Caixa'' Foundation (ID 100010434) with code LCF/BQ/DI22/11940001 (PIP). VR acknowledges financial support from the Ministerio de Ciencia e Innovación through the Plan Nacional (PID2020-117011GB-I00) and funding from the European Union’s Horizon EIC-ESMEA Pathfinder program under grant agreement No 101046620. Declaration of interests The findings presented in this study are covered under patent application number EP25382180.5, filed on 27/02/2025. Author contributions Conceptualization: EM-R, MCF, PIP, BW Methodology: SM-V, EM-R, MCF, PIP, FP, FA, JLC, BW, FM, LR, SW Investigation: SM-V, EM-R, MCF, PIP, BW Visualization: SM-V, EM-R, PIP, LM Funding acquisition: MCF Project administration: MCF Supervision: YZ, VR, AR, PP, MCF Writing – original draft: EM-R, SM-V, MCF, PIP Writing – review & editing: AR, VR, YZ, PP, MCF References Verovskaya EV, Dellorusso PV, Passegué E (2019) Losing Sense of Self and Surroundings: Hematopoietic Stem Cell Aging and Leukemic Transformation. Trends Mol Med 25(6):494–515. https://doi.org/10.1016/J.MOLMED.2019.04.006 Brunet A, Goodell MA, Rando TA (2022) Ageing and Rejuvenation of Tissue Stem Cells and Their Niches. Nat Rev Mol Cell Biol 1–18. https://doi.org/10.1038/s41580-022-00510-w Mejia-Ramirez E, Florian MC (2020) Understanding Intrinsic Hematopoietic Stem Cell Aging. Haematologica 105(1):22–37. https://doi.org/10.3324/haematol.2018.211342 Geiger H, de Haan G, Florian MC (2013) The Ageing Haematopoietic Stem Cell Compartment. Nat Rev Immunol 13(5):376–389. https://doi.org/10.1038/nri3433 Poscablo DM, Worthington AK, Smith-Berdan S, Rommel MGE, Manso BA, Adili R, Mok L, Reggiardo RE, Cool T, Mogharrab R, Myers J, Dahmen S, Medina P, Beaudin AE, Boyer SW, Holinstat M, Jonsson VD, Forsberg EC (2024) An Age-Progressive Platelet Differentiation Path from Hematopoietic Stem Cells Causes Exacerbated Thrombosis. Cell 0(0). https://doi.org/10.1016/j.cell.2024.04.018 Matteini F, Montserrat-Vazquez S, Florian MC (2024) Rejuvenating Aged Stem Cells: Therapeutic Strategies to Extend Health and Lifespan. FEBS Lett 598(22):2776–2787. https://doi.org/10.1002/1873-3468.14865 Li H, Luo Q, Shan W, Cai S, Tie R, Xu Y, Lin Y, Qian P, Huang H (2021) Biomechanical Cues as Master Regulators of Hematopoietic Stem Cell Fate. Cell Mol Life Sci 78(16):5881–5902. https://doi.org/10.1007/s00018-021-03882-y Lundin V, Sugden WW, Theodore LN, Sousa PM, Han A, Chou S, Wrighton PJ, Cox AG, Ingber DE, Goessling W, Daley GQ, North TE (2020) YAP Regulates Hematopoietic Stem Cell Formation in Response to the Biomechanical Forces of Blood Flow. Dev Cell 52(4):446–460e5. https://doi.org/10.1016/j.devcel.2020.01.006 Shin J-W, Swift J, Ivanovska I, Spinler KR, Buxboim A, Discher DE (2013) Mechanobiology of Bone Marrow Stem Cells: From Myosin-II Forces to Compliance of Matrix and Nucleus in Cell Forms and Fates. Differentiation 86(3):77–86. https://doi.org/10.1016/j.diff.2013.05.001 Ivanovska IL, Shin JW, Swift J, Discher DE (2015) Stem Cell Mechanobiology: Diverse Lessons from Bone Marrow. Trends Cell Biol. https:/. /doi.org/S0962-8924(15)00072-0 [pii] 10.1016/j.tcb.2015.04.003 Starodubtseva MN (2011) Mechanical Properties of Cells and Ageing. Ageing Res Rev 10(1):16–25. https://doi.org/10.1016/j.arr.2009.10.005 Phillip JM, Aifuwa I, Walston J, Wirtz D (2015) The Mechanobiology of Aging. Annu Rev Biomed Eng 17:113–141. https://doi.org/10.1146/annurev-bioeng-071114-040829 López-Otín C, Blasco MA, Partridge L, Serrano M, Kroemer G (2023) Hallmarks of Aging: An Expanding Universe. Cell 186(2):243–278. https://doi.org/10.1016/j.cell.2022.11.001 Lessey EC, Guilluy C, Burridge K (2012) From Mechanical Force to RhoA Activation. Biochemistry (Mosc). 51 (38), 7420–7432. https://doi.org/10.1021/bi300758e Lessey EC, Guilluy C, Burridge K (2012) From Mechanical Force to RhoA Activation. Biochemistry 51(38):7420–7432. https://doi.org/10.1021/bi300758e Burridge K, Monaghan-Benson E, Graham DM, Mechanotransduction (2019) From the Cell Surface to the Nucleus via RhoA. Philosophical Trans Royal Soc B: Biol Sci 374(1779):20180229. https://doi.org/10.1098/rstb.2018.0229 Goldyn AM, Rioja BA, Spatz JP, Ballestrem C, Kemkemer R (2009) Force-Induced Cell Polarisation Is Linked to RhoA-Driven Microtubule-Independent Focal-Adhesion Sliding. J Cell Sci 122(20):3644–3651. https://doi.org/10.1242/jcs.054866 Venturini V, Pezzano F, Català Castro F, Häkkinen H-M, Jiménez-Delgado S, Colomer-Rosell M, Marro M, Tolosa-Ramon Q, Paz-López S, Valverde MA, Weghuber J, Loza-Alvarez P, Krieg M, Wieser S, Ruprecht V (2020) The Nucleus Measures Shape Changes for Cellular Proprioception to Control Dynamic Cell Behavior. Science 370(6514):eaba2644. https://doi.org/10.1126/science.aba2644 Mistriotis P, Wisniewski EO, Bera K, Keys J, Li Y, Tuntithavornwat S, Law RA, Perez-Gonzalez NA, Erdogmus E, Zhang Y, Zhao R, Sun SX, Kalab P, Lammerding J, Konstantopoulos K (2019) Confinement Hinders Motility by Inducing RhoA-Mediated Nuclear Influx, Volume Expansion, and Blebbing. J Cell Biol 218(12):4093–4111. https://doi.org/10.1083/jcb.201902057 Zhou X, Florian MC, Arumugam P, Chen X, Cancelas JA, Lang R, Malik P, Geiger H, Zheng Y (2013) RhoA GTPase Controls Cytokinesis and Programmed Necrosis of Hematopoietic Progenitors. J Exp Med 210(11):2371–2385. https://doi.org/10.1084/jem.20122348 Enyedi B, Niethammer P (2017) Nuclear Membrane Stretch and Its Role in Mechanotransduction. Nucleus 8 (2), 156–161. https://doi.org/10.1080/19491034.2016.1263411 Enyedi B, Jelcic M, Niethammer P (2016) The Cell Nucleus Serves as a Mechanotransducer of Tissue Damage-Induced Inflammation. Cell 165(5):1160–1170. https://doi.org/10.1016/j.cell.2016.04.016 Lomakin AJ, Cattin CJ, Cuvelier D, Alraies Z, Molina M, Nader GPF, Srivastava N, Sáez PJ, Garcia-Arcos JM, Zhitnyak IY, Bhargava A, Driscoll MK, Welf ES, Fiolka R, Petrie RJ, De Silva NS, González-Granado JM, Manel N, Lennon-Duménil AM, Müller DJ, Piel M (2020) The Nucleus Acts as a Ruler Tailoring Cell Responses to Spatial Constraints. Science 370(6514):eaba2894. https://doi.org/10.1126/science.aba2894 Le Berre M, Aubertin J, Piel M (2012) Fine Control of Nuclear Confinement Identifies a Threshold Deformation Leading to Lamina Rupture and Induction of Specific Genes. Integr Biol 4(11):1406. https://doi.org/10.1039/c2ib20056b Venturini V, Pezzano F, Català Castro F, Häkkinen H-M, Jiménez-Delgado S, Colomer-Rosell M, Marro M, Tolosa-Ramon Q, Paz-López S, Valverde MA, Weghuber J, Loza-Alvarez P, Krieg M, Wieser S, Ruprecht V (2020) The Nucleus Measures Shape Changes for Cellular Proprioception to Control Dynamic Cell Behavior. Science 370(6514):eaba2644. https://doi.org/10.1126/science.aba2644 Lomakin AJ, Cattin CJ, Cuvelier D, Alraies Z, Molina M, Nader GPF, Srivastava N, Sáez PJ, Garcia-Arcos JM, Zhitnyak IY, Bhargava A, Driscoll MK, Welf ES, Fiolka R, Petrie RJ, De Silva NS, González-Granado JM, Manel N, Lennon-Duménil AM, Müller DJ, Piel M (2020) The Nucleus Acts as a Ruler Tailoring Cell Responses to Spatial Constraints. Science 370(6514):eaba2894. https://doi.org/10.1126/science.aba2894 Nielsen LK, Risbo J, Callisen TH, Bjørnholm T (1999) Lag-Burst Kinetics in Phospholipase A2 Hydrolysis of DPPC Bilayers Visualized by Atomic Force Microscopy. Biochim et Biophys Acta (BBA) - Biomembr 1420(1):266–271. https://doi.org/10.1016/S0005-2736(99)00103-0 Leidy C, Ocampo J, Duelund L, Mouritsen OG, Jørgensen K, Peters GH (2011) Membrane Restructuring by Phospholipase A2 Is Regulated by the Presence of Lipid Domains. Biophys J 101(1):90–99. https://doi.org/10.1016/j.bpj.2011.02.062 Garcia MC, Williams J, Johnson K, Olden K, Roberts JD (2011) Arachidonic Acid Stimulates Formation of a Novel Complex Containing Nucleolin and RhoA. FEBS Lett 585(4):618–622. https://doi.org/10.1016/j.febslet.2011.01.035 Garcia MC, Ray DM, Lackford B, Rubino M, Olden K, Roberts JD (2009) Arachidonic Acid Stimulates Cell Adhesion through a Novel P38 MAPK-RhoA Signaling Pathway That Involves Heat Shock Protein 27*. J Biol Chem 284(31):20936–20945. https://doi.org/10.1074/jbc.M109.020271 Chen T, Sun H, Lu J, Zhao Y, Tao D, Li X, Huang B (2002) Histone Acetylation Is Involved in Hsp70 Gene Transcription Regulation in Drosophila Melanogaster. Arch Biochem Biophys 408(2):171–176. https://doi.org/10.1016/S0003-9861(02)00564-7 Kulka LAM, Fangmann P-V, Panfilova D, Olzscha H (2020) Impact of HDAC Inhibitors on Protein Quality Control Systems: Consequences for Precision Medicine in Malignant Disease. Frontiers in Cell and Developmental Biology 8 Grigoryan A, Guidi N, Senger K, Liehr T, Soller K, Marka G, Vollmer A, Markaki Y, Leonhardt H, Buske C, Lipka DB, Plass C, Zheng Y, Mulaw MA, Geiger H, Florian MC (2018) LaminA/C Regulates Epigenetic and Chromatin Architecture Changes upon Aging of Hematopoietic Stem Cells. Genome biology 19 (1), 189–189. https://doi.org/10.1186/s13059-018-1557-3 Roffay C, Molinard G, Kim K, Urbanska M, Andrade V, Barbarasa V, Nowak P, Mercier V, García-Calvo J, Matile S, Loewith R, Echard A, Guck J, Lenz M, Roux A (2021) Passive Coupling of Membrane Tension and Cell Volume during Active Response of Cells to Osmosis. Proc Natl Acad Sci U S A 118(47):e2103228118. https://doi.org/10.1073/pnas.2103228118 Piccolo S, Dupont S, Cordenonsi M (2014) The Biology of YAP/TAZ: Hippo Signaling and Beyond. Physiol Rev 94(4):1287–1312. https://doi.org/10.1152/physrev.00005.2014 Dupont S, Morsut L, Aragona M, Enzo E, Giulitti S, Cordenonsi M, Zanconato F, Le Digabel J, Forcato M, Bicciato S, Elvassore N, Piccolo S (2011) Role of YAP/TAZ in Mechanotransduction. Nature 474(7350):179–183. https://doi.org/10.1038/nature10137 Chen X, Hughes R, Mullin N, Hawkins RJ, Holen I, Brown NJ, Hobbs JK (2020) Mechanical Heterogeneity in the Bone Microenvironment as Characterized by Atomic Force Microscopy. Biophys J 119(3):502–513. https://doi.org/10.1016/j.bpj.2020.06.026 Zhang P, Zhang C, Li J, Han J, Liu X, Yang H (2019) The Physical Microenvironment of Hematopoietic Stem Cells and Its Emerging Roles in Engineering Applications. Stem Cell Res Ther 10(1):327. https://doi.org/10.1186/s13287-019-1422-7 Ingallina E, Sorrentino G, Bertolio R, Lisek K, Zannini A, Azzolin L, Severino LU, Scaini D, Mano M, Mantovani F, Rosato A, Bicciato S, Piccolo S, Del Sal G (2018) Mechanical Cues Control Mutant P53 Stability through a Mevalonate-RhoA Axis. Nat Cell Biol 20(1):28–35. https://doi.org/10.1038/s41556-017-0009-8 Phillip JM, Aifuwa I, Walston J, Wirtz D (2015) The Mechanobiology of Aging. Annu Rev Biomed Eng 17(1):113–141. https://doi.org/10.1146/annurev-bioeng-071114-040829 Koester J, Miroshnikova YA, Ghatak S, Chacón-Martínez CA, Morgner J, Li X, Atanassov I, Altmüller J, Birk DE, Koch M, Bloch W, Bartusel M, Niessen CM, Rada-Iglesias A, Wickström SA (2021) Niche Stiffening Compromises Hair Follicle Stem Cell Potential during Ageing by Reducing Bivalent Promoter Accessibility. Nat Cell Biol 23(7):771–781. https://doi.org/10.1038/s41556-021-00705-x Zhang X, Cao D, Xu L, Xu Y, Gao Z, Pan Y, Jiang M, Wei Y, Wang L, Liao Y, Wang Q, Yang L, Xu X, Gao Y, Gao S, Wang J, Yue R (2023) Harnessing Matrix Stiffness to Engineer a Bone Marrow Niche for Hematopoietic Stem Cell Rejuvenation. Cell Stem Cell 30(4):378–395e8. https://doi.org/10.1016/j.stem.2023.03.005 Shang X, Marchioni F, Sipes N, Evelyn CR, Jerabek-Willemsen M, Duhr S, Seibel W, Wortman M, Zheng Y (2012) Rational Design of Small Molecule Inhibitors Targeting RhoA Subfamily Rho GTPases. Chem Biol 19(6):699–710. https://doi.org/10.1016/j.chembiol.2012.05.009 Al Jord A, Letort G, Chanet S, Tsai F-C, Antoniewski C, Eichmuller A, Da Silva C, Huynh J-R, Gov NS, Voituriez R, Terret M-É, Verlhac M-H (2022) Cytoplasmic Forces Functionally Reorganize Nuclear Condensates in Oocytes. Nat Commun 13(1):5070. https://doi.org/10.1038/s41467-022-32675-5 Jackson JA, Romeo N, Mietke A, Burns KJ, Totz JF, Martin AC, Dunkel J, Alsous JI (2023) Scaling Behaviour and Control of Nuclear Wrinkling. Nat Phys 19(12):1927–1935 Totaro A, Panciera T, Piccolo S (2018) YAP/TAZ Upstream Signals and Downstream Responses. Nat Cell Biol 20(8):888–899. https://doi.org/10.1038/s41556-018-0142-z Kalukula Y, Stephens AD, Lammerding J, Gabriele S (2022) Mechanics and Functional Consequences of Nuclear Deformations. Nat Rev Mol Cell Biol 23(9):583–602. https://doi.org/10.1038/s41580-022-00480-z Kim KM, Mura-Meszaros A, Tollot M, Krishnan MS, Gründl M, Neubert L, Groth M, Rodriguez-Fraticelli A, Svendsen AF, Campaner S, Andreas N, Kamradt T, Hoffmann S, Camargo FD, Heidel FH, Bystrykh LV, de Haan G (2022) Eyss, B. Taz Protects Hematopoietic Stem Cells from an Aging-Dependent Decrease in PU.1 Activity. Nat Commun 13(1):5187. https://doi.org/10.1038/s41467-022-32970-1 Mascetti G, Carrara S, Vergani L (2001) Relationship between Chromatin Compactness and Dye Uptake for in Situ Chromatin Stained with DAPI. Cytometry 44 (2), 113–119. https://doi.org/10.1002/1097-0320(20010601)44:2<113::AID-CYTO1089>3.0.CO;2-A Linhoff MW, Garg SK, Mandel GA, High-Resolution (2015) Imaging Approach to Investigate Chromatin Architecture in Complex Tissues. Cell 163(1):246–255. https://doi.org/10.1016/j.cell.2015.09.002 Long JT, Lammerding J (2021) Nuclear Deformation Lets Cells Gauge Their Physical Confinement. Dev Cell 56(2):156–158. https://doi.org/10.1016/j.devcel.2021.01.002 McInnes L, Healy J, Saul N, Großberger LUMAP (2018) Uniform Manifold Approximation and Projection. J Open Source Softw 3(29):861. https://doi.org/10.21105/joss.00861 Goldman RD, Shumaker DK, Erdos MR, Eriksson M, Goldman AE, Gordon LB, Gruenbaum Y, Khuon S, Mendez M, Varga R, Collins FS (2004) Accumulation of Mutant Lamin A Causes Progressive Changes in Nuclear Architecture in Hutchinson–Gilford Progeria Syndrome. Proc. Natl. Acad. Sci. U.S.A. 101 (24), 8963–8968. https://doi.org/10.1073/pnas.0402943101 Chambers SM, Shaw CA, Gatza C, Fisk CJ, Donehower LA, Goodell MA (2007) Aging Hematopoietic Stem Cells Decline in Function and Exhibit Epigenetic Dysregulation. PLoS Biol 5 (8), e201 Chambers SM, Goodell MA (2007) Hematopoietic Stem Cell Aging: Wrinkles in Stem Cell Potential. Stem Cell Rev 3(3):201–211 Sun D, Luo M, Jeong M, Rodriguez B, Xia Z, Hannah R, Wang H, Le T, Faull KF, Chen R, Gu H, Bock C, Meissner A, Gottgens B, Darlington GJ, Li W, Goodell MA (2014) Epigenomic Profiling of Young and Aged HSCs Reveals Concerted Changes during Aging That Reinforce Self-Renewal. Cell Stem Cell 14 (5), 673–688. S1934-5909(14)00096-4 [pii] 10.1016/j.stem.2014.03.002 Pal S, Tyler JK, Epigenetics, Aging (2016) Sci Adv 2(7):e1600584. https://doi.org/10.1126/sciadv.1600584 Tsurumi A, Li WX (2012) Global Heterochromatin Loss: A Unifying Theory of Aging? Epigenetics 7 (7), 680–688. https://doi.org/10.4161/epi.20540 Padeken J, Methot SP, Gasser SM (2022) Establishment of H3K9-Methylated Heterochromatin and Its Functions in Tissue Differentiation and Maintenance. Nat Rev Mol Cell Biol 23(9):623–640. https://doi.org/10.1038/s41580-022-00483-w Kirby TJ, Lammerding J (2018) Emerging Views of the Nucleus as a Cellular Mechanosensor. Nat Cell Biol 20(4):373–381. https://doi.org/10.1038/s41556-018-0038-y Zhang D, Zhang R, Song X, Yan KC, Liang H (2021) Uniaxial Cyclic Stretching Promotes Chromatin Accessibility of Gene Loci Associated With Mesenchymal Stem Cells Morphogenesis and Osteogenesis. Front Cell Dev Biol 9:664545. https://doi.org/10.3389/fcell.2021.664545 Nava MM, Miroshnikova YA, Biggs LC, Whitefield DB, Metge F, Boucas J, Vihinen H, Jokitalo E, Li X, García Arcos JM, Hoffmann B, Merkel R, Niessen CM, Dahl KN, Wickström SA (2020) Heterochromatin-Driven Nuclear Softening Protects the Genome against Mechanical Stress-Induced Damage. Cell 181(4):800–817e22. https://doi.org/10.1016/j.cell.2020.03.052 Grigoryan A, Pospiech J, Krämer S, Lipka D, Liehr T, Geiger H, Kimura H, Mulaw MA, Florian MC (2021) Attrition of X Chromosome Inactivation in Aged Hematopoietic Stem Cells. Stem Cell Rep 16(4):708–716. https://doi.org/10.1016/j.stemcr.2021.03.007 Itokawa N, Oshima M, Koide S, Takayama N, Kuribayashi W, Nakajima-Takagi Y, Aoyama K, Yamazaki S, Yamaguchi K, Furukawa Y, Eto K, Iwama A (2022) Epigenetic Traits Inscribed in Chromatin Accessibility in Aged Hematopoietic Stem Cells. Nat Commun 13(1):2691. https://doi.org/10.1038/s41467-022-30440-2 Gorbunova V, Seluanov A, Mita P, McKerrow W, Fenyö D, Boeke JD, Linker SB, Gage FH, Kreiling JA, Petrashen AP, Woodham TA, Taylor JR, Helfand SL, Sedivy JM (2021) The Role of Retrotransposable Elements in Aging and Age-Associated Diseases. Nature 596(7870):43–53. https://doi.org/10.1038/s41586-021-03542-y Lara-Astiaso D, Weiner A, Lorenzo-Vivas E, Zaretsky I, Jaitin DA, David E, Keren-Shaul H, Mildner A, Winter D, Jung S, Friedman N, Amit I (2014) Immunogenetics. Chromatin State Dynamics during Blood Formation. Science 345(6199):943–949. https://doi.org/10.1126/science.1256271 De Cecco M, Ito T, Petrashen AP, Elias AE, Skvir NJ, Criscione SW, Caligiana A, Brocculi G, Adney EM, Boeke JD, Le O, Beausejour C, Ambati J, Ambati K, Simon M, Seluanov A, Gorbunova V, Slagboom PE, Helfand SL, Neretti N, Sedivy JM (2019) L1 Drives IFN in Senescent Cells and Promotes Age-Associated Inflammation. Nature 566(7742):73–78. https://doi.org/10.1038/s41586-018-0784-9 Della Valle F, Reddy P, Yamamoto M, Liu P, Saera-Vila A, Bensaddek D, Zhang H, Prieto Martinez J, Abassi L, Celii M, Ocampo A, Nuñez Delicado E, Mangiavacchi A, Aiese Cigliano R, Rodriguez Esteban C, Horvath S, Izpisua Belmonte JC, Orlando V (2022) LINE-1 RNA Causes Heterochromatin Erosion and Is a Target for Amelioration of Senescent Phenotypes in Progeroid Syndromes. Sci Transl Med 14(657):eabl6057. https://doi.org/10.1126/scitranslmed.abl6057 Hidaoui D, Porquet A, Chelbi R, Bohm M, Polyzou A, Alcazer V, Depil S, Imanci A, Morabito M, Renneville A, Selimoglu-Buet D, Thépot S, Itzykson R, Laplane L, Droin N, Trompouki E, Elvira-Matelot E, Solary E, Porteu F (2024) Targeting Heterochromatin Eliminates Chronic Myelomonocytic Leukemia Malignant Stem Cells through Reactivation of Retroelements and Immune Pathways. Commun Biol 7:1555. https://doi.org/10.1038/s42003-024-07214-1 Clapes T, Polyzou A, Prater P, Sagar; Morales-Hernández A, Ferrarini MG, Kehrer N, Lefkopoulos S, Bergo V, Hummel B, Obier N, Maticzka D, Bridgeman A, Herman JS, Ilik I, Klaeylé L, Rehwinkel J, McKinney-Freeman S, Backofen R, Akhtar A, Cabezas-Wallscheid N, Sawarkar R, Rebollo R, Grün D, Trompouki E (2021) Chemotherapy-Induced Transposable Elements Activate MDA5 to Enhance Haematopoietic Regeneration. Nat Cell Biol 23(7):704–717. https://doi.org/10.1038/s41556-021-00707-9 Rusinova I, Forster S, Yu S, Kannan A, Masse M, Cumming H, Chapman R, Hertzog PJ (2013) INTERFEROME v2.0: An Updated Database of Annotated Interferon-Regulated Genes. Nucleic Acids Res 41(D1):D1040–D1046. https://doi.org/10.1093/nar/gks1215 Bouman BJ, Demerdash Y, Sood S, Grünschläger F, Pilz F, Itani AR, Kuck A, Marot-Lassauzaie V, Haas S, Haghverdi L, Essers MA (2024) Single-Cell Time Series Analysis Reveals the Dynamics of HSPC Response to Inflammation. Life Sci Alliance 7(3). https://doi.org/10.26508/lsa.202302309 Flohr Svendsen A, Yang D, Kim K, Lazare S, Skinder N, Zwart E, Mura-Meszaros A, Ausema A, von Eyss B, de Haan G, Bystrykh L (2021) A Comprehensive Transcriptome Signature of Murine Hematopoietic Stem Cell Aging. Blood 138(6):439–451. https://doi.org/10.1182/blood.2020009729 Edginton-White B, Maytum A, Kellaway SG, Goode DK, Keane P, Pagnuco I, Assi SA, Ames L, Clarke M, Cockerill PN, Göttgens B, Cazier JB, Bonifer CA (2023) Genome-Wide Relay of Signalling-Responsive Enhancers Drives Hematopoietic Specification. Nat Commun 14(1):267. https://doi.org/10.1038/s41467-023-35910-9 Di Giammartino DC, Kloetgen A, Polyzos A, Liu Y, Kim D, Murphy D, Abuhashem A, Cavaliere P, Aronson B, Shah V, Dephoure N, Stadtfeld M, Tsirigos A, Apostolou E (2019) KLF4 Is Involved in the Organization and Regulation of Pluripotency-Associated Three-Dimensional Enhancer Networks. Nat Cell Biol 21(10):1179–1190. https://doi.org/10.1038/s41556-019-0390-6 Park CS, Shen Y, Lewis A, Lacorazza HD (2016) Role of the Reprogramming Factor KLF4 in Blood Formation. J Leukoc Biol 99(5):673–685. https://doi.org/10.1189/jlb.1RU1215-539R Xie L, Torigoe SE, Xiao J, Mai DH, Li L, Davis FP, Dong P, Marie-Nelly H, Grimm J, Lavis L, Darzacq X, Cattoglio C, Liu Z, Tjian R (2017) A Dynamic Interplay of Enhancer Elements Regulates Klf4 Expression in Naïve Pluripotency. Genes Dev 31(17):1795–1808. https://doi.org/10.1101/gad.303321.117 Roisman A, Adelman ER, Huang H-T, Wade D, Bilbao D, Figueroa ME (2019) Loss of KLF6 Recapitulates Molecular and Functional Changes Associated with Aging in Human Hematopoietic Stem and Progenitor Cells. Blood 134(Supplement1):447. https://doi.org/10.1182/blood-2019-130800 Adelman ER, Huang HT, Roisman A, Olsson A, Colaprico A, Qin T, Lindsley RC, Bejar R, Salomonis N, Grimes HL, Figueroa ME (2019) Aging Human Hematopoietic Stem Cells Manifest Profound Epigenetic Reprogramming of Enhancers That May Predispose to Leukemia. Cancer Discov 9(8):1080–1101. https://doi.org/10.1158/2159-8290.CD-18-1474 McGinn J, Hallou A, Han S, Krizic K, Ulyanchenko S, Iglesias-Bartolome R, England FJ, Verstreken C, Chalut KJ, Jensen KB, Simons BD, Alcolea MP (2021) A Biomechanical Switch Regulates the Transition towards Homeostasis in Oesophageal Epithelium. Nat Cell Biol 23(5):511–525. https://doi.org/10.1038/s41556-021-00679-w Mas G, Santoro F, Blanco E, Gamarra Figueroa GP, Le Dily F, Frigè G, Vidal E, Mugianesi F, Ballaré C, Gutierrez A, Sparavier A, Marti-Renom MA, Minucci S, Di Croce L (2022) Vivo Temporal Resolution of Acute Promyelocytic Leukemia Progression Reveals a Role of Klf4 in Suppressing Early Leukemic Transformation. Genes Dev 36(7–8):451–467. https://doi.org/10.1101/gad.349115.121 Wilson NK, Kent DG, Buettner F, Shehata M, Macaulay IC, Calero-Nieto FJ, Sanchez Castillo M, Oedekoven CA, Diamanti E, Schulte R, Ponting CP, Voet T, Caldas C, Stingl J, Green AR, Theis FJ, Gottgens B (2015) Combined Single-Cell Functional and Gene Expression Analysis Resolves Heterogeneity within Stem Cell Populations. Cell Stem Cell 16(6):712–724. https://doi.org/10.1016/j.stem.2015.04.004 Rodriguez-Fraticelli AE, Weinreb C, Wang S-W, Migueles RP, Jankovic M, Usart M, Klein AM, Lowell S, Camargo FD (2020) Single-Cell Lineage Tracing Unveils a Role for TCF15 in Haematopoiesis. Nature 583(7817):585–589. https://doi.org/10.1038/s41586-020-2503-6 Cabezas-Wallscheid N, Buettner F, Sommerkamp P, Klimmeck D, Ladel L, Thalheimer FB, Pastor-Flores D, Roma LP, Renders S, Zeisberger P, Przybylla A, Schonberger K, Scognamiglio R, Altamura S, Florian CM, Fawaz M, Vonficht D, Tesio M, Collier P, Pavlinic D, Geiger H, Schroeder T, Benes V, Dick TP, Rieger MA, Stegle O, Trumpp A (2017) Vitamin A-Retinoic Acid Signaling Regulates Hematopoietic Stem Cell Dormancy. Cell 169(5):807–823e19. https://doi.org/10.1016/j.cell.2017.04.018 Pereira C-F, Chang B, Gomes A, Bernitz J, Papatsenko D, Niu X, Swiers G, Azzoni E, de Bruijn MFTR, Schaniel C, Lemischka IR, Moore KA (2016) Hematopoietic Reprogramming In Vitro Informs In Vivo Identification of Hemogenic Precursors to Definitive Hematopoietic Stem Cells. Dev Cell 36(5):525–539. https://doi.org/10.1016/j.devcel.2016.02.011 Florian MC, Dörr K, Niebel A, Daria D, Schrezenmeier H, Rojewski M, Filippi M-D, Hasenberg A, Gunzer M, Scharffetter-Kochanek K, Zheng Y, Geiger H (2012) Cdc42 Activity Regulates Hematopoietic Stem Cell Aging and Rejuvenation. Cell Stem Cell 10(5):520–530. https://doi.org/10.1016/j.stem.2012.04.007 Florian MC, Klose M, Sacma M, Jablanovic J, Knudson L, Nattamai KJ, Marka G, Vollmer A, Soller K, Sakk V, Cabezas-Wallscheid N, Zheng Y, Mulaw MA, Glauche I, Geiger H (2018) Aging Alters the Epigenetic Asymmetry of HSC Division. PLoS Biol 16(9):e2003389–e2003389. https://doi.org/10.1371/journal.pbio.2003389 Montserrat-Vazquez S, Ali NJ, Matteini F, Lozano J, Zhaowei T, Mejia-Ramirez E, Marka G, Vollmer A, Soller K, Sacma M, Sakk V, Mularoni L, Mallm JP, Plass M, Zheng Y, Geiger H, Florian MC (2022) Transplanting Rejuvenated Blood Stem Cells Extends Lifespan of Aged Immunocompromised Mice. npj Regen Med 7(1):1–17. https://doi.org/10.1038/s41536-022-00275-y Luche H, Weber O, Nageswara Rao T, Blum C, Fehling HJ (2007) Faithful Activation of an Extra-Bright Red Fluorescent Protein in Knock-in Cre-Reporter Mice Ideally Suited for Lineage Tracing Studies. Eur J Immunol 37(1):43–53. https://doi.org/10.1002/eji.200636745 Matteini F, Montserrat-Vazquez S, Florian MC Rejuvenating Aged Stem Cells: Therapeutic Strategies to Extend Health and Lifespan., FEBS Letters n/a (n/a). https://doi.org/10.1002/1873-3468.14865 Maurer M, Lammerding J (2019) The Driving Force: Nuclear Mechanotransduction in Cellular Function, Fate, and Disease. Annu Rev Biomed Eng 21:443–468. https://doi.org/10.1146/annurev-bioeng-060418-052139 Xia Y, Pfeifer CR, Cho S, Discher DE, Irianto J (2018) Nuclear Mechanosensing. Emerg Top Life Sci 2(5):713–725. https://doi.org/10.1042/ETLS20180051 Hegde S, Akbar H, Wellendorf AM, Nestheide S, Johnson JF, Zhao X, Setchell KD, Zheng Y, Cancelas JA (2024) Inhibition of RHOA Activity Preserves the Survival and Hemostasis Function of Long-Term Cold-Stored Platelets. Blood 144(16):1732–1746. https://doi.org/10.1182/blood.2023021453 De Cecco M, Criscione SW, Peterson AL, Neretti N, Sedivy JM, Kreiling JA (2013) Transposable Elements Become Active and Mobile in the Genomes of Aging Mammalian Somatic Tissues. Aging 5(12):867–883. https://doi.org/10.18632/aging.100621 Wang Y, Zheng J, Luo Y, Wang J, Xu L, Wang J, Sedivy JM, Song Z, Wang H, Ju Z (2020) L1 Drives HSC Aging and Affects Prognosis of Chronic Myelomonocytic Leukemia. Sig Transduct Target Ther 5(1):1–4. https://doi.org/10.1038/s41392-020-00279-4 Jaganathan BG, Anjos-Afonso F, Kumar A, Bonnet D, Active (2013) RHOA Favors Retention of Human Hematopoietic Stem/Progenitor Cells in Their Niche. J Biomed Sci 20:66. https://doi.org/10.1186/1423-0127-20-66 Yu Y, Schleich K, Yue B, Ji S, Lohneis P, Kemper K, Silvis MR, Qutob N, van Rooijen E, Werner-Klein M, Li L, Dhawan D, Meierjohann S, Reimann M, Elkahloun A, Treitschke S, Dorken B, Speck C, Mallette FA, Zon LI, Holmen SL, Peeper DS, Samuels Y, Schmitt CA, Lee S (2018) Targeting the Senescence-Overriding Cooperative Activity of Structurally Unrelated H3K9 Demethylases in Melanoma. Cancer Cell 33(4):785. https://doi.org/10.1016/j.ccell.2018.03.009 Adolfsson J, Månsson R, Buza-Vidas N, Hultquist A, Liuba K, Jensen CT, Bryder D, Yang L, Borge O-J, Thoren LAM, Anderson K, Sitnicka E, Sasaki Y, Sigvardsson M, Jacobsen SEW (2005) Identification of Flt3 + Lympho-Myeloid Stem Cells Lacking Erythro-Megakaryocytic Potential: A Revised Road Map for Adult Blood Lineage Commitment. Cell 121(2):295–306. https://doi.org/10.1016/j.cell.2005.02.013 Chambolle A (2004) An Algorithm for Total Variation Minimization and Applications. J Math Imaging Vis 20(1):89–97. https://doi.org/10.1023/B:JMIV.0000011325.36760.1e Otsu N (1979) A Threshold Selection Method from Gray-Level Histograms. IEEE Trans Syst Man Cybernetics 9(1):62–66. https://doi.org/10.1109/TSMC.1979.4310076 Walt S (2014) Schönberger, J. L.; Nunez-Iglesias, J.; Boulogne, F.; Warner, J. D.; Yager, N.; Gouillart, E.; Yu, T. Scikit-Image: Image Processing in Python. PeerJ 2 , e453. https://doi.org/10.7717/peerj.453 Hartigan JA, Wong MA, Algorithm (1979) AS 136: A K-Means Clustering Algorithm. J Royal Stat Soc Ser C (Applied Statistics) 28(1):100–108. https://doi.org/10.2307/2346830 Saçma M, Matteini F, Mulaw MA, Hageb A, Bogeska R, Sakk V, Vollmer A, Marka G, Soller K, Milsom MD, Florian MC, Geiger H (2022) Fast and High-Fidelity in Situ 3D Imaging Protocol for Stem Cells and Niche Components for Mouse Organs and Tissues. STAR Protocols 3(3):101483. https://doi.org/10.1016/j.xpro.2022.101483 Le Berre M, Zlotek-Zlotkiewicz E, Bonazzi D, Lautenschlaeger F, Piel M (2014) Methods for Two-Dimensional Cell Confinement. Methods Cell Biol 121:213–229. https://doi.org/10.1016/B978-0-12-800281-0.00014-2 Danecek P, Bonfield JK, Liddle J, Marshall J, Ohan V, Pollard MO, Whitwham A, Keane T, McCarthy SA, Davies RM, Li H (2021) Twelve Years of SAMtools and BCFtools. GigaScience 10 (2), giab008. https://doi.org/10.1093/gigascience/giab008 Ou J, Liu H, Yu J, Kelliher MA, Castilla LH, Lawson ND, Zhu LJ (2018) ATACseqQC: A Bioconductor Package for Post-Alignment Quality Assessment of ATAC-Seq Data. BMC Genomics 19(1):169. https://doi.org/10.1186/s12864-018-4559-3 Ramírez F, Ryan DP, Grüning B, Bhardwaj V, Kilpert F, Richter AS, Heyne S, Dündar F, Manke T (2016) deepTools2: A next Generation Web Server for Deep-Sequencing Data Analysis. Nucleic Acids Res 44(W1):W160–W165. https://doi.org/10.1093/nar/gkw257 Thorvaldsdóttir H, Robinson JT, Mesirov JP (2013) Integrative Genomics Viewer (IGV): High-Performance Genomics Data Visualization and Exploration. Brief Bioinform 14(2):178–192. https://doi.org/10.1093/bib/bbs017 Heinz S, Benner C, Spann N, Bertolino E, Lin YC, Laslo P, Cheng JX, Murre C, Singh H, Glass CK (2010) Simple Combinations of Lineage-Determining Transcription Factors Prime Cis-Regulatory Elements Required for Macrophage and B Cell Identities. Mol Cell 38(4):576–589. https://doi.org/10.1016/j.molcel.2010.05.004 Liao Y, Smyth GK, Shi W (2019) The R Package Rsubread Is Easier, Faster, Cheaper and Better for Alignment and Quantification of RNA Sequencing Reads. Nucleic Acids Res 47(8):e47. https://doi.org/10.1093/nar/gkz114 Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, Smyth GK (2015) Limma Powers Differential Expression Analyses for RNA-Sequencing and Microarray Studies. Nucleic Acids Res 43(7):e47. https://doi.org/10.1093/nar/gkv007 Lê S, Josse J, Husson F, FactoMineR (2008) An R Package for Multivariate Analysis. J Stat Softw 25:1–18. https://doi.org/10.18637/jss.v025.i01 Chen H, VennDiagram (2021) Generate High-Resolution Venn and Euler Plots. https://CRAN.R-project.org/package=VennDiagram (accessed 2022-03-21) Wu T, Hu E, Xu S, Chen M, Guo P, Dai Z, Feng T, Zhou L, Tang W, Zhan L, Fu X, Liu S, Bo X, Yu G (2021) clusterProfiler 4.0: A Universal Enrichment Tool for Interpreting Omics Data. Innov 2(3):100141. https://doi.org/10.1016/j.xinn.2021.100141 Martin M (2011) Cutadapt Removes Adapter Sequences from High-Throughput Sequencing Reads. EMBnet.journal 17 (1), 10–12. https://doi.org/10.14806/ej.17.1.200 Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, Batut P, Chaisson M, Gingeras TR (2013) STAR: Ultrafast Universal RNA-Seq Aligner. Bioinformatics 29(1):15–21. https://doi.org/10.1093/bioinformatics/bts635 Robinson MD, McCarthy DJ, Smyth GK, edgeR: (2010) A Bioconductor Package for Differential Expression Analysis of Digital Gene Expression Data. Bioinformatics 26(1):139–140. https://doi.org/10.1093/bioinformatics/btp616 Jin Y, Tam OH, Paniagua E, Hammell M, TEtranscripts (2015) A Package for Including Transposable Elements in Differential Expression Analysis of RNA-Seq Datasets. Bioinformatics 31(22):3593–3599. https://doi.org/10.1093/bioinformatics/btv422 Gu Z, Eils R, Schlesner M (2016) Complex Heatmaps Reveal Patterns and Correlations in Multidimensional Genomic Data. Bioinformatics 32(18):2847–2849. https://doi.org/10.1093/bioinformatics/btw313 Hao Y, Hao S, Andersen-Nissen E, Mauck WM, Zheng S, Butler A, Lee MJ, Wilk AJ, Darby C, Zager M, Hoffman P, Stoeckius M, Papalexi E, Mimitou EP, Jain J, Srivastava A, Stuart T, Fleming LM, Yeung B, Rogers AJ, McElrath JM, Blish CA, Gottardo R, Smibert P, Satija R (2021) Cell 184(13):3573–3587e29. https://doi.org/10.1016/j.cell.2021.04.048 . Integrated Analysis of Multimodal Single-Cell Data Kowalczyk MS, Tirosh I, Heckl D, Nageswara Rao T, Dixit A, Haas BJ, Schneider R, Wagers AJ, Ebert BL, Regev A (2015) Single Cell RNA-Seq Reveals Changes in Cell Cycle and Differentiation Programs upon Aging of Hematopoietic Stem Cells. Genome Res. https://doi.org/gr.192237.115 Zappia L, Oshlack A, Clustering Trees (2018) A Visualization for Evaluating Clusterings at Multiple Resolutions. GigaScience 7 (7), giy083. https://doi.org/10.1093/gigascience/giy083 Büttner M, Ostner J, Müller CL, Theis FJ, Schubert B (2021) scCODA Is a Bayesian Model for Compositional Single-Cell Data Analysis. Nat Commun 12(1):6876. https://doi.org/10.1038/s41467-021-27150-6 Crowell HL, Soneson C, Germain P-L, Calini D, Collin L, Raposo C, Malhotra D, Robinson MD (2020) Nat Commun 11(1):6077. https://doi.org/10.1038/s41467-020-19894-4 . Muscat Detects Subpopulation-Specific State Transitions from Multi-Sample Multi-Condition Single-Cell Transcriptomics Data Aibar S, González-Blas CB, Moerman T, Huynh-Thu VA, Imrichova H, Hulselmans G, Rambow F, Marine J-C, Geurts P, Aerts J, van den Oord J, Atak ZK, Wouters J, Aerts SSCENIC (2017) Single-Cell Regulatory Network Inference and Clustering. Nat Methods 14(11):1083–1086. https://doi.org/10.1038/nmeth.4463 Supplementary Tables Supplementary Tables S2-S5 are not available with this version. Table S2 | Data for the ATAC-seq experiment on young, aged and aged+Ri HSCs. Information on the samples, the consensus peaks, the DARs, the GO enrichment, the TF motif analysis, and the Klf4-targeted genes. Table S3 | Data for the ATAC-seq experiment on young and young 5 μ m confined HSCs. Information on the samples, the consensus peaks, and the DARs. Table S4 | Data for the RNA-seq experiment on young, aged and aged+Ri HSCs. Information on the samples, the DE genes, the DE retrotransposons, the GSEA, and the GO enrichment of upregulated Klf4-targeted genes. Table S5 | Data for the scRNA-seq experiment on young, aged and aged+Ri LSKs. Information on the samples, the cluster markers, the compositional analysis, the DE genes, and the differentially active regulons. Additional Declarations Yes there is potential Competing Interest. The findings presented in this study are covered under patent application number EP25382180.5, filed on 27/02/2025. Supplementary Files NAFIGURESUPP3.pdf Fig S1-S7 SupplementaryTable1.pdf Table S1 | Morphometric and intensity features extracted from DAPI microscope images used for the multivariate feature analysis Movie1Fig1cunconf.mp4 Video S1 | Related to Figure 1. Representative 3D immunofluorescence reconstructions of an uncofined HSC. The nucleus is stained by DAPI (gray). RhoAGTP was stained with an RhoAGTP antibody, and is shown in red. Scale bars=1 µm. Movie2Fig1c8um.mp4 Video S2 | Related to Figure 1. Representative 3D immunofluorescence reconstructions of 8µm-confined HSC. The nucleus is stained by DAPI (gray). RhoAGTP was stained with an RhoAGTP antibody, and is shown in red. Scale bars=1 µm. Movie3Fig1c5um.mp4 Video S3 | Related to Figure 1. Representative 3D immunofluorescence reconstructions of 5µm-confined HSC. The nucleus is stained by DAPI (gray). RhoAGTP was stained with an RhoAGTP antibody, and is shown in red. Scale bars=1 µm. Movie4Fig1c3um.mp4 Video S4 | Related to Figure 1. Representative 3D immunofluorescence reconstructions of 3µm-confined HSC. The nucleus is stained by DAPI (gray). RhoAGTP was stained with an RhoAGTP antibody and is shown in red. Scale bars=1 µm. Movie5fig1gcontrol.mp4 Video S5 | Related to Figure 1. Representative 3D confocal reconstructions of HSCs showing phospho-cPLA2 (PcPLA2) (light pink) and DAPI (gray) in young control (Movie5) and young NaB (Movie6) treated HSCs. By using image analysis software Imaris we have compartmentalized PcPLA2 signal within the nucleus and at the nuclear envelope (NE). We have assigned light pink to NE PcPLA2 and magenta to nuclear PcPLA2 after compartmentalization. Surfaces have been created for better visualization. Scale bar=1µm Movie6youngNaBfig1g.mp4 Video S6 | Related to Figure 1. Representative 3D confocal reconstructions of HSCs showing phospho-cPLA2 (PcPLA2) (light pink) and DAPI (gray) in young control (Movie5) and young NaB (Movie6) treated HSCs. By using image analysis software Imaris we have compartmentalized PcPLA2 signal within the nucleus and at the nuclear envelope (NE). We have assigned light pink to NE PcPLA2 and magenta to nuclear PcPLA2 after compartmentalization. Surfaces have been created for better visualization. Scale bar=1µm SupplementaryFigureLegends.docx Cite Share Download PDF Status: Published Journal Publication published 24 Nov, 2025 Read the published version in Nature Aging → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-6333603\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Article\",\"associatedPublications\":[],\"authors\":[{\"id\":448091211,\"identity\":\"d1ce52df-3bc3-4abb-8d96-8d2376442dfe\",\"order_by\":0,\"name\":\"Maria Carolina Florian\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA20lEQVRIie3PsQrCMBCA4SuBdol0rSC+gZAiFAfxXYLgJFJxKSiYUujq2sdQBGfloF36DioOroqriEkdlVY3h/zjcR/HAeh0/xhBAcAAbAIMATqNamLyF6lHBXGonBmimsjYFhj5irRiIzwFfm/UzsgK/UASC8+HMuKlRuTmrD/x0PQxySWhA7f0ircP47pghG+QMqzFkjhQQVJDkTlfR4o8FLGu3xDkS6KIUIRWXolcwTKeFL+kDjXpcJyUE7I7ivuULxa4vvmzbtO2suWljLxn/rau0+l0uk89AUuXRlyUhaiFAAAAAElFTkSuQmCC\",\"orcid\":\"https://orcid.org/0000-0002-5791-1310\",\"institution\":\"ICREA, Stem Cell Aging Group, The Bellvitge Institute for Biomedical Research (IDIBELL), L'Hospitalet de Llobregat\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Maria\",\"middleName\":\"Carolina\",\"lastName\":\"Florian\",\"suffix\":\"\"},{\"id\":448091212,\"identity\":\"4805f808-a372-4144-a3fc-ca92eb3ea419\",\"order_by\":1,\"name\":\"Eva Mejia-Ramirez\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"The Bellvitge Institute for Biomedical Research (IDIBELL), L'Hospitalet de Llobregat\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Eva\",\"middleName\":\"\",\"lastName\":\"Mejia-Ramirez\",\"suffix\":\"\"},{\"id\":448091213,\"identity\":\"f4eddbda-2663-47de-84dc-ca958c8ab156\",\"order_by\":2,\"name\":\"Pablo Iañez Picazo\",\"email\":\"\",\"orcid\":\"https://orcid.org/0000-0001-7174-3264\",\"institution\":\"Barcelona Institute for Global Health (ISGlobal); 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A, \\u003c/strong\\u003eRepresentative HSC gating strategy and experimental set-up for the confinement experiments. \\u003cstrong\\u003eB. \\u003c/strong\\u003eTwo representative nuclei are depicted to illustrate the strategy to measure the Nuclear Height Average (NHA) and diameter. NHA is calculated as the sum of \\u003cem\\u003en\\u003c/em\\u003e measurements of the nucleus height in the YZ axis, divided by the \\u003cem\\u003en\\u003c/em\\u003e number of measurements. Nuclear Height Average (NHA) and Diameter are represented against the different confinement conditions used in the experiments and a correlation plot between both is shown from 3 independent experiments. Data on graphic bars have been analyzed using Mann-Whitney two-tailed tests **p\\u0026lt;0.01; ***p\\u0026lt;0.001, ****p\\u0026lt;0.0001. Data on correlation plot have been analyzed by simple linear regression. Pearson r coefficient and P value are shown. ****P\\u0026lt;0.0001 \\u003cstrong\\u003eC. \\u003c/strong\\u003eRepresentative images of 3D immunofluorescence reconstruction from XY and YZ axes of single HSCs under different levels of confinement of 3 independent experiments. Antibody anti-RhoAGTP was used to stain active RhoA (red). The nuclei are stained by DAPI (gray). Scale bars=1 µm. Movies of these confocal acquisitions are available in \\u003cstrong\\u003eVideo S1-4\\u003c/strong\\u003e. Quantification of RhoAGTP (volume of positive signal by intensity) under different confinements: unconfined, 8 µm, 5 µm and 3 µm. Mann-Whitney-test, two-tailed \\u003cem\\u003en\\u003c/em\\u003e=3*\\u003cem\\u003ep\\u0026lt;0.05; **p\\u0026lt;0.01, ***p\\u0026lt;0.001\\u003c/em\\u003e. Correlation plot in between the RhoAGTP and the diameter is shown. Data on correlation have been analyzed by simple linear regression. Data is obtained from 3 independent experiments. Pearson r coefficient and P value are shown. *\\u003cem\\u003ep\\u0026lt;0.05\\u003c/em\\u003e. \\u003cstrong\\u003eD, \\u003c/strong\\u003eRepresentative images of 3D confocal reconstruction showing LT-HSC from RhoA\\u003csup\\u003efl/fl\\u003c/sup\\u003e and RhoA\\u003csup\\u003e-/-\\u003c/sup\\u003e mice. Cells were treated \\u003cem\\u003ein vitro \\u003c/em\\u003ewith 4OH-tamoxifen o/n and then confined under 5 µm for 2 hours. Cells were stained for RhoAGTP (red) and DAPI (gray). Graph shows volume in µm\\u003csup\\u003e3\\u003c/sup\\u003e of RhoAGTP signal. n=3 Mann-Whitney two-tailed, ****p\\u0026lt;0.0001 \\u003cstrong\\u003eE,\\u003c/strong\\u003e Representative images of 3D confocal reconstruction showing RhoAGTP signal (red) and DAPI (gray) of HSCs treated with NaB. Graph shows volume in µm\\u003csup\\u003e3 \\u003c/sup\\u003eof RhoAGTP signal. n=3, Mann-Whitney two-tailed ****\\u003cem\\u003ep\\u003c/em\\u003e\\u0026lt;0.0001. \\u003cstrong\\u003eF, \\u003c/strong\\u003eRepresentative images of 3D confocal reconstruction showing DAPI staining of nuclei from young control LT-HSC and young treated with NaB. Graph shows the maximum diameter at XY in µm at each condition. n=3, Mann-whitney test one-tailed *\\u003cem\\u003ep\\u003c/em\\u003e\\u0026lt;0,05. \\u003cstrong\\u003eG,\\u003c/strong\\u003e Representative images of 3D confocal reconstruction showing phospho-cPLA2 (PcPLA2) (pink) and DAPI (gray) in young control and young NaB treated LT-HSCs. By using image analysis software (Imaris and Volocity) we have compartmentalize PcPLA2 signal within the nucleus and at the nuclear envelope (NE). We have assigned light pink to NE PcPLA2 and magenta to nuclear PcPLA2. NE PcPLA2 only is also shown. The zoom inset shows membrane localization of PcPLA2 which has been reconstructed in light pink in the last panel. Graph showing the volume in µm\\u003csup\\u003e3\\u003c/sup\\u003e of PcPLA2 signal at the NE. Movies of representative confocal acquisitions are available in \\u003cstrong\\u003eVideo S5-6\\u003c/strong\\u003e.\\u0026nbsp; n=3, Mann-Whitney, two-tailed ****\\u003cem\\u003ep\\u003c/em\\u003e\\u0026lt;0.0001. \\u003cstrong\\u003eH, \\u003c/strong\\u003eRepresentative images of 3D confocal reconstruction showing HSC after 16h incubation on hydrogels of 1 KPa or 40 KPa stiffness, stained with RhoAGTP (red) and DAPI (gray). Graph shows volume in µm\\u003csup\\u003e3\\u003c/sup\\u003e of RhoAGTP signal. n=2; Mann-Whitney, two-tailed. \\u003cstrong\\u003eI, \\u003c/strong\\u003eGraphs showing the number of colonies and the number of cells on CFU assays of wt and RhoAKO HSCs after incubation on hydrogels of different stiffness. n=4, Mann-Whitney,two-tailed. Scale bars=1µm. \\u003cstrong\\u003eJ and K, \\u003c/strong\\u003eGraphs showing haematopoietic progenitor cells (c-Kit+) (J) and myeloid cells (mac1+gr1+ and mac1+) (K) in CFU assays of Rhoa\\u003csup\\u003efl/fl\\u003c/sup\\u003e and RhoA\\u003csup\\u003e-/-\\u003c/sup\\u003e LT-HSC after incubation on hydrogels at 1KPa or 40KPa. n=4, Mann-Whitney, two-tailed, *\\u003cem\\u003ep\\u003c/em\\u003e\\u0026lt;0.05. \\u003cstrong\\u003eL, \\u003c/strong\\u003eGraphics depicting RhoAGTP and PcPLA2 under normal conditions and nuclear stretching, either by confinement or NaB treatment.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"NAFigures31.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6333603/v1/06dc4a87d4d4c717feb9795b.png\"},{\"id\":81962855,\"identity\":\"7bf50698-28b7-4d97-9d75-05fc8d1822f0\",\"added_by\":\"auto\",\"created_at\":\"2025-05-05 11:14:47\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":1024575,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eNE tension and RhoA activity are increased in aged HSCs. A, \\u003c/strong\\u003eRepresentative HSC gating strategy and experimental set-up for \\u003cem\\u003ein vitro \\u003c/em\\u003eculturing experiments with and without treatment. \\u003cstrong\\u003eB, \\u003c/strong\\u003eRepresentative images of 3D confocal reconstruction of young, aged and aged+Ri HSC stained with anti-RhoAGTP antibody (red) and DAPI (grey). Graph on the left shows RhoAGTP volume based on intensity of the signal. n=3, Mann-Whitney two-tailed test ****p\\u0026lt;0.0001. Graph on the right shows HSC maximum diameter in \\u003cem\\u003exy\\u003c/em\\u003e for each condition. *p\\u0026lt;0.05; **p\\u0026lt;0.01 \\u003cstrong\\u003eC, \\u003c/strong\\u003eRepresentative images of 3D confocal reconstruction and analysis of young, aged and aged treated with AACOCF3 HSCs stained with anti-PcPLA2 showing compartmentalized PcPLA2 at the NE after segmentation. NE PcPLA2 is shown in green and DAPI in gray. The graph shows measurements of percentage volume of PcPLA2 at NE signal normalized against total volume of PcPLA2. n=3, Mann-Whitney two-tailed test ***p\\u0026lt;0.001. \\u003cstrong\\u003eD, \\u003c/strong\\u003eRepresentative images of 3D confocal reconstruction of young, aged and aged+ AACOCF3 treated HSC stained with anti-RhoAGTP antibody (red) and DAPI (gray). The graph shows volume of RhoAGTP signal. n=3, Mann-Whitney two-tailed test *p\\u0026lt;0.05; **p\\u0026lt;0.01; ****p\\u0026lt;0.0001.\\u003cstrong\\u003e E, \\u003c/strong\\u003eRepresentative images of 3D reconstruction and sections at the three axis \\u003cem\\u003exy, xz \\u003c/em\\u003eand \\u003cem\\u003eyz\\u003c/em\\u003e of young, aged and aged Ri treated LT-HSC stained with LaminB in yellow and DAPI in gray. Arrows in white point to NE wrinkles. \\u003cstrong\\u003eF, \\u003c/strong\\u003eNucleus wrinkling analysis. Nuclear envelope (NE) circularity of the nucleus (4𝜋𝐴⁄𝑃\\u003csup\\u003e2\\u003c/sup\\u003e, with 𝐴 the area of the nucleus and 𝑃 the total perimeter of the nucleus) for aged HSCs cells (n=29), aged HSCs treated with RhoA inhibitor (n=32) and young HSCs cells (n=30) from 3 independent experiments. Kruskal Wallis test for multiple comparisons (****p\\u0026lt;0.0001 and ***p\\u0026lt;0.001). Quantification of NE excess folding parameter (1 − 𝑝⁄𝑃, with 𝑝 the outline of the nucleus and 𝑃 the total perimeter of the nucleus) for aged HSCs (n=29), aged HSCs treated with RhoA inhibitor (n=32) and young HSCs cells (n=30) from 3 independent experiments. Kruskal Wallis test for multiple comparisons (****p\\u0026lt;0.0001 and ***p\\u0026lt;0.001). \\u003cstrong\\u003eG.\\u003c/strong\\u003e Cartoon scheme summarizing the findings.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"NAFigures32.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6333603/v1/5205c03660dcf9437574ceab.png\"},{\"id\":81962851,\"identity\":\"75b1e94c-2905-4a05-b342-7fb3b2823859\",\"added_by\":\"auto\",\"created_at\":\"2025-05-05 11:14:47\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":1308252,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eDrivers of aging identified by individual image-derived morphometric and DAPI intensity features. A, \\u003c/strong\\u003eBoxplot displaying differences in selected feature distributions among HSCs young, aged, and young confined to 8 μm. Mean values are represented by a yellow dot on top of each distribution, with a yellow line aiding to visualize trends among conditions. Statistical significance was determined using the Mann-Whitney test. \\u003cstrong\\u003eB. \\u003c/strong\\u003eLine plot depicting the normalized DAPI intensity of nuclei obtained from young, aged, and aged + Ri conditions as a function of 3D iso-distant intervals of 0.1μm for up to 1.5μm from the nuclear border. A magnified view of the intensity within the initial 0.2 μm from the nuclear border is provided on the top right.\\u003cstrong\\u003e C.\\u003c/strong\\u003e Representative images of young, aged, and aged + Ri nuclear intensity images are shown, with DIRs highlighted by a white contour. Boxplots showing the standardized DAPI intensity of young, aged, and aged + Ri nuclei as a function of distance in 0.5 μm intervals. Statistical significance was determined using the Mann-Whitney test. \\u003cstrong\\u003eD.\\u003c/strong\\u003eScatterplot displaying the UMAP embedding of all nuclei images, with marginal distributions for both components, colored by condition. The estimated kernel density helps to visualize high-density areas for each condition. \\u003cstrong\\u003eE.\\u003c/strong\\u003eUMAP embedding with background areas colored by clusters identified using the K-Means clustering algorithm (BI: Border Intensity, CI: Central Intensity, HS: High Size, LS: Low Size). Donut charts depicting the proportion of each condition per cluster are included on top. Marginal distributions for both UMAP components are also displayed. \\u003cstrong\\u003eF\\u003c/strong\\u003e. Lineplots showing the change in average values for selected morphometric-related features across clusters, ordered from the cluster with highest to lowest frequency of young HSCs. \\u003cstrong\\u003eG. \\u003c/strong\\u003eLineplots showing the change in average values for selected intensity-related features across clusters, ordered from the cluster with lowest to highest frequency of aged+Ri HSCs.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"NAFigures33.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6333603/v1/ce39b2803d5c233d1f2b5bdc.png\"},{\"id\":81963662,\"identity\":\"f6b55467-bf76-4a0e-aa18-26f92ed252eb\",\"added_by\":\"auto\",\"created_at\":\"2025-05-05 11:22:47\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":1097352,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eReduced methylation of H3K9 increases RhoA activation and drives aging of HSCs. A, \\u003c/strong\\u003eRepresentative images of 3D confocal reconstruction of young, aged and aged+Ri treated HSCs stained with anti-H3K9me2 antibody (green) and DAPI (grey). Representative images of 3D confocal reconstruction of young, aged and aged+Ri with UNC0631 treatment is shown. At least 10 cells were acquired per experiment per condition. n=4-8 Graph shows H3K9me2 signal volume normalized against DAPI signal volume. Mann-Whitney two-tailed, n=8, **p\\u0026lt;0.01, *p\\u0026lt;0.05, ####p\\u0026lt;0.0001.\\u003cstrong\\u003e B.\\u0026nbsp;\\u003c/strong\\u003eExperimental set up for retroviral transduction and transplantation of young LSK overexpressing wild type H3K9 or mutant H3R9 with\\u0026nbsp;mCherry\\u0026nbsp;as a reporter. Transduced cells were transplanted into lethally irradiated mice. Bone marrow of transplanted mice was analyzed by flow\\u0026nbsp;cytometry to evaluate the regenerative capacity of transduced HSCs.\\u0026nbsp;mCherry\\u003csup\\u003e+\\u003c/sup\\u003e Myeloid Progenitors (MP) were sorted to measure H3K9me2 levels.\\u0026nbsp;mCherry\\u003csup\\u003e+\\u003c/sup\\u003e HSCs were sorted for the analysis of\\u0026nbsp;RhoA\\u0026nbsp;activation and nuclear stretching.\\u0026nbsp;\\u003cstrong\\u003eC. \\u003c/strong\\u003eRepresentative images of sorted MP transduced with H3K9 and H3R9 stained with anti-H3K9me2 antibody (magenta) and DAPI (blue). The graph shows H3K9me2 signal volume normalized against DAPI signal volume. Mann-Whitney two-tailed n=7 H3K9; n=6 H3R9, ****p\\u0026lt;0.0001. \\u003cstrong\\u003eD.\\u003c/strong\\u003e Representative images of 3D confocal reconstruction of HSC overexpressing H3K9 or H3R9 isolated from transplanted mice (H3K9 n=7; H3R9 n=6). HSCs were stained with anti-RhoAGTP\\u0026nbsp;antibody (red) and DAPI (grey). Mann-Whitney, two-tailed statistics are shown for\\u0026nbsp;RhoAGTP\\u0026nbsp;quantification analysis\\u0026nbsp;**p\\u0026lt;0.01. For nuclear volume (DAPI volume) unpaired t-test (one-tailed) was used *p\\u0026lt;0.05. \\u003cstrong\\u003eE.\\u003c/strong\\u003e Gating strategy for the analysis of bone marrow progenitors after 12-20 weeks post transplantation. The analysis of the different populations is shown for mice transplanted with LSK transduced with either wild type H3K9 (n=7) or H3R9 (n=6). Mann-Whitney statistics are shown two-tailed **p\\u0026lt;0.01. \\u003cstrong\\u003eF. \\u003c/strong\\u003eEngraftment\\u0026nbsp;of\\u0026nbsp;mCherry\\u003csup\\u003e+\\u003c/sup\\u003e-H3K9 or mCherry\\u003csup\\u003e+\\u003c/sup\\u003e-H3R9 HSCs in mice. Representative flow cytometry charts for bone marrow engraftment are shown. The box plots display\\u0026nbsp;engraftment of\\u0026nbsp;mCherry\\u003csup\\u003e+\\u003c/sup\\u003e-H3K9 or mCherry\\u003csup\\u003e+\\u003c/sup\\u003e-H3R9 HSCs in mice 12-20 weeks post-transplant in BM and peripheral blood. Unpaired t-test statistics is shown *p\\u0026lt;0.05, **p\\u0026lt;0.01.\\u0026nbsp;\\u003cstrong\\u003eG.\\u003c/strong\\u003e\\u0026nbsp;Gating strategy\\u0026nbsp;for the analysis of B cells (B220+), T cells (Cd3+) and myeloid cells (Gr1+ and Mac1+) within the BM after 12-24 weeks post transplantation. The analysis of the different populations is shown for mice\\u0026nbsp;transplanted with wild type H3K9 (n=7) transduced LSK and mutant H3R9 LSK (n=6). Mann-Whitney statistics are shown *p\\u0026lt;0.05, **p\\u0026lt;0.01.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"NAFigures34.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6333603/v1/3f321da39bbf81f66ca59b76.png\"},{\"id\":81962852,\"identity\":\"ffe08c7a-a405-4189-a3b6-97744d10f0b9\",\"added_by\":\"auto\",\"created_at\":\"2025-05-05 11:14:47\",\"extension\":\"png\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":754952,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eRhoA regulates chromatin accessibility at repetitive elements in aged HSCs. A\\u003c/strong\\u003e, Representative HSC gating strategy and experimental strategy for ATAC-seq and RNA-seq of Ri treated aged HSCs. \\u003cstrong\\u003eB\\u003c/strong\\u003e, Volcano plot of DARs in aged \\u003cem\\u003evs\\u003c/em\\u003eyoung and aged+Ri \\u003cem\\u003evs\\u003c/em\\u003e aged (p\\u0026lt;0.005). The annotation of selected DARs is shown. \\u003cstrong\\u003eC\\u003c/strong\\u003e, Heatmap of the normalized accessibility in the 144 common DARs in aged \\u003cem\\u003evs\\u003c/em\\u003eyoung and aged+Ri \\u003cem\\u003evs\\u003c/em\\u003e aged. \\u003cstrong\\u003eD\\u003c/strong\\u003e, Logarithmic fold change (log2FC) of the proportion of each genomic region type among the different sets of DARs over the proportions among the consensus peaks. Fisher’s exact test p-value is shown. One-proportion z-test for each genomic region is shown as *FDR\\u0026lt;0.05, **FDR\\u0026lt;0.005, ***FDR\\u0026lt;0.0005, ****FDR\\u0026lt;0.00005. \\u003cstrong\\u003eE\\u003c/strong\\u003e, Genome tracks for the normalized ATAC-seq read distribution in several LTRs and LINEs. Location of DARs is indicated with purple lines. \\u003cstrong\\u003eF\\u003c/strong\\u003e, Experimental strategy for ATAC-seq of young HSCs confined in 5μm. \\u003cstrong\\u003eG\\u003c/strong\\u003e, Volcano plot of DARs in young 5μm \\u003cem\\u003evs\\u003c/em\\u003e young (p\\u0026lt;0.001). The annotation of selected DARs is shown. \\u003cstrong\\u003eH\\u003c/strong\\u003e, Heatmap of the normalized accessibility in the 468 DARs in young 5μm \\u003cem\\u003evs\\u003c/em\\u003e young. \\u003cstrong\\u003eI\\u003c/strong\\u003e, Log2FC of the proportion of each genomic region type among the different sets of DARs over the proportions among the consensus peaks. “nd” indicates that no regions of that type were detected as DARs. Statistics as in d. \\u003cstrong\\u003eJ\\u003c/strong\\u003e, Heatmap of the normalized expression of the retrotransposon subfamilies upregulated with aging (p\\u0026lt;0.005). Legend as in c. Boxplot shows the normalized expression per condition of the retrotransposon subfamiles downregulated with Ri (p\\u0026lt;0.005). A red asterisk indicates the retrotransposon subfamilies that show significant differences in both comparisons. \\u003cstrong\\u003eK\\u003c/strong\\u003e, Radar chart showing the normalized enrichment scores (NES) for some significantly negatively enriched GOs with Ri (FDR\\u0026lt;0.05).\\u003cstrong\\u003e L-O\\u003c/strong\\u003e, Enrichment plot and volcano plot for the acute inflammatory response GO term (L), the Interferome.org signature (M), the interferon-stimulated genes (N) and the aging signature (O) in aged+Ri \\u003cem\\u003evs\\u003c/em\\u003e aged. FDR = false discovery rate.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"NAFigures35.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6333603/v1/62caf53adeaabd137c93f93b.png\"},{\"id\":81962857,\"identity\":\"a3aaa09c-ca19-45f7-a6ca-398a40e2b5c2\",\"added_by\":\"auto\",\"created_at\":\"2025-05-05 11:14:47\",\"extension\":\"png\",\"order_by\":6,\"title\":\"Figure 6\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":923733,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eInhibiting RhoA activity induces Klf4 expression and partial reprogramming in aged HSCs. A\\u003c/strong\\u003e, Genome tracks for the normalized RNA-seq read distribution in Klf genes.\\u003cstrong\\u003e B\\u003c/strong\\u003e,\\u003cstrong\\u003e \\u003c/strong\\u003eTop 2 TF-binding motifs found among the DARs opening with Ri. \\u003cstrong\\u003eC\\u003c/strong\\u003e, Genome tracks for the normalized ATAC-seq read distribution in Klf4 gene and its enhancer in –119kb (orange shadow). A green line indicates the DAR opening with Ri. \\u003cstrong\\u003eD\\u003c/strong\\u003e,\\u003cstrong\\u003e \\u003c/strong\\u003eRadar chart showing the minus logarithmic FDR for some significantly enriched GOs among the Klf4-targeted genes (FDR \\u0026lt; 0.05).\\u003cstrong\\u003e E\\u003c/strong\\u003e, Representative images of 3D confocal reconstruction of young, aged and aged+Ri LT-HSC stained with Phalloidin-488 (green) and DAPI (grey). The graph shows measurements of phalloidin (actin) signal volume, n=3. Mann-Whitney, two tailed ***p\\u0026lt;0.001. \\u003cstrong\\u003eF\\u003c/strong\\u003e, Clustered and annotated UMAP for the integrated scRNA-seq data for young, aged and aged+Ri LSKs. HSC: hematopoietic stem cells; MPP: multipotent progenitors; LMPP: lymphoid multipotent progenitors; CMP: common myeloid progenitors; CLP: common lymphoid progenitors. \\u003cstrong\\u003eG\\u003c/strong\\u003e, Klf4 normalized expression in the UMAP for young, aged and aged+Ri. The lower right panel shows the difference in the percentage of Klf4+ cells in aged+Ri \\u003cem\\u003evs\\u003c/em\\u003eaged per cluster. \\u003cstrong\\u003eH\\u003c/strong\\u003e, UMAP based on the TF activity scores in HSCs colored by condition. \\u003cstrong\\u003eI\\u003c/strong\\u003e, Klf4 active cells (AUC \\u0026gt; 0.128) in the UMAP. \\u003cstrong\\u003eJ\\u003c/strong\\u003e, Heatmap of the activity scores for the top 30 TFs that are differentially active in aged+Ri \\u003cem\\u003evs\\u003c/em\\u003e aged (|log2FC| \\u0026gt; 0.5 and FDR \\u0026lt; 0.05). Color legend as in h. \\u003cstrong\\u003eK\\u003c/strong\\u003e, Enrichment plot for the hemogenic precursors signature in aged+Ri \\u003cem\\u003evs\\u003c/em\\u003e aged.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"NAFigures36.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6333603/v1/b4b26822dab8ee13897eec86.png\"},{\"id\":81962856,\"identity\":\"8401cdd3-f458-418c-8e9d-0a70f9f1c86a\",\"added_by\":\"auto\",\"created_at\":\"2025-05-05 11:14:47\",\"extension\":\"png\",\"order_by\":7,\"title\":\"Figure 7\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":378445,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eInhibiting RhoA activity improves function of aged HSCs. A\\u003c/strong\\u003e,\\u003cstrong\\u003e \\u003c/strong\\u003eExperimental strategy for transplantation of young, aged and aged+Ri RFP+ HSCs into NBSGW mice. \\u003cstrong\\u003eB\\u003c/strong\\u003e,\\u003cstrong\\u003e \\u003c/strong\\u003ePercentage of engraftment in PB along the course of the transplantation at 6, 12 and 18 weeks. 4 independent transplantation experiments were performed, and the initial number of recipient mice used per experiment was 3-4 per group. Young n=9; aged n=10; aged+Ri n=10. Mann-Whitney test, unpaired, two-tailed *p\\u0026lt;0.05, **p\\u0026lt;0.01, ***p\\u0026lt;0.001. \\u003cstrong\\u003eC\\u003c/strong\\u003e,\\u003cstrong\\u003e \\u003c/strong\\u003eRepresentative flow cytometry gating strategies for RFP+ cells, lymphoid cells (CD3+ and B220+), and myeloid cells (Gr1+, Gr1+Mac1+ and Mac1+) in PB at 18 weeks. \\u003cstrong\\u003eD\\u003c/strong\\u003e,\\u003cstrong\\u003e \\u003c/strong\\u003eGraphs showing the percentage of donor derived B220+ cells, myeloid (Gr1+, Gr1+Mac1+ and Mac1+ cells) and CD3+ cells in PB at 6, 12 and 18 weeks after transplant. Young n=9; aged n=10; aged+Ri n=10.; Mann-Whitney test, unpaired two-tailed; *\\u003cem\\u003ep\\u003c/em\\u003e\\u0026lt;0.05, ***p\\u0026lt;0.001. \\u003cstrong\\u003eE\\u003c/strong\\u003e, Cartoon scheme summarizing the main features rejuvenated by Ri treatment in aged HSCs.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"NAFigures37.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6333603/v1/cf26d2d486e0a04b50988ca3.png\"},{\"id\":96699790,\"identity\":\"9168a3ec-3b92-46d9-88d2-f105f11c3ee2\",\"added_by\":\"auto\",\"created_at\":\"2025-11-25 08:16:03\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":8700888,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6333603/v1/69ef4b26-ecc7-4a89-89a7-175b5a89c3a0.pdf\"},{\"id\":81963665,\"identity\":\"b8114194-8a7f-47b9-bd4b-52a2e4147593\",\"added_by\":\"auto\",\"created_at\":\"2025-05-05 11:22:47\",\"extension\":\"pdf\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":6424077,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eFig S1-S7\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"NAFIGURESUPP3.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6333603/v1/45618bf8920e1b47b2f9f49e.pdf\"},{\"id\":81962850,\"identity\":\"96d05102-14b2-4a59-bd64-1dcecdcdbecb\",\"added_by\":\"auto\",\"created_at\":\"2025-05-05 11:14:46\",\"extension\":\"pdf\",\"order_by\":2,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":58520,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eTable S1 | Morphometric and intensity features extracted from DAPI microscope images used for the multivariate feature analysis\\u003c/strong\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"SupplementaryTable1.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6333603/v1/5476c6629a9fd888a4153343.pdf\"},{\"id\":81962865,\"identity\":\"1590188e-c0cb-4157-8c7e-04074825ba32\",\"added_by\":\"auto\",\"created_at\":\"2025-05-05 11:14:47\",\"extension\":\"mp4\",\"order_by\":3,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":11821875,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eVideo S1 | Related to Figure 1.\\u003c/strong\\u003e Representative 3D immunofluorescence reconstructions of an uncofined HSC. The nucleus is stained by DAPI (gray). RhoAGTP was stained with an RhoAGTP antibody, and is shown in red. Scale bars=1 µm.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Movie1Fig1cunconf.mp4\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6333603/v1/ded7fb8367ae0c4fbfa27819.mp4\"},{\"id\":81962868,\"identity\":\"c38a81db-3cbb-46c8-8e93-195fe4f90e42\",\"added_by\":\"auto\",\"created_at\":\"2025-05-05 11:14:47\",\"extension\":\"mp4\",\"order_by\":4,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":27742310,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eVideo S2 | Related to Figure 1.\\u003c/strong\\u003e Representative 3D immunofluorescence reconstructions of 8µm-confined HSC. The nucleus is stained by DAPI (gray). RhoAGTP was stained with an RhoAGTP antibody, and is shown in red. Scale bars=1 µm.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Movie2Fig1c8um.mp4\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6333603/v1/a0103fe526e9fb4c9817be6c.mp4\"},{\"id\":81962864,\"identity\":\"8bd9aa15-6315-4e8a-9280-4a666b3fcc62\",\"added_by\":\"auto\",\"created_at\":\"2025-05-05 11:14:47\",\"extension\":\"mp4\",\"order_by\":5,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":22441566,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eVideo S3 | Related to Figure 1.\\u003c/strong\\u003e Representative 3D immunofluorescence reconstructions of 5µm-confined HSC. The nucleus is stained by DAPI (gray). RhoAGTP was stained with an RhoAGTP antibody, and is shown in red. Scale bars=1 µm.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Movie3Fig1c5um.mp4\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6333603/v1/4a881bab7ec95e27bd8183e0.mp4\"},{\"id\":81962867,\"identity\":\"15aa0330-1b34-45d9-967c-c203caab18fd\",\"added_by\":\"auto\",\"created_at\":\"2025-05-05 11:14:47\",\"extension\":\"mp4\",\"order_by\":6,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":16299956,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eVideo S4 | Related to Figure 1.\\u003c/strong\\u003e Representative 3D immunofluorescence reconstructions of 3µm-confined HSC. The nucleus is stained by DAPI (gray). RhoAGTP was stained with an RhoAGTP antibody and is shown in red. Scale bars=1 µm.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Movie4Fig1c3um.mp4\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6333603/v1/0aaf408ac11b9ef032a711b3.mp4\"},{\"id\":81962870,\"identity\":\"9975106e-db6e-44b2-977e-aa80bdbb1e4e\",\"added_by\":\"auto\",\"created_at\":\"2025-05-05 11:14:47\",\"extension\":\"mp4\",\"order_by\":7,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":25377526,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eVideo S5 | Related to Figure 1.\\u003c/strong\\u003e Representative 3D confocal reconstructions of HSCs showing phospho-cPLA2 (PcPLA2) (light pink) and DAPI (gray) in young control (Movie5) and young NaB (Movie6) treated HSCs. By using image analysis software Imaris we have compartmentalized PcPLA2 signal within the nucleus and at the nuclear envelope (NE). We have assigned light pink to NE PcPLA2 and magenta to nuclear PcPLA2 after compartmentalization. Surfaces have been created for better visualization. Scale bar=1µm\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Movie5fig1gcontrol.mp4\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6333603/v1/81c222c17aca3a5adb2e4375.mp4\"},{\"id\":81962863,\"identity\":\"bb0efdaa-7cad-4b9f-b549-3cb2c2280311\",\"added_by\":\"auto\",\"created_at\":\"2025-05-05 11:14:47\",\"extension\":\"mp4\",\"order_by\":8,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":11927138,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eVideo S6 | Related to Figure 1.\\u003c/strong\\u003e Representative 3D confocal reconstructions of HSCs showing phospho-cPLA2 (PcPLA2) (light pink) and DAPI (gray) in young control (Movie5) and young NaB (Movie6) treated HSCs. By using image analysis software Imaris we have compartmentalized PcPLA2 signal within the nucleus and at the nuclear envelope (NE). We have assigned light pink to NE PcPLA2 and magenta to nuclear PcPLA2 after compartmentalization. Surfaces have been created for better visualization. Scale bar=1µm\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Movie6youngNaBfig1g.mp4\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6333603/v1/a33113ba337ec169ab78eff4.mp4\"},{\"id\":81965661,\"identity\":\"818e6000-1fbf-474f-9156-4a7dd7494dda\",\"added_by\":\"auto\",\"created_at\":\"2025-05-05 11:30:47\",\"extension\":\"docx\",\"order_by\":9,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":18865,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"SupplementaryFigureLegends.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6333603/v1/61464f40a93a3c07b1bc14a7.docx\"}],\"financialInterests\":\"\\u003cb\\u003eYes\\u003c/b\\u003e there is potential Competing Interest.\\nThe findings presented in this study are covered under patent application number EP25382180.5, filed on 27/02/2025.\",\"formattedTitle\":\"Targeting RhoA activity rejuvenates aged hematopoietic stem cells\",\"fulltext\":[{\"header\":\"INTRODUCTION\",\"content\":\"\\u003cp\\u003eAging is characterized by the decline in tissue function and is the primary risk factor for major diseases. In particular, aged HSC functional decline critically impacts not only on their ability to regenerate the hematopoietic system and to support lymphoid cell production over time\\u003csup\\u003e\\u003cspan additionalcitationids=\\\"CR2 CR3\\\" citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e\\u003c/sup\\u003e but also directly contributes to major aging-related diseases\\u003csup\\u003e\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e\\u003c/sup\\u003e. Aging is a complex, multifaceted process that is accompanied by biomechanical changes affecting tissues and organs and also cells and subcellular organelles\\u003csup\\u003e\\u003cspan additionalcitationids=\\\"CR8 CR9 CR10 CR11\\\" citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e\\u003c/sup\\u003e. Among others, these biomechanical changes include alterations in NE tension and aging correlates with progressive changes in the nucleus mechanical integrity and impaired mechanotransduction\\u003csup\\u003e\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e\\u003c/sup\\u003e. However, how to possibly target changes in nuclear mechano-signaling to explain and prevent aging of somatic stem cells is still largely under-investigated.\\u003c/p\\u003e \\u003cp\\u003eIn addition, epigenetic alterations are considered one of the primary hallmarks of aging\\u003csup\\u003e\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e\\u003c/sup\\u003e and despite the large amount of data demonstrating the occurrence of an epigenetic drift upon stem cell aging and disease, there is a lack of knowledge on molecular mechanisms to explain this epigenetic drift and whether it possibly associates to mechanical alterations of the nuclear and chromatin architecture.\\u003c/p\\u003e \\u003cp\\u003eMechanical forces trigger multiple signaling pathways that converge in the activation of RhoA\\u003csup\\u003e\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e\\u003c/sup\\u003e, which is a small RhoGTPase that can cycle between an active (RhoA-GTP) and an inactive (RhoA-GDP) status. RhoA is a key regulator of mechanotransduction\\u003csup\\u003e\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e\\u003c/sup\\u003e regardless of whether the activating mechanical stimulus is cell extrinsic, as occurs in cells responding to alterations of substrate stiffness\\u003csup\\u003e\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e\\u003c/sup\\u003e, or cell intrinsic, like for example when the cell nucleus acts as a mechanosensor of genomic changes\\u003csup\\u003e\\u003cspan additionalcitationids=\\\"CR18\\\" citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e\\u003c/sup\\u003e. So far, in HSCs RhoA has been shown to be important for cytokinesis\\u003csup\\u003e\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e\\u003c/sup\\u003e. \\u003cem\\u003eIn vivo\\u003c/em\\u003e, knocking out RhoA in bone marrow cells induces a dramatic phenotype, characterized by a multilineage hematopoietic failure due to programmed necrosis of hematopoietic progenitors, while HSCs retain long-term engraftment potential but fail to produce hematopoietic progenitors and lineage-defined blood cells\\u003csup\\u003e\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e\\u003c/sup\\u003e.\\u003c/p\\u003e \\u003cp\\u003eHere, we investigate the role of RhoA in nuclear mechanotransduction in HSCs and its involvement in preserving nuclear architecture and stem cell function upon aging. Our data reveal that RhoA activity increases upon increased NE tension in HSCs, which is intrinsically altered upon aging and can be targeted to improve \\u003cem\\u003ein vivo\\u003c/em\\u003e function of aged blood stem cells.\\u003c/p\\u003e\"},{\"header\":\"RESULTS\",\"content\":\"\\u003cp\\u003e \\u003cb\\u003eRhoA is necessary for HSCs to survive under increased NE tension.\\u003c/b\\u003e \\u003c/p\\u003e \\u003cp\\u003eThe nuclear membrane is able to stretch under various pathophysiological conditions involving nuclear lamina weakening, which include aging and laminopathies\\u003csup\\u003e\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e\\u003c/sup\\u003e, and stretching of the nucleus is thought to be a fundamental mechanism engaging nuclear mechnotransduction\\u003csup\\u003e\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e\\u003c/sup\\u003e. Therefore, we wondered if nuclear stretching might be engaging RhoA mechano-signaling pathways in HSCs. HSCs are notoriously small sized non adherent cells with an average cell diameter of 10 \\u0026micro;m and a high nuclear/cytosol ratio. To induce nuclear stretching in HSCs, we took advantage of a previously established cell confinement device\\u003csup\\u003e\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e\\u003c/sup\\u003e (\\u003cb\\u003eFigure \\u003cspan refid=\\\"MOESM1\\\" class=\\\"InternalRef\\\"\\u003eS1\\u003c/span\\u003eA\\u003c/b\\u003e). We isolated HSCs (sorted as Cd11b\\u003csup\\u003e\\u0026minus;\\u003c/sup\\u003e, Ter119\\u003csup\\u003e\\u0026minus;\\u003c/sup\\u003e, Cd8\\u003csup\\u003e\\u0026minus;\\u003c/sup\\u003e, Cd5\\u003csup\\u003e\\u0026minus;\\u003c/sup\\u003e, B220\\u003csup\\u003e\\u0026minus;\\u003c/sup\\u003e, Gr1\\u003csup\\u003e\\u0026minus;\\u003c/sup\\u003e, c-kit\\u003csup\\u003e+\\u003c/sup\\u003e, Sca1\\u003csup\\u003e+\\u003c/sup\\u003e, Flt3\\u003csup\\u003e\\u0026minus;\\u003c/sup\\u003e, CD34\\u003csup\\u003e\\u0026minus;\\u003c/sup\\u003e) from the bone marrow (BM) of young adult (8 to 20 weeks old) C57Bl6 mice and we seeded them on fibronectin-coated coverslips followed by confinement with non-adhesive coverslips functionalized with pillars of 8, 5 and 3 \\u0026micro;m height for 2 hours (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eA and \\u003cb\\u003eFigure \\u003cspan refid=\\\"MOESM1\\\" class=\\\"InternalRef\\\"\\u003eS1\\u003c/span\\u003eA-B\\u003c/b\\u003e). Upon confinement, HSCs were fixed and stained with DAPI (4\\u0026prime;,6-diamidino-2-phenylindole) to image and measure the nuclear stretching, and with an anti-RhoA-GTP antibody for quantifying the activation levels of RhoA. By 3D single-cell confocal microscopy, we observed that cell confinement produced a reduction of the nuclear height proportional to the applied level of confinement (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eB). The maximum nuclear diameter increased significantly already under 8 \\u0026micro;m confinement, and it reached its maximum at almost 10 \\u0026micro;m when HSCs were confined at 5 \\u0026micro;m, showing a significant inverse correlation with nuclear height (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eB). We observed the formation of nucleoplasm containing blebs, which was progressive with confinement and particularly prominent when HSCs were confined at 5\\u0026micro;m (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eC). This can be quantified looking at the Excess of Perimeter (EOP) of the largest 2D slide\\u003csup\\u003e\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e\\u003c/sup\\u003e and at the major axis length, which significantly increased in HSCs under the 5 \\u0026micro;m confinement conditions (\\u003cb\\u003eFigure \\u003cspan refid=\\\"MOESM1\\\" class=\\\"InternalRef\\\"\\u003eS1\\u003c/span\\u003eC-D\\u003c/b\\u003e). Interestingly, nuclear stretching was paralleled by a progressive alteration of the DAPI intensity profile of the nucleus and by a sharp increase in the activation levels of RhoA, which was mildly but directly correlated with the increase in nuclear diameter (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eC and \\u003cb\\u003eFigure \\u003cspan refid=\\\"MOESM1\\\" class=\\\"InternalRef\\\"\\u003eS1\\u003c/span\\u003eE\\u003c/b\\u003e). Indeed, RhoA-GTP levels increased significantly in direct correlation to the level of confinement at 8 \\u0026micro;m and 5 \\u0026micro;m (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eC). When the nuclear height was reduced to 3 \\u0026micro;m, RhoA-GTP levels decreased dramatically. In this condition of tight confinement, HSCs displayed severe nuclear rupture, which suggests cell distress and the inability to react against such a reduction of nuclear height, as evidenced by the dramatic reduction of the nuclear volume and DAPI intensity profile, the appearance of nuclear blebs and the extreme standard deviation variability of the EOP (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eC and \\u003cb\\u003eFigure \\u003cspan refid=\\\"MOESM1\\\" class=\\\"InternalRef\\\"\\u003eS1\\u003c/span\\u003eC-E\\u003c/b\\u003e and \\u003cb\\u003eVideo S1-4\\u003c/b\\u003e). This data is consistent with a threshold of up to 70% compression of the nuclear height for HSC survival and consequent RhoA activation before irreversible nuclear lamina rupture, in agreement with what was described for other cells\\u003csup\\u003e\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e\\u003c/sup\\u003e.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eTo investigate whether RhoA is necessary for the response to nuclear deformation in HSCs, we isolated HSCs from \\u003cem\\u003eCreERt2\\u003c/em\\u003eX\\u003cem\\u003eRhoA\\u003c/em\\u003e\\u003csup\\u003e\\u003cem\\u003eflox/flox\\u003c/em\\u003e\\u003c/sup\\u003e mice, in which the activity of the Cre recombinase to knock-out \\u003cem\\u003eRhoA\\u003c/em\\u003e can be induced \\u003cem\\u003ein vitro\\u003c/em\\u003e by overnight treatment with 4-hydroxy tamoxifen (\\u003cb\\u003eFigure \\u003cspan refid=\\\"MOESM1\\\" class=\\\"InternalRef\\\"\\u003eS1\\u003c/span\\u003eF;\\u003c/b\\u003e hereafter referred to as RhoA knock-out (KO) HSCs). To note, after 12\\u0026ndash;16 hours from induction of the Cre-recombinase, levels of RhoA-GTP were clearly reduced (\\u003cb\\u003eFigure \\u003cspan refid=\\\"MOESM1\\\" class=\\\"InternalRef\\\"\\u003eS1\\u003c/span\\u003eG\\u003c/b\\u003e). Next, we confined the cells at 5 \\u0026micro;m, according to the protocol described above to image nuclei and quantify the impact of RhoA knock-out. Surprisingly, in RhoAKO HSCs, the nuclei were completely broken, indicating that RhoAKO HSCs were unable to resist the 5 \\u0026micro;m confinement (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eD).\\u003c/p\\u003e \\u003cp\\u003eSince our experiments were performed \\u003cem\\u003ein vitro\\u003c/em\\u003e on sorted non adherent HSCs, we reasoned that RhoA activation might be intrinsically induced by the mechanical tension at the nuclear envelope (NE). To investigate this hypothesis, we decided to quantify the phosphorylated form of the nuclear protein cPLA2 (P-cPLA2), which translocates to the NE due to a physical process mediated by tension at the NE\\u003csup\\u003e\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e\\u003c/sup\\u003e. NE P-cPLA2 catalyzes the hydrolysis of phospholipids releasing arachidonic acid (AA)\\u003csup\\u003e\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e\\u003c/sup\\u003e, which is a well-established activator of RhoA\\u003csup\\u003e\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e\\u003c/sup\\u003e. We stained P-cPLA2 and quantified its NE localization, which increased when HSCs are under confinement, consistent with the increased RhoA activation (\\u003cb\\u003eFigure \\u003cspan refid=\\\"MOESM1\\\" class=\\\"InternalRef\\\"\\u003eS1\\u003c/span\\u003eH\\u003c/b\\u003e).\\u003c/p\\u003e \\u003cp\\u003eTo further investigate whether intrinsic RhoA activation is induced by NE tension, we treated freshly sorted HSCs with sodium butyrate (NaB), a histone deacetylase inhibitor known to increase levels of histone acetylation and induce chromatin decompaction (intrinsically increasing NE tension) in different cells and also in HSCs\\u003csup\\u003e\\u003cspan additionalcitationids=\\\"CR32\\\" citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e33\\u003c/span\\u003e\\u003c/sup\\u003e. RhoA activity levels were sharply upregulated in HSCs treated with 5mM NaB, in parallel with the increase in NE tension, as shown by the increase in the nuclear diameter and P-cPLA2 translocation to the NE (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eE-G and \\u003cb\\u003eVideo S5-6\\u003c/b\\u003e)\\u003c/p\\u003e \\u003cp\\u003eTo further support that P-cPLA2 translocation to the NE can be triggered by changes in NE tension\\u003csup\\u003e\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e\\u003c/sup\\u003e, we used hypotonic shock which was previously used to increase nuclear volume\\u003csup\\u003e\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e34\\u003c/span\\u003e\\u003c/sup\\u003e and NE tension and we quantified NE P-cPLA2 and RhoA activation. Consistently, the data shows an increase in NE P-cPLA2 and RhoA-GTP levels in HSCs after hypotonic osmotic shock (\\u003cb\\u003eFigure \\u003cspan refid=\\\"MOESM1\\\" class=\\\"InternalRef\\\"\\u003eS1\\u003c/span\\u003eI\\u003c/b\\u003e).\\u003c/p\\u003e \\u003cp\\u003eOverall, the data demonstrates that intrinsic changes in the NE tension activate RhoA in HSCs. However, RhoA activity might also be triggered by the extracellular matrix (niche or extrinsic activation). To explore this possibility, we focused on tissue stiffness, a well-described mechanical stimulus inducing RhoA activity in different cells and tissues\\u003csup\\u003e\\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e35\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e36\\u003c/span\\u003e\\u003c/sup\\u003e. Since in adult mammals HSCs reside in the BM, we first measured the stiffness of this compartment, which is a semi-solid tissue with viscoelastic properties and a quite heterogeneous mechanical behavior\\u003csup\\u003e\\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e37\\u003c/span\\u003e\\u003c/sup\\u003e. To this end, we used a Nanoindenter device equipped with a small and sensitive cantilever tip. We designed a matrix of several points to cover the whole area of the femoral BM tissue and obtain a map of the stiffness of the murine BM cavity (\\u003cb\\u003eFigure \\u003cspan refid=\\\"MOESM1\\\" class=\\\"InternalRef\\\"\\u003eS1\\u003c/span\\u003eJ\\u003c/b\\u003e). In agreement with previous observations\\u003csup\\u003e\\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e38\\u003c/span\\u003e\\u003c/sup\\u003e, within the BM we detected areas with different levels of stiffness, ranging from 0.5 to 10kPa (on average 4kPa) in the inner marrow to a range of 5 to 50 kPa (on average 12kPa) at the endosteum (\\u003cb\\u003eFigure \\u003cspan refid=\\\"MOESM1\\\" class=\\\"InternalRef\\\"\\u003eS1\\u003c/span\\u003eK\\u003c/b\\u003e). Based on these results, we prepared polyacrylamide hydrogels functionalized with fibronectin (FN) to mimic \\u003cem\\u003ein vitro\\u003c/em\\u003e the lowest (at the inner marrow; ~1kPa) and the highest (endosteum; ~40kPa) stiffness values that we detected in our murine BM samples. We then isolated HSCs and cultured them overnight on the functionalized hydrogels with different stiffness values. HSCs were then recovered and stained for RhoA-GTP and DAPI and used for a colony-forming unit (CFU) assay (\\u003cb\\u003eFigure \\u003cspan refid=\\\"MOESM1\\\" class=\\\"InternalRef\\\"\\u003eS1\\u003c/span\\u003eL\\u003c/b\\u003e). Interestingly, RhoA-GTP levels did not changed in HSCs cultured on the stiff (40kPa) hydrogels compared to those cultured on the 1kPa hydrogels, contrary to what expected for RhoA being activated by an increased stiffness of the substrate\\u003csup\\u003e\\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e39\\u003c/span\\u003e\\u003c/sup\\u003e (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eH). As for the CFU assay, we did not detect differences in colony number, but the number of c-kit\\u0026thinsp;+\\u0026thinsp;cells (hematopoietic progenitors) and the total cell number were reduced in the 40kPa-stiff hydrogels with no differences in the number of myeloid cells (Gr1\\u0026thinsp;+\\u0026thinsp;and Mac1\\u0026thinsp;+\\u0026thinsp;cells) (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eI-K and \\u003cb\\u003eFigure \\u003cspan refid=\\\"MOESM1\\\" class=\\\"InternalRef\\\"\\u003eS1\\u003c/span\\u003eM\\u003c/b\\u003e). Intriguingly, RhoAKO HSCs behaved as their wild-type control for all the measured parameters (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eI-K and \\u003cb\\u003eFigure \\u003cspan refid=\\\"MOESM1\\\" class=\\\"InternalRef\\\"\\u003eS1\\u003c/span\\u003eM\\u003c/b\\u003e).\\u003c/p\\u003e \\u003cp\\u003eTherefore, our data reveals that in HSCs RhoA is dispensable in the response to changes in extracellular stiffness, while RhoA is necessary to survive intrinsic changes in NE tension. In HSCs RhoA activity sharply increases after NE tension raise induced by confinement, osmotic shock and chromatin decompaction after NaB treatment (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eL).\\u003c/p\\u003e \\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eRhoA activity is increased in aged HSCs\\u003c/h2\\u003e \\u003cp\\u003eAging alters the biomechanical properties of tissues and cells\\u003csup\\u003e\\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e40\\u003c/span\\u003e\\u003c/sup\\u003e. Since increased tissue stiffening upon aging has been reported to induce an increase in RhoA activity levels for example in the hair follicle\\u003csup\\u003e\\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e41\\u003c/span\\u003e\\u003c/sup\\u003e, we wondered if RhoA activity was altered in aged haematopoietic stem and progenitor cells (HSPCs) and whether it was correlated with increased stiffness of the aged BM\\u003csup\\u003e\\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e42\\u003c/span\\u003e\\u003c/sup\\u003e. We profiled the stiffness of the aged BM by performing indentation experiments using the same NanoIndenter device as reported above (\\u003cb\\u003eFigure \\u003cspan refid=\\\"MOESM1\\\" class=\\\"InternalRef\\\"\\u003eS1\\u003c/span\\u003eJ\\u003c/b\\u003e). We collected several measurements alongside a matrix to cover the overall BM cavity of femurs and tibiae comparing in parallel young (8 to 20 weeks old) and aged C57Bl6 (\\u0026gt;\\u0026thinsp;80 weeks old) mice. Strikingly, the indentation measurements showed an overall decreased bone marrow stiffness in aged samples compared to young, resulting in a homogenously low stiffness along the transversal section of the bone marrow (\\u003cb\\u003eFigure \\u003cspan refid=\\\"MOESM2\\\" class=\\\"InternalRef\\\"\\u003eS2\\u003c/span\\u003eA-B\\u003c/b\\u003e). To investigate whether the decreased BM stiffness was associated to changes in RhoA activity, we performed western blot and pull-down assays on HSPCs isolated from young and aged mice. We detected a significant upregulation of RhoA activity levels in aged BM cells, that could be targeted and significantly reduced by treatment with a selective RhoA inhibitor (Rhosin, here referred to as Ri)\\u003csup\\u003e\\u003cspan citationid=\\\"CR43\\\" class=\\\"CitationRef\\\"\\u003e43\\u003c/span\\u003e\\u003c/sup\\u003e (\\u003cb\\u003eFigure \\u003cspan refid=\\\"MOESM2\\\" class=\\\"InternalRef\\\"\\u003eS2\\u003c/span\\u003eC\\u003c/b\\u003e). Further, we measured RhoA-GTP levels in sorted HSCs by immunofluorescence. To this end, HSCs were harvested from aged mice and incubated with or without Ri. Young HSCs were sorted alongside as control (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eA and \\u003cb\\u003eFigure \\u003cspan refid=\\\"MOESM2\\\" class=\\\"InternalRef\\\"\\u003eS2\\u003c/span\\u003eD\\u003c/b\\u003e). Aged HSCs showed a dramatic upregulation of RhoA activity and treatment with 100\\u0026micro;M Ri significantly decreased levels of RhoA-GTP in aged HSCs (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eB), consistent with the western blot/pulldown results on HSPCs (\\u003cb\\u003eFigure \\u003cspan refid=\\\"MOESM2\\\" class=\\\"InternalRef\\\"\\u003eS2\\u003c/span\\u003eC\\u003c/b\\u003e). Therefore, in agreement with our conclusions based on the hydrogel experiments (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eI-K), RhoA activity appears not to be associated to the extracellular stiffness and, while the aged BM stiffness decreases, RhoA activity increases in HSCs upon aging, supporting an intrinsic activation of RhoA in HSCs upon aging.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eSince we reported above that changes in NE tension can intrinsically activate RhoA in HSCs, we hypothesized that RhoA-GTP levels are higher in aged HSCs because their nucleus might be experiencing higher nuclear tension. Consistent with this hypothesis, we detected a significant increase of NE P-cPLA2 in aged stem cells (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eC and \\u003cb\\u003eFigure \\u003cspan refid=\\\"MOESM2\\\" class=\\\"InternalRef\\\"\\u003eS2\\u003c/span\\u003eE\\u003c/b\\u003e). By treatment with the cPLA2 inhibitor (AACOCF3), NE translocation of P-cPLA2 is clearly reduced across all measurement metrics (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eC and \\u003cb\\u003eFigure \\u003cspan refid=\\\"MOESM2\\\" class=\\\"InternalRef\\\"\\u003eS2\\u003c/span\\u003eE\\u003c/b\\u003e). Importantly, levels of RhoA-GTP in aged cells were sharply reduced to levels similar to young HSCs after treatment with AACOCF3, supporting that changes in NE tension are necessary to activate RhoA in aged HSCs (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eD).\\u003c/p\\u003e \\u003cp\\u003eTo further investigate the increased NE tension in aged stem cells, we performed additional experiments to measure the wrinkling of the NE, a structural feature of nuclear architecture that has previously been used as a measure of NE tension\\u003csup\\u003e\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR44\\\" class=\\\"CitationRef\\\"\\u003e44\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR45\\\" class=\\\"CitationRef\\\"\\u003e45\\u003c/span\\u003e\\u003c/sup\\u003e. LaminB staining clearly shows increased NE circularity and reduced NE excess folding in aged HSCs compared to young (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eE-F). This data strongly supports the results based on NE P-cPLA2 increase in aged stem cells and therefore that the NE tension in aged HSCs is higher compared to young stem cells (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eE-F). Strikingly, RhoA inhibition sharply decreases NE circularity and NE excess folding in aged HSCs (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eE-F).\\u003c/p\\u003e \\u003cp\\u003eTo corroborate further these observations, we quantified also the nuclear import of the mechanosensitive transcription factor TAZ, which is known to accumulate in the nucleus with increasing NE tension\\u003csup\\u003e\\u003cspan citationid=\\\"CR46\\\" class=\\\"CitationRef\\\"\\u003e46\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR47\\\" class=\\\"CitationRef\\\"\\u003e47\\u003c/span\\u003e\\u003c/sup\\u003e. In agreement, we measured increased nuclear translocation of TAZ after 8\\u0026micro;m and 5\\u0026micro;m HSC confinement (\\u003cb\\u003eFigure \\u003cspan refid=\\\"MOESM2\\\" class=\\\"InternalRef\\\"\\u003eS2\\u003c/span\\u003eF\\u003c/b\\u003e). As reported also previously\\u003csup\\u003e\\u003cspan citationid=\\\"CR48\\\" class=\\\"CitationRef\\\"\\u003e48\\u003c/span\\u003e\\u003c/sup\\u003e, we quantified higher level of nuclear TAZ in aged HSCs compared to young, which is dependent on RhoA activity (\\u003cb\\u003eFigure \\u003cspan refid=\\\"MOESM2\\\" class=\\\"InternalRef\\\"\\u003eS2\\u003c/span\\u003eF\\u003c/b\\u003e). Altogether, the results support the functional connection between changes in NE tension and RhoA activity as a mechano-sensitive regulator of HSC ageing (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eG).\\u003c/p\\u003e \\u003c/div\\u003e\\n\\u003ch3\\u003eRhoA inhibition restores DAPI-Intense Regions in aged HSCs\\u003c/h3\\u003e\\n\\u003cp\\u003eTo explore if changes in NE tension impact on chromatin of aged HSCs, we developed a computational approach leveraging image analysis algorithms to extract morphometric and fluorescence intensity features from 3D-confocal images of HSC nuclei stained with DAPI, which is a photostable fluorescent DNA dye and its fluorescence intensity has been used in several applications to quantify DNA amount and chromatin condensation in intact nuclei\\u003csup\\u003e\\u003cspan citationid=\\\"CR49\\\" class=\\\"CitationRef\\\"\\u003e49\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR50\\\" class=\\\"CitationRef\\\"\\u003e50\\u003c/span\\u003e\\u003c/sup\\u003e. First, consistent with increased NE tension, the results show that aged HSCs display a significant increase in nuclear volume, nuclear diameter, surface area, perimeter of the largest Z slide and DAPI-Intense Regions (DIRs) volume, among other related features, compared to young HSCs (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eA). To note, HSC confinement (8\\u0026micro;m) induced a similar larger increase in the same morphometric features, supporting that these alterations of nuclear volume, size and shape in aged HSCs are compatible with increased NE tension (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eA)\\u003csup\\u003e\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR51\\\" class=\\\"CitationRef\\\"\\u003e51\\u003c/span\\u003e\\u003c/sup\\u003e.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eNext, since RhoA inhibition sharply decreases NE circularity and NE excess folding in aged HSCs (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eE-F), we asked whether decreasing RhoA activity might feedback to the nucleus inducing any change in morphometric and fluorescence intensity features of aged HSC nuclei. Surprisingly, the data revealed differences mainly in the pattern of DAPI intensity, that we extracted by quantifying fluorescence intensities along 3D iso-distant intervals from the nuclear border (\\u003cb\\u003eFigure S3A\\u003c/b\\u003e and Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eB\\u003cb\\u003e)\\u003c/b\\u003e. The pattern of DAPI intensities showed lower values near the nuclear border for aged HSCs compared to young and in aged HSCs DIRs localize relatively far from the nuclear border towards the central part of the nucleus (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eC). Most aged\\u0026thinsp;+\\u0026thinsp;Ri HSCs nuclei displayed a large proportion of DIRs near the NE, similar to young HSCs (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eC). In addition, other DAPI intensity features were different between young and aged HSCs and were restored to a youthful level after RhoA inhibition, namely DIRs height, DIRs major axis length, DIR distance to the border and number of DIRs (\\u003cb\\u003eFigure S3B\\u003c/b\\u003e).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eNext, as we calculated multiple morphometric and intensity features, we proceeded to conduct dimensionality reduction analyses on our feature set to explore similarity patterns within our HSC nucleus images (\\u003cb\\u003eFigure S3C-D\\u003c/b\\u003e). First, we performed feature engineering and clustering analysis on all available nucleus images to uncover potential mechanisms underlying nuclear remodeling in young, aged and aged Ri-treated HSCs. The extracted imaging features are categorized into three groups: (\\u003cem\\u003ei\\u003c/em\\u003e) whole nucleus level features, (\\u003cem\\u003eii\\u003c/em\\u003e) DIRs level features, and (\\u003cem\\u003eiii\\u003c/em\\u003e) features computed from the largest 2D \\u003cem\\u003ez\\u003c/em\\u003e slide in the \\u003cem\\u003exy\\u003c/em\\u003e plane. A comprehensive summary of these features is provided in \\u003cb\\u003eTable \\u003cspan refid=\\\"MOESM1\\\" class=\\\"InternalRef\\\"\\u003eS1\\u003c/span\\u003e\\u003c/b\\u003e. To address the high dimensionality and notable correlation of our feature space, we employed the non-linear dimensionality reduction technique Uniform Manifold Approximation and Projection\\u003csup\\u003e\\u003cspan citationid=\\\"CR52\\\" class=\\\"CitationRef\\\"\\u003e52\\u003c/span\\u003e\\u003c/sup\\u003e (UMAP). Feature selection involved identifying statistically significant features (Mann Whitney U-test p-value\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05) among young and aged HSCs, and among aged and aged Ri-treated HSCs (\\u003cb\\u003eFigure S3C-D\\u003c/b\\u003e). These significant features were then combined and filtered to remove those exhibiting high Pearson correlation coefficient (absolute value of correlation\\u0026thinsp;\\u0026gt;\\u0026thinsp;80%), resulting in a final set of 20 features (\\u003cb\\u003eFigure S3E\\u003c/b\\u003e). The UMAP revealed that young, aged and aged\\u0026thinsp;+\\u0026thinsp;Ri HSCs exhibit overlapping yet distinct distributions, indicating underlying differences in their chromatin properties (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eD). By grouping the nucleus data points using the K-Means clustering algorithm on the original feature space, we identified four distinct clusters that differ in morphometrics and intensity characteristics (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eE and \\u003cb\\u003eFigure S3F-G\\u003c/b\\u003e). Analysis of individual features\\u0026rsquo; impact over the UMAP representation revealed that morphometric-related features polarize the embedding vertically, whereas intensity and DIRs-related features polarize the embedding horizontally (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eE and \\u003cb\\u003eFigure S3H\\u003c/b\\u003e). Subsequently, by assessing the most representative features and biological populations, we annotated the clusters as Low Size (LS), High Size (HS), Border Intensity (BI) and Central Intensity (CI) (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eE). Feature importance for each cluster was evaluated by measuring feature statistical significance and fold change per cluster (\\u003cb\\u003eFigure S3I\\u003c/b\\u003e). Cluster BI includes mostly young and aged\\u0026thinsp;+\\u0026thinsp;Ri HSCs exhibiting high intensity near the NE and lower intensity in the nuclear center, along with an increased number of small DIRs closer to the border, which tend to be less spherical compared to nuclei in other clusters (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eE-G and \\u003cb\\u003eFigure S3H-I\\u003c/b\\u003e). Cluster CI predominantly consists of aged nuclei, with a reduced number of aged Ri-treated nuclei. These nuclei present with larger DIRs located farther from the NE and are notably spherical, with decreased intensity near the nuclear border (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eE-G and \\u003cb\\u003eFigure S3H-I\\u003c/b\\u003e). Cluster LS mainly contains young nuclei characterized by smaller size and lower mean intensity of DIRs, with surprisingly not much accentuated intensity values near the border (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eE-G and \\u003cb\\u003eFigure S3H-I\\u003c/b\\u003e). Cluster HS contains a mix of biological conditions, with nuclei characterized by larger size, DIRs positioned away from the nuclear border, and a decreased aspect ratio indicating these nuclei are wider than they are tall (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eE-G and \\u003cb\\u003eFigure S3H-I\\u003c/b\\u003e). By plotting feature trajectories across clusters, morphometric features progressively decrease or increase with decreasing frequencies of young HSCs within the clusters spanning from LS to HS clusters (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eF). Interestingly, intensity features, and DIR-related features progressively decrease or increase with increasing frequencies of aged\\u0026thinsp;+\\u0026thinsp;Ri HSCs from CI to BI clusters (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eG). Therefore, aged Ri-treated nuclei appear to share some morphometric similarities with aged nuclei yet exhibit intensity and DIR-related characteristics notably similar to those of young HSCs.\\u003c/p\\u003e \\u003cp\\u003eOverall, our computational approach suggests that DAPI imaging features elucidate chromatin differences in stem cells revealing a Ri-associated nuclear remodeling, which is mainly linked to changes in DAPI intensity and DIR volume, number and localization.\\u003c/p\\u003e\\n\\u003ch3\\u003eRhoA inhibition restores H3K9me2 at heterochromatin\\u003c/h3\\u003e\\n\\u003cp\\u003eAlterations in the mechanobiology of the cell have been associated with aging-dependent changes in chromatin architecture\\u003csup\\u003e\\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e40\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR53\\\" class=\\\"CitationRef\\\"\\u003e53\\u003c/span\\u003e\\u003c/sup\\u003e and several epigenetic alterations characterize intrinsic HSC aging\\u003csup\\u003e\\u003cspan additionalcitationids=\\\"CR55\\\" citationid=\\\"CR54\\\" class=\\\"CitationRef\\\"\\u003e54\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR56\\\" class=\\\"CitationRef\\\"\\u003e56\\u003c/span\\u003e\\u003c/sup\\u003e, among which it has been previously reported a general loss of heterochromatin\\u003csup\\u003e\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR57\\\" class=\\\"CitationRef\\\"\\u003e57\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR58\\\" class=\\\"CitationRef\\\"\\u003e58\\u003c/span\\u003e\\u003c/sup\\u003e. Intrigued by the observation that in aged HSCs treated with Ri some nuclear DAPI intensity and DIR-related features were significantly reverted to the level found in young HSCs (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eC and \\u003cb\\u003eFigure S3B\\u003c/b\\u003e) and are associated to the nuclear remodeling induced by Ri (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eG), we focused on heterochromatin because especially DIRs are related to the most condensed portion of chromatin. Therefore, we analyzed the levels and distribution of H3K9me2, a known heterochromatin histone mark which is altered in aged HSCs\\u003csup\\u003e\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e33\\u003c/span\\u003e\\u003c/sup\\u003e. By 3D-IF analyses, we measure a significant increase of H3K9me2 levels in aged\\u0026thinsp;+\\u0026thinsp;Ri HSCs compared to aged controls, together with a partial re-localization at the nuclear border like in young HSCs (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig7\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eA). Interestingly, treatment with UNC0631, a selective inhibitor of the histone methyltransferase G9a specific to H3K9me2\\u003csup\\u003e59\\u003c/sup\\u003e, blunts completely the effect of Ri on H3K9me2 in aged HSCs (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig7\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eA). This data suggests the requirement of G9a to increase levels of H3K9me2 in aged\\u0026thinsp;+\\u0026thinsp;Ri HSCs. Moreover, it reveals that aged HSCs are not affected by UNC0631 treatment alone, most likely because of the very low expression of G9a in these cells in basal conditions, which is partially rescued by Ri treatment (\\u003cb\\u003eFigure S4A\\u003c/b\\u003e). To causally explain the role of decreased H3K9me2 in HSCs, we transduced young hematopoietic stem and progenitor cells (HSPCs or LSKs, gated as Lin\\u003csup\\u003e\\u0026minus;\\u003c/sup\\u003ec-Kit\\u003csup\\u003e+\\u003c/sup\\u003eSca-1\\u003csup\\u003e+\\u003c/sup\\u003e) with a retroviral vector, codifying for a histone variant in which the lysine of H3K9 is replaced by an arginine (H3R9) (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig7\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eB and \\u003cb\\u003eFigure S4B\\u003c/b\\u003e). The arginine in position 9 on H3 cannot be methylated, enforcing H3K9me reduction in HSCs. We functionally validated the strategy and the H3R9 incorporation by transplanting transduced LSKs into lethally irradiated recipient mice. We measured a significant reduction of H3K9me in H3R9 myeloid progenitors (MPs) compared to control vector transduced H3K9 MPs (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig7\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eC). Young H3R9 HSCs isolated from transplanted mice present with higher RhoA-GTP levels and increased nuclear stretching compared to controls (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig7\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eD). Moreover, H3R9 HSCs show a clear premature-aging phenotype upon transplantations \\u003cem\\u003ein vivo\\u003c/em\\u003e, characterized by expansion of LSK and granulocyte-monocyte progenitors (GMP), reduced BM and peripheral blood (PB) regeneration, reduced B-lymphopoiesis and myeloid skewing (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig7\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eE-G). Altogether, this data demonstrates that reduced methylation of H3K9 causes nuclear stretching, increasing RhoA activation and driving aging of HSCs. Importantly, RhoA inhibition restores H3K9 methylation levels in aged HSCs.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e\\n\\u003ch3\\u003eRhoA regulates chromatin accessibility at retrotransposons in aged HSCs\\u003c/h3\\u003e\\n\\u003cp\\u003ePreviously, it has been proposed that the cell nucleus can directly respond to mechanical stress by inducing chromatin remodeling, altering polymerase and transcription factor accessibility and activity\\u003csup\\u003e\\u003cspan additionalcitationids=\\\"CR61\\\" citationid=\\\"CR60\\\" class=\\\"CitationRef\\\"\\u003e60\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR62\\\" class=\\\"CitationRef\\\"\\u003e62\\u003c/span\\u003e\\u003c/sup\\u003e, while increased chromatin accessibility as measured by ATAC-seq has been already characterized as an epigenetic alteration intrinsic to HSC aging\\u003csup\\u003e\\u003cspan citationid=\\\"CR63\\\" class=\\\"CitationRef\\\"\\u003e63\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR64\\\" class=\\\"CitationRef\\\"\\u003e64\\u003c/span\\u003e\\u003c/sup\\u003e. Intrigued by possibility that RhoA inhibition might underscore a link between increased NE tension and increased chromatin accessibility in aged HSCs, we investigated further the chromatin remodeling associated with Ri treatment by performing ATAC-seq profiling of sorted young, aged, and aged\\u0026thinsp;+\\u0026thinsp;Ri HSCs (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig9\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003eA). Peak-calling identified a total of 57,289 accessible regions consistent between samples, most of them located in introns, intergenic regions, and gene promoters (\\u003cb\\u003eFigure S5A-B\\u003c/b\\u003e and \\u003cb\\u003eTable \\u003cspan refid=\\\"MOESM2\\\" class=\\\"InternalRef\\\"\\u003eS2\\u003c/span\\u003e\\u003c/b\\u003e). In agreement with previously published data\\u003csup\\u003e\\u003cspan citationid=\\\"CR63\\\" class=\\\"CitationRef\\\"\\u003e63\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR64\\\" class=\\\"CitationRef\\\"\\u003e64\\u003c/span\\u003e\\u003c/sup\\u003e, we detected an increase in open differentially accessible regions (DARs) with aging (2713 DARs open and 1103 DARs closed in aged compared to young HSCs; ~8% FPR; Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig9\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003eB and \\u003cb\\u003eTable \\u003cspan refid=\\\"MOESM2\\\" class=\\\"InternalRef\\\"\\u003eS2\\u003c/span\\u003e\\u003c/b\\u003e). After applying the Ri treatment to aged HSCs, 743 chromatin regions opened and 355 closed (~\\u0026thinsp;8% FPR; Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig9\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003eB and \\u003cb\\u003eTable \\u003cspan refid=\\\"MOESM2\\\" class=\\\"InternalRef\\\"\\u003eS2\\u003c/span\\u003e\\u003c/b\\u003e). Overall, 144 DARs were detected in both comparisons (aged \\u003cem\\u003evs\\u003c/em\\u003e young and aged\\u0026thinsp;+\\u0026thinsp;Ri \\u003cem\\u003evs\\u003c/em\\u003e aged), with 85.42% of them showing accessibility levels after the Ri treatment changing in the direction of the young levels (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig9\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003eC and \\u003cb\\u003eFigure S5C\\u003c/b\\u003e and \\u003cb\\u003eTable \\u003cspan refid=\\\"MOESM2\\\" class=\\\"InternalRef\\\"\\u003eS2\\u003c/span\\u003e\\u003c/b\\u003e). Among the regions opening in aged HSCs treated with Ri, Gene ontology (GO) enrichment analysis revealed pathways related to cell migration, morphogenesis, adhesion, and chemotaxis (\\u003cb\\u003eFigure S5D\\u003c/b\\u003e and \\u003cb\\u003eTable \\u003cspan refid=\\\"MOESM2\\\" class=\\\"InternalRef\\\"\\u003eS2\\u003c/span\\u003e\\u003c/b\\u003e). Interestingly, no GOs were significantly enriched among the DARs closing in aged HSCs\\u0026thinsp;+\\u0026thinsp;Ri and a high percentage of these closing DARs were located at retrotransposons (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig9\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003eD \\u003cb\\u003elower right panel\\u003c/b\\u003e), especially Long Terminal Repeats (LTRs) and Long INnterspersed Elements (LINE), like ERVL-MaLR, ERVK, ERV1, and L1 families (2-fold/~1 log2FC higher percentage compared to the percentage in consensus peaks; FDR\\u0026thinsp;=\\u0026thinsp;0.0018 for LTRs and FDR\\u0026thinsp;=\\u0026thinsp;0.014 for LINEs in a one-proportion z-test; Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig9\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003eD-E and \\u003cb\\u003eTable \\u003cspan refid=\\\"MOESM2\\\" class=\\\"InternalRef\\\"\\u003eS2\\u003c/span\\u003e\\u003c/b\\u003e)\\u003csup\\u003e\\u003cspan citationid=\\\"CR65\\\" class=\\\"CitationRef\\\"\\u003e65\\u003c/span\\u003e\\u003c/sup\\u003e. Notably, in aged HSCs we observe an opening of chromatin at retrotransposons, with a 1.65-fold (0.72 log2FC) and a 1.25-fold (0.33 log2FC) increase in the percentage of open DARs localizing in LTRs and LINEs, respectively (FDR\\u0026thinsp;=\\u0026thinsp;3.2x10\\u003csup\\u003e\\u0026minus;\\u0026thinsp;13\\u003c/sup\\u003e for LTRs and FDR\\u0026thinsp;=\\u0026thinsp;0.04998 for LINEs in a one-proportion z-test; Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig9\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003eD-E and \\u003cb\\u003eTable \\u003cspan refid=\\\"MOESM2\\\" class=\\\"InternalRef\\\"\\u003eS2\\u003c/span\\u003e\\u003c/b\\u003e). Some of these DARs at LTRs and LINEs were overlapping with enhancers described previously\\u003csup\\u003e\\u003cspan citationid=\\\"CR66\\\" class=\\\"CitationRef\\\"\\u003e66\\u003c/span\\u003e\\u003c/sup\\u003e, while others were located in intronic and intergenic regions (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig9\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003eE and \\u003cb\\u003eTable \\u003cspan refid=\\\"MOESM2\\\" class=\\\"InternalRef\\\"\\u003eS2\\u003c/span\\u003e\\u003c/b\\u003e). Interestingly, retrotransposons and in particular LINE-1 have been suggested to directly contribute to aging of somatic cells and aging-related diseases\\u003csup\\u003e\\u003cspan citationid=\\\"CR65\\\" class=\\\"CitationRef\\\"\\u003e65\\u003c/span\\u003e,\\u003cspan additionalcitationids=\\\"CR68\\\" citationid=\\\"CR67\\\" class=\\\"CitationRef\\\"\\u003e67\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR69\\\" class=\\\"CitationRef\\\"\\u003e69\\u003c/span\\u003e\\u003c/sup\\u003e. To explore if the increase in chromatin accessibility at retrotransposons upon HSC aging is linked to increased NE tension, we performed ATAC-seq of young HSCs under 5\\u0026micro;m confinement and compared their chromatin accessibility profiles to those of unconfined HSCs sorted in parallel from the same mice (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig9\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003eF). As a reference for DAR identification, we used the 42,632 consensus peaks identified between young and aged HSCs samples (\\u003cb\\u003eTable S3\\u003c/b\\u003e). Data show 443 DARs opening and only 25 DARs closing in young HSCs under confinement (~\\u0026thinsp;6.6% FPR; Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig9\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003eG-H and \\u003cb\\u003eFigure S5E\\u003c/b\\u003e and \\u003cb\\u003eTable S3\\u003c/b\\u003e). Strikingly, while the DARs that are closing in confined HSCs are located at promoter-TSS and 5\\u0026rsquo;UTR (1.67 log2FC over consensus peaks; FDR\\u0026thinsp;=\\u0026thinsp;4.1x10\\u003csup\\u003e\\u0026minus;\\u0026thinsp;6\\u003c/sup\\u003e in a one-proportion z-test; Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig9\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003eI \\u003cb\\u003elower panel)\\u003c/b\\u003e, the DARs that are opening are mainly at LTRs and LINEs (0.75 and 1.18 log2FC over consensus peaks, respectively; FDR\\u0026thinsp;=\\u0026thinsp;0.008 for LTRs and FDR\\u0026thinsp;=\\u0026thinsp;0.003 for LINEs in a one-proportion z-test; Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig9\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003eI \\u003cb\\u003eupper panel\\u003c/b\\u003e). Overall, the results support that the increased accessibility at REs observed in the aged HSCs and reverted (closed) by Ri treatment are located at LTR and LINE, which are the same type of genomic regions that are open by increasing NE tension by mechanical confinement.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eNext, we investigated transcriptional changes by bulk RNA-seq analysis of young, aged and aged\\u0026thinsp;+\\u0026thinsp;Ri HSCs. We identified 38 genes upregulated and 27 downregulated in aged HSCs after treatment with Ri (~\\u0026thinsp;5% FPR; \\u003cb\\u003eFigure S5F-H\\u003c/b\\u003e and \\u003cb\\u003eTable S4\\u003c/b\\u003e). Consistently with the ATAC-seq data, after Ri treatment we detected a downregulation in the transcription of several retrotransposons subfamilies that are upregulated in aged HSCs, mainly LTRs but also some LINEs and DNA transposon subfamilies, like L1 (L1ME1 and L1ME3A), ERV1 (MER110-int) and hAT-Blackjack (MER63C) (~\\u0026thinsp;8% FPR; Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig9\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003eJ and \\u003cb\\u003eFigure S5I\\u003c/b\\u003e and \\u003cb\\u003eTable S4\\u003c/b\\u003e). Gene Set Enrichment Analysis (GSEA) revealed enrichment for several GOs related to inflammation, innate immune response activation and interferon response among the genes downregulated after Ri treatment, again consistent with a downregulation of retrotransposons\\u003csup\\u003e\\u003cspan citationid=\\\"CR65\\\" class=\\\"CitationRef\\\"\\u003e65\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR68\\\" class=\\\"CitationRef\\\"\\u003e68\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR70\\\" class=\\\"CitationRef\\\"\\u003e70\\u003c/span\\u003e\\u003c/sup\\u003e (FDR\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05; Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig9\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003eK-L and \\u003cb\\u003eFigure S5J\\u003c/b\\u003e and \\u003cb\\u003eTable S4\\u003c/b\\u003e). Using GSEA, we also detected a decrease in the Interferome.org gene signature\\u003csup\\u003e\\u003cspan citationid=\\\"CR71\\\" class=\\\"CitationRef\\\"\\u003e71\\u003c/span\\u003e\\u003c/sup\\u003e and of interferon-stimulated genes (ISG)\\u003csup\\u003e\\u003cspan citationid=\\\"CR72\\\" class=\\\"CitationRef\\\"\\u003e72\\u003c/span\\u003e\\u003c/sup\\u003e in aged\\u0026thinsp;+\\u0026thinsp;Ri HSCs (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig9\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003eM-N and \\u003cb\\u003eFigure S5K-L\\u003c/b\\u003e). Moreover, we measured a significantly negative enrichment score in aged\\u0026thinsp;+\\u0026thinsp;Ri \\u003cem\\u003evs\\u003c/em\\u003e aged samples for the HSC aging signature defined by Svendsen et al.\\u003csup\\u003e\\u003cspan citationid=\\\"CR73\\\" class=\\\"CitationRef\\\"\\u003e73\\u003c/span\\u003e\\u003c/sup\\u003e, while the same signature was clearly positively enriched in the aged samples compared to the young ones (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig9\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003eO and \\u003cb\\u003eFigure S5M\\u003c/b\\u003e).\\u003c/p\\u003e \\u003cp\\u003eIn summary, by ATAC-seq profiling, we detect changes in chromatin accessibility in aged Ri-treated HSCs that revealed reduction of open regions at LTRs and LINE, belonging mainly to ERVL-MaLR, ERVK, ERV1 and L1 families. ATAC-seq profiling of young 5\\u0026micro;m-confined HSCs supports that the increased accessibility at retrotransposones observed in the aged HSCs and reverted by Ri treatment could be a consequence of increased nuclear stretching. Consistently, by bulk RNA-seq profiling of aged\\u0026thinsp;+\\u0026thinsp;Ri HSCs we detected a reduction in the transcription of LTRs and LINEs and a downregulation of the immune response, inflammatory response, interferon response and aging gene signatures compared to aged control HSCs.\\u003c/p\\u003e\\n\\u003ch3\\u003eInhibiting RhoA activity improves function of aged HSCs\\u003c/h3\\u003e\\n\\u003cp\\u003eNext, to further characterize the transcriptional changes in aged HSCs after Ri treatment, we focused on the few upregulated genes (\\u003cb\\u003eFigure S5G-H Table S4\\u003c/b\\u003e) and, interestingly, we noticed three transcription factors (TF) belonging to the same family: \\u003cem\\u003eKlf4\\u003c/em\\u003e, \\u003cem\\u003eKlf6\\u003c/em\\u003e and \\u003cem\\u003eKlf2\\u003c/em\\u003e (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig11\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003eA and \\u003cb\\u003eTable S4\\u003c/b\\u003e). Noteworthy, the upregulation of Klfs is consistent with the opening of Klf4 motifs detected by ATAC-seq TF-binding motif analysis, which revealed enrichment in motifs for AP-1 and Klf4 (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig11\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003eB and \\u003cb\\u003eTable \\u003cspan refid=\\\"MOESM2\\\" class=\\\"InternalRef\\\"\\u003eS2\\u003c/span\\u003e\\u003c/b\\u003e). In hematopoietic cells, AP-1 can interact with chromatin remodelers to assist in the binding of other TFs\\u003csup\\u003e\\u003cspan citationid=\\\"CR74\\\" class=\\\"CitationRef\\\"\\u003e74\\u003c/span\\u003e\\u003c/sup\\u003e and Klf4 has been previously shown to be important in cell reprogramming, blood formation and mechanosensing\\u003csup\\u003e\\u003cspan additionalcitationids=\\\"CR76 CR77 CR78 CR79\\\" citationid=\\\"CR75\\\" class=\\\"CitationRef\\\"\\u003e75\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR80\\\" class=\\\"CitationRef\\\"\\u003e80\\u003c/span\\u003e\\u003c/sup\\u003e. In addition, we identified an open DAR in aged\\u0026thinsp;+\\u0026thinsp;Ri HSCs in correspondence with a recently annotated Klf4 enhancer\\u003csup\\u003e\\u003cspan citationid=\\\"CR81\\\" class=\\\"CitationRef\\\"\\u003e81\\u003c/span\\u003e\\u003c/sup\\u003e (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig11\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003eC) and we measured a clear increase in Klf4 protein in the nucleus of aged stem cells after Ri treatment (\\u003cb\\u003eFigure S6A\\u003c/b\\u003e). Next, we analyzed the expression level of the genes targeted by a higher accessibility of Klf4-binding motifs and most of them increased their expression after Ri (\\u003cb\\u003eFigure S6B\\u003c/b\\u003e and \\u003cb\\u003eTable \\u003cspan refid=\\\"MOESM2\\\" class=\\\"InternalRef\\\"\\u003eS2\\u003c/span\\u003e\\u003c/b\\u003e). GO enrichment analysis of these genes revealed enrichment for morphogenesis, differentiation, and actin polymerization (FDR\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05; Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig11\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003eD and \\u003cb\\u003eTable S4\\u003c/b\\u003e). Notably, actin polymerization (filamentous actin or F-actin) has been reported to restrict nuclear stretching\\u003csup\\u003e\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e\\u003c/sup\\u003e. Consistently, while in aged HSCs F-actin is decreased compared to young, F-actin levels increase upon Ri treatment similarly to young HSCs (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig11\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003eE).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eTo gain further insights into the transcriptional rewiring of aged HSCs treated with Ri, we performed scRNA-seq of young, aged, and aged Ri-treated LSK cells. We obtained a total of 60,648 cells expressing 15,049 genes. Clustering of the integrated UMAP identified 11 clusters (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig11\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003eF) that were annotated as based on known marker gene expression and enrichment for several previously identified HSC and LSK signatures\\u003csup\\u003e\\u003cspan additionalcitationids=\\\"CR83\\\" citationid=\\\"CR82\\\" class=\\\"CitationRef\\\"\\u003e82\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR84\\\" class=\\\"CitationRef\\\"\\u003e84\\u003c/span\\u003e\\u003c/sup\\u003e (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig11\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003eC-F and \\u003cb\\u003eTable S5)\\u003c/b\\u003e. Compositional analysis of cell clusters showed the expected increase in the percentage of HSCs in all aged samples compared to the young ones (\\u003cb\\u003eFigure S6G\\u003c/b\\u003e and \\u003cb\\u003eTable S5\\u003c/b\\u003e). It also revealed an increase in the percentage of MPP3 with aging and its decrease with the Ri treatment (\\u003cb\\u003eFigure S6G\\u003c/b\\u003e and \\u003cb\\u003eTable S5\\u003c/b\\u003e). Interestingly, the scRNA-seq dataset showed that the increase of Klf4 expression in aged\\u0026thinsp;+\\u0026thinsp;Ri LSKs is more prominent in the HSC and the MPP1 clusters (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig11\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003eG). In detail, 43.58% of aged\\u0026thinsp;+\\u0026thinsp;Ri HSCs are expressing Klf4, while only 2.46% and 2.80% of young and aged HSCs, respectively, express this TF. Differential gene expression analysis between the three conditions in the HSC cluster confirms the upregulation of several genes of the Klf family after Ri treatment (|log2FC| \\u0026gt; 1 and FDR\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05; \\u003cb\\u003eFigure S6H\\u003c/b\\u003e and \\u003cb\\u003eTable S5\\u003c/b\\u003e). To further analyze the activity of the different TFs and identify the network of regulated genes, we calculated TF activity scores in each single HSC using SCENIC and generated a UMAP based on TF activity scores (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig11\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003eH). Interestingly, the percentage of aged\\u0026thinsp;+\\u0026thinsp;Ri HSCs with active Klf4 is 89.52%, while it is negligible in young and aged HSCs (1.89% and 0.69% respectively) (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig11\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003eI and \\u003cb\\u003eFigure S6I\\u003c/b\\u003e). Differential analysis of the activity scores in between conditions confirms the increased activity of several Klf TFs, as well as other TFs (|log2FC| \\u0026gt; 0.5 and FDR\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05; Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig11\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003eJ). Since Klf4 is known for its role in cell reprogramming and dedifferentiation, we wondered if aged\\u0026thinsp;+\\u0026thinsp;Ri HSCs show a more dedifferentiated transcriptional state. Interestingly, GSEA reveals an enrichment of the hemogenic precursor signature defined by Pereira et al.\\u003csup\\u003e\\u003cspan citationid=\\\"CR85\\\" class=\\\"CitationRef\\\"\\u003e85\\u003c/span\\u003e\\u003c/sup\\u003e in the aged\\u0026thinsp;+\\u0026thinsp;Ri HSCs compared to the aged controls (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig11\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003eK), supporting that Ri treatment might induce a partial transcriptional reprogramming of aged HSCs.\\u003c/p\\u003e \\u003cp\\u003eSince the scRNA-seq analysis indicates a partial reprogramming suggestive at the transcriptomic level of a possible functional improvement, we decided to assess the regenerative capacity of aged HSCs after Ri treatment \\u003cem\\u003ein vivo\\u003c/em\\u003e. Previously, we correlated changes in cell epigenetic polarity of H4K16ac to function of HSCs\\u003csup\\u003e\\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e33\\u003c/span\\u003e,\\u003cspan additionalcitationids=\\\"CR87\\\" citationid=\\\"CR86\\\" class=\\\"CitationRef\\\"\\u003e86\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR88\\\" class=\\\"CitationRef\\\"\\u003e88\\u003c/span\\u003e\\u003c/sup\\u003e. 3D-IF staining of young, aged and aged\\u0026thinsp;+\\u0026thinsp;Ri HSCs clearly showed that Ri treatment restores H4K16ac polarity in aged stem cells (\\u003cb\\u003eFigure S7A\\u003c/b\\u003e). Next, we tested in a non-competitive transplantation assay into young immunocompromised and \\u003cem\\u003eKit\\u003c/em\\u003e\\u003csup\\u003e\\u003cem\\u003eW\\u0026minus;41J\\u003c/em\\u003e\\u003c/sup\\u003e mutant mice (NBSGW) the regenerative capacity of aged Ri treated HSCs compared to young and aged control stem cells. We transplanted 200 aged HSCs treated overnight \\u003cem\\u003eex vivo\\u003c/em\\u003e with 100\\u0026micro;M Ri, alongside control recipient mice transplanted with solvent treated young or aged HSC (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig13\\\" class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003eA). Donor mice were genetically tagged by constitutive expression in Rosa26 locus of a bright and stable red-fluorescent protein (tdRFP)\\u003csup\\u003e\\u003cspan citationid=\\\"CR89\\\" class=\\\"CitationRef\\\"\\u003e89\\u003c/span\\u003e\\u003c/sup\\u003e and we measured donor-derived contribution in peripheral blood (PB) by detecting RFP\\u003csup\\u003e+\\u003c/sup\\u003e cells at several time-points after transplantation. Notably, aged Ri-treated HSCs engrafted at the endpoint similarly to young HSCs, showing a significant increase in their peripheral blood (PB) regenerative capacity compared to aged control HSCs (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig13\\\" class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003eB-C). Remarkably, Ri treatment also significantly increased the B cell lymphoid differentiation potential of aged HSCs and decreased the contribution to the myeloid lineage 18 weeks after transplantation (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig13\\\" class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003eB-D). Ri treatment did not change the differentiation to the T cell compartment (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig13\\\" class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003eB-D). Engraftment in BM and HSC compartments was not significantly different in Ri treated recipients compared with either young or aged control recipients, showing a trend for both parameters to resemble young donor HSCs (\\u003cb\\u003eFigure S7B-C\\u003c/b\\u003e). Overall, we conclude that inactivation of RhoA in aged HSCs functionally improves the regenerative capacity of old stem cells and their myeloid/B-lymphoid skewing \\u003cem\\u003ein vivo\\u003c/em\\u003e.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e\"},{\"header\":\"DISCUSSION\",\"content\":\"\\u003cp\\u003eThe regenerative potential of HSCs declines upon aging\\u003csup\\u003e\\u003cspan citationid=\\\"CR90\\\" class=\\\"CitationRef\\\"\\u003e90\\u003c/span\\u003e\\u003c/sup\\u003e. Moreover, aged HSCs are skewed toward myeloid differentiation, which contributes to immunosenescence and to the increased incidence of hematopoietic disorders in the elderly\\u003csup\\u003e\\u003cspan additionalcitationids=\\\"CR2 CR3\\\" citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e\\u003c/sup\\u003e. Previously, intrinsic epigenetic alterations have been associated with HSC aging, such as for example increased chromatin accessibility\\u003csup\\u003e\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e33\\u003c/span\\u003e,\\u003cspan additionalcitationids=\\\"CR55\\\" citationid=\\\"CR54\\\" class=\\\"CitationRef\\\"\\u003e54\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR56\\\" class=\\\"CitationRef\\\"\\u003e56\\u003c/span\\u003e\\u003c/sup\\u003e. We also described that some of these epigenetic alterations depend on a reduction of LaminA/C expression in aged HSCs\\u003csup\\u003e\\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e33\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR63\\\" class=\\\"CitationRef\\\"\\u003e63\\u003c/span\\u003e\\u003c/sup\\u003e, which is suggestive of a possible impairment of the mechanical properties of the nucleus. Supporting a novel emerging perspective that focuses on nuclear mechanoregulation\\u003csup\\u003e\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR47\\\" class=\\\"CitationRef\\\"\\u003e47\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR91\\\" class=\\\"CitationRef\\\"\\u003e91\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR92\\\" class=\\\"CitationRef\\\"\\u003e92\\u003c/span\\u003e\\u003c/sup\\u003e, here we investigate RhoA, a small GTPase critical for HSC cytokinesis and differentiation\\u003csup\\u003e\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e\\u003c/sup\\u003e that has been involved also in mechanotransduction in different cell types\\u003csup\\u003e\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e\\u003c/sup\\u003e.\\u003c/p\\u003e \\u003cp\\u003eOur data shows that in HSCs RhoA is activated by increasing NE tension under cell confinement, by chromatin decompaction after NaB treatment, by nuclear swelling after hypotonic osmotic shock and after reduction of H3K9 methylation levels. To note, we identify the loss of methylation of H3K9 as a likely cause not only of RhoA over-activation and increased nuclear stretching in aged HSCs, but also of many phenotypes associated with functional aging of HSCs (reduced regenerative capacity, expansion of the primitive GMPs and LSKs populations and myeloid/lymphoid skewing in BM). Interestingly, G9a activity is required for Ri restoration of H3K9me2 levels in aged HSCs. Further work is necessary to clarify mechanistically how RhoA activity downregulation affects the activity of this histone methyltransferase.\\u003c/p\\u003e \\u003cp\\u003eTo note, the data reveals also that RhoA is necessary to survive cell confinement, which intrinsically induces RhoA activation. Differently from other cell types\\u003csup\\u003e\\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e39\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e41\\u003c/span\\u003e\\u003c/sup\\u003e, we show that in HSCs RhoA is not involved in transducing changes in extracellular stiffness, since RhoA is dispensable in the response to changes induced by culturing HSC on hydrogel with high stiffness.\\u003c/p\\u003e \\u003cp\\u003eFurthermore, our results reveal that NE tension is physiologically increased upon HSC aging and that NE tension is necessary to activate RhoA in aged HSCs. RhoA activity in aged HSCs can be targeted by a specific small molecule inhibitor\\u003csup\\u003e\\u003cspan citationid=\\\"CR43\\\" class=\\\"CitationRef\\\"\\u003e43\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR93\\\" class=\\\"CitationRef\\\"\\u003e93\\u003c/span\\u003e\\u003c/sup\\u003e, which restores H3K9me2 levels and recovers the phenotypes of young HSC nuclei such as DAPI intensity, DIR volume, number and localization. This is further supported by our machine learning approach, which demonstrates that DAPI-imaging morphometric and intensity features can be used to explain differences between HSCs and to inform on the age and fitness of the stem cells.\\u003c/p\\u003e \\u003cp\\u003eImportantly, by ATAC-seq and RNA-seq we detect a downregulation in chromatin accessibility and transcription at LTRs and LINE and a decrease in inflammation, immune response, interferon responsive genes and aging signatures in aged Ri-treated HSCs. Moreover, after treatment of aged HSCs with Ri we measure an increased transcription of Klf4, an opening of Klf4-binding motifs and an increased activity of Klf4 TF. Genes targeted by Klf4 are enriched for pathways related to actin polymerization and the increased levels of F-actin are likely acting to restrict nuclear stretching in aged Ri-treated HSCs. Moreover, Klf4 targets also genes enriched for a signature of hemogenic precursors, compatible with a partial reprogramming of aged HSCs, supporting the functional improvement in the regenerative capacity and myeloid/lymphoid skewing of aged\\u0026thinsp;+\\u0026thinsp;Ri stem cells in transplantation assays (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig13\\\" class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003eE).\\u003c/p\\u003e \\u003cp\\u003eSo far, several reports associated the aging process of different cell types with an epigenetic drift involving loos of heterochromatin and H3K9me and a dis-regulation of normally silenced and closed retrotransposons\\u003csup\\u003e\\u003cspan citationid=\\\"CR65\\\" class=\\\"CitationRef\\\"\\u003e65\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR67\\\" class=\\\"CitationRef\\\"\\u003e67\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR68\\\" class=\\\"CitationRef\\\"\\u003e68\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR94\\\" class=\\\"CitationRef\\\"\\u003e94\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR95\\\" class=\\\"CitationRef\\\"\\u003e95\\u003c/span\\u003e\\u003c/sup\\u003e. Here we report that an intrinsic nuclear mechanosignalling pathway dependent on RhoA can be pharmacologically targeted to revert these drifted epigenetic features, improving function of aged somatic stem cells (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig13\\\" class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003eE). To note, overactivation of RhoA has been previously reported to be associated with functional impairment of human HSCs, supporting possible translations of our findings\\u003csup\\u003e\\u003cspan citationid=\\\"CR96\\\" class=\\\"CitationRef\\\"\\u003e96\\u003c/span\\u003e\\u003c/sup\\u003e. Altogether our data sheds light on a new perspective of intrinsic nuclear mechanotransduction to control the aging-related epigenetic drift of somatic stem cells as a potential target for improving tissue homeostasis over time.\\u003c/p\\u003e\"},{\"header\":\"METHODS\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eReagents\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eA list of reagents, chemicals, commercial kits and antibodies is provided as \\u003cstrong\\u003eSource Data file\\u003c/strong\\u003e.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eMice\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eYoung and aged C57BL/6 mice were obtained from the internal divisional stock (derived from mice obtained from The Jackson Laboratory). Young and aged acRFP C57BL/6 mice were obtained from the internal divisional stock (originally kindly donated by Prof. Fehling, Ulm University and previously described\\u003csup\\u003e89\\u003c/sup\\u003e). The NBSGW mice were obtained from the internal divisional stock (derived from mice obtained from The Jackson Laboratory, JAXStock No.026622) and were maintained as homozygotes. RhoAflox mice were described previously\\u003csup\\u003e20\\u003c/sup\\u003e and crossed with CreErt2 mice (JAXStock No.008463). All mice were housed in the animal barrier facility under pathogen-free conditions at the Biomedical Research Institute of Bellvitge (IDIBELL). Throughout the manuscript, young mice are between 10 and 20 weeks old and aged mice are at least 80 weeks old. C57BL/6 mice were randomized for sex.\\u003c/p\\u003e\\n\\u003cp\\u003eFor the transplantation study NBSGW mice were randomized for sex and equal number of male and female mice were used across samples. Mice that failed to recover from blood sampling and mice that died due to laboratory errors were excluded. Mice that needed to be euthanized because they were scored as \\u0026ldquo;weak and about to die\\u0026rdquo; according to our approved animal license protocol for evaluating mouse health status remained part of the dataset. Allocation to control or treated group was done randomly (4-5 mice each experimental group in four different experimental batches). All animals were maintained according to the recommendations of the European Convention for the Protection of Vertebrate Animals used for Experimental and other Scientific Purposes (ETS 123). Animals were housed in groups of up to 4 animals per cage in Macrolon Type II (long) cages with bedding and paper nesting material. The animals had access to food (V1124-3, ssniff\\u0026reg;) and water \\u003cem\\u003ead libitum\\u003c/em\\u003e. Animals were kept at a day/night rhythm of 12/12 hours throughout the entire experiment.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eEthical compliance for mouse experiments\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAll mouse experiments were performed in compliance with the ethical regulations according to the Spanish Law for Animal Protection and Welfare Code and were previously approved in the project AR18008/10399 by IDIBELL\\u0026rsquo;s Ethical Committee for Animal Experimentation (CEEA-IDIBELL) as well as by Generalitat of Catalunya.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eHSC transplantation in NBSGW\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eFor HSC transplantation, young and aged acRFP C57BL/6 were used as donors. Two-hundred HSCs were sorted into 72-well Terasaki multi-well plates (Merck, M5812) and cultured for 16 h in HBSS + 10% FBS\\u0026thinsp; + 1% Penicylin/Streptomycin (P/S) with or without Rhosin (RhoA inhibitor or Ri)\\u003csup\\u003e43\\u003c/sup\\u003e at 100\\u0026micro;M in a water-jacketed incubator at 37\\u0026thinsp;\\u0026deg;C, 5% CO\\u003csub\\u003e2\\u003c/sub\\u003e, 3% O\\u003csub\\u003e2\\u003c/sub\\u003e. Cells were transplanted via retro-orbital vein injection. Peripheral blood chimerism was determined by FACS analysis every 6\\u0026thinsp;weeks up to 18\\u0026thinsp;weeks after transplants. The transplantation experiment was performed four times with a cohort of four recipient mice per group each transplant. In general, transplanted mice were regarded as engrafted when peripheral blood chimerism was higher or equal to 0.2% and contribution was detected in all lineages.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eRetroviral vector construction and viral production\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eH3K9 and the mutant form H3R9 sequences were kindly provided by the laboratory of Prof. Clemens Schmitt\\u003csup\\u003e97\\u003c/sup\\u003e. They are based on the H3.1 histone isoform with the K9 mutated to R9. Sequences were subcloned into the retroviral backbone pMSCVII (pMSCVII was a gift from Maki Nakayama (Addgene plasmid # 162750; http://n2t.net/addgene:162750 ; RRID:Addgene_162750) adding by PCR a P2A site and mCherry as a reporter. Resultant vectors were transfected into Phoenix-ECO using Lipofectamine 2000 (Thermo-Fisher) following manufacturer instructions. Supernatant containing retroviral particles was collected at 48h and 72h post-transfection and kept at 4\\u0026ordm;C until concentration 72h post-transfection. We used Millipore Centricon Plus-70, Ultracel-PL Membrane 100kDa (UFC710008 Millipore) for viral concentration. Concentrated retroviral particles were kept at -80\\u0026ordm;C prior to transduction. Retroviral particles were titrated in dilutions ranging from 1/10 to 1/500000 in NIH-3T3 cells (mouse embryonic fibroblasts). The titration was analyzed 48h later assessing mCherry-frequencies by flow cytometry (Cyto Flex SRT, MoFlo Cell Sorter). Viral infectious units (VIU) were calculated based on the initial cell-input, dilution, mCherry-frequency and volume of retroviral supernatant. The resulting values were plotted and the average, representing the transducing units (TU) was calculated from the linear portions of the graph.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFlow cytometry and cell sorting\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003ePB and BM cell immunostaining was performed according to standard procedures and samples were analysed by Beckman Coulter Gallios Analyzer (Beckman Coulter). RFP signal was used to distinguish donor from recipient cells. For PB and BM lineage analysis the antibodies used were all from eBioscience: anti-CD3\\u0026epsilon; (clone 145-2C11), anti-B220 (clone RA3-6B2), anti-Mac-1 (clone M1/70) and anti-Gr-1 (clone RC57BL/6-8C5). Lineage FACS analysis data are plotted as the percentage of B220\\u003csup\\u003e+\\u003c/sup\\u003e, CD3\\u003csup\\u003e+\\u003c/sup\\u003e and myeloid (Mac-1\\u003csup\\u003e+\\u003c/sup\\u003e and Gr-1\\u003csup\\u003e+\\u003c/sup\\u003eMac-1\\u003csup\\u003e+\\u003c/sup\\u003e) cells among donor-derived RFP\\u003csup\\u003e+\\u003c/sup\\u003e cells in case of a transplantation experiment or among total white blood cells within the bone marrow. As for early haematopoiesis analysis, mononuclear cells were isolated by low-density centrifugation (Histopaque 1083, Sigma) and stained with a cocktail of biotinylated lineage antibodies. Biotinylated antibodies used for lineage staining were all rat anti-mouse antibodies: anti-CD11b (clone M1/70), anti-B220 (clone RA3-6B2), anti-CD5 (clone 53-7.3) anti-Gr-1 (clone RB6-8C5), anti-Ter119 and anti-CD8a (clone 53-6.7) (all from eBioscience). After lineage depletion by magnetic separation (Dynabeads, Invitrogen), cells were stained with anti-Sca-1 (clone D7) (eBioscience), anti-c-Kit (clone 2B8) (eBioscience), anti-CD34 (clone RAM34) (eBioscience), anti-Flk-2 (clone A2F10) (eBioscience) and streptavidin (eBioscience). Early haematopoiesis FACS analysis data were plotted as percentage of long-term haematopoietic stem cells (HSCs, gated as LSK CD34\\u003csup\\u003e\\u0026minus;/low\\u003c/sup\\u003eFlk2\\u003csup\\u003e\\u0026minus;\\u003c/sup\\u003e), short-term haematopoietic stem cells (ST-HSCs, gated as LSK CD34\\u003csup\\u003e+\\u003c/sup\\u003eFlk2\\u003csup\\u003e\\u0026minus;\\u003c/sup\\u003e) and lymphoid-primed multipotent progenitors (LMPPs, gated as LSK CD34\\u003csup\\u003e+\\u003c/sup\\u003eFlk2\\u003csup\\u003e+\\u003c/sup\\u003e)\\u003csup\\u003e98\\u003c/sup\\u003e distributed among donor-derived LSKs (Lin\\u003csup\\u003eneg\\u003c/sup\\u003ec-Kit\\u003csup\\u003e+\\u003c/sup\\u003eSca-1\\u003csup\\u003e+\\u003c/sup\\u003e cells). To isolate HSCs, lineage depletion was performed to enrich for lineage-negative cells. Lineage-negative enriched cells were then stained as mentioned above and sorted using Beckman Coulter High Speed Cell Sorter Moflo-XDP (Beckman Coulter) and CytoFLEX SRT Cell Sorter (Beckman Coulter). For analysis of haematopoietic progenitors in the experiment of H3K9 and H3R9 transduction followed by transplantation, same procedure for BM analysis was carried out except for the staining after lineage magnetic depletion. Cells were stained with anti-IL7Ra (clone A7R34), anti-c-Kit (clone 2B8), anti-Sca1 (clone D7), anti-CD16/32 (clone 2.4G2) and anti CD34 (clone RAM34). Flow cytometry analysis data was plotted as percentage of MP (lin\\u003csup\\u003e-\\u003c/sup\\u003e, mCherry\\u003csup\\u003e+\\u003c/sup\\u003e, Il7Ra\\u003csup\\u003e-\\u003c/sup\\u003e, c-Kit\\u003csup\\u003e+\\u003c/sup\\u003e and Sca1\\u003csup\\u003e-\\u003c/sup\\u003e), MEP (lin\\u003csup\\u003e-\\u003c/sup\\u003e, mCherry\\u003csup\\u003e+\\u003c/sup\\u003e, Il7Ra\\u003csup\\u003e-\\u003c/sup\\u003e, c-Kit\\u003csup\\u003e+\\u003c/sup\\u003e, Sca1\\u003csup\\u003e-\\u003c/sup\\u003e, CD16/32\\u003csup\\u003e-\\u003c/sup\\u003e, CD34\\u003csup\\u003e-\\u003c/sup\\u003e), CMP (lin\\u003csup\\u003e-\\u003c/sup\\u003e, mCherry\\u003csup\\u003e+\\u003c/sup\\u003e, Il7Ra\\u003csup\\u003e-\\u003c/sup\\u003e, c-Kit\\u003csup\\u003e+\\u003c/sup\\u003e, Sca1\\u003csup\\u003e-\\u003c/sup\\u003e, CD16/32\\u003csup\\u003e-\\u003c/sup\\u003e CD34\\u003csup\\u003e+\\u003c/sup\\u003e), GMP (lin\\u003csup\\u003e-\\u003c/sup\\u003e, mCherry\\u003csup\\u003e+\\u003c/sup\\u003e, Il7Ra\\u003csup\\u003e-\\u003c/sup\\u003e, c-Kit\\u003csup\\u003e+\\u003c/sup\\u003e, Sca1\\u003csup\\u003e-\\u003c/sup\\u003e, CD16/32\\u003csup\\u003e+\\u003c/sup\\u003e CD34\\u003csup\\u003e+\\u003c/sup\\u003e) and CLP (lin\\u003csup\\u003e-\\u003c/sup\\u003e, mCherry\\u003csup\\u003e+\\u003c/sup\\u003e, Il7Ra\\u003csup\\u003e+\\u003c/sup\\u003e, c-Kit\\u003csup\\u003elow\\u003c/sup\\u003e, Sca1\\u003csup\\u003elow\\u003c/sup\\u003e).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eLSK retroviral transduction and competitive transplantation in lethally irradiated mice.\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eLSK (Lin\\u003csup\\u003eneg\\u003c/sup\\u003ec-Kit\\u003csup\\u003e+\\u003c/sup\\u003eSca-1\\u003csup\\u003e+\\u003c/sup\\u003e) cells were sorted in growth medium (IMDM 10%FBS 1%P/S medium with cytokines mSCF, mTPO and G-CSF at 1\\u0026micro;g/ml). They were seeded on fibronectin functionalized wells (50ng/ml) in 96 well plate, 10K cells per 50\\u0026micro;l of medium and maintained in normoxia at 37\\u0026ordm;C and 5%CO\\u003csub\\u003e2\\u003c/sub\\u003e for 20h. Then, medium was changed to growth medium containing polybrene (1/1000, Sigma-Aldrich) and 30MOI of the retroviral vector. Incubate the cells with the retroviral particles for 6-8h. Change medium to normal growth medium and incubate over night at 37\\u0026ordm;C, normoxia and 5%CO\\u003csub\\u003e2\\u003c/sub\\u003e. Cells were lifted with trypsin 0.05% for 5 minutes, quenched with medium with no cytokines, washed and recovered. A fraction of the cells was saved for transduction efficiency analysis and the rest was used for transplantation in lethally irradiated (9Gy) C57Bl6 mice. Cells for transplantation were mixed with BM competitor cells from non-irradiated C57Bl6 in a ratio 1/15 (20000 transduced LSK and 300000 competitor cells) in cold PBS.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eImmunofluorescence staining and confocal images acquisition\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eFreshly sorted HSCs were seeded on fibronectin-coated glass coverslips. For polarity staining, HSCs were incubated for 12\\u0026ndash;16h in HBSS + 10% FBS + 1% P/S and when indicated treated with 100\\u0026thinsp;\\u0026micro;M Rhosin\\u003csup\\u003e43\\u003c/sup\\u003e, 5mM NaB, 20 \\u0026micro;M cPLA2 inhibitor (AACOCF3\\u003csup\\u003e23\\u003c/sup\\u003e), hypotonic medium or left untreated. After incubation at 37\\u0026thinsp;\\u0026deg;C, 5% CO\\u003csub\\u003e2\\u003c/sub\\u003e, 3% O\\u003csub\\u003e2\\u003c/sub\\u003e in growth factor-free medium, cells were fixed with BD Cytofix fixation buffer (BD Biosciences). After fixation cells were gently washed with PBS, permeabilized with 0.2% Triton X-100 (Sigma) in PBS for 20\\u0026thinsp;min and blocked with 10% donkey serum (Sigma) for 30\\u0026thinsp;min. Primary and secondary antibody incubations were performed for 1\\u0026thinsp;h at room temperature. Cells were stained with a DAPI dilution 1:500 in PBS of DAPI 1\\u0026micro;g/\\u0026micro;l (Thermo, ref) for 10 minutes at room temperature and washed twice with PBS. Coverslips were mounted with ProLong Gold Antifade reagent without DAPI (Invitrogen, Molecular Probes). A list of antibodies used for immunofluorescence staining is provided in Supplementary Data. Samples were imaged with an AxioObserver Z1 microscope (Zeiss) equipped with a \\u0026times;63 PH objective. Alternatively, samples were analysed with an LSM880 confocal microscope (Zeiss) equipped with a \\u0026times;63 objective. \\u003cem\\u003eZ\\u003c/em\\u003e-stacks were obtained by automatically scanning along the \\u003cem\\u003ez\\u003c/em\\u003e axis of the cell with a confocal microscope and acquiring a picture of the in-focus plane every 0.2-0.4 \\u0026mu;m.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eImmunofluorescence Image Analysis and Rendering\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eSamples for immunofluorescence quantification analysis were rigorously sorted, stained and imaged in parallel within the same experiment to minimize any possible technical variability. Image acquisition at the confocal has been carried out consistently in between experiments regarding laser power, z-stack size and gain master. Antibody specificities have been validated using a knockout model in the case of RhoAGTP or using a control sample only with the secondary antibody in the staining protocol for the rest of the stainings. Quantification of protein signals has been done using Volocity Software 6.5 (Quorum technologies) using the \\u0026ldquo;volume by intensity\\u0026rdquo; tool, which sets a threshold for the positive signal against the negative. Positive signal threshold is set for each experiment by using RhoAKO for RhoAGTP and with the secondary antibody for the rest of the stainings. Morphometric measurements were done using Volocity Software 6.5 or by our computational vision approach. Quantifications of DAPI volume were done with the \\u0026ldquo;volume\\u0026rdquo; tool and Nuclear Height Average (NHA) and diameter was done using the tool \\u0026ldquo;line\\u0026rdquo;. Immunofluorescence images 3D reconstruction and rendering have been performed using Imaris 9.5.0 (Oxford Instruments) using the \\u0026ldquo;surface\\u0026rdquo; tool keeping the threshold constant for the signal in between experiments and conditions. As for polarity scoring, the localization of each single stained protein was considered polarized when a clear asymmetric distribution was visible by drawing a line across the middle of the cell. A total of 50 to 100 HSCs were singularly analysed per sample. Data are plotted as percentage of the total number of cells scored per sample.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e3D nuclear DAPI images preprocessing\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eHSCs nucleus microscopy images were exported as Carl Zeiss CZI files for downstream analysis and processed using Python programming language. Images acquired with a different microscope to the ones specified above and images belonging to experiments in which more than 30 days passed between nucleus staining and image data acquisition were excluded from these analyses. As a first quality control, images displaying total pixel intensities higher than 4\\u0026times;108 were labeled as overexposed to DAPI and discarded. Similarly, images with increased Gaussian noise (estimated noise standard deviation \\u0026gt; 8) were labeled as noisy and also excluded from further analyses.\\u003cstrong\\u003e\\u0026nbsp;\\u003c/strong\\u003eThe remaining images were then corrected using Chambolle\\u0026rsquo;s Total Variation denoising method\\u003csup\\u003e99\\u003c/sup\\u003e. Since image acquisition measurements were dynamically adjusted to the size of the nucleus, each 3D image was resized to achieve a uniform resolution of 10 pixels per micrometer in all dimensions through isotropic interpolation that accounted for variations in the number of z-stacks obtained. For each image, a nucleus binary mask was extracted using the Otsu segmentation method\\u003csup\\u003e100\\u003c/sup\\u003e, after smoothing with a Gaussian filter and allowing for hysteresis to preserve nuclear border with higher confidence Potential holes in the binary mask due to low intensity areas within the nucleus were filled and marked as part of the segmentation. Both the intensity image and respective nucleus mask were centered in the container array grid by trimming the background and padding the image borders symmetrically. Marginal intensities outside the nucleus mask were removed. To mitigate batch effects associated with technical variations in the fluorescence signal, we standardized the pixel intensity distribution within each nucleus mask using Z-score normalization to enhance comparability among conditions in downstream analysis.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eIntensity by distance to nuclear border analyses\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eIntensity by distance analyses were performed at iso-distance intervals of 0.1\\u0026micro;m from the boundary of the nucleus segmentation, using a distance-transformed mask. A distance-transformed mask is a mask in which each pixel value represents the shortest geodesic distance to the nearest mask boundary. The average intensity value of all pixels within a 3D band with a thickness of 0.15\\u0026micro;m was reported at each measurement interval. Young, aged and aged+Ri conditions were measured up to a distance of 1.6\\u0026micro;m from the nuclear border. Boxplots of DAPI intensity by discrete distance ranges are computed taking into account the average intensity of all pixels within the specified distance boundaries for each interval.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eMultivariate feature analyses\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eMost morphometric and intensity features were measured with the scikit-image Python library\\u003csup\\u003e101\\u003c/sup\\u003e regionprops_table() function at the nuclear mask level, DIR level, and on the largest 2D slide in the XY plane from each 3D image, comprising a total of 39 features\\u003cstrong\\u003e\\u0026nbsp;(Table S1).\\u0026nbsp;\\u003c/strong\\u003eThe width, length, and height of each nucleus were obtained from its bounding box. Height deviation was calculated as the average standard deviation of height in the X dimension for each YZ slide. The aspect ratio was defined as the ratio of height to length. The surface area was calculated using the Marching Cubes algorithm after smoothing the nucleus mask with a Gaussian filter. The intensity ratio is computed as the ratio of average intensity within the 1 - 1.5 \\u0026micro;m distance interval to the 0 - 0.5 \\u0026micro;m distance interval from the nuclear border. The Excess of Perimeter (EOP) was computed as the proportion of the difference of the nuclear perimeter compared to the perimeter of an ellipse with the same major and minor axis length as the nucleus mask. Detailed information about each of these features is shown in\\u003cstrong\\u003e\\u0026nbsp;Table S1.\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eDIRs were segmented using the Watershed method on the thresholded images. The intensity standardization of the images allowed for the choice of an absolute thresholding value for all nuclei.\\u003cstrong\\u003e\\u0026nbsp;\\u003c/strong\\u003eWe set this parameter to the quantile 80% of the intensity distribution for all young nuclei. Individual DIRs were labelled and segmented with Watershed by identifying the intensity peaks as the local maxima in the Euclidean distance transform of the thresholded images. From the total of segmented DIRs, we filtered out those displaying a voxel volume smaller than 0.2\\u0026micro;m. Morphometric and intensity features were measured on the resulting DIRs segmentation mask in the same manner as with the nuclear mask. DIRs distance to border was computed as the average of the distances for each voxel within each DIR to the nuclear mask border. \\u0026nbsp;After the calculation of these measurements, a second quality control is conducted to eliminate images that produced artifacts in the nuclear and DIR segmentation. The images were filtered out if they produced nuclear masks with a voxel volume smaller than 40\\u0026micro;m\\u003csup\\u003e3\\u003c/sup\\u003e, an EOP larger than 0.25 or DIRs volume larger than 5\\u0026micro;m3, which mostly belonged to confined nuclei that were damaged during the experimental setup and failed nuclear mask segmentations. We proceeded with 177 young nuclei, 164 aged nuclei, 144 aged+Ri nuclei.\\u003c/p\\u003e\\n\\u003cp\\u003eFeature selection was performed on the original set of features to maximize the separation of samples according to our biological conditions of interest and minimize the information redundancy. First, statistically significant features which differ among young \\u003cem\\u003evs.\\u003c/em\\u003e aged and among aged \\u003cem\\u003evs.\\u003c/em\\u003e aged Ri-treated were found (Mann-Whitney U-test, p-value \\u0026lt; 0.05). Later, features that exhibited absolute correlation values higher than 0.8 were discarded and the resulting sets from each pairwise comparison were merged to form a combined set of 20 features. These features were standardized and used to train the UMAP\\u003csup\\u003e52\\u003c/sup\\u003e model on young, aged and aged Ri-treated nuclei. Clustering was conducted in the original multidimensional parameter space using the K-means algorithm\\u003csup\\u003e102\\u003c/sup\\u003e with k=4 and depicted in the UMAP embedding. This value of k yielded the best silhouette scores for all clusters \\u003cstrong\\u003e(Figure S3F).\\u0026nbsp;\\u003c/strong\\u003eThe resulting clusters revealed biologically relevant groups combining images from the young, aged, and aged+Ri conditions. Volcano plots showing feature importance for each of the identified clusters were generated by calculating the statistical significance (Mann-Whitney U-test) and log2 fold change of the average of each feature between the cluster and the average of the remaining clusters.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eBone samples preparation for NanoIndenter\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eSamples were obtained from young and aged mice. Mice were perfused with PFA 4% in PBS, bones were collected as shown in\\u003csup\\u003e103\\u003c/sup\\u003e and embedded in liquid OCT (CellPath, KMA-0100-00A) and frozen at -80\\u0026deg;C. OCT is a cryo-embedding matrix, designed for cryostat sectioning at -10\\u0026deg;C or below. For these experiments, humeri and femurs were used as they show an optimal ratio between rigidity of the bone walls and internal surface area. Samples for Chiaro NanoIndenter analysis were prepared by opening the bone structure and exposing the internal area of the bone marrow by gradually cutting transversally with a cryostat, keeping the sample at -20\\u0026deg;C to avoid OCT thawing and exposing the bone marrow inner surface. The open bones were transferred to a 4-well Ibidi \\u0026micro;-slide (Ibidi, 80427) and partially embedded in agarose gel 4% (Condalab, 8010). The inner surface was covered with PBS to avoid tissue drying.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eMeasurements with Chiaro NanoIndenter\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eFor measuring stiffness of the tissue, Chiaro NanoIndenter was employed, and the data were elaborated through Piuma software (Optics11Life). 0.5 N/m - 25,6 \\u0026micro;m-diameter probes (Optics11Life) were used for these experiments. The probes were calibrated through the Piuma software automated procedure, using a glass Petri dish filled with PBS. After the calibration, the Petri dish was replaced with the Ibidi \\u0026micro;-slide containing the samples of interest. The measurement was performed programming a 30-points matrix, a group of sequential measurements covering the whole diameter of the bone marrow. The points were distant in width (D\\u003csub\\u003ey\\u003c/sub\\u003e) 350 \\u0026micro;m for the humerus and 450 \\u0026micro;m for the femur; in length instead the distance (D\\u003csub\\u003ex\\u003c/sub\\u003e) was 150 \\u0026micro;m for both humerus and femur. At least 3 matrixes per sample were performed, two on the peri-epiphyseal part and one on the diaphyseal part. The effective Young\\u0026rsquo;s modulus was derived from force vs. indentation curves, using a Hertzian model. The stiffness of the bone marrow was measured and calculated in kPa (kiloPascals) units.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eProtein Sample preparation\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eBone marrow cells for western blot were isolated by flushing bones from young and aged mice into HBSS (Gibco, 24020117), complemented with FBS 10% and P/S 1%. Cells were then centrifuged at 1500 RPM for 5 minutes at RT, resuspended in 3 ml HBSS per mouse and filtered through a 70 \\u0026micro;m cell strainer. For each mouse, 27 ml of 1X Red Blood Lysis Buffer (Biolegend, 420301) were added and incubated for 5 minutes at RT in the dark. At the end of the incubation, PBS was added to the top of the tube, to stop the lysis reaction and dilute the buffer. Cells were then centrifuged at 1500 RPM for 5 minutes at 4\\u0026deg;C, washed twice and resuspended in PBS. After cell count, cells were pelleted, washed with PBS and lysed with RIPA buffer (Pierce\\u0026trade; RIPA Buffer, ThermoFisher, 89900) complemented with protease inhibitor cocktail (cOmplete, Sigma Aldrich, 11697498001), for 15 minutes on ice, flicking the tube several times during the incubation. The cells were then centrifuged at 14,000 RPM for 15 minutes at 4\\u0026deg;C; the supernatant containing the protein lysate was stored at -80\\u0026ordm;C.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eRhoA-GTP bound Pull Down\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eTo isolate the active isoform of RhoA, GTP-bound RhoA, Active RhoA Detection Kit (Cell Signaling, 8820) was used following manufacturer instructions.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eWestern Blotting\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAcrylamide gel was prepared, run and transferred in nitrocellulose membranes following the indications of the kit manufacturer (TGX\\u0026trade; FastCast\\u0026trade; Acrylamide Kit 12%, BioRad, 1610185). The transferred membrane was cut as needed and washed twice in TBS-T (Tris-buffered saline (TBS) 1x \\u0026nbsp;Tween20 0.1%) for 15 minutes each time, then blocked with low-fat milk 5% in TBS-T on a rocker for 1 hour at RT. Anti-RhoA (rabbit monoclonal, Cell Signaling, 2117) and anti-actin (mouse monoclonal, Sigma Aldrich, A2228-100UL) primary antibodies were diluted 1:100 and 1:2000 respectively in milk 5% in TBS-T and incubated in a cold room with the samples at 4\\u0026deg;C overnight. After incubation, the membranes were washed twice in TBS-T for 15 minutes each, and incubated with HPR-goat anti-rabbit (Biorad, 1706515) and anti-mouse (Biorad, secondary antibodies (BioRad), diluted in milk 5% in TBS-T, at a concentration of 1:2000 and 1:5000 respectively, for 1 hour at RT. The membranes were then washed twice in TBS-T for 15 minutes. ECL Prime Western Blotting Detection Reagents (GE Healthcare, 28980926) was used for developing, by pipetting 500 \\u0026micro;l of reagent A and B on the membrane and incubating it for 5 minutes in the dark. The antibodies were detected by UV reveal in BioRad\\u0026rsquo;s ChemiDoc and analyzed using Image Lab 6.1 software (BioRad).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConfinement assay\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe confinement device used in this protocol was adapted to a single well plate from a \\u0026nbsp; previously described method\\u003csup\\u003e25,104\\u003c/sup\\u003e . Briefly, the confinement device was made by a magnetic container, two metallic rings, a polymeric ring and a closing ring (\\u003cstrong\\u003eFigure S1A\\u003c/strong\\u003e). The compression is mediated by a pillar coverslip, a polymeric piston and the magnetic lid of the device that exerts the confinement pressure. 3,000 HSCs were seeded in a volume of 40 \\u0026micro;l HBSS 10% FBS 1% P/S onto a 35-mm glass coverslip. The coverslip was previously functionalized with fibronectin: 40 \\u0026micro;l of fibronectin (50 \\u0026micro;g/ml) were applied upon the surface of the coverslip for 2 hours at RT, blocked with the same volume of BSA 2% for 1h at 37\\u0026deg;C and washed with PBS Ca\\u003csup\\u003e2+\\u003c/sup\\u003e/Mg\\u003csup\\u003e2+\\u003c/sup\\u003e. The coverslip with cells was mounted in the confinement device and compressed with pillars of 3, 5 and 8 \\u0026micro;m for 2 hours at 37\\u0026deg;C in a hypoxic incubator (5% CO\\u003csub\\u003e2\\u003c/sub\\u003e, 3% O\\u003csub\\u003e2\\u003c/sub\\u003e). Cells were then fixed directly in the confinement device with 1 ml of PFA 4.21% (BD Cytofix, Thermo Fisher, 15817828) at 4\\u0026deg;C for 15\\u0026rsquo;. The sample on the coverslip was then removed from the confiner and stocked at 4\\u0026deg;C in PBS for a maximum of 2 weeks before staining.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eHypotonic shock assay\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eBriefly, HSCs were cultured on fibronectin functionalized coverslips in 30 \\u0026micro;l of isotonic medium (HBSS 10% FBS 1% P/S) for 12h after sorting in a hypoxic incubator (5% CO\\u003csub\\u003e2\\u003c/sub\\u003e, 3% O\\u003csub\\u003e2\\u003c/sub\\u003e). Then, the medium was removed almost completely carefully and replaced by fresh isotonic medium or by hypotonic medium. Hypotonic medium consists on a 0.75X dilution of isotonic medium with sterile distillated water. HSCs were incubated for 2h in the same hypoxic conditions and then fixed as explained above. Protocol of staining was performed as usual.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eNuclear Wrinkling Analysis\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eTo analyze nuclear wrinkling in LaminB stained HSCs, we developed an automatic image analysis pipeline in Matlab (Mathworks, R2024b). Fluorescence images of LaminB were acquired in a LSM880 confocal microscope (Zeiss) commented in section above \\u0026ldquo;\\u003cem\\u003eImmunofluorescence staining and confocal images acquisition\\u0026rdquo;\\u003c/em\\u003e For each dataset, the equatorial maximum cross-section section was identified from Z-slices and 3 slices were selected above and below, covering approximately 50 percent of the nucleus surface with optimal resolution of surface wrinkles in the lateral X-Y dimension. Selected image slices were then subjected to binning (factor n=3) using a bilinear interpolation to reduce noise and facilitate subsequent processing steps, effectively preserving key structural information. A threshold intensity of 25 counts was applied to create a binary mask and to segment the regions of interest representing the nuclear envelope. A Gaussian blur with a sigma of 3 pixels was applied to the binary mask to smooth the segmented regions and facilitate the subsequent skeletonization of nucleus envelope features based on a morphological thinning algorithm. The skeletonization process enabled to extract the structural features of the segmented Lamin signal, highlighting the wrinkles of the nuclear envelope. To remove structural artefacts outside the nuclear region of interest, the skeletonized image was filled to generate an inner region (representing the inside of the nuclear wrinkles) and an outer region (around the nucleus periphery) by applying an inverted mask to the filled skeleton. This allowed to separate the nuclear envelope from the surrounding structures and detect the points representing nuclear invaginations. The coordinates of the nuclear surface and inner invaginations represent the total nuclear periphery and were extracted for further quantitative metrics of nuclear wrinkles. We derived the nuclear circularity (𝐶 = 4𝜋𝐴\\u0026frasl;𝑃\\u003csup\\u003e2\\u003c/sup\\u003e) from the total perimeter 𝑃 and area 𝐴 of nuclear cross-sections from multiple Z-slices for each cell. The circularity provides a quantitative measure of nuclear envelope deformations, with values close to 1 indicating a more circular shape and values \\u0026lt; 1 a highly deformed and wrinkled nuclear surface. In addition, we derived the excess folding parameter (𝐸 = 1 \\u0026minus; 𝑝\\u0026frasl;𝑃) as the ratio of the boundary outline of the nucleus 𝑝 to the total perimeter 𝑃 of nuclear cross-sections, with values close to 0 resembling a circular shape and values closer to 1 indicating increased nuclear wrinkling. Statistical data analysis was performed in Prism (Version 10.2.3.) using a Kruskal-Wallis test for multiple comparisons.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eBulk ATAC-seq of HSCs\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eHSCs were isolated from young and aged animals via FACS sorting. Between 2,000 and 5,000 cells were isolated for each library and we prepared 3-5 biological replicates for each sample arm. Cells were cultured overnight without growth factors at 3% O2 and washed twice with PBS before processing. Aged cells from same animal were used as aged control sample and aged treated with Ri sample. Young cells from same animal were used as young control sample and young confined under 5\\u0026mu;m sample.\\u0026nbsp;Cells were subjected to fragmentation of open chromatin regions using Tn5 transposase (Illumina), followed by a pre-amplification step, library preparation and subsequent paired-end sequencing. For the pre-amplification, NEBNext Ultra II Q5 Master Mix was used with Primer 1: 5\\u0026rsquo;GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAG3\\u0026rsquo; and Primer 2: 5\\u0026rsquo;TCGTCGGCAGCGTCAGATGTGTATAAGAGACAG3\\u0026rsquo;. For dual-indexing, 10 \\u0026mu;L of the pre-amplified ATAC reaction was used as input for Nextera XT index kit (Illumina). The generated libraries were quantified using an Agilent Bioanalyzer and a qPCR kit (New England Biolabs), pooled and subjected to next-generation sequencing in a NextSeq550\\u0026nbsp;or Illumina HiSeq 2000\\u0026nbsp;for paired-end 150 bp\\u0026nbsp;or 250 bp\\u0026nbsp;sequencing condition. Initial quality control was performed with FastQC v0.11.5. The ENCODE ATAC-seq pipeline v2.1.3 was\\u0026nbsp;used to process the FASTQ files, including adapter trimming, alignment to the reference genome (mm10), filtering and peak calling, and using the parameters \\u0026quot;atac.pipeline_type\\u0026quot; : \\u0026quot;atac\\u0026quot;, \\u0026quot;atac.align_only\\u0026quot; : false, \\u0026quot;atac.true_rep_only\\u0026quot; : false, \\u0026quot;atac.paired_end\\u0026quot; : true, \\u0026quot;atac.auto_detect_adapter\\u0026quot; : true, \\u0026quot;atac.multimapping\\u0026quot; : 20, \\u0026quot;atac.mapq_thresh\\u0026quot; : 20. Samtools v1.14\\u003csup\\u003e105\\u003c/sup\\u003e was used to index BAM files. ATACseqQC v1.20.2\\u003csup\\u003e106\\u003c/sup\\u003e, BSgenome v1.64.0, Bsgenome.Mmusculus.UCSC.mm10 v1.4.3 and TxDb.Mmusculus.UCSC.mm10.knownGene v3.10.0 in R v4.2.0 and Bioconductor v3.15.2 were used for quality control and Tn5 shifting. Deeptools v3.5.1\\u003csup\\u003e107\\u003c/sup\\u003e was used to transform BAM files to normalized BigWig files (with parameters --effectiveGenomeSize 2407883318 --normalizeUsing RPKM --exactScaling --binSize 50 \\u0026ndash;extendReads) to be visualized in the Integrative Genomics Viewer (IGV)\\u003csup\\u003e108\\u003c/sup\\u003e web app and to check for read enrichment in transcription start sites (TSS). A consensus set of peaks was defined by taking those peaks detected in at least 6 samples. This threshold was determined using Monte Carlo simulation. Briefly, a binary matrix was generated including all detected peaks as rows and all samples as columns, with 1 for presence of the peaks and 0 for absence. The binary matrix was randomized 1,000 times and the number of peaks detected in all possible minimum number of samples was calculated for each randomized matrix. The chosen minimum number of samples was the one where the mean number of consensus peaks in the simulated data was \\u0026asymp;10% of the number of consensus peaks in the empirical data (false positive rate, FPR). The final 57,289 consensus peaks were resized to have a width of 700 bp. In the case of the young 5\\u0026mu;m confined analysis, 42,632 consensus peaks were defined following the same strategy but using only the young and aged control samples. Peaks were annotated using annotatePeaks function from Homer v4.11.1\\u003csup\\u003e109\\u003c/sup\\u003e and a donut chart was plotted using ggplot2 v3.3.6, RColorBrewer v1.1-3 and ggrepel v0.9.1.\\u003c/p\\u003e\\n\\u003cp\\u003eIn R v4.2.0 and Bioconductor v3.15.2, the featureCounts function from Rsubread v2.10.5\\u003csup\\u003e110\\u003c/sup\\u003e was used to count the number of reads in consensus peaks with parameters largestOverlap = TRUE, isPairedEnd = TRUE, countReadPairs = TRUE, requireBothEndsMapped = TRUE, checkFragLength = TRUE, minFragLength = 0, maxFragLength = 2000. The count matrix was transformed and normalized using the voom function from limma v3.52.1\\u003csup\\u003e111\\u003c/sup\\u003e and the quantile normalization method. Batch effects were removed with the removeBatchEffect function only for visualization purposes. Principal component analysis (PCA) was performed using the PCA function from FactoMineR v2.6\\u003csup\\u003e112\\u003c/sup\\u003e and the top 10,000 most variable peaks. limma was used to perform differential accessibility (DA) analysis between conditions, adding the batch as a covariate. The p-value threshold to determine the significance of the DA was defined using Monte Carlo simulation. Briefly, the normalized count matrix was randomized 1,000 times and the number of DARs at different p-value thresholds was calculated for each randomized matrix. The chosen p-value threshold was the one where the number of DARs in the simulated data was \\u0026asymp;6-8% of the number of DARs in the empirical data (FPR), a p-value of 0.005 in the case of the Ri analysis and 0.001 in the case of the young 5\\u0026mu;m confined analysis. Volcano plots were plotted using ggplot2, ggrepel and patchwork v1.1.1. Venn diagram was plotted using VennDiagram v1.7.3\\u003csup\\u003e113\\u003c/sup\\u003e. Heatmap was plotted using pheatmap v1.0.12. GO enrichment analysis of genes close to DARs (considered close if the distance of the DAR to the TSS of the gene was less than 100 kb upstream or 25 kb downstream) was performed using clusterProfiler v4.4.1\\u003csup\\u003e114\\u003c/sup\\u003e, AnnotationDbi v1.58.0 and org.Mm.eg.db v3.15.0 with the function enrichGO and parameters ont = \\u0026quot;BP\\u0026quot;, pvalueCutoff = 0.1, pAdjustMethod = \\u0026quot;BH\\u0026quot;, minGSSize = 10, maxGSSize = 500, readable = TRUE. Selected GOs were plotted in radar plots using the function radarchart from fmsb v0.7.4. Barplots representing log2FCs in the proportions of genomic region types among the DARs from the different comparisons compared to the proportions in the consensus peaks were plotted using ggplot2, RColorBrewer and patchwork. Fisher\\u0026rsquo;s exact tests were performed to compare the proportions in DARs vs the proportions in consensus peaks. One-proportion z-tests were used to compare the proportion of a specific genomic region (i.e. intron) among the DARs vs among the consensus peaks. TF-binding motifs in the different sets of DARs were found using the findMotifsGenome function from Homer, using the consensus peaks as background. In R v4.3.0 and Bioconductor v3.17, ATACseqTFEA v1.2.0 was used to obtain the coordinates of the Klf4 motif (Jaspar MA0039.4) across the DARs opening with Ri, which were later used to determine the Klf4-targeted genes (\\u0026lt;100kb upstream or \\u0026lt;25kb downstream).\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eBulk RNA-seq of HSCs\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eRNA-seq libraries were generated from 2,000 pooled young and aged HSCs (n=3 biological repeats per condition). Cells were cultured overnight without growth factors at 3% O2 and washed twice with PBS before processing. Aged cells from same animal were used as control sample and aged treated with Ri sample. SMART-Seq\\u0026reg; v4 Ultra\\u0026reg; Low Input RNA Kit for Sequencing manufacturer\\u0026rsquo;s protocol was strictly followed. We quantified the cDNA quality and quantity using an Agilent Bioanalyzer. For the library preparation, 150 pg of cDNA were used per sample using the NexteraXT index kit. We performed quality control of our libraries using an Agilent Bioanalyzer and quantifying with qPCR kit (New England Biolabs). Libraries were then pooled to be subjected to next generation sequencing in a NextSeq550 for paired-end 150 bp sequencing condition.\\u003c/p\\u003e\\n\\u003cp\\u003eAfter performing quality control with FastQC v0.11.5, adapters were removed from the FASTQ files using Cutadapt v1.18\\u003csup\\u003e115\\u003c/sup\\u003e with parameters -m 20 -O 6 -q 20. Reads were mapped to the reference genome\\u0026nbsp;mm10 (Mus Musculus GRCm38, Ensembl 102 Nov 2020) using STAR v2.7.0\\u003csup\\u003e116\\u003c/sup\\u003e and BAM files were sorted and indexed using Samtools v1.14\\u003csup\\u003e105\\u003c/sup\\u003e. Library complexity was estimated using Picard v2.26.7. Deeptools v3.5.1\\u003csup\\u003e107\\u003c/sup\\u003e was used to transform BAM files to normalized BigWig files (with parameters --effectiveGenomeSize 2407883318 --normalizeUsing CPM --exactScaling --binSize 50) to be visualized in IGV\\u003csup\\u003e108\\u003c/sup\\u003e web app and to check for read enrichment in exons.\\u003c/p\\u003e\\n\\u003cp\\u003eIn R v4.2.0 and Bioconductor v3.15.2, the featureCounts function from Rsubread v2.10.5\\u003csup\\u003e110\\u003c/sup\\u003e was used to count the number of reads in genes for each sample, using Ensembl GTF annotation for GRCm38 102 version, filtered for protein coding genes and with parameters GTF.featureType = \\u0026quot;exon\\u0026quot;, GTF.attrType = \\u0026quot;gene_name\\u0026quot;, useMetaFeatures = TRUE, isPairedEnd = TRUE, countReadPairs = TRUE, requireBothEndsMapped = TRUE, countMultiMappingReads = TRUE, fraction = TRUE. The function filterByExpr from edgeR v3.38.1\\u003csup\\u003e117\\u003c/sup\\u003e was used to keep only those genes with more than 20 counts in at least three samples and a minimum total count of 60. The count matrix was transformed and normalized using the voom function from limma v3.52.1\\u003csup\\u003e111\\u003c/sup\\u003e and the quantile normalization method.\\u003csup\\u003e112\\u003c/sup\\u003e limma was used to perform differential expression (DE) analysis between the sequencing batches and 24 genes were removed from the analysis for showing significant batch effect (with \\u0026lt;5% FPR; p-value \\u0026lt; 0.0001). The DE analysis between conditions was also performed with limma. The p-value threshold to determine the significance of the DE was defined using Monte Carlo simulation as for the DA analysis in ATAC-seq. In this case, the chosen p-value threshold was the one where the FPR was \\u0026asymp;5%, a p-value of 0.001. Volcano plots were plotted using ggplot2 v3.3.6, ggrepel v0.9.1 and patchwork v1.1.1. Venn diagram was plotted using VennDiagram v1.7.3\\u003csup\\u003e113\\u003c/sup\\u003e. GSEA was performed using clusterProfiler v4.4.1\\u003csup\\u003e114\\u003c/sup\\u003e, AnnotationDbi v1.58.0 and org.Mm.eg.db v3.15.0 with the function gseGO and parameters ont = \\u0026quot;BP\\u0026quot;, pvalueCutoff = 0.05, pAdjustMethod = \\u0026quot;BH\\u0026quot;, minGSSize = 10, maxGSSize = 500, seed = TRUE, eps = 0. Selected GOs were plotted in radar plots using the function radarchart from fmsb v0.7.4 and the enrichment curve for \\u0026ldquo;acute inflammatory response\\u0026rdquo; was plotted using the gseaplot2 function of enrichplot v1.16.1. GSEA for the Interferome.org\\u003csup\\u003e71\\u003c/sup\\u003e (filtering by Species: Mus musculus; System: Haemopoietic/Immune; Cell: HSC or haematopoietic stem cells; and FC: 2), interferon-stimulated\\u003csup\\u003e72\\u003c/sup\\u003e and aging\\u003csup\\u003e73\\u003c/sup\\u003e signatures was performed with the function GSEA of clusterProfiler and parameters minGSSize = 1, maxGSSize = Inf, pvalueCutoff = Inf, pAdjustMethod = \\u0026quot;BH\\u0026quot;, seed = TRUE and plotted using the gseaplot2 function of enrichplot.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eTo analyse REs, the STAR alignment was repeated with parameters --winAnchorMultimapNmax 200 --outFilterMultimapNmax 100 to allow for more multimapping. The function TEcount from TEtranscripts v2.2.1\\u003csup\\u003e118\\u003c/sup\\u003e was used to count the reads mapping to RE subfamilies, using the annotation downloaded from Hammell\\u0026rsquo;s lab website for GRCm38 Ensembl rmsk. The resulting count matrix was processed as previously described for the genes but keeping only those RE subfamilies with more than 10 counts in at least three samples and a minimum total count of 30. No RE subfamilies were significantly affected by the sequencing batch. The DE significance p-value threshold used was 0.005 (FPR ~8%). Venn diagram was plotted using VennDiagram. Heatmap was plotted using ComplexHeatmap v2.12.1\\u003csup\\u003e119\\u003c/sup\\u003e. Boxplot was plotted using ggplot2.\\u003c/p\\u003e\\n\\u003cp\\u003eThe t-statistics for the Klf4-targeted genes were plotted with ggplot2 and ggrepel. GO enrichment of upregulated Klf4-targeted genes was performed using clusterProfiler, AnnotationDbi and org.Mm.eg.db with the function enrichGO and parameters ont = \\u0026quot;BP\\u0026quot;, pvalueCutoff = 0.05, pAdjustMethod = \\u0026quot;BH\\u0026quot;, minGSSize = 10, maxGSSize = 500, readable = TRUE. Selected GOs were plotted in a radar plot using the function radarchart from fmsb.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eSingle-cell RNA seq\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eLSK cells were sorted as explained previously from young and aged mice (n = 3 for each). Cells were incubated 16h in IMDM 10%FBS 1%P/S in the presence or absence of Ri 100\\u0026micro;M and then profiled by using standard 10x Genomics protocols for single cell sequencing. After performing the quality control of the FASTQ files with FastQC v0.11.5, reads were aligned to the reference genome (GRCm38/mm10, annotation Ensembl 98), filtered, and counted using Cell Ranger software v7.2.0 (10x Genomics). Count matrices were pre-processed using R v4.2.0 and Seurat package v4.1.1\\u003csup\\u003e120\\u003c/sup\\u003e. In an initial filtering step, genes expressed in \\u0026lt;10 cells and cells expressing \\u0026lt;10 genes were discarded. Cells with \\u0026gt;6% of mitochondrial RNA, \\u0026lt;1,500 genes, \\u0026gt;7,000 genes, \\u0026lt;1,500 UMI counts, or \\u0026gt;40,000 UMI counts were also discarded. We further discarded genes with less than 400 UMI counts (decided based on the distribution of the total counts per gene). We obtained a total of 60,648 cells and 15,049 genes to continue with the analysis. After log-normalizing the data, the genes defined by Kowalczyk et al.\\u003csup\\u003e121\\u003c/sup\\u003e associated to G0, early G1, late G1, S and G2/M cell cycle phases were used to score each cell for the average expression of each set of genes using the AddModuleScore function of Seurat. A cell cycle phase was assigned to each cell according to the highest score. Seurat\\u0026rsquo;s SCTransform function was used to normalize the gene counts for each condition and sequencing batch separately, regressing out the effect of the number of genes, the number of UMI counts, the percentage of mitochondrial RNA, and the scores for the cell cycle phases in every cell and returning 3000 variable genes for each condition. Then, integration was performed following Seurat\\u0026rsquo;s integration workflow, using 3000 integration features and canonical correlation analysis with 30 dimensions. The integrated dataset, 50 principal components (PCs), and 500 epochs were used to generate the UMAP. Clustering was performed with Seurat\\u0026rsquo;s FindNeighbors and FindClusters functions, using 50 PCs, Louvain algorithm, and a resolution of 0.2, chosen after evaluating several resolutions with the clustree package v0.4.4\\u003csup\\u003e122\\u003c/sup\\u003e. The resulting clusters were annotated based on their markers (obtained after running the FindAllMarkers function on the log-normalized data with parameters test.use = \\u0026apos;wilcox\\u0026apos;, logfc.threshold = 0.25, min.pct = 0.1, only.pos = TRUE, return.thresh = 0.05), plotting the expression of genes known to be expressed in HSPCs, and projecting on our data different gene signatures defined in previous studies (like the MolO signature defined by Wilson et al.\\u003csup\\u003e82\\u003c/sup\\u003e, the low/high-output HSC signature defined by Rodriguez-Fraticelli et al.\\u003csup\\u003e83\\u003c/sup\\u003e, and the dormant/active HSC signature defined by Cabezas-Wallscheid et al.\\u003csup\\u003e84\\u003c/sup\\u003e) using the AddModuleScore function of Seurat. Plots were generated using Seurat\\u0026rsquo;s plotting functions and ggplot2 v3.3.6. scCODA\\u003csup\\u003e123\\u003c/sup\\u003e v0.1.9 was used to perform compositional analysis, using the cluster CyclingCells_2 as the reference (as automatically selected by the tool), and running the model 10 times to account for the\\u0026nbsp;randomness introduced by the MCMC sampling. Differences were considered statistically credible if at least 7 of the runs showed an effect. In the HSC cluster, differential expression analysis between the three conditions was performed using muscat\\u003csup\\u003e124\\u003c/sup\\u003e v1.10.1 (Bioconductor v3.15.2) and the muscat_analysis function of the muscatWrapper v1.0.0 R package on the log-normalized data with parameter de_method_oi = \\u0026quot;limma-voom\\u0026quot; and regressing out the effect of the sequencing batch. Significance was considered if |log2FC| \\u0026gt; 1 and FDR \\u0026lt; 0.05. MAplots were plotted using ggplot2 and ggrepel v0.9.1. To perform the GSEA of the signature for hemogenic precursors defined by Pereira et al.\\u003csup\\u003e85\\u003c/sup\\u003e, we first ordered the genes by their -log10(p-value) * sign(log2FC). Then the GSEA function from clusterProfiler v4.4.1\\u003csup\\u003e114\\u003c/sup\\u003e was used to determine the enrichment of the signature with the parameters minGSSize = 1, maxGSSize = Inf, pvalueCutoff = Inf, pAdjustMethod = \\u0026quot;BH\\u0026quot;, seed = TRUE, eps = 0. The p-values were adjusted across the three pairwise comparisons between the conditions. The enrichment plot was generated using the gseaplot2 function of the package enrichplot v1.16.1. SCENIC v1.3.1\\u003csup\\u003e125\\u003c/sup\\u003e was used to calculate regulons (TF + target genes) activity per cell in the HSC cluster. These 225 regulon activity scores were used to integrate the data by condition and sequencing batch following Seurat\\u0026rsquo;s integration workflow, using all regulons as integration features and canonical correlation analysis with 30 dimensions. The integrated data was scaled and centered using the ScaleData function of Seurat and the effect of the number of genes, the number of UMI counts, the percentage of mitochondrial RNA, and the scores for the cell cycle phases in every cell was regressed out. The UMAP was generated using 25 PCs and 500 epochs. The activity scores of interesting regulons were binarized based on the distribution of their values (AUC = 0.128, 0.007 and 0.01 for Klf4, Ctcf and Hoxa10, respectively). Differential regulon activity analysis between conditions was performed using muscat\\u003csup\\u003e124\\u003c/sup\\u003e and the muscat_analysis function of the muscatWrapper R package on the AUC values with parameter de_method_oi = \\u0026quot;limma-voom\\u0026quot; and regressing out the effect of the sequencing batch. Significance was considered if |log2FC| \\u0026gt; 0.5 and FDR \\u0026lt; 0.05. The activity scores of the significantly different regulons were plotted in a heatmap using ComplexHeatmap v2.12.1\\u003csup\\u003e119\\u003c/sup\\u003e.\\u0026nbsp;\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eData availability statement\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe source data underlying \\u003cstrong\\u003eFigure 1-6\\u003c/strong\\u003e and \\u003cstrong\\u003eFigure S1-6\\u003c/strong\\u003e is provided as a \\u003cstrong\\u003eSource Data file\\u003c/strong\\u003e. The source code for the microscopy image analyses showed on \\u003cstrong\\u003eFigure 3\\u003c/strong\\u003e and \\u003cstrong\\u003eFigure S3\\u003c/strong\\u003e is available at HYPERLINK \\u0026quot;https://github.com/biomedical-data-science/hsc_rhoa\\u0026quot;. Sequencing data underlining \\u003cstrong\\u003eFigure 4-5\\u003c/strong\\u003e and \\u003cstrong\\u003eFigure S4-5\\u003c/strong\\u003e and \\u003cstrong\\u003eTable S2-5\\u003c/strong\\u003e is deposited together with codes under the repository DOI https://doi.org/10.34810/data697. ATAC-seq, RNA-seq and scRNA-seq data are deposited at GEO (accession number GSE233989). Dilutions and catalogue numbers of all commercial antibodies are provided in the \\u003cstrong\\u003eSource Data file\\u003c/strong\\u003e.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgments \\u0026amp; Funding Sources\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eWe thank support from Dr. Laia Traveset Martinez, IDIBELL Innovation Unit. We acknowledge support from Dr. Merc\\u0026egrave; Marti Gaudes, head of Technical Facilities at IDIBELL together with Jos\\u0026eacute; Andres Vaquero (IDIBELL FACS and Flow cytometry SCT), Antoni Ventura (IDIBELL Mouse Facility SCT) and Joan Repulles and Saioa Mendizuri (IDIBELL Bioimaging SCT). We thank Esther Casta\\u0026ntilde;o, Beatriz Barroso and Benjamin Torrejon (CCiT-UB, Bellvitge). We thank Giulia Lunazzi and the National Center for Genomic Analysis (CNAG, Barcelona) for the support with scRNA-seq experiments. \\u0026nbsp;We thank CERCA Program/Generalitat de Catalunya for institutional support. We thank Conxi Lazaro (LCAM laboratory, ICO-HUB) for supporting sequencing experiments. We acknowledge the funding sources: European Research Council (ERC) grant 101002453 (MCF), Spanish Ministry of Science, Innovation and University grants RYC2018-025979-I (MCF) and PGC2018-102049-B-I00 (MCF) and INPhINIT Incoming fellowship from \\u0026ldquo;la Caixa\\u0026apos;\\u0026apos; Foundation (ID 100010434) with code LCF/BQ/DI22/11940001 (PIP). VR acknowledges financial support from the Ministerio de Ciencia e Innovaci\\u0026oacute;n through the Plan Nacional (PID2020-117011GB-I00) and funding from the European Union\\u0026rsquo;s Horizon EIC-ESMEA Pathfinder program under grant agreement No 101046620.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eDeclaration of interests\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe findings presented in this study are covered under patent application number EP25382180.5, filed on 27/02/2025.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthor contributions\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eConceptualization: EM-R, MCF, PIP, BW\\u003c/p\\u003e\\n\\u003cp\\u003eMethodology: SM-V, EM-R, MCF, PIP, FP, FA, JLC, BW, FM, LR, SW\\u003c/p\\u003e\\n\\u003cp\\u003eInvestigation: SM-V, EM-R, MCF, PIP, BW\\u003c/p\\u003e\\n\\u003cp\\u003eVisualization: SM-V, EM-R, PIP, LM\\u003c/p\\u003e\\n\\u003cp\\u003eFunding acquisition: MCF\\u003c/p\\u003e\\n\\u003cp\\u003eProject administration: MCF\\u003c/p\\u003e\\n\\u003cp\\u003eSupervision: YZ, VR, AR, PP, MCF\\u003c/p\\u003e\\n\\u003cp\\u003eWriting \\u0026ndash; original draft: EM-R, SM-V, MCF, PIP\\u003c/p\\u003e\\n\\u003cp\\u003eWriting \\u0026ndash; review \\u0026amp; editing: AR, VR, YZ, PP, MCF\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003eVerovskaya EV, Dellorusso PV, Passegu\\u0026eacute; E (2019) Losing Sense of Self and Surroundings: Hematopoietic Stem Cell Aging and Leukemic Transformation. Trends Mol Med 25(6):494\\u0026ndash;515. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/J.MOLMED.2019.04.006\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/J.MOLMED.2019.04.006\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eBrunet A, Goodell MA, Rando TA (2022) Ageing and Rejuvenation of Tissue Stem Cells and Their Niches. Nat Rev Mol Cell Biol 1\\u0026ndash;18. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1038/s41580-022-00510-w\\u003c/span\\u003e\\u003cspan address=\\\"10.1038/s41580-022-00510-w\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eMejia-Ramirez E, Florian MC (2020) Understanding Intrinsic Hematopoietic Stem Cell Aging. Haematologica 105(1):22\\u0026ndash;37. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.3324/haematol.2018.211342\\u003c/span\\u003e\\u003cspan address=\\\"10.3324/haematol.2018.211342\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eGeiger H, de Haan G, Florian MC (2013) The Ageing Haematopoietic Stem Cell Compartment. Nat Rev Immunol 13(5):376\\u0026ndash;389. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1038/nri3433\\u003c/span\\u003e\\u003cspan address=\\\"10.1038/nri3433\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003ePoscablo DM, Worthington AK, Smith-Berdan S, Rommel MGE, Manso BA, Adili R, Mok L, Reggiardo RE, Cool T, Mogharrab R, Myers J, Dahmen S, Medina P, Beaudin AE, Boyer SW, Holinstat M, Jonsson VD, Forsberg EC (2024) An Age-Progressive Platelet Differentiation Path from Hematopoietic Stem Cells Causes Exacerbated Thrombosis. Cell 0(0). \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/j.cell.2024.04.018\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.cell.2024.04.018\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eMatteini F, Montserrat-Vazquez S, Florian MC (2024) Rejuvenating Aged Stem Cells: Therapeutic Strategies to Extend Health and Lifespan. FEBS Lett 598(22):2776\\u0026ndash;2787. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1002/1873-3468.14865\\u003c/span\\u003e\\u003cspan address=\\\"10.1002/1873-3468.14865\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLi H, Luo Q, Shan W, Cai S, Tie R, Xu Y, Lin Y, Qian P, Huang H (2021) Biomechanical Cues as Master Regulators of Hematopoietic Stem Cell Fate. Cell Mol Life Sci 78(16):5881\\u0026ndash;5902. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1007/s00018-021-03882-y\\u003c/span\\u003e\\u003cspan address=\\\"10.1007/s00018-021-03882-y\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLundin V, Sugden WW, Theodore LN, Sousa PM, Han A, Chou S, Wrighton PJ, Cox AG, Ingber DE, Goessling W, Daley GQ, North TE (2020) YAP Regulates Hematopoietic Stem Cell Formation in Response to the Biomechanical Forces of Blood Flow. Dev Cell 52(4):446\\u0026ndash;460e5. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/j.devcel.2020.01.006\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.devcel.2020.01.006\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eShin J-W, Swift J, Ivanovska I, Spinler KR, Buxboim A, Discher DE (2013) Mechanobiology of Bone Marrow Stem Cells: From Myosin-II Forces to Compliance of Matrix and Nucleus in Cell Forms and Fates. Differentiation 86(3):77\\u0026ndash;86. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/j.diff.2013.05.001\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.diff.2013.05.001\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eIvanovska IL, Shin JW, Swift J, Discher DE (2015) Stem Cell Mechanobiology: Diverse Lessons from Bone Marrow. Trends Cell Biol. https:/. /doi.org/S0962-8924(15)00072-0 [pii] 10.1016/j.tcb.2015.04.003\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eStarodubtseva MN (2011) Mechanical Properties of Cells and Ageing. Ageing Res Rev 10(1):16\\u0026ndash;25. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/j.arr.2009.10.005\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.arr.2009.10.005\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003ePhillip JM, Aifuwa I, Walston J, Wirtz D (2015) The Mechanobiology of Aging. Annu Rev Biomed Eng 17:113\\u0026ndash;141. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1146/annurev-bioeng-071114-040829\\u003c/span\\u003e\\u003cspan address=\\\"10.1146/annurev-bioeng-071114-040829\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eL\\u0026oacute;pez-Ot\\u0026iacute;n C, Blasco MA, Partridge L, Serrano M, Kroemer G (2023) Hallmarks of Aging: An Expanding Universe. Cell 186(2):243\\u0026ndash;278. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/j.cell.2022.11.001\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.cell.2022.11.001\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLessey EC, Guilluy C, Burridge K (2012) From Mechanical Force to RhoA Activation. \\u003cem\\u003eBiochemistry (Mosc). 51\\u003c/em\\u003e (38), 7420\\u0026ndash;7432. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1021/bi300758e\\u003c/span\\u003e\\u003cspan address=\\\"10.1021/bi300758e\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLessey EC, Guilluy C, Burridge K (2012) From Mechanical Force to RhoA Activation. Biochemistry 51(38):7420\\u0026ndash;7432. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1021/bi300758e\\u003c/span\\u003e\\u003cspan address=\\\"10.1021/bi300758e\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eBurridge K, Monaghan-Benson E, Graham DM, Mechanotransduction (2019) From the Cell Surface to the Nucleus via RhoA. Philosophical Trans Royal Soc B: Biol Sci 374(1779):20180229. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1098/rstb.2018.0229\\u003c/span\\u003e\\u003cspan address=\\\"10.1098/rstb.2018.0229\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eGoldyn AM, Rioja BA, Spatz JP, Ballestrem C, Kemkemer R (2009) Force-Induced Cell Polarisation Is Linked to RhoA-Driven Microtubule-Independent Focal-Adhesion Sliding. J Cell Sci 122(20):3644\\u0026ndash;3651. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1242/jcs.054866\\u003c/span\\u003e\\u003cspan address=\\\"10.1242/jcs.054866\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eVenturini V, Pezzano F, Catal\\u0026agrave; Castro F, H\\u0026auml;kkinen H-M, Jim\\u0026eacute;nez-Delgado S, Colomer-Rosell M, Marro M, Tolosa-Ramon Q, Paz-L\\u0026oacute;pez S, Valverde MA, Weghuber J, Loza-Alvarez P, Krieg M, Wieser S, Ruprecht V (2020) The Nucleus Measures Shape Changes for Cellular Proprioception to Control Dynamic Cell Behavior. Science 370(6514):eaba2644. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1126/science.aba2644\\u003c/span\\u003e\\u003cspan address=\\\"10.1126/science.aba2644\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eMistriotis P, Wisniewski EO, Bera K, Keys J, Li Y, Tuntithavornwat S, Law RA, Perez-Gonzalez NA, Erdogmus E, Zhang Y, Zhao R, Sun SX, Kalab P, Lammerding J, Konstantopoulos K (2019) Confinement Hinders Motility by Inducing RhoA-Mediated Nuclear Influx, Volume Expansion, and Blebbing. J Cell Biol 218(12):4093\\u0026ndash;4111. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1083/jcb.201902057\\u003c/span\\u003e\\u003cspan address=\\\"10.1083/jcb.201902057\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eZhou X, Florian MC, Arumugam P, Chen X, Cancelas JA, Lang R, Malik P, Geiger H, Zheng Y (2013) RhoA GTPase Controls Cytokinesis and Programmed Necrosis of Hematopoietic Progenitors. J Exp Med 210(11):2371\\u0026ndash;2385. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1084/jem.20122348\\u003c/span\\u003e\\u003cspan address=\\\"10.1084/jem.20122348\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eEnyedi B, Niethammer P (2017) Nuclear Membrane Stretch and Its Role in Mechanotransduction. \\u003cem\\u003eNucleus 8\\u003c/em\\u003e (2), 156\\u0026ndash;161. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1080/19491034.2016.1263411\\u003c/span\\u003e\\u003cspan address=\\\"10.1080/19491034.2016.1263411\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eEnyedi B, Jelcic M, Niethammer P (2016) The Cell Nucleus Serves as a Mechanotransducer of Tissue Damage-Induced Inflammation. Cell 165(5):1160\\u0026ndash;1170. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/j.cell.2016.04.016\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.cell.2016.04.016\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLomakin AJ, Cattin CJ, Cuvelier D, Alraies Z, Molina M, Nader GPF, Srivastava N, S\\u0026aacute;ez PJ, Garcia-Arcos JM, Zhitnyak IY, Bhargava A, Driscoll MK, Welf ES, Fiolka R, Petrie RJ, De Silva NS, Gonz\\u0026aacute;lez-Granado JM, Manel N, Lennon-Dum\\u0026eacute;nil AM, M\\u0026uuml;ller DJ, Piel M (2020) The Nucleus Acts as a Ruler Tailoring Cell Responses to Spatial Constraints. Science 370(6514):eaba2894. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1126/science.aba2894\\u003c/span\\u003e\\u003cspan address=\\\"10.1126/science.aba2894\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLe Berre M, Aubertin J, Piel M (2012) Fine Control of Nuclear Confinement Identifies a Threshold Deformation Leading to Lamina Rupture and Induction of Specific Genes. Integr Biol 4(11):1406. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1039/c2ib20056b\\u003c/span\\u003e\\u003cspan address=\\\"10.1039/c2ib20056b\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eVenturini V, Pezzano F, Catal\\u0026agrave; Castro F, H\\u0026auml;kkinen H-M, Jim\\u0026eacute;nez-Delgado S, Colomer-Rosell M, Marro M, Tolosa-Ramon Q, Paz-L\\u0026oacute;pez S, Valverde MA, Weghuber J, Loza-Alvarez P, Krieg M, Wieser S, Ruprecht V (2020) The Nucleus Measures Shape Changes for Cellular Proprioception to Control Dynamic Cell Behavior. Science 370(6514):eaba2644. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1126/science.aba2644\\u003c/span\\u003e\\u003cspan address=\\\"10.1126/science.aba2644\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLomakin AJ, Cattin CJ, Cuvelier D, Alraies Z, Molina M, Nader GPF, Srivastava N, S\\u0026aacute;ez PJ, Garcia-Arcos JM, Zhitnyak IY, Bhargava A, Driscoll MK, Welf ES, Fiolka R, Petrie RJ, De Silva NS, Gonz\\u0026aacute;lez-Granado JM, Manel N, Lennon-Dum\\u0026eacute;nil AM, M\\u0026uuml;ller DJ, Piel M (2020) The Nucleus Acts as a Ruler Tailoring Cell Responses to Spatial Constraints. Science 370(6514):eaba2894. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1126/science.aba2894\\u003c/span\\u003e\\u003cspan address=\\\"10.1126/science.aba2894\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eNielsen LK, Risbo J, Callisen TH, Bj\\u0026oslash;rnholm T (1999) Lag-Burst Kinetics in Phospholipase A2 Hydrolysis of DPPC Bilayers Visualized by Atomic Force Microscopy. Biochim et Biophys Acta (BBA) - Biomembr 1420(1):266\\u0026ndash;271. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/S0005-2736(99)00103-0\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/S0005-2736(99)00103-0\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLeidy C, Ocampo J, Duelund L, Mouritsen OG, J\\u0026oslash;rgensen K, Peters GH (2011) Membrane Restructuring by Phospholipase A2 Is Regulated by the Presence of Lipid Domains. Biophys J 101(1):90\\u0026ndash;99. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/j.bpj.2011.02.062\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.bpj.2011.02.062\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eGarcia MC, Williams J, Johnson K, Olden K, Roberts JD (2011) Arachidonic Acid Stimulates Formation of a Novel Complex Containing Nucleolin and RhoA. FEBS Lett 585(4):618\\u0026ndash;622. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/j.febslet.2011.01.035\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.febslet.2011.01.035\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eGarcia MC, Ray DM, Lackford B, Rubino M, Olden K, Roberts JD (2009) Arachidonic Acid Stimulates Cell Adhesion through a Novel P38 MAPK-RhoA Signaling Pathway That Involves Heat Shock Protein 27*. J Biol Chem 284(31):20936\\u0026ndash;20945. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1074/jbc.M109.020271\\u003c/span\\u003e\\u003cspan address=\\\"10.1074/jbc.M109.020271\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eChen T, Sun H, Lu J, Zhao Y, Tao D, Li X, Huang B (2002) Histone Acetylation Is Involved in Hsp70 Gene Transcription Regulation in Drosophila Melanogaster. Arch Biochem Biophys 408(2):171\\u0026ndash;176. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/S0003-9861(02)00564-7\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/S0003-9861(02)00564-7\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eKulka LAM, Fangmann P-V, Panfilova D, Olzscha H (2020) Impact of HDAC Inhibitors on Protein Quality Control Systems: Consequences for Precision Medicine in Malignant Disease. \\u003cem\\u003eFrontiers in Cell and Developmental Biology 8\\u003c/em\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eGrigoryan A, Guidi N, Senger K, Liehr T, Soller K, Marka G, Vollmer A, Markaki Y, Leonhardt H, Buske C, Lipka DB, Plass C, Zheng Y, Mulaw MA, Geiger H, Florian MC (2018) LaminA/C Regulates Epigenetic and Chromatin Architecture Changes upon Aging of Hematopoietic Stem Cells. \\u003cem\\u003eGenome biology 19\\u003c/em\\u003e (1), 189\\u0026ndash;189. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1186/s13059-018-1557-3\\u003c/span\\u003e\\u003cspan address=\\\"10.1186/s13059-018-1557-3\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eRoffay C, Molinard G, Kim K, Urbanska M, Andrade V, Barbarasa V, Nowak P, Mercier V, Garc\\u0026iacute;a-Calvo J, Matile S, Loewith R, Echard A, Guck J, Lenz M, Roux A (2021) Passive Coupling of Membrane Tension and Cell Volume during Active Response of Cells to Osmosis. Proc Natl Acad Sci U S A 118(47):e2103228118. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1073/pnas.2103228118\\u003c/span\\u003e\\u003cspan address=\\\"10.1073/pnas.2103228118\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003ePiccolo S, Dupont S, Cordenonsi M (2014) The Biology of YAP/TAZ: Hippo Signaling and Beyond. Physiol Rev 94(4):1287\\u0026ndash;1312. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1152/physrev.00005.2014\\u003c/span\\u003e\\u003cspan address=\\\"10.1152/physrev.00005.2014\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eDupont S, Morsut L, Aragona M, Enzo E, Giulitti S, Cordenonsi M, Zanconato F, Le Digabel J, Forcato M, Bicciato S, Elvassore N, Piccolo S (2011) Role of YAP/TAZ in Mechanotransduction. Nature 474(7350):179\\u0026ndash;183. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1038/nature10137\\u003c/span\\u003e\\u003cspan address=\\\"10.1038/nature10137\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eChen X, Hughes R, Mullin N, Hawkins RJ, Holen I, Brown NJ, Hobbs JK (2020) Mechanical Heterogeneity in the Bone Microenvironment as Characterized by Atomic Force Microscopy. Biophys J 119(3):502\\u0026ndash;513. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/j.bpj.2020.06.026\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.bpj.2020.06.026\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eZhang P, Zhang C, Li J, Han J, Liu X, Yang H (2019) The Physical Microenvironment of Hematopoietic Stem Cells and Its Emerging Roles in Engineering Applications. Stem Cell Res Ther 10(1):327. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1186/s13287-019-1422-7\\u003c/span\\u003e\\u003cspan address=\\\"10.1186/s13287-019-1422-7\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eIngallina E, Sorrentino G, Bertolio R, Lisek K, Zannini A, Azzolin L, Severino LU, Scaini D, Mano M, Mantovani F, Rosato A, Bicciato S, Piccolo S, Del Sal G (2018) Mechanical Cues Control Mutant P53 Stability through a Mevalonate-RhoA Axis. Nat Cell Biol 20(1):28\\u0026ndash;35. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1038/s41556-017-0009-8\\u003c/span\\u003e\\u003cspan address=\\\"10.1038/s41556-017-0009-8\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003ePhillip JM, Aifuwa I, Walston J, Wirtz D (2015) The Mechanobiology of Aging. Annu Rev Biomed Eng 17(1):113\\u0026ndash;141. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1146/annurev-bioeng-071114-040829\\u003c/span\\u003e\\u003cspan address=\\\"10.1146/annurev-bioeng-071114-040829\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eKoester J, Miroshnikova YA, Ghatak S, Chac\\u0026oacute;n-Mart\\u0026iacute;nez CA, Morgner J, Li X, Atanassov I, Altm\\u0026uuml;ller J, Birk DE, Koch M, Bloch W, Bartusel M, Niessen CM, Rada-Iglesias A, Wickstr\\u0026ouml;m SA (2021) Niche Stiffening Compromises Hair Follicle Stem Cell Potential during Ageing by Reducing Bivalent Promoter Accessibility. Nat Cell Biol 23(7):771\\u0026ndash;781. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1038/s41556-021-00705-x\\u003c/span\\u003e\\u003cspan address=\\\"10.1038/s41556-021-00705-x\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eZhang X, Cao D, Xu L, Xu Y, Gao Z, Pan Y, Jiang M, Wei Y, Wang L, Liao Y, Wang Q, Yang L, Xu X, Gao Y, Gao S, Wang J, Yue R (2023) Harnessing Matrix Stiffness to Engineer a Bone Marrow Niche for Hematopoietic Stem Cell Rejuvenation. Cell Stem Cell 30(4):378\\u0026ndash;395e8. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/j.stem.2023.03.005\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.stem.2023.03.005\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eShang X, Marchioni F, Sipes N, Evelyn CR, Jerabek-Willemsen M, Duhr S, Seibel W, Wortman M, Zheng Y (2012) Rational Design of Small Molecule Inhibitors Targeting RhoA Subfamily Rho GTPases. Chem Biol 19(6):699\\u0026ndash;710. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/j.chembiol.2012.05.009\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.chembiol.2012.05.009\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eAl Jord A, Letort G, Chanet S, Tsai F-C, Antoniewski C, Eichmuller A, Da Silva C, Huynh J-R, Gov NS, Voituriez R, Terret M-\\u0026Eacute;, Verlhac M-H (2022) Cytoplasmic Forces Functionally Reorganize Nuclear Condensates in Oocytes. Nat Commun 13(1):5070. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1038/s41467-022-32675-5\\u003c/span\\u003e\\u003cspan address=\\\"10.1038/s41467-022-32675-5\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eJackson JA, Romeo N, Mietke A, Burns KJ, Totz JF, Martin AC, Dunkel J, Alsous JI (2023) Scaling Behaviour and Control of Nuclear Wrinkling. Nat Phys 19(12):1927\\u0026ndash;1935\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eTotaro A, Panciera T, Piccolo S (2018) YAP/TAZ Upstream Signals and Downstream Responses. Nat Cell Biol 20(8):888\\u0026ndash;899. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1038/s41556-018-0142-z\\u003c/span\\u003e\\u003cspan address=\\\"10.1038/s41556-018-0142-z\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eKalukula Y, Stephens AD, Lammerding J, Gabriele S (2022) Mechanics and Functional Consequences of Nuclear Deformations. Nat Rev Mol Cell Biol 23(9):583\\u0026ndash;602. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1038/s41580-022-00480-z\\u003c/span\\u003e\\u003cspan address=\\\"10.1038/s41580-022-00480-z\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eKim KM, Mura-Meszaros A, Tollot M, Krishnan MS, Gr\\u0026uuml;ndl M, Neubert L, Groth M, Rodriguez-Fraticelli A, Svendsen AF, Campaner S, Andreas N, Kamradt T, Hoffmann S, Camargo FD, Heidel FH, Bystrykh LV, de Haan G (2022) Eyss, B. Taz Protects Hematopoietic Stem Cells from an Aging-Dependent Decrease in PU.1 Activity. Nat Commun 13(1):5187. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1038/s41467-022-32970-1\\u003c/span\\u003e\\u003cspan address=\\\"10.1038/s41467-022-32970-1\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eMascetti G, Carrara S, Vergani L (2001) Relationship between Chromatin Compactness and Dye Uptake for in Situ Chromatin Stained with DAPI. \\u003cem\\u003eCytometry 44\\u003c/em\\u003e (2), 113\\u0026ndash;119. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1002/1097-0320(20010601)44:2\\u0026lt;113::AID-CYTO1089\\u0026gt;3.0.CO;2-A\\u003c/span\\u003e\\u003cspan address=\\\"10.1002/1097-0320(20010601)44:2%3C113::AID-CYTO1089%3E3.0.CO;2-A\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLinhoff MW, Garg SK, Mandel GA, High-Resolution (2015) Imaging Approach to Investigate Chromatin Architecture in Complex Tissues. Cell 163(1):246\\u0026ndash;255. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/j.cell.2015.09.002\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.cell.2015.09.002\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLong JT, Lammerding J (2021) Nuclear Deformation Lets Cells Gauge Their Physical Confinement. Dev Cell 56(2):156\\u0026ndash;158. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/j.devcel.2021.01.002\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.devcel.2021.01.002\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eMcInnes L, Healy J, Saul N, Gro\\u0026szlig;berger LUMAP (2018) Uniform Manifold Approximation and Projection. J Open Source Softw 3(29):861. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.21105/joss.00861\\u003c/span\\u003e\\u003cspan address=\\\"10.21105/joss.00861\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eGoldman RD, Shumaker DK, Erdos MR, Eriksson M, Goldman AE, Gordon LB, Gruenbaum Y, Khuon S, Mendez M, Varga R, Collins FS (2004) Accumulation of Mutant Lamin A Causes Progressive Changes in Nuclear Architecture in Hutchinson\\u0026ndash;Gilford Progeria Syndrome. \\u003cem\\u003eProc. Natl. Acad. Sci. U.S.A. 101\\u003c/em\\u003e (24), 8963\\u0026ndash;8968. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1073/pnas.0402943101\\u003c/span\\u003e\\u003cspan address=\\\"10.1073/pnas.0402943101\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eChambers SM, Shaw CA, Gatza C, Fisk CJ, Donehower LA, Goodell MA (2007) Aging Hematopoietic Stem Cells Decline in Function and Exhibit Epigenetic Dysregulation. PLoS Biol 5 (8), e201\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eChambers SM, Goodell MA (2007) Hematopoietic Stem Cell Aging: Wrinkles in Stem Cell Potential. Stem Cell Rev 3(3):201\\u0026ndash;211\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eSun D, Luo M, Jeong M, Rodriguez B, Xia Z, Hannah R, Wang H, Le T, Faull KF, Chen R, Gu H, Bock C, Meissner A, Gottgens B, Darlington GJ, Li W, Goodell MA (2014) Epigenomic Profiling of Young and Aged HSCs Reveals Concerted Changes during Aging That Reinforce Self-Renewal. \\u003cem\\u003eCell Stem Cell 14\\u003c/em\\u003e (5), 673\\u0026ndash;688. S1934-5909(14)00096-4 [pii] 10.1016/j.stem.2014.03.002\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003ePal S, Tyler JK, Epigenetics, Aging (2016) Sci Adv 2(7):e1600584. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1126/sciadv.1600584\\u003c/span\\u003e\\u003cspan address=\\\"10.1126/sciadv.1600584\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eTsurumi A, Li WX (2012) Global Heterochromatin Loss: A Unifying Theory of Aging? \\u003cem\\u003eEpigenetics 7\\u003c/em\\u003e (7), 680\\u0026ndash;688. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.4161/epi.20540\\u003c/span\\u003e\\u003cspan address=\\\"10.4161/epi.20540\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003ePadeken J, Methot SP, Gasser SM (2022) Establishment of H3K9-Methylated Heterochromatin and Its Functions in Tissue Differentiation and Maintenance. Nat Rev Mol Cell Biol 23(9):623\\u0026ndash;640. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1038/s41580-022-00483-w\\u003c/span\\u003e\\u003cspan address=\\\"10.1038/s41580-022-00483-w\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eKirby TJ, Lammerding J (2018) Emerging Views of the Nucleus as a Cellular Mechanosensor. Nat Cell Biol 20(4):373\\u0026ndash;381. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1038/s41556-018-0038-y\\u003c/span\\u003e\\u003cspan address=\\\"10.1038/s41556-018-0038-y\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eZhang D, Zhang R, Song X, Yan KC, Liang H (2021) Uniaxial Cyclic Stretching Promotes Chromatin Accessibility of Gene Loci Associated With Mesenchymal Stem Cells Morphogenesis and Osteogenesis. Front Cell Dev Biol 9:664545. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.3389/fcell.2021.664545\\u003c/span\\u003e\\u003cspan address=\\\"10.3389/fcell.2021.664545\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eNava MM, Miroshnikova YA, Biggs LC, Whitefield DB, Metge F, Boucas J, Vihinen H, Jokitalo E, Li X, Garc\\u0026iacute;a Arcos JM, Hoffmann B, Merkel R, Niessen CM, Dahl KN, Wickstr\\u0026ouml;m SA (2020) Heterochromatin-Driven Nuclear Softening Protects the Genome against Mechanical Stress-Induced Damage. Cell 181(4):800\\u0026ndash;817e22. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/j.cell.2020.03.052\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.cell.2020.03.052\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eGrigoryan A, Pospiech J, Kr\\u0026auml;mer S, Lipka D, Liehr T, Geiger H, Kimura H, Mulaw MA, Florian MC (2021) Attrition of X Chromosome Inactivation in Aged Hematopoietic Stem Cells. Stem Cell Rep 16(4):708\\u0026ndash;716. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/j.stemcr.2021.03.007\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.stemcr.2021.03.007\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eItokawa N, Oshima M, Koide S, Takayama N, Kuribayashi W, Nakajima-Takagi Y, Aoyama K, Yamazaki S, Yamaguchi K, Furukawa Y, Eto K, Iwama A (2022) Epigenetic Traits Inscribed in Chromatin Accessibility in Aged Hematopoietic Stem Cells. Nat Commun 13(1):2691. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1038/s41467-022-30440-2\\u003c/span\\u003e\\u003cspan address=\\\"10.1038/s41467-022-30440-2\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eGorbunova V, Seluanov A, Mita P, McKerrow W, Feny\\u0026ouml; D, Boeke JD, Linker SB, Gage FH, Kreiling JA, Petrashen AP, Woodham TA, Taylor JR, Helfand SL, Sedivy JM (2021) The Role of Retrotransposable Elements in Aging and Age-Associated Diseases. Nature 596(7870):43\\u0026ndash;53. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1038/s41586-021-03542-y\\u003c/span\\u003e\\u003cspan address=\\\"10.1038/s41586-021-03542-y\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLara-Astiaso D, Weiner A, Lorenzo-Vivas E, Zaretsky I, Jaitin DA, David E, Keren-Shaul H, Mildner A, Winter D, Jung S, Friedman N, Amit I (2014) Immunogenetics. Chromatin State Dynamics during Blood Formation. Science 345(6199):943\\u0026ndash;949. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1126/science.1256271\\u003c/span\\u003e\\u003cspan address=\\\"10.1126/science.1256271\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eDe Cecco M, Ito T, Petrashen AP, Elias AE, Skvir NJ, Criscione SW, Caligiana A, Brocculi G, Adney EM, Boeke JD, Le O, Beausejour C, Ambati J, Ambati K, Simon M, Seluanov A, Gorbunova V, Slagboom PE, Helfand SL, Neretti N, Sedivy JM (2019) L1 Drives IFN in Senescent Cells and Promotes Age-Associated Inflammation. Nature 566(7742):73\\u0026ndash;78. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1038/s41586-018-0784-9\\u003c/span\\u003e\\u003cspan address=\\\"10.1038/s41586-018-0784-9\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eDella Valle F, Reddy P, Yamamoto M, Liu P, Saera-Vila A, Bensaddek D, Zhang H, Prieto Martinez J, Abassi L, Celii M, Ocampo A, Nu\\u0026ntilde;ez Delicado E, Mangiavacchi A, Aiese Cigliano R, Rodriguez Esteban C, Horvath S, Izpisua Belmonte JC, Orlando V (2022) LINE-1 RNA Causes Heterochromatin Erosion and Is a Target for Amelioration of Senescent Phenotypes in Progeroid Syndromes. Sci Transl Med 14(657):eabl6057. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1126/scitranslmed.abl6057\\u003c/span\\u003e\\u003cspan address=\\\"10.1126/scitranslmed.abl6057\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eHidaoui D, Porquet A, Chelbi R, Bohm M, Polyzou A, Alcazer V, Depil S, Imanci A, Morabito M, Renneville A, Selimoglu-Buet D, Th\\u0026eacute;pot S, Itzykson R, Laplane L, Droin N, Trompouki E, Elvira-Matelot E, Solary E, Porteu F (2024) Targeting Heterochromatin Eliminates Chronic Myelomonocytic Leukemia Malignant Stem Cells through Reactivation of Retroelements and Immune Pathways. Commun Biol 7:1555. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1038/s42003-024-07214-1\\u003c/span\\u003e\\u003cspan address=\\\"10.1038/s42003-024-07214-1\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eClapes T, Polyzou A, Prater P, Sagar; Morales-Hern\\u0026aacute;ndez A, Ferrarini MG, Kehrer N, Lefkopoulos S, Bergo V, Hummel B, Obier N, Maticzka D, Bridgeman A, Herman JS, Ilik I, Klaeyl\\u0026eacute; L, Rehwinkel J, McKinney-Freeman S, Backofen R, Akhtar A, Cabezas-Wallscheid N, Sawarkar R, Rebollo R, Gr\\u0026uuml;n D, Trompouki E (2021) Chemotherapy-Induced Transposable Elements Activate MDA5 to Enhance Haematopoietic Regeneration. Nat Cell Biol 23(7):704\\u0026ndash;717. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1038/s41556-021-00707-9\\u003c/span\\u003e\\u003cspan address=\\\"10.1038/s41556-021-00707-9\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eRusinova I, Forster S, Yu S, Kannan A, Masse M, Cumming H, Chapman R, Hertzog PJ (2013) INTERFEROME v2.0: An Updated Database of Annotated Interferon-Regulated Genes. Nucleic Acids Res 41(D1):D1040\\u0026ndash;D1046. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1093/nar/gks1215\\u003c/span\\u003e\\u003cspan address=\\\"10.1093/nar/gks1215\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eBouman BJ, Demerdash Y, Sood S, Gr\\u0026uuml;nschl\\u0026auml;ger F, Pilz F, Itani AR, Kuck A, Marot-Lassauzaie V, Haas S, Haghverdi L, Essers MA (2024) Single-Cell Time Series Analysis Reveals the Dynamics of HSPC Response to Inflammation. Life Sci Alliance 7(3). \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.26508/lsa.202302309\\u003c/span\\u003e\\u003cspan address=\\\"10.26508/lsa.202302309\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eFlohr Svendsen A, Yang D, Kim K, Lazare S, Skinder N, Zwart E, Mura-Meszaros A, Ausema A, von Eyss B, de Haan G, Bystrykh L (2021) A Comprehensive Transcriptome Signature of Murine Hematopoietic Stem Cell Aging. Blood 138(6):439\\u0026ndash;451. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1182/blood.2020009729\\u003c/span\\u003e\\u003cspan address=\\\"10.1182/blood.2020009729\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eEdginton-White B, Maytum A, Kellaway SG, Goode DK, Keane P, Pagnuco I, Assi SA, Ames L, Clarke M, Cockerill PN, G\\u0026ouml;ttgens B, Cazier JB, Bonifer CA (2023) Genome-Wide Relay of Signalling-Responsive Enhancers Drives Hematopoietic Specification. Nat Commun 14(1):267. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1038/s41467-023-35910-9\\u003c/span\\u003e\\u003cspan address=\\\"10.1038/s41467-023-35910-9\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eDi Giammartino DC, Kloetgen A, Polyzos A, Liu Y, Kim D, Murphy D, Abuhashem A, Cavaliere P, Aronson B, Shah V, Dephoure N, Stadtfeld M, Tsirigos A, Apostolou E (2019) KLF4 Is Involved in the Organization and Regulation of Pluripotency-Associated Three-Dimensional Enhancer Networks. Nat Cell Biol 21(10):1179\\u0026ndash;1190. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1038/s41556-019-0390-6\\u003c/span\\u003e\\u003cspan address=\\\"10.1038/s41556-019-0390-6\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003ePark CS, Shen Y, Lewis A, Lacorazza HD (2016) Role of the Reprogramming Factor KLF4 in Blood Formation. J Leukoc Biol 99(5):673\\u0026ndash;685. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1189/jlb.1RU1215-539R\\u003c/span\\u003e\\u003cspan address=\\\"10.1189/jlb.1RU1215-539R\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eXie L, Torigoe SE, Xiao J, Mai DH, Li L, Davis FP, Dong P, Marie-Nelly H, Grimm J, Lavis L, Darzacq X, Cattoglio C, Liu Z, Tjian R (2017) A Dynamic Interplay of Enhancer Elements Regulates Klf4 Expression in Na\\u0026iuml;ve Pluripotency. Genes Dev 31(17):1795\\u0026ndash;1808. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1101/gad.303321.117\\u003c/span\\u003e\\u003cspan address=\\\"10.1101/gad.303321.117\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eRoisman A, Adelman ER, Huang H-T, Wade D, Bilbao D, Figueroa ME (2019) Loss of KLF6 Recapitulates Molecular and Functional Changes Associated with Aging in Human Hematopoietic Stem and Progenitor Cells. Blood 134(Supplement1):447. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1182/blood-2019-130800\\u003c/span\\u003e\\u003cspan address=\\\"10.1182/blood-2019-130800\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eAdelman ER, Huang HT, Roisman A, Olsson A, Colaprico A, Qin T, Lindsley RC, Bejar R, Salomonis N, Grimes HL, Figueroa ME (2019) Aging Human Hematopoietic Stem Cells Manifest Profound Epigenetic Reprogramming of Enhancers That May Predispose to Leukemia. Cancer Discov 9(8):1080\\u0026ndash;1101. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1158/2159-8290.CD-18-1474\\u003c/span\\u003e\\u003cspan address=\\\"10.1158/2159-8290.CD-18-1474\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eMcGinn J, Hallou A, Han S, Krizic K, Ulyanchenko S, Iglesias-Bartolome R, England FJ, Verstreken C, Chalut KJ, Jensen KB, Simons BD, Alcolea MP (2021) A Biomechanical Switch Regulates the Transition towards Homeostasis in Oesophageal Epithelium. Nat Cell Biol 23(5):511\\u0026ndash;525. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1038/s41556-021-00679-w\\u003c/span\\u003e\\u003cspan address=\\\"10.1038/s41556-021-00679-w\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eMas G, Santoro F, Blanco E, Gamarra Figueroa GP, Le Dily F, Frig\\u0026egrave; G, Vidal E, Mugianesi F, Ballar\\u0026eacute; C, Gutierrez A, Sparavier A, Marti-Renom MA, Minucci S, Di Croce L (2022) Vivo Temporal Resolution of Acute Promyelocytic Leukemia Progression Reveals a Role of Klf4 in Suppressing Early Leukemic Transformation. Genes Dev 36(7\\u0026ndash;8):451\\u0026ndash;467. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1101/gad.349115.121\\u003c/span\\u003e\\u003cspan address=\\\"10.1101/gad.349115.121\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eWilson NK, Kent DG, Buettner F, Shehata M, Macaulay IC, Calero-Nieto FJ, Sanchez Castillo M, Oedekoven CA, Diamanti E, Schulte R, Ponting CP, Voet T, Caldas C, Stingl J, Green AR, Theis FJ, Gottgens B (2015) Combined Single-Cell Functional and Gene Expression Analysis Resolves Heterogeneity within Stem Cell Populations. Cell Stem Cell 16(6):712\\u0026ndash;724. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/j.stem.2015.04.004\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.stem.2015.04.004\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eRodriguez-Fraticelli AE, Weinreb C, Wang S-W, Migueles RP, Jankovic M, Usart M, Klein AM, Lowell S, Camargo FD (2020) Single-Cell Lineage Tracing Unveils a Role for TCF15 in Haematopoiesis. Nature 583(7817):585\\u0026ndash;589. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1038/s41586-020-2503-6\\u003c/span\\u003e\\u003cspan address=\\\"10.1038/s41586-020-2503-6\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eCabezas-Wallscheid N, Buettner F, Sommerkamp P, Klimmeck D, Ladel L, Thalheimer FB, Pastor-Flores D, Roma LP, Renders S, Zeisberger P, Przybylla A, Schonberger K, Scognamiglio R, Altamura S, Florian CM, Fawaz M, Vonficht D, Tesio M, Collier P, Pavlinic D, Geiger H, Schroeder T, Benes V, Dick TP, Rieger MA, Stegle O, Trumpp A (2017) Vitamin A-Retinoic Acid Signaling Regulates Hematopoietic Stem Cell Dormancy. Cell 169(5):807\\u0026ndash;823e19. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/j.cell.2017.04.018\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.cell.2017.04.018\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003ePereira C-F, Chang B, Gomes A, Bernitz J, Papatsenko D, Niu X, Swiers G, Azzoni E, de Bruijn MFTR, Schaniel C, Lemischka IR, Moore KA (2016) Hematopoietic Reprogramming In Vitro Informs In Vivo Identification of Hemogenic Precursors to Definitive Hematopoietic Stem Cells. Dev Cell 36(5):525\\u0026ndash;539. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/j.devcel.2016.02.011\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.devcel.2016.02.011\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eFlorian MC, D\\u0026ouml;rr K, Niebel A, Daria D, Schrezenmeier H, Rojewski M, Filippi M-D, Hasenberg A, Gunzer M, Scharffetter-Kochanek K, Zheng Y, Geiger H (2012) Cdc42 Activity Regulates Hematopoietic Stem Cell Aging and Rejuvenation. Cell Stem Cell 10(5):520\\u0026ndash;530. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/j.stem.2012.04.007\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.stem.2012.04.007\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eFlorian MC, Klose M, Sacma M, Jablanovic J, Knudson L, Nattamai KJ, Marka G, Vollmer A, Soller K, Sakk V, Cabezas-Wallscheid N, Zheng Y, Mulaw MA, Glauche I, Geiger H (2018) Aging Alters the Epigenetic Asymmetry of HSC Division. PLoS Biol 16(9):e2003389\\u0026ndash;e2003389. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1371/journal.pbio.2003389\\u003c/span\\u003e\\u003cspan address=\\\"10.1371/journal.pbio.2003389\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eMontserrat-Vazquez S, Ali NJ, Matteini F, Lozano J, Zhaowei T, Mejia-Ramirez E, Marka G, Vollmer A, Soller K, Sacma M, Sakk V, Mularoni L, Mallm JP, Plass M, Zheng Y, Geiger H, Florian MC (2022) Transplanting Rejuvenated Blood Stem Cells Extends Lifespan of Aged Immunocompromised Mice. npj Regen Med 7(1):1\\u0026ndash;17. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1038/s41536-022-00275-y\\u003c/span\\u003e\\u003cspan address=\\\"10.1038/s41536-022-00275-y\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLuche H, Weber O, Nageswara Rao T, Blum C, Fehling HJ (2007) Faithful Activation of an Extra-Bright Red Fluorescent Protein in Knock-in Cre-Reporter Mice Ideally Suited for Lineage Tracing Studies. Eur J Immunol 37(1):43\\u0026ndash;53. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1002/eji.200636745\\u003c/span\\u003e\\u003cspan address=\\\"10.1002/eji.200636745\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eMatteini F, Montserrat-Vazquez S, Florian MC Rejuvenating Aged Stem Cells: Therapeutic Strategies to Extend Health and Lifespan., FEBS Letters \\u003cem\\u003en/a\\u003c/em\\u003e (n/a). \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1002/1873-3468.14865\\u003c/span\\u003e\\u003cspan address=\\\"10.1002/1873-3468.14865\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eMaurer M, Lammerding J (2019) The Driving Force: Nuclear Mechanotransduction in Cellular Function, Fate, and Disease. Annu Rev Biomed Eng 21:443\\u0026ndash;468. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1146/annurev-bioeng-060418-052139\\u003c/span\\u003e\\u003cspan address=\\\"10.1146/annurev-bioeng-060418-052139\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eXia Y, Pfeifer CR, Cho S, Discher DE, Irianto J (2018) Nuclear Mechanosensing. Emerg Top Life Sci 2(5):713\\u0026ndash;725. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1042/ETLS20180051\\u003c/span\\u003e\\u003cspan address=\\\"10.1042/ETLS20180051\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eHegde S, Akbar H, Wellendorf AM, Nestheide S, Johnson JF, Zhao X, Setchell KD, Zheng Y, Cancelas JA (2024) Inhibition of RHOA Activity Preserves the Survival and Hemostasis Function of Long-Term Cold-Stored Platelets. Blood 144(16):1732\\u0026ndash;1746. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1182/blood.2023021453\\u003c/span\\u003e\\u003cspan address=\\\"10.1182/blood.2023021453\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eDe Cecco M, Criscione SW, Peterson AL, Neretti N, Sedivy JM, Kreiling JA (2013) Transposable Elements Become Active and Mobile in the Genomes of Aging Mammalian Somatic Tissues. Aging 5(12):867\\u0026ndash;883. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.18632/aging.100621\\u003c/span\\u003e\\u003cspan address=\\\"10.18632/aging.100621\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eWang Y, Zheng J, Luo Y, Wang J, Xu L, Wang J, Sedivy JM, Song Z, Wang H, Ju Z (2020) L1 Drives HSC Aging and Affects Prognosis of Chronic Myelomonocytic Leukemia. Sig Transduct Target Ther 5(1):1\\u0026ndash;4. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1038/s41392-020-00279-4\\u003c/span\\u003e\\u003cspan address=\\\"10.1038/s41392-020-00279-4\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eJaganathan BG, Anjos-Afonso F, Kumar A, Bonnet D, Active (2013) RHOA Favors Retention of Human Hematopoietic Stem/Progenitor Cells in Their Niche. J Biomed Sci 20:66. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1186/1423-0127-20-66\\u003c/span\\u003e\\u003cspan address=\\\"10.1186/1423-0127-20-66\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eYu Y, Schleich K, Yue B, Ji S, Lohneis P, Kemper K, Silvis MR, Qutob N, van Rooijen E, Werner-Klein M, Li L, Dhawan D, Meierjohann S, Reimann M, Elkahloun A, Treitschke S, Dorken B, Speck C, Mallette FA, Zon LI, Holmen SL, Peeper DS, Samuels Y, Schmitt CA, Lee S (2018) Targeting the Senescence-Overriding Cooperative Activity of Structurally Unrelated H3K9 Demethylases in Melanoma. Cancer Cell 33(4):785. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/j.ccell.2018.03.009\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.ccell.2018.03.009\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eAdolfsson J, M\\u0026aring;nsson R, Buza-Vidas N, Hultquist A, Liuba K, Jensen CT, Bryder D, Yang L, Borge O-J, Thoren LAM, Anderson K, Sitnicka E, Sasaki Y, Sigvardsson M, Jacobsen SEW (2005) Identification of Flt3\\u0026thinsp;+\\u0026thinsp;Lympho-Myeloid Stem Cells Lacking Erythro-Megakaryocytic Potential: A Revised Road Map for Adult Blood Lineage Commitment. Cell 121(2):295\\u0026ndash;306. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/j.cell.2005.02.013\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.cell.2005.02.013\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eChambolle A (2004) An Algorithm for Total Variation Minimization and Applications. J Math Imaging Vis 20(1):89\\u0026ndash;97. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1023/B:JMIV.0000011325.36760.1e\\u003c/span\\u003e\\u003cspan address=\\\"10.1023/B:JMIV.0000011325.36760.1e\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eOtsu N (1979) A Threshold Selection Method from Gray-Level Histograms. IEEE Trans Syst Man Cybernetics 9(1):62\\u0026ndash;66. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1109/TSMC.1979.4310076\\u003c/span\\u003e\\u003cspan address=\\\"10.1109/TSMC.1979.4310076\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eWalt S (2014) Sch\\u0026ouml;nberger, J. L.; Nunez-Iglesias, J.; Boulogne, F.; Warner, J. D.; Yager, N.; Gouillart, E.; Yu, T. Scikit-Image: Image Processing in Python. \\u003cem\\u003ePeerJ 2\\u003c/em\\u003e, e453. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.7717/peerj.453\\u003c/span\\u003e\\u003cspan address=\\\"10.7717/peerj.453\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eHartigan JA, Wong MA, Algorithm (1979) AS 136: A K-Means Clustering Algorithm. J Royal Stat Soc Ser C (Applied Statistics) 28(1):100\\u0026ndash;108. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.2307/2346830\\u003c/span\\u003e\\u003cspan address=\\\"10.2307/2346830\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eSa\\u0026ccedil;ma M, Matteini F, Mulaw MA, Hageb A, Bogeska R, Sakk V, Vollmer A, Marka G, Soller K, Milsom MD, Florian MC, Geiger H (2022) Fast and High-Fidelity in Situ 3D Imaging Protocol for Stem Cells and Niche Components for Mouse Organs and Tissues. STAR Protocols 3(3):101483. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/j.xpro.2022.101483\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.xpro.2022.101483\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLe Berre M, Zlotek-Zlotkiewicz E, Bonazzi D, Lautenschlaeger F, Piel M (2014) Methods for Two-Dimensional Cell Confinement. Methods Cell Biol 121:213\\u0026ndash;229. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/B978-0-12-800281-0.00014-2\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/B978-0-12-800281-0.00014-2\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eDanecek P, Bonfield JK, Liddle J, Marshall J, Ohan V, Pollard MO, Whitwham A, Keane T, McCarthy SA, Davies RM, Li H (2021) Twelve Years of SAMtools and BCFtools. \\u003cem\\u003eGigaScience 10\\u003c/em\\u003e (2), giab008. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1093/gigascience/giab008\\u003c/span\\u003e\\u003cspan address=\\\"10.1093/gigascience/giab008\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eOu J, Liu H, Yu J, Kelliher MA, Castilla LH, Lawson ND, Zhu LJ (2018) ATACseqQC: A Bioconductor Package for Post-Alignment Quality Assessment of ATAC-Seq Data. BMC Genomics 19(1):169. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1186/s12864-018-4559-3\\u003c/span\\u003e\\u003cspan address=\\\"10.1186/s12864-018-4559-3\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eRam\\u0026iacute;rez F, Ryan DP, Gr\\u0026uuml;ning B, Bhardwaj V, Kilpert F, Richter AS, Heyne S, D\\u0026uuml;ndar F, Manke T (2016) deepTools2: A next Generation Web Server for Deep-Sequencing Data Analysis. Nucleic Acids Res 44(W1):W160\\u0026ndash;W165. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1093/nar/gkw257\\u003c/span\\u003e\\u003cspan address=\\\"10.1093/nar/gkw257\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eThorvaldsd\\u0026oacute;ttir H, Robinson JT, Mesirov JP (2013) Integrative Genomics Viewer (IGV): High-Performance Genomics Data Visualization and Exploration. Brief Bioinform 14(2):178\\u0026ndash;192. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1093/bib/bbs017\\u003c/span\\u003e\\u003cspan address=\\\"10.1093/bib/bbs017\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eHeinz S, Benner C, Spann N, Bertolino E, Lin YC, Laslo P, Cheng JX, Murre C, Singh H, Glass CK (2010) Simple Combinations of Lineage-Determining Transcription Factors Prime Cis-Regulatory Elements Required for Macrophage and B Cell Identities. Mol Cell 38(4):576\\u0026ndash;589. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/j.molcel.2010.05.004\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.molcel.2010.05.004\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLiao Y, Smyth GK, Shi W (2019) The R Package Rsubread Is Easier, Faster, Cheaper and Better for Alignment and Quantification of RNA Sequencing Reads. Nucleic Acids Res 47(8):e47. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1093/nar/gkz114\\u003c/span\\u003e\\u003cspan address=\\\"10.1093/nar/gkz114\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eRitchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, Smyth GK (2015) Limma Powers Differential Expression Analyses for RNA-Sequencing and Microarray Studies. Nucleic Acids Res 43(7):e47. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1093/nar/gkv007\\u003c/span\\u003e\\u003cspan address=\\\"10.1093/nar/gkv007\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eL\\u0026ecirc; S, Josse J, Husson F, FactoMineR (2008) An R Package for Multivariate Analysis. J Stat Softw 25:1\\u0026ndash;18. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.18637/jss.v025.i01\\u003c/span\\u003e\\u003cspan address=\\\"10.18637/jss.v025.i01\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eChen H, VennDiagram (2021) Generate High-Resolution Venn and Euler Plots. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://CRAN.R-project.org/package=VennDiagram\\u003c/span\\u003e\\u003cspan address=\\\"https://CRAN.R-project.org/package=VennDiagram\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e (accessed 2022-03-21)\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eWu T, Hu E, Xu S, Chen M, Guo P, Dai Z, Feng T, Zhou L, Tang W, Zhan L, Fu X, Liu S, Bo X, Yu G (2021) clusterProfiler 4.0: A Universal Enrichment Tool for Interpreting Omics Data. Innov 2(3):100141. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/j.xinn.2021.100141\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.xinn.2021.100141\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eMartin M (2011) Cutadapt Removes Adapter Sequences from High-Throughput Sequencing Reads. \\u003cem\\u003eEMBnet.journal 17\\u003c/em\\u003e (1), 10\\u0026ndash;12. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.14806/ej.17.1.200\\u003c/span\\u003e\\u003cspan address=\\\"10.14806/ej.17.1.200\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eDobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, Batut P, Chaisson M, Gingeras TR (2013) STAR: Ultrafast Universal RNA-Seq Aligner. Bioinformatics 29(1):15\\u0026ndash;21. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1093/bioinformatics/bts635\\u003c/span\\u003e\\u003cspan address=\\\"10.1093/bioinformatics/bts635\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eRobinson MD, McCarthy DJ, Smyth GK, edgeR: (2010) A Bioconductor Package for Differential Expression Analysis of Digital Gene Expression Data. Bioinformatics 26(1):139\\u0026ndash;140. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1093/bioinformatics/btp616\\u003c/span\\u003e\\u003cspan address=\\\"10.1093/bioinformatics/btp616\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eJin Y, Tam OH, Paniagua E, Hammell M, TEtranscripts (2015) A Package for Including Transposable Elements in Differential Expression Analysis of RNA-Seq Datasets. Bioinformatics 31(22):3593\\u0026ndash;3599. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1093/bioinformatics/btv422\\u003c/span\\u003e\\u003cspan address=\\\"10.1093/bioinformatics/btv422\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eGu Z, Eils R, Schlesner M (2016) Complex Heatmaps Reveal Patterns and Correlations in Multidimensional Genomic Data. Bioinformatics 32(18):2847\\u0026ndash;2849. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1093/bioinformatics/btw313\\u003c/span\\u003e\\u003cspan address=\\\"10.1093/bioinformatics/btw313\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eHao Y, Hao S, Andersen-Nissen E, Mauck WM, Zheng S, Butler A, Lee MJ, Wilk AJ, Darby C, Zager M, Hoffman P, Stoeckius M, Papalexi E, Mimitou EP, Jain J, Srivastava A, Stuart T, Fleming LM, Yeung B, Rogers AJ, McElrath JM, Blish CA, Gottardo R, Smibert P, Satija R (2021) Cell 184(13):3573\\u0026ndash;3587e29. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/j.cell.2021.04.048\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.cell.2021.04.048\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e. Integrated Analysis of Multimodal Single-Cell Data\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eKowalczyk MS, Tirosh I, Heckl D, Nageswara Rao T, Dixit A, Haas BJ, Schneider R, Wagers AJ, Ebert BL, Regev A (2015) Single Cell RNA-Seq Reveals Changes in Cell Cycle and Differentiation Programs upon Aging of Hematopoietic Stem Cells. Genome Res. https://doi.org/gr.192237.115\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eZappia L, Oshlack A, Clustering Trees (2018) A Visualization for Evaluating Clusterings at Multiple Resolutions. \\u003cem\\u003eGigaScience 7\\u003c/em\\u003e (7), giy083. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1093/gigascience/giy083\\u003c/span\\u003e\\u003cspan address=\\\"10.1093/gigascience/giy083\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eB\\u0026uuml;ttner M, Ostner J, M\\u0026uuml;ller CL, Theis FJ, Schubert B (2021) scCODA Is a Bayesian Model for Compositional Single-Cell Data Analysis. Nat Commun 12(1):6876. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1038/s41467-021-27150-6\\u003c/span\\u003e\\u003cspan address=\\\"10.1038/s41467-021-27150-6\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eCrowell HL, Soneson C, Germain P-L, Calini D, Collin L, Raposo C, Malhotra D, Robinson MD (2020) Nat Commun 11(1):6077. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1038/s41467-020-19894-4\\u003c/span\\u003e\\u003cspan address=\\\"10.1038/s41467-020-19894-4\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e. Muscat Detects Subpopulation-Specific State Transitions from Multi-Sample Multi-Condition Single-Cell Transcriptomics Data\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eAibar S, Gonz\\u0026aacute;lez-Blas CB, Moerman T, Huynh-Thu VA, Imrichova H, Hulselmans G, Rambow F, Marine J-C, Geurts P, Aerts J, van den Oord J, Atak ZK, Wouters J, Aerts SSCENIC (2017) Single-Cell Regulatory Network Inference and Clustering. Nat Methods 14(11):1083\\u0026ndash;1086. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1038/nmeth.4463\\u003c/span\\u003e\\u003cspan address=\\\"10.1038/nmeth.4463\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e\\u003c/ol\\u003e\"},{\"header\":\"Supplementary Tables\",\"content\":\"\\u003cp\\u003eSupplementary Tables S2-S5 are not available with this version. \\u003c/p\\u003e\\u003cp\\u003e\\u003cstrong\\u003eTable S2 | Data for the ATAC-seq experiment on young, aged and aged+Ri HSCs.\\u0026nbsp;\\u003c/strong\\u003eInformation on the samples, the consensus peaks, the DARs, the GO enrichment, the TF motif analysis, and the Klf4-targeted genes.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTable S3 | Data for the ATAC-seq experiment on young and young 5\\u003c/strong\\u003e\\u003cstrong\\u003e\\u0026mu;\\u003c/strong\\u003e\\u003cstrong\\u003em confined HSCs.\\u0026nbsp;\\u003c/strong\\u003eInformation on the samples, the consensus peaks, and the DARs.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTable S4 | Data for the RNA-seq experiment on young, aged and aged+Ri HSCs.\\u0026nbsp;\\u003c/strong\\u003eInformation on the samples, the DE genes, the DE retrotransposons, the GSEA, and the GO enrichment of upregulated Klf4-targeted genes.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTable S5 | Data for the scRNA-seq experiment on young, aged and aged+Ri LSKs.\\u0026nbsp;\\u003c/strong\\u003eInformation on the samples, the cluster markers, the compositional analysis, the DE genes, and the differentially active regulons.\\u003c/p\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":true,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":true,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"nature-portfolio\",\"isNatureJournal\":true,\"hasQc\":false,\"allowDirectSubmit\":false,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"\",\"title\":\"Nature Portfolio\",\"twitterHandle\":\"\",\"acdcEnabled\":false,\"dfaEnabled\":false,\"editorialSystem\":\"ejp\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":false},\"keywords\":\"Aging, hematopoietic stem cells, RhoA, regeneration, hematopoiesis, nuclear envelope, cell confinement, chromatin accessibility, retrotransposones, LINE-1, LTR, Klf4\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-6333603/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-6333603/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eBiomechanical alterations contribute to the decreased regenerative capacity of hematopoietic stem cells (HSCs) upon aging. RhoA is a key regulator of mechano-signaling but its role for mechanotransduction in stem cell aging has not been investigated yet.\\u003c/p\\u003e \\u003cp\\u003eHere, we show that murine HSCs respond to increased nuclear envelope (NE) tension by inducing NE translocation of P-cPLA2, which cell intrinsically activates RhoA. Interestingly, aged HSCs experience physiologically higher intrinsic NE tension, associated with increased NE P-cPLA2 and RhoA activity. Reducing RhoA activity lowers NE tension in aged HSCs. Feature image analysis of HSC nuclei reveals that chromatin remodeling is associated to RhoA inhibition, which includes the restoration of youthful levels of the heterochromatin marker H3K9me2 and a decrease in chromatin accessibility and transcription at retrotransposons. Eventually, we demonstrate that RhoA inhibition upregulates Klf4 expression and transcriptional activity, improving aged HSCs regenerative capacity and lympho/myeloid skewing \\u003cem\\u003ein vivo\\u003c/em\\u003e.\\u003c/p\\u003e \\u003cp\\u003eOverall, our data support that an intrinsic mechano-signaling axis dependent on RhoA can be pharmacologically targeted to rejuvenate stem cell function upon aging.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Targeting RhoA activity rejuvenates aged hematopoietic stem cells\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-05-05 11:14:42\",\"doi\":\"10.21203/rs.3.rs-6333603/v1\",\"editorialEvents\":[],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"nature-aging\",\"isNatureJournal\":true,\"hasQc\":false,\"allowDirectSubmit\":false,\"externalIdentity\":\"nataging\",\"sideBox\":\"Learn more about [Nature Aging](https://www.nature.com/nataging/)\",\"snPcode\":\"\",\"submissionUrl\":\"\",\"title\":\"Nature Aging\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"ejp\",\"reportingPortfolio\":\"Nature Research\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":false}}],\"origin\":\"\",\"ownerIdentity\":\"56a218c4-609d-40db-8c15-2b3d49b867bd\",\"owner\":[],\"postedDate\":\"May 5th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"published-in-journal\",\"subjectAreas\":[{\"id\":47696149,\"name\":\"Biological sciences/Stem cells/Ageing\"},{\"id\":47696150,\"name\":\"Biological sciences/Stem cells/Adult stem cells\"},{\"id\":47696151,\"name\":\"Biological sciences/Stem cells/Epigenetic memory\"}],\"tags\":[],\"updatedAt\":\"2025-11-25T08:15:53+00:00\",\"versionOfRecord\":{\"articleIdentity\":\"rs-6333603\",\"link\":\"https://doi.org/10.1038/s43587-025-01014-w\",\"journal\":{\"identity\":\"nature-aging\",\"isVorOnly\":false,\"title\":\"Nature Aging\"},\"publishedOn\":\"2025-11-24 05:00:00\",\"publishedOnDateReadable\":\"November 24th, 2025\"},\"versionCreatedAt\":\"2025-05-05 11:14:42\",\"video\":\"\",\"vorDoi\":\"10.1038/s43587-025-01014-w\",\"vorDoiUrl\":\"https://doi.org/10.1038/s43587-025-01014-w\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-6333603\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-6333603\",\"identity\":\"rs-6333603\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}