Soluble DLK1 secreted by telomere-shortening-induced senescent microglia impairs myelination and alters neuronal activity

Soluble DLK1 secreted by telomere-shortening-induced senescent microglia impairs myelination and alters neuronal activity | 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 Soluble DLK1 secreted by telomere-shortening-induced senescent microglia impairs myelination and alters neuronal activity Li Gan, Bangyan Liu, Maria Telpoukhovskaia, Li Fan, Alice Giani, and 11 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5014333/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Aging has a critical role in the development of neurodegenerative disorders, such as Alzheimer’s disease and Parkinson’s disease. In the current study, we investigated the impact of aging on the brain through telomere shortening, a physiological change correlated with aging. Animals with shortened telomeres exhibit cognitive decline and exacerbated lipofuscinosis in the brain. Our single-nuclei transcriptome analysis revealed that telomere shortening led to the emergence of a senescent microglia population reminiscent of a senescence-associated secretory phenotype signature, and oligodendrocyte lineage cells with disrupted maturation and differentiation profiles. Using iPSC-derived microglia with shortened telomeres, we identified DLK1 as a novel senescence-associated ligand secreted by senescent microglia. Depletion of microglia abolished the DLK1 elevation in the cerebral spinal fluid of telomere-shortened mice. Elevation of soluble DLK1 induced demyelination and disruption of neuronal calcium signaling. Our findings highlighted the induction of microglia senescence by telomere shortening and identified DLK1 as a new senescence-associated ligand by which senescent microglia disrupts normal myelination and neuronal calcium activity. Biological sciences/Neuroscience/Neural ageing Biological sciences/Cell biology/Senescence Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Aging is the most critical risk factor for many neurodegenerative disorders, including Alzheimer’s disease (AD), the most common cause for dementia that contributes to up to 80% of all dementia cases 1 . Rather than presenting with a single neurodegenerative pathology, a large proportion of the elderly dementia patients exhibit mixed brain pathologies, including AD and other degenerative and vascular brain pathologies simultaneously 2 , 3 . The comprehensive disruption of brain functions and structural integrity can be attributed to pathological brain aging, which is characterized by more rapid deterioration due to genetic predisposition and environmental factors 4 – 6 . Understanding the mechanisms underlying pathological brain aging is critical for unveiling the pathogenesis of neurodegenerative disorders and other age-dependent brain conditions. Telomeres are repeated nucleotide sequences at the ends of chromosomes that prevent nucleolytic degradation and chromosome end-to-end fusion. The telomere is a key indicator of an organism’s biological age 7 . Human telomeres range from 2 to 30 kb and gradually lose their length due to the end replication problem during aging 8 , 9 . Telomere sequence and function are conserved between human and mouse, but mouse telomeres are 5–10 times longer than human, and mouse lifespan is 30 times shorter, making it unfeasible to study the mechanisms affected by critically shortened telomeres in naturally aged mice 10 . To overcome this, TERC knock out has been used to study the effect of telomere shortening in the mice. TERC is the RNA component of telomerase, an RNA-protein complex that elongates telomere length in cells with infinite proliferative potential and serves as the replication template for telomere sequence 11 . Knockout of TERC hampers the telomerase activity without affecting other enzymatic functions of TERT, the protein component of telomerase, and causes telomere shortening to be inherited through generations by disabling telomere elongations in the mouse germline. Previous studies have showcased that three consecutive generations of TERC knock out induces aging-like phenotypes 12 . Critical shortening of telomeres by end replication problem induces cell-cycle arrest and causes the cell to enter replicative senescence 13 . Glial cells in the brain (e.g., microglia, astrocytes, and oligodendrocytes) retain proliferative capabilities after development and become more proliferative in response to damage to the central nervous system (CNS) and other stressors. Thus, glial cells are under heavy replicative stress and telomere shortening is detected in the white matter, whereas telomere length in the grey matter remains relatively unchanged 14 , 15 . Glial senescence has been suggested to transform the brain from normal aging to pathological aging and to drive the buildup and spread of AD pathologies 16 . Microglia are the resident macrophages in the CNS responsible for immune surveillance and innate immune responses to damage and pathogenic species 17 . Microglia are susceptible to more replicative stress associated with the reactivation of the proliferative program caused by the responses to neurodegenerative pathologies, including tauopathy and Aβ accumulation 18 , 19 . Telomere shortening and replicative senescence may disrupt normal microglia functions under aging and neurodegenerative conditions 20 – 22 . However, how microglia senescence contributes to pathological aging and affects other brain cell types remains unknown. To determine the effects of telomere shortening in the brain, we assessed pathological and functional outcomes while characterizing the cell-type-specific transcriptomic alterations. We expanded our study on telomere-shortened mouse microglia in human iPSC-derived microglia with shortened telomeres. Here we report direct evidence that senescent microglia exert detrimental influences on other cell types through an altered secretion profile. Importantly, we identified delta-like non-canonical Notch ligand 1 (DLK1) as a new senescent microglia-derived ligand. DLK1 was originally identified as a member of the epidermal growth factor (EGF)-like family and a negative regulator of adipocyte differentiation (pre-adipocyte inhibitor factor 1, Pref1) 23 . Overexpression of soluble DLK1 (sDLK1) in the mouse brain caused defects in oligodendrocyte differentiation and myelinating activities. Higher level of sDLK1 also induced abnormal Ca 2+ activities in the mouse and human iPSC-derived neurons. This study profiles cellular signatures associated with shorter telomeres in the mouse brain and identified replicative microglia senescence along with the associated ligands as a key contributor to pathological brain aging. Results Telomere shortening exacerbates brain lipofuscinosis and memory deficit To understand the effects of telomere shortening on brain functions, we used the TERC knockout model. We bred mice with a homologous TERC deletion for three consecutive generations (G3 Terc −/− ) (Fig. 1a). Terc −/− mice have progressive telomere loss with increasing generations 24 . Using in situ hybridization, we measured the fluorescence intensities of the probe complementary to the telomere sequence that directly correlate with telomere length (Fig. 1b) 25 . Indeed, quantification of the fluorescence intensity showed that G3 Terc −/− mice exhibited significantly less fluorescence in all and Iba1-positive cells than age-matched wild-type (WT) mice (Fig. 1c, d). Thus, three consecutive generations of TERC knockout significantly reduced the telomere length in microglia and other brain cells, as expected. Lipofuscinosis is an aging hallmark in the brain that can be visualized as autofluorescence 26 . Autofluorescence was strikingly increased throughout the brain, as detected in the primary somatosensory cortex (Fig. 1e). Quantification of the autofluorescence revealed a significant increase of lipofuscinosis in the G3 Terc −/− mice (Fig. 1f-h). To determine if telomere shortening caused any age-related cognitive declines in the mice, we used the contextual fear conditioning (CFC) test (Fig. 1i). During the conditioning session, G3 Terc −/− mice showed no change in the percentage of time frozen during the learning period (Fig. 1j). However, the G3 Terc −/− mice had significantly less frozen time than WT mice during the test session when they were exposed to the same environment without being shocked, suggesting impaired contextual memory (Fig. 1k). To assess memory impairment not associated with fear, we used novel object recognition (NOR), which measures a form of declarative memory (Fig. 1l). During the sample-object exposure session, G3 Terc −/− and WT mice showed no preference on the two objects as expected (Fig. 1m). In contrast, during the novel-object exposure session, the WT mice showed significant preferences for the novel object, indicating normal memory, but G3 Terc −/− mice again failed to show a preference for the novel object (Fig. 1n). Thus, shortening telomere length in brain cells elevated lipofuscinosis and impaired memory function, phenotypes commonly observed in aged animals 26 , 27 . Telomere shortening accelerates aging in glial cells and enhances senescence signaling in microglia To dissect the mechanism by which telomere shortening exacerbates the phenotypes of brain aging, we performed snRNA-seq to examine hippocampal tissues from WT and G3 Terc −/− animals. We utilized a pre-established cold mechanical dissociation protocol to prepare the snRNA library and sequenced 83,223 nuclei 28 . Of these, 9,131 nuclei were filtered out, based on the gene counts, unique molecular identifier (UMI), and percent mitochondrial genes per nucleus (Supp Fig. 1). We further removed potential multiplets identified by DoubletFinder and selected 72,330 nuclei for downstream analysis 29 . Graph-based clustering identified eight major clusters, and reference gene sets were used to annotate the clusters to eight cell types in the hippocampus region (Fig. 2a; Supp Fig. 1a). Telomere shortening did not significantly affect the composition of the major cell types in the mouse hippocampus (Supp Fig. 1b). We focused on microglia, astrocytes, neurons, oligodendrocytes, and oligodendrocyte precursor cells for the downstream analysis. To identify the magnitude of telomere shortening effect on each cell type, we performed differential expression analysis for each cell type, comparing the transcriptomes of G3 Terc −/− and WT tissues. Telomere shortening led to significant alterations in all six analyzed cell types. The number of differentially expressed genes (DEGs) in each cell type was normalized, using the average number of UMIs detected (Fig. 2b, Supplementary table 1). Examination of the DEGs of microglia, astrocyte, and oligodendrocyte revealed that telomere shortening resulted in unique transcriptomic changes in individual cell types (Fig. 2c, Supp Fig. 2a). Nonetheless, we identified 31 DEGs shared among the three cell types, and the majority of the overlapped DEGs (26/31) were changed in the same direction by telomere shortening (Fig. 2d). Noticeably, AC149090.1 was significantly upregulated in all three cell types. AC149090.1 is a mouse ortholog to human PISD, a gene encodes for a phospholipid decarboxylase that catalyzes the conversion of phosphatidylserine to phosphatidylethanolamine in the inner mitochondrial membrane and is essential in lipid metabolism and autophagy 30 . AC149090.1 is a major marker of cell-type-specific aging in the neurogenic region of the subventricular zone 31 . Utilizing the aging clock built by the Brunet group 32 , we examined the effect of telomere shortening on predicted biological age in oligodendrocyte, microglia, and astrocytes (Supp Fig. 2b). Although the aging clocks are based on different gene contributors for different cell types (Supp Fig. 2c), we found that telomere shortening uniformly increased the predicted biological age in all three cell types with microglia affected the most (Fig. 2e, f). We next analyzed transcriptomic changes induced by telomere shortening in microglia. The WT and G3 Terc −/− samples were subclustered into three distinct transcriptional states (Fig. 2g). A significant shift of microglia transcriptomic states was observed in microglia from WT and G3 Terc −/− brains. Specifically, subcluster 3 (MG3) microglia in G3 Terc −/− animals was enriched (Fig. 2h). We next asked if telomere shortening mimics the transcriptomic change during normal aging in microglia. As expected, when we applied the aging clock to predict the biological age of MG3, a portion of MG3 was predicted to be significantly “older” than the other microglia, linking the molecular signatures of telomere shortening induced senescent microglia with those in chronically aging brain (Fig. 2i). Analyses of top marker genes of MG3 revealed genes associated with replication stress survival and senescence, such as Pold4 and Cdk5r1 (Fig. 2j) 33 , 34 . Upregulated pathways identified in MG3 by Ingenuity Pathway Analysis (IPA) included senescence pathway and production of IL-15, a known senescence-associated secretory phenotype (SASP) cytokine (Fig. 2k). We also examined expression changes of known SASP ligands and found 12 SASP ligands upregulated in MG3 (Fig. 2l). Single-cell regulatory network inference and clustering (SCENIC) predicted 10 up-stream transcriptional factors specific to MG3 (Fig. 2m). Remarkably, among the 10, CUX1 directly regulates replicative senescence, and ATF6 regulates morphological changes associated with senescence 35 , 36 . The intercellular signaling between microglia and neuronal cells was examined with CellChat. Using canonical neuronal markers, we identified 11 neuronal subtypes in our snRNA dataset (Supp Fig. 3a, b). Alongside 268 common signaling pathways from microglia to neurons, we identified 13 unique to MG3, including CD47, CNTN2, FGFR2, FN1, LPL, and somatostatin (Supp Fig. 3c, d). MG3 sends strong CD47 signal to granule cells, neuroblasts and pyramidal neurons (Supp Fig. 3e). Increased CD47 expression has been associated with replicative senescence in mouse lung fibroblast, and CD47 is a potential therapeutic target for multiple sclerosis 37 , 38 . Telomere shortening impairs oligodendrocyte maturation and disrupts normal myelination Loss of white matter volume is a well-documented phenomenon in the aging brain 39 . We next examined the changes induced by telomere shortening in oligodendrocyte lineage cells by combining and re-clustering the oligodendrocyte precursor cells (OPCs) and oligodendrocytes in the UMAP (Fig. 3a). We found that telomere shortening caused a striking shift from OG2 to OG1 (Fig. 3b). With OPC as a starting point, we used pseudotime analyses to examine the maturation process from OPC to OG. Telomere shortening appeared to shift the oligodendrocyte further away from the OPCs, consistent with an abnormal transformation of oligodendrocyte beyond normal maturation (Fig. 3c). Notably, genes associated with normal formation of myelin structures and neuroprotective effects, such as ANLN, SPOCK3, and APOD, were significantly reduced in OG1 (Fig. 3d) 40 – 42 . Analyses of the expression dynamics of ANLN, SPOCK3, and APOD revealed a similar pattern, where the expression levels increased during the differentiation from the OPCs and reduced further away from the starting point, suggesting a connection between the disruption of normal myelin formation and increased pseudotime telomere shortening (Fig. 3e). We then directly examined the effects of telomere shortening on oligodendrocytes. Immunostaining of corpus collosum revealed a significant reduction of ANLN + oligodendrocytes in the G3 Terc −/− animals (Fig. 3f, g). Moreover, a western blot of myelin basic protein (MBP) revealed a significant reduction in the cortex of G3 Terc −/− brains, compared to their WT counterparts, supporting the notion that telomere shortening leads to demyelination (Fig. 3h, i). Identification of DLK1 as a novel senescence associated ligand of microglia in human microglia model To extend the analyses from mouse microglia to human microglia and facilitate mechanistic studies, we developed an in vitro senescent microglia model, based on iPSC-derived microglia 43 . We applied a TERT inhibitor, BIBR1532, to iPSC cultures for 30 days to significantly reduce telomere length, and subsequentially differentiated them into human microglial-like cells (hMGLs) (Fig. 4a). After differentiation, etoposide was used to induce DNA damage and senescence in the hMGLs. Before microglial differentiation, we measured the relative telomere length by a qPCR-based approach and verified a significant reduction in telomere length in the iPSCs (Fig. 4b) 44 . Using unsupervised hierarchical clustering, we categorized cells treated with BIBR1532, etoposide, or both to four groups, based on the expression levels of canonical senescence markers, p16 and p21, detected by bulk RNA sequencing (Fig. 4c). We then compared the DEGs between hMGLs with high expression levels of p16 and p21 senescent hMGLs with those with low p16 and p21 levels (Supplementary table2). Pathway analysis demonstrated that interleukin signaling, and secretory activity were among the most dysregulated pathways in the senescent hMGLs (Fig. 4d). We then examined expression levels of intercellular cell signaling genes in association with p16 and p21 expression levels. To identify the signaling genes, we used the curated ligand-receptor dataset in the CellPhoneDB v2.0 package to select for the ligand genes 45 . The correlation between log fold changes in ligand gene expression and the levels of p21 and p16 revealed DLK1, a canonical ligand for NOTCH pathway, as the top ligand associated with these senescent markers (Fig. 4e, Supplementary Table 3). Weighted gene co-expression network analysis (WGCNA) identified 27 unique co-expression modules (Supp Fig. 4a). Among those, eight modules were significantly correlated to DLK1, p21, or p16 expression (Supp Fig. 4b). The modules significantly correlated to DLK1 (brown module and green module) were also significantly correlated with both p21 and p16, confirming a strong link between DLK1 expression and microglial senescence (Supp Fig. 4c, Supplementary Table 4). Further analyses revealed that the brown module is enriched with genes linked to hippo signaling pathway (Supp Fig. 4d), and the green module is enriched with genes linked to steroid hormone biosynthesis and complement system, which is a key factor in innate immune response (Supp Fig. 4e). In the adult brain, DLK1 expression regulates neurogenesis and is restricted to neural stem cells and niche astrocytes in neurogenic regions, such as the dentate gyrus 46 , 47 . However, when we examined if DLK1 is expressed in the senescent microglia in vivo , we detected low levels of DLK1 transcripts in over 60% of G3 Terc-/- microglia in our snRNA-seq data, but DLK1 was not found in WT microglia (Fig. 4f). To determine if DLK1 is expressed and secreted by senescent microglia in G3 Terc-/- mice, we treated G3 Terc-/- mice with a CSF1R antagonist (PLX 5622) to deplete most microglia 48 . Levels of sDLK1 protein in the cerebrospinal fluid (CSF) were quantified with an ELISA assay (Fig. 4g). In agreement with our snRNA-seq and our findings in hMGLs, levels of sDLK1 protein were elevated in the CSF of G3 Terc-/- mice. Moreover, depletion of microglia abolished the elevation of sDLK1 in in the CSF of G3 Terc-/- mice. Thus, senescent microglia are likely to be major source of elevated sDLK1 in vivo (Fig. 4h). Soluble DLK1 disrupts normal functions of oligodendrocyte lineage cells To investigate the role of sDLK1 on different brain cells, we applied AAV-sDLK1 (AAV-PHPeB) to overexpress sDLK1 in the mouse brain via intravenous injection (Fig. 5a, Supp Fig. 5a). Using a DLK1 ELISA, we confirmed successful induction of sDLK1 expression in the brain tissues 2 months after viral injection (Fig. 5b). To determine the cell type–specific effects of sDLK1 overexpression, we performed snRNA sequencing of hippocampus. Eight major cell types were identified after applying the same quality control standard we used for G3 Terc-/- animals (Supp Fig. 6). DLK1 expression was elevated in astrocytes and endothelial cells of DLK-AAV animals (Supp Fig. 5b). Interestingly, oligodendrocytes had the highest normalized DEG numbers (Fig. 5c, Supplementary Table 5). To explore the role of elevated sDLK1 in the G3 Terc-/- model, we first focused on the overlap of DEGs in both conditions (Fig. 5d). Among the DEGs with the same direction of change induced by telomere shortening and elevated sDLK1 level, genes involved in myelination and oligodendrocyte differentiation were strongly correlated (Fig. 5e). At the same time, among the top 500 differentially expressed gene, 50 genes were associated with abnormal myelination or diseases related to abnormal myelination, with key myelination genes downregulated in the brains with higher sDLK1 levels (Supp Fig. 5c, 5d). To further examine the effect of sDLK1 on the oligodendrocyte lineage cells, we analyzed the OPCs and identified five distinct OPC subclusters with OPC3 significantly enriched with cells from the brains injected with DLK1-AAV (Fig. 5f, g). When we looked at the marker genes defining OPC3, we found the top markers genes, such as Tns3 and Grin2b, that are associated with OPC differentiation and white matter alteration, respectively (Fig. 5h) 49 , 50 . GSEA revealed that the marker genes of OPC3 were negatively enriched with oligodendrocyte differentiation, indicating that DLK1 caused the emergence of an OPC population with abnormal proliferation and differentiation (Fig. 5i). To confirm the detrimental effects of sDLK1 on myelination, we measured the expression of MBP and MOBP in the animals treated with the AAVs (Fig. 5j). In agreement with our predictions from the snRNA data, protein levels of MBP and MOBP were significantly reduced in animals with higher sDLK1 expression, indicating a direct negative effect of sDLK1 on the myelination process (Fig. 5k, l). DLK1 directly affects calcium influx in neuronal cells We also examined the effect of DLK1 on neurons. Comparing the DEGs in G3 Terc-/- neurons and DLK-AAV neurons, we found that 297 of 746 upregulated DEGs in DLK-AAV neurons were also upregulated in the G3 Terc-/- neurons (Fig. 6a). Calcium-signal-related pathways were the top pathways predicted by the DEGs shared between DLK1-AAV and G3 Terc-/- neurons (Fig. 6b). GSEA analysis showed that the overlapped DEGs were enriched for calcium ion transmembrane transport but negatively enriched for calcium ion binding, leading us to hypothesis that DLK1 induces an abnormal calcium signaling in the excitatory neurons (Fig. 6c, d). To further determine the function of DLK1 and its potential effects on calcium activities in neurons, we established an in vitro neuronal culture with a calcium reporter. We used the GCaMP lentivirus to transfect maturing neurons at day 7 of differentiation and treated the neurons with recombinant human DLK1 for 7 days starting at day 14 to model a chronic effect of DLK1 (Fig. 6e). With single-neuron tracing of the calcium signaling, we looked at the calcium activities in the neurons treated with either vehicle or recombinant human DLK1 (Fig. 6f, g). DLK1 had no effect on the synchronized firing rate of the neuronal network (Fig. 6h). No changes were noted in firing amplitude or firing rate of the calcium activities in the neuron treated with DLK1 (Fig. 6i, j). However, when we depolarized the neurons with KCl, we observed a significant increase in the peak calcium signal and a delayed rise to the peak (Fig. 6k-n). This observation confirmed the effect of DLK1 predicted by our in vivo data, and notably, this change in neuronal calcium activity is consistent with the change of calcium signal in aged rat CA1 neurons 51 . At the same time, we analyzed the transcriptomic changes of the neurons induced by DLK1 treatment (Supp Fig. 7a). We found a predominant association of significant DEGs with synaptic regions, indicating that an elevated DLK1 signal disrupts normal synaptic structure and function (Supp Fig. 7b). Intriguingly, we identified a subset of DEGs induced by DLK1 treatment also implicated in incipient AD patients (Supp Fig. 7c) 52 . Discussion In this study, we demonstrated that telomere shortening, an evolutionarily conserved aging mechanism, causes pathological aging phenotypes within the mouse brain. Moreover, we comprehensively investigated the underlying mechanisms by which telomere shortening exerts detrimental effects on the integrity and functionality of the brain. Telomere shortening results in a premature accumulation of lipofuscin, a prominent hallmark of aging in the brain. Lipofuscin is a complex mixture primarily of oxidized protein and lipid remnants that resists cellular degradation and elimination and tends to accumulate in post-mitotic cells, such as neurons 26 . Formation of lipofuscin arises from the impaired degradation of damaged mitochondria by lysosomes. Intriguingly, the connection between lipofuscin and telomere shortening was made in a recent study where telomere shortening in the leukocyte was associated with the accumulation of lipofuscin in the serum 53 . Conventionally, the accumulation of lipofuscin is associated with the mitochondrial-lysosomal axis of aging 54 . However, we found that telomere shortening actively facilitates the accrual of lipofuscin, necessitating further investigations into the interplay of telomere and mitochondrial-lysosomal functions. Telomere shortening was also deleterious to memory, thereby corroborating an additional phenotype commonly observed in aged humans and mice. Our in-situ hybridization data showed a significant reduction in telomere length in neuronal and glial cells of the G3 TERC −/− animals. Thus, it is imperative to investigate the mechanistic connection between telomere shortening and memory deficit, specifically to determine if the memory deficit is attributed to an inherent neuronal effect or a secondary glial effect. TERC −/− animals are characterized by truncated telomeres at the embryonic stage, resulting in shorter telomeres in all somatic cells than WT animals. Consistently, we found heterogeneous transcriptional responses to telomere shortening across all identified cell types, but individual cell types had different susceptibilities. We found that excitatory neurons experienced the most pronounced impact, which contradicted our original hypothesis. We had expected neurons to be less susceptible to telomere shortening as the neuronal cells progress into the post-mitotic stage during early development and undergo fewer cell division cycles than glial cells. This finding prompted us to explore the non-cell-autonomous mechanism by which telomere shortening affects neuronal transcriptome as glial cells endure more severer telomere attrition stress due to their inherent capability for proliferation. The small overlap between differentially expressed genes in the major glial cells reveals that telomere shortening induces diverse effects on the glial population. Remarkably, amidst the small overlap of the differentially expressed genes, AC149090.1 was identified as a commonly upregulated gene in microglia, astrocytes, and oligodendrocytes. AC149090.1 was identified as a gene upregulated by aging in the hippocampus and neurogenic regions of the brain, and a major contributor to a transcriptome-based aging clock for naturally aged brain cells 32 , 55 . Although the mechanism by which AC149090.1 is connected to aging remains unclear, our transcriptomic data suggest a connection to telomere shortening and links natural aging in the glial cells to telomere length. The neuroimmune system, along with its critical constituent microglia, has drawn substantial attention in the field of research focusing on the pathophysiology of neurodegenerative disorders 56 . Impairments and dysregulations of microglial functions have been implicated in neurodegenerative disorders ranging from AD to frontotemporal dementia 57 . Nevertheless, a clear understanding of the underlying factors that drive the initial deterioration of microglia remains elusive. Unlike the other cell types in the CNS, the microglia population derives from microglia progenitors that originated from the yolk sac during early embryonic development 58 . The progenitors undergo extensive replication cycles to achieve the adult microglia population during embryonic and early postnatal stage 59 . Despite the common belief that microglia are long-lived cells with slow turnovers, new evidence has shown that the microglia population undergoes several rounds of proliferation-mediated renewal during the lifetime in both mouse and human 60 . We hypothesized that telomere attrition induced by microglia self-replication, along with its associated replicative senescence, plays a crucial role in disrupting microglial homeostasis and characterized the microglia with shortened telomeres in our TERC model. Our snRNA-seq data showed a striking transformation in the microglia state upon telomere shortening, resulting in the emergence of a distinct subpopulation exhibiting a prominent senescent signature. Senescent microglia exhibited robust upregulation of IL15, a well-established SASP cytokine, and interferon alpha. Both are key hallmarks of senescence 61 . We detected a strong upregulation of other SASP genes and genes known as pathology-associated microglia markers within the senescent microglia population. Through the transcriptome-based aging clock, we demonstrated the physiological relevance of the microglial senescence induced by telomere shortening and its potential as a tool to study microglia in brain aging. A growing body of evidence supports the connection of microglia senescence and neurodegenerative diseases 20 , 62 , 63 . However, no investigation has examined the intricate transcriptomic changes within senescent microglia and how senescent microglia exert deleterious effects on its surroundings. In our in vitro model of microglial senescence, p16 and p21 were upregulated, indicating a mature senescence phenotype. Importantly, the manifestations of senescence, particularly the SASP, exhibit substantial heterogeneity across different cell and tissue types 64 , 65 . With our in vitro model, we gained a comprehensive insight into the transcriptomic alterations within senescent microglia. Given that p21 and p16 represent differential mechanisms in the establishment and sustenance of senescence, we identified genes with positive correlations with either p21 or p16 66 . Our main objective was to elucidate the mechanism by which senescent microglia contribute to brain aging, so we sought to identify genes involved in intercellular signaling among those correlated with p21 and p16. To our surprise, along with cytokines known to be involved in the SASP, we identified that DLK1 expression was strongly correlated with both p21 and p16. Dlk1 is a single-pass transmembrane protein containing a TACE-mediated cleavage site and is a noncanonical member of the Delta-Notch signaling pathway 67 . Dlk1 expression is high during embryonic development but is restricted in adulthood by genomic imprinting 68 , 69 . The aberrant elevation of DLK1 expression in the senescent microglia might be attributed to dysregulation of the imprinted DLK1-DIO3 locus, as miRNAs in the DLK1-DIO3 increase in adipose-derived stem cells undergoing replicative senescence 70 . Using a microglia depletion regimen, we confirmed that the expression level of DLK1 is increased in our G3 TERC −/− animals and that the increase unequivocally originated from the microglia. Our snRNA-seq data also point to senescent microglia as the main source of DLK1 expression, reinforcing the link between DLK1 expression and microglial senescence. Although there has been no evidence showing expression of DLK1 in microglia, our data established DLK1 as a novel member of the microglial SASP. The involvement of oligodendrocyte lineage cells in brain aging and the development of age-dependent neurodegenerative disorders, including diseases not primarily associated with myelination defects (e.g., AD and PD), has attracted increasing attention in recent years 71 . Increased DLK1 signaling from senescent microglia may disrupt the normal myelinating functions of the oligodendrocytes. For example, RHEB-knockout-induced upregulation of DLK1 from neurons impairs oligodendrocyte differentiation and myelination 72 . Indeed, we observed loss of myelination and disrupted oligodendrocyte differentiation in the G3 Terc −/− mouse brains. However, the proliferative nature of OPCs renders it inconclusive whether the myelination phenotype is attributed to increased DLK1 signaling or to the cell-autonomous effect of telomere shortening. After overexpressing the soluble form of DLK1 in mouse brain, we confirmed that the soluble form of DLK1 causes defects in myelination and oligodendrocyte differentiation. This finding provides another mechanism by which DLK1 affects normal myelination and evidence that senescent microglia can induce neurodegenerative pathologies in other cell types through a unique secreting profile. With our in vivo and in vitro models, we showed that the increase of DLK1 signaling in the mouse also altered Ca2 + signaling pathways in the neurons, which provides a potential explanation for the memory deficits in the G3 TERC −/− animals. Our finding that chronic treatment of DLK1 dysregulated the expression of synaptic genes as well as AD-associated genes further supports the involvement of DLK1 in the mechanism underlying the increased susceptibility of neurodegeneration associated with aging. The mechanisms underlying pathological brain aging and associated neurodegenerative disorders remain largely elusive, especially their involvement in the disease development before disease manifestations such as misfolded proteins. Although telomere length is only one of many metrics of aging, we found that telomere shortening and microglia senescence induced by it can give rise to key phenotypes of pathological brain aging, proving its potential as a tool to further understand the alterations caused by age and early disease development. While this study revealed the potential mechanisms by which telomere shortening causes pathological phenotypes such as myelination defects and altered calcium signaling of neurons, we did not focus on any disease-specific phenotypes. The logical next step is to combine the telomere shortening model with disease-specific risk genes, such as APOE or TREM2, to further examine the mechanisms underlying the age dependency of neurodegenerative disease and potential methods for early disease diagnosis and intervention. Method Mice Male and female G0 TERC -/- breeders were purchased from the Jackson Laboratory (The Jackson Laboratory, 004132) and bred for three generations to generate G3 TERC -/- animals. Age-matched WT C57BL6/J mice from the NIA. Animals were housed no more than five per cage in a pathogen-free barrier facility at 21–23°C with 30–70% humidity on a 12-hour light/dark cycle. The animals were given ad libitum access to food and water. Both male and female animals were used for histological analysis. Only female animals were used for behavioral and biochemical analyses. Animals were 8–9 months of age when used for histological analysis. Animals underwent behavioral testing at 9 months of age and had not been used previously for any other experiments. At 10 months of age, the same mice were used for pathology and RNA-seq studies. For microglia depletion regimen, PLX5622 was given to the animals with food at 8 months of age for 2 weeks before the behavioral experiment and the subsequent pathology studies. All mouse protocols were approved by the Institutional Animal Care and Use Committee, University of California, San Francisco, and Weill Cornell Medicine. Brian tissue collection Mice were euthanized using Fatal-Plus (pentobarbital sodium) and transcardially perfused with PBS. The brains were hemisected, and the hemibrains were flashed-frozen at -80°C or fixed in 4% paraformaldehyde for 48 hours, which was then followed by 48-hour 30% sucrose infiltration at 4°C. The fixed hemibrains were sectioned into 30-µm slices using freezing microtome (Leica) and stored at -20°C in cryoprotectant before staining. Contextual fear conditioning Mice were tested for contextual fear learning and memory using sound-attenuated chambers (Med Associates, VT, USA). During fear acquisition, mice freely explored a novel environment. After a 2-min habituation, mice were exposed to a 2-s foot shock (0.5 mA) followed by a 60-s interstimulus interval for a total of three shocks. At 24 hours after fear acquisition, hippocampal-dependent fear memory was measured by recording the percent total time spent freezing in a 5-min context test (Video Freeze software). Novel object recognition Mice were habituated to opaque open field arenas (40 × 40 cm) by allowing them to freely explore the arena for two 10-min trials spaced on the 2 days leading up to the object recognition test. On the test day, two identical objects (plastic geometric object) were placed in the center of each arena. Mice were allowed to freely explore the objects for a 15-min trial. After 24 hours, one of the geometric objects was replaced by a novel object of a different shape and color. The animal was then allowed to freely explore the new objects for a 15-min trial. Video recording and tracking (Ethovision v15, Noldus) were used to track the movement of animals. The time mice spent exploring each object was determined automatically by the Ethovision software. Preference was calculated based on the total time an individual mouse spent exploring both objects. Telomere length measurement Telomere length labeling was done using TelC-Cy3 (PNA Bio Cat. No. F1002), according to the manufacturer’s protocol with modifications. PFA-fixed brain sections were incubated in 1% Tween-20 in PBS for 1 minute, followed by boiling in antigen unmasking solution (Vector Cat. No. H-3300) at 90°C for 35 minutes. After cooling for 5 minutes, the sections were rinsed with PBS. In a PCR tube, 50 µL of TelC-Cy3 solution (1:250 dilution of 250 µg/ml formamide TelC-Cy3 stock solution into PNA staining solution (70% formamide, 10 mM Tris pH 7.5, 0.5% B/M Blocking Reagent solution (Sigma Cat. No. 11096176001, prepared according to manufacturer’s instructions)) was added to a brain section and heated at 84 o C for 5 minutes. The sections were left overnight at room temperature and protected from light. Thereafter, the sections were washed twice for 15 minutes with PNA Wash Solution (70% formamide, 10 mM Tris pH 7.5). Finally, the sections were washed with 0.01% Tween-20 in PBS. To identify TelC-Cy3 signal in microglia, Iba-1 labeling was performed according to a published procedure, and counterstained with DAPI 73 . Brain sections were mounted and imaged using Zeiss LSM880 inverted scanning confocal microscope (Carl Zeiss Microscopy, Thornwood, New York) with 10 series of 1 µm sections. Z-max projections were analyzed; TelC-Cy3 signal was quantified using a published method 74 . Briefly, using FIJI 75 , the background-corrected TelC-C3 signal in the nucleus of Iba-1 + microglia was calculated by subtracting background from the average of top 20% of red pixels. For each genotype, four male and four female mice were analyzed, with three images taken per animal, and 9–20 Iba1 + cells per animal were quantified. On average, WT animals were 8.5 months old, and G3 animals were 8.62 months old. Nuclei isolation from frozen mouse hippocampi Five female animals of each genotype were used for snRNA-seq. Nuclei isolation was done as described, with modifications 31 , 76 . In brief, mouse hippocampi were dissected from frozen brain tissue before placed in 1,500 µL of Sigma nuclei PURE lysis buffer (Sigma, NUC201-1KT). Hippocampal samples were homogenized with a Dounce tissue grinder (Sigma, D8938-1SET) with 20 strokes using pestle A, followed by 15 strokes using pestle B. After homogenization, the tissue was filtered through a 35-µm cell strainer, followed by centrifugation at 600g for 5 minutes at 4°C. The resulting pellet was washed three times with 1 mL of PBS containing 1% bovine serum albumin (BSA), 20 mM DTT, and 0.2 UµL − 1 recombinant RNase inhibitor. The nuclei sample were then centrifuged at 600g for 5 minutes at 4°C and subsequently resuspended in 800 µL of PBS containing 0.04% BSA and 1X DAPI, followed by fluorescence-activated cell sorting (FACS) to remove cell debris. Droplet-based snRNA-seq snRNA-seq libraries were prepared with Chromium Single Cell 3’ Reagent kits (v3; 10X Genomics, PN-1000075). The libraries were sequenced on a NovaSeq 6000 sequencer (Illumina) with 100 cycles. Analysis of droplet-based snRNA-seq data RNA reads sequenced from the snRNA-seq library were aligned to mm10 genome using Cell Ranger software (v.3.1.0; 10X Genomics) to generate raw gene counts. Reads mapped to pre-mRNA were counted to include un-spliced nuclear transcripts. Cell barcodes were called using Cell Ranger 3.1.0 default parameters. We further removed genes expressed in no more than three cells, cells with unique gene counts over 8,000 or less than 100, and cells with more than 5% mitochondrial reads. High-confidence doublets were removed from individual samples using DoubletFinder 29 . Sample integration, normalization and clustering were done with the Seurat package v3.0.1 77 . In brief, integration anchors were computed for the datasets using the top 30 principal components, and the datasets were integrated using the anchor set. The integrated dataset was scaled by the total library size multiplied by a scale factor (10,000) and transformed to log space. Principle component analysis was done on the highly variable genes, and t-distributed stochastic neighbor embedding was run on the top 30 principal components. k.param was computed using the top 30 principal components, and cells clusters were identified using the FindCluster function with a resolution of 0.02. Uniform Manifold Approximation and Projection (UMAP) was run on the top 30 principal components. Cell-type labels were assigned to teach cluster using statistical enrichment for sets of marker genes and performing manual evaluation of gene expression for small sets of known marker genes. The dataset was then split into individual datasets based on cell-type identity. Differential gene expression analysis was done using the FindMarkers function with MAST as the method used. Ingenuity Pathway Analysis (IPA) and MSigDB gene annotation database were used to identify gene ontology and pathways enriched in the differentially expressed genes (DEGs). To address multiple testing, Benjamini-Hochberg approach was used to generate corrected false discovery rate (FDR). For trajectory analysis, the dataset was converted to a monocle3 object and analyzed using the Monocle3 package 78 , 79 . Pseudotime analysis was done on the microglia data using WT microglia cluster as the origin. To evaluate cellular interactions between microglia and neuronal cells, we applied CellChat (v.1.6.1) to examine the ligand-receptor interactions inferred by the dataset 80 . Normalized integrated data was used as input, and the analysis followed the CellChat tutorial with default parameters and CellChatDB.mouse as the interaction database. Ligand-receptor interactions were plotted using netVisual_chord_gene and netVisual_bubble functions. Bulk RNA-seq Primary microglia and neurons were disassociated from the tissue plates using Accutase (ThermoFisher, 00-4555) and collected by 3-minute centrifugation at 500 × g. mRNA was extracted according to the manufacturer’s protocol (ZYMO Research, Quick -RNA Microprep Kit). Isolated RNA was sent to Novogene Co. for quality control, library preparation, and sequencing. RNA-seq read mapping was performed using the STAR program and with the GENCODE GRCh38.p13 as reference. The read count table was generated with the Rsubread package. Differential gene expression was calculated with the DEseq2 package. Lipofuscin imaging and quantification PFA-fixed hemibrain sections were washed with 0.5% Triton in PBS and mounted for imaging on Keyence BZ-9000 inverted epifluorescence microscope (Keyence, Osaka, Japan). The entire hemibrain section was scanned at 10x magnification using Cy3 channel, and the individual images were stitched together using Keyence BZ-X Analyzer software. All images were thresholded uniformly, and a 400x400 pixel square was cropped in primary somatosensory cortical region for endogenous fluorescence quantification. Using “Analyze Particles” in FIJI, the area of lipofuscin particles was quantified. Four mice for WT (two males and two females, average age: 9 months old) and three for G3 (one male and two females, average age: 9.3 months old) were quantified, with eight sections per mouse. Average numbers of puncta were 93 per animal for Terc +/+ and 168 for G3 Terc −/− . Cerebrospinal fluid extraction CSF extraction procedure was described in detail 81 . Briefly, mice were deeply anesthetized using Fatal-Plus (pentobarbital sodium) and positioned prone over a 15-mL conical tube to place the cervical spine in flexion. The mouse occiput was palpated to locate the cisternal magna, and a 30G insulin needle was punctured and advanced less than 4mm deep. CSF was collected by slowly pulling the syringe plunge. Approximately 15 µL CSF was collected from each animal. CSF was spun down at 600g for 5 minutes in 4⁰C. Supernatant was collected, flash-frozen on dry ice and stored at -80⁰C until analysis. ELISA and multiplex bead-based immunoassay Extracted CSF was diluted in PBS and assayed using the Mouse Pref-1/DLK-1/FA1 ELISA Kit (Invitrogen, EM66RB), according to the manufacturer’s instructions. Other SASP cytokines were measured with a MILLIPLEX MAO mouse cytokine/chemokine magnetic bead kit (Millipore) using a MAGPIX system. Human iPSC differentiation into microglia and senescence induction iPSCs from an adult female with no known diseases were purchased from WiCell (UCSD072i-1-3) for the differentiation of microglia. Cells were cultured in an incubator at 37°C with 5% CO 2 . iPSCs were passed three times for expansion purpose before the start of experiments. Bibr1532 (60 µM; Millipore Sigma, 508839) was added to iPSCs culture after starting the experiments, and the iPSCs were cultured in BIBr1532-containing medium for 30 days before microglia differentiation. iPSCs were passed at a ratio of 1:12 every time the culture reaches 80% confluency. Bibr1532 was withdrawn after 30 days, and the iPSCs were differentiated into macrophage progenitors via a 10-day protocol, as described 43 . The macrophage progenitors were then further matured into microglia-like cells via a 13-day protocol with macrophage colony–stimulating factor and interleukin 34 in RPMI supplemented with 10% FBS as described 43 . Differentiation quality control was conducted through IBA1 (Abcam, ab5076) and TMEM119 (Sigma, HPA051870) immunocytochemistry. Matured microglia were treated with 50 µM Etoposide (Millipore Sigma, E1383) for 24 hours and then recovered for 24 hours before subsequent experiments. Human iNeuron differentiation Human iNeurons are differentiated as described, where WTC11, iPSCs from a male, were engineered for inducible expression of Ngn2 from a transgene integrated in the AAVS1 locus 81 , 82 . The differentiation process followed the published protocol 81 . Briefly, the iPSCs were pre-differentiated to neuronal precursor cells in pre-differential medium containing doxycycline. On day 0, neuronal precursor cells were replated in maturation medium containing doxycycline. Doxycycline was removed from the maturation medium on day 7. Thereafter, one-half of the maturation medium was replaced with fresh medium weekly until the cells were collected. Immunocytochemistry iPSC-derived microglia and neurons on coverslips were fixed in 4% paraformaldehyde in PBS for 30 minutes and then washed three times for 5 minutes each with PBS. The cells were then permeabilized with 0.1% Triton X-100 in PBS (PBS-T) before blocking with 5% normal donkey serum (NDS) in PBS-T for 1 hour at room temperature. Cells were washed three times with PBS after blocking. Primary antibodies were added in PBS-T and incubated at 4°C overnight. The secondary antibodies were added to the cells for 2 hours at room temperature after washing with PBS for three times. DAPI was added to the cells for nuclei labeling for 10 minutes before visualization. Images were acquired with a laser scanning confocal microscope (Zeiss, LSM 700) using a 63X oil objective or a Apotome3 microscope (Zeiss, Axio Observer). The image acquisition settings were chosen to prevent most of the brightest pixel intensities from reaching saturation. Calcium imaging iPSCs were differentiated into neurons on coverslips as described. Neurons were transduced with hSyn-jGCaMP8f lentivirus on D6. The lentiviral construct was made using the third generation lentiviral plasmid FUGW (Addgene, 14883), where the Pacl + EcoRI fragment was replaced by the hSyn-GCaMP8f fragment 83 . The medium containing the lentiviral construct was replaced by fresh medium on Day 7. DLK1 treatment started on Day 14, and the cells were imaged and sequenced on Day 21. At the time of imaging, the coverslip was gently washed and placed into a glass-bottom chamber (Warner Instruments, RC-26G) containing Ca 2+ imaging buffer (20 mM HEPES, 119 mM NaCl, 5 mM KCl, 2 mM MgCl 2 , 30 mM glucose, 2 mM CaCl 2 , pH 7.2–7.4). The temperature was maintained at 37°C by a dual chamber heat controller (Warner Instruments, TC-344C). Fluorescence time-lapse images were collected on a microscope (Nikon, FN1) using a 60X, 1.0 NA objective (Nikon, CFI APO 60XW NIR) and a C-FL GFP filter cube. An X-CITE LED illuminator (Nikon) was used for excitation. Images were collected using an ORCA-Fusion CMOS camera (Hamamatsu) with 4×4 binning (576×576-pixel resolution, 16-bit grayscale depth, 0.43 µm/pix) and NIS-Elements AR software (Nikon). Exposure time was set to 20 milliseconds. For spontaneous activities, eight fields per coverslip were acquired at 30 Hz for 2 minutes. After imaging spontaneous activity, one filed per coverslip was imaged for 10 minutes at 20 Hz to minimize photo-bleaching immediately after 50 mM KCl perfusion. Generation of AAV The extracellular domain of mouse Dlk (aa residues 1–170) (sDLK1), followed by a T2A and a GFP gene, was made under a CAG promoter and placed into an AAV-PHPeB plasmid. The AAV-PHPeB virus was made at the University of Pennsylvania ( https://gtp.med.upenn.edu/vector-core ). The intravenous injection of AAV particles encoding the mouse sDLK1 was performed, and a control PHP-eB AAV encoding GFP only was applied as a negative control in C57BL/6 mice. 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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-5014333","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":352686002,"identity":"aede4f7c-5cd8-4c06-a236-36d2d06e1f59","order_by":0,"name":"Li 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Medicine","correspondingAuthor":false,"prefix":"","firstName":"Alice","middleName":"","lastName":"Giani","suffix":""},{"id":352686007,"identity":"7ecb3cfa-d316-4a43-844d-0ffd1a23039c","order_by":5,"name":"Eileen Torres","email":"","orcid":"","institution":"Weill Cornell Medicine","correspondingAuthor":false,"prefix":"","firstName":"Eileen","middleName":"","lastName":"Torres","suffix":""},{"id":352686008,"identity":"d74f06de-bd01-49af-b3d7-035c42d6353f","order_by":6,"name":"Lihong Zhan","email":"","orcid":"","institution":"Gladstone Institutes","correspondingAuthor":false,"prefix":"","firstName":"Lihong","middleName":"","lastName":"Zhan","suffix":""},{"id":352686009,"identity":"4484e4e5-fbce-4f76-92bb-5d18de2bbeb1","order_by":7,"name":"Pearly Ye","email":"","orcid":"","institution":"Weill Cornell 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Medicine","correspondingAuthor":false,"prefix":"","firstName":"Daphne","middleName":"","lastName":"Zhu","suffix":""},{"id":352686013,"identity":"1b3500c1-7184-4b70-b7dd-97e67c7e135e","order_by":11,"name":"Xinran Tong","email":"","orcid":"","institution":"Weill Cornell Medicine","correspondingAuthor":false,"prefix":"","firstName":"Xinran","middleName":"","lastName":"Tong","suffix":""},{"id":352686014,"identity":"9992adb3-9428-42a5-941a-ba42176d075c","order_by":12,"name":"Deepak Srivastava","email":"","orcid":"","institution":"Gladstone Institutes","correspondingAuthor":false,"prefix":"","firstName":"Deepak","middleName":"","lastName":"Srivastava","suffix":""},{"id":352686015,"identity":"cbb6c4d7-9c39-43ac-927b-30a965576f72","order_by":13,"name":"Christina Theodoris","email":"","orcid":"https://orcid.org/0000-0003-1658-1447","institution":"Gladstone Institutes","correspondingAuthor":false,"prefix":"","firstName":"Christina","middleName":"","lastName":"Theodoris","suffix":""},{"id":352686016,"identity":"f8ae6f9f-74ce-4ced-b7dd-422897036971","order_by":14,"name":"Shiaoching Gong","email":"","orcid":"","institution":"Weill Cornell Medicine","correspondingAuthor":false,"prefix":"","firstName":"Shiaoching","middleName":"","lastName":"Gong","suffix":""},{"id":352686017,"identity":"3269c829-d466-4fc1-8594-9d981866067f","order_by":15,"name":"Mingrui Zhao","email":"","orcid":"","institution":"Weill Cornell Medicine","correspondingAuthor":false,"prefix":"","firstName":"Mingrui","middleName":"","lastName":"Zhao","suffix":""}],"badges":[],"createdAt":"2024-09-01 20:45:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5014333/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5014333/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":69558892,"identity":"abd56951-2b4a-44f7-a83a-0e5d0a093e6d","added_by":"auto","created_at":"2024-11-21 16:03:38","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":310540,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTelomere shortening exacerbates brain lipofuscinosis and memory deficit. a.\u003c/strong\u003e Schematic showing the generation of G3 TERC\u003csup\u003e-/-\u003c/sup\u003e mice. \u003cstrong\u003eb.\u003c/strong\u003e Representative immunofluorescence images of telomere length measurements by \u003cem\u003ein situ\u003c/em\u003e hybridization with TelC-Cy3 (white), DAPI (blue), and anti-IBA1 (green). Zoomed in images on the top right and TelC-Cy3-only image on the bottom right. \u003cstrong\u003ec.\u003c/strong\u003e Quantification of TelC-Cy3 fluorescence intensities in all cells (IBA1+ \u0026amp; IBA1-), showing reduced telomere length in the G3 Terc\u003csup\u003e-/-\u003c/sup\u003e brains. Results are presented as mean intensity measurements from eight animals. Data were analyzed by two-tailed unpaired \u003cem\u003et\u003c/em\u003e-test. *\u003cem\u003ep\u003c/em\u003e=0.0036 \u003cstrong\u003ed.\u003c/strong\u003e Quantification of TelC-Cy3 fluorescence intensities in IBA1+ cells. Results are presented as mean intensity measurements from eight animals. Data were analyzed by two-tailed unpaired \u003cem\u003et\u003c/em\u003e-test. *\u003cem\u003ep\u003c/em\u003e=0.016 \u003cstrong\u003ee. \u003c/strong\u003eRepresentative autofluorescence images showing lipofuscin in the primary somatosensory cortex. \u003cstrong\u003ef.\u003c/strong\u003e Quantification of lipofuscin puncta counts. Results are presented as mean puncta counts from 24–32 sections. Data were reported as box \u0026amp; whisker plot showing min to max and analyzed by unpaired \u003cem\u003et\u003c/em\u003e-test. ****\u003cem\u003ep\u003c/em\u003e\u0026lt;0.0001. \u003cstrong\u003eg.\u003c/strong\u003e Quantification of area covered by lipofuscin puncta. Results are presented as mean puncta area from 24–32 sections. Data were reported as box \u0026amp; whisker plot showing min to max and analyzed by unpaired \u003cem\u003et\u003c/em\u003e-test. ****\u003cem\u003ep\u003c/em\u003e\u0026lt;0.0001. \u003cstrong\u003eh.\u003c/strong\u003e Quantification of lipofuscin puncta intensities. Results are presented as mean puncta intensity measurements from 24–32 sections. Data were reported as box \u0026amp; whisker plot showing min to max and analyzed by unpaired \u003cem\u003et\u003c/em\u003e-test. **\u003cem\u003ep=\u003c/em\u003e0.0077. \u003cstrong\u003ei.\u003c/strong\u003e Diagram showing the setup for contextual fear conditioning. \u003cstrong\u003ej.\u003c/strong\u003e Percentage of freezing time the animals spent on the training day. The data show a change of animal behavior at baseline and after receiving shocks. No significant difference was observed. Data were reported as mean ± s.e.m. and analyzed by two-way ANOVA. \u003cstrong\u003ek.\u003c/strong\u003e Percentage of freezing time the animals spent on the test day. G3 Terc\u003csup\u003e-/-\u003c/sup\u003e animals showed a reduction in the percentage time. Each dot represents an animal. Data were reported as box \u0026amp; whisker plot showing min to max and analyzed by unpaired \u003cem\u003et\u003c/em\u003e-test. **p=0.0057. \u003cstrong\u003el.\u003c/strong\u003e Diagram showing the setup for novel object recognition test. \u003cstrong\u003em.\u003c/strong\u003e Percentage of time animals spent around one of two objects on the training day. Each dot represents an animal. Data were reported as box \u0026amp; whisker plot showing min to max and analyzed by unpaired \u003cem\u003et\u003c/em\u003e-test. \u003cstrong\u003en.\u003c/strong\u003e Percentage time the animals spent around the novel object. Each dot represents an animal. Data were reported as box \u0026amp; whisker plot showing min to max and analyzed by unpaired \u003cem\u003et\u003c/em\u003e-test. *\u003cem\u003ep\u003c/em\u003e=0.0273\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5014333/v1/c78cd81c8bbfbc3eb617d4d0.jpeg"},{"id":69560178,"identity":"30a07782-f062-4673-983d-3ba7c78907f1","added_by":"auto","created_at":"2024-11-21 16:11:36","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":359767,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTelomere shortening accelerates aging in glial cells and enhances senescence signaling in microglia. a.\u003c/strong\u003e Uniform Manifold Approximation and Projection (UMAP) plots showing eight major cell types identified in the mouse hippocampus. \u003cstrong\u003eb.\u003c/strong\u003e Normalized differentially expressed gene numbers in different cell types. The result shows the number of differentially expressed gene per 1000 UMI detected in each cell type. \u003cstrong\u003ec.\u003c/strong\u003e Venn diagram showing numbers and overlaps of DEGs in oligodendrocyte, astrocyte, and microglia. DEGs common among the three cell types are listed in the rectangle on the right. \u003cstrong\u003ed.\u003c/strong\u003e Heatmap comparing the average log fold-change of the common DEGs in astrocytes, oligodendrocytes, and microglia in G3 Terc\u003csup\u003e-/- \u003c/sup\u003eto WT mice. \u003cstrong\u003ee.\u003c/strong\u003e Ridge plot of the predicted chronological ages for oligodendrocyte, astrocyte, and microglia in G3 Terc\u003csup\u003e-/-\u003c/sup\u003e and WT mice. \u003cstrong\u003ef.\u003c/strong\u003e Point plot showing the mean and standard deviation of the predicted age of oligodendrocytes, astrocytes, and microglia in WT and G3 Terc \u003csup\u003e-/-\u003c/sup\u003e mice. \u003cstrong\u003eg.\u003c/strong\u003e UMAP plot showing a shift from cluster 2 (red) to cluster 3 (blue) caused by G3 Terc\u003csup\u003e-/-\u003c/sup\u003e. \u003cstrong\u003eh.\u003c/strong\u003e Bar plot showing the composition of microglia clusters. Data of each cluster were analyzed by Kruskal Willis test. **\u003cem\u003ep\u003c/em\u003e=0.0013; *\u003cem\u003ep\u003c/em\u003e=0.011 \u003cstrong\u003ei.\u003c/strong\u003e Density plot showing the predicted age of different microglia clusters. The red arrow points to a younger microglia population, and the blue arrow points at an aged microglia population. \u003cstrong\u003ej.\u003c/strong\u003e Volcano plot of marker genes of microglia cluster 3 (MG3). Red and blue dots represent genes with a log2FC \u0026gt; 0.1 or \u0026lt; -0.1, respectively. All other genes are colored gray. Top differential genes are labeled. \u003cstrong\u003ek.\u003c/strong\u003e Top canonical pathways identified by Ingenuity Pathway Analysis that are associated with DEGs in MG3. \u003cstrong\u003el.\u003c/strong\u003e Heatmap showing the SASP genes upregulated in MG3. \u003cstrong\u003em. \u003c/strong\u003eHeatmap showing the regulon activity predicted by SCENIC in individual microglia subclusters.\u003c/p\u003e","description":"","filename":"image2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5014333/v1/86975a2fbf1923524a50cbf5.jpeg"},{"id":69558888,"identity":"f811b4a0-6214-4cca-9c81-867533cbd9f3","added_by":"auto","created_at":"2024-11-21 16:03:36","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":302948,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTelomere shortening impairs oligodendrocyte maturation and disrupts normal myelination.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea. \u003c/strong\u003eUMAP plot showing a shift from OG2 (blue) to OG1 (red) caused by G3 Terc\u003csup\u003e-/-\u003c/sup\u003e. \u003cstrong\u003eb.\u003c/strong\u003e Bar plot showing the composition of oligodendrocyte lineage cell clusters. Data are reported as mean ± s.e.m. and analyzed by two-way ANOVA. \u003cstrong\u003ec. \u003c/strong\u003ePseudo-time trajectory depicting the development of oligodendrocyte from oligodendrocyte precursor cells. \u003cstrong\u003ed. \u003c/strong\u003eVolcano plot of marker genes of OG1. Red and blue dots represent genes with a log2FC \u0026gt; 0.15 or \u0026lt; -0.15, respectively. All other genes are colored gray. Top differential genes are labeled. \u003cstrong\u003ee.\u003c/strong\u003e Expression dynamics of Anln, Apod, and Spock3 as a function of pseudo-time.\u003cstrong\u003e f.\u003c/strong\u003e Representative images of Anln-positive oligodendrocyte in corpus collosum. Each dot represents an animal. Data were reported as box \u0026amp; whisker plot showing min to max and analyzed by unpaired \u003cem\u003et\u003c/em\u003e-test. \u003cstrong\u003eg.\u003c/strong\u003e Quantification of Anln-positive oligodendrocyte in corpus collosum (n = 4). \u003cstrong\u003eh.\u003c/strong\u003e Western blot of MBP in the cortex. \u003cstrong\u003ei.\u003c/strong\u003e Quantification of MBP signal normalized to β-actin.\u003cstrong\u003e \u003c/strong\u003eData were analyzed by two-tailed unpaired \u003cem\u003et\u003c/em\u003e-test.\u003c/p\u003e","description":"","filename":"image3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5014333/v1/c8a66b3a5eca10a4783b6f7b.jpeg"},{"id":69560177,"identity":"22f40356-7ae5-4391-b1d8-bea03e5ca87a","added_by":"auto","created_at":"2024-11-21 16:11:36","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":348760,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEstablishment of a human iPSC-derived microglial senescence model and identification of DLK1 as a microglial-senescence-associated secretory protein.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea.\u003c/strong\u003e Schematics of inducing microglia senescence from human iPSCs. \u003cstrong\u003eb.\u003c/strong\u003e Quantification of iPSC telomere length measured by qPCR. The data are presented as the mean T/S ratio ± s.e.m. Each dot represents cells from an individual well. Data were analyzed by unpaired \u003cem\u003et\u003c/em\u003e-test. ****\u003cem\u003ep\u003c/em\u003e\u0026lt;0.0001. \u003cstrong\u003ec.\u003c/strong\u003e Scatter plot showing the expression levels of p16 and p21 in all RNA samples. Samples are clustered by k-means. \u003cstrong\u003ed.\u003c/strong\u003e Differentially regulated Reactome pathways predicted from senescent DEGs. \u003cstrong\u003ee.\u003c/strong\u003e Correlation plot showing the genes correlated to expression levels of p16 and p21 in senescent microglia. DLK1 was strongly correlated with both. \u003cstrong\u003ef.\u003c/strong\u003e Percentage of microglia expressing DLK1 mRNA in the snRNA dataset. Data are reported as mean ± s.e.m. and analyzed by t-test. \u003cstrong\u003eg.\u003c/strong\u003e Schematic of microglia depletion experiment set up. The animals were fed normal food or food containing PLX5622 before CSF extraction. \u003cstrong\u003eh.\u003c/strong\u003e Quantification of DLK1 measured in the mouse CSF. G3 Terc\u003csup\u003e-/-\u003c/sup\u003e mice had elevated DLK1 level in the CSF, and the elevation was diminished by PLX5622 treatment. Each dot represents an animal, and the data were analyzed by one-way ANOVA. *\u003cem\u003ep\u003c/em\u003e=0.0328.\u003c/p\u003e","description":"","filename":"image4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5014333/v1/0346331b78523a825a80463a.jpeg"},{"id":69558887,"identity":"7d9d8c75-cbe8-488a-a822-38ccc44714c0","added_by":"auto","created_at":"2024-11-21 16:03:36","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":375633,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSoluble DLK1 disrupts normal myelination and oligodendrocyte precursor cell functions. a. \u003c/strong\u003eSchematic showing the IV injection of the EGFP-AAV or sDLK1-AAV into the mice. \u003cstrong\u003eb.\u003c/strong\u003e Bar plot showing expression level of sDLK1 in the cortex of the mice, measured by mouse DLK1 ELISA. \u003cstrong\u003ec.\u003c/strong\u003e Normalized DEG numbers in different cell types. The result shows the number of DEGs per 1000 genes detected in each cell type.\u003cstrong\u003ed.\u003c/strong\u003e Venn diagram showing the overlap of upregulated DEGs in G3 Terc-/- and sDLK-AAV injected oligodendrocytes (top) and of downregulated DEGs in G3 Terc-/- and sDLK-AAV injected oligodendrocytes (bottom). \u003cstrong\u003ee.\u003c/strong\u003e Scatter plot showing the positively correlated genes between DEGs in G3 Terc-/- and sDLK-AAV injected oligodendrocytes. \u003cstrong\u003ef.\u003c/strong\u003e UMAP plot showing the enrichment of OPC2 caused by increased sDLK1. \u003cstrong\u003eg.\u003c/strong\u003e Bar plot showing the composition of OPC clusters. Data are reported as mean ± s.e.m. and analyzed by two-way ANOVA. *\u003cem\u003ep\u003c/em\u003e=0.0432. \u0026nbsp;\u003cstrong\u003eh.\u003c/strong\u003e Volcano plot of marker genes of OPC3. Red and blue dots represent DEGs with a log2FC \u0026gt; 0.1 or \u0026lt; -0.1, respectively. All other genes are colored gray. Genes linked to known OPC functions are labeled. \u003cstrong\u003ei.\u003c/strong\u003e Running enrichment score and pre-ranked list showing a negative enrichment of oligodendrocyte differentiation predicted by OPC3 markers. \u003cstrong\u003ej.\u003c/strong\u003e Western bolts of MBP (top) and MOBP (bottom). \u003cstrong\u003ek.\u003c/strong\u003eQuantification of the MBP western plot. Data were analyzed by two-tailed unpaired \u003cem\u003et\u003c/em\u003e-test. **\u003cem\u003ep\u003c/em\u003e=0.0095 \u003cstrong\u003el.\u003c/strong\u003e Quantification of the MOBP western blot. Data were analyzed by two-tailed unpaired \u003cem\u003et\u003c/em\u003e-test. ***\u003cem\u003ep\u003c/em\u003e=0.0009.\u003c/p\u003e","description":"","filename":"image5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5014333/v1/409b66f445bddeb3e69cecfb.jpeg"},{"id":69560725,"identity":"e2f131f9-8731-42d6-94f9-1b0f601671b0","added_by":"auto","created_at":"2024-11-21 16:19:36","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":799193,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDLK1 is neural active and affects calcium influx in neurons. a.\u003c/strong\u003e Venn diagram showing the overlap of upregulated (top) and downregulated (bottom) DEGs in G3 Terc-/- and sDLK-AAV-injected excitatory neurons. \u003cstrong\u003eb.\u003c/strong\u003e Dot plot of the top 10 Reactome pathways inferred by the upregulated overlapping DEGs in G3 Terc-/- and sDLK-AAV injected excitatory neurons. \u003cstrong\u003ec,d.\u003c/strong\u003e Running enrichment score and pre-ranked list showing a positive (c) and negative (d) enrichment of calcium ion transmembrane transport predicted by the upregulated overlapping DEGs in G3 Terc-/- and sDLK-AAV injected excitatory neurons. \u003cstrong\u003ee.\u003c/strong\u003e Schematic illustrating the experiment setup to test the chronic effects of DLK1 on neuronal activities. \u003cstrong\u003ef.\u003c/strong\u003eRepresentative fluorescence image of iPSC-derived GCaMP8f-expressing neurons showing spontaneous activity. \u003cstrong\u003eg.\u003c/strong\u003e Representative spontaneous calcium traces. \u003cstrong\u003eh.\u003c/strong\u003e Quantification of synchronized firing rate. \u003cstrong\u003ei.\u003c/strong\u003eQuantification of firing amplitude. \u003cstrong\u003ej.\u003c/strong\u003e Quantification of spontaneous firing rate. \u003cstrong\u003ek.\u003c/strong\u003e Representative averaged calcium traces from one KCl stimulation experiment in neurons treated with DLK1 (red) and the untreated control neurons (black). Recording 400 seconds. Mean± s.e.m. \u003cstrong\u003el.\u003c/strong\u003e Quantification of peak amplitude from KCl stimulation-induced neuronal responses. Data are presented as mean ± s.e.m. and analyzed by unpaired \u003cem\u003et\u003c/em\u003e-test. **p=0.0055. \u003cstrong\u003em.\u003c/strong\u003e Quantification of the delayed KCl stimulation-induced neuronal responses. \u003cstrong\u003en.\u003c/strong\u003e Quantification of the time each neuron spent to reach peak intensity. Data are presented as mean ±s.e.m. and analyzed by unpaired \u003cem\u003et\u003c/em\u003e-test. ****p\u0026lt;0.0001.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-5014333/v1/25f6e8eec44bc32a8b6de4dc.png"},{"id":69561547,"identity":"75305af4-9b33-4039-9a5a-225a9641bc04","added_by":"auto","created_at":"2024-11-21 16:27:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3408313,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5014333/v1/14a3a2de-dee7-4eaa-b11d-578acf5855aa.pdf"},{"id":69558893,"identity":"5d166514-da8e-4981-aa47-037a7d9d7c3f","added_by":"auto","created_at":"2024-11-21 16:03:39","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2567684,"visible":true,"origin":"","legend":"","description":"","filename":"SuppFig.docx","url":"https://assets-eu.researchsquare.com/files/rs-5014333/v1/41ff0c5504fcd15d647fd046.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Soluble DLK1 secreted by telomere-shortening-induced senescent microglia impairs myelination and alters neuronal activity","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAging is the most critical risk factor for many neurodegenerative disorders, including Alzheimer\u0026rsquo;s disease (AD), the most common cause for dementia that contributes to up to 80% of all dementia cases\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Rather than presenting with a single neurodegenerative pathology, a large proportion of the elderly dementia patients exhibit mixed brain pathologies, including AD and other degenerative and vascular brain pathologies simultaneously\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. The comprehensive disruption of brain functions and structural integrity can be attributed to pathological brain aging, which is characterized by more rapid deterioration due to genetic predisposition and environmental factors\u003csup\u003e\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Understanding the mechanisms underlying pathological brain aging is critical for unveiling the pathogenesis of neurodegenerative disorders and other age-dependent brain conditions.\u003c/p\u003e \u003cp\u003eTelomeres are repeated nucleotide sequences at the ends of chromosomes that prevent nucleolytic degradation and chromosome end-to-end fusion. The telomere is a key indicator of an organism\u0026rsquo;s biological age\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Human telomeres range from 2 to 30 kb and gradually lose their length due to the end replication problem during aging\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Telomere sequence and function are conserved between human and mouse, but mouse telomeres are 5\u0026ndash;10 times longer than human, and mouse lifespan is 30 times shorter, making it unfeasible to study the mechanisms affected by critically shortened telomeres in naturally aged mice\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTo overcome this, TERC knock out has been used to study the effect of telomere shortening in the mice. TERC is the RNA component of telomerase, an RNA-protein complex that elongates telomere length in cells with infinite proliferative potential and serves as the replication template for telomere sequence\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Knockout of TERC hampers the telomerase activity without affecting other enzymatic functions of TERT, the protein component of telomerase, and causes telomere shortening to be inherited through generations by disabling telomere elongations in the mouse germline. Previous studies have showcased that three consecutive generations of TERC knock out induces aging-like phenotypes\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eCritical shortening of telomeres by end replication problem induces cell-cycle arrest and causes the cell to enter replicative senescence\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Glial cells in the brain (e.g., microglia, astrocytes, and oligodendrocytes) retain proliferative capabilities after development and become more proliferative in response to damage to the central nervous system (CNS) and other stressors. Thus, glial cells are under heavy replicative stress and telomere shortening is detected in the white matter, whereas telomere length in the grey matter remains relatively unchanged\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eGlial senescence has been suggested to transform the brain from normal aging to pathological aging and to drive the buildup and spread of AD pathologies\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Microglia are the resident macrophages in the CNS responsible for immune surveillance and innate immune responses to damage and pathogenic species\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Microglia are susceptible to more replicative stress associated with the reactivation of the proliferative program caused by the responses to neurodegenerative pathologies, including tauopathy and Aβ accumulation\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Telomere shortening and replicative senescence may disrupt normal microglia functions under aging and neurodegenerative conditions\u003csup\u003e\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. However, how microglia senescence contributes to pathological aging and affects other brain cell types remains unknown.\u003c/p\u003e \u003cp\u003eTo determine the effects of telomere shortening in the brain, we assessed pathological and functional outcomes while characterizing the cell-type-specific transcriptomic alterations. We expanded our study on telomere-shortened mouse microglia in human iPSC-derived microglia with shortened telomeres. Here we report direct evidence that senescent microglia exert detrimental influences on other cell types through an altered secretion profile. Importantly, we identified delta-like non-canonical Notch ligand 1 (DLK1) as a new senescent microglia-derived ligand. DLK1 was originally identified as a member of the epidermal growth factor (EGF)-like family and a negative regulator of adipocyte differentiation (pre-adipocyte inhibitor factor 1, Pref1)\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Overexpression of soluble DLK1 (sDLK1) in the mouse brain caused defects in oligodendrocyte differentiation and myelinating activities. Higher level of sDLK1 also induced abnormal Ca\u003csup\u003e2+\u003c/sup\u003e activities in the mouse and human iPSC-derived neurons. This study profiles cellular signatures associated with shorter telomeres in the mouse brain and identified replicative microglia senescence along with the associated ligands as a key contributor to pathological brain aging.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\"\u003e\n \u003ch2\u003eTelomere shortening exacerbates brain lipofuscinosis and memory deficit\u003c/h2\u003e\n \u003cp\u003eTo understand the effects of telomere shortening on brain functions, we used the TERC knockout model. We bred mice with a homologous TERC deletion for three consecutive generations (G3 Terc\u003csup\u003e\u0026minus;/\u0026minus;\u003c/sup\u003e) (Fig.\u0026nbsp;1a). Terc\u003csup\u003e\u0026minus;/\u0026minus;\u003c/sup\u003e mice have progressive telomere loss with increasing generations\u003csup\u003e\u003cspan\u003e24\u003c/span\u003e\u003c/sup\u003e. Using \u003cem\u003ein situ\u003c/em\u003e hybridization, we measured the fluorescence intensities of the probe complementary to the telomere sequence that directly correlate with telomere length (Fig.\u0026nbsp;1b)\u003csup\u003e\u003cspan\u003e25\u003c/span\u003e\u003c/sup\u003e. Indeed, quantification of the fluorescence intensity showed that G3 Terc\u003csup\u003e\u0026minus;/\u0026minus;\u003c/sup\u003e mice exhibited significantly less fluorescence in all and Iba1-positive cells than age-matched wild-type (WT) mice (Fig.\u0026nbsp;1c, d). Thus, three consecutive generations of TERC knockout significantly reduced the telomere length in microglia and other brain cells, as expected.\u003c/p\u003e\n \u003cp\u003eLipofuscinosis is an aging hallmark in the brain that can be visualized as autofluorescence \u003csup\u003e\u003cspan\u003e26\u003c/span\u003e\u003c/sup\u003e. Autofluorescence was strikingly increased throughout the brain, as detected in the primary somatosensory cortex (Fig.\u0026nbsp;1e). Quantification of the autofluorescence revealed a significant increase of lipofuscinosis in the G3 Terc\u003csup\u003e\u0026minus;/\u0026minus;\u003c/sup\u003e mice (Fig.\u0026nbsp;1f-h).\u003c/p\u003e\n \u003cp\u003eTo determine if telomere shortening caused any age-related cognitive declines in the mice, we used the contextual fear conditioning (CFC) test (Fig.\u0026nbsp;1i). During the conditioning session, G3 Terc\u003csup\u003e\u0026minus;/\u0026minus;\u003c/sup\u003e mice showed no change in the percentage of time frozen during the learning period (Fig.\u0026nbsp;1j). However, the G3 Terc\u003csup\u003e\u0026minus;/\u0026minus;\u003c/sup\u003e mice had significantly less frozen time than WT mice during the test session when they were exposed to the same environment without being shocked, suggesting impaired contextual memory (Fig.\u0026nbsp;1k). To assess memory impairment not associated with fear, we used novel object recognition (NOR), which measures a form of declarative memory (Fig.\u0026nbsp;1l). During the sample-object exposure session, G3 Terc\u003csup\u003e\u0026minus;/\u0026minus;\u003c/sup\u003e and WT mice showed no preference on the two objects as expected (Fig.\u0026nbsp;1m). In contrast, during the novel-object exposure session, the WT mice showed significant preferences for the novel object, indicating normal memory, but G3 Terc\u003csup\u003e\u0026minus;/\u0026minus;\u003c/sup\u003e mice again failed to show a preference for the novel object (Fig.\u0026nbsp;1n). Thus, shortening telomere length in brain cells elevated lipofuscinosis and impaired memory function, phenotypes commonly observed in aged animals\u003csup\u003e\u003cspan\u003e26\u003c/span\u003e,\u003cspan\u003e27\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\"\u003e\n \u003ch2\u003eTelomere shortening accelerates aging in glial cells and enhances senescence signaling in microglia\u003c/h2\u003e\n \u003cp\u003eTo dissect the mechanism by which telomere shortening exacerbates the phenotypes of brain aging, we performed snRNA-seq to examine hippocampal tissues from WT and G3 Terc\u003csup\u003e\u0026minus;/\u0026minus;\u003c/sup\u003e animals. We utilized a pre-established cold mechanical dissociation protocol to prepare the snRNA library and sequenced 83,223 nuclei \u003csup\u003e\u003cspan\u003e28\u003c/span\u003e\u003c/sup\u003e. Of these, 9,131 nuclei were filtered out, based on the gene counts, unique molecular identifier (UMI), and percent mitochondrial genes per nucleus (Supp Fig.\u0026nbsp;1). We further removed potential multiplets identified by DoubletFinder and selected 72,330 nuclei for downstream analysis\u003csup\u003e\u003cspan\u003e29\u003c/span\u003e\u003c/sup\u003e. Graph-based clustering identified eight major clusters, and reference gene sets were used to annotate the clusters to eight cell types in the hippocampus region (Fig.\u0026nbsp;2a; Supp Fig.\u0026nbsp;1a). Telomere shortening did not significantly affect the composition of the major cell types in the mouse hippocampus (Supp Fig.\u0026nbsp;1b).\u003c/p\u003e\n \u003cp\u003eWe focused on microglia, astrocytes, neurons, oligodendrocytes, and oligodendrocyte precursor cells for the downstream analysis. To identify the magnitude of telomere shortening effect on each cell type, we performed differential expression analysis for each cell type, comparing the transcriptomes of G3 Terc\u003csup\u003e\u0026minus;/\u0026minus;\u003c/sup\u003e and WT tissues. Telomere shortening led to significant alterations in all six analyzed cell types. The number of differentially expressed genes (DEGs) in each cell type was normalized, using the average number of UMIs detected (Fig.\u0026nbsp;2b, Supplementary table 1). Examination of the DEGs of microglia, astrocyte, and oligodendrocyte revealed that telomere shortening resulted in unique transcriptomic changes in individual cell types (Fig.\u0026nbsp;2c, Supp Fig.\u0026nbsp;2a). Nonetheless, we identified 31 DEGs shared among the three cell types, and the majority of the overlapped DEGs (26/31) were changed in the same direction by telomere shortening (Fig.\u0026nbsp;2d). Noticeably, AC149090.1 was significantly upregulated in all three cell types. AC149090.1 is a mouse ortholog to human PISD, a gene encodes for a phospholipid decarboxylase that catalyzes the conversion of phosphatidylserine to phosphatidylethanolamine in the inner mitochondrial membrane and is essential in lipid metabolism and autophagy \u003csup\u003e\u003cspan\u003e30\u003c/span\u003e\u003c/sup\u003e. AC149090.1 is a major marker of cell-type-specific aging in the neurogenic region of the subventricular zone \u003csup\u003e\u003cspan\u003e31\u003c/span\u003e\u003c/sup\u003e. Utilizing the aging clock built by the Brunet group\u003csup\u003e\u003cspan\u003e32\u003c/span\u003e\u003c/sup\u003e, we examined the effect of telomere shortening on predicted biological age in oligodendrocyte, microglia, and astrocytes (Supp Fig.\u0026nbsp;2b). Although the aging clocks are based on different gene contributors for different cell types (Supp Fig.\u0026nbsp;2c), we found that telomere shortening uniformly increased the predicted biological age in all three cell types with microglia affected the most (Fig.\u0026nbsp;2e, f).\u003c/p\u003e\n \u003cp\u003eWe next analyzed transcriptomic changes induced by telomere shortening in microglia. The WT and G3 Terc\u003csup\u003e\u0026minus;/\u0026minus;\u003c/sup\u003e samples were subclustered into three distinct transcriptional states (Fig.\u0026nbsp;2g). A significant shift of microglia transcriptomic states was observed in microglia from WT and G3 Terc \u003csup\u003e\u0026minus;/\u0026minus;\u003c/sup\u003e brains. Specifically, subcluster 3 (MG3) microglia in G3 Terc\u003csup\u003e\u0026minus;/\u0026minus;\u003c/sup\u003e animals was enriched (Fig.\u0026nbsp;2h). We next asked if telomere shortening mimics the transcriptomic change during normal aging in microglia. As expected, when we applied the aging clock to predict the biological age of MG3, a portion of MG3 was predicted to be significantly \u0026ldquo;older\u0026rdquo; than the other microglia, linking the molecular signatures of telomere shortening induced senescent microglia with those in chronically aging brain (Fig.\u0026nbsp;2i).\u003c/p\u003e\n \u003cp\u003eAnalyses of top marker genes of MG3 revealed genes associated with replication stress survival and senescence, such as Pold4 and Cdk5r1 (Fig.\u0026nbsp;2j)\u003csup\u003e\u003cspan\u003e33\u003c/span\u003e,\u003cspan\u003e34\u003c/span\u003e\u003c/sup\u003e. Upregulated pathways identified in MG3 by Ingenuity Pathway Analysis (IPA) included senescence pathway and production of IL-15, a known senescence-associated secretory phenotype (SASP) cytokine (Fig.\u0026nbsp;2k). We also examined expression changes of known SASP ligands and found 12 SASP ligands upregulated in MG3 (Fig.\u0026nbsp;2l). Single-cell regulatory network inference and clustering (SCENIC) predicted 10 up-stream transcriptional factors specific to MG3 (Fig.\u0026nbsp;2m). Remarkably, among the 10, CUX1 directly regulates replicative senescence, and ATF6 regulates morphological changes associated with senescence\u003csup\u003e\u003cspan\u003e35\u003c/span\u003e,\u003cspan\u003e36\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n \u003cp\u003eThe intercellular signaling between microglia and neuronal cells was examined with CellChat. Using canonical neuronal markers, we identified 11 neuronal subtypes in our snRNA dataset (Supp Fig.\u0026nbsp;3a, b). Alongside 268 common signaling pathways from microglia to neurons, we identified 13 unique to MG3, including CD47, CNTN2, FGFR2, FN1, LPL, and somatostatin (Supp Fig.\u0026nbsp;3c, d). MG3 sends strong CD47 signal to granule cells, neuroblasts and pyramidal neurons (Supp Fig.\u0026nbsp;3e). Increased CD47 expression has been associated with replicative senescence in mouse lung fibroblast, and CD47 is a potential therapeutic target for multiple sclerosis\u003csup\u003e\u003cspan\u003e37\u003c/span\u003e,\u003cspan\u003e38\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\"\u003e\n \u003ch2\u003eTelomere shortening impairs oligodendrocyte maturation and disrupts normal myelination\u003c/h2\u003e\n \u003cp\u003eLoss of white matter volume is a well-documented phenomenon in the aging brain\u003csup\u003e\u003cspan\u003e39\u003c/span\u003e\u003c/sup\u003e. We next examined the changes induced by telomere shortening in oligodendrocyte lineage cells by combining and re-clustering the oligodendrocyte precursor cells (OPCs) and oligodendrocytes in the UMAP (Fig.\u0026nbsp;3a). We found that telomere shortening caused a striking shift from OG2 to OG1 (Fig.\u0026nbsp;3b). With OPC as a starting point, we used pseudotime analyses to examine the maturation process from OPC to OG. Telomere shortening appeared to shift the oligodendrocyte further away from the OPCs, consistent with an abnormal transformation of oligodendrocyte beyond normal maturation (Fig.\u0026nbsp;3c). Notably, genes associated with normal formation of myelin structures and neuroprotective effects, such as ANLN, SPOCK3, and APOD, were significantly reduced in OG1 (Fig.\u0026nbsp;3d)\u003csup\u003e\u003cspan\u003e40\u003c/span\u003e\u0026ndash;\u003cspan\u003e42\u003c/span\u003e\u003c/sup\u003e. Analyses of the expression dynamics of ANLN, SPOCK3, and APOD revealed a similar pattern, where the expression levels increased during the differentiation from the OPCs and reduced further away from the starting point, suggesting a connection between the disruption of normal myelin formation and increased pseudotime telomere shortening (Fig.\u0026nbsp;3e). We then directly examined the effects of telomere shortening on oligodendrocytes. Immunostaining of corpus collosum revealed a significant reduction of ANLN\u0026thinsp;+\u0026thinsp;oligodendrocytes in the G3 Terc\u003csup\u003e\u0026minus;/\u0026minus;\u003c/sup\u003e animals (Fig.\u0026nbsp;3f, g). Moreover, a western blot of myelin basic protein (MBP) revealed a significant reduction in the cortex of G3 Terc\u003csup\u003e\u0026minus;/\u0026minus;\u003c/sup\u003e brains, compared to their WT counterparts, supporting the notion that telomere shortening leads to demyelination (Fig.\u0026nbsp;3h, i).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\"\u003e\n \u003ch2\u003eIdentification of DLK1 as a novel senescence associated ligand of microglia in human microglia model\u003c/h2\u003e\n \u003cp\u003eTo extend the analyses from mouse microglia to human microglia and facilitate mechanistic studies, we developed an \u003cem\u003ein vitro\u003c/em\u003e senescent microglia model, based on iPSC-derived microglia\u003csup\u003e\u003cspan\u003e43\u003c/span\u003e\u003c/sup\u003e. We applied a TERT inhibitor, BIBR1532, to iPSC cultures for 30 days to significantly reduce telomere length, and subsequentially differentiated them into human microglial-like cells (hMGLs) (Fig.\u0026nbsp;4a). After differentiation, etoposide was used to induce DNA damage and senescence in the hMGLs. Before microglial differentiation, we measured the relative telomere length by a qPCR-based approach and verified a significant reduction in telomere length in the iPSCs (Fig.\u0026nbsp;4b)\u003csup\u003e\u003cspan\u003e44\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n \u003cp\u003eUsing unsupervised hierarchical clustering, we categorized cells treated with BIBR1532, etoposide, or both to four groups, based on the expression levels of canonical senescence markers, p16 and p21, detected by bulk RNA sequencing (Fig.\u0026nbsp;4c). We then compared the DEGs between hMGLs with high expression levels of p16 and p21 senescent hMGLs with those with low p16 and p21 levels (Supplementary table2). Pathway analysis demonstrated that interleukin signaling, and secretory activity were among the most dysregulated pathways in the senescent hMGLs (Fig.\u0026nbsp;4d). We then examined expression levels of intercellular cell signaling genes in association with p16 and p21 expression levels. To identify the signaling genes, we used the curated ligand-receptor dataset in the CellPhoneDB v2.0 package to select for the ligand genes\u003csup\u003e\u003cspan\u003e45\u003c/span\u003e\u003c/sup\u003e. The correlation between log fold changes in ligand gene expression and the levels of p21 and p16 revealed DLK1, a canonical ligand for NOTCH pathway, as the top ligand associated with these senescent markers (Fig.\u0026nbsp;4e, Supplementary Table\u0026nbsp;3). Weighted gene co-expression network analysis (WGCNA) identified 27 unique co-expression modules (Supp Fig.\u0026nbsp;4a). Among those, eight modules were significantly correlated to DLK1, p21, or p16 expression (Supp Fig.\u0026nbsp;4b). The modules significantly correlated to DLK1 (brown module and green module) were also significantly correlated with both p21 and p16, confirming a strong link between DLK1 expression and microglial senescence (Supp Fig.\u0026nbsp;4c, Supplementary Table\u0026nbsp;4). Further analyses revealed that the brown module is enriched with genes linked to hippo signaling pathway (Supp Fig.\u0026nbsp;4d), and the green module is enriched with genes linked to steroid hormone biosynthesis and complement system, which is a key factor in innate immune response (Supp Fig.\u0026nbsp;4e).\u003c/p\u003e\n \u003cp\u003eIn the adult brain, DLK1 expression regulates neurogenesis and is restricted to neural stem cells and niche astrocytes in neurogenic regions, such as the dentate gyrus\u003csup\u003e\u003cspan\u003e46\u003c/span\u003e,\u003cspan\u003e47\u003c/span\u003e\u003c/sup\u003e. However, when we examined if DLK1 is expressed in the senescent microglia \u003cem\u003ein vivo\u003c/em\u003e, we detected low levels of DLK1 transcripts in over 60% of G3 Terc-/- microglia in our snRNA-seq data, but DLK1 was not found in WT microglia (Fig.\u0026nbsp;4f). To determine if DLK1 is expressed and secreted by senescent microglia in G3 Terc-/- mice, we treated G3 Terc-/- mice with a CSF1R antagonist (PLX 5622) to deplete most microglia\u003csup\u003e\u003cspan\u003e48\u003c/span\u003e\u003c/sup\u003e. Levels of sDLK1 protein in the cerebrospinal fluid (CSF) were quantified with an ELISA assay (Fig.\u0026nbsp;4g). In agreement with our snRNA-seq and our findings in hMGLs, levels of sDLK1 protein were elevated in the CSF of G3 Terc-/- mice. Moreover, depletion of microglia abolished the elevation of sDLK1 in in the CSF of G3 Terc-/- mice. Thus, senescent microglia are likely to be major source of elevated sDLK1 \u003cem\u003ein vivo\u003c/em\u003e (Fig.\u0026nbsp;4h).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\"\u003e\n \u003ch2\u003eSoluble DLK1 disrupts normal functions of oligodendrocyte lineage cells\u003c/h2\u003e\n \u003cp\u003eTo investigate the role of sDLK1 on different brain cells, we applied AAV-sDLK1 (AAV-PHPeB) to overexpress sDLK1 in the mouse brain via intravenous injection (Fig.\u0026nbsp;5a, Supp Fig.\u0026nbsp;5a). Using a DLK1 ELISA, we confirmed successful induction of sDLK1 expression in the brain tissues 2 months after viral injection (Fig.\u0026nbsp;5b). To determine the cell type\u0026ndash;specific effects of sDLK1 overexpression, we performed snRNA sequencing of hippocampus. Eight major cell types were identified after applying the same quality control standard we used for G3 Terc-/- animals (Supp Fig.\u0026nbsp;6). DLK1 expression was elevated in astrocytes and endothelial cells of DLK-AAV animals (Supp Fig.\u0026nbsp;5b). Interestingly, oligodendrocytes had the highest normalized DEG numbers (Fig.\u0026nbsp;5c, Supplementary Table\u0026nbsp;5). To explore the role of elevated sDLK1 in the G3 Terc-/- model, we first focused on the overlap of DEGs in both conditions (Fig.\u0026nbsp;5d). Among the DEGs with the same direction of change induced by telomere shortening and elevated sDLK1 level, genes involved in myelination and oligodendrocyte differentiation were strongly correlated (Fig.\u0026nbsp;5e). At the same time, among the top 500 differentially expressed gene, 50 genes were associated with abnormal myelination or diseases related to abnormal myelination, with key myelination genes downregulated in the brains with higher sDLK1 levels (Supp Fig.\u0026nbsp;5c, 5d).\u003c/p\u003e\n \u003cp\u003eTo further examine the effect of sDLK1 on the oligodendrocyte lineage cells, we analyzed the OPCs and identified five distinct OPC subclusters with OPC3 significantly enriched with cells from the brains injected with DLK1-AAV (Fig.\u0026nbsp;5f, g). When we looked at the marker genes defining OPC3, we found the top markers genes, such as Tns3 and Grin2b, that are associated with OPC differentiation and white matter alteration, respectively (Fig.\u0026nbsp;5h)\u003csup\u003e\u003cspan\u003e49\u003c/span\u003e,\u003cspan\u003e50\u003c/span\u003e\u003c/sup\u003e. GSEA revealed that the marker genes of OPC3 were negatively enriched with oligodendrocyte differentiation, indicating that DLK1 caused the emergence of an OPC population with abnormal proliferation and differentiation (Fig.\u0026nbsp;5i). To confirm the detrimental effects of sDLK1 on myelination, we measured the expression of MBP and MOBP in the animals treated with the AAVs (Fig.\u0026nbsp;5j). In agreement with our predictions from the snRNA data, protein levels of MBP and MOBP were significantly reduced in animals with higher sDLK1 expression, indicating a direct negative effect of sDLK1 on the myelination process (Fig.\u0026nbsp;5k, l).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\"\u003e\n \u003ch2\u003eDLK1 directly affects calcium influx in neuronal cells\u003c/h2\u003e\n \u003cp\u003eWe also examined the effect of DLK1 on neurons. Comparing the DEGs in G3 Terc-/- neurons and DLK-AAV neurons, we found that 297 of 746 upregulated DEGs in DLK-AAV neurons were also upregulated in the G3 Terc-/- neurons (Fig.\u0026nbsp;6a). Calcium-signal-related pathways were the top pathways predicted by the DEGs shared between DLK1-AAV and G3 Terc-/- neurons (Fig.\u0026nbsp;6b). GSEA analysis showed that the overlapped DEGs were enriched for calcium ion transmembrane transport but negatively enriched for calcium ion binding, leading us to hypothesis that DLK1 induces an abnormal calcium signaling in the excitatory neurons (Fig.\u0026nbsp;6c, d).\u003c/p\u003e\n \u003cp\u003eTo further determine the function of DLK1 and its potential effects on calcium activities in neurons, we established an \u003cem\u003ein vitro\u003c/em\u003e neuronal culture with a calcium reporter. We used the GCaMP lentivirus to transfect maturing neurons at day 7 of differentiation and treated the neurons with recombinant human DLK1 for 7 days starting at day 14 to model a chronic effect of DLK1 (Fig.\u0026nbsp;6e). With single-neuron tracing of the calcium signaling, we looked at the calcium activities in the neurons treated with either vehicle or recombinant human DLK1 (Fig.\u0026nbsp;6f, g). DLK1 had no effect on the synchronized firing rate of the neuronal network (Fig.\u0026nbsp;6h). No changes were noted in firing amplitude or firing rate of the calcium activities in the neuron treated with DLK1 (Fig.\u0026nbsp;6i, j). However, when we depolarized the neurons with KCl, we observed a significant increase in the peak calcium signal and a delayed rise to the peak (Fig.\u0026nbsp;6k-n). This observation confirmed the effect of DLK1 predicted by our \u003cem\u003ein vivo\u003c/em\u003e data, and notably, this change in neuronal calcium activity is consistent with the change of calcium signal in aged rat CA1 neurons\u003csup\u003e\u003cspan\u003e51\u003c/span\u003e\u003c/sup\u003e. At the same time, we analyzed the transcriptomic changes of the neurons induced by DLK1 treatment (Supp Fig.\u0026nbsp;7a). We found a predominant association of significant DEGs with synaptic regions, indicating that an elevated DLK1 signal disrupts normal synaptic structure and function (Supp Fig.\u0026nbsp;7b). Intriguingly, we identified a subset of DEGs induced by DLK1 treatment also implicated in incipient AD patients (Supp Fig.\u0026nbsp;7c)\u003csup\u003e\u003cspan\u003e52\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we demonstrated that telomere shortening, an evolutionarily conserved aging mechanism, causes pathological aging phenotypes within the mouse brain. Moreover, we comprehensively investigated the underlying mechanisms by which telomere shortening exerts detrimental effects on the integrity and functionality of the brain. Telomere shortening results in a premature accumulation of lipofuscin, a prominent hallmark of aging in the brain. Lipofuscin is a complex mixture primarily of oxidized protein and lipid remnants that resists cellular degradation and elimination and tends to accumulate in post-mitotic cells, such as neurons\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Formation of lipofuscin arises from the impaired degradation of damaged mitochondria by lysosomes. Intriguingly, the connection between lipofuscin and telomere shortening was made in a recent study where telomere shortening in the leukocyte was associated with the accumulation of lipofuscin in the serum\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. Conventionally, the accumulation of lipofuscin is associated with the mitochondrial-lysosomal axis of aging\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. However, we found that telomere shortening actively facilitates the accrual of lipofuscin, necessitating further investigations into the interplay of telomere and mitochondrial-lysosomal functions. Telomere shortening was also deleterious to memory, thereby corroborating an additional phenotype commonly observed in aged humans and mice. Our \u003cem\u003ein-situ\u003c/em\u003e hybridization data showed a significant reduction in telomere length in neuronal and glial cells of the G3 TERC\u003csup\u003e\u0026minus;/\u0026minus;\u003c/sup\u003e animals. Thus, it is imperative to investigate the mechanistic connection between telomere shortening and memory deficit, specifically to determine if the memory deficit is attributed to an inherent neuronal effect or a secondary glial effect.\u003c/p\u003e\n\u003cp\u003eTERC\u003csup\u003e\u0026minus;/\u0026minus;\u003c/sup\u003e animals are characterized by truncated telomeres at the embryonic stage, resulting in shorter telomeres in all somatic cells than WT animals. Consistently, we found heterogeneous transcriptional responses to telomere shortening across all identified cell types, but individual cell types had different susceptibilities. We found that excitatory neurons experienced the most pronounced impact, which contradicted our original hypothesis. We had expected neurons to be less susceptible to telomere shortening as the neuronal cells progress into the post-mitotic stage during early development and undergo fewer cell division cycles than glial cells. This finding prompted us to explore the non-cell-autonomous mechanism by which telomere shortening affects neuronal transcriptome as glial cells endure more severer telomere attrition stress due to their inherent capability for proliferation. The small overlap between differentially expressed genes in the major glial cells reveals that telomere shortening induces diverse effects on the glial population. Remarkably, amidst the small overlap of the differentially expressed genes, AC149090.1 was identified as a commonly upregulated gene in microglia, astrocytes, and oligodendrocytes. AC149090.1 was identified as a gene upregulated by aging in the hippocampus and neurogenic regions of the brain, and a major contributor to a transcriptome-based aging clock for naturally aged brain cells\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. Although the mechanism by which AC149090.1 is connected to aging remains unclear, our transcriptomic data suggest a connection to telomere shortening and links natural aging in the glial cells to telomere length.\u003c/p\u003e\n\u003cp\u003eThe neuroimmune system, along with its critical constituent microglia, has drawn substantial attention in the field of research focusing on the pathophysiology of neurodegenerative disorders\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e. Impairments and dysregulations of microglial functions have been implicated in neurodegenerative disorders ranging from AD to frontotemporal dementia\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e. Nevertheless, a clear understanding of the underlying factors that drive the initial deterioration of microglia remains elusive. Unlike the other cell types in the CNS, the microglia population derives from microglia progenitors that originated from the yolk sac during early embryonic development\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e. The progenitors undergo extensive replication cycles to achieve the adult microglia population during embryonic and early postnatal stage\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e. Despite the common belief that microglia are long-lived cells with slow turnovers, new evidence has shown that the microglia population undergoes several rounds of proliferation-mediated renewal during the lifetime in both mouse and human\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e. We hypothesized that telomere attrition induced by microglia self-replication, along with its associated replicative senescence, plays a crucial role in disrupting microglial homeostasis and characterized the microglia with shortened telomeres in our TERC model. Our snRNA-seq data showed a striking transformation in the microglia state upon telomere shortening, resulting in the emergence of a distinct subpopulation exhibiting a prominent senescent signature. Senescent microglia exhibited robust upregulation of IL15, a well-established SASP cytokine, and interferon alpha. Both are key hallmarks of senescence\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e. We detected a strong upregulation of other SASP genes and genes known as pathology-associated microglia markers within the senescent microglia population. Through the transcriptome-based aging clock, we demonstrated the physiological relevance of the microglial senescence induced by telomere shortening and its potential as a tool to study microglia in brain aging.\u003c/p\u003e\n\u003cp\u003eA growing body of evidence supports the connection of microglia senescence and neurodegenerative diseases\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e62\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e. However, no investigation has examined the intricate transcriptomic changes within senescent microglia and how senescent microglia exert deleterious effects on its surroundings. In our \u003cem\u003ein vitro\u003c/em\u003e model of microglial senescence, p16 and p21 were upregulated, indicating a mature senescence phenotype. Importantly, the manifestations of senescence, particularly the SASP, exhibit substantial heterogeneity across different cell and tissue types\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e64\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e. With our \u003cem\u003ein vitro\u003c/em\u003e model, we gained a comprehensive insight into the transcriptomic alterations within senescent microglia. Given that p21 and p16 represent differential mechanisms in the establishment and sustenance of senescence, we identified genes with positive correlations with either p21 or p16\u003csup\u003e66\u003c/sup\u003e. Our main objective was to elucidate the mechanism by which senescent microglia contribute to brain aging, so we sought to identify genes involved in intercellular signaling among those correlated with p21 and p16. To our surprise, along with cytokines known to be involved in the SASP, we identified that DLK1 expression was strongly correlated with both p21 and p16. Dlk1 is a single-pass transmembrane protein containing a TACE-mediated cleavage site and is a noncanonical member of the Delta-Notch signaling pathway\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e. Dlk1 expression is high during embryonic development but is restricted in adulthood by genomic imprinting\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e68\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e. The aberrant elevation of DLK1 expression in the senescent microglia might be attributed to dysregulation of the imprinted DLK1-DIO3 locus, as miRNAs in the DLK1-DIO3 increase in adipose-derived stem cells undergoing replicative senescence\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e. Using a microglia depletion regimen, we confirmed that the expression level of DLK1 is increased in our G3 TERC\u003csup\u003e\u0026minus;/\u0026minus;\u003c/sup\u003e animals and that the increase unequivocally originated from the microglia. Our snRNA-seq data also point to senescent microglia as the main source of DLK1 expression, reinforcing the link between DLK1 expression and microglial senescence. Although there has been no evidence showing expression of DLK1 in microglia, our data established DLK1 as a novel member of the microglial SASP.\u003c/p\u003e\n\u003cp\u003eThe involvement of oligodendrocyte lineage cells in brain aging and the development of age-dependent neurodegenerative disorders, including diseases not primarily associated with myelination defects (e.g., AD and PD), has attracted increasing attention in recent years\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e. Increased DLK1 signaling from senescent microglia may disrupt the normal myelinating functions of the oligodendrocytes. For example, RHEB-knockout-induced upregulation of DLK1 from neurons impairs oligodendrocyte differentiation and myelination\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e72\u003c/span\u003e\u003c/sup\u003e. Indeed, we observed loss of myelination and disrupted oligodendrocyte differentiation in the G3 Terc\u003csup\u003e\u0026minus;/\u0026minus;\u003c/sup\u003e mouse brains. However, the proliferative nature of OPCs renders it inconclusive whether the myelination phenotype is attributed to increased DLK1 signaling or to the cell-autonomous effect of telomere shortening. After overexpressing the soluble form of DLK1 in mouse brain, we confirmed that the soluble form of DLK1 causes defects in myelination and oligodendrocyte differentiation. This finding provides another mechanism by which DLK1 affects normal myelination and evidence that senescent microglia can induce neurodegenerative pathologies in other cell types through a unique secreting profile.\u003c/p\u003e\n\u003cp\u003eWith our \u003cem\u003ein vivo\u003c/em\u003e and \u003cem\u003ein vitro\u003c/em\u003e models, we showed that the increase of DLK1 signaling in the mouse also altered Ca2\u0026thinsp;+\u0026thinsp;signaling pathways in the neurons, which provides a potential explanation for the memory deficits in the G3 TERC\u003csup\u003e\u0026minus;/\u0026minus;\u003c/sup\u003e animals. Our finding that chronic treatment of DLK1 dysregulated the expression of synaptic genes as well as AD-associated genes further supports the involvement of DLK1 in the mechanism underlying the increased susceptibility of neurodegeneration associated with aging.\u003c/p\u003e\n\u003cp\u003eThe mechanisms underlying pathological brain aging and associated neurodegenerative disorders remain largely elusive, especially their involvement in the disease development before disease manifestations such as misfolded proteins. Although telomere length is only one of many metrics of aging, we found that telomere shortening and microglia senescence induced by it can give rise to key phenotypes of pathological brain aging, proving its potential as a tool to further understand the alterations caused by age and early disease development. While this study revealed the potential mechanisms by which telomere shortening causes pathological phenotypes such as myelination defects and altered calcium signaling of neurons, we did not focus on any disease-specific phenotypes. The logical next step is to combine the telomere shortening model with disease-specific risk genes, such as APOE or TREM2, to further examine the mechanisms underlying the age dependency of neurodegenerative disease and potential methods for early disease diagnosis and intervention.\u003c/p\u003e"},{"header":"Method","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eMice\u003c/h2\u003e \u003cp\u003eMale and female G0 TERC\u003csup\u003e-/-\u003c/sup\u003e breeders were purchased from the Jackson Laboratory (The Jackson Laboratory, 004132) and bred for three generations to generate G3 TERC\u003csup\u003e-/-\u003c/sup\u003e animals. Age-matched WT C57BL6/J mice from the NIA. Animals were housed no more than five per cage in a pathogen-free barrier facility at 21\u0026ndash;23\u0026deg;C with 30\u0026ndash;70% humidity on a 12-hour light/dark cycle. The animals were given \u003cem\u003ead libitum\u003c/em\u003e access to food and water. Both male and female animals were used for histological analysis. Only female animals were used for behavioral and biochemical analyses. Animals were 8\u0026ndash;9 months of age when used for histological analysis. Animals underwent behavioral testing at 9 months of age and had not been used previously for any other experiments. At 10 months of age, the same mice were used for pathology and RNA-seq studies. For microglia depletion regimen, PLX5622 was given to the animals with food at 8 months of age for 2 weeks before the behavioral experiment and the subsequent pathology studies. All mouse protocols were approved by the Institutional Animal Care and Use Committee, University of California, San Francisco, and Weill Cornell Medicine.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eBrian tissue collection\u003c/h2\u003e \u003cp\u003eMice were euthanized using Fatal-Plus (pentobarbital sodium) and transcardially perfused with PBS. The brains were hemisected, and the hemibrains were flashed-frozen at -80\u0026deg;C or fixed in 4% paraformaldehyde for 48 hours, which was then followed by 48-hour 30% sucrose infiltration at 4\u0026deg;C. The fixed hemibrains were sectioned into 30-\u0026micro;m slices using freezing microtome (Leica) and stored at -20\u0026deg;C in cryoprotectant before staining.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eContextual fear conditioning\u003c/h2\u003e \u003cp\u003eMice were tested for contextual fear learning and memory using sound-attenuated chambers (Med Associates, VT, USA). During fear acquisition, mice freely explored a novel environment. After a 2-min habituation, mice were exposed to a 2-s foot shock (0.5 mA) followed by a 60-s interstimulus interval for a total of three shocks. At 24 hours after fear acquisition, hippocampal-dependent fear memory was measured by recording the percent total time spent freezing in a 5-min context test (Video Freeze software).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eNovel object recognition\u003c/h2\u003e \u003cp\u003eMice were habituated to opaque open field arenas (40 \u0026times; 40 cm) by allowing them to freely explore the arena for two 10-min trials spaced on the 2 days leading up to the object recognition test. On the test day, two identical objects (plastic geometric object) were placed in the center of each arena. Mice were allowed to freely explore the objects for a 15-min trial. After 24 hours, one of the geometric objects was replaced by a novel object of a different shape and color. The animal was then allowed to freely explore the new objects for a 15-min trial. Video recording and tracking (Ethovision v15, Noldus) were used to track the movement of animals. The time mice spent exploring each object was determined automatically by the Ethovision software. Preference was calculated based on the total time an individual mouse spent exploring both objects.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eTelomere length measurement\u003c/h2\u003e \u003cp\u003eTelomere length labeling was done using TelC-Cy3 (PNA Bio Cat. No. F1002), according to the manufacturer\u0026rsquo;s protocol with modifications. PFA-fixed brain sections were incubated in 1% Tween-20 in PBS for 1 minute, followed by boiling in antigen unmasking solution (Vector Cat. No. H-3300) at 90\u0026deg;C for 35 minutes. After cooling for 5 minutes, the sections were rinsed with PBS. In a PCR tube, 50 \u0026micro;L of TelC-Cy3 solution (1:250 dilution of 250 \u0026micro;g/ml formamide TelC-Cy3 stock solution into PNA staining solution (70% formamide, 10 mM Tris pH 7.5, 0.5% B/M Blocking Reagent solution (Sigma Cat. No. 11096176001, prepared according to manufacturer\u0026rsquo;s instructions)) was added to a brain section and heated at 84 \u003csup\u003eo\u003c/sup\u003eC for 5 minutes. The sections were left overnight at room temperature and protected from light. Thereafter, the sections were washed twice for 15 minutes with PNA Wash Solution (70% formamide, 10 mM Tris pH 7.5). Finally, the sections were washed with 0.01% Tween-20 in PBS. To identify TelC-Cy3 signal in microglia, Iba-1 labeling was performed according to a published procedure, and counterstained with DAPI\u003csup\u003e\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e\u003c/sup\u003e. Brain sections were mounted and imaged using Zeiss LSM880 inverted scanning confocal microscope (Carl Zeiss Microscopy, Thornwood, New York) with 10 series of 1 \u0026micro;m sections. Z-max projections were analyzed; TelC-Cy3 signal was quantified using a published method\u003csup\u003e\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u003c/sup\u003e. Briefly, using FIJI\u003csup\u003e\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e\u003c/sup\u003e, the background-corrected TelC-C3 signal in the nucleus of Iba-1\u0026thinsp;+\u0026thinsp;microglia was calculated by subtracting background from the average of top 20% of red pixels. For each genotype, four male and four female mice were analyzed, with three images taken per animal, and 9\u0026ndash;20 Iba1\u0026thinsp;+\u0026thinsp;cells per animal were quantified. On average, WT animals were 8.5 months old, and G3 animals were 8.62 months old.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eNuclei isolation from frozen mouse hippocampi\u003c/h2\u003e \u003cp\u003eFive female animals of each genotype were used for snRNA-seq.\u0026nbsp;Nuclei isolation was done as described, with modifications\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e\u003c/sup\u003e. In brief, mouse hippocampi were dissected from frozen brain tissue before placed in 1,500 \u0026micro;L of Sigma nuclei PURE lysis buffer (Sigma, NUC201-1KT). Hippocampal samples were homogenized with a Dounce tissue grinder (Sigma, D8938-1SET) with 20 strokes using pestle A, followed by 15 strokes using pestle B. After homogenization, the tissue was filtered through a 35-\u0026micro;m cell strainer, followed by centrifugation at 600g for 5 minutes at 4\u0026deg;C. The resulting pellet was washed three times with 1 mL of PBS containing 1% bovine serum albumin (BSA), 20 mM DTT, and 0.2 U\u0026micro;L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e recombinant RNase inhibitor. The nuclei sample were then centrifuged at 600g for 5 minutes at 4\u0026deg;C and subsequently resuspended in 800 \u0026micro;L of PBS containing 0.04% BSA and 1X DAPI, followed by fluorescence-activated cell sorting (FACS) to remove cell debris.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eDroplet-based snRNA-seq\u003c/h2\u003e \u003cp\u003esnRNA-seq libraries were prepared with Chromium Single Cell 3\u0026rsquo; Reagent kits (v3; 10X Genomics, PN-1000075). The libraries were sequenced on a NovaSeq 6000 sequencer (Illumina) with 100 cycles.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of droplet-based snRNA-seq data\u003c/h2\u003e \u003cp\u003eRNA reads sequenced from the snRNA-seq library were aligned to mm10 genome using Cell Ranger software (v.3.1.0; 10X Genomics) to generate raw gene counts. Reads mapped to pre-mRNA were counted to include un-spliced nuclear transcripts. Cell barcodes were called using Cell Ranger 3.1.0 default parameters. We further removed genes expressed in no more than three cells, cells with unique gene counts over 8,000 or less than 100, and cells with more than 5% mitochondrial reads. High-confidence doublets were removed from individual samples using DoubletFinder\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Sample integration, normalization and clustering were done with the Seurat package v3.0.1\u003csup\u003e77\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn brief, integration anchors were computed for the datasets using the top 30 principal components, and the datasets were integrated using the anchor set. The integrated dataset was scaled by the total library size multiplied by a scale factor (10,000) and transformed to log space. Principle component analysis was done on the highly variable genes, and t-distributed stochastic neighbor embedding was run on the top 30 principal components. k.param was computed using the top 30 principal components, and cells clusters were identified using the FindCluster function with a resolution of 0.02. Uniform Manifold Approximation and Projection (UMAP) was run on the top 30 principal components. Cell-type labels were assigned to teach cluster using statistical enrichment for sets of marker genes and performing manual evaluation of gene expression for small sets of known marker genes. The dataset was then split into individual datasets based on cell-type identity. Differential gene expression analysis was done using the FindMarkers function with MAST as the method used. Ingenuity Pathway Analysis (IPA) and MSigDB gene annotation database were used to identify gene ontology and pathways enriched in the differentially expressed genes (DEGs). To address multiple testing, Benjamini-Hochberg approach was used to generate corrected false discovery rate (FDR). For trajectory analysis, the dataset was converted to a monocle3 object and analyzed using the Monocle3 package\u003csup\u003e\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e,\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e\u003c/sup\u003e. Pseudotime analysis was done on the microglia data using WT microglia cluster as the origin. To evaluate cellular interactions between microglia and neuronal cells, we applied CellChat (v.1.6.1) to examine the ligand-receptor interactions inferred by the dataset\u003csup\u003e\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e\u003c/sup\u003e. Normalized integrated data was used as input, and the analysis followed the CellChat tutorial with default parameters and CellChatDB.mouse as the interaction database. Ligand-receptor interactions were plotted using netVisual_chord_gene and netVisual_bubble functions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eBulk RNA-seq\u003c/h2\u003e \u003cp\u003ePrimary microglia and neurons were disassociated from the tissue plates using Accutase (ThermoFisher, 00-4555) and collected by 3-minute centrifugation at 500 \u0026times; g. mRNA was extracted according to the manufacturer\u0026rsquo;s protocol (ZYMO Research, \u003cem\u003eQuick\u003c/em\u003e-RNA Microprep Kit). Isolated RNA was sent to Novogene Co. for quality control, library preparation, and sequencing. RNA-seq read mapping was performed using the STAR program and with the GENCODE GRCh38.p13 as reference. The read count table was generated with the Rsubread package. Differential gene expression was calculated with the DEseq2 package.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eLipofuscin imaging and quantification\u003c/h2\u003e \u003cp\u003ePFA-fixed hemibrain sections were washed with 0.5% Triton in PBS and mounted for imaging on Keyence BZ-9000 inverted epifluorescence microscope (Keyence, Osaka, Japan). The entire hemibrain section was scanned at 10x magnification using Cy3 channel, and the individual images were stitched together using Keyence BZ-X Analyzer software. All images were thresholded uniformly, and a 400x400 pixel square was cropped in primary somatosensory cortical region for endogenous fluorescence quantification. Using \u0026ldquo;Analyze Particles\u0026rdquo; in FIJI, the area of lipofuscin particles was quantified. Four mice for WT (two males and two females, average age: 9 months old) and three for G3 (one male and two females, average age: 9.3 months old) were quantified, with eight sections per mouse. Average numbers of puncta were 93 per animal for Terc\u003csup\u003e+/+\u003c/sup\u003e and 168 for G3 Terc\u003csup\u003e\u0026minus;/\u0026minus;\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eCerebrospinal fluid extraction\u003c/h2\u003e \u003cp\u003eCSF extraction procedure was described in detail\u003csup\u003e\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e\u003c/sup\u003e. Briefly, mice were deeply anesthetized using Fatal-Plus (pentobarbital sodium) and positioned prone over a 15-mL conical tube to place the cervical spine in flexion. The mouse occiput was palpated to locate the cisternal magna, and a 30G insulin needle was punctured and advanced less than 4mm deep. CSF was collected by slowly pulling the syringe plunge. Approximately 15 \u0026micro;L CSF was collected from each animal. CSF was spun down at 600g for 5 minutes in 4⁰C. Supernatant was collected, flash-frozen on dry ice and stored at -80⁰C until analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eELISA and multiplex bead-based immunoassay\u003c/h2\u003e \u003cp\u003e Extracted CSF was diluted in PBS and assayed using the Mouse Pref-1/DLK-1/FA1 ELISA Kit (Invitrogen, EM66RB), according to the manufacturer\u0026rsquo;s instructions. Other SASP cytokines were measured with a MILLIPLEX MAO mouse cytokine/chemokine magnetic bead kit (Millipore) using a MAGPIX system.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eHuman iPSC differentiation into microglia and senescence induction\u003c/h2\u003e \u003cp\u003eiPSCs from an adult female with no known diseases were purchased from WiCell (UCSD072i-1-3) for the differentiation of microglia. Cells were cultured in an incubator at 37\u0026deg;C with 5% CO\u003csub\u003e2\u003c/sub\u003e. iPSCs were passed three times for expansion purpose before the start of experiments. Bibr1532 (60 \u0026micro;M; Millipore Sigma, 508839) was added to iPSCs culture after starting the experiments, and the iPSCs were cultured in BIBr1532-containing medium for 30 days before microglia differentiation. iPSCs were passed at a ratio of 1:12 every time the culture reaches 80% confluency. Bibr1532 was withdrawn after 30 days, and the iPSCs were differentiated into macrophage progenitors via a 10-day protocol, as described\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. The macrophage progenitors were then further matured into microglia-like cells via a 13-day protocol with macrophage colony\u0026ndash;stimulating factor and interleukin 34 in RPMI supplemented with 10% FBS as described\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. Differentiation quality control was conducted through IBA1 (Abcam, ab5076) and TMEM119 (Sigma, HPA051870) immunocytochemistry. Matured microglia were treated with 50 \u0026micro;M Etoposide (Millipore Sigma, E1383) for 24 hours and then recovered for 24 hours before subsequent experiments.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eHuman iNeuron differentiation\u003c/h2\u003e \u003cp\u003eHuman iNeurons are differentiated as described, where WTC11, iPSCs from a male, were engineered for inducible expression of Ngn2 from a transgene integrated in the AAVS1 locus\u003csup\u003e\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e,\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e\u003c/sup\u003e. The differentiation process followed the published protocol\u003csup\u003e\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e\u003c/sup\u003e. Briefly, the iPSCs were pre-differentiated to neuronal precursor cells in pre-differential medium containing doxycycline. On day 0, neuronal precursor cells were replated in maturation medium containing doxycycline. Doxycycline was removed from the maturation medium on day 7. Thereafter, one-half of the maturation medium was replaced with fresh medium weekly until the cells were collected.\u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eImmunocytochemistry\u003c/h2\u003e \u003cp\u003eiPSC-derived microglia and neurons on coverslips were fixed in 4% paraformaldehyde in PBS for 30 minutes and then washed three times for 5 minutes each with PBS. The cells were then permeabilized with 0.1% Triton X-100 in PBS (PBS-T) before blocking with 5% normal donkey serum (NDS) in PBS-T for 1 hour at room temperature. Cells were washed three times with PBS after blocking. Primary antibodies were added in PBS-T and incubated at 4\u0026deg;C overnight. The secondary antibodies were added to the cells for 2 hours at room temperature after washing with PBS for three times. DAPI was added to the cells for nuclei labeling for 10 minutes before visualization. Images were acquired with a laser scanning confocal microscope (Zeiss, LSM 700) using a 63X oil objective or a Apotome3 microscope (Zeiss, Axio Observer). The image acquisition settings were chosen to prevent most of the brightest pixel intensities from reaching saturation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003eCalcium imaging\u003c/h2\u003e \u003cp\u003eiPSCs were differentiated into neurons on coverslips as described. Neurons were transduced with hSyn-jGCaMP8f lentivirus on D6. The lentiviral construct was made using the third generation lentiviral plasmid FUGW (Addgene, 14883), where the Pacl\u0026thinsp;+\u0026thinsp;EcoRI fragment was replaced by the hSyn-GCaMP8f fragment\u003csup\u003e\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e\u003c/sup\u003e. The medium containing the lentiviral construct was replaced by fresh medium on Day 7. DLK1 treatment started on Day 14, and the cells were imaged and sequenced on Day 21. At the time of imaging, the coverslip was gently washed and placed into a glass-bottom chamber (Warner Instruments, RC-26G) containing Ca\u003csup\u003e2+\u003c/sup\u003e imaging buffer (20 mM HEPES, 119 mM NaCl, 5 mM KCl, 2 mM MgCl\u003csub\u003e2\u003c/sub\u003e, 30 mM glucose, 2 mM CaCl\u003csub\u003e2\u003c/sub\u003e, pH 7.2\u0026ndash;7.4). The temperature was maintained at 37\u0026deg;C by a dual chamber heat controller (Warner Instruments, TC-344C). Fluorescence time-lapse images were collected on a microscope (Nikon, FN1) using a 60X, 1.0 NA objective (Nikon, CFI APO 60XW NIR) and a C-FL GFP filter cube. An X-CITE LED illuminator (Nikon) was used for excitation. Images were collected using an ORCA-Fusion CMOS camera (Hamamatsu) with 4\u0026times;4 binning (576\u0026times;576-pixel resolution, 16-bit grayscale depth, 0.43 \u0026micro;m/pix) and NIS-Elements AR software (Nikon). Exposure time was set to 20 milliseconds. For spontaneous activities, eight fields per coverslip were acquired at 30 Hz for 2 minutes. After imaging spontaneous activity, one filed per coverslip was imaged for 10 minutes at 20 Hz to minimize photo-bleaching immediately after 50 mM KCl perfusion.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003ch2\u003eGeneration of AAV\u003c/h2\u003e \u003cp\u003eThe extracellular domain of mouse Dlk (aa residues 1\u0026ndash;170) (sDLK1), followed by a T2A and a GFP gene, was made under a CAG promoter and placed into an AAV-PHPeB plasmid. The AAV-PHPeB virus was made at the University of Pennsylvania (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gtp.med.upenn.edu/vector-core\u003c/span\u003e\u003cspan address=\"https://gtp.med.upenn.edu/vector-core\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The intravenous injection of AAV particles encoding the mouse sDLK1 was performed, and a control PHP-eB AAV encoding GFP only was applied as a negative control in C57BL/6 mice. The titer for each AAV particles was 1 x 10\u003csup\u003e11\u003c/sup\u003e per mouse, and both males and females were used in this study. At 1\u0026ndash;2 months after injection, mice were anesthetized and perfused with ice-cold PBS. Samples were harvested for biochemical or histological analysis.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe sample size for each experiments was determined on the basis of previous publications\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e\u003c/sup\u003e. Statistical analyses were performed using R v.4.1.0 (R Foundation for Statistical Computing, Vienna, Austria) or GraphPad Prism 9 (Graphpad, San Diego, California) as indicated in the legends and Methods. 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Nat Neurosci 26:737\u0026ndash;750\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Supplementary Tables","content":"\u003cp\u003eSupplementary Tables 1 to 5 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","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":"","lastPublishedDoi":"10.21203/rs.3.rs-5014333/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5014333/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAging has a critical role in the development of neurodegenerative disorders, such as Alzheimer\u0026rsquo;s disease and Parkinson\u0026rsquo;s disease. In the current study, we investigated the impact of aging on the brain through telomere shortening, a physiological change correlated with aging. Animals with shortened telomeres exhibit cognitive decline and exacerbated lipofuscinosis in the brain. Our single-nuclei transcriptome analysis revealed that telomere shortening led to the emergence of a senescent microglia population reminiscent of a senescence-associated secretory phenotype signature, and oligodendrocyte lineage cells with disrupted maturation and differentiation profiles. Using iPSC-derived microglia with shortened telomeres, we identified DLK1 as a novel senescence-associated ligand secreted by senescent microglia. Depletion of microglia abolished the DLK1 elevation in the cerebral spinal fluid of telomere-shortened mice. Elevation of soluble DLK1 induced demyelination and disruption of neuronal calcium signaling. Our findings highlighted the induction of microglia senescence by telomere shortening and identified DLK1 as a new senescence-associated ligand by which senescent microglia disrupts normal myelination and neuronal calcium activity.\u003c/p\u003e","manuscriptTitle":"Soluble DLK1 secreted by telomere-shortening-induced senescent microglia impairs myelination and alters neuronal activity","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-21 16:03:31","doi":"10.21203/rs.3.rs-5014333/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","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":"6f42c10a-a787-49c2-b66e-cb6f5ed1f56a","owner":[],"postedDate":"November 21st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":37461244,"name":"Biological sciences/Neuroscience/Neural ageing"},{"id":37461245,"name":"Biological sciences/Cell biology/Senescence"}],"tags":[],"updatedAt":"2024-11-21T16:03:31+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-21 16:03:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5014333","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5014333","identity":"rs-5014333","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}