Transcriptional and epigenetic repression of hematopoietic stem cells underlies bone marrow failure after spinal cord injury

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Rodgers , View ORCID Profile Elizabeth A.R. Garfinkle , View ORCID Profile Kristina A. Kigerl , View ORCID Profile Elham Asghari Adib , View ORCID Profile Katherine A. Mifflin , View ORCID Profile Jodie Hall , View ORCID Profile Rohan Kulkarni , View ORCID Profile Cankun Wang , View ORCID Profile Chinmayee Goda , Ana C Rodrigues Dias , Zhen Guan , Malith Karunasiri , Qin Ma , Katherine E. Miller , Adrienne M. Dorrance , View ORCID Profile Phillip G. Popovich doi: https://doi.org/10.1101/2025.10.05.680535 Kyleigh A. Rodgers 1 Department of Neuroscience, The Ohio State University 2 Medical Scientist Training Program, The Ohio State University Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Kyleigh A. Rodgers Elizabeth A.R. Garfinkle 3 The Steve and Cindy Rasmussen Institute for Genomic Medicine, Abigail Wexner Research Institute at Nationwide Children’s Hospital 4 Department of Pediatrics, The Ohio State University Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Elizabeth A.R. Garfinkle Kristina A. Kigerl 1 Department of Neuroscience, The Ohio State University 5 Belford Center for Spinal Cord Injury, The Ohio State University Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Kristina A. Kigerl Elham Asghari Adib 1 Department of Neuroscience, The Ohio State University Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Elham Asghari Adib Katherine A. Mifflin 1 Department of Neuroscience, The Ohio State University 5 Belford Center for Spinal Cord Injury, The Ohio State University Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Katherine A. Mifflin Jodie Hall 1 Department of Neuroscience, The Ohio State University 5 Belford Center for Spinal Cord Injury, The Ohio State University Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jodie Hall Rohan Kulkarni 6 Huntsman Cancer Institute at the University of Utah , Salt Lake City, UT Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Rohan Kulkarni Cankun Wang 7 Department of Biomedical Informatics, The Ohio State University Wexner Medical Center Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Cankun Wang Chinmayee Goda 6 Huntsman Cancer Institute at the University of Utah , Salt Lake City, UT Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Chinmayee Goda Ana C Rodrigues Dias 1 Department of Neuroscience, The Ohio State University 5 Belford Center for Spinal Cord Injury, The Ohio State University Find this author on Google Scholar Find this author on PubMed Search for this author on this site Zhen Guan 1 Department of Neuroscience, The Ohio State University Find this author on Google Scholar Find this author on PubMed Search for this author on this site Malith Karunasiri 8 Comprehensive Cancer Center, The Ohio State University Find this author on Google Scholar Find this author on PubMed Search for this author on this site Qin Ma 7 Department of Biomedical Informatics, The Ohio State University Wexner Medical Center Find this author on Google Scholar Find this author on PubMed Search for this author on this site Katherine E. Miller 3 The Steve and Cindy Rasmussen Institute for Genomic Medicine, Abigail Wexner Research Institute at Nationwide Children’s Hospital 4 Department of Pediatrics, The Ohio State University Find this author on Google Scholar Find this author on PubMed Search for this author on this site Adrienne M. Dorrance 6 Huntsman Cancer Institute at the University of Utah , Salt Lake City, UT Find this author on Google Scholar Find this author on PubMed Search for this author on this site Phillip G. Popovich 1 Department of Neuroscience, The Ohio State University 2 Medical Scientist Training Program, The Ohio State University 5 Belford Center for Spinal Cord Injury, The Ohio State University Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Phillip G. Popovich For correspondence: Phillip.popovich{at}osumc.edu Abstract Full Text Info/History Metrics Preview PDF Abstract Spinal cord injury (SCI) exerts profound systemic effects that extend beyond the nervous system, including the onset of bone marrow failure. Here, we show that SCI impairs the ability of hematopoietic stem cells (HSCs) to exit quiescence, proliferate, and differentiate, ultimately compromising long-term hematopoiesis. Using in-vivo transplantation assays, single-cell transcriptomics, and chromatin accessibility profiling, we show that SCI suppresses canonical stress-induced transcriptional programs in HSCs, including those governing cell cycle progression and DNA repair. These transcriptional changes are accompanied by epigenetic remodeling, with reduced chromatin accessibility at key genomic loci required for genome maintenance. Functionally, SCI HSCs exhibit impaired proliferation, persistent DNA damage, and an inability to resolve oxidative stress, even in the absence of ongoing injury. These defects culminate in bone marrow failure and pancytopenia in recipient mice. Our findings reveal a previously unrecognized systemic consequence of SCI and underscore the need for therapeutic strategies to preserve hematopoietic integrity following SCI. Download figure Open in new tab Key Findings SCI prevents stress-induced transcriptional programs in HSCs. DNA repair genes in HSPCs are epigenetically silenced after SCI. SCI HSCs accumulate ROS and DNA damage. SCI HSCs are hypersensitive to genotoxic stress. SCI HSCs fail long-term hematopoiesis post-transplant. Introduction Hematopoiesis is a tightly regulated and continuous process by which all mature blood cells are created, primarily in bone marrow. This process is orchestrated by a hierarchy of cells starting with hematopoietic stem cells (HSCs) and then progressing to hematopoietic progenitor cells (HPCs) ( Laurenti & Göttgens, 2018 ; Pinho & Frenette, 2019 ; Rieger & Schroeder, 2012 ; Sawai et al., 2016 ; Seita & Weissman, 2010 ). HSCs are multipotent cells capable of self-renewal and differentiation into multiple cell lineages ( Bernitz et al., 2016 ; Notta et al., 2011 ). HPCs, which derive from HSCs, lack self-renewal capacity but exhibit robust proliferative potential, enabling rapid expansion and differentiation into mature blood cells ( Cheshier et al., 1999 ; Johnson et al., 2020 ; Majeti et al., 2007 ; J. Sun et al., 2014 ; van Velthoven & Rando, 2019 ). These functional distinctions allow HSCs to support long-term hematopoiesis, while HPCs ensure short-term hematopoietic production, particularly in response to infection, injury, or inflammation ( Baldridge et al., 2010 ; Courties et al., 2014 ; Heidt et al., 2014 ; Vasamsetti et al., 2018 ). The capacity of HSCs and HPCs (collectively referred to as HSPCs) to adapt their hematopoietic output to meet the organism’s requirements is linked to their ability to manage replicative and genotoxic stress ( Asai et al., 2012 ; Essers et al., 2009 ; Singh et al., 2020 ). In response to a break in homeostasis, HSPCs proliferate and differentiate. This replication stress exposes them to elevated levels of reactive oxygen species (ROS) and causes DNA damage, which can impair HSC function ( Flach et al., 2014 ; Flint et al., 2007 ; Jacobs et al., 2022 ; N. Li et al., 2022 ; Porto et al., 2015 ; Walter et al., 2015 ; A. Wilson et al., 2008 ). To preserve hematopoietic fidelity, damaged cells may halt cell cycle progression or induce cell death to prevent the propagation of mutations that could lead to hematological malignancies or bone marrow failure ( Asai et al., 2012 ; King & Cidlowski, 1998 ; Y. Liu et al., 2009 ; Wang et al., 2012 ). Inadequate stress responses caused by faulty DNA repair, poor ROS handling, or disrupted cell cycling can impair hematopoiesis, resulting in bone marrow failure, immunodeficiency, anemia, and higher risk of infection ( Jacobs et al., 2022 ; Till & Mcculloch, 1961 ; Wang et al., 2012 ). Emerging evidence suggests that acquired injuries, including those affecting the central nervous system (CNS), such as spinal cord injury (SCI), stroke, or traumatic brain injury can disrupt hematopoiesis ( Carpenter et al., 2020 ; Chernykh et al., 2006 ; Courties et al., 2014 , 2019 ; Iversen et al., 2000 ; W. Liu et al., 2023 ; Shi et al., 2021 ). SCI is associated with various hematological complications, including chronic immune suppression, anemia, high infection risk, and impaired wound healing ( Brommer et al., 2016 ; Carpenter et al., 2020 ; Chernykh et al., 2006 ; Evans et al., 2008 ; Failli et al., 2012 ; Frisbie, 2010 ; Garcia-Arguello et al., 2017 ; Iversen et al., 2000 ; Jaja et al., 2019 ; Kopp et al., 2013 , 2017 ; Marbourg et al., 2017 ; Sekhon & Fehlings, 2001 ; X. Sun et al., 2016 ; Szymczak et al., 2021 ). Dysregulated systemic inflammation and altered platelet function further exacerbate vascular complications and hinder recovery ( DiSabato et al., 2024 ; Failli et al., 2012 ; Furlan & Fehlings, 2008 ; Kigerl et al., 2016 ; Myllynen et al., 1985 ; Popa et al., 2010 ; Rodgers et al., 2022 ). The mechanisms by which SCI alters hematopoiesis, particularly in the early post-injury period, remain incompletely characterized ( Rodgers et al., 2022 ). Previous research shows that SCI damages bone marrow HSPCs ( Carpenter et al., 2020 ; Chernykh et al., 2006 ; Iversen et al., 2000 ). HSPCs isolated from SCI mice as early as 3 days post-injury (dpi) exhibit aberrant patterns of proliferation and reduced ability to engraft lethally-irradiated recipient mice ( Carpenter et al., 2020 ). Given the indispensable role of functional HSPCs in hematopoiesis, immunity, and health, this study examined mechanisms underlying SCI-induced changes in HSPC function. New data show that within 1 dpi, HSCs from SCI mice exhibit significant downregulation of gene expression, increased DNA damage, and impaired ROS management. These defects prevent SCI HSCs from exiting quiescence and entering the cell cycle. Consequently, SCI HSCs fail to restore hematopoiesis when transplanted into uninjured, irradiated recipient mice. These data suggest that hematopoietic defects are cell-autonomous and persist within the HSCs over time. Further, we found that this impairment results, at least in part, from epigenetically altered HSPC chromatin accessibility and reduced DNA repair, which was evident within 1 dpi. Collectively, these results demonstrate that SCI induces rapid and persistent intrinsic defects in HSCs, disrupting hematopoiesis and likely contributing to many systemic complications seen in Individuals with SCI. This work emphasizes the importance of addressing hematopoietic dysfunction as a critical and modifiable component of SCI pathology to improve patient outcomes following SCI. Additionally, these data underscore the importance of the spinal cord in regulating hematopoiesis. Results Spinal cord injury impairs bone marrow hematopoietic stem cell function and lineage commitment To study how HSPC dysfunction emerges after SCI, we used single cell RNA-sequencing (scRNA-seq) to profile transcriptomic changes in HSCs and their downstream progenitors (HPCs), which collectively form the blood system. Whole bone marrow enriched for cKit+ HSPCs was collected at 1 dpi, preceding times of known HSC dysfunction ( Carpenter et al., 2020 ). To differentiate SCI-specific molecular effects from general stress responses caused by surgery and anesthesia, three groups were compared: naïve mice, sham mice, and mice that received a complete, high-thoracic spinal cord transection injury (SCI). Sham mice receive a laminectomy, but not SCI. These mice are anesthetized and experience surgical bleeding and muscle damage like SCI mice, but without any damage to the spinal cord. Unsupervised clustering of 16,431 bone marrow cells revealed a heterogeneous mix of 18 HSPC clusters ( Fig. 1a ). Cell clusters were annotated using differentially expressed genes (DEGs) and known lineage markers ( Fig. S1a-b ) ( Dahlin et al., 2018 ; Kucinski et al., 2024 ; Nestorowa et al., 2016 ; N. K. Wilson et al., 2015 ). Phenotypic analysis revealed SCI-induced shifts in cell composition ( Fig. S1c ), prompting further examination of distinct HSPC functional classes ( Fig. 1b ): LSKs (HSCs and MPPs) and LKs (common myeloid progenitors [CMPs], common lymphoid progenitors [CLPs], granulocyte-monocyte progenitors [GMPs], and megakaryocyte-erythroid progenitors [MEPs]) as described ( Dahlin et al., 2018 ; Komic et al., 2025 ; Kucinski et al., 2024 ; Nestorowa et al., 2016 ). Download figure Open in new tab Fig S1. Additional scRNA-seq data in bone marrow immune cell precursors at 1 dpi (a) Dot plot depicting scaled expression of canonical marker genes used for cell type annotation. (b) Feature plots showing expression of canonical LSK marker genes. (c) Percentages of each cell cluster within the sequenced population. (d) Number of DEGs in MPP subsets comparing sham to naïve and SCI to sham. (e) GSEA pathway analysis of DEGs in MPP4s after SCI (compared to sham). Download figure Open in new tab Fig 1. Single cell RNA-sequencing reveals impaired stress response in HSCs after SCI (a) UMAP projection of all sequenced bone marrow cells from naïve, sham, and SCI mice at 1 dpi. (b) Schematic demonstrating functional classes of HSPCs: LSKs (Lineage-Sca1+cKit+) and LKs (Lineage-Sca1-cKit+). LSKs differentiate into LKs, which then derive downstream precursors (other). (c) scRNA-seq clusters were classified as LSKs (HSCs, MPP2-4) and LKs (GMP, CLP, MEP, CMP) or other (non-LSKs and non-LKs) and represented as a percentage of cells sequenced. (d) LSK subtypes as a % of LSKs sequenced. (e) Number of upregulated and downregulated DEGs in HSCs from sham vs. naïve and SCI vs. sham mice (f-g) GSEA pathway analysis of DEGs in HSCs in sham vs. naïve (f) and SCI vs. sham groups (g). To establish a baseline for evaluating the effects of SCI, we first analyzed the effects of sham surgery, a stressor known to affect hematopoiesis ( Carpenter et al., 2020 ). Compared to naïve controls, sham surgery induced a canonical stress response, expanding both LSKs and LKs ( Fig. 1c ) ( Baldridge et al., 2010 ; Essers et al., 2009 ; Leimkühler et al., 2019 ). In contrast, SCI further increased LSK cells, but LK populations were markedly decreased ( Fig. 1c ), resulting in skewed hematopoiesis. Subtype analysis revealed that SCI samples were enriched for HSCs within the LSK compartment ( Fig. 1d ). These compositional changes were associated with distinct transcriptional profiles. In sham HSCs, 84% of DEGs (n=84 of 100) were upregulated ( Fig. 1e ), including genes involved in metabolism, survival, and proliferation, although these pathways were not significant (padj >0.05) ( Fig. 1f ) ( Carow & Rottenberg, 2014 ; Saito et al., 2024 ). Downregulated genes were mostly associated with biosynthetic and RNA-related processes ( Fig. 1f ) ( Khajuria et al., 2018 ). Sham MPPs also showed increased transcriptional activity, with most DEGs being upregulated compared to naive ( Fig. S1d ). In contrast, SCI caused widespread transcriptional suppression in HSCs, with ∼68% of DEGs (n=541 of 799) downregulated relative to sham HSCs ( Fig. 1e ). These downregulated genes are involved in DNA strand break repair, chromosomal organization, cell cycling, and DNA replication ( Fig. 1g ) ( Deutsch et al., 2003 ; Neizer-Ashun & Bhattacharya, 2021 ; Patil et al., 2013 ; Vasanthakumar et al., 2016 ). Upregulated genes were enriched in immune activation, transcriptional regulation, cellular trafficking, and membrane dynamics ( Fig. 1g ) ( Chia et al., 2025 ; Freire & Conneely, 2018 ; Land et al., 2013 ; C. Li et al., 2024 ). SCI did not uniformly suppress gene transcription across MPP subsets ( Fig. S1d ). Only in MPP4 cells was downregulation of DEGs linked to significant (padj < 0.05) pathway enrichment, notably in biosynthetic, metabolic, and proliferative pathways ( Fig. S1e ). scRNA-seq data indicate stress and SCI-dependent changes in HSPC proliferation and differentiation. To validate these data, we directly measured these readouts ( Fig. 2a ) using flow cytometric analysis of intracellular Ki67 staining in HSPCs. Compared to sham controls, SCI reduced the percentage of HSCs and progenitors in S-G2-M phases of the cell cycle ( Fig. 2b-g ). These data were confirmed in separate cell preparations using BrdU incorporation and immunophenotyping ( Fig. S2 ). The MPP-to-HSC ratio, used here as a proxy for productive differentiation ( Nishi et al., 2025 ), was reduced in SCI mice relative to both naïve and sham groups, suggesting impaired transition from HSCs to MPPs ( Fig. 2h ). Sham-induced HSPC proliferation was accompanied by increased ex vivo proliferative capacity, reflecting productive differentiation and activation, whereas SCI prevented this response ( Fig. 2i ). Download figure Open in new tab Fig S2. BrdU incorporation assay (a) Schematic to assess HSPC proliferation in-vivo using the BrdU incorporation assay which labels newly synthesized DNA. (b-e) Proliferating (BrdU+) bone marrow HSCs, MPPs, HPC1s, and HPC2s at 1 dpi from 4 independent experiments. Data are box-and-whisker plots with min-max with the line at the median. Individual data points overlaid. (f-i) Total numbers of HSCs, MPPs, HPC1s, and HPC2s in bone marrow at 1 dpi from 4 independent experiments. Data are box-and-whisker plots with min-max with the line at the median. Individual data points overlaid. Values in (b-i) were obtained by back-calculation of flow cytometry percentages to automated hemacytometry counts. These values were averaged for sham mice, and each experimental sample was divided by this value and multiplied by 100 to get a percentage of sham such that values of y=100 indicate equivalence to sham. This was done to normalize for batch effects between independent studies. Download figure Open in new tab Fig 2. SCI prevents stress-induced HSC proliferation (a) Schematic of hematopoiesis demonstrating that HSCs self-renew or differentiate into downstream progenitors distinguished by flow cytometry cell surface markers shown (b-f) % of HSPCs positive for Ki67 and nuclear DNA stain (Hoechst 33342) by flow cytometry. Ki67 distinguishes proliferation status while the DNA stain determines the phase status. Data are box-and-whisker plots with min-max with the line at the median. Individual data points overlaid. One SCI mouse was excluded in (b) due to issues with flow staining in the HSC gate. (g) Represenative select flow plots from data in (b-f). Gated on the cell populations labeled. (h) MPP-to-HSC ratio, used to assess the ratio of downstream progenitors to HSCs in bone marrow. (i) In-vitro Colony Forming Unit assay performed at 1 dpi. Whole bone marrow was plated in 3 mL of MethoCult M3434, and total colonies were enumerated per 20k WBM cells as a readout of functional progenitor proliferative capacity. (j-m) Cell cycle analysis of the scRNA-seq dataset in LSKs at 1 dpi. Finally, we analyzed cell cycle phase-specific gene expression in scRNA-seq LSKs ( Fig. 2j-m ). A large proportion (>50%) of sham HSCs were in S phase compared to naïve, suggesting active stress-induced cycling of sham HSCs. Conversely, most (∼78%) SCI HSCs were arrested in G1, with few HSCs entering S or G2/M phases ( Fig. 2j ). Similar SCI-dependent suppression of cell cycle genes was observed in MPPs ( Fig. 2k-m ). Collectively, these results demonstrate that SCI induces profound and distinct alterations in HSPC composition and transcriptional activity, characterized by expansion of stem-like LSK cells and reduction of downstream progenitors, distinguishing SCI-specific hematopoietic disruption from general stress responses observed after sham surgery. SCI-induced epigenetic remodeling restricts chromatin accessibility in hematopoietic stem and progenitor cells During HSC proliferation and differentiation, chromatin is remodeled, and distinct epigenetic marks are introduced that affect the HSC transcriptome ( Iwama et al., 2005 ; Martin et al., 2021 ; Meng & Nerlov, 2025 ; Sashida & Iwama, 2012 ; Sharma & Gurudutta, 2016 ; Yu et al., 2016 ). To determine if the epigenetic identity of HSCs is influenced by SCI, we used ATAC-seq to explore the epigenome of cKit+ bone marrow cells ( Fig. 3a , S3a-c). PCA revealed markedly different chromatin accessibility profiles in SCI HSPCs compared to both sham and naïve( Fig. 3b ). Overall, 55,068 accessible regions (ARs) were identified, mostly in non-promoter regions ( Fig. 3c ). Differential chromatin accessibility analysis revealed 809 differentially accessible regions (DARs) in the SCI HSPC genome (1.47% of 55,068 ARs), with most (n=484 DARs, ∼60% of total) being less accessible compared to sham HSPCs ( Fig. 3d ). Lower accessibility dominated across all annotations (i.e., introns, exons, promoters, etc.,) after SCI ( Fig. 3d ). Sham and naïve HSPCs had similar DAR profiles. A heatmap of z-scores illustrates the top 20 DARs (using peak to nearest gene), capturing both more and less accessible regions in SCI versus sham and naive HSPCs ( Fig. 3e ). Download figure Open in new tab Fig 3. SCI alters the chromatin landscape of bone marrow immune cell precursors within 1 dpi (a) Schematic of experimental design to assess chromatin accessibility in bone marrow HSPCs at 1 dpi. (b) PCA of chromatin accessibility profiles in the total consensus peak set. (c) Genome annotation of ATAC-seq peaks in the total consensus. Accessible Regions (ARs) were identified here, prior to assessing SCI-specific effects (i.e., differential accessibility). (d) Differentially accessible regions (DARs) identified as FDR <0.05 after SCI compared to sham. % of all 809 DARs, was split by annotation (i.e., distance from transcription start site; TSS). There were no statistically significant DARs due to sham surgery alone, thus those data are not shown. Annotations (A-E) match those labeled in panel (c): A, Intron; B, TTS; C, Exon; D, Promoter; E, Intergenic. (e) Heatmap displaying Z-scores for the top 40 DARs (20 with increased and 20 with decreased accessibility) across individual samples. Each column represents an individual sample (i.e., mouse). Blue indicates decreased accessibility. Red indicates increased accessibility. (f) Pathway analysis of ATAC-seq genes mapped to nearest DARs. For SCI HSPCs, GSEA analysis of DARs mapped to nearest genes revealed less accessible regions in genes controlling DNA damage response, cytoskeletal organization, transcriptional regulation, and stress responses, including gamma radiation and heat stress ( Fig. 3f ). These findings suggest that SCI promotes epigenetic silencing of protective and homeostatic programs in HSPCs ( Jacobs et al., 2022 ; Yu et al., 2016 ). In contrast, more accessible regions of DNA in HSPCs after SCI were enriched for genes encoding pathways related to immune activation, cytokine signaling, and cell motility, such as lamellipodium morphogenesis and neutrophil degranulation ( Fig. 3f ). This shift in chromatin accessibility reflects epigenetic reprogramming of HSPCs toward inflammatory and stress-related states and is consistent with the suppressed transcriptional profile observed in SCI HSCs ( Fig 1 ). SCI suppresses DNA repair programs and reduces hematopoietic stem cell resilience to genotoxic stress We integrated our HSPC scRNA-seq and ATAC-seq datasets to assess which genes showed similar changes after SCI, with emphasis on promoter DARs that are likely associated with direct gene regulation ( Fig. S4a ) ( Huang et al., 2021 ; Starks et al., 2019 ). Six genes were identified as downregulated and having less accessible promoter regions ( Fig. S4a ). Fen1 and Lig1 mapped to HSCs within the scRNA-seq dataset, while Lig1, Cecr2, Gfra1, Cd79a, and Itga4 mapped to MPP4s ( Fig. S4a ). Fen1 and Lig1 facilitate DNA Okazaki fragment end-joining and participate in base and nucleotide-excision repair in damaged DNA. These functions are essential for preserving genome integrity, which is necessary for HSC self-renewal and differentiation (N. Li et al., 2022 ; Walter et al., 2015 ). Other genes including Lig1 , Cecr2 , Gfra1 , Cd79a , and Itga4 , support lymphoid lineage commitment ( Minegishi et al., 1999 ), chromatin remodeling ( Banting et al., 2005 ), DNA repair, and niche interactions. Lig1 and Cecr2 are involved in DNA replication and chromatin accessibility, respectively, and their suppression may impair genome maintenance and transcriptional activation. Gfra1 and Itga4 mediate survival and adhesion signals from the bone marrow niche ( Cui et al., 2017 ; L. Li et al., 2016 ), while Cd79a is essential for B cell receptor signaling and lymphoid specification ( Minegishi et al., 1999 ). The coordinated downregulation and promoter closure of these genes in MPP4s suggest that SCI disrupts lymphoid lineage commitment, by impairing transcriptional readiness, niche responsiveness, and lineage-specific gene activation. Download figure Open in new tab Fig S3. Additional ATAC-seq data in bone marrow immune cell precursors at 1 dp (a-b) Flow cytometry data on an aliquot of samples sent for ATAC-seq stained for viability via DAPI (a) and purity via cKit (b). Samples are highly viable and pure without differences between groups. Each datapoint is an individual mouse that was sequenced. (c) Representative flow plots for viability (top) and cKit purity (bottom). A tube of cells was heated to 55 degrees Celcius to obtain a strong true death signal (Heat killed). A tube of cells was not stained for cKit to obtain a true negative (No cKit). Download figure Open in new tab Fig S4. Additional HSC DNA damage and cell death data (a) Manual integration of scRNA-seq downregulated DEGs after SCI compared to sham in LSKs (HSCs, MPP2s, MPP3s, and MPP4s) with DARs from ATAC-seq of HSPCs. (b) Same dataset as presented in Fig. 4d , with the addition of cohort-matched naïve mice to demonstrate the absence of significant differences between naïve and sham groups. (c) Schematic of experiment to test for increased LSK susceptibility to irradiation-induced cell death in LSKs harvested at 1 dpi. (d-e) Flow cytometric data of the % of LSKs positive for viability dye (positive is necrotic or late apoptotic) was quantified before irradiation and divided by the value at either 1 h or 4 h post-irradiation to obtain a ratio (i.e., fold change) in cell death at each timepoint. The pre-irradiation value was set to 1 for each group (i.e., value at pre-irradiation divided by the same value = 1). Values are pooled from n=9-10 mice/ group. (f) Flow cytometric analysis of y-H2AX MFI in HSCs harvested at 1 dpi. Values are pooled from n=9-10 mice/ group. Data in Fig. 1 indicate that SCI broadly suppresses transcription of DNA repair pathway genes in HSCs, implying impaired DNA repair mechanisms. Notably, downregulated genes included Fen1, Lig1, Brca1, Brca2, Rad51b, Chek1, Atm, Pclaf, and Fanc -family genes ( Fig. 4a ), famously associated with cancer and known to be required for normal HSC function ( Fortin et al., 2021 ; Mgbemena et al., 2017 ). In contrast, compared to naïve HSCs, sham surgery induced differential expression of four DNA repair genes ( Rps3 , Mcm2 , Npm1 , Ier3 ) in HSCs, and all were upregulated. Download figure Open in new tab Fig 4. SCI induces DNA damage in HSCs (a) HSC typed cells were computationally submit from the scRNA-seq dataset. Expression of select genes involved in DNA repair are plotted in violin plots. Each dot represents expression of the indicated gene per cell. Statistical significance was calculated across comparative samples using unpaired two-tailed Wilcoxon rank test (b,d) Comet assay to assess DNA damage (% of DNA in the gel i.e., tail rather than nucleus) in FACS-purified HSCs at 1 dpi at baseline (i.e., without irradiation) as in (b) or after irradiation to induce genotoxic stress as in (d). Each data point is an individual HSC. Data are from n=9-10 pooled mice per group (c,e) Representative comet structures visualized by confocal microscopy. Z-stack images from panels were processed using maximum intensity projection. DNA was stained with Vista Green dye to visualize nuclear and fragmented DNA. High-intensity signal around the head (comet head; H) corresponds to intact nuclear DNA, while the left-trailing signal represents electrophoretically migrated DNA fragments (comet tail; T, arrowheads), indicative of strand breaks. Comet morphology is absent or minimal in undamaged cells (c, sham sample). DNA damage is expected in the 10 Gy condition (e), but more DNA remains in the head in sham HSCs, indicating less DNA damage. DNA damage inhibits cell cycle progression ( Cassimere et al., 2016 ; Hao et al., 2016 ; King & Cidlowski, 1998 ; López et al., 2025 ; Morris-Hanon et al., 2017 ; Patil et al., 2013 ; Plett et al., 2003 ; Schmitt et al., 2007 ). Our findings indicate that SCI disrupts core DNA repair programs and reduces proliferation, leading us to predict increased de novo DNA damage in HSCs isolated from SCI mice. Quantification confirmed higher levels of single- and double-strand breaks in SCI HSC DNA, compared to sham controls ( Fig. 4b-c ). No significant differences were observed between sham and naïve HSCs ( Fig. S4b ). Elevated baseline DNA damage cannot predict resilience of HSCs to subsequent genotoxic insults. Therefore, we assessed DNA damage in HSCs exposed to x-ray irradiation. Irradiation significantly increased DNA damage in all HSCs; but damage was most prominent in SCI HSCs ( Fig. 4d-e ). Irradiation also induced cell death in LSK cells from all groups, but again, the most significant changes occurred in SCI LSKs ( Fig. S4c-e ). γ-H2AX levels, indicative of DNA damage, were also increased in SCI HSCs relative to sham controls ( Fig. S4f ). Collectively, these results demonstrate that SCI causes the coordinated downregulation of DNA repair and lymphoid lineage genes, increased DNA damage, and heightened sensitivity to genotoxic stress in bone marrow HSPCs. SCI impairs antioxidant defense and exacerbates oxidative stress in hematopoietic stem cells A reduction or loss of antioxidant defense mechanisms would predispose SCI HSCs to greater DNA damage and explain cell cycle arrest, exacerbated cell death, and subsequent HSC dysfunction ( Ito et al., 2004 ; Naka et al., 2008 ; Shao et al., 2012 ; Tan & Suda, 2018 ; Tothova et al., 2007 ; Urao & Ushio-Fukai, 2014 ). In addition to the DNA repair genes that were downregulated after SCI ( Fig. 4 ), genes encoding redox regulatory and antioxidant defense proteins also were downregulated ( Fig. 5a ). Interestingly, the stress of sham surgery increased these same genes in sham HSCs compared to naïve HSCs ( Fig. 5a ). Download figure Open in new tab Fig 5. SCI impairs intracellular oxidative species reduction in HSCs (a) HSC typed cells were computationally submit from the scRNA-seq dataset. Expression of select genes involved in ROS reduction are plotted in violin plots. Each dot represents expression of the indicated gene per cell. Statistical significance was calculated across comparative samples using unpaired two-tailed Wilcoxon rank test. (b) The ability of HSCs to reduce H 2 O 2 which passively diffuses across the cell membrane and directly increases intracellular ROS content was measured at 1 dpi. Any change over time is attributed to cellular reduction via functional proteins in the cell. Flow cytometry data showing the number of HSCs positive for ROS before and over time after H 2 O 2 exposure. Values were normalized to the average prior to exposure (i.e., baseline) for each group so that a value of 1 indicates baseline. Dotted lines= final reading at 15min (mapped to aid visiblity against y-axis). n=5 mice per group were pooled. (c) Representative flow histograms. The presence of a peak at the arrow indicates ROS+ HSCs. (d) Intracellular ROS content (i.e., mean fluorescence intensity; MFI of the H 2 -DCFDA probe) in bone marrow HSCs at 15 minutes after H 2 O 2 exposure. Values were normalized to average sham values so that a value of 1 indicates equivalent to sham. To determine the functional consequence of blunted oxidative stress gene expression, SCI and sham HSCs were treated with hydrogen peroxide (H₂O₂), a potent oxidizer that, after diffusing into the cell, must be rapidly reduced to prevent toxicity ( Hanschmann et al., 2013 ; Lennicke et al., 2015 ; Picou et al., 2019 ). A typical oxidative stress response was observed in sham HSCs: exposure to H₂O₂ provoked a rapid increase in intracellular ROS, followed by a gradual return to baseline, indicating effective antioxidant defense in sham HSCs ( Fig. 5b-c ). In contrast, SCI HSCs did not reduce oxidative stress ( Fig. 5b-c ), as intracellular ROS levels remained elevated compared to baseline throughout the analysis period and were approximately 2.5 times higher than those observed in sham HSCs ( Fig. 5d ). SCI impairs long-term hematopoietic stem cell self-renewal despite preserved short-term engraftment Previously, we discovered that when bone marrow was isolated from SCI donors at 3 dpi, then transplanted into lethally-irradiated naïve recipient mice, HSPCs survived and successfully engrafted in recipient mice, a phenomenon likely explained by preferential activation of multipotent progenitor cells ( Carpenter et al., 2020 ). However, the delayed clonogenic potential of SCI HSPCs was impaired, suggesting defects in more immature HSCs ( Carpenter et al., 2020 ). New data in this report show that SCI impairs the ability of HSCs to exit quiescence, proliferate, and differentiate, in part by downregulating expression of genes involved in controlling cell cycle, oxidative stress and DNA repair. This HSC phenotype is fundamentally different from the “activated” progenitor phenotype described previously ( Carpenter et al., 2020 ) and emerges at least two days before the expansion of progenitor cells. To determine the functional implications of these early defects in SCI HSCs, we performed in-vivo competitive repopulation assays ( Fig. 6a ). One day after sham surgery or SCI, donor bone marrow cells (CD45.2+) were collected then mixed in equal ratios with sex- and age-matched congenic CD45.1 naïve competitor bone marrow cells. Mixed cell suspensions were injected into the tail veins of lethally irradiated recipient CD45.1 mice, and percent donor chimerism calculated as 100*[CD45.2%/(CD45.1% + CD45.2%)]. Our results show that SCI impairs HSC function. Although short-term hematopoiesis remained unaffected ( Fig. 6b ), recipient bone marrow had lower HSC chimerism in the SCI group after about 8 months, indicating reduced long-term repopulating ability ( Fig. 6c , S5a). Progenitor chimerism (MPP, HPC1, and HPC2) was unchanged or slightly increased, suggesting early engraftment depended on progenitor cells, not functional HSCs ( Fig. 6b,c ). Download figure Open in new tab Fig 6. SCI induces long-term HSC defects within 1 dpi (a) Schematic of primary and secondary transplant design. Donor and competitor cells are distinguishable via flow cytometry surface marker analysis of CD45.2 (donor) vs. CD45.1 (competitor) antigens (b, d) Flow cytometry data of blood from primary recipients (b) and secondary recipients (d) as % donor chimerism, calculated as 100*[CD45.2%/(CD45.1% + CD45.2%)]. (c) Flow cytometry data of bone marrow of primary recipients at endpoint as % donor chimerism in LSKs and LSK subtypes. Data are box-and-whisker plots with min-max with the line at the median. Individual data points overlaid. (e) Bone marrow from a subset of secondary recipients at endpoint was purified for primary donor cells (CD45.2) via FACS and transplanted into lethally irradiated recipient mice ( Fig. S5h ). Data are shown at 16 days after transplant due to recipients of SCI cells reaching early removal criteria (body weight loss and severe anemia). (f) Still shots of video just prior to euthanasia shows mice receiving SCI cells displayed overt lethargy and reduced locomotor activity. Yellow lines indicate individual mouse movement every approx. 5 seconds. Download figure Open in new tab Fig S5. Additional transplant data of sham or SCI bone marrow into naïve mice (a) Representative flow plots of data in ( Fig. 6c ). LT-HSCs were gated on CD150+CD48- LSKs and defined by lack of CD34 surface expression. (b) Flow plots of bone marrow transplanted into secondary recipients after samples were enriched for primary donor cells (CD45.2+) through magnetic depletion of competitor CD45.1+ cells. This bone marrow was transplanted into secondary recipients shown in Fig. 6a (c-f) Flow cytometry data of blood from primary recipients showing individual mice from Fig. 6b . Input values plotted as dottled lines (red= SCI input, blue= Sham input). (g) Representative flow plots of of data from (f) show more competitor cells (CD45.1, red arrowheads) in recipients of SCI compared to sham bone marrow. (h) Schematic of tertiary transplant design. Primary donor CD45.2+ bone marrow cells were FACS- purified from from secondary recipients. These cells were transplanted into new lethally irradiated BoyJ tertiary recipients. (i-v) Automated veterinary hemacytometer data in the blood (i-l), bone marrow (m-q) and spleen (r-v) at endpoint in tertiary recipients at 16 days after mice met early removal criteria due to weight loss > 30% with overt lethargy. SCI-induced defects in HSCs were sustained and amplified in secondary transplant recipients ( Fig. 6d ), a bona fide test of LT-HSC function ( Harrison, 1980 ). Recipients of SCI donor cells showed significantly lower peripheral blood chimerism throughout the ∼10 month monitoring period ( Fig. 6d , S5b-g). Together these data indicate that SCI impairs the self-renewal and maintenance of primitive HSCs, a deficit that is initially masked by compensatory activity in progenitors. HSC defects can be fatal ( Dykstra et al., 2011 ), and individuals with SCI have higher mortality and shorter lifespans than those with intact spinal cords ( DeVivo et al., 1989 ; DeVivo, Rutt, et al., 1992 ; Kasbekar et al., 2023 ; Savic et al., 2017 ; Shavelle et al., 2006 , 2015 ; Strauss et al., 2006 ). Because competitor cells can mask functional defects in HSCs, we transplanted purified CD45.2+ SCI or sham donor bone marrow cells into lethally irradiated tertiary recipient mice ( Fig. S5h ). Within 16 days of transplanting purified SCI donor cells, recipient mice became ill and required euthanasia based on early-removal criteria. These mice exhibited engraftment failure signs including significant (>30%) body weight loss and were severely anemic ( Fig. 6e ) compared to recipients of sham donor cells. Recipients of SCI cells also exhibited ruffled fur, microphthalmia, and overt lethargy ( Fig. 6f ) due to bone marrow failure as lymphoid organs including the blood, bone marrow, and spleen exhibited trending panleukopenia ( Fig. S5i-v ). Collectively, these data demonstrate that SCI induces durable, intrinsic defects in HSCs that compromise hematopoietic recovery and negatively affect host survival. Discussion SCI is traditionally viewed through the lens of impaired neurological functions, yet its systemic consequences, particularly those affecting hematopoiesis, remain underexplored. This study reveals that SCI imposes a profound and rapid disruption of HSC function, not as a secondary consequence of infection or immobility, but as a direct, cell-intrinsic response that develops following discrete and complete severing of the spinal cord. Within 1 dpi, SCI imprints a maladaptive program on the bone marrow stem cell compartment, characterized by transcriptional silencing, epigenetic remodeling, impaired DNA repair, and oxidative stress. These changes are not transient; they persist even when SCI-derived HSCs are transplanted into naïve, uninjured mice with intact spinal cords, suggesting a durable reprogramming of stem and progenitor cell identity by unknown mechanisms set in motion by SCI. A central finding of this study is that SCI blocks the canonical stress-induced activation of HSCs ( Flach & Milyavsky, 2018 ; Hanna & Hedrick, n.d.; Heidt et al., 2014 ; Jacobs et al., 2022 ; Singh et al., 2020 ). HSPCs, which are evolutionarily adapted to regulate hematopoietic output in response to stress ( Flach & Milyavsky, 2018 ; Heidt et al., 2014 ; Singh et al., 2020 ; Tothova et al., 2007 ), can be impaired by the very stress signals intended to activate them. In contrast to sham surgery, which triggers a robust transcriptional response in HSCs and HPCs, SCI suppresses biosynthetic and proliferative programs while activating immune-related pathways. This uncoupling of HSC activation from downstream progenitor expansion disrupts early hematopoietic hierarchy and may underlie the immune dysfunction observed in animals and humans with SCI ( Brommer et al., 2016 ; Evans et al., 2008 ; Garcia-Arguello et al., 2017 ; Jaja et al., 2019 ; Mifflin et al., 2022 ). Notably, the transcriptional suppression in SCI HSCs is accompanied by reduced chromatin accessibility at loci governing DNA repair, cell cycle progression, and oxidative stress responses, hallmarks of a dysfunctional stem cell state ( Flint et al., 2007 ; Jang & Sharkis, 2007 ; Martin et al., 2021 ; Schmitt et al., 2007 ; Shao et al., 2012 ; Takizawa et al., 2011 ; Walter et al., 2015 ). Our integrated transcriptomic and epigenomic analyses identify a core set of genes, such as Fen1 and Lig1 , that are both transcriptionally downregulated and epigenetically silenced in SCI HSCs and MPP4s, the lymphoid-primed progenitor population. In MPP4s, SCI induces coordinated downregulation of lymphoid specification genes ( Cecr2, Cd79a, Itga4, Gfra1 ) and reduces promoter accessibility at loci required for lymphoid lineage commitment. This transcriptional and epigenetic silencing suggests a lineage bias away from lymphopoiesis. Clinically, this is consistent with observations in humans with SCI, who exhibit lymphopenia and impaired lymphocyte development in secondary lymphoid organs such as the spleen ( Brommer et al., 2016 ; Lucin et al., 2007 , 2009 ; Meisel et al., 2005 ; Noble et al., 2022 ; Prüss et al., 2017 ; Riegger et al., 2007 ). This early lineage bias may contribute to the chronic immune suppression observed in Individuals with SCI and suggests that restoring lymphoid lineage fate in MPPs could be a viable therapeutic strategy. The absence of significant DARs in our ATAC-seq analysis due to sham surgery (compared to naïve) is intriguing but could indicate that a single acute stressor is not sufficient to irreversibly modify the HSPC genome or the function of these cells. Functionally, SCI HSCs exhibit persistent DNA damage, hypersensitivity to genotoxic stress, and an inability to resolve intracellular oxidative species, culminating in cell cycle arrest and death. Because competitive repopulation assays rely on successful engraftment, they may selectively capture only the least impaired HSCs. Severely damaged HSCs may fail to engraft altogether, meaning the observed deficits likely underestimate the true extent of dysfunction. Tertiary transplants, which eliminate competitor cells, reveal the full impact of SCI on hematopoietic failure. The intrinsic failure of HSCs following SCI may explain why individuals with SCI experience shortened lifespans despite advances in medical care ( DeVivo et al., 1989 ; DeVivo, Rutt, et al., 1992 ; DeVivo, Stover, et al., 1992 ; DeVivo & Farris, 2011 ; Kopp et al., 2017 ; Savic et al., 2017 ; Shavelle et al., 2006 , 2015 ; Strauss et al., 2006 ). HSCs are essential for sustaining long-term hematopoiesis and immune competence, particularly under conditions of physiological stress ( Jacobs et al., 2022 ; Kasbekar et al., 2023 ; Singh et al., 2020 ). While our data show that progenitor cells retain short-term function and can support early hematopoietic recovery, this compensation is not durable. Over time, the inability of SCI HSCs to self-renew and replenish the progenitor pool may lead to cumulative defects in immune cell production, contributing to chronic immune suppression, anemia, and increased infection risk, hallmarks of SCI pathology ( Frisbie, 2010 ; Harrigan et al., 2023 ; Mifflin et al., 2022 ; Noble et al., 2022 ; Prüss et al., 2017 ; Rodgers et al., 2022 ; Zhang et al., 2013 ). These findings suggest that HSC failure is not merely a laboratory observation but a clinically relevant mechanism that may underlie the progressive decline in health and survival observed in individuals living with SCI. Repeated infections, which are common after SCI, may accelerate HSC exhaustion ( Flach et al., 2014 ; Hao et al., 2016 ; Pietras et al., 2011 , 2016 ). Each infectious episode demands hematopoietic adaptation, placing stress on the stem cell compartment. In healthy individuals with intact spinal cords, HSCs respond by transiently exiting quiescence to replenish immune cells ( Hao et al., 2016 ; Pietras et al., 2011 ; Schmitt et al., 2007 ; van Velthoven & Rando, 2019 ). However, SCI HSCs are transcriptionally and epigenetically silenced, unable to mount this response. As progenitor reserves are depleted (evidenced in our transplant and proliferative data), the burden shifts back to HSCs, which, due to their dysfunction, fail to meet hematopoietic demands. This maladaptive cycle may contribute to the high rates of sepsis, poor wound healing, and early mortality in SCI populations ( DeVivo et al., 1989 ; DeVivo & Farris, 2011 ). Therapeutic strategies that preserve HSC function, prevent defects from occurring, or enhance their resilience to stress may therefore be critical for improving long-term outcomes after SCI. HSC defects may be driven by SCI-induced bone marrow hypoxia. Indeed, neurogenic shock and systemic hypotension occur after SCI and peak up to 4 dpi, with chronic orthostatic hypotension persisting in most individuals ( Boontoterm et al., 2025 ; Ditunno et al., 2004 ; Furlan et al., 2006 ; Furlan & Fehlings, 2008 ; Mojtahedzadeh et al., 2019 ; Popa et al., 2010 ; Titus Grigorean et al., 2009 ; Zhou et al., 2023 ). Hypotension can result in reduced blood flow, leading to end organ damage due to hypoxia and toxic metabolite build up. Treatment of individuals with antioxidants could provide a promising strategy to prevent unfavorable hematopoietic outcomes if this is a mechanism by which the bone marrow fails. Indeed, administration of N-acetyl-L-cysteine (NAC) has been shown to improve HSC function ( Ishida et al., 2017 ). Whether this can be leveraged as a therapeutic to improve long-term hematopoiesis after SCI has not been tested. Our study has several translational implications. First, it highlights the need to monitor hematopoietic health in Individuals with SCI, particularly during the acute post-injury period when dysfunctional genomic and epigenomic changes are imprinted in HSCs ( Carpenter et al., 2020 ). Second, it identifies candidate genes and pathways, such as DNA repair and ROS-handling programs, that may be therapeutically targetable to restore HSC function ( Ishida et al., 2017 ). Third, it raises the possibility that early interventions aimed at preserving bone marrow perfusion, mitigating oxidative stress, or reversing epigenetic silencing could improve long-term immune competence and recovery after SCI. Future studies should explore whether pharmacological activation of suppressed genes or antioxidant therapy can rescue HSC function in-vivo. Additionally, dissecting the upstream signals that trigger epigenetic remodeling, such as sympathetic dysregulation, inflammation, or hypoxia, may reveal new therapeutic targets. Finally, given the parallels between SCI-induced HSC dysfunction and other forms of acquired bone marrow failure, our findings may have broader relevance for understanding how other injuries, especially those with neurological impairment (e.g., stroke, traumatic brain injuries, or neurodegenerative diseases), reprogram stem cell compartments across organ systems. In summary, this work establishes that SCI induces rapid and durable failure of HSC activation, driven by transcriptional and epigenetic suppression of genome maintenance programs. These findings redefine the systemic consequences of SCI and open new avenues for therapeutic intervention to preserve hematopoietic integrity in injured individuals. Materials & Methods Mice & housing The Institutional Animal Care and Use Committee of the Office of Responsible Research Practices at The Ohio State University approved all animal protocols for this study. All experiments were performed in accordance with the guidelines and regulations of The Ohio State University and outlined in the Guide for the Care and Use of Laboratory Animals from the National Institutes of Health. C57BL/6J (strain #000664; CD45.2) and BoyJ mice (strain #002014; C57BL/6-CD45.1) were purchased from The Jackson Laboratory. Mice were fed commercial food pellets and chlorinated reverse osmosis water ad libitum while housed (≤5/ cage) in ventilated microisolator cages layered with corn cob or alpha-dry soft bedding in a 12 h light-dark cycle at a constant temperature (20 ± 2 °C). Adult male mice (8 weeks or older) were used for all experiments because 78% of SCI occur in males and published literature suggests that bone marrow failure is independent of sex in mice ( Traumatic Spinal Cord Injury Facts and Figures at a Glance, 2024 ). All mice were euthanized between Zeitgeber Time 1-5, unless otherwise indicated, to control circadian changes ( Méndez-Ferrer et al., 2008 ; Mendez-Ferrer et al., 2009 ). Surgery & post-surgical care Mice were assigned to groups using a random number generator. The SCI group received a complete spinal cord transection injury at the third thoracic spinal level after laminectomy of the T3 vertebrae. The surgical sham group (sham) received a T3 laminectomy like the SCI group, but without a SCI. Naïve mice were prepped as done for surgical mice (anesthetized, shaved, and cleaned), but immediately placed back into their cages. Mice were not given dietary supplementation (e.g., high calorie pellets) after surgery to control changes in gut microbiota and/or body fat as a confounding variable. For all surgical procedures, mice were anesthetized with ketamine (120 mg/kg, i.p.) and xylazine (10 mg/kg, i.p.). Mice did not receive prophylactic antibiotics as these have been shown to alter hematopoiesis ( Josefsdottir et al., 2017 ). Mice that survived longer than 1 dpi received daily saline injections s.c. for the first 5 dpi. On the day of euthanasia, bladder expression was omitted to reduce stress-related changes in leukocyte mobilization. Blinding & randomization Mice were randomized at each appropriate step of experiments. On arrival to the animal housing facility all age-, sex-, and strain-matched mice were combined to one bin from different delivery boxes to minimize shipping stress or cohort confound. Mice were distributed to separate cages in succession (ex., 1st mouse into 1st cage, 2nd mouse into 2nd cage., etc) to ensure random distribution of mice per cage (i.e., the ease with which a mouse was selected was not associated with given cage). Cages were then randomized to group using a number generator. During sample processing, mice were assigned a number ID without group identifier to prevent sample handling bias. Samples were unblinded during flow cytometric instrument set-up as baseline gates and voltages were set on control groups and ensure the panel worked properly prior to data acquisition. For cell culture assays including the Methocult assay, the researcher plating and counting was blinded to group. For quantification of CFU, a second identifier on a removable sticker was placed on top of the original label to prevent bias in colony enumeration as often CFU was counted after experiment completion and unblinding for analyses of other outcomes. For transplant assays, the researcher was blinded to group to prevent unequal treatment throughout the engraftment periods. Tissue collection & processing Mice were terminally anesthetized with ketamine (0.1mL, 100 mg/mL) and xylazine (0.1 mL, 5 mg/mL) i.p. for euthanasia and lack of pain response confirmed. Blood : Approximately 0.2-1mL of blood was collected via right cardiac puncture through a 26 G needle (BD, cat# 309597) into EDTA tubes (Fisher, cat# 22030402) for analysis on a Veterinary Hematology Analyzer (Element HT5, Heska Corp Inc.) and/or by flow cytometry. Bones : Bones (femurs and tibiae) were removed using ethanol-sterilized gloves and scissors/forceps and cleaned of muscle using kimwipes and placed into conical tubes containing 5 mL of PBS. Under aseptic conditions in a cell culture hood, bones were transferred to a mortar and excess solution vacuumed and immediately replaced with 2-5 mL of PBS+ 2-10% (consistent volume between mice) before being crushed with a pestle. Suspensions were filtered through 40-70um cell strainers using a serological pipette and placed on ice for downstream assays. Spleens : Spleens were grossly dissected and adherent pancreatic tissue removed. Spleens were expressed through 40-70um cell strainers using the rubber end of 3 or 5-ml syringes and rinsed with 5mL of PBS+ 2-10% FBS before being placed on ice for downstream assays. Bone marrow mononuclear cell isolation and depletion of lineage+ cells Whole bone marrow was processed into single cell suspensions as above, brought to room temp, and slowly layered over room temp Histopaque 1083 (Sigma, cat# 10831) before centrifugation (9 acc, 3 dec,1600 rpm, 20 min). The top 2 mL of media containing platelets was discarded and the middle layer containing mononuclear cells (MNCs) transferred into a new 15 mL polystyrene conical tube and brought to 15 mL with PBS + 2% FBS. This was centrifuged at room temp (!) (9 acc, 9 dec, 350-400 x g, 8 min). The pellet was resuspended in PBS + 2% FBS and placed on ice. 125 µL of a biotinylated lineage cocktail containing the following were added per 100x10 6 cells: anti-TER119 (Fisher, cat# BDB553672), anti-B220 (Fisher, cat# BDB553086), anti-CD11b (Fisher, cat# BDB553309), anti-CD8 (Fisher, cat# BDB553029), anti-Gr1 (Fisher, cat# BDB553125), and anti-CD5 (Fisher, cat #BDB553019). This was incubated for 45 min at 4°C shaking at 40 rpm horizontally and brought to 15 mL with PBS + 2% FBS before centrifugation at 4°C (9 acc, 9 dec, 350 x g, 5 min). Dynabeads Biotin Binder beads (Fisher, cat# 11047) were washed per manufacturer’s instructions and added to the cells at 10 µL of beads per 1x10 6 cells. Beads and cells were incubated for 30 min at 4°C shaking at 40 rpm horizontally and the volume increased to 7 mL with PBS + 2% FBS. Samples were placed in Dynamag-15 (Fisher, cat# 12301D) for 2 min. The bead was tightened with twisting and supernatant removed carefully in 1 mL aliquots into a new tube. This lineage-depleted MNC sample was then centrifuged at 4°C (9 acc, 9 dec, 350 x g, 8 min) and resuspended in PBS + 2% FBS for flow staining. Single cell RNA-sequencing Single cell RNA-sequencing (scRNA-seq) was performed to characterize transcriptional changes in HSPCs following SCI. Bone marrow was harvested from femurs and tibias, and density gradient centrifugation was used to remove multinucleated leukocytes and erythrocytes. Stem and progenitor cells were enriched by magnetic selection for cKit⁺ surface expression using the MACS system. Up to 16,000 cKit⁺ cells per condition were used as input for single-cell library generation using the 10X Genomics Chromium Next GEM Single Cell 3’ v2 (Dual Index) platform, following the manufacturer’s protocol. Libraries were sequenced on an Illumina NovaSeq6000. Raw BCL files were converted to FASTQ format using Cell Ranger v8.0.0 mkfastq, and alignment to the mm10 mouse genome was performed using Cell Ranger count with default settings, including filtering, barcode counting, and UMI quantification. Downstream analysis was conducted in R v4.3.3 using Seurat v5 [PMCID: PMC10928517]. Cells were filtered to exclude those with 9,000 detected features, >25,000 total counts, or >5% mitochondrial reads. Data were log-normalized and integrated using anchor-based canonical correlation analysis (CCA). Cell types were annotated based on canonical marker gene expression. Differential expression analysis was performed using the FindMarkers function (min.pct = 0.1, logfc.threshold = 0.25), and genes with adjusted p-values <0.05 were considered significant. Gene set enrichment analysis (GSEA) was performed using the fgsea package [ https://www.biorxiv.org/content/10.1101/060012v3 ] and the MSigDB Gene Ontology Mouse Biological Process reference [PMCID: PMC1239896]. Cell cycle phase assignment was conducted using the CellCycleScoring function in Seurat. Violin plots were generated using ggplot2 (RRID:SCR_014601), and statistical comparisons were made using unpaired two-tailed Wilcoxon rank-sum tests. Data availability statement The single cell RNA-sequencing gene expression data presented in this publication will be deposited in NCBI’s Gene Expression Omnibus and are accessible through Gene Expression Omnibus Series accession number (upon peer-review publication). Single cell RNA-sequencing code is available upon request. ATAC-seq preprocessing and peak calling Bone marrow lineage-depleted, cKit enriched mononuclear cells were isolated as described above at 1 dpi and libraries prepped and sequenced (CD Genomics). Raw ATAC-seq reads were processed using the nf-core/atacseq v2.1.2 pipeline executed via Nextflow v23.10.1 ( Tommaso et al., 2017 ). Briefly, raw read quality was assessed with FastQC v0.11.9, adapters were trimmed with Trim Galore! v0.6.7, and reads were aligned to the GRCm38/mm10 reference genome using BWA-MEM v0.7.17 (H. Li & Durbin, 2009 ). Alignments were sorted and indexed with SAMtools v1.17 (H. Li et al., 2009 ), and duplicate reads were removed using Picard v3.0.0. Reads mapping to mitochondrial DNA or ENCODE blacklisted regions ( Amemiya et al., 2019 ) were subsequently filtered out. Normalized bigWig tracks were generated for visualization using deepTools v3.5.1 ( Ramírez et al., 2016 ). Narrow peaks were called with MACS2 v2.2.7.1 (--nomodel --shift -100 --extsize 200 -q 0.05) (Y. Zhang et al., 2008 ), and a consensus peak set was generated across all samples with BEDTools v2.30.0 ( Quinlan & Hall, 2010 ). Reads within consensus peaks were quantified using featureCounts (subread v2.0.1) ( Liao et al., 2014 ), and peaks were annotated to the nearest transcription start site (TSS) with HOMER v4.11 ( Heinz et al., 2010 ). ATAC-seq differential accessibility and functional analysis Differential chromatin accessibility was determined in R v4.4.1 using the edgeR package ( Robinson et al., 2009 ). The count matrix was filtered for low-abundance peaks, normalized, and fitted to a generalized linear model. Contrasts between experimental groups were performed, and differentially accessible regions (DARs) were defined as those with a false discovery rate (FDR) < 0.05. Principal component analysis (PCA) was performed on variance-stabilizing transformed counts using the DESeq2 package v1.28.0 ( Love et al., 2014 ) to visualize sample relationships. Functional enrichment analysis of DARs was conducted with the rGREAT R package ( Gu & Hübschmann, 2023 ). Genomic coordinates of DARs (FDR < 0.05), stratified by directionality, were tested for enrichment against Gene Ontology (GO) databases (Biological Process, Cellular Component, and Molecular Function) ( Ashburner et al., 2000 ). GO terms with an adjusted p-value < 0.05 were considered significant. HSPC staining of whole bone marrow for flow cytometry 2-10x10 6 cells were aliquoted per sample or pooled together and distributed across control tubes. A biotinylated lineage antibody cocktail was made combining anti-TER119 (Fisher, cat# BDB553672), anti-B220 (Fisher, cat# BDB553086), anti-CD11b (Fisher, cat# BDB553309), anti-CD8 (Fisher, cat# BDB553029), anti-Gr1 (Fisher, cat# BDB553125), and anti-CD5 (Fisher, cat #BDB553019) and incubating it will cells for 15 min at room temp without shaking in the dark. Tubes were washed with 1mL of PBS + 2% FBS and centrifuged (9 acc, 9 dec, 300-400 x g, 5 min) and resuspended in 100-200 µL of PBS + 2% FBS. The live/dead control tube was heated to 55C for 10 min or received a few drops of 70% ethanol to create strong positive peaks. A mixture of fluorophore-labeled antibodies containing the following were added and incubated with samples for 15 min as before to label LSK subtypes: PerCP Cy5.5 Streptavidin (Biolegend, cat# 4052140, PE anti-mouse Sca1 (Biolegend, cat# 108107), APC-Cy7 anti-mouse cKit (Biolegend, cat# 105826), APC anti-mouse CD150 (Biolegend, cat# 115910), PE-Cy7 CD48 (Biolegend, cat# 103424), with or without FITC anti-mouse CD34 (Fisher, cat# BDB553733), with or without Zombie Aqua Fixable Viability Dye (Biolegend, cat# 423102). For chimeric studies, anti-CD45.1 or CD45.2 were also added. Cells were washed with 1mL of PBS + 2% FBS and centrifuged, as before, prior to resuspension in either PBS + 2% FBS or with 1x DAPI (Fisher, cat# H21492). Single color controls were stained using 1-2 µL of the appropriate antibodies per tube. Fluorescence-Minus-One (FMO) controls were created for appropriate color controls. Data were acquired on a BD SORP Fortessa (R647800L6068) supported by The Ohio State University Comprehensive Cancer Center Flow Cytometry Shared Resources Center. Total cell counts were back calculated using hemocytometry data. Flow cytometry staining of blood Submandibular blood (1-2 drops) or right cardiac blood (0.2-1 mL) was collected into EDTA tubes (Fisher, cat# 22030402). 100 µL of PBS + 2% FBS was added to these tubes and mixed gently before transfer into 5 mL polystyrene flow tubes (Fisher, cat# 149591A). 10 µL of each sample was combined and equally distributed across control tubes. 1 mL of BD Lysing Buffer (Fisher, cat #BDB555899) was added per tube and antibodies immediately added at 1-2 µL of each: FITC anti-mouse CD4 (Biolegend, cat# 100406), PerCP Cy 5.5 anti-mouse CD8a (Biolegend, cat# 100734), PE-Cy7 anti-mouse Ly6G (Biolegend, cat# 127617) or APC-Cy7 anti-mouse Ly6G (Biolegend, cat# 127624), PE-Cy7 anti-mouse B220 (Biolegend, cat# 103222) or APC-Cy7 anti-mouse B220 (Biolegend, cat# 103224) with or without chimeric antibodies: PE anti-mouse CD45.2 (Biolegend, cat# 109808) and APC anti-mouse CD45.1 (Biolegend, cat# 110714). BrdU incorporation assay Mice received intraperitoneal BrdU injections (50 µg/g body weight) immediately and at 12 h post-surgery. Lineage-depleted bone marrow MNCs were fixed and permeabilized using BD Cytofix/CytoPerm Buffer (BD, cat# 554714) for 15-30 min at room temp or on ice in the dark. Following centrifugation (300 x g, 5 min), cells were washed with 2 mL 1x BD Perm/Wash Buffer and resuspended in 250 µL BD CytoPerm Permeabilization Buffer Plus (Invitrogen, cat# 561651) at 50 µL per 1 million cells. After a 10 min incubation on ice, cells were washed again and re-fixed/permeabilized in 500 µL Cytofix/CytoPerm Buffer (100 µL per 1 million cells) for 15-30 min. To expose incorporated BrdU, cells were resuspended in 500 µL of 300 µg/mL DNAse solution (delivering 150 µg per 5 million cells) and incubated for 1 hour at 37 °C. Cells were then washed and stained with 250 µL BrdU stain (FITC anti-mouse BrdU; Fisher, cat# 501030842) solution per 5 million cells for 20 min at room temp in the dark. After washing, cells were stained with 700 µL DAPI stain solution per 5 million cells for 15 min at room temp in the dark. Final washes were performed, and cells were resuspended in 300 µL 1x Perm/Wash Buffer for acquisition. Values were averaged for sham mice, and each experimental sample (sham or SCI) was divided by this value and multiplied by 100 to get a percentage of sham. This was done to normalize batch effects between all 4 independent studies. HSC Ki67 and Hoechst 33342 flow cytometry Lineage-depleted bone marrow MNCs were stained for LSKs as described and then for Ki67 and Hoechst 33342 staining. Cells were fixed and permeabilized as follows using the Foxp3 / Transcription Factor Staining Buffer Set (Fisher, cat# 005523). Briefly, these cells were fixed in 1X Fixation Buffer for 30 min on ice, then permeabilized twice using 1X Permeabilization Buffer. Anti-mouse Ki67 antibody (Biolegend, cat# 652410) was added at 2-5 µL per tube and incubated for 30 min on ice. After washing, cells were stained with Hoechst 33342 (2 µg/mL in PBS + 2% FBS) for 30 min on ice, washed again, and resuspended in Permeabilization Buffer. Samples were stored overnight at 4°C protected from light and analyzed by flow cytometry the following day. ROS flow cytometry assay Lineage-depleted bone marrow MNCs were pooled per group (to obtain enough cells) and stained for LSKs as above and suspended in 1x (5 uM) Probe Solution. 1x Probe Solution was created by adding 100 µL of 100x Probe Stock to 9.9 mL of PBS + 2% FBS. The 100x (500 uM) Probe Stock was created by dissolving 50 µg of H2-DCFDA (Invitrogen, cat# C6827) into 173 µL of DMSO. 100-200 µL of hydrogen peroxide (H2O2) per tube to induce intracellular ROS and sampled over time via flow cytometry. Ex vivo colony formation assay Colony-forming unit (CFU) assays were performed using MethoCult™ GF M3434 methylcellulose medium (Stemcell Technologies, Cat# 3444), which contains erythropoietin and other cytokines to support multilineage hematopoietic colony formation from single HSPCs. Bone marrow cells were plated at a density of 20,000 cells per well in 6-well plates, corresponding to 60,000 cells per 3 mL of MethoCult medium. Cultures were prepared and maintained as previously described ( Carpenter et al., 2020 ). Colonies were enumerated under an inverted microscope between days 9 and 14 post-plating. Colony types were identified based on morphology and size, following manufacturer guidelines. DNA comet assay To assess DNA damage in hematopoietic stem cells (HSCs), whole bone marrow was harvested from 5–10 mice per group and pooled into separate samples. Lineage-depleted mononuclear cells (MNCs) were isolated and stained for HSCs, defined as Lin⁻ c-Kit⁺ Sca-1⁺ CD150⁺ CD48⁻, and fluorescence-activated cell sorting (FACS) was performed using a BD FACSAria III. Cells were sorted directly into filtered fetal bovine serum (FBS), washed, and resuspended in phosphate-buffered saline (PBS) prior to assay. For irradiation experiments, purified HSCs were exposed to 10 Gy X-ray irradiation and incubated for 4 hours at 37°C before DNA damage was assessed using the OxiSelect Comet Assay Kit (Fisher Scientific, Cat# NC0588768) and 3-Well Comet Assay Slides (Fisher Scientific, Cat# NC0569359). Cells were centrifuged and washed twice with ice-cold PBS lacking Mg²⁺ and Ca²⁺, then resuspended at 1 × 10⁵ cells/mL. Comet slides were pre-warmed to 37°C, and cells were mixed 1:10 (v/v) with Comet Agarose. A volume of 75 µL was added per well. Slides were incubated horizontally at 4°C in the dark for 15–30 minutes, then immersed in pre-chilled lysis buffer (∼25 mL/slide) for 30–60 minutes. Following lysis, slides were transferred to pre-chilled alkaline unwinding solution (∼25 mL/slide) for 1 hour. Electrophoresis was performed in alkaline buffer at 1 V/cm for 30 minutes, maintaining a current of 300 mA. Slides were rinsed twice in pre-chilled distilled water (5 minutes each), followed by a 5-minute wash in cold 70% ethanol. After drying at 37°C for 15 minutes, DNA was stained with 100 µL/well of diluted Vista Green DNA dye for 15 minutes at room temperature. Imaging was performed using a Nikon AXR Scanning Confocal Microscope with a FITC filter. For each condition, 30–50 cells were imaged per well using an air 20× objective (resolution: 2048 × 2048; line averaging: 4×; zoom factor: 2×; z-stack depth: 30 µm; z-step size: 0.127 µm). Comet tail DNA percentage was quantified using ImageJ software, calculated as tail DNA%= (100*Tail DNA Intensity)/ (Head DNA Intensity) where intensity was the pixel intensity over the ROI drawn around the tail vs. head ( Ni et al., 2023 ; Rodríguez et al., 2021 ). Irradiation-induced cell death and H2AX assay To evaluate irradiation-induced cell death and DNA damage, lineage-depleted bone marrow mononuclear cells (MNCs) were isolated from pooled samples of 9–10 mice per group and stained for LSK (Lin⁻ Sca-1⁺ c-Kit⁺) populations as described above. Cells were exposed to 5 Gy X-ray irradiation using the RS2000 irradiator. Following irradiation, samples were incubated at 37°C with shaking (60 rpm, uncapped) for either 1 or 4 hours, with aliquots collected at each timepoint. Baseline cell death was assessed prior to irradiation using DAPI staining. At designated post-irradiation timepoints, 1.5 µL of Zombie Aqua Fixable Viability Dye (BioLegend, Cat# 423102) was added per tube and incubated for 10 minutes at room temperature. Samples were washed with PBS and centrifuged at 4°C (400 × g, 5 min; acceleration/deceleration settings: 9/9). Following viability staining, cells were fixed and permeabilized using the Foxp3/Transcription Factor Staining Buffer Set (Fisher Scientific, Cat# 005523). Cells were resuspended in 200 µL of 1× Fixation Buffer and incubated for 30 minutes at 4°C, protected from light. Permeabilization was performed by adding 1 mL of 1× Permeabilization Buffer, followed by centrifugation at 4°C (500 × g, 5 min; 9/9 settings). This step was repeated once to ensure complete permeabilization. Pellets were resuspended in residual volume and adjusted to 200 µL with 1× Permeabilization Buffer. Cells were stained with anti-H2A.X Phospho (Ser139) antibody (BioLegend, Cat# 613404) at 1.5 µL per tube for 30 minutes on ice. After staining, samples were washed with 1 mL of 1× Permeabilization Buffer and centrifuged (500 × g, 5 min; 9/9 settings). Final resuspension was performed in 300–500 µL of 1× Permeabilization Buffer. Samples were stored at 4°C, protected from light, and analyzed by flow cytometry within 48 hours. To normalize across groups, pre-irradiation values were set to 1 (i.e., each pre-irradiation value was divided by itself), allowing relative quantification of post-irradiation changes. Bone marrow transplants Competitive repopulation assays were performed to assess the functional fitness of HSPCs under experimental conditions relative to a control population. The protocol was adapted as previously published ( Carpenter et al., 2020 ). Briefly, whole bone marrow was isolated and mixed in equal parts from the experimental group (CD45.2) with naïve non-irradiated congenic BoyJ (CD45.1) whole bone marrow. This suspension was injected into lethally irradiated (10 Gy split-dose x-ray irradiation approx. 10-24 h apart) CD45.1 recipients via lateral tail vein, delivering a total of 4 × 10⁶ cells per mouse in sterile saline without supplements. Peripheral blood was sampled via submandibular veins periodically throughout the engraftment window to assess chimerism. Chimerism was calculated as CD45.2/ (CD45.1+CD45.2) with or without normalization to the input values. Secondary transplantation assays were performed to specifically evaluate long-term hematopoietic stem cell (LT-HSC) function. These followed the same protocol as primary transplants, except that CD45.1 competitor cells were depleted from the donor suspension using magnetic bead-based negative selection (Dynabeads system). A new cohort of lethally irradiated BoyJ recipients was used for secondary transplantation. Tertiary transplantation assays were conducted to further assess LT-HSC durability and self-renewal. Donor-derived CD45.2⁺ cells were fluorescence-activated cell sorted (FACS) to >90% purity (confirmed post-sort; data not shown) and injected at a dose of 3.5 × 10⁵ total WBCs per lethally irradiated BoyJ recipient. Statistics & outlier exclusion Data were excluded only in cases of significant behavioral deviation following SCI or irradiation (e.g., bladder infection, severe lethargy) or technical errors during sample processing (e.g., flow cytometry antibody staining issues). All exclusions are disclosed in figure legends and were made in conjunction with statistical outlier detection using Grubb’s test and the ROUT method. Randomization and blinding procedures are detailed in the dedicated section. Data are represented as mean ± SEM, with individual data points representing independent biological replicates unless otherwise stated. Group sizes were determined a priori when possible, by analyzing preliminary and published data and/or using G*Power with α0.05 and 80% power. All statistical tests were two-tailed. All data were checked for normality using Shapiro-Wilk with normal qq plotting. T-tests were used for two-sample comparisons. If non-Gaussian, the Mann-Whitney nonparametric t-test was performed. If Gaussian, unequal variance was assumed and unpaired t-tests with Welch’s correction were performed. For comparisons of three groups with one independent variable, one-way ANOVA was performed followed by Tukey post hoc test. For comparisons of two or more independent variables without repeated testing in the same subjects, a two-way ANOVA was conducted followed by either Tukey or Bonferroni post hoc testing depending on whether only a subset of comparisons were planned. For comparisons of two or more independent variables with repeated sampling from the same mice over time, a mixed effect or two-way ANOVA with repeated measures was conducted followed by Bonferroni. Data were analyzed using GraphPad Prism software v10.2.1 (GraphPad Software Inc., San Diego, CA). Illustrations were created with BioRender using a lab-paid subscription ( biorender.com ). Figures were generated in Adobe Illustrator under a subscription (Adobe Systems Inc., San Jose, CA). Funding This work has been funded by the NIND (R35NS111582), The Ray W Poppleton Endowment (PGP), the NINDS T32 (NS105864), and The Ohio State University Presidential Fellowship. Author Contributions KAR, AMD, and PGP designed experiments; KAR, EARG, KAK, EAA, KAM, JH, RK, ACRD, ZG, and MK performed experiments; EARG, KAR, and KEM performed scRNA-seq analysis. CW and QM performed ATAC-seq analysis. KAR, AMD, and PGP wrote the manuscript; all authors reviewed and edited the manuscript. Acknowledgements The authors would like to thank Dr. Christina Marion, Rochelle Diebert, and Ajay Medipally for assistance in caring for the mice throughout the post-transplantation period; Eric Naumann for assistance in tissue harvest in the early stages of project development; Dr. Patrick Collins and Mariam Salem for ATAC-seq library prep and submission for sequencing. Funder Information Declared National Institute of Neurological Disorders and Stroke, https://ror.org/01s5ya894 , R35NS111582 The Ray W Poppleton Endowment (PGP) National Institute of Neurological Disorders and Stroke, https://ror.org/01s5ya894 , NS105864 The Ohio State University, https://ror.org/00rs6vg23 , Presidential Fellowship Footnotes ↵ † AMD and PGP are co-last authors References ↵ Amemiya , H. M. , Kundaje , A. , & Boyle , A. P . ( 2019 ). The ENCODE Blacklist: Identification of Problematic Regions of the Genome . 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