Abstract
Traumatic brain injury (TBI) remains a leading cause of chronic neurological impairment, yet the cellular mechanisms underlying long-term neurodegeneration in TBI remain incompletely understood. Astrocytes, the most abundant glial cell type, are central to maintaining neuroglial and neurovascular homeostasis. Following TBI, however, astrocytic activation contributes to sustained inflammation and neurotoxicity. In this study, we employed immunohistochemistry and RNA sequencing to longitudinally profile astrocyte morphology and transcriptional states at acute (2 days), subacute (2 weeks), and chronic (1 year) stages after controlled cortical impact in mice. We identified a temporally evolving astrocyte response—beginning with a pro-inflammatory profile acutely, transitioning through a profile suggestive of mixed inflammatory and neurodegenerative signatures subacutely, and culminating in a chronic state marked generally by expression of Alzheimer’s and Parkinson’s disease-associated genes. Notably, a subset of astrocyte-derived progenitor cells also was found up to one-year post-injury, expressing markers associated with neurogenesis. These findings reveal that astrocyte activation is not transient but persists chronically, undergoing a dynamic shift from inflammation to degeneration. The observed parallels between astrocyte states in chronic TBI and neurodegenerative disorders underscore their potential role in post-traumatic cognitive decline and highlight astrocyte-targeted interventions as a promising avenue for therapeutic development.
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Introduction
Traumatic brain injury (TBI) remains a leading global cause of death and disability, contributing to substantial long-term morbidity and healthcare burden1,2. Survivors of TBI often develop chronic medical, neurological, and psychiatric3,4 comorbidities1,5, including neurodegenerative disease such Alzheimer’s disease (AD)6, and chronic traumatic encephalopathy7. These outcomes reflect the complex pathophysiology of TBI, which involves immediate mechanical injury followed by prolonged secondary cascades of biochemical and cellular events. A central feature of these secondary processes is neuroinflammation—mediated by both neuroglia8 and infiltrating peripheral immune cells9—which plays a paradoxical role by promoting both tissue damage and repair10. Despite this, therapeutic strategies targeting neuroinflammation have had limited success in clinical trials11,12,13, underscoring the need to refine our understanding of its context- and time-dependent effects.
Astrocytes, the most abundant glial cells in the central nervous system (CNS), are integral to maintaining neuronal homeostasis, regulating blood-brain barrier integrity, and modulating synaptic function14. Following CNS injury, astrocytes undergo reactive transformation, characterized by morphological, molecular, and functional changes15. While reactive astrocytes can exacerbate neuronal injury through the release of excitotoxic glutamate, reactive oxygen species, and pro-inflammatory cytokines16,17,18,19, they also contribute to repair by supporting neurogenesis, neurovascular remodeling, and scar formation20,21. This dual functionality has complicated efforts to therapeutically target astrocytes, and the temporal dynamics of their responses—especially in the chronic phase of TBI—remain insufficiently defined.
To address this gap, we performed high-throughput RNA sequencing (RNA-seq) of astrocytes isolated at acute, subacute, and chronic time points up to 1 year following experimental TBI. Our analyses revealed sustained astrocyte activation across all phases, with transcriptional signatures evolving from an early pro-inflammatory profile to a mixed phenotype featuring both neurodegenerative and regenerative elements. The persistent expression of genes associated with several neurodegenerative diseases suggests that TBI may predispose to—or mechanistically converge with—pathways involved in neurodegeneration. At the same time, the maintenance of neurogenic gene expression in a subset of astrocytes for up to 1 year indicates a prolonged window for potential regenerative intervention.
Together, our findings provide a longitudinal map of astrocyte transcriptional states after TBI and identify key windows and pathways that could be leveraged to enhance repair or mitigate neurodegeneration. Targeting these evolving astrocyte states may offer a promising avenue for therapeutic development in TBI and related neurodegenerative conditions.
Results
Astrocyte activation persists up to 1 year after TBI
To first characterize the histological changes that occur in brain parenchyma after TBI, we employed the reproducible controlled cortical impact (CCI) model of TBI in 8-week-old mice. Representative photomicrographs show cortical astrocytes in the ipsilateral hemisphere at acute (2 days after injury), subacute (2 weeks after injury), and chronic (1 year after injury) time points post-injury. CCI caused a significant increase in astrocyte activation at all time points, including the surrounding cortex, underlying hippocampus, and regions as deep as the thalamus (Fig. 1A, C). Morphological changes consistent with astrocytes activation were seen at all three time points in the ipsilateral cortex, including shorter, thicker, and less ramified processes and swollen or stretched cell bodies, with a stepwise increase in the ratio of activated to inactivated astrocytes seen between time points (Fig. 1B, D).
A Immunofluorescence staining of the injury site using DAPI (blue) and GFAP (red) in Sham, acute, subacute, and chronic TBI. Dotted boxes indicate focused regions of interest affecting astrocyte morphology in the thalamus. Scale bar: 800 µm and 50 µm. B Immunofluorescence staining of homeostatic (white border) and activated (green border) pericontusional GFAP+ cells using DAPI (blue) and GFAP (red). C Pericontusional GFAP+ cells quantified using ImageJ (Sham n = 4, Acute TBI n = 4, Subacute TBI n = 4, Chronic TBI n = 4), analyzed by one-way ANOVA with Tukey’s multiple comparisons. Data are shown as mean ± s.e.m. D GFAP+ cells quantified using Imaris software (Sham n = 3, Acute n = 3, Subacute n = 3, and Chronic n = 3), analyzed by one-way ANOVA with Tukey’s multiple comparisons. Data are shown as mean ± s.e.m. Scale bar 10 µm.
Astrocyte transcriptomic profile after TBI
To investigate the molecular signature and function of astrocytes in acute and chronic TBI (Supplementary Fig. 1A), single-cell suspensions of sorted astrocytes were generated from mouse brains using a papain-based isolation method22. We first optimized our astrocyte sorting strategy to exclude other cells such as oligodendrocytes, myeloid cells, and lymphocytes (Supplementary Fig. 1B). RNA-seq of sorted astrocytes isolated from naïve WT mice confirmed the purity of the cell population with relatively high expression of astrocyte markers (Slc1a3, Aldh1l1, Sox9, Aqp4, S100b, Gfap) and almost no detection of markers of microglia (Cx3cr1, Tmem119, Fcrls), oligodendrocytes (Sox10, Pdgfra, Plp, Mog) or neurons (Snap25, Kcc2, Syt1, Gabra1) (Supplementary Fig. 1C).
Astrocyte gene expression at 2 days post-injury revealed a pro-inflammatory transcriptome profile
RNA-seq of astrocytes isolated from acute TBI mice compared to sham controls revealed a total of 22,351 expressed genes, with 2588 differentially regulated transcripts (Padj < 0.05) (Supplementary Data 1). Differential gene expression analysis revealed significant downregulation of genes associated with lipid metabolism (Acsl3), ion channel regulation (Slc1a3), synaptic and cellular communication (Kirrel3, Farp1, Syn3, S1pr1), neurotransmission (Grin3a, Gria2, Mtch2), cell junction assembly and adhesion (Ptprt, Nrcam, Cdh19), cell projection organization (Fut9, Prkd1, Atp1b2, Ntrk2), and neuroprotection (Dkk3)23,24 (Fig. 2A, B, Supplementary Data 1). Conversely, genes associated with astrocyte reactivity (Gfap, Nes, Vim), chaperone activity (Hspb1, Cryab), protein secretion (Serpina3n, Thbs1), and neuroinflammation (Egr1, Tnf, Ccl2, Cxcl10, Il1b, Il17rc, Stat3, Sbno2, Junb, Irf5, Irf8) were significantly upregulated. Additionally, genes involved in Toll-like receptor signaling (Tlr2, Myd88), complement activation (C4b), neurodegeneration (Trem2, Sod3), and apoptosis (Tradd) were prominently enriched in acute TBI astrocytes23,24 (Fig. 2A, B).
A Heatmap of the differentially expressed genes in astrocytes isolated from Sham and TBI mice 2 days (acute) after injury. B MA plot of the TBI vs. Sham differentially expressed astrocyte genes. C GSEA summary plot of TBI vs. Sham astrocyte hallmark pathways. D Individual TBI vs. Sham GSEA plots showing the normalized enrichment score and the corresponding FDR-adjusted P value for each enriched astrocyte hallmark pathway and a top driving gene. E Cnetplot depicting the network of interacting genes among the enriched pathways. Biologically independent samples were used. Data shown as mean ± s.e.m. P values were calculated per the FDR-adjusted P values with DEseq2 using the Wald test for significance following fitting to a negative binomial linear model and the Benjamini-Hochberg procedure to control for false discoveries. F Heatmap of the differentially expressed genes in astrocytes isolated from aged Sham and aged TBI mice 1 year after TBI. G MA plot of the TBI vs. Sham differentially expressed astrocyte genes. H GSEA summary plot of TBI vs. Sham astrocyte hallmark pathways. I Individual TBI vs. Sham GSEA plots showing the normalized enrichment score and the corresponding FDR-adjusted P value for each enriched astrocyte hallmark pathway and a top driving gene. J Cnetplot depicting the network of interacting genes among the enriched pathways. Biologically independent samples were used. Data shown as mean ± s.e.m. P values were calculated per the FDR-adjusted P values with DEseq2 using the Wald test for significance following fitting to a negative binomial linear model and the Benjamini-Hochberg procedure to control for false discoveries. DEG differentially expressed gene, NES normalized enrichment score.
Gene set enrichment analysis (GSEA) further revealed significant enrichment in inflammatory and stress-related pathways, including interferon-α (IFN-α) and interferon-γ (IFN-γ) responses, TNFα signaling, complement activation, reactive oxygen species production, E2F signaling, and apoptosis (Fig. 2C, D). An interaction cnetplot illustrating the interconnected biological pathways altered in acute TBI astrocytes is shown in (Fig. 2E), which demonstrates overlapping pathways largely involved in the inflammatory response, and a potentially separate group of activated genes related to E2F signaling, which are more often related to cellular growth and proliferation.
Thus, our RNA-seq profiling of astrocytes in acute TBI highlights a distinct neuroinflammatory signature (A1-like24), with a separate module involved in proliferation (A2-like24). This reveals astrocyte-specific pathways during acute TBI, which may be targeted to mitigate neuroinflammation and improve post-TBI outcomes.
Astrocyte gene expression at 1 year post-injury revealed a transcriptome of persistent stress and neurodegenerative state
Our initial immunohistochemistry analysis of mice subjected to CCI vs sham-injury revealed persistent astrocyte activation over 1 year after TBI (Fig. 1B–D). To further investigate the molecular signature and functional changes in these astrocytes during chronic TBI, we performed RNA-seq on sorted astrocytes from animals 1 year post-CCI injury. We identified 589 differentially expressed transcripts (Padj < 0.05) in astrocytes from chronic TBI mice compared to age-matched sham controls (Supplementary Data 1). The chronic TBI astrocyte signature included 383 upregulated genes and 206 downregulated genes. Differential gene expression analysis of astrocytes in chronic TBI revealed significant downregulation of genes associated with lysosomal processing (Hexb)25, phagocytosis (Mfge8)26, neurotransmission and vesicle trafficking (Syn3, S1pr1, Gria2)23,27, oxidative regulation (Scara3, Bckdk)28,29, protein synthesis (Sh2b3, Hdac5, Emc4)30,31 cell adhesion (Vcam1) and angiogenesis (Kcnj8)32 (Fig. 2F, G). Conversely, genes associated with astrocyte cytoskeletal reactivity (Gfap, Tuba4a, Itgb5, S100b, Ptprd, Ntrk2, Atp1b2), neurodegeneration (Apoe, Axl, Litaf, Bin1)33,34, neurotoxicity and channelopathies (Sgk1, Kcna1, Kcnj10, Lrrc8b, Cox2)35,36,37,38,39, and proteolysis (Psmb6, Ube2q1, Pcsk1n)40 were upregulated. However, also upregulated were genes associated with reduction of reactive oxygen species (Sod1, Sod2), growth factor signaling (Nell2, Hdgf)41,42, and protein chaperone/stress response (Cryab)23,24 (Fig. 2F, G, Supplementary Data 1).
GSEA revealed enrichment of biological pathways associated with reactive oxygen species, oxidative phosphorylation, glycolysis, fatty acid metabolism, and mTOR signaling in chronic TBI astrocytes (Fig. 2H, I). An interaction cnetplot illustrating the interconnectivity of altered biological pathways in chronic TBI astrocytes is shown in (Fig. 2J). These findings suggest that astrocytes in chronic TBI acquire a neurodegenerative signature, characterized by dysregulation of metabolic, inflammatory, and neurotoxic pathways, but also upregulation of pathways involved with supporting neuroglial cooperation, cell survival, and adaptation to chronic injury. This persistent astrocytic reactivity highlights their complex role in the long-term pathophysiology of TBI, reinforcing the duality of astrocyte function post-TBI, and suggests potential therapeutic targets for mitigating long-term outcomes.
Astrocyte transcriptome: a dynamic shift from inflammation to neurodegeneration
Given the remarkable differences between the astrocyte transcriptomes in acute versus chronic TBI, we also performed RNA-seq of sorted astrocytes at a subacute time point, 2 weeks post-TBI, and performed a temporal analysis of astrocytic transcriptomes between acute, subacute, and chronic TBI. Compared to subacute and chronic TBI, the acute astrocytic transcriptome revealed upregulation of several oxidative stress and immune response pathways (pro-inflammatory cytokines such as IL-1, IL-6, TNF, and IFN-γ, leukocyte migration, cell chemotaxis, and regulation of immune response) (Fig. 3A, Supplementary Data 2). We found significant upregulation of genes such as Serping1, Gfap, Vim, and S1pr3, suggestive of neurotoxic and pan-reactive astrocyte subsets, but also a reactively neuroprotective subset expressing genes such as Clcf1, Tgm1, S100a10, and TM4sf124,43.
A–C Heatmap of the differentially expressed astrocyte genes at the acute, subacute, and chronic time points of TBI. Clustering was done with K-means using k = 3. Enriched GO terms (q value < 0.05) of each cluster are displayed on the right with selected corresponding genes labeled on the left. Total number of genes in each enriched GO term is shown in brackets. D Top IPA predicted upstream regulators (P < 0.05 & |z| > 0) of the following pairwise comparisons obtained with DESeq2 using the Wald test: Subacute TBI vs. Acute TBI, Chronic TBI vs. Acute TBI, and Chronic TBI vs. Subacute TBI. E, F Bar graphs depicting expression levels of selected genes (Acute TBI n = 3, Subacute TBI n = 4, Chronic TBI n = 3). Data shown as mean ± s.e.m. P values were calculated using a two-sided likelihood-ratio test with an FDR-corrected P < 0.05 (A–C), right-tailed Fisher’s exact test (D), and one-way ANOVA with Tukey post hoc analysis (E, F). *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001, ns not significant. Type: DEG differentially expressed gene, IPA ingenuity pathway analysis.
In the subacute phase of TBI, the astrocyte transcriptome revealed persistent low-level upregulation in pro-inflammatory signaling. We found that most of the inflammatory genes that were upregulated in acute TBI were no longer differentially expressed (Tnf, Ccl2, Cxcl10, Il1b, Il17rc, Stat3) or expressed at reduced levels (Junb, Egr1, Ccl3, Ccl4) in the subacute phase. On the other hand, we found that genes involved in neurodegenerative processes (Per144, C1qa45, Trem246, Spp147, Fcer1g46, Tmsb4x48, Tyrobp46, Sparc49, C1qc45, and Inpp5d50) were upregulated. We also found upregulation of several regulatory and homeostatic pathways, including pathways involved with the regulation and maintenance of synaptic structure and function, axonogenesis, and neuronal stem cell maintenance compared to the acute and chronic phases of TBI (Fig. 3B, Supplementary Data 2). These findings suggest that the transcriptome of astrocytes at 2 weeks after TBI exhibited a transition state between inflammation and neurodegeneration.
Like the subacute time point, the chronic-phase astrocytic transcriptome revealed upregulation in several homeostatic pathways such as axonogenesis and dendrite development, neuron ensheathment, and synaptic organization (Fig. 3C, Supplementary Data 2). Genes in several neurodegenerative pathways, including mitochondrial and neuronal apoptotic pathways, were also upregulated in the chronic phase compared to acute and subacute phases of TBI. Temporal analysis further revealed that genes involved in neuron-astrocyte interactions, such as Il33, Clasp2, and Fgfr2, as well as those involved in trafficking (Ssr3), autophagy (Lamp2), metabolism (Nsdhi, Sqle), and chronic neurodegeneration (App, S1pr1), were significantly upregulated in chronic TBI (Fig. 4A–C), with a stepwise increase in several genes associated with neurodegeneration observed over time (Fig. 4C, D). The transition to a neurodegenerative state was further supported by colocalization of GFAP and APP proteins: While this was seen to some extent acutely, it largely resolved by 2 weeks post-injury. Colocalization was significantly higher in chronic TBI than in any other group (Fig. 4E). Astrocytic expression of APP is thought to propagate the spread of amyloid beta (Aβ) throughout the brain51, in addition to directly increasing the total Aβ burden52. Ingenuity pathway analysis (IPA) analysis demonstrated that APOE and NCSTN, two key genes involved in Aβ deposition, were top upstream regulators in the chronic phase of TBI (Fig. 3D, Supplementary Data 2). Individual gene plots shown in Fig. 3E, F. Taken together, these findings suggest that astrocytes undergo a shift in function during the subacute phase of TBI away from acute inflammation and towards a neurodegenerative phenotype that is prominent by 1 year post-injury.
A–C Bar graphs depicting expression levels of selected genes categorized by their upregulation in chronic TBI (Acute TBI n = 3, Subacute TBI n = 4, Chronic TBI n = 3), analyzed by one-way ANOVA with Tukey’s multiple comparisons. Data are shown as mean ± s.e.m. with P values shown. D Bar plot showing the percent change in the log2FoldChange of astrocyte genes at the subacute (2 weeks after TBI) time point relative to the acute (2 days after TBI) time point. E Immunofluorescence staining of GFAP+ (green) and APP+ (red) cells and DAPI (blue). Dotted boxes indicate a focused region of interest. Scale bars: 50 µm and 10 µm. Arrows indicate APP+/GFAP+ cells. F Circos plot showing the overlap of the astrocyte transcriptomes in acute and chronic TBI relative to that of Alzheimer’s disease, Parkinson’s disease, epilepsy, aging, and amyotrophic lateral sclerosis. Pericontusional GFAP+/APP+ cells quantified using Imaris software (Sham n = 3, Acute n = 3, Subacute n = 3, and Chronic n = 3), analyzed by one-way ANOVA with Tukey’s multiple comparisons. Data are shown as mean ± s.e.m. with P values as indicated. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001, ns not significant.
The astrocyte transcriptome of chronic TBI overlaps with that of neurodegenerative disease
To investigate whether the phenotype of astrocytes in chronic TBI is similar to that of other neurodegenerative diseases, we compared the differentially expressed genes of astrocytes at 1 year after TBI with those that are differentially regulated in other chronic neurological conditions. We compared our chronic astrocyte transcriptome to previously published astrocyte gene datasets of epilepsy53, aging40, AD54, Parkinson’s disease (PD)55, and amyotrophic lateral sclerosis56. We found substantial overlap in the molecular signature of astrocytes in chronic TBI with that of AD as well as PD (Fig. 4F, Supplementary Data 3). Notably, common genes include Gfap, Kcnj10, S100a1, Ntrk2, and Mapt. Among the genes that were uniquely upregulated in chronic TBI compared to other TBI groups, we found Atp5c1and Pink1 (depleted in AD57 and PD58, respectively) and Igfbp5 (enriched in AD59). Ntrk2, a pro-survival BDNF receptor that may protect against neurodegeneration60, was downregulated acutely after TBI, but upregulated in the chronic phase (Supplementary Data 1). Thus, astrocytes may partially drive the development of neurodegenerative diseases among TBI survivors6, but may also play a key role in the body’s adaptive response to neurodegeneration, suggesting a potential regenerative role as well.
Astrocyte-derived progenitor cells maintain their neuroregenerative function in chronic TBI
Recent studies showed that neural stem cells, including astrocyte-derived progenitor cells, display increased markers of proliferation20 and migration21 in acute and subacute periods after TBI. In line with these findings, our subacute and chronic TBI astrocytic transcriptomes revealed upregulation in pathways enriched for regulation of synaptic structure, axonogenesis, and neuronal stem cell maintenance, and at least a subset of astrocytes in the acute phase demonstrated increased expression of E2F-related signaling, involved in cell proliferation and mitosis61,62,63. Beyond the transcriptomic changes described above, RNA-seq of total isolated astrocytes at 1 year after TBI also showed increased expression of Ndrg1 (highly expressed in brain tumors such as glioblastoma and astrocytoma64) and Rps11 (involved in protein translation65), whereas Rbpj (which when deleted promotes neurogenesis66) was downregulated, suggesting an increase in proliferative gene programs beyond that seen in age-matched sham, potentially in a subset of astrocytes. Therefore, we sought to investigate whether astrocyte-derived progenitor cells maintain a regenerative phenotype in chronic TBI. Histopathologic evaluation of the perilesional area revealed colocalization of GFAP and EAAT2 (astrocyte markers) and Tuj1 (beta III tubulin, an early marker of neuronal lineage67), colocalization of GFAP and Nestin (marker of neural stem cells68,69), as well as colocalization of Nestin and pS6 at 1 year after injury. This suggests that neuroglial progenitor cells, including those of astrocytic origin, continue to proliferate at least a year after TBI (Fig. 5A, Supplementary Fig. 1D, E). Flow cytometric quantification of CD45−GLAST(ACSA1)+PROM1+ neural stem cells70, that include astrocyte progenitor cells, showed increased expression of cell surface EGFR71 and CXCR472 as well as intracellular SOX273 (Fig. 5B) when compared to age-matched sham, suggesting an astrocyte-derived subset with a neurogenic function (Fig. 5C). We then isolated single-cell suspensions of CD45−GLAST(ACSA1)+PROM1+ neural stem cells from chronic-phase mice for RNA-seq. We detected 103 transcripts that were differentially regulated (Padj < 0.05) in neural stem cells isolated from aged chronic TBI mice compared to age-matched Sham (Fig. 5D, Supplementary Data 1). Chronic TBI neural stem cells expressed higher levels of Pcdh11x, which decreases neural differentiation and increases proliferation of neuronal stem cells74, Bst2, which has been reported to be upregulated in hiPSC-derived astrocytes68 and is involved in hematopoietic stem cell activation75, Serpina3n, which has been associated with neurogenesis and cognitive improvement76, Wdr36, which plays an essential role in cellular self-renewal and differentiation77, and Bdnf, which is a key factor in neuronal differentiation21,78. These findings demonstrate that a subset of astrocyte-derived neuroglial progenitors maintain neurogenic potential up to at least 1 year after injury and that these cells are more abundant after TBI.
A Immunofluorescence staining of the pericontusional brain cortex for DAPI (blue), GFAP (red), EAAT2 (green), and Tuj1 (violet) (top) and for DAPI (blue), Nestin (red), and GFAP (green) (bottom) in TBI mice 1 year after injury. Dotted boxes show a focused area of interest. Arrows indicate GFAP+/Nestin+ cells (yellow). Scale bar: 70 μm. B Gating strategy (top) and flow cytometric quantification of EGFR+, SOX2+, and CXCR4+ cells gated from CD45-GLAST(ACSA1)+PROM1+ neural stem cells with FMOs shown for PROM1, EGFR, SOX2, and CXCR4 (Sham n = 4, TBI n = 4). C Individual plots of genes of interest of the isolated ACSA2+ astrocytes (Sham n = 3, TBI n = 3). D Heatmap and individual plots of genes of interest of the isolated CD45−O4−CD31−CD24−PSA/NCAM−GLAST(ACSA1)+PROM1+ neural stem cells (Sham n = 3, TBI n = 3). Biologically independent samples were used. Data shown as mean ± s.e.m. P values were calculated using the two-tailed unpaired Student’s t-test (B–D). FMO fluorescence minus one.
Discussion
TBI induces a temporally dynamic and multifaceted astrocytic response that is central to both injury propagation and recovery. In this study, we characterized astrocyte transcriptomic profiles across the acute, subacute, and chronic phases of TBI, identifying a progressive shift from a predominantly pro-inflammatory response to a chronic neurodegenerative signature. Importantly, we also discovered a persistent regenerative astrocyte subset that retained transcriptional signatures associated with neurogenesis up to 1 year post-injury. These findings reveal previously unrecognized temporal changes in astrocyte phenotypes that may be therapeutically targetable.
Our acute-phase findings align with previous studies reporting robust upregulation of inflammatory genes79,80 and activation of interferon- and interleukin-mediated pathways in astrocytes following TBI81,82. Key pan-reactive astrocyte markers (e.g., Gfap, Apoe, Cd44, Serpina3n, Cxcl10) were prominently expressed, confirming conserved injury responses across studies83,84,85,86. Notably, we observed early co-expression of neuroprotective genes, consistent with data from stab injury models showing dual reactive phenotypes84. This biphasic behavior underscores the need to move beyond simplistic “A1/A2” astrocyte models in favor of temporally and contextually nuanced frameworks24.
In the subacute phase, we observed a transcriptomic shift away from acute inflammation and toward signatures associated with early neurodegenerative processes. Upregulated genes such as Sparc, Mertk, and C1q components (C1qa) are known to mediate synapse pruning and were previously implicated in AD and multiple sclerosis models45,87,88,89. Vcam1, elevated in inflammatory astrocytes90 and in the CSF of AD patients91, was also upregulated in our dataset. Additionally, Tmsb4x, linked to impaired neuronal development in PD models48, and immune-modulating genes (Trem2, Tyrobp, Inpp5d)—all previously associated with AD pathogenesis46,50,92—were significantly upregulated. Trem2, Tyrobp, and Inpp5d are known to be expressed in microglia rather than astrocytes. However, emerging evidence using advanced RNA-seq technology concurrently with improved purification protocols of astrocytes enabled the discovery of new genes expressed in astrocytes, including Trem293 and Tyrobp94. Overall, these findings suggest that astrocytic transcriptional programs during subacute TBI converge with molecular mechanisms observed in chronic neurodegeneration.
Both our group and others have demonstrated that TBI is associated with a significantly increased risk of developing several neurodegenerative diseases6, however, mechanisms linked to neurodegeneration are largely unexplored. By 1 year post-TBI, we found that the astrocyte transcriptome closely resembled that observed in AD and PD, and found increased accumulation of APP within astrocytes. Genes such as Mapt (encodes the various isoforms of the tau protein), S100a1, and Kcnj10 (encoding Kir4.1)95 were significantly upregulated. Elevated Kir4.1 levels have been shown to correlate with multiple markers of Alzheimer’s histopathology in AD models96. Furthermore, chronic astrocytes expressed both maladaptive (Vcam1, Spp1, Fcer1g) and compensatory (Sod1, Cryab, Ntrk2, Hdgf) genes, suggesting concurrent neurotoxic and neuroprotective programs. This duality supports the hypothesis that astrocytes contribute not only to the progression of neurodegeneration but also to ongoing attempts at neuroadaptation and repair. These molecular and cellular findings contribute to a potential framework for understanding the increased risk of neurodegeneration in TBI survivors.
Consistent with recent studies highlighting astrocytic roles in neurogenesis and neuroblast migration post-TBI20,21, our data demonstrate persistence of a regenerative astrocyte subset beyond the acute injury window. The presence of Bst2-positive astrocyte-derived progenitors at 1 year supports a sustained neurogenic capacity. This aligns with earlier findings in the fluid percussion injury model97 and suggests a protracted window of neuroplasticity in the chronic phase. The detection of Bst2 in GFAP+ astrocytes from post-mortem AD brains further underscores potential overlap between regenerative and neurodegenerative programs98.
Despite these insights, our study has limitations. Bulk RNA-seq might obscure regionally and functionally distinct astrocyte states and as it lacks the resolution to adequately distinguish discrete astrocyte subtypes or to capture spatial heterogeneity across CNS regions. Future single-cell and spatial transcriptomic studies will be essential to define the lineage and fate of neuroglial progenitors and to clarify whether neuroprotective and neurodegenerative astrocytes represent distinct or transitional states. Although immunohistochemistry confirmed several key transcriptional findings, comprehensive proteomic analysis is needed to fully capture post-transcriptional regulation and astrocyte functionality at chronic time points. While the current work conveys transcriptomic trends suggestive of a potential shift in astrocyte phenotype, we recognize that causality may not be definitively inferred from the current evidence alone. Future gain and loss of function investigations are warranted to validate those observations. Finally, while the CCI model recapitulates contusional TBI—a common subtype—it does not reflect diffuse injuries such as subdural hematoma or diffuse axonal injury, which constitute a large proportion of clinical TBI cases99.
In summary, our study reveals a temporally distinct, phase-specific transcriptional trajectory in astrocytes following TBI, encompassing profiles suggestive of pro-inflammatory, neurodegenerative, and neuroregenerative states. Notably, the persistence of regenerative astrocytes into the chronic phase highlights a potential therapeutic opportunity to harness endogenous repair mechanisms. These findings underscore the dual role of astrocytes as both mediators of neurodegeneration and agents of recovery and suggest that precision targeting of astrocyte subtypes at defined time points may offer novel strategies for improving long-term outcomes after TBI.
Methods
Animals
Eight-to-twelve-week-old male C57BL/6J mice (#000664, Jackson Laboratory) were used in this study. Animals were housed in a conventional specific pathogen-free facility at the Building for Transformative Medicine under standard 12-h light/dark cycle conditions. Carbon dioxide administered for 2 min from a compressed gas tank, followed by cervical dislocation, was used as the method of euthanasia. All experiments were reviewed and overseen by the institutional animal care and use committee at Brigham and Women’s Hospital in accordance with NIH guidelines for the humane treatment of animals.
Controlled cortical impact
An electromagnetic open-skull CCI model of TBI was employed as previously described100. Animals were given buprenorphine HCl 0.1 mg/kg subcutaneously once 1 h prior to surgery. The procedure was performed in a biosafety hood while mice were placed on a heat pad, and anesthesia was provided throughout the procedure. Mice were fixed on a stereotaxic frame and anesthetized with a combination of isoflurane and oxygen. Moderate TBI was induced using the CCI Impact One device (Leica Biosystems). Sterile surgical equipment was used. First, the dorsal aspect of the animal’s head was shaved and sterilized, followed by a midline skin incision. Skin was retracted, exposing the surface of the skull. Craniotomy was performed by removing a 3-mm skull bone overlying the right parietal cortex. A 1.5 mm metal impactor tip was extended and lowered at a 20° angle at the following coordinates: 1.8 mm caudal to bregma, 2.0 mm laterally. The tip was retracted, and the electromagnetic device was triggered using the following parameters: 1 mm depth, 5.0 m/s velocity, and a dwell time of 0.8 s. Following the impact, the bone flap was returned and glued using a sterile bone wax. The skin was sutured using 6-0 black nylon nonabsorbable monofilament sutures as 3–5 interrupted sterile sutures. Mice were allowed to recover on another heat pad and then returned to new cages. Sham mice underwent the same procedure, including craniotomy, but without impact. Instead, the impact tip was discharged in the air.
Brain dissection and dissociation
Mice were intracardially perfused with ice-cold Mg2+ and Ca2+-free 1X Hank’s balanced salt solution (HBSS; Gibco, #14175). Brains were gently dissected from the overlying skull, with the peri-contusion brain tissue quickly removed and placed on ice. Pericontusional brain tissue was obtained by cutting 1 inch below, above, and lateral to the site of brain injury. Brains were gently minced using a razor blade into smaller pieces. Chemical digestion was performed using the papain-based adult brain dissociation kit (Miltenyi Biotech; #130-107-677) with slight modifications. For the isolation of neural stem cells, chemical digestion was performed using the trypsin-based neural tissue dissociation kit (Miltenyi Biotech; #130-093-231). In either case, cells were incubated with papain enzyme mix 1 for 30 min at 37 °C on a shaker, followed by 10-min incubation with enzyme mix 2 as per the manufacturer’s instructions. Cells were then washed with HBSS, passed through an HBSS pre-wet 70 μm cell strainer (Fisher Scientific, #22363548), and sedimented by centrifugation at 300 g for 5 min at 4 °C. Debris and myelin were removed using a room-temperature Percoll gradient101. Briefly, cell pellets were resuspended in 5 mL room-temperature 30% Percoll Plus cell separation liquid media (GE Healthcare Biosciences, #17-5445-01) diluted in HBSS and then spun for 20 min at 350 × g with acceleration rate of 5 and deceleration rate of 3. Cells subsequently pellet at the bottom of the 15 mL tubes with myelin and debris floating above the Percoll Plus solution. The cell pellet was washed with 10 mL ice-cold FACS buffer (Mg2+ and Ca2+ free HBSS with 2% FBS, 0.4% EDTA, 0.5 M, and 2.5% HEPES 1 M) and spun again for 5 min at 300 × g at 4 °C. All samples were then resuspended in FACS buffer for further processing.
FACS staining and isolation of astrocyte single cell suspensions
Samples were resuspended in 100 μl of ice-cold FACS buffer containing APC/Cy7 anti-CD45 (Biolegend, #103116, 1:100), PE/Cy7 anti-CD11b (Biolegend, #101216, 1:100), APC anti-ACSA-2 (Miltenyi Biotech, #130-117-535, 1:50), PerCP anti-Ly-6C (Biolegend, #128028, 1:400), PerCP anti-Ly-6G (Biolegend, #127654, 1:400), PerCP anti-CD3ε (Biolegend, #100326, 1:400), PerCP anti-CD11c (Biolegend, #117326, 1:400), PerCP anti-CD19 (Biolegend, #115532, 1:400), PerCP anti-NK1.1 (Biolegend, #108726, 1:400), PE anti-PSA/NCAM (Miltenyi Biotech, #130-117-394, 1:50), PE anti-A2B5 (Miltenyi Biotech, #130-093-581, 1:50), PE anti-CD24 (Miltenyi Biotech, #130-102-732, 1:50), PE anti-CD31 (Miltenyi Biotech, #130-119-653, 1:50), PE anti-Prom1 (eBioscience, #12-1331-80, 1:50), PE anti-CD45R/B220 (Biolegend, #103208, 1:100), PE anti-Ter119 (Biolegend, #116208, 1:100), PE anti-O4 (R&D Systems, #FAB1326P, 1:100), PE anti-CD105 (eBioscience, #12-1051-83, 1:100), PE anti-CD140a (eBioscience, #12-1401-81, 1:100). The PerCP and PE channels were used as dump channels to minimize contamination from related cell types22. Cells were stained for 20 min on ice. Samples were then washed in 10 mL ice-cold FACS buffer and spun for 5 min at 300 × g and then resuspended in 200 μl ice-cold FACS buffer. 1000 live CD45−CD11b−CD3ε−Ly-6C−Ly6G−CD11c−CD19−NK1.1−PSA/NCAM−A2B5−CD24−CD31−Prom1−CD45R/B220−Ter119−O4−CD105−CD140a−ACSA2+ astrocytes were sorted on a BD FACSAria II using the 70 μm nozzle with a sort speed of approximately 10,000 events per second. Dead cells were excluded using 7-AAD viability staining solution (Biolegend, #420404, 1:100). Gating strategy is shown in Supplementary Fig. 1B. For neural stem cells, the staining antibodies used are APC/Cy7 anti-CD45 (Biolegend, #103116, 1:100), PE anti-O4 (Miltenyi Biotech, #130-117-507, 1:200), PE anti-CD31 (Miltenyi Biotech, #130-119-653, 1:200), PE anti-CD24 (Miltenyi Biotech, #130-102-732, 1:200), PE anti-PSA/NCAM (Miltenyi Biotech, #130-117-394, 1:200), APC anti-GLAST (Miltenyi Biotech, #130-098-803, 1:10), PE/Cy7 anti-Prom1 (Biolegend, #141209, 1:200). Neural stem cells were isolated as CD45−O4−CD31−CD24−PSA/NCAM−GLAST(ACSA1)+PROM1+. In all cases, cells were sorted into prechilled 1.5 mL tubes containing 1% 2-mercaptoethanol in TCL buffer (Qiagen, #1031576). After sorting, cells were immediately short-spun, placed on dry ice, and stored in the −80 °C freezer.
RNA sequencing
After FACS sorting, single-cell suspensions were plated in a prechilled 96-well twin.tec PCR plate LoBind, full skirted plate (Eppendorf, #0030129512) and shipped to the Broad Institute for Smart-seq2 RNA-seq102. Samples were processed for cDNA generation and Illumina Nextera XT library construction. Sequencing data was generated using 2 × 38 bp paired-end sequencing on the NextSeq500. Transcript-level gene expression analysis of the raw RNA-seq reads was performed as previously described103. First, the quality of the raw RNA-seq reads was assessed using the FastQC quality control tool for high-throughput sequence data104. Reads were concatenated, then trimmed using Trimmomatic105. The RNA reads were aligned to the mouse mm10 reference genome using HISAT2106. HISAT2-generated SAM files were sorted and converted into BAM files using Samtools107. The sorted reads were assembled into transcripts using StringTie108. Next, StringTie-generated transcript lengths and abundance estimates were converted into count matrices using Tximport109. Differential gene expression analysis was performed with false discovery rate (FDR)-adjusted P values using DESeq2 with an adjusted P cutoff value of 0.05110. Data visualization was performed in R (version 4.0.3). Heatmaps and clustering were generated using heatmap.2 from the gplots package. For clustering, z-scores were calculated using the mean expression of biological replicates per disease stage/condition and then subsequently clustered using K-means. MA plots were generated using ggmaplot from the ggpubr package. Pathway analysis was performed using GSEA111,112. GSEA or GSEA Preranked analyses were used to generate enrichment plots for RNA-seq data using MSigDB molecular signatures for canonical pathways: hallmark (h.all) pathways. From the enrichplot package, enrichment plots were generated using gseaplot2, dot plots were generated using dotplot, and gene networks were visualized using cnetplot. Venn diagrams were generated in R using the venn package, and circos plots were generated using the circlize package. To identify regulators of gene expression networks, IPA software (Qiagen) was used by inputting gene expression datasets. “Canonical pathways” and “upstream analysis” metrics were considered significant at P < 0.05. To identify regulatory networks, once a specific regulator was identified, the “Build>Grow” function was used to identify molecules of the selected network. Statistical analysis using Qiagen IPA was carried out with a right-tailed Fisher’s exact test.
Flow cytometry
Brain tissue was dissociated as described above. For assessment of neural stem cells, chemical digestion was performed using the trypsin-based neural tissue dissociation kit (Miltenyi Biotech; #130-093-231). Surface markers were stained for 20 min at 4 °C in FACS buffer, then fixed and permeabilized with Foxp3/Transcription Factor Staining Buffer Set (eBioscience, #00-5523-00). Cells were then stained for intracellular markers and washed in FACS buffer. Zombie Aqua Fixable Viability Kit (Biolegend, #423102, 1:1000) was used to exclude dead cells. Fc receptors were blocked with anti-mouse CD16/CD32 (Bio X Cell, #BE0307, 1:50) for 15 min on ice. Flow cytometric acquisition was performed on LSRFortessa (BD Biosciences) using DIVA software (BD Biosciences), and data were analyzed with FlowJo software version 10 (TreeStar Inc.). The staining antibodies used are APC/Cy7 anti-CD45 (Biolegend, #103116, 1:100), PE anti-O4 (Miltenyi Biotech, #130-117-507, 1:200), PE anti-CD31 (Miltenyi Biotech, #130-119-653, 1:200), PE anti-CD24 (Miltenyi Biotech, #130-102-732, 1:200), PE anti-PSA/NCAM (Miltenyi Biotech, #130-117-394, 1:200), APC anti-GLAST (Miltenyi Biotech, #130-098-803, 1:10), PE/Cy7 anti-Prom1 (Biolegend, #141209, 1:200), PE anti-GLAST (Miltenyi Biotech, #130-118-483, 1:100), AF488 anti-EGFR (eBio, #E13345, 1:200), AF647 anti-SOX2 (Biolegend, #656108, 1:200), BV421 anti-CXCR4 (Biolegend, # 146511, 1:200).
Immunohistochemistry
Mice were intracardially perfused with 10 mL ice-cold 1X phosphate-buffered saline, followed by 10 mL ice-cold 4% paraformaldehyde. Brains were gently dissected, post-fixed in 4% paraformaldehyde overnight at 4 °C, and dehydrated in 30% sucrose for 3 days at 4 °C. Brains were then flash frozen in Tissue-Tek OCT (Sakura, compound 4583) and stored at −80 °C until the time of sectioning. Brains were subsequently sectioned at −20 °C using a cryostat at the bregma position for each targeted brain. Sections were cut using at 0.2 mm in a sixfold series interval. Six total sections were placed on frosted plus microscope slides (Thermo Fisher Scientific, 22-037-246) and stored at −20 °C until the time of staining. For immunohistochemistry, sections were blocked in a 10% normal horse serum solution, containing 0.1% Triton X-100, and 2% bovine serum albumin. Slides were incubated overnight at 4 °C overnight. The following day, sections were washed and incubated with appropriate secondary antibodies. Sections were incubated with the following primary antibodies: Mouse monoclonal anti-GFAP-Alexa Fluor 488 conjugated antibody (Thermo Fisher Scientific, #53-98-92-82. 1:200), mouse monoclonal anti-nestin (Thermo Fisher Scientific, #MA1-110, 1:300), rabbit polyclonal anti-beta III tubulin (“anti-Tuj1”, Abcam, #ab18207, 1:5000, citrate antigen retrieval), rabbit polyclonal anti-EAAT2 (Abcam, #ab41621, 1:5000, EDTA antigen retrieval), rabbit anti-pS6 polyclonal (Abcam, #ab101691, 1:2000, citrate antigen retrieval). For immunofluorescence, the following fluorophores were used: Alexa Fluor 488 Tyramide (Life Technologies, #B40953), Alexa Fluor 555 Tyramide (Life Technologies, #B40955), Alexa Fluor 594 Tyramide (Life Technologies, #B40957), and Alexa Fluor 647 Tyramide (Life Technologies, #B40958). For amyloid precursor protein immunofluorescence Opal Staining method using Opal 3-plex Anti-RB detection kit (Akoya Biosciences, SKU NEL830001KT) was employed, following the manufacturer’s instructions, using rabbit anti-APP (Abcam, #ab32136, 1:250). All slides were mounted and counterstained using mounting medium with DAPI (Vectashield, Vector Laboratories, H-1200-10). Images were taken using the Zeiss LSM 710 confocal microscope or the Zeiss Axioscan 7.
Image analysis
Analysis of APP and GFAP colocalization was performed on the ipsilateral lesion hemisphere using the “Coloc” function of Imaris version 10.2 (bitplane, an Oxford Instruments Company). Activation of astrocytes was determined using the “surfaces” function on Imaris version 10.2. All other images were analyzed using ImageJ software (National Institutes of Health: https://imagej.nih.gov/ij).
Statistics and reproducibility
Statistical analysis was performed using GraphPad Prism 9 software. Data are presented as mean ± s.e.m. and One-way ANOVA with Tukey post hoc correction (p < 0.05) was used to assess statistical significance, except where otherwise noted. Statistical analysis in GSEA was determined by a one-tailed t-test in GSEA. Data for each experiment were collected and processed randomly, and animals were assigned to various experimental groups randomly as well. No statistical methods were used to predetermine sample size. Sample sizes were chosen in accordance with previous studies in the field79. Each experiment was repeated 2–3 times. All n and P values and statistical tests are indicated in the figure legends.
Ethics approval and consent to participate
The Institutional Animal Care and Use Committee approved all animal experiments described in this study. The authors have complied with all relevant ethical regulations for animal use.
Reporting summary
Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.
Data availability
All data generated or analyzed during this study are included in this published article and its supplementary information files. RNA-seq data samples were deposited into the Sequence Read Archive (SRA) of the National Center for Biotechnology Information (NCBI), BioProject accession number PRJNA1280541.
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Acknowledgements
This work was funded by NIH grant number 5K08NS123503-03 and Stepping Strong Innovator Award awarded to Dr. Saef Izzy, and in part, by a Brigham Research Institute microgrant awarded to Dr. Hadi Abou-El-Hassan. The authors thank the NeuroTechnology Studio at the Brigham and Women’s Hospital for providing instrument access and consultation on data acquisition and data analysis.
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H.A. designed and performed experiments and wrote the manuscript. S.I. and R.M.R. designed and supervised experiments and wrote the manuscript. T.Y., B.Z., O. A., H.T.I., J.J.Y., F.P., J.R.C., M.H.A., K.L., G.G., B.K.T., K.J.H., J.R.L., and A.H.M. performed experiments. J.J.Y. contributed to the review and editing of the manuscript. F.J.Q., H.L.W., and S.I. supervised the project and wrote the manuscript.
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Abou-El-Hassan, H., Yahya, T., Zusman, B.E. et al. Astrocyte activation persists one year after TBI: a dynamic shift from inflammation to neurodegeneration. Commun Biol 8, 1745 (2025). https://doi.org/10.1038/s42003-025-09138-w
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DOI: https://doi.org/10.1038/s42003-025-09138-w
