Single − Cell Transcriptomic Landscape of Sciatic Nerve After Transection Injury

Single − Cell Transcriptomic Landscape of Sciatic Nerve After Transection Injury | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Single − Cell Transcriptomic Landscape of Sciatic Nerve After Transection Injury Yiben Ouyang, Mingqian Yu, Tieyuan Zhang, Haofeng Cheng, Liang Zuo, and 15 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6582223/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 23 Aug, 2025 Read the published version in Journal of Neuroinflammation → Version 1 posted 10 You are reading this latest preprint version Abstract Peripheral nerve injuries, especially those involving the sciatic nerve, often result in persistent deficits with limited treatment options. To dissect the cellular mechanisms of nerve repair, we performed single − cell RNA sequencing on rat sciatic nerves at seven time points (Day 0, 1, 3, 5, 7, 10, 14) following transection injury. We identified dynamic changes in four major cell compartments—neurofibroblasts (NFs), glial cells (Glis), immune cells, and vascular cells. Early responses (Day 1) featured immune cell infiltration, followed by expansion of proliferative mesenchymal cells (NF5) and repair Schwann cells (Gli0) by Day 3–5. Vascular cells expanded from Day 7 to Day 10, and by Day 14, Glis transitioned into mature myelinating states while NF and immune cells stabilized. Compared to crush injury, transection induced a stronger early immune response and delayed Schwann cell recovery. These findings provide a time − resolved atlas of sciatic nerve regeneration and highlight stage − specific therapeutic targets, particularly macrophage activation and NF–Gli signaling. Sciatic nerve transection Single − cell RNA − seq Immune microenvironment Macrophage heterogeneity Schwann cell remyelination Neurofibroblast activation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Peripheral nerve injuries, particularly those involving the sciatic nerve, pose significant clinical challenges with profound socioeconomic impacts. Globally, sciatic nerve trauma has an incidence of 7.7% in specific high − risk populations (e.g., acetabular fracture patients), among whom iatrogenic injuries account for 12.87% of postoperative complications [1]. The primary etiologies include traumatic accidents [1], surgical interventions [1, 2], and metabolic disorders such as diabetic neuropathy [3]. Despite advances in microsurgical techniques, functional recovery rates remain suboptimal, with only 50% of patients achieving clinically meaningful recovery, while persistent motor deficits and chronic neuropathic pain—strongly correlated with reduced quality of life in physical functioning and social participation domains [4, 5]—continue to burden survivors. This incomplete recovery underscores the urgent need to dissect the multicellular dynamics that underlie nerve repair—particularly the role of macrophage (Mac) subpopulations and transcriptional reprogramming in axonal regeneration—as revealed by recent single − cell RNA sequencing (scRNA − seq) studies [6]. The primary therapeutic strategies for sciatic nerve injury, such as nerve grafting and neurotrophic factor administration, remain limited by incomplete axonal regeneration and misdirected reinnervation [7]. These limitations arise primarily due to the inability of traditional bulk RNA sequencing approaches to resolve the spatiotemporal coordination among Schwann cells, fibroblasts, immune cells, and vascular cells during regeneration. By averaging signals across heterogeneous cell populations, bulk RNA sequencing obscures critical cell type − specific responses [8]. Schwann cells exhibit dynamic phenotypic transitions between dedifferentiated, pro − regenerative, and repair states [9, 10], while Macs undergo polarization from pro − inflammatory to pro − repair subtypes [11]—processes that bulk RNA sequencing fails to resolve. Emerging scRNA − seq technologies now enable unprecedented cellular − resolution dissection of neural repair mechanisms. In central nervous system injury models—such as ischemic stroke and spinal cord injury—scRNA − seq has revealed transitional cellular states and intercellular crosstalk networks that dictate regenerative outcomes [12–14]. More recently, in peripheral nerve injury models, scRNA − seq has been employed to unravel the heterogeneity and functional states of Schwann cells, fibroblasts, immune subsets, and vascular cells. For instance, distinct transcriptional alterations have been identified in both myelinating and non − myelinating Schwann cells under autoimmune conditions, providing insights into glial plasticity [15]. Endothelial Plexin − D1 has been shown to play dual roles in peripheral nerve repair by not only guiding the directional growth of endothelial cells (ECs) but also regulating angiogenic patterning [16]. In parallel, studies using Aire − deficient mouse models demonstrated that T cell–derived IFN− \(\:\gamma\:\) induces Mac TNF− \(\:\alpha\:\) expression, thereby driving Mac phenotype switching and amplifying inflammatory responses [17]. Furthermore, in trauma−induced heterotopic ossification, scRNA−seq revealed a high degree of spatial colocalization between peripheral nerves and blood vessels [18], implicating coordinated neurovascular remodeling. Collectively, these findings underscore the power of scRNA−seq to disclose cellular plasticity, spatial interactions, and regulatory networks that remain obscured in conventional bulk transcriptomic analysis. Critical knowledge gaps persist regarding the spatiotemporal regulation of cellular responses during sciatic nerve regeneration. Despite recent advances identifying transcription factors (e.g., Sox10 [19] and Zeb2 [20]) and signaling pathways (e.g., Neuregulin − 1/ErbB [21] and Wnt/ \(\:\beta\:\) −catenin [22]) as key regulators of Schwann cell development, the molecular mechanisms governing Schwann cell fate decisions—particularly the transition into repair − promoting phenotypes following nerve injury—remain incompletely understood.​ Immune cell dynamics, including the recruitment of bone marrow − derived Macs and neutrophils, exhibit temporal specificity—Macs peak at Day 3 post − injury, while neutrophils surge within 24 hours—a pattern consistent with the time − dependent immune activation originally observed in the sciatic nerve crush injury model [6]. Vascular ECs in the sciatic nerve demonstrate distinct subtypes, including epineurial, endoneurial, and lymphatic endothelial cells (lyECs)—each characterized by unique gene expression profiles. Marker genes such as Spock2 , Rgcc , and Lrg1 have been validated in vivo for the identification of these subtypes, offering improved specificity over classical pan − endothelial markers like Pecam1 [23]. To bridge gaps in our understanding of peripheral nerve repair, we conducted a longitudinal scRNA − seq study to investigate dynamic cellular transitions during sciatic nerve injury and repair. Our research focuses on the influence of Schwann cells on fibroblast behavior, explores cell communication networks, and identifies key genes involved in the repair process using pseudotemporal trajectory analysis. We also examine the differentiation of Macs and monocytes (Mos) into Mac − like cells and assess the role of other immune and vascular cells during repair. Additionally, we compare the repair processes following both sciatic nerve transection and crush injuries, highlighting both their similarities and differences. This study provides an in − depth insight into the molecular mechanisms underlying nerve regeneration and highlights potential therapeutic targets for clinical application. Methods Animal model of sciatic nerve transection Adult female Sprague–Dawley rats (8–10 weeks old, 220–250 g) were anesthetized by intraperitoneal injection of sodium pentobarbital (30 mg/kg). Under sterile conditions, the left sciatic nerve was exposed via a mid‑thigh skin incision, freed from surrounding tissue, and sharply transected approximately 10 mm proximal to its trifurcation. The muscle fascia and skin were then closed in two layers with 4‑0 absorbable sutures. After recovery from anesthesia, animals were housed in standard plastic cages, with free access to water and a 12‑hour light/12‑hour dark cycle. Five rats were used per time point. Tissue Collection and Single − Cell Dissociation At each designated post‑injury time point (Day 0, 1, 3, 5, 7, 10, and 14), five rats were re‑anesthetized and the original incision reopened. For Day 0 controls, a 6 mm segment of intact sciatic nerve was harvested at the mid‑thigh. For Day 1–3 (before nerve‑bridge formation), a 2 mm segment was collected from each stump (proximal and distal to the transection site). For Day 5–14 (after nerve‑bridge formation), a \(\:\pm\:\) 2 mm segment of the newly formed inter‑stump nerve bridge was harvested. Tissues from the five animals at each time point were pooled, immediately placed in ice‑cold Hank’s Balanced Salt Solution (HBSS), and enzymatically digested in collagenase IV (1 mg/mL) and DNase I (50 U/mL) at 37°C for 30 min. The resulting cell suspension was filtered through a 40 µm cell strainer, centrifuged at 300 × g for 5 min, and resuspended in phosphate−buffered saline (PBS) containing 0.04% bovine serum albumin (BSA) for subsequent scRNA−seq library preparation. ScRNA − seq and Preprocessing scRNA − seq was performed using the Chromium Single Cell Gene Expression Solution (10x Genomics), which enables the isolation and labeling of 500 − 10,000 individual cells. This technology is based on the GemCode microfluidics platform, where barcoded gel beads and single cells are encapsulated within oil droplets (Gel Bead − In − EMulsions, GEMs). Within each GEM, gel beads dissolve, and cells undergo lysis, releasing mRNA that is reverse − transcribed into barcoded cDNA. After breaking the emulsion, cDNA was amplified via PCR, followed by quality control to assess fragment size and yield. The amplified cDNA was then fragmented to 200–300 bp, end − repaired, A − tailed, and ligated with sequencing adapters before undergoing index PCR amplification. Library quality was validated before sequencing on the Illumina NovaSeq 6000 platform to obtain high − throughput single − cell gene expression data. Raw sequencing reads were processed using the Cell Ranger pipeline (10x Genomics), including read alignment to the rat genome (Rnor_6.0), barcode assignment, and unique molecular identifier (UMI) counting. Low − quality cells with high mitochondrial gene content (> 10%) or low total UMI counts (< 500) were removed. Doublet detection was performed using DoubletFinder , and doublets were excluded from downstream analysis. Clustering and Cell Type Annotation Dimensionality reduction was performed using principal component analysis (PCA) on the top 3000 variable genes. The first 30 principal components were used for uniform manifold approximation and projection (UMAP) visualization. Unsupervised clustering was performed using the Seurat package [24] (version 5.1.0) with the Louvain algorithm at a resolution of 0.8. Cell types were annotated based on the expression of established marker genes: NFs ( Col1a1, Dcn, Col3a1 ), Glis ( Mpz, S100b, Mag ), immune cells ( Aif1, Cd68, Cd3e ), and vascular cells ( Vtn, Esam, Plvap ). Further subclustering within major cell populations was performed to identify distinct cellular subtypes. Bioinformatic Analysis and Statistics We employed Seurat for downstream analysis, following a structured pipeline to process and analyse the scRNA − seq data. The analysis was conducted in seven key steps: Normalization: Raw gene expression counts were normalized on a per − cell basis using log − normalization (log1p transformation), in which the natural logarithm of 1 plus the counts per 10,000 was computed. This step ensures that expression levels are comparable across cells and suitable for downstream analyses. Highly Variable Gene Selection and Batch Effect Correction: The top 3,000 highly variable genes were identified using the FindVariableFeatures function, capturing genes with the greatest variability across the dataset and likely reflecting biologically meaningful signals. To mitigate batch effects, integration anchors were first identified using the FindIntegrationAnchors function and then used to integrate the datasets with IntegrateData . Data Scaling: Gene expression values were standardized across all cells using Z − score transformation via the ScaleData function. This step adjusts for differences in average gene expression levels, facilitating cross − cell comparisons and downstream dimensionality reduction. PCA: PCA was performed on the scaled expression matrix of the highly variable genes to reduce dimensionality and capture the primary axes of variation in the dataset. UMAP Visualization: UMAP was used to project the high − dimensional data into a two − dimensional space for visualization. The RunUMAP function in Seurat was executed using principal components 1 through 15 (dims = 1:15), as determined from the PCA on the subsetted data. Clustering: Cell clustering was carried out using a graph − based approach with the Louvain algorithm, as implemented in the FindClusters function. A shared nearest neighbor (SNN) graph was first constructed using FindNeighbors , and clustering was performed with a resolution parameter of 0.3 to define discrete cell populations within the sciatic nerve dataset. Differential Gene Expression (DGE) Analysis: To identify differentially expressed genes (DEGs) for each cluster, the Wilcoxon rank − sum test was applied using the FindMarkers function. The analysis was conducted on the log − normalized expression matrix, with min.pct = 0.25 set to include genes expressed in at least 25% of cells in either group. All other parameters were kept at their default values, including only.pos = TRUE, logfc.threshold = 0.1, and max.cells.per.ident = Inf. This strategy enabled robust identification of cluster − specific marker genes while maintaining sensitivity to subtle expression differences. Cell Type Annotation For unsupervised cell type annotation, we utilized the SingleR package [25] (version 2.8.0) with the crush sciatic nerve injury single − cell dataset (GSE198582 [6]) as a reference. Cell type assignments were further manually validated by examining the DEGs for the presence of canonical marker genes for each cell type. Based on this analysis, we assigned metacells to various cell types, including NF, Gli, PC, SMC, Mac, Gran, DC, and T /NK/B cell. Subclustering and Further Resolution To achieve higher resolution of cell states, subclustering was performed on major cell types by first subsetting specific populations (e.g., fibroblasts) from the integrated dataset. For each subset, data normalization and scaling were repeated using the SCTransform function to ensure consistency in preprocessing. Dimensionality reduction was carried out via PCA, and the appropriate number of principal components (PCs) was determined using ElbowPlot . The selected PCs (typically 1–10/15) were then used for constructing a SNN graph and performing graph − based clustering using the FindNeighbors and FindClusters functions, with an appropriate resolution to capture finer subpopulations. Low − dimensional embeddings were generated using UMAP based on the selected PCs. DEGs between subclusters were identified using the FindMarkers function with the parameter min.pct = 0.25, while other parameters remained at their default settings. DEGs were ranked by log2 fold − change values to identify representative marker genes. For visualization, z − scores were computed based on the average expression levels of each gene across subclusters, and the resulting matrix was visualized using heatmaps, enabling clear comparison of transcriptional profiles and aiding in the refinement of subpopulation annotations. DGE and Enrichment Analysis DGE analysis was performed using the FindMarkers function in Seurat. For identifying marker genes of general cell subtypes, genes were considered significant if they were both upregulated in the target cluster and had a p − value < 0.05. This dual criterion ensured that selected genes were not only statistically significant but also biologically relevant in distinguishing cell populations. This analysis enabled the classification of distinct cellular subtypes and facilitated the identification of key regulatory genes within each lineage. For the comparative analysis between crush injury and transection injury, a stricter threshold was applied to identify DEGs that distinguished the two injury models. After using FindMarkers , genes were considered significantly different if they met the criteria of fold − change > 1 and adjusted p − value < 0.01. These DEGs were subsequently subjected to Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis using clusterProfiler [26] to identify biological pathways that were commonly activated in both injury types or uniquely enriched in either crush or transection injuries. This approach enabled a detailed characterization of both shared and distinct molecular mechanisms underlying nerve repair in different injury models. Pseudotime and Trajectory Analysis To investigate dynamic cellular transitions during nerve repair, pseudotime trajectory analysis was performed using Monocle [27] for glial cells (Glis) and slingshot [28] for monocyte − to − macrophage (Mo − to − Mac) differentiation. For glial cells, Monocle was used to model the lineage trajectory from repair Schwann cells (Gli0) to myelinating Schwann cells (Gli5). After filtering out uninjured samples, cells from Day 5 to Day 14 were selected for analysis. Cells were re − embedded based on highly variable genes, and a subset of genes expressed in at least 350 cells was retained. To define ordering genes for trajectory inference, we performed differential expression analysis across timepoints using differentialGeneTest , and genes with p < 1e − 15 were selected. The top pseudotime − associated genes were then analyzed for expression trends using plot_pseudotime_heatmap , and k − means clustering ( k = 3) was applied to identify co − expression modules. These clusters were interpreted as representing early − stage regulatory genes, intermediate − phase modulators, and late − stage effectors, corresponding to successive waves of gene activation during Schwann cell reprogramming and remyelination. To explore the biological significance of these gene modules, we conducted GO enrichment analysis for each gene cluster using clusterProfiler [26], revealing functional themes associated with glial plasticity, differentiation, and remyelination. For the immune compartment, slingshot was used to reconstruct lineage relationships between infiltrating monocytes (Mo0 and Mo1) and differentiated macrophage subtypes (Mac0–Mac4). The trajectory was inferred based on UMAP embeddings, with pseudotime values extracted along inferred lineages. For downstream analysis, cells with valid pseudotime values were filtered, and a generalized additive model (GAM) was fitted to gene expression using the tradeSeq framework. Genes exhibiting significant changes along the trajectory were identified using associationTest and startVsEndTest , highlighting transcriptional programs that govern Mo − to − Mac fate transitions. Top − ranked genes visualized with plotSmoothers were prioritized for further interpretation. Together, these analyses provided comprehensive insights into the temporal orchestration of glial reprogramming and immune differentiation during peripheral nerve regeneration. Cell − Cell Communication Analysis Cell − cell communication analysis was performed to investigate intercellular signaling dynamics during sciatic nerve repair. The CellChat package [29] was used to infer ligand − receptor interactions among different cell populations. Normalized single − cell RNA − seq data were used as input, and the analysis was conducted separately for different time points to track temporal changes in signaling activity. First, the expression of known ligand − receptor pairs was assessed across all major cell types, including NFs, Glis, immune cells (Mac, Mo, Gran, T cells, B cells, NK cells, and DCs), and vascular cells (PCs, ECs, SMCs, and lyECs). Communication networks were constructed based on the strength and specificity of ligand − receptor interactions. The computeCommunProb function was applied to calculate interaction probabilities, followed by computeCommunProbPathway to identify active signaling pathways. To visualize signaling patterns, network diagrams and heatmaps were generated to depict interactions between cell types. The netVisual_circle function was used to illustrate global intercellular communication, while netVisual_bubble provided insights into specific ligand − receptor pairs. Pathway − level analysis was conducted to identify key signaling cascades, with a focus on those implicated in nerve regeneration, including collagen − related signaling, PTN signaling between Glis and NFs, and Mac − mediated extracellular matrix remodeling. The strength and directionality of communication were assessed by examining changes in outgoing and incoming signaling patterns for specific cell populations over time. Statistical analyses were corrected for multiple comparisons using the Benjamini − Hochberg method to control the false discovery rate. Comparative Analysis of Crush Injury and Transection Injury Models To compare the cellular and molecular responses between crush injury and transection injury models, we conducted a detailed comparative analysis using scRNA − seq data. The scRNA − seq data from both injury models were integrated using the FindIntegrationAnchors function in Seurat, which identifies mutual nearest neighbors (anchors) between datasets to correct for batch effects and technical variations while preserving biological differences. Following anchor identification, the datasets were harmonized using the IntegrateData function, generating a combined dataset with minimized batch effects. The integrated dataset was log − normalized using the natural log1p normalization, and the 3,000 most variable genes were identified using the FindVariableFeatures function. Expression values were standardized across cells using Z − score transformation, and PCA was performed on the scaled variable gene matrix. Clustering was performed using the Louvain algorithm implemented in the FindClusters function, with a resolution setting of 0.7 to identify distinct cell populations. Cell type annotations were based on the classification derived from the crush injury dataset, which included various functional subtypes such as fibroblasts (e.g., proliferating, differentiating, and matrix − stabilizing subtypes), Schwann cells (e.g., proliferating, repairing, and myelinating subtypes), Macs (e.g., pro − inflammatory, pro − repair, and proliferating subtypes), Mo, Gran, T cells, B cells, ECs, and PCs. These annotations were manually validated by examining the expression of canonical marker genes for each cell type. DGE analysis was conducted using the FindMarkers function, with genes considered significantly differentially expressed if they exhibited a fold change > 1 and an adjusted p − value < 0.01. GO and KEGG pathway enrichment analysis were conducted using the clusterProfiler package, with pathways considered significantly enriched if they had an adjusted p − value < 0.05. This comprehensive comparative analysis provided insights into the distinct cellular and molecular mechanisms underlying nerve repair in crush injury and transection injury models, highlighting potential therapeutic targets for enhancing nerve regeneration in different injury contexts. Results 1. Single − Cell Transcriptomics Reveals Dynamic Cellular Heterogeneity and Intercellular Communication in Sciatic Nerve Repair We conducted scRNA − seq on rat sciatic nerve tissues following transection injury by analyzing 58,943 high − quality cells across seven time points (Day 0, 1, 3, 5, 7, 10, 14) (Additional file 1: Fig. S1 ). Unsupervised clustering and UMAP dimensionality reduction revealed four major cellular compartments: neurofibroblasts (NFs), glial cells (Glis), immune cells (Mac, Mo, Gran, T/B/NK/DC cells), and vascular cells (PC, EC, SMC, lyEC) (Fig. 1 and Additional file 2: Fig. S2 a). Subclustering and annotation analysis revealed distinct cellular subtypes within each major compartment (Additional file 2: Fig. S2 and Additional file 3: Fig. S3 a). In the NF lineage, we identified fibroblasts and mesenchymal cell populations, along with their respective subtypes (Additional file 2: Fig. S2 b). Glis exhibited functional heterogeneity, comprising proliferating, repairing, myelinating, and non − myelinating subtypes (Additional file 2: Fig. S2 c). Immune cell diversity was characterized by distinct Mac populations, including proliferating Macs, as well as plasmacytoid and mature/migrating dendritic cells (DCs) (Additional file 2: Fig. S2 f − k). Within the vascular compartment, we identified arterial pericytes (PCs), proliferating PCs and additional SMC − associated subtypes (Additional file 2: Fig. S2 d,e). These findings highlight the cellular complexity and dynamic responses underlying sciatic nerve repair. Dynamic changes in cellular composition were observed following sciatic nerve transection (Fig. 1 a − c). In the uninjured state, NFs constituted the predominant population, accounting for 81.8% of the total cells, followed by vascular and immune cells, with Glis being the least abundant (Additional file 3: Fig. S3 c). NFs were characterized by the expression of key extracellular matrix − related genes such as Col1a1, Dcn, Col3a1, Col6a2 , and Lum , which play a fundamental role in maintaining tissue integrity (Fig. 1 d). Vascular cells were identified by markers including Vtn, Esam, Plvap, Acta2 , and Des , while immune cells exhibited distinct signatures such as Aif1, Cd68, Cd3e, Cd3g, Inpp5d , and Adgre1 . Glial cells, primarily Schwann cells, expressed Mpz, S100b, Mbp , and Mag , underscoring their role in nerve support and myelination. By Day 1 post − injury, immune cells expanded dramatically, exceeding 95% of the total cell population (Additional file 3: Fig. S3 c). This early inflammatory response was dominated by Macs and granulocytes (Grans), consistent with their essential roles in debris clearance and initiating the repair process [30, 31]. Mac subsets were characterized by markers such as F13a1, Pf4, C1qc, C1qb, Ms4a7 , and Folr2 , whereas Grans displayed gene signatures including S100a8, S100a9, Il1r2 , and Dgat2 (Additional file 5: Fig. S5 e). The marked immune infiltration suggested a rapid activation of innate immune mechanisms to facilitate the removal of myelin debris [32]. By Day 5, NFs re − emerged in substantial numbers, marking a transition from the inflammatory phase to the regenerative phase. This increase in NFs coincided with a notable rise in Glis, which play a critical role in axonal regeneration and remyelination. At the same time, immune cell numbers began to decline, indicating a shift toward tissue remodeling and repair. By Day 7, vascular cells nearly doubled, primarily comprising PCs and ECs (Fig. 1 c and Fig. 5 b). This vascular expansion likely reflects an increase in angiogenesis and the establishment of a supportive microenvironment for regenerating axons. Given the observed strong cell–cell communication between Glis and vascular cells (Additional file 3: Fig. S3 b), their coordinated function may be essential in restoring nerve homeostasis and promoting functional recovery [33]. The analysis of cellular communication during nerve repair reveals complex interactions between different cell types (Additional file 3: Fig. S3 b). Glis and vascular cells exhibit strong cell − cell communication both in normal and injured states, suggesting their pivotal roles in maintaining nerve homeostasis and promoting tissue repair. NFs and Glis show robust signaling interactions, with potential regulatory pathways that facilitate cellular differentiation and tissue regeneration [34]. While immune cells play a crucial role in the early stages of injury, their cell − cell communication with other cell types is relatively weaker both in normal tissue and throughout the repair process [29]. The intricate communication networks observed during the repair process highlight the need for coordinated signaling between multiple cell types to ensure efficient tissue regeneration and functional recovery. In summary, this study provides a comprehensive overview of the cellular landscape during sciatic nerve injury and repair, revealing dynamic shifts in cellular proportions and highlighting the complex cellular interactions that drive tissue regeneration. The identification of key cell subtype markers and their associated signaling pathways offers insights into the molecular mechanisms underlying nerve repair, which may inform future therapeutic strategies aimed at enhancing nerve regeneration. 2. Temporal Dynamics and Subtype − Specific Remodeling of the Immune Landscape During Sciatic Nerve Repair In the uninjured sciatic nerve, Macs constitute the predominant immune cell population, followed by T cells and DCs (Fig. 2 a,b). Following injury, the immune response undergoes dynamic changes over time. During the early phase (Day 1–Day 3), the immune landscape is dominated by Macs and Grans, reflecting their crucial role in the immediate inflammatory response and debris clearance [30, 35]. As the repair process progresses (Day 5–Day 14), the proportion of T cells and DCs increases, while Macs gradually decline. This transition highlights the shift from an inflammatory environment to an adaptive immune response and tissue remodeling [36, 37]. T cells exhibit distinct subtypes that vary throughout the repair process (Fig. 2 f,m). The uninjured sciatic nerve primarily harbors T0 and T2 cells, with T0 gradually decreasing post − injury. In contrast, T1, T2, and T3 subpopulations expand during the repair phase. Marker gene analysis reveals that T0 cells are characterized by Calhm6 and S1pr1 expression, whereas T1 cells express Ccl5, Ccl3 , and Ccl4 , suggesting their involvement in immune activation and recruitment. T2 cells, marked by Btrc, Tnfrsf4 , and Foxp3 , may correspond to regulatory T cells contributing to immune modulation. T3 cells, enriched in genes related to chromosome segregation and mitotic activity, such as Ncapg , Cdca3 , and Top2a , likely represent proliferative T cell subsets. Functional enrichment analysis further supports these findings (Fig. 3 a), as T0 cells are associated with type II interferon production and ATP export, T1 cells with lymphocyte − mediated immunity and dopamine biosynthesis, T2 cells with cytokine regulation and mononuclear cell proliferation, and T3 cells with chromatin remodeling and amino acid metabolism. B cells, although present at low levels throughout the repair process (Fig. 2 g,n), undergo subtype − specific changes. The primary subsets include B0, B1, and B2 cells. Marker gene analysis indicates that B0 cells express Cd40, Lbh , and Ms4a1 , while B1 cells upregulate Gpr171, Grn , and Cd7 . Notably, B2 cells, characterized by Nipal1, Ctla4 , and Tnfrsf17 expression, primarily correspond to plasma blasts. Functional enrichment analysis suggests that B0 cells participate in ribosome biogenesis and interleukin − 2 regulation, B1 cells contribute to autophagosome assembly and lectin receptor signaling, and B2 cells engage in endoplasmic reticulum stress responses and protein localization (Fig. 3 a). Natural killer (NK) cells are present at low levels in both the uninjured and repairing nerve (Fig. 2 h,o), with no significant changes in their proportions during the repair process. Two major NK cell subtypes are identified: NK0 and NK1. NK0 cells are enriched in Gata3, Cd27 , and Gpr183 , while NK1 cells express Batf, Il21r , and Gzma . Functional annotation suggests that NK0 cells contribute to T cell chemotaxis and calcium ion transport, whereas NK1 cells are involved in Gran chemotaxis and tissue disruption, implying a role in immune surveillance and cytotoxic activity (Fig. 3 a). DCs display dynamic changes in their subpopulations (Fig. 2 e). The uninjured sciatic nerve primarily contains DC0 cells, which decrease during repair, while DC1 cells increase. Subtype characterization reveals that DC0 cells, expressing Ccl17, Mfge8 , and Clec4a1 , are Mo − derived DCs. DC1 cells, marked by Mctp2, Slco4a1, and Siglech , predominantly represent plasmacytoid DCs, whereas DC3 cells ( Gls2, Fscn1 , and Lad1 ) correspond to mature/migrating DCs, and DC2 cells ( Naaa, Slpi , and Xcr1 ) align with conventional DCs (Fig. 2 l and Additional file 2: Fig. S2 h). Functional analysis indicates that DC0 cells are involved in biotic stimulus responses and Toll − like receptor signaling, DC1 cells participate in endoplasmic reticulum stress responses and nuclear receptor signaling, DC2 cells regulate innate immune responses and lipid absorption, and DC3 cells mediate actin filament organization and Toll − like receptor signaling (Fig. 3 a). These findings suggest a coordinated DC response that facilitates antigen presentation and immune modulation during nerve repair. Grans also exhibit significant changes post − injury (Fig. 2 d). The predominant subsets in the uninjured nerve are Gran0 and Gran1, with Gran0 increasing and Gran1 decreasing during repair. Additionally, two new Gran subtypes, Gran2 and Gran3, emerge post − injury. Marker gene analysis identifies Gran0 as expressing Riok3 , Gadd45a , and S100a8 , while Gran1 expresses Rps28 and Hnrnpf . Gran2 cells upregulate Ccl2 and Gpnmb , whereas Gran3 expresses Atp6v0d1, Psmb3 , and Capg (Fig. 2 k). Functional enrichment analysis suggests that Gran0 is involved in tumor necrosis factor signaling and collagen catabolism, Gran1 contributes to translation and Toll − like receptor signaling, and Gran2 and Gran3 play roles in sphingoid metabolism (Fig. 3 a). These findings highlight the dynamic Gran response in nerve repair, potentially influencing inflammation resolution and tissue remodeling. Collectively, these results demonstrate that immune cell populations undergo distinct temporal and subtype − specific changes during sciatic nerve repair. The early phase is dominated by Macs and Grans, facilitating debris clearance and acute inflammation. As repair progresses, T cells, DCs, and B cells expand, contributing to adaptive immunity and tissue remodeling. These findings provide insights into the immune landscape following nerve injury, with potential implications for therapeutic strategies targeting immune modulation in peripheral nerve repair. 3. Mo − Derived Mac Diversification and Functional Trajectories Shape the Immune Microenvironment During Sciatic Nerve Repair Macs and Mos play critical roles in the repair process following sciatic nerve injury, orchestrating a dynamic response that evolves over time. In the uninjured sciatic nerve, Macs are predominantly of the Mac3 subtype, which is marked by high expression of genes such as Slco2b1, Cd4 , and Selenop . Following injury, the immune landscape undergoes significant shifts, with distinct Mac subtypes emerging at different stages (Fig. 2 c,j). During the early phase (Day 1), the Mac0 subtype dominates. These cells are characterized not only by their involvement in acute − phase responses and metabolic processes related to amino acid metabolism (Fig. 3 a) but also by a unique marker profile that includes Chi3l1, Cxcl3, Ereg, Slc2a6, Slpi, Ass1, Vcan, Ccl24 , and Nrg1 (Fig. 2 j). By Day 3, there is a marked shift with Mac1 and Mac2 subtypes becoming more prominent. Mac1 cells—displaying markers such as Fxyd2, Hpse, Asgr2, Kctd4, Pdgfc, Akr1b8, Gdf15, Gsta1, Htr2b , and C6 —likely represent a substantial fraction of blood − derived Macs, while Mac2 cells are defined by the expression of Dcn, Col3a1 , and Col1a1 (Fig. 2 j and Additional file 2: Fig. S2 f). These subtypes are involved in lipid homeostasis, cytokine regulation, and extracellular matrix remodeling. Their numbers decline by Day 5 and nearly vanish by Day 7, coinciding with the progressive increase in Mac3 and Mac4 populations. The Mac4 subset, identified by a distinct proliferative marker profile ( Cep55, Mastl, Hist1h1b, Kif20b, Hmmr, Aspm, Kif4a, Sgo2, Pclaf , and Espl1 ), gradually increases in the later repair stages (Day 7, Day 10, and Day 14), underscoring its importance in cell proliferation and tissue regeneration. Enrichment analysis further support these findings (Fig. 3 a). Specifically, Mac0 cells are enriched in pathways related to acute − phase response and both proteinogenic and non − proteinogenic amino acid metabolism. In contrast, Mac1 cells are associated with negative regulation of cytokine production, lipid homeostasis, and complement activation, among other immune regulatory processes. Mac2 cells are linked to responses to mechanical stimuli and collagen fibril organization. Meanwhile, Mac3 cells contribute to the regulation of hemopoiesis and non − membrane − bound organelle assembly, and Mac4 cells not only support the proliferative capacity by engaging in chromosome segregation and DNA repair − dependent chromatin remodeling but also modulate epigenetic regulation through pathways such as constitutive heterochromatin formation and negative regulation of the cGAS/STING signaling pathway. Mos, largely absent in the uninjured nerve, infiltrate the lesion site post − injury, where they differentiate into Macs (Fig. 2 i,p). Two major Mo subsets are observed: Mo0 and Mo1. Mo0 cells are marked by Cdc42ep4, Polr2j, Dap, Polr2e, Cct3, Ccl2, Cd81, Card19, Rhoc , and Sh3kbp1 , and their enrichment analysis shows a strong association with mRNA metabolism, nucleic acid catabolic processes, and T cell − mediated immunity, among other functions. In comparison, Mo1 cells—exhibiting markers such as Cops2, Dhx58, Psmg4, Isg15, Tsta3, Phc2, Pik3cd, Krt75, Gps1 , and Polr3k —are enriched in pathways involved in RNA catabolism and the cellular response to increased oxygen levels. The stable presence and balanced proportions of these Mo subsets throughout the repair process highlight their crucial role in shaping the heterogeneity and functionality of the Mac populations. Pseudotime trajectory analysis reveals two distinct differentiation pathways for Mo − derived Macs, both originating from Mos (the root state) and progressing through intermediate stages (Fig. 4 c,f). Trajectory 1 follows a sequential progression whereby Mos transition through early − stage Mac0, intermediate − stage Mac2, and ultimately differentiate into late − stage Mac1 and Mac3 subsets. In Trajectory 2, the cells share the early − to − intermediate stages (Mac0 \(\:\to\:\) Mac2) but diverge in the late phase, giving rise to a mix of Mac1, Mac3, and the proliferative Mac4 subpopulation. These trajectories are accompanied by dynamic transcriptional reprogramming. For instance, genes such as Apoe, Pltp, C1qa, C1qb , and C1qc peak in late − stage Mac of Trajectory 1 and then decline, indicating transient yet essential roles in lipid metabolism, cholesterol efflux, and complement system regulation (Fig. 4 e). In Trajectory 2, the late − phase upregulation of proliferation − associated genes—including Tuba1b, Hmgb2l1, Tubb5 , and Cst3 —highlights the support for microtubule dynamics, chromatin remodeling, and lysosomal functions in promoting the expansion of the Mac4 subset. The fluctuating expression of additional markers such as Ctsz and Vim further underscores their roles in proteolysis and cytoskeletal reorganization during tissue repair (Fig. 4 h). Finally, cell − cell communication analysis reveals that collagen − mediated signaling between Macs and NFs is significantly enhanced during the repair process (Fig. 4 b). Although this interaction is minimal at Day 0 and absent at Day 1, it intensifies markedly from Day 3 onward. This observation reinforces the significance of collagen in extracellular matrix remodeling and nerve regeneration, as well as the critical interplay between Macs and NFs in facilitating tissue repair. The integration of marker gene analysis with functional and temporal data not only refines our understanding of Macs and Mos heterogeneity but also provides important insights for developing therapeutic strategies aimed at harnessing Mac − mediated regenerative processes. 4. Temporal Dynamics and Functional Specialization of Vascular Cell Populations During Sciatic Nerve Repair In the uninjured sciatic nerve, vascular cell populations were predominantly composed of Smooth muscle cells (SMCs) (Fig. 5 a,b). Following nerve injury, vascular cell dynamics underwent significant shifts across different time points. Notably, no vascular cell infiltration was observed on Day 1 post − injury. From Day 3 to Day 14, ECs remained the dominant vascular cell type, with PCs emerging on Day 5 and subsequently maintaining a stable proportion throughout the repair process. A substantial increase in vascular cell numbers was observed from Day 7, showing a rise that persisted in the later stages. In contrast, SMCs and lyECs remained relatively scarce throughout the repair timeline. EC subtypes exhibited distinct temporal patterns and functional enrichments during nerve repair (Fig. 5 c,g). In the uninjured nerve, ECs were rare, and no ECs were detected at Day 1 post − injury. From Day 3 to Day 10, EC0 and EC1 constituted the predominant endothelial populations contributing to repair. By Day 14, EC2 became the dominant subtype. GO analysis revealed functional specialization among these subpopulations (Fig. 3 b). EC0 was enriched in pathways associated with epithelial tube morphogenesis, negative regulation of cell migration, and response to reactive oxygen species, highlighting its role in early − stage tissue remodeling. EC1 displayed enrichment in inflammatory response regulation, nitric oxide metabolism, and maintenance of blood vessel diameter, indicating its involvement in modulating vascular tone and oxidative stress response. EC2, emerging in the later stages, was linked to apoptotic signaling regulation, nucleic acid catabolism, and toll − like receptor signaling, suggesting a role in resolving inflammation and tissue remodeling. lyECs were scarcely present in the normal sciatic nerve, with lyEC1 constituting the primary population (Fig. 5 f,i). Following injury, lyECs began to emerge at Day 5, predominantly consisting of lyEC0. Functional analysis of lyEC subtypes revealed that lyEC0 was enriched in apoptotic signaling regulation, TGF− \(\:\beta\:\) receptor signaling, and nucleic acid catabolic processes, indicating its potential role in immune modulation and extracellular remodeling (Fig. 3 b). lyEC1, on the other hand, shared enrichment in TGF− \(\:\beta\:\) receptor signaling but was also linked to protein catabolism, epigenetic regulation of gene expression, and mitochondrial import, suggesting its involvement in cellular adaptation and metabolic regulation during the later stages of repair. Pericytes (PCs) exhibited a distinct temporal pattern, with minimal presence in the uninjured nerve (Fig. 5 e,h). PC populations emerged at Day 5 and followed a dynamic shift in subtypes over time. In the early repair phase (Day 5–Day 10), PC0 was the predominant PC subtype, while by Day 14, PC2 became the dominant contributor. Functional analysis of PC subtypes highlighted PC0's role in extracellular matrix organization, regulation of blood circulation, and TGF− \(\:\beta\:\) signaling, reflecting its involvement in early vascular stabilization and tissue remodeling (Fig. 3 b). PC1 was enriched in muscle cell differentiation, cholesterol metabolism, and viral release pathways, indicating a role in metabolic support and immune response. PC2 exhibited enrichment in apoptotic signaling regulation, nuclear protein import, and pigmentation regulation, suggesting its involvement in later−stage tissue homeostasis. A minor population of PC3, detected at later stages, was enriched in chromosome segregation, oxidative stress response, and viral life cycle regulation, indicating a potential role in cellular proliferation and stress adaptation. SMCs were present in very low numbers throughout the repair process, appearing only at Day 7 post − injury (Fig. 5 d,j). SMC0 and SMC1 were identified as the primary subtypes, each displaying distinct functional properties. SMC0 exhibited enrichment in extracellular matrix organization, muscle cell proliferation, nucleocytoplasmic transport, and calcium ion transmembrane transport, suggesting its involvement in vascular support and contractile function (Fig. 3 b). SMC1 shared enrichment in extracellular matrix organization and TGF− \(\:\beta\:\) signaling but also displayed enrichment in amino acid metabolic processes, potentially linking it to metabolic regulation within the vascular microenvironment during nerve repair. Overall, vascular cell dynamics during sciatic nerve repair revealed a highly coordinated response, with ECs playing a central role in early repair, followed by the recruitment of PCs and the late emergence of SMCs. The functional diversity of these subtypes suggests specialized roles in angiogenesis, inflammatory modulation, and extracellular matrix remodeling, highlighting their critical contributions to the regeneration process. 5. Dynamic Shifts in NF Subpopulations Reveal Phase − Specific Roles in Nerve Regeneration Dynamic changes in NF subpopulations were observed throughout the sciatic nerve repair process, highlighting their diverse functional roles in different phases of regeneration (Fig. 6 a − c). At Day 0, NF2 cells, identified by marker genes Crispld2, Sqle, Aldh1a1, Idi1, Ralgps2, Kcnk2, Myoc, Cttnbp2, Col9a1 , and Col9a2 , were the predominant population (Fig. 6 d). These cells were classified as endoneurial mesenchymal cells and were primarily involved in extracellular matrix organization and transforming growth factor beta ( TGF− \(\:\beta\:\) ) receptor superfamily signaling pathways, supporting the structural integrity of the nerve and facilitating initial cellular responses post − injury (Fig. 6 e and Additional file 2: Fig. S2 b). In the early inflammatory phase (Day 1 to Day 3), NF5 cells, characterized by Ncapg, Tpx2, Hmmr, Ect2, Kif4a, Cenpu, Cenpf, Sgo2, Pclaf , and Kif20b , exhibited significant expansion (Fig. 6 c,d). These cells, categorized as proliferating mesenchymal cells, played a critical role in chromosome segregation and non − membrane − bounded organelle assembly (Fig. 6 e and Additional file 2: Fig. S2 b) [38, 39]. Their increased activity suggested a surge in cell proliferation, likely contributing to the rapid remodeling of the extracellular environment to accommodate immune cell infiltration and debris clearance [40, 41]. By Day 5, NF0 cells, defined by Spon1 , displayed a notable peak, surpassing their levels at Day 3 and Day 7 (Fig. 6 c,d). NF0 cells, along with NF1 and NF3, were classified as differentiating mesenchymal cells (Additional file 2: Fig. S2 b). NF0 cells were specifically enriched in pathways related to extracellular matrix organization, TGF− \(\:\beta\:\) receptor signaling and nuclear receptor−mediated signaling (Fig. 6 e). The increased presence of NF0 at this stage indicated their crucial role in transitioning from the inflammatory to the regenerative phase, facilitating extracellular matrix remodeling and tissue stabilization. Simultaneously, NF4 cells, characterized by the expression of Lrg1 and Plvap —genes commonly implicated in vascular biology [42, 43]—were enriched in collagen fibril organization and glycolytic pathways, indicating a role in matrix remodeling and metabolic adaptation essential for repair (Fig. 6 c−e) [44]. During the mid − to − late regenerative phase (Day 7 to Day 14), NF0 cells continued to increase gradually, whereas NF3 cells, marked by Sbsn, Cdkn2a, Bhlhe22, Apod, Rdh10, Slc16a11, A2m, Scn3b, Plcxd3 , and Mrap2 , showed a progressive decline (Fig. 6 c,d). NF3 cells were involved in epithelial cell proliferation regulation and TGF− \(\:\beta\:\) receptor signaling, suggesting their involvement in early repair mechanisms that diminished as regeneration progressed (Fig. 6 e). NF6 cells, identified by Fst, Mpzl2, Nkain4, Dpep1, Cntfr, Aox3, Inmt, Mmp27, Prlr , and Spock2 , were primarily fibroblasts involved in connective tissue development and nuclear receptor − mediated signaling pathways (Fig. 6 e). These cells played a key role in the final stages of repair by contributing to the reconstruction of the extracellular matrix and supporting the structural and functional restoration of the nerve tissue [45, 46]. Overall, the dynamic shifts in NF subpopulations underscore their essential contributions to different phases of nerve repair. The transition from NF5 − driven proliferative responses to NF4 − mediated metabolic transformation and NF0 − associated extracellular matrix remodeling highlights the coordinated interplay of these NF subsets. Their involvement in key signaling pathways, particularly TGF− \(\:\beta\:\) receptor superfamily signaling [47], suggests that modulating these pathways could be a potential therapeutic strategy to enhance nerve regeneration. 6. Dynamic Heterogeneity and Temporal Fate Transitions of Glis during Sciatic Nerve Regeneration Dynamic changes in Gli populations were observed during the repair process following sciatic nerve transection (Fig. 7 a − c). In the uninjured state, the predominant Gli subtype was Gli5, which represents myelinating Schwann cells (Additional file 2: Fig. S2 c). Following nerve injury, Glis underwent significant phenotypic transitions, with Gli0, identified as repair − associated Schwann cells, emerging as the dominant subtype during the early phase of regeneration (Fig. 7 c and Additional file 2: Fig. S2 c). As the repair process progressed, Gli0 cells gradually declined, while Gli2 cells increased, suggesting a shift toward remyelination and structural recovery (Fig. 7 c). Gli0 − 4 contribute to nerve regeneration by engaging the TGF− \(\:\beta\:\) receptor superfamily signaling pathway to modulate cellular responses, orchestrating extracellular matrix organization to reshape the tissue microenvironment, and regulating axon ensheathment to support the restoration of nerve function (Fig. 6 f). Single − cell transcriptomic analysis identified distinct marker genes associated with different Gli subtypes (Fig. 7 e–h and Additional file 7: Fig. S7 a, b). Gli5, characteristic of myelinating Schwann cells, expressed markers such as Ncmap, Sema5a, Mt1 and Kcna1 . In contrast, Gli0 (repair Schwann cells) exhibited increased expression of genes such as Clcf1, Met, Artn, and Runx2 , which are associated with regeneration and extracellular matrix reorganization (Fig. 6 f). Gli3 (proliferating Schwann cells) exhibited high expression of Ube2c, Kif14, Kif4a and Plk1 , indicating active cell cycle progression and proliferation. The transition toward remyelination was marked by the emergence of Gli2, which expressed genes such as Nefm, Cuedc2, Cldn19 , and Mag , indicating a functional shift toward axon ensheathment and nerve fiber stabilization [48, 49]. Functional enrichment analysis further elucidated the biological roles of different Gli subtypes during the repair process (Fig. 6 f). Gli0 was associated with pathways involved in TGF− \(\:\beta\:\) receptor signaling, neuron projection guidance, and glycoprotein metabolism, all of which are critical for early nerve repair. Gli1, which plays a role in extracellular matrix remodeling, was enriched for pathways related to TGF− \(\:\beta\:\) signaling, peptide cross−linking, and interferon−mediated signaling. Gli2 exhibited functional enrichment in myelination−related processes, including amine transport and apical protein localization, consistent with its role in late−stage nerve repair. Gli3 was primarily associated with cell cycle regulation, with enrichment in chromosome segregation and organelle assembly. Gli4 was linked to extracellular matrix organization and nuclear receptor−mediated signaling, suggesting involvement in tissue remodeling. Finally, Gli5, as the mature myelinating Schwann cell population, was enriched for pathways regulating axon ensheathment, potassium ion transport, and sterol biosynthesis, all of which are crucial for maintaining functional nerve architecture. Pseudotime trajectory analysis revealed two major regenerative pathways governing Gli fate transitions (Fig. 7 e − h and Additional file 7: Fig. S7 a − b). The first trajectory was characterized by increased expression of genes associated with myelination, proliferation, autophagy, and metabolic regulation, including Col3a1, Csrp2, Mbp, Mpz, posten , and Pmp22 (Additional file 7: Fig. S7 b). These genes were upregulated during later stages of repair, highlighting their role in structural restoration [50–52]. The second trajectory was primarily associated with growth factor secretion and extracellular matrix modulation, with early upregulation of genes such as Apod, Apoe, Col1a1 , and Fn1 (Additional file 7: Fig. S7 b) [53, 54]. The dynamic interplay between these two pathways underscores the complex cellular mec7anisms governing Schwann cell function during nerve regeneration. Additionally, cell − cell communication analysis indicated that Glis actively interacted with NFs during repair, particularly through enhanced PTN signaling (Fig. 7 i and Additional file 7: Fig. S7 c). This interaction was not only observed during nerve regeneration but has also been implicated in pathological conditions such as neurofibromatosis, where Gli − NF communication is dysregulated (Fig. 7 j and Additional file 8: Fig. S8 ) [55]. Collectively, these findings provide a comprehensive understanding of Gli heterogeneity, their temporal dynamics, and their critical roles in coordinating nerve repair following injury. 7. Divergent Cellular and Molecular Repair Programs in Crush Versus Transection Models of Sciatic Nerve Injury The comparative analysis of crush and transection injuries revealed distinct cellular responses and molecular pathways involved in the repair process (Fig. 8 ). In the early stages following crush injury, Macs were the predominant immune cell type, maintaining a stable presence from Day 1 to Day 7 (Fig. 8 a). In contrast, in transection injury, Mac presence was more pronounced in the early phases, with a higher proportion on Day 1 and Day 3 compared to crush injury. However, by Day 7, the proportion of Macs had significantly declined. Additionally, Gran were more abundant in the early stages of transection injury (Day 1 and Day 3) than in crush injury, but their presence diminished markedly by Day 7. Schwann cell dynamics also varied between the two injury models (Fig. 8 b − e). In crush injury, Schwann cells progressively increased from Day 1 to Day 7, contributing to nerve regeneration. Conversely, in transection injury, Schwann cells were scarce in the early stages (Day 1 and Day 3), with a notable presence only emerging by Day 7. These findings suggest that Schwann cell recruitment and proliferation are delayed in transection injury, potentially affecting the overall repair process. Mesenchymal stem cells exhibited a distinct pattern in both models (Fig. 8 b − e). In crush injury, they were continuously present throughout the repair timeline, suggesting a sustained role in tissue remodeling and repair. In contrast, in transection injury, mesenchymal stem cells were present at lower levels during the early stages (Day 1 and Day 3) but showed a substantial increase by Day 7, indicating a delayed yet significant involvement in the repair process. The enrichment analysis of shared upregulated genes between crush and transection injuries highlighted several key pathways (Fig. 8 g). On Day 1, common pathways included leukocyte migration, positive regulation of cytosolic calcium ion concentration, interferon − mediated signaling, and toll − like receptor signaling. By Day 3, pathways related to chromosome segregation, multicellular homeostasis, and M phase regulation were enriched, reflecting active cell division and immune responses. On Day 7, pathways such as chromosome segregation and structural disruption in another organism remained prevalent, indicating ongoing cellular proliferation and inflammatory responses. In contrast, pathways specifically enriched in transection injury revealed distinct molecular mechanisms (Fig. 8 h). On Day 1, pathways such as regulation of angiogenesis, extracellular matrix organization, inflammatory response modulation, and neuron apoptotic processes were significantly enriched, suggesting a robust early vascular and immune response. By Day 3, TGF− \(\:\beta\:\) receptor signaling and potassium ion transport were notably active, highlighting their role in tissue remodeling. On Day 7, pathways involved in cytosolic calcium ion regulation, interleukin−1 response, and fatty acid biosynthesis were enriched, indicating a shift toward metabolic and immune modulation during later repair phases. These findings underscore the fundamental differences between crush and transection injuries in terms of immune cell recruitment, Schwann cell involvement, and mesenchymal stem cell dynamics. The enrichment analysis further elucidates the distinct molecular pathways governing nerve repair in each model, with transection injury demonstrating a more complex and delayed regenerative process. The differential activation of immune and metabolic pathways suggests potential therapeutic targets to enhance nerve regeneration in severe injury conditions. Discussion The present study integrates our single − cell transcriptomic findings into a comprehensive narrative of sciatic nerve repair post − transection, revealing a dynamic, multi − phasic process that diverges from traditional theories of nerve regeneration. In contrast to classical views that primarily emphasize Wallerian degeneration and subsequent axonal regrowth, our results demonstrate that multiple cell types participate in a temporally coordinated response. Notably, the immediate post − injury phase (Day 1–3) is marked by a significant influx of immune cells. During this critical window, distinct Mac subsets emerge—characterized by the expression of Slpi [56] and Ass 1 [57]—alongside Gran identifiable by markers such as S100a8/ 9 [58], Anxa1 [59], Mmp8 [60], and Pglyrp1 [61]. These observations concur with earlier studies indicating that early immune activation is indispensable for efficient debris clearance via phagocytosis and proteolytic mechanisms. As repair commences, the inflammatory landscape gradually shifts. Immune cells, which dominate the early stages with over 95% of the cell population by Day 1, give way to a robust re − emergence of NFs and Schwann cells. Detailed subclustering of NF populations reveals that the NFs, which are predominant in the uninjured nerve, begin to undergo significant reprogramming post − injury. By Day 5, a specialized NF subpopulation—NF4, distinguished by high Lrg1 [43, 44] and Plvap [42, 62] expression—appears. This NF4 subset is hypothesized to be a pivotal intermediary, potentially serving as a key transitional cell type that integrates signals from the early immune response and fosters the subsequent emergence of mature fibroblasts, Glis, and vascular cells. Such a role is consistent with the observation that NF4 marks the transformation of NFs from a proliferative state towards a more differentiated, mature phenotype as part of the evolving tissue repair process. Concurrently, Schwann cells exhibit significant phenotypic transitions that underscore their essential role in restoring nerve function. In uninjured tissue, myelinating Schwann cells dominate; however, following injury, repair − associated subtypes rapidly emerge. The initial surge of Gli0 cells, characterized by the upregulation of regeneration − associated genes such as Clcf1 [63] and Runx [64, 65], facilitates early axonal guidance and extracellular matrix reorganization. As regeneration progresses, a shift occurs with the increase of Gli2 cells, which express markers including Mag [66] and Cldn19 [67], signifying a transition towards remyelination and stabilization of the axonal architecture. This sequential switch—from an initial repair − focused profile to one committed to remyelination—underscores a dynamic process in which Schwann cell functionality is finely tuned to meet the evolving requirements of the regenerating nerve. Vascular remodeling also plays a critical role in the repair process. Although vascular cells are relatively sparse in the immediate aftermath of injury, ECs begin to accumulate from Day 3 onward, and by Day 7, there is a dramatic surge in PC numbers. The robust intercellular communication between Schwann cells and vascular cells, coupled with the emergence of PC subsets involved in TGF− \(\:\beta\:\) signaling, establishes a supportive microenvironment essential for angiogenesis. Such vascular expansion not only restores blood flow but also creates a niche that is vital for sustaining axonal regrowth and overall tissue homeostasis. Moreover, the trajectory analysis of Mo − derived Macs reveals distinct differentiation pathways that are intimately linked with the regenerative process. Early Mac subsets engaged in inflammatory clearance gradually give way to populations involved in tissue remodeling and extracellular matrix deposition. The interplay between these immune cells and NFs, mediated by enhanced collagen signaling, highlights an integrated network that regulates both inflammation resolution and tissue repair. In summary, our time − resolved single − cell atlas of sciatic nerve transection injury reveals a coordinated, multi − phasic repair program that progresses through three principal biological phases: early immune activation, extracellular matrix remodeling, and Schwann cell − driven remyelination. Initially, the robust infiltration of specialized macrophages and granulocytes not only facilitates debris clearance but also establishes a pro − regenerative cytokine environment. This is followed by a transitional phase marked by the emergence of NF4 fibroblasts and proliferative mesenchymal subsets, which remodel the extracellular matrix through TGF− \(\:\beta\:\) and collagen−related signaling, setting the foundation for tissue repair. In the later phase, Schwann cells exhibit dynamic fate transitions—from repair−associated Gli0 to myelinating Gli2 subtypes—underscoring their essential role in axonal ensheathment and functional restoration. Importantly, our cross − species integration with human neurofibroma data highlights conserved PTN signaling between neurofibroblasts and Schwann cells, implicating a broader relevance of the NF–Gli axis in both regenerative and pathological contexts. Moreover, by comparing crush and transection injury models, we demonstrate that the delayed engagement of Schwann cells and mesenchymal subtypes in transection injury likely contributes to impaired regeneration, pointing to a narrower therapeutic window for intervention. Together, these findings provide not only a granular understanding of the cellular and molecular mechanisms orchestrating nerve repair, but also identify temporal checkpoints—such as early macrophage heterogeneity, mid − phase NF4 activation, and late Schwann cell remyelination—as actionable targets for stage − specific therapeutic modulation. This work lays a foundation for developing time − tuned regenerative therapies tailored to the specific needs of each phase of peripheral nerve repair. Conclusion In summary, we present a high-resolution, time − resolved single − cell atlas of rat sciatic nerve transection injury that reveals three successive, tightly coordinated phases of regeneration. First, within 24 h of injury there is massive infiltration of pro − inflammatory macrophages and granulocytes, together with expansion of proliferative mesenchymal fibroblasts (NF5), which together clear debris and establish a pro − regenerative cytokine milieu​​. Second, between Days 3–7, proliferative NF4 and NF0 subsets drive extracellular − matrix remodeling via TGF− \(\:\beta\:\) and collagen signaling, while repair Schwann cells (Gli0) emerge to guide axon outgrowth and re−establish cell–cell communication with fibroblasts and endothelium​​. Third, from Day 7 onward, Schwann cells transition into myelinating states (Gli2/5), vascular cells (ECs, PCs) expand to rebuild blood supply, and immune populations shift toward tissue−remodeling and resolution phenotypes (Mac3/4), culminating in restoration of nerve architecture by Day 14​​. Compared to crush injury, transection elicits a stronger early immune response and delays Schwann cell−driven remyelination, identifying a narrowed therapeutic window for intervention​​. Together, these data define phase−specific cellular and molecular targets—early macrophage heterogeneity, mid−phase NF4 activation, and late Schwann cell remyelination—for the development of time−tuned therapies to enhance peripheral nerve repair. Abbreviations scRNA−seq single−cell RNA sequencing NF neurofibroblasts Gli glial cells Mac macrophages Mo monocytes Gran granulocytes DC dendritic cells T T cells NK natural killer cells B B cells PC pericytes SMC smooth muscle cells EC endothelial cells lyEC lymphatic endothelial cells DEGs differentially expressed genes DGE differential gene expression UMI unique molecular identifier PCA principal component analysis UMAP uniform manifold approximation and projection SNN shared nearest−neighbor TGF − transforming growth factor−beta PBS phosphate−buffered saline BSA bovine serum albumin DAPI 4’,6−diamidino−2−phenylindole GO Gene Ontology KEGG Kyoto Encyclopedia of Genes and Genomes Declarations Acknowledgements We thank Beijing Capital Biotechnology Co., Ltd. for assistance with single−cell RNA sequencing. We also acknowledge the use of publicly available datasets (GSE198582, human neurofibroma scRNA−seq data) which greatly supported the cross−validation and annotation processes. The authors thank the developers of CellChat, Monocle, Seurat, and related tools for their contributions to open science. Author contributions Yiben Ouyang, Mingqian Yu, Haolin Liu and Haofeng Cheng contributed equally to this work. The study was conceptualized and designed by Yiben Ouyang, Jiang Peng and Yu Wang. Methodology development and animal experiments were performed by Tieyuan Zhang, Haolin Liu, Haofeng Cheng, Liang Zuo, and Yiben Ouyang, while single−cell library preparation and sequencing were carried out by Yanjun Guan and Sice Wang. Data processing and formal analysis were conducted by Mingqian Yu, Ao Liu, Ruichao He, Xiaoyang Fu, and Jiajie Chen, with bioinformatics and pseudotime trajectory analyses by Mingqian Yu, Yixiao Tan, Yuhui Cui, Junli Wang, and Yiben Ouyang. Cell–cell communication and enrichment analyses were performed by Jinjuan Zhao, Ao Liu, Xiaochun Zhang and Tianqi Su, and visualization was produced by Mingqian Yu and Yiben Ouyang. The original draft was written by Yiben Ouyang and Mingqian Yu, and all authors contributed to review and editing. Supervision and funding acquisition were provided by Jiang Peng and Yu Wang. Funding This study was funded by the Key Technologies Research and Development Program (2024YFC3406806) and the National Natural Science Foundation of China (32171356). Data Availability Statement The raw sequencing data generated in this study have been deposited in the NCBI Sequence Read Archive (SRA) under BioProject accession number SAMN48188757. These data will be made publicly available upon acceptance of the manuscript. Prior to publication, the data are accessible to editors and reviewers upon request. Ethics approval All experimental procedures involving animals were approved by the International Council for Laboratory Animal Science and conducted in accordance with guidelines for the care and use of laboratory animals. Efforts were made to minimize animal suffering and reduce the number of animals used in the study. All procedures were approved by the Institutional Animal Care and Use Committee of PLA General Hospital (approval number: 2016‑x9‑07) and conformed to national guidelines for animal care. Competing interests The authors declare no competing interests. Author details 1 School of Medicine, Nankai University, No. 94, Weijin Road, Nankai District, Tianjin, 300071, PR China. 2 Institute of Orthopedics, The Fourth Medical Center of Chinese PLA General Hospital, Beijing Key Lab of Regenerative Medicine in Orthopedics, Key Laboratory of Musculoskeletal Trauma & War Injuries PLA, No. 51 Fucheng Road, Beijing, 100048, PR China. 3 Co-innovation Center of Neuroregeneration, Nantong University Nantong, Jiangsu Province, 226007, PR China. 4 Cheeloo College of Medicine, Shandong University, 44 West Wenhua Road, Lixia District, Jinan, Shandong 250012, P.R. 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(b) Number of detected genes per cell (nFeature). (c) Percentage of mitochondrial gene expression (percent.mt) across identified cell types. (d–e) Scatter plots illustrating quality control assessments: (d) Correlation between nCount and percent.mt, exhibiting a weak negative correlation (R = −0.06). (e) Correlation between nCount and nFeature, demonstrating a strong positive correlation (R = 0.94). Additionalfile2.png Additional file 2: Fig. S2. Single−Cell Annotation Using Reference−Based Classification. (a) Violin plot displaying the expression levels of characteristic marker genes for each identified cell cluster in the integrated dataset. (b–k) Heatmap depicting the proportion of cells in each cluster (columns) assigned to a specific cell subtype classifier (rows) after unsupervised annotation using SingleR with reference data from GSE198582. The annotation was performed at the subtype level within individual cell types, providing a high−resolution classification of cellular heterogeneity. The following cell types and their respective subpopulations are identified: (b) Neurofibroblasts (NF): Fb: Fibroblasts; dMES: Differentiating mesenchymal cells; prol.MES: Proliferating mesenchymal cells; eMES: Endoneurial mesenchymal cells; pMES: Perineurial mesenchymal cells; (c) Glial cells (Gli): nmSC: Non−myelinating Schwann cells; mSC: Myelinating Schwann cells; rSC: Repair Schwann cells; prol.SC: Proliferating Schwann cells; (d) Pericytes (PC): vPC: Venous pericytes; aPC: Arterial pericytes; prol.PC: Proliferating pericytes; vSMC_PC: Vascular smooth muscle cells and pericytes; (e) Smooth muscle cells (SMC): vSMC: Vascular smooth muscle cells; (f) Macrophages (Mac): tissue prol.Mac: Tissue proliferating macrophages; tissue epi−Mac: Tissue epineurial macrophages; tissue endo−Mac: Tissue endoneurial macrophages; blood Mac: Blood macrophages; (g) Granulocytes (Gran): tissue GC−L: Tissue granule cell−like granulocytes; tissue GC: Tissue granulocytes; blood iGC: Blood immature granulocytes; blood mGC: Blood mature granulocytes; (h) Dendritic cells (DC): tissue MoDC: Tissue monocyte−derived DCs; cDC: Conventional DCs; tissue prol.DC: Tissue proliferating DCs; tissue pDC: Tissue plasmacytoid DCs; tissue DCx: mature/migrating DCs; blood DC: Blood DCs; (i) T cells (T): blood TC: Blood T cells; tissue TC: Tissue T cells; tissue T_NK: Tissue T/NK cells; (j) NK cells (NK): blood NK: Blood NK cells; tissue NK: Tissue NK cells; tissue T_NK: Tissue T/NK cells; (k) B cells (B): BC: B cells; PB: plasma blasts. Additionalfile3.png Additional file 3: Fig. S3. Cell Type Annotation, Cell−Cell Communication, and Temporal Proportions. (a) Heatmap displaying the proportion of cells in each cluster (columns) assigned to a specific cell type classifier (rows) after unsupervised annotation using SingleR with reference data from GSE198582. (b) Heatmap displaying cell−cell communication results across all cell types. The numbers represent the count of signaling pathways detected between cell types at each time point. The higher the number, the stronger the communication, represented by a more intense color. This heatmap includes both signaling sent and signaling received by the cell types. (c) Heatmap showing the proportional distribution of different cell types across seven time points post−injury. Additionalfile4.png Additional file 4: Fig. S4. Temporal Cell−Cell Communication Between Neurofibroblasts and Other Cell Types. (a) Heatmap illustrating cell−cell communication between neurofibroblasts (NF) and different immune cell types across various time points. The numbers represent the count of signaling pathways detected between the two cell types at each time point. Redder colors indicate a higher number of interactions. The heatmap includes both signaling sent and received by NF. (b) Heatmap illustrating cell−cell communication between NF and different vascular cell types across various time points. The numbers represent the count of signaling pathways detected between the two cell types at each time point. Redder colors indicate a higher number of interactions. The heatmap includes both signaling sent and received by NF. (c) Heatmap depicting the predicted strength of signaling pathways between NF and pericyte (PC) subtypes across different time points. Redder colors indicate stronger predicted signaling interactions. The heatmap specifically highlights pathways where PC serve as the signal senders and NF as the signal receivers. Additionalfile5.png Additional file 5: Fig. S5. scRNA−seq Analysis of Immune Cells During Sciatic Nerve Injury. (a) UMAP clustering of immune cells across all time points. Each dot represents a single immune cell, colored according to the time point. (b) UMAP visualization of immune cells at different time points, with dots colored by cell type (T cells, NK cells, Monocytes (Mo), Macrophages (Mac), Granulocytes (Gran), Dendritic cells (DC), B cells). (c) UMAP clustering of immune cells after extraction and re−clustering using an unsupervised approach, revealing distinct immune cell subtypes (T, NK, Mo, Mac, Gran, DC, and B cells). Dots are colored by time point, as in (a). (d) Heatmap of DEGs identified from snRNA sequencing across major immune cell clusters. Red indicates high expression, while gray represents low expression. (e) Bubble plot illustrating marker gene expression for T, NK, Mo, Mac, Gran, DC, and B cells. Dot size represents the percentage of cells expressing the marker gene, while dot color indicates the average expression level. This panel characterizes the distribution of canonical marker genes across major immune cell populations. Additionalfile6.png Additional file 6: Fig. S6. scRNA−seq Analysis of Vascular Cells During Sciatic Nerve Injury. (a) UMAP clustering of vascular cells across all time points. Each dot represents a single vascular cell, and its color corresponds to the time point. (b) UMAP visualization of vascular cells at different time points, with dots colored by cell type, including Endothelial Cells (EC), Smooth Muscle Cells (SMC), Pericytes (PC), and Lymphatic Endothelial Cells (lyEC). (c) UMAP clustering after extracting vascular cells and re−clustering using an unsupervised approach, showing distinct vascular cell subtypes (EC, SMC, PC, lyEC). Dots are colored by time points, as in (a). (d) Heatmap of DEGs identified from snRNA sequencing across major vascular cell clusters. Red indicates high expression, while gray represents low expression. (e) Bubble plot showing marker gene expression for EC, SMC, PC, and lyEC. Dot size represents the percentage of cells expressing the marker gene, and dot color indicates average gene expression levels. This panel characterizes the distribution of canonical marker genes across major vascular cell populations. Additionalfile7.png Additional file 7: Fig. S7. Pseudotime Analysis and Cell−Cell Communication Between Glial Cells and Neurofibroblasts. (a) Pseudotime trajectory analysis of glial cells (Gli). Left: Cell state classification along the trajectory. Right: Distribution of pseudotime values ranging from 0 to 16. (b) Expression dynamics of individual genes along different pseudotime trajectories. The dot plot displays log−normalized gene expression as a function of pseudotime across different cell subtypes. Each dot represents the expression level of a gene in a single cell, with colors indicating different time points. The lines represent smoothed expression values fitted using a generalized additive model (GAM). Top: Trajectory 1; Bottom: Trajectory 2. (c) Heatmap depicting the predicted strength of signaling pathways between neurofibroblasts (NF) and different Gli subtypes at various time points. Redder colors indicate stronger predicted signaling interactions. The heatmap highlights pathways in which Gli act as signal senders and NF as signal receivers. Additionalfile8.png Additional file 8: Fig. S8. Single−Cell Analysis of GSE198582 Dataset and Cell−Cell Communication. (a) UMAP visualization of dimensionally reduced clustering based on the GSE198582 single−cell dataset, categorizing cells into six major types: APCs (antigen−presenting cells), endothelial cells, fibroblasts, hematopoietic cells, pericytes, and Schwann cells. (b) UMAP plot displaying the distribution of cells from different patients within the clustering results. cNF: cutaneous neurofibroma. (c) Bar plot showing the proportional distribution of different cell types across patients. (d) UMAP visualization depicting the expression distribution of marker genes for different cell types. (e) Bubble plot illustrating the marker gene expression across major cell types. The color intensity represents the average expression level, while the dot size indicates the percentage of cells expressing the gene. (f) Cell−cell communication analysis based on CellChat, showing interaction networks between different cell types. Cite Share Download PDF Status: Published Journal Publication published 23 Aug, 2025 Read the published version in Journal of Neuroinflammation → Version 1 posted Editorial decision: Revision requested 19 May, 2025 Reviews received at journal 19 May, 2025 Reviewers agreed at journal 18 May, 2025 Reviewers agreed at journal 17 May, 2025 Reviews received at journal 15 May, 2025 Reviewers agreed at journal 07 May, 2025 Reviewers invited by journal 06 May, 2025 Editor assigned by journal 05 May, 2025 Submission checks completed at journal 03 May, 2025 First submitted to journal 03 May, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABC0lEQVRIiWNgGAWjYFCDw0D8ocJGjo29+QDxWhhnnEkz5uM5lkCkFqDRzJxthxPnSeQo4FVocPzs4dc8NXfs+o4zP3vMwJaW3saQw8Dwo2Ibbi1n8tKseY49S555mM3cuIDHJreN4ewBxp4zt3FqMTuQY2acw3Y42eAwg5n0DIm03DbGvgRmxjY8Ws6/AWr5B9LC/k2ax+BwOhszjwF+LTdyjB/nth22MzjMYybNk3A4gY2NgBb7G2/MmP/2HU6QPMxTJjnjQJphGw9bwkF8fpHszzH+OOPbYXu+88e3SXz8ZyMvP//xwQc/KnBrAQI2CSCR2IAsdACfeiBg/gByIAFFo2AUjIJRMJIBAHCyXWQSvfv4AAAAAElFTkSuQmCC","orcid":"","institution":"The Fourth Medical Center of Chinese PLA General Hospital","correspondingAuthor":true,"prefix":"","firstName":"Yu","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2025-05-03 06:08:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6582223/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6582223/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12974-025-03514-3","type":"published","date":"2025-08-23T16:29:38+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":82511500,"identity":"86ae04a1-29cb-4c81-9c9e-294831914c7c","added_by":"auto","created_at":"2025-05-12 10:50:11","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":9618825,"visible":true,"origin":"","legend":"\u003cp\u003eThe Temporal Heterogeneity of the Sciatic Nerve Injury Cell Atlas. (a) Uniform Manifold Approximation and Projection (UMAP) visualization of integrated single−cell RNA sequencing data from different time points post−injury. Each dot represents a single cell, with its color corresponding to the time point. (b) Left: UMAP plot showing the clustering of cells into major cell types. Right: UMAP plot illustrating the distribution of these cell types across different time points, categorized into neurofibroblasts (NF), immune cells, glial cells (Gli), and vascular cells. The color of the dots indicates the time points. (c) Proportional changes in the distribution of cell types at various time points post−injury. (d) The dot color represents the average gene expression level, while the dot size corresponds to the percentage of cells where the gene is detected with at least one unique molecular identifier (UMI). The plot highlights the characteristic distribution of canonical markers across major cell populations. (e) Heatmap displaying differentially expressed genes (DEGs) within the primary cell populations identified by snRNA sequencing. Red indicates high expression, while gray indicates low expression. (f) Volcano plots of DEGs for each major cell type. The top 10 significant DEGs are labeled, and the y−axis represents the log fold change (logFC).\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-6582223/v1/f2a9e35cd156de073b8a270b.png"},{"id":82511494,"identity":"0ebee557-ca70-45ac-9c71-06c90826ecc7","added_by":"auto","created_at":"2025-05-12 10:50:11","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2237020,"visible":true,"origin":"","legend":"\u003cp\u003eImmune Cell Clustering, Proportions, and Marker Gene Expression. (a) UMAP plot showing the clustering of immune cells across all time points, categorizing cells into B cells, T cells, NK cells, macrophages, monocytes, dendritic cells, and granulocytes. (b) Bar plot showing the proportional distribution of different immune cell types at various time points. (c−d) Unsupervised clustering of specific immune cell types into distinct subtypes. Left: UMAP visualization of immune cell subclusters. Right: Bar plot showing the proportional distribution of each immune cell subtype at different time points. (c) Macrophage subtypes: Mac0–Mac4. (d) Granulocyte subtypes: Gran0–Gran3. (e) Dendritic cell (DC) subtypes: DC0–DC3. (f) T cell subtypes: T0–T3. (g) B cell subtypes: B0–B2. (h) NK cell subtypes: NK0–NK1. (i) Monocyte subtypes: Mo0–Mo1. (j−p) Heatmaps of upregulated marker genes across immune cell subclusters (Z−score, log). (j) Macrophages, (k) Granulocytes, (l) Dendritic cells, (m) T cells, (n) B cells, (o) NK cells, (p) Monocytes.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-6582223/v1/01fb440b060145717a0b01b5.png"},{"id":82511503,"identity":"8fb97825-007b-4306-815f-9e21c34f36ce","added_by":"auto","created_at":"2025-05-12 10:50:11","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":6294554,"visible":true,"origin":"","legend":"\u003cp\u003eGene Ontology Biological Process Enrichment Analysis of Immune and Vascular Cell Subtypes. (a) Gene Ontology (GO) Biological Process enrichment analysis of immune cell subtypes. (b) GO Biological Process enrichment analysis of vascular cell subtypes, including endothelial cells (EC), smooth muscle cells (SMC), pericytes (PC) and lymphatic endothelial cells (lyEC).\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-6582223/v1/1625f6af71763c22c1ed7cdc.png"},{"id":82511496,"identity":"a31f75b9-11dd-465c-8c0a-65d38f5d4056","added_by":"auto","created_at":"2025-05-12 10:50:11","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1407273,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of Monocyte and Macrophage Trajectories and Cell–Cell Communication. (a) UMAP clustering of monocyte (Mo) and macrophage (Mac) at post−injury time points. Cells were clustered using an unsupervised approach and annotated based on previously identified subtypes. Each dot represents a single cell, colored by its cluster. (b) Collagen signaling interactions inferred by CellChat between Mac and neurofibroblasts (NF) at various time points. The chord diagram illustrates interaction strength (thicker edges indicate stronger interactions). The color of each chord corresponds to the sender cell type, while the stacked bars next to each sender indicate the relative contribution of different subclusters. (c, f) Slingshot trajectory inference based on the UMAP clustering in (a). Each dot represents a single cell, colored by its predicted pseudotime. Panel (c) corresponds to trajectory 1, and panel (f) corresponds to trajectory 2. (d, g) Density distributions of Mac and Mo subtypes along different pseudotime trajectories. Panel (d) shows the distribution for trajectory 1, and panel (g) shows the distribution for trajectory 2. (e, h) Expression dynamics of individual genes along different pseudotime trajectories. Dot plots display the log−normalized expression levels of genes as a function of pseudotime across various subtypes. Each dot represents a single cell, colored by its subtype, with solid lines representing smoothed expression trends fitted using a generalized additive model (GAM). Panel (e) corresponds to trajectory 1, and panel (h) corresponds to trajectory 2.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-6582223/v1/cece43507a9303b70f650f52.png"},{"id":82511502,"identity":"75386928-7e63-489f-bf61-36d3ac7c7996","added_by":"auto","created_at":"2025-05-12 10:50:11","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2407641,"visible":true,"origin":"","legend":"\u003cp\u003eCharacterization of Neurofibroblasts Subtypes During Sciatic Nerve Repair. (a) UMAP visualization of integrated neurofibroblasts (NF) cells across different time points post−injury. (b) Left: UMAP plot showing the clustering of NF subtypes. Right: UMAP plot illustrating the distribution of NF subtypes across different time points, categorized into NF0–NF6. (c) Proportional changes in NF subtypes at various time points post−injury. (d) Heatmap of upregulated marker genes for NF subtypes. The color scale represents the Z−score (log) of average gene expression. (e) Gene Ontology (GO) functional enrichment analysis of NF subtypes, highlighting the biological processes enriched in each NF subtype. (f) GO functional enrichment analysis of glial cell (Gli) subtypes, highlighting the enriched biological processes associated with each subtype.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-6582223/v1/0fc789c4818c98d5c60d97e8.png"},{"id":82512278,"identity":"1bf158f7-e0fa-48c8-adcc-89350f613440","added_by":"auto","created_at":"2025-05-12 10:58:11","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":2067959,"visible":true,"origin":"","legend":"\u003cp\u003eCharacterization of Glial cell Subtypes and Signaling Interactions During Sciatic Nerve Repair (a) UMAP visualization of integrated glial cells (Gli) populations across different time points post−injury. (b) Left: UMAP plot showing the clustering of Gli subtypes. Right: UMAP plot illustrating the distribution of Gli subtypes across different time points, classified into Gli0–Gli5. (c) Proportional changes in Gli subtypes at various time points post−injury. (d) Heatmap showing upregulated marker genes for Gli subtypes. The color scale represents the Z−score (log) of average gene expression. (e) Pseudotime trajectory analysis of Gli cells from Day 5 to Day 14, identifying two distinct differentiation pathways. (f) Heatmaps of highly variable gene expression patterns along two trajectories (Z−score, log). Top: Trajectory 2; Bottom: Trajectory 1. (g) Density plots showing cell distribution along the two pseudotime trajectories at different time points. Top: Trajectory 2; Bottom: Trajectory 1. (h) Bubble plot showing activation of signaling pathways across different time points along each trajectory. Larger dots indicate a higher degree of pathway activation. (i) Chord diagram generated by CellChat, depicting the predicted PTEN signaling interactions from Gli to NF at different time points. The thickness of the edges represents interaction strength, and the color of the chords corresponds to the sender cell cluster. The number and weight of receptor clusters involved in the interaction are indicated by the stacked color−matched bars next to each sender. (j) Bubble plot showing Gli−derived signaling pathwaysacting on NF dataset. Redder dots indicate higher predicted pathway activity.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-6582223/v1/4b7b85a2933ac4fa28b16721.png"},{"id":82512761,"identity":"9765878d-a5b8-45c4-8b91-4a0d07a1fb3b","added_by":"auto","created_at":"2025-05-12 11:06:11","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":2054651,"visible":true,"origin":"","legend":"\u003cp\u003eIntegrated Single−Cell Analysis of Sciatic Nerve Crush Injury and Transection Injury. (a) UMAP plot showing the distribution of cells across different time points (Day 0, 1, 3, 7) after sciatic nerve crush injury (SNCI) and sciatic nerve tansection injury (SNTI). (b) Cell type annotation using SingleR based on the Injured Sciatic Nerve Atlas (iSNAT) single−cell reference dataset. Annotated cell types include Endothelial cells (EC), Dendritic cells (DC), Fibroblasts (Fb), Lymphatic endothelial cells (lyEC), Granulocytes (Gc), Monocytes (Mo), Mesenchymal stem cells (MES), Pericytes (PC), Leukocytes (Lk), NK cells (NK), Schwann cells (SC), Smooth muscle cells (SMC), Macrophages (MAC), T cells (Tc), B cells (Bc), and other cell types. (c) UMAP visualization of SNCI cells only, extracted from (a), showing their distribution at different time points. Each dot represents a single cell colored by cell type. (d) UMAP visualization of SNTI cells only, extracted from (a), showing their distribution at different time points. Each dot represents a single cell colored by cell type. (e) Bar plot comparing the proportions of different cell types between SNCI and SNTI at each time point. (f) Scatter plots of differentially expressed genes (DEGs) in SNCI and SNTI at Day 1, Day 3, and Day 7, relative to Day 0. Genes with log₂(FC) \u0026gt; ±1 and FDR \u0026lt; 0.05 are highlighted: Red: Upregulated in both SNCI and SNTI. Green: Downregulated in both SNCI and SNTI. The number of DEGs in each subgroup is displayed in the corresponding quadrants. (g) Gene Ontology (GO) Biological Process enrichment analysis of genes commonly upregulated in either SNCI or SNTI (green dots in f). (h) GO Biological Process enrichment analysis of injury−specific upregulated genes, defined as log\u003csub\u003e2\u003c/sub\u003e(FC) \u0026gt; 1 in SNCI or SNTI, but excluding the commonly upregulated genes from (f).\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-6582223/v1/777aba4b1f283247086b242b.png"},{"id":89847332,"identity":"4fb1666a-971b-4260-ad96-f319ca458dad","added_by":"auto","created_at":"2025-08-25 16:43:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":31783188,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6582223/v1/03eff5fd-7a74-49a3-807f-011ddf6524a5.pdf"},{"id":82511498,"identity":"8fbba0fc-bef7-4bc0-b675-4b8305a3a403","added_by":"auto","created_at":"2025-05-12 10:50:11","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":3442123,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAdditional file 1: Fig. S1. Quality Control Metrics of Single−Cell RNA Sequencing (scRNA−seq) Data. (a–c) Violin plots depicting key quality control metrics across all single cells at seven post−injury time points: (a) Total UMI count per cell (nCount). (b) Number of detected genes per cell (nFeature). (c) Percentage of mitochondrial gene expression (percent.mt) across identified cell types. (d–e) Scatter plots illustrating quality control assessments: (d) Correlation between nCount and percent.mt, exhibiting a weak negative correlation (R = −0.06). (e) Correlation between nCount and nFeature, demonstrating a strong positive correlation (R = 0.94).\u003c/p\u003e","description":"","filename":"Additionalfile1.png","url":"https://assets-eu.researchsquare.com/files/rs-6582223/v1/799c2a954d747e097b3d5e58.png"},{"id":82512283,"identity":"637e970d-eb7d-4895-af1f-4976bab11926","added_by":"auto","created_at":"2025-05-12 10:58:11","extension":"png","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":7122837,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional file 2: Fig. S2. Single−Cell Annotation Using Reference−Based Classification. (a) Violin plot displaying the expression levels of characteristic marker genes for each identified cell cluster in the integrated dataset. (b–k) Heatmap depicting the proportion of cells in each cluster (columns) assigned to a specific cell subtype classifier (rows) after unsupervised annotation using SingleR with reference data from GSE198582. The annotation was performed at the subtype level within individual cell types, providing a high−resolution classification of cellular heterogeneity. The following cell types and their respective subpopulations are identified: (b) Neurofibroblasts (NF): Fb: Fibroblasts; dMES: Differentiating mesenchymal cells; prol.MES: Proliferating mesenchymal cells; eMES: Endoneurial mesenchymal cells; pMES: Perineurial mesenchymal cells; (c) Glial cells (Gli): nmSC: Non−myelinating Schwann cells; mSC: Myelinating Schwann cells; rSC: Repair Schwann cells; prol.SC: Proliferating Schwann cells; (d) Pericytes (PC): vPC: Venous pericytes; aPC: Arterial pericytes; prol.PC: Proliferating pericytes; vSMC_PC: Vascular smooth muscle cells and pericytes; (e) Smooth muscle cells (SMC): vSMC: Vascular smooth muscle cells; (f) Macrophages (Mac): tissue prol.Mac: Tissue proliferating macrophages; tissue epi−Mac: Tissue epineurial macrophages; tissue endo−Mac: Tissue endoneurial macrophages; blood Mac: Blood macrophages; (g) Granulocytes (Gran): tissue GC−L: Tissue granule cell−like granulocytes; tissue GC: Tissue granulocytes; blood iGC: Blood immature granulocytes; blood mGC: Blood mature granulocytes; (h) Dendritic cells (DC): tissue MoDC: Tissue monocyte−derived DCs; cDC: Conventional DCs; tissue prol.DC: Tissue proliferating DCs; tissue pDC: Tissue plasmacytoid DCs; tissue DCx: mature/migrating DCs; blood DC: Blood DCs; (i) T cells (T): blood TC: Blood T cells; tissue TC: Tissue T cells; tissue T_NK: Tissue T/NK cells; (j) NK cells (NK): blood NK: Blood NK cells; tissue NK: Tissue NK cells; tissue T_NK: Tissue T/NK cells; (k) B cells (B): BC: B cells; PB: plasma blasts.\u003c/p\u003e","description":"","filename":"Additionalfile2.png","url":"https://assets-eu.researchsquare.com/files/rs-6582223/v1/636e5b4563e32c3f7dfba8df.png"},{"id":82511514,"identity":"e0bf2314-6262-4b57-af8f-551febda807a","added_by":"auto","created_at":"2025-05-12 10:50:11","extension":"png","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":7340869,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional file 3: Fig. S3. Cell Type Annotation, Cell−Cell Communication, and Temporal Proportions. (a) Heatmap displaying the proportion of cells in each cluster (columns) assigned to a specific cell type classifier (rows) after unsupervised annotation using SingleR with reference data from GSE198582. (b) Heatmap displaying cell−cell communication results across all cell types. The numbers represent the count of signaling pathways detected between cell types at each time point. The higher the number, the stronger the communication, represented by a more intense color. This heatmap includes both signaling sent and signaling received by the cell types. (c) Heatmap showing the proportional distribution of different cell types across seven time points post−injury.\u003c/p\u003e","description":"","filename":"Additionalfile3.png","url":"https://assets-eu.researchsquare.com/files/rs-6582223/v1/997816da166f3c02e084014f.png"},{"id":82512760,"identity":"229a11aa-59d5-4605-ab21-39a57f8f0048","added_by":"auto","created_at":"2025-05-12 11:06:11","extension":"png","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":6779730,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional file 4: Fig. S4. Temporal Cell−Cell Communication Between Neurofibroblasts and Other Cell Types. (a) Heatmap illustrating cell−cell communication between neurofibroblasts (NF) and different immune cell types across various time points. The numbers represent the count of signaling pathways detected between the two cell types at each time point. Redder colors indicate a higher number of interactions. The heatmap includes both signaling sent and received by NF. (b) Heatmap illustrating cell−cell communication between NF and different vascular cell types across various time points. The numbers represent the count of signaling pathways detected between the two cell types at each time point. Redder colors indicate a higher number of interactions. The heatmap includes both signaling sent and received by NF. (c) Heatmap depicting the predicted strength of signaling pathways between NF and pericyte (PC) subtypes across different time points. Redder colors indicate stronger predicted signaling interactions. The heatmap specifically highlights pathways where PC serve as the signal senders and NF as the signal receivers.\u003c/p\u003e","description":"","filename":"Additionalfile4.png","url":"https://assets-eu.researchsquare.com/files/rs-6582223/v1/2adf6553b5c5711398922157.png"},{"id":82511518,"identity":"5b0ddbe7-cebc-491c-a821-8333fc993a1c","added_by":"auto","created_at":"2025-05-12 10:50:11","extension":"png","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":7401749,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional file 5: Fig. S5. scRNA−seq Analysis of Immune Cells During Sciatic Nerve Injury. (a) UMAP clustering of immune cells across all time points. Each dot represents a single immune cell, colored according to the time point. (b) UMAP visualization of immune cells at different time points, with dots colored by cell type (T cells, NK cells, Monocytes (Mo), Macrophages (Mac), Granulocytes (Gran), Dendritic cells (DC), B cells). (c) UMAP clustering of immune cells after extraction and re−clustering using an unsupervised approach, revealing distinct immune cell subtypes (T, NK, Mo, Mac, Gran, DC, and B cells). Dots are colored by time point, as in (a). (d) Heatmap of DEGs identified from snRNA sequencing across major immune cell clusters. Red indicates high expression, while gray represents low expression. (e) Bubble plot illustrating marker gene expression for T, NK, Mo, Mac, Gran, DC, and B cells. Dot size represents the percentage of cells expressing the marker gene, while dot color indicates the average expression level. This panel characterizes the distribution of canonical marker genes across major immune cell populations.\u003c/p\u003e","description":"","filename":"Additionalfile5.png","url":"https://assets-eu.researchsquare.com/files/rs-6582223/v1/a59a48c974dd3b6748295f3b.png"},{"id":82511516,"identity":"a28eb923-dc90-48e0-bc98-32f3aef570bb","added_by":"auto","created_at":"2025-05-12 10:50:11","extension":"png","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":7163815,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional file 6: Fig. S6. scRNA−seq Analysis of Vascular Cells During Sciatic Nerve Injury. (a) UMAP clustering of vascular cells across all time points. Each dot represents a single vascular cell, and its color corresponds to the time point. (b) UMAP visualization of vascular cells at different time points, with dots colored by cell type, including Endothelial Cells (EC), Smooth Muscle Cells (SMC), Pericytes (PC), and Lymphatic Endothelial Cells (lyEC). (c) UMAP clustering after extracting vascular cells and re−clustering using an unsupervised approach, showing distinct vascular cell subtypes (EC, SMC, PC, lyEC). Dots are colored by time points, as in (a). (d) Heatmap of DEGs identified from snRNA sequencing across major vascular cell clusters. Red indicates high expression, while gray represents low expression. (e) Bubble plot showing marker gene expression for EC, SMC, PC, and lyEC. Dot size represents the percentage of cells expressing the marker gene, and dot color indicates average gene expression levels. This panel characterizes the distribution of canonical marker genes across major vascular cell populations.\u003c/p\u003e","description":"","filename":"Additionalfile6.png","url":"https://assets-eu.researchsquare.com/files/rs-6582223/v1/6905630c670c58955593c4e3.png"},{"id":82511527,"identity":"a09cea57-554e-405f-b7da-4a1f81c4ac91","added_by":"auto","created_at":"2025-05-12 10:50:12","extension":"png","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":5815201,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional file 7: Fig. S7. Pseudotime Analysis and Cell−Cell Communication Between Glial Cells and Neurofibroblasts. (a) Pseudotime trajectory analysis of glial cells (Gli). Left: Cell state classification along the trajectory. Right: Distribution of pseudotime values ranging from 0 to 16. (b) Expression dynamics of individual genes along different pseudotime trajectories. The dot plot displays log−normalized gene expression as a function of pseudotime across different cell subtypes. Each dot represents the expression level of a gene in a single cell, with colors indicating different time points. The lines represent smoothed expression values fitted using a generalized additive model (GAM). Top: Trajectory 1; Bottom: Trajectory 2. (c) Heatmap depicting the predicted strength of signaling pathways between neurofibroblasts (NF) and different Gli subtypes at various time points. Redder colors indicate stronger predicted signaling interactions. The heatmap highlights pathways in which Gli act as signal senders and NF as signal receivers.\u003c/p\u003e","description":"","filename":"Additionalfile7.png","url":"https://assets-eu.researchsquare.com/files/rs-6582223/v1/519dd4a6835b131c4b9abe2a.png"},{"id":82512292,"identity":"f74045c1-0840-40d7-a38d-e346df7d8c68","added_by":"auto","created_at":"2025-05-12 10:58:12","extension":"png","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":8483819,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional file 8: Fig. S8. Single−Cell Analysis of GSE198582 Dataset and Cell−Cell Communication. (a) UMAP visualization of dimensionally reduced clustering based on the GSE198582 single−cell dataset, categorizing cells into six major types: APCs (antigen−presenting cells), endothelial cells, fibroblasts, hematopoietic cells, pericytes, and Schwann cells. (b) UMAP plot displaying the distribution of cells from different patients within the clustering results. cNF: cutaneous neurofibroma. (c) Bar plot showing the proportional distribution of different cell types across patients. (d) UMAP visualization depicting the expression distribution of marker genes for different cell types. (e) Bubble plot illustrating the marker gene expression across major cell types. The color intensity represents the average expression level, while the dot size indicates the percentage of cells expressing the gene. (f) Cell−cell communication analysis based on CellChat, showing interaction networks between different cell types.\u003c/p\u003e","description":"","filename":"Additionalfile8.png","url":"https://assets-eu.researchsquare.com/files/rs-6582223/v1/801922255a7f843394c8f052.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"Single − Cell Transcriptomic Landscape of Sciatic Nerve After Transection Injury","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePeripheral nerve injuries, particularly those involving the sciatic nerve, pose significant clinical challenges with profound socioeconomic impacts. Globally, sciatic nerve trauma has an incidence of 7.7% in specific high\u0026thinsp;\u0026minus;\u0026thinsp;risk populations (e.g., acetabular fracture patients), among whom iatrogenic injuries account for 12.87% of postoperative complications [1]. The primary etiologies include traumatic accidents [1], surgical interventions [1, 2], and metabolic disorders such as diabetic neuropathy [3]. Despite advances in microsurgical techniques, functional recovery rates remain suboptimal, with only 50% of patients achieving clinically meaningful recovery, while persistent motor deficits and chronic neuropathic pain\u0026mdash;strongly correlated with reduced quality of life in physical functioning and social participation domains [4, 5]\u0026mdash;continue to burden survivors. This incomplete recovery underscores the urgent need to dissect the multicellular dynamics that underlie nerve repair\u0026mdash;particularly the role of macrophage (Mac) subpopulations and transcriptional reprogramming in axonal regeneration\u0026mdash;as revealed by recent single\u0026thinsp;\u0026minus;\u0026thinsp;cell RNA sequencing (scRNA\u0026thinsp;\u0026minus;\u0026thinsp;seq) studies [6].\u003c/p\u003e \u003cp\u003eThe primary therapeutic strategies for sciatic nerve injury, such as nerve grafting and neurotrophic factor administration, remain limited by incomplete axonal regeneration and misdirected reinnervation [7]. These limitations arise primarily due to the inability of traditional bulk RNA sequencing approaches to resolve the spatiotemporal coordination among Schwann cells, fibroblasts, immune cells, and vascular cells during regeneration. By averaging signals across heterogeneous cell populations, bulk RNA sequencing obscures critical cell type\u0026thinsp;\u0026minus;\u0026thinsp;specific responses [8]. Schwann cells exhibit dynamic phenotypic transitions between dedifferentiated, pro\u0026thinsp;\u0026minus;\u0026thinsp;regenerative, and repair states [9, 10], while Macs undergo polarization from pro\u0026thinsp;\u0026minus;\u0026thinsp;inflammatory to pro\u0026thinsp;\u0026minus;\u0026thinsp;repair subtypes [11]\u0026mdash;processes that bulk RNA sequencing fails to resolve.\u003c/p\u003e \u003cp\u003eEmerging scRNA\u0026thinsp;\u0026minus;\u0026thinsp;seq technologies now enable unprecedented cellular\u0026thinsp;\u0026minus;\u0026thinsp;resolution dissection of neural repair mechanisms. In central nervous system injury models\u0026mdash;such as ischemic stroke and spinal cord injury\u0026mdash;scRNA\u0026thinsp;\u0026minus;\u0026thinsp;seq has revealed transitional cellular states and intercellular crosstalk networks that dictate regenerative outcomes [12\u0026ndash;14]. More recently, in peripheral nerve injury models, scRNA\u0026thinsp;\u0026minus;\u0026thinsp;seq has been employed to unravel the heterogeneity and functional states of Schwann cells, fibroblasts, immune subsets, and vascular cells. For instance, distinct transcriptional alterations have been identified in both myelinating and non\u0026thinsp;\u0026minus;\u0026thinsp;myelinating Schwann cells under autoimmune conditions, providing insights into glial plasticity [15]. Endothelial Plexin\u0026thinsp;\u0026minus;\u0026thinsp;D1 has been shown to play dual roles in peripheral nerve repair by not only guiding the directional growth of endothelial cells (ECs) but also regulating angiogenic patterning [16]. In parallel, studies using Aire\u0026thinsp;\u0026minus;\u0026thinsp;deficient mouse models demonstrated that T cell\u0026ndash;derived \u003cem\u003eIFN\u0026minus;\u003c/em\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\gamma\\:\\)\u003c/span\u003e\u003c/span\u003e induces Mac \u003cem\u003eTNF\u0026minus;\u003c/em\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\alpha\\:\\)\u003c/span\u003e\u003c/span\u003e expression, thereby driving Mac phenotype switching and amplifying inflammatory responses [17]. Furthermore, in trauma\u0026minus;induced heterotopic ossification, scRNA\u0026minus;seq revealed a high degree of spatial colocalization between peripheral nerves and blood vessels [18], implicating coordinated neurovascular remodeling. Collectively, these findings underscore the power of scRNA\u0026minus;seq to disclose cellular plasticity, spatial interactions, and regulatory networks that remain obscured in conventional bulk transcriptomic analysis.\u003c/p\u003e \u003cp\u003eCritical knowledge gaps persist regarding the spatiotemporal regulation of cellular responses during sciatic nerve regeneration. Despite recent advances identifying transcription factors (e.g., \u003cem\u003eSox10\u003c/em\u003e [19] and \u003cem\u003eZeb2\u003c/em\u003e [20]) and signaling pathways (e.g., \u003cem\u003eNeuregulin\u0026thinsp;\u0026minus;\u0026thinsp;1/ErbB\u003c/em\u003e [21] and \u003cem\u003eWnt/\u003c/em\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\beta\\:\\)\u003c/span\u003e\u003c/span\u003e\u003cem\u003e\u0026minus;catenin\u003c/em\u003e [22]) as key regulators of Schwann cell development, the molecular mechanisms governing Schwann cell fate decisions\u0026mdash;particularly the transition into repair\u0026thinsp;\u0026minus;\u0026thinsp;promoting phenotypes following nerve injury\u0026mdash;remain incompletely understood.​ Immune cell dynamics, including the recruitment of bone marrow\u0026thinsp;\u0026minus;\u0026thinsp;derived Macs and neutrophils, exhibit temporal specificity\u0026mdash;Macs peak at Day 3 post\u0026thinsp;\u0026minus;\u0026thinsp;injury, while neutrophils surge within 24 hours\u0026mdash;a pattern consistent with the time\u0026thinsp;\u0026minus;\u0026thinsp;dependent immune activation originally observed in the sciatic nerve crush injury model [6]. Vascular ECs in the sciatic nerve demonstrate distinct subtypes, including epineurial, endoneurial, and lymphatic endothelial cells (lyECs)\u0026mdash;each characterized by unique gene expression profiles. Marker genes such as \u003cem\u003eSpock2\u003c/em\u003e, \u003cem\u003eRgcc\u003c/em\u003e, and \u003cem\u003eLrg1\u003c/em\u003e have been validated in vivo for the identification of these subtypes, offering improved specificity over classical pan\u0026thinsp;\u0026minus;\u0026thinsp;endothelial markers like \u003cem\u003ePecam1\u003c/em\u003e [23].\u003c/p\u003e \u003cp\u003eTo bridge gaps in our understanding of peripheral nerve repair, we conducted a longitudinal scRNA\u0026thinsp;\u0026minus;\u0026thinsp;seq study to investigate dynamic cellular transitions during sciatic nerve injury and repair. Our research focuses on the influence of Schwann cells on fibroblast behavior, explores cell communication networks, and identifies key genes involved in the repair process using pseudotemporal trajectory analysis. We also examine the differentiation of Macs and monocytes (Mos) into Mac\u0026thinsp;\u0026minus;\u0026thinsp;like cells and assess the role of other immune and vascular cells during repair. Additionally, we compare the repair processes following both sciatic nerve transection and crush injuries, highlighting both their similarities and differences. This study provides an in \u0026minus;\u0026thinsp;depth insight into the molecular mechanisms underlying nerve regeneration and highlights potential therapeutic targets for clinical application.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eAnimal model of sciatic nerve transection\u003c/h2\u003e \u003cp\u003eAdult female Sprague–Dawley rats (8–10 weeks old, 220–250 g) were anesthetized by intraperitoneal injection of sodium pentobarbital (30 mg/kg). Under sterile conditions, the left sciatic nerve was exposed via a mid‑thigh skin incision, freed from surrounding tissue, and sharply transected approximately 10 mm proximal to its trifurcation. The muscle fascia and skin were then closed in two layers with 4‑0 absorbable sutures. After recovery from anesthesia, animals were housed in standard plastic cages, with free access to water and a 12‑hour light/12‑hour dark cycle. Five rats were used per time point.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eTissue Collection and Single − Cell Dissociation\u003c/h3\u003e\n\u003cp\u003eAt each designated post‑injury time point (Day 0, 1, 3, 5, 7, 10, and 14), five rats were re‑anesthetized and the original incision reopened. For Day 0 controls, a 6 mm segment of intact sciatic nerve was harvested at the mid‑thigh. For Day 1–3 (before nerve‑bridge formation), a 2 mm segment was collected from each stump (proximal and distal to the transection site). For Day 5–14 (after nerve‑bridge formation), a \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\pm\\:\\)\u003c/span\u003e\u003c/span\u003e2 mm segment of the newly formed inter‑stump nerve bridge was harvested. Tissues from the five animals at each time point were pooled, immediately placed in ice‑cold Hank’s Balanced Salt Solution (HBSS), and enzymatically digested in collagenase IV (1 mg/mL) and DNase I (50 U/mL) at 37°C for 30 min. The resulting cell suspension was filtered through a 40 µm cell strainer, centrifuged at 300 × g for 5 min, and resuspended in phosphate−buffered saline (PBS) containing 0.04% bovine serum albumin (BSA) for subsequent scRNA−seq library preparation.\u003c/p\u003e\n\u003ch3\u003eScRNA − seq and Preprocessing\u003c/h3\u003e\n\u003cp\u003escRNA − seq was performed using the Chromium Single Cell Gene Expression Solution (10x Genomics), which enables the isolation and labeling of 500 − 10,000 individual cells. This technology is based on the GemCode microfluidics platform, where barcoded gel beads and single cells are encapsulated within oil droplets (Gel Bead − In − EMulsions, GEMs). Within each GEM, gel beads dissolve, and cells undergo lysis, releasing mRNA that is reverse − transcribed into barcoded cDNA. After breaking the emulsion, cDNA was amplified via PCR, followed by quality control to assess fragment size and yield. The amplified cDNA was then fragmented to 200–300 bp, end − repaired, A − tailed, and ligated with sequencing adapters before undergoing index PCR amplification. Library quality was validated before sequencing on the Illumina NovaSeq 6000 platform to obtain high − throughput single − cell gene expression data.\u003c/p\u003e \u003cp\u003eRaw sequencing reads were processed using the Cell Ranger pipeline (10x Genomics), including read alignment to the rat genome (Rnor_6.0), barcode assignment, and unique molecular identifier (UMI) counting. Low − quality cells with high mitochondrial gene content (\u0026gt; 10%) or low total UMI counts (\u0026lt; 500) were removed. Doublet detection was performed using \u003cem\u003eDoubletFinder\u003c/em\u003e, and doublets were excluded from downstream analysis.\u003c/p\u003e\n\u003ch3\u003eClustering and Cell Type Annotation\u003c/h3\u003e\n\u003cp\u003eDimensionality reduction was performed using principal component analysis (PCA) on the top 3000 variable genes. The first 30 principal components were used for uniform manifold approximation and projection (UMAP) visualization. Unsupervised clustering was performed using the \u003cem\u003eSeurat\u003c/em\u003e package [24] (version 5.1.0) with the Louvain algorithm at a resolution of 0.8. Cell types were annotated based on the expression of established marker genes: NFs (\u003cem\u003eCol1a1, Dcn, Col3a1\u003c/em\u003e), Glis (\u003cem\u003eMpz, S100b, Mag\u003c/em\u003e), immune cells (\u003cem\u003eAif1, Cd68, Cd3e\u003c/em\u003e), and vascular cells (\u003cem\u003eVtn, Esam, Plvap\u003c/em\u003e). Further subclustering within major cell populations was performed to identify distinct cellular subtypes.\u003c/p\u003e\n\u003ch3\u003eBioinformatic Analysis and Statistics\u003c/h3\u003e\n\u003cp\u003eWe employed Seurat for downstream analysis, following a structured pipeline to process and analyse the scRNA − seq data. The analysis was conducted in seven key steps:\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eNormalization: Raw gene expression counts were normalized on a per − cell basis using log − normalization (log1p transformation), in which the natural logarithm of 1 plus the counts per 10,000 was computed. This step ensures that expression levels are comparable across cells and suitable for downstream analyses.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eHighly Variable Gene Selection and Batch Effect Correction: The top 3,000 highly variable genes were identified using the \u003cem\u003eFindVariableFeatures\u003c/em\u003e function, capturing genes with the greatest variability across the dataset and likely reflecting biologically meaningful signals. To mitigate batch effects, integration anchors were first identified using the \u003cem\u003eFindIntegrationAnchors\u003c/em\u003e function and then used to integrate the datasets with \u003cem\u003eIntegrateData\u003c/em\u003e.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eData Scaling: Gene expression values were standardized across all cells using Z − score transformation via the \u003cem\u003eScaleData\u003c/em\u003e function. This step adjusts for differences in average gene expression levels, facilitating cross − cell comparisons and downstream dimensionality reduction.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003ePCA: PCA was performed on the scaled expression matrix of the highly variable genes to reduce dimensionality and capture the primary axes of variation in the dataset.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eUMAP Visualization: UMAP was used to project the high − dimensional data into a two − dimensional space for visualization. The \u003cem\u003eRunUMAP\u003c/em\u003e function in Seurat was executed using principal components 1 through 15 (dims = 1:15), as determined from the PCA on the subsetted data.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eClustering: Cell clustering was carried out using a graph − based approach with the Louvain algorithm, as implemented in the \u003cem\u003eFindClusters\u003c/em\u003e function. A shared nearest neighbor (SNN) graph was first constructed using \u003cem\u003eFindNeighbors\u003c/em\u003e, and clustering was performed with a \u003cem\u003eresolution\u003c/em\u003e parameter of 0.3 to define discrete cell populations within the sciatic nerve dataset.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eDifferential Gene Expression (DGE) Analysis: To identify differentially expressed genes (DEGs) for each cluster, the Wilcoxon rank − sum test was applied using the \u003cem\u003eFindMarkers\u003c/em\u003e function. The analysis was conducted on the log − normalized expression matrix, with \u003cem\u003emin.pct\u003c/em\u003e = 0.25 set to include genes expressed in at least 25% of cells in either group. All other parameters were kept at their default values, including \u003cem\u003eonly.pos\u003c/em\u003e = TRUE, \u003cem\u003elogfc.threshold\u003c/em\u003e = 0.1, and \u003cem\u003emax.cells.per.ident\u003c/em\u003e = Inf. This strategy enabled robust identification of cluster − specific marker genes while maintaining sensitivity to subtle expression differences.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003cp\u003e\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eCell Type Annotation\u003c/h2\u003e \u003cp\u003eFor unsupervised cell type annotation, we utilized the \u003cem\u003eSingleR\u003c/em\u003e package [25] (version 2.8.0) with the crush sciatic nerve injury single − cell dataset (GSE198582 [6]) as a reference. Cell type assignments were further manually validated by examining the DEGs for the presence of canonical marker genes for each cell type. Based on this analysis, we assigned metacells to various cell types, including NF, Gli, PC, SMC, Mac, Gran, DC, and T /NK/B cell.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSubclustering and Further Resolution\u003c/h3\u003e\n\u003cp\u003eTo achieve higher resolution of cell states, subclustering was performed on major cell types by first subsetting specific populations (e.g., fibroblasts) from the integrated dataset. For each subset, data normalization and scaling were repeated using the \u003cem\u003eSCTransform\u003c/em\u003e function to ensure consistency in preprocessing. Dimensionality reduction was carried out via PCA, and the appropriate number of principal components (PCs) was determined using \u003cem\u003eElbowPlot\u003c/em\u003e. The selected PCs (typically 1–10/15) were then used for constructing a SNN graph and performing graph − based clustering using the \u003cem\u003eFindNeighbors\u003c/em\u003e and \u003cem\u003eFindClusters\u003c/em\u003e functions, with an appropriate resolution to capture finer subpopulations. Low − dimensional embeddings were generated using UMAP based on the selected PCs.\u003c/p\u003e \u003cp\u003eDEGs between subclusters were identified using the \u003cem\u003eFindMarkers\u003c/em\u003e function with the parameter \u003cem\u003emin.pct\u003c/em\u003e = 0.25, while other parameters remained at their default settings. DEGs were ranked by log2 fold − change values to identify representative marker genes. For visualization, z − scores were computed based on the average expression levels of each gene across subclusters, and the resulting matrix was visualized using heatmaps, enabling clear comparison of transcriptional profiles and aiding in the refinement of subpopulation annotations.\u003c/p\u003e\n\u003ch3\u003eDGE and Enrichment Analysis\u003c/h3\u003e\n\u003cp\u003eDGE analysis was performed using the \u003cem\u003eFindMarkers\u003c/em\u003e function in Seurat. For identifying marker genes of general cell subtypes, genes were considered significant if they were both upregulated in the target cluster and had a \u003cem\u003ep\u003c/em\u003e − value \u0026lt; 0.05. This dual criterion ensured that selected genes were not only statistically significant but also biologically relevant in distinguishing cell populations. This analysis enabled the classification of distinct cellular subtypes and facilitated the identification of key regulatory genes within each lineage. For the comparative analysis between crush injury and transection injury, a stricter threshold was applied to identify DEGs that distinguished the two injury models. After using \u003cem\u003eFindMarkers\u003c/em\u003e, genes were considered significantly different if they met the criteria of fold − change \u0026gt; 1 and adjusted \u003cem\u003ep − value\u003c/em\u003e \u0026lt; 0.01. These DEGs were subsequently subjected to Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis using \u003cem\u003eclusterProfiler\u003c/em\u003e [26] to identify biological pathways that were commonly activated in both injury types or uniquely enriched in either crush or transection injuries. This approach enabled a detailed characterization of both shared and distinct molecular mechanisms underlying nerve repair in different injury models.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003ePseudotime and Trajectory Analysis\u003c/h2\u003e \u003cp\u003eTo investigate dynamic cellular transitions during nerve repair, pseudotime trajectory analysis was performed using \u003cem\u003eMonocle\u003c/em\u003e [27] for glial cells (Glis) and \u003cem\u003eslingshot\u003c/em\u003e [28] for monocyte − to − macrophage (Mo − to − Mac) differentiation. For glial cells, \u003cem\u003eMonocle\u003c/em\u003e was used to model the lineage trajectory from repair Schwann cells (Gli0) to myelinating Schwann cells (Gli5). After filtering out uninjured samples, cells from Day 5 to Day 14 were selected for analysis. Cells were re − embedded based on highly variable genes, and a subset of genes expressed in at least 350 cells was retained. To define ordering genes for trajectory inference, we performed differential expression analysis across timepoints using \u003cem\u003edifferentialGeneTest\u003c/em\u003e, and genes with \u003cem\u003ep\u003c/em\u003e \u0026lt; \u003cem\u003e1e\u003c/em\u003e\u003csup\u003e\u003cem\u003e− 15\u003c/em\u003e\u003c/sup\u003e were selected. The top pseudotime − associated genes were then analyzed for expression trends using \u003cem\u003eplot_pseudotime_heatmap\u003c/em\u003e, and k − means clustering (\u003cem\u003ek\u003c/em\u003e = 3) was applied to identify co − expression modules. These clusters were interpreted as representing early − stage regulatory genes, intermediate − phase modulators, and late − stage effectors, corresponding to successive waves of gene activation during Schwann cell reprogramming and remyelination. To explore the biological significance of these gene modules, we conducted GO enrichment analysis for each gene cluster using \u003cem\u003eclusterProfiler\u003c/em\u003e [26], revealing functional themes associated with glial plasticity, differentiation, and remyelination. For the immune compartment, \u003cem\u003eslingshot\u003c/em\u003e was used to reconstruct lineage relationships between infiltrating monocytes (Mo0 and Mo1) and differentiated macrophage subtypes (Mac0–Mac4). The trajectory was inferred based on UMAP embeddings, with pseudotime values extracted along inferred lineages. For downstream analysis, cells with valid pseudotime values were filtered, and a generalized additive model (GAM) was fitted to gene expression using the tradeSeq framework. Genes exhibiting significant changes along the trajectory were identified using \u003cem\u003eassociationTest\u003c/em\u003e and \u003cem\u003estartVsEndTest\u003c/em\u003e, highlighting transcriptional programs that govern Mo − to − Mac fate transitions. Top − ranked genes visualized with \u003cem\u003eplotSmoothers\u003c/em\u003e were prioritized for further interpretation. Together, these analyses provided comprehensive insights into the temporal orchestration of glial reprogramming and immune differentiation during peripheral nerve regeneration.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eCell − Cell Communication Analysis\u003c/h2\u003e \u003cp\u003eCell − cell communication analysis was performed to investigate intercellular signaling dynamics during sciatic nerve repair. The \u003cem\u003eCellChat\u003c/em\u003e package [29] was used to infer ligand − receptor interactions among different cell populations. Normalized single − cell RNA − seq data were used as input, and the analysis was conducted separately for different time points to track temporal changes in signaling activity. First, the expression of known ligand − receptor pairs was assessed across all major cell types, including NFs, Glis, immune cells (Mac, Mo, Gran, T cells, B cells, NK cells, and DCs), and vascular cells (PCs, ECs, SMCs, and lyECs). Communication networks were constructed based on the strength and specificity of ligand − receptor interactions. The \u003cem\u003ecomputeCommunProb\u003c/em\u003e function was applied to calculate interaction probabilities, followed by \u003cem\u003ecomputeCommunProbPathway\u003c/em\u003e to identify active signaling pathways. To visualize signaling patterns, network diagrams and heatmaps were generated to depict interactions between cell types. The \u003cem\u003enetVisual_circle\u003c/em\u003e function was used to illustrate global intercellular communication, while \u003cem\u003enetVisual_bubble\u003c/em\u003e provided insights into specific ligand − receptor pairs. Pathway − level analysis was conducted to identify key signaling cascades, with a focus on those implicated in nerve regeneration, including collagen − related signaling, \u003cem\u003ePTN\u003c/em\u003e signaling between Glis and NFs, and Mac − mediated extracellular matrix remodeling. The strength and directionality of communication were assessed by examining changes in outgoing and incoming signaling patterns for specific cell populations over time. Statistical analyses were corrected for multiple comparisons using the Benjamini − Hochberg method to control the false discovery rate.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eComparative Analysis of Crush Injury and Transection Injury Models\u003c/h2\u003e \u003cp\u003eTo compare the cellular and molecular responses between crush injury and transection injury models, we conducted a detailed comparative analysis using scRNA − seq data. The scRNA − seq data from both injury models were integrated using the \u003cem\u003eFindIntegrationAnchors\u003c/em\u003e function in Seurat, which identifies mutual nearest neighbors (anchors) between datasets to correct for batch effects and technical variations while preserving biological differences. Following anchor identification, the datasets were harmonized using the \u003cem\u003eIntegrateData\u003c/em\u003e function, generating a combined dataset with minimized batch effects. The integrated dataset was log − normalized using the natural log1p normalization, and the 3,000 most variable genes were identified using the \u003cem\u003eFindVariableFeatures\u003c/em\u003e function. Expression values were standardized across cells using Z − score transformation, and PCA was performed on the scaled variable gene matrix. Clustering was performed using the Louvain algorithm implemented in the \u003cem\u003eFindClusters\u003c/em\u003e function, with a resolution setting of 0.7 to identify distinct cell populations. Cell type annotations were based on the classification derived from the crush injury dataset, which included various functional subtypes such as fibroblasts (e.g., proliferating, differentiating, and matrix − stabilizing subtypes), Schwann cells (e.g., proliferating, repairing, and myelinating subtypes), Macs (e.g., pro − inflammatory, pro − repair, and proliferating subtypes), Mo, Gran, T cells, B cells, ECs, and PCs. These annotations were manually validated by examining the expression of canonical marker genes for each cell type. DGE analysis was conducted using the \u003cem\u003eFindMarkers\u003c/em\u003e function, with genes considered significantly differentially expressed if they exhibited a fold change \u0026gt; 1 and an adjusted \u003cem\u003ep\u003c/em\u003e − value \u0026lt; 0.01. GO and KEGG pathway enrichment analysis were conducted using the \u003cem\u003eclusterProfiler\u003c/em\u003e package, with pathways considered significantly enriched if they had an adjusted \u003cem\u003ep\u003c/em\u003e − value \u0026lt; 0.05. This comprehensive comparative analysis provided insights into the distinct cellular and molecular mechanisms underlying nerve repair in crush injury and transection injury models, highlighting potential therapeutic targets for enhancing nerve regeneration in different injury contexts.\u003c/p\u003e"},{"header":"Results","content":"\u003ch2\u003e1. Single − Cell Transcriptomics Reveals Dynamic Cellular Heterogeneity and Intercellular Communication in Sciatic Nerve Repair\u003c/h2\u003e\u003cp\u003eWe conducted scRNA − seq on rat sciatic nerve tissues following transection injury by analyzing 58,943 high − quality cells across seven time points (Day 0, 1, 3, 5, 7, 10, 14) (Additional file 1: Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Unsupervised clustering and UMAP dimensionality reduction revealed four major cellular compartments: neurofibroblasts (NFs), glial cells (Glis), immune cells (Mac, Mo, Gran, T/B/NK/DC cells), and vascular cells (PC, EC, SMC, lyEC) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Additional file 2: Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003ea). Subclustering and annotation analysis revealed distinct cellular subtypes within each major compartment (Additional file 2: Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e and Additional file 3: Fig. \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003ea). In the NF lineage, we identified fibroblasts and mesenchymal cell populations, along with their respective subtypes (Additional file 2: Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eb). Glis exhibited functional heterogeneity, comprising proliferating, repairing, myelinating, and non − myelinating subtypes (Additional file 2: Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003ec). Immune cell diversity was characterized by distinct Mac populations, including proliferating Macs, as well as plasmacytoid and mature/migrating dendritic cells (DCs) (Additional file 2: Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003ef − k). Within the vascular compartment, we identified arterial pericytes (PCs), proliferating PCs and additional SMC − associated subtypes (Additional file 2: Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003ed,e). These findings highlight the cellular complexity and dynamic responses underlying sciatic nerve repair.\u003c/p\u003e\u003cp\u003eDynamic changes in cellular composition were observed following sciatic nerve transection (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea − c). In the uninjured state, NFs constituted the predominant population, accounting for 81.8% of the total cells, followed by vascular and immune cells, with Glis being the least abundant (Additional file 3: Fig. \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003ec). NFs were characterized by the expression of key extracellular matrix − related genes such as \u003cem\u003eCol1a1, Dcn, Col3a1, Col6a2\u003c/em\u003e, and \u003cem\u003eLum\u003c/em\u003e, which play a fundamental role in maintaining tissue integrity (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed). Vascular cells were identified by markers including \u003cem\u003eVtn, Esam, Plvap, Acta2\u003c/em\u003e, and \u003cem\u003eDes\u003c/em\u003e, while immune cells exhibited distinct signatures such as \u003cem\u003eAif1, Cd68, Cd3e, Cd3g, Inpp5d\u003c/em\u003e, and \u003cem\u003eAdgre1\u003c/em\u003e. Glial cells, primarily Schwann cells, expressed \u003cem\u003eMpz, S100b, Mbp\u003c/em\u003e, and \u003cem\u003eMag\u003c/em\u003e, underscoring their role in nerve support and myelination. By Day 1 post − injury, immune cells expanded dramatically, exceeding 95% of the total cell population (Additional file 3: Fig. \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003ec). This early inflammatory response was dominated by Macs and granulocytes (Grans), consistent with their essential roles in debris clearance and initiating the repair process [30, 31]. Mac subsets were characterized by markers such as \u003cem\u003eF13a1, Pf4, C1qc, C1qb, Ms4a7\u003c/em\u003e, and \u003cem\u003eFolr2\u003c/em\u003e, whereas Grans displayed gene signatures including \u003cem\u003eS100a8, S100a9, Il1r2\u003c/em\u003e, and \u003cem\u003eDgat2\u003c/em\u003e (Additional file 5: Fig. \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003ee). The marked immune infiltration suggested a rapid activation of innate immune mechanisms to facilitate the removal of myelin debris [32]. By Day 5, NFs re − emerged in substantial numbers, marking a transition from the inflammatory phase to the regenerative phase. This increase in NFs coincided with a notable rise in Glis, which play a critical role in axonal regeneration and remyelination. At the same time, immune cell numbers began to decline, indicating a shift toward tissue remodeling and repair. By Day 7, vascular cells nearly doubled, primarily comprising PCs and ECs (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). This vascular expansion likely reflects an increase in angiogenesis and the establishment of a supportive microenvironment for regenerating axons. Given the observed strong cell–cell communication between Glis and vascular cells (Additional file 3: Fig. \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003eb), their coordinated function may be essential in restoring nerve homeostasis and promoting functional recovery [33].\u003c/p\u003e\u003cp\u003eThe analysis of cellular communication during nerve repair reveals complex interactions between different cell types (Additional file 3: Fig. \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003eb). Glis and vascular cells exhibit strong cell − cell communication both in normal and injured states, suggesting their pivotal roles in maintaining nerve homeostasis and promoting tissue repair. NFs and Glis show robust signaling interactions, with potential regulatory pathways that facilitate cellular differentiation and tissue regeneration [34]. While immune cells play a crucial role in the early stages of injury, their cell − cell communication with other cell types is relatively weaker both in normal tissue and throughout the repair process [29]. The intricate communication networks observed during the repair process highlight the need for coordinated signaling between multiple cell types to ensure efficient tissue regeneration and functional recovery.\u003c/p\u003e\u003cp\u003eIn summary, this study provides a comprehensive overview of the cellular landscape during sciatic nerve injury and repair, revealing dynamic shifts in cellular proportions and highlighting the complex cellular interactions that drive tissue regeneration. The identification of key cell subtype markers and their associated signaling pathways offers insights into the molecular mechanisms underlying nerve repair, which may inform future therapeutic strategies aimed at enhancing nerve regeneration.\u003c/p\u003e\u003ch2\u003e2. Temporal Dynamics and Subtype − Specific Remodeling of the Immune Landscape During Sciatic Nerve Repair\u003c/h2\u003e\u003cp\u003eIn the uninjured sciatic nerve, Macs constitute the predominant immune cell population, followed by T cells and DCs (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003ea,b). Following injury, the immune response undergoes dynamic changes over time. During the early phase (Day 1–Day 3), the immune landscape is dominated by Macs and Grans, reflecting their crucial role in the immediate inflammatory response and debris clearance [30, 35]. As the repair process progresses (Day 5–Day 14), the proportion of T cells and DCs increases, while Macs gradually decline. This transition highlights the shift from an inflammatory environment to an adaptive immune response and tissue remodeling [36, 37].\u003c/p\u003e\u003cp\u003eT cells exhibit distinct subtypes that vary throughout the repair process (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003ef,m). The uninjured sciatic nerve primarily harbors T0 and T2 cells, with T0 gradually decreasing post − injury. In contrast, T1, T2, and T3 subpopulations expand during the repair phase. Marker gene analysis reveals that T0 cells are characterized by \u003cem\u003eCalhm6\u003c/em\u003e and \u003cem\u003eS1pr1\u003c/em\u003e expression, whereas T1 cells express \u003cem\u003eCcl5, Ccl3\u003c/em\u003e, and \u003cem\u003eCcl4\u003c/em\u003e, suggesting their involvement in immune activation and recruitment. T2 cells, marked by \u003cem\u003eBtrc, Tnfrsf4\u003c/em\u003e, and \u003cem\u003eFoxp3\u003c/em\u003e, may correspond to regulatory T cells contributing to immune modulation. T3 cells, enriched in genes related to chromosome segregation and mitotic activity, such as \u003cem\u003eNcapg\u003c/em\u003e, \u003cem\u003eCdca3\u003c/em\u003e, and \u003cem\u003eTop2a\u003c/em\u003e, likely represent proliferative T cell subsets. Functional enrichment analysis further supports these findings (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003ea), as T0 cells are associated with type II interferon production and ATP export, T1 cells with lymphocyte − mediated immunity and dopamine biosynthesis, T2 cells with cytokine regulation and mononuclear cell proliferation, and T3 cells with chromatin remodeling and amino acid metabolism.\u003c/p\u003e\u003cp\u003eB cells, although present at low levels throughout the repair process (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eg,n), undergo subtype − specific changes. The primary subsets include B0, B1, and B2 cells. Marker gene analysis indicates that B0 cells express \u003cem\u003eCd40, Lbh\u003c/em\u003e, and \u003cem\u003eMs4a1\u003c/em\u003e, while B1 cells upregulate \u003cem\u003eGpr171, Grn\u003c/em\u003e, and \u003cem\u003eCd7\u003c/em\u003e. Notably, B2 cells, characterized by \u003cem\u003eNipal1, Ctla4\u003c/em\u003e, and \u003cem\u003eTnfrsf17\u003c/em\u003e expression, primarily correspond to plasma blasts. Functional enrichment analysis suggests that B0 cells participate in ribosome biogenesis and interleukin − 2 regulation, B1 cells contribute to autophagosome assembly and lectin receptor signaling, and B2 cells engage in endoplasmic reticulum stress responses and protein localization (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003ea).\u003c/p\u003e\u003cp\u003eNatural killer (NK) cells are present at low levels in both the uninjured and repairing nerve (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eh,o), with no significant changes in their proportions during the repair process. Two major NK cell subtypes are identified: NK0 and NK1. NK0 cells are enriched in \u003cem\u003eGata3, Cd27\u003c/em\u003e, and \u003cem\u003eGpr183\u003c/em\u003e, while NK1 cells express \u003cem\u003eBatf, Il21r\u003c/em\u003e, and \u003cem\u003eGzma\u003c/em\u003e. Functional annotation suggests that NK0 cells contribute to T cell chemotaxis and calcium ion transport, whereas NK1 cells are involved in Gran chemotaxis and tissue disruption, implying a role in immune surveillance and cytotoxic activity (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003ea).\u003c/p\u003e\u003cp\u003eDCs display dynamic changes in their subpopulations (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003ee). The uninjured sciatic nerve primarily contains DC0 cells, which decrease during repair, while DC1 cells increase. Subtype characterization reveals that DC0 cells, expressing \u003cem\u003eCcl17, Mfge8\u003c/em\u003e, and \u003cem\u003eClec4a1\u003c/em\u003e, are Mo − derived DCs. DC1 cells, marked by Mctp2, Slco4a1, and \u003cem\u003eSiglech\u003c/em\u003e, predominantly represent plasmacytoid DCs, whereas DC3 cells (\u003cem\u003eGls2, Fscn1\u003c/em\u003e, and \u003cem\u003eLad1\u003c/em\u003e) correspond to mature/migrating DCs, and DC2 cells (\u003cem\u003eNaaa, Slpi\u003c/em\u003e, and \u003cem\u003eXcr1\u003c/em\u003e) align with conventional DCs (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003el and Additional file 2: Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eh). Functional analysis indicates that DC0 cells are involved in biotic stimulus responses and Toll − like receptor signaling, DC1 cells participate in endoplasmic reticulum stress responses and nuclear receptor signaling, DC2 cells regulate innate immune responses and lipid absorption, and DC3 cells mediate actin filament organization and Toll − like receptor signaling (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). These findings suggest a coordinated DC response that facilitates antigen presentation and immune modulation during nerve repair.\u003c/p\u003e\u003cp\u003eGrans also exhibit significant changes post − injury (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003ed). The predominant subsets in the uninjured nerve are Gran0 and Gran1, with Gran0 increasing and Gran1 decreasing during repair. Additionally, two new Gran subtypes, Gran2 and Gran3, emerge post − injury. Marker gene analysis identifies Gran0 as expressing \u003cem\u003eRiok3\u003c/em\u003e, \u003cem\u003eGadd45a\u003c/em\u003e, and \u003cem\u003eS100a8\u003c/em\u003e, while Gran1 expresses \u003cem\u003eRps28\u003c/em\u003e and \u003cem\u003eHnrnpf\u003c/em\u003e. Gran2 cells upregulate \u003cem\u003eCcl2\u003c/em\u003e and \u003cem\u003eGpnmb\u003c/em\u003e, whereas Gran3 expresses \u003cem\u003eAtp6v0d1, Psmb3\u003c/em\u003e, and \u003cem\u003eCapg\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003ek). Functional enrichment analysis suggests that Gran0 is involved in tumor necrosis factor signaling and collagen catabolism, Gran1 contributes to translation and Toll − like receptor signaling, and Gran2 and Gran3 play roles in sphingoid metabolism (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). These findings highlight the dynamic Gran response in nerve repair, potentially influencing inflammation resolution and tissue remodeling.\u003c/p\u003e\u003cp\u003eCollectively, these results demonstrate that immune cell populations undergo distinct temporal and subtype − specific changes during sciatic nerve repair. The early phase is dominated by Macs and Grans, facilitating debris clearance and acute inflammation. As repair progresses, T cells, DCs, and B cells expand, contributing to adaptive immunity and tissue remodeling. These findings provide insights into the immune landscape following nerve injury, with potential implications for therapeutic strategies targeting immune modulation in peripheral nerve repair.\u003c/p\u003e\u003ch2\u003e3. Mo − Derived Mac Diversification and Functional Trajectories Shape the Immune Microenvironment During Sciatic Nerve Repair\u003c/h2\u003e\u003cp\u003eMacs and Mos play critical roles in the repair process following sciatic nerve injury, orchestrating a dynamic response that evolves over time. In the uninjured sciatic nerve, Macs are predominantly of the Mac3 subtype, which is marked by high expression of genes such as \u003cem\u003eSlco2b1, Cd4\u003c/em\u003e, and \u003cem\u003eSelenop\u003c/em\u003e. Following injury, the immune landscape undergoes significant shifts, with distinct Mac subtypes emerging at different stages (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003ec,j).\u003c/p\u003e\u003cp\u003eDuring the early phase (Day 1), the Mac0 subtype dominates. These cells are characterized not only by their involvement in acute − phase responses and metabolic processes related to amino acid metabolism (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003ea) but also by a unique marker profile that includes \u003cem\u003eChi3l1, Cxcl3, Ereg, Slc2a6, Slpi, Ass1, Vcan, Ccl24\u003c/em\u003e, and \u003cem\u003eNrg1\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003ej). By Day 3, there is a marked shift with Mac1 and Mac2 subtypes becoming more prominent. Mac1 cells—displaying markers such as \u003cem\u003eFxyd2, Hpse, Asgr2, Kctd4, Pdgfc, Akr1b8, Gdf15, Gsta1, Htr2b\u003c/em\u003e, and \u003cem\u003eC6\u003c/em\u003e—likely represent a substantial fraction of blood − derived Macs, while Mac2 cells are defined by the expression of \u003cem\u003eDcn, Col3a1\u003c/em\u003e, and \u003cem\u003eCol1a1\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003ej and Additional file 2: Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003ef). These subtypes are involved in lipid homeostasis, cytokine regulation, and extracellular matrix remodeling. Their numbers decline by Day 5 and nearly vanish by Day 7, coinciding with the progressive increase in Mac3 and Mac4 populations. The Mac4 subset, identified by a distinct proliferative marker profile (\u003cem\u003eCep55, Mastl, Hist1h1b, Kif20b, Hmmr, Aspm, Kif4a, Sgo2, Pclaf\u003c/em\u003e, and \u003cem\u003eEspl1\u003c/em\u003e), gradually increases in the later repair stages (Day 7, Day 10, and Day 14), underscoring its importance in cell proliferation and tissue regeneration.\u003c/p\u003e\u003cp\u003eEnrichment analysis further support these findings (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). Specifically, Mac0 cells are enriched in pathways related to acute − phase response and both proteinogenic and non − proteinogenic amino acid metabolism. In contrast, Mac1 cells are associated with negative regulation of cytokine production, lipid homeostasis, and complement activation, among other immune regulatory processes. Mac2 cells are linked to responses to mechanical stimuli and collagen fibril organization. Meanwhile, Mac3 cells contribute to the regulation of hemopoiesis and non − membrane − bound organelle assembly, and Mac4 cells not only support the proliferative capacity by engaging in chromosome segregation and DNA repair − dependent chromatin remodeling but also modulate epigenetic regulation through pathways such as constitutive heterochromatin formation and negative regulation of the \u003cem\u003ecGAS/STING\u003c/em\u003e signaling pathway.\u003c/p\u003e\u003cp\u003eMos, largely absent in the uninjured nerve, infiltrate the lesion site post − injury, where they differentiate into Macs (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003ei,p). Two major Mo subsets are observed: Mo0 and Mo1. Mo0 cells are marked by \u003cem\u003eCdc42ep4, Polr2j, Dap, Polr2e, Cct3, Ccl2, Cd81, Card19, Rhoc\u003c/em\u003e, and \u003cem\u003eSh3kbp1\u003c/em\u003e, and their enrichment analysis shows a strong association with mRNA metabolism, nucleic acid catabolic processes, and T cell − mediated immunity, among other functions. In comparison, Mo1 cells—exhibiting markers such as \u003cem\u003eCops2, Dhx58, Psmg4, Isg15, Tsta3, Phc2, Pik3cd, Krt75, Gps1\u003c/em\u003e, and \u003cem\u003ePolr3k\u003c/em\u003e—are enriched in pathways involved in RNA catabolism and the cellular response to increased oxygen levels. The stable presence and balanced proportions of these Mo subsets throughout the repair process highlight their crucial role in shaping the heterogeneity and functionality of the Mac populations.\u003c/p\u003e\u003cp\u003ePseudotime trajectory analysis reveals two distinct differentiation pathways for Mo − derived Macs, both originating from Mos (the root state) and progressing through intermediate stages (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003ec,f). Trajectory 1 follows a sequential progression whereby Mos transition through early − stage Mac0, intermediate − stage Mac2, and ultimately differentiate into late − stage Mac1 and Mac3 subsets. In Trajectory 2, the cells share the early − to − intermediate stages (Mac0 \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\to\\:\\)\u003c/span\u003e\u003c/span\u003e Mac2) but diverge in the late phase, giving rise to a mix of Mac1, Mac3, and the proliferative Mac4 subpopulation. These trajectories are accompanied by dynamic transcriptional reprogramming. For instance, genes such as \u003cem\u003eApoe, Pltp, C1qa, C1qb\u003c/em\u003e, and \u003cem\u003eC1qc\u003c/em\u003e peak in late − stage Mac of Trajectory 1 and then decline, indicating transient yet essential roles in lipid metabolism, cholesterol efflux, and complement system regulation (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003ee). In Trajectory 2, the late − phase upregulation of proliferation − associated genes—including \u003cem\u003eTuba1b, Hmgb2l1, Tubb5\u003c/em\u003e, and \u003cem\u003eCst3\u003c/em\u003e—highlights the support for microtubule dynamics, chromatin remodeling, and lysosomal functions in promoting the expansion of the Mac4 subset. The fluctuating expression of additional markers such as \u003cem\u003eCtsz\u003c/em\u003e and \u003cem\u003eVim\u003c/em\u003e further underscores their roles in proteolysis and cytoskeletal reorganization during tissue repair (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003eh).\u003c/p\u003e\u003cp\u003eFinally, cell − cell communication analysis reveals that collagen − mediated signaling between Macs and NFs is significantly enhanced during the repair process (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). Although this interaction is minimal at Day 0 and absent at Day 1, it intensifies markedly from Day 3 onward. This observation reinforces the significance of collagen in extracellular matrix remodeling and nerve regeneration, as well as the critical interplay between Macs and NFs in facilitating tissue repair. The integration of marker gene analysis with functional and temporal data not only refines our understanding of Macs and Mos heterogeneity but also provides important insights for developing therapeutic strategies aimed at harnessing Mac − mediated regenerative processes.\u003c/p\u003e\u003ch2\u003e4. Temporal Dynamics and Functional Specialization of Vascular Cell Populations During Sciatic Nerve Repair\u003c/h2\u003e\u003cp\u003eIn the uninjured sciatic nerve, vascular cell populations were predominantly composed of Smooth muscle cells (SMCs) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e5\u003c/span\u003ea,b). Following nerve injury, vascular cell dynamics underwent significant shifts across different time points. Notably, no vascular cell infiltration was observed on Day 1 post − injury. From Day 3 to Day 14, ECs remained the dominant vascular cell type, with PCs emerging on Day 5 and subsequently maintaining a stable proportion throughout the repair process. A substantial increase in vascular cell numbers was observed from Day 7, showing a rise that persisted in the later stages. In contrast, SMCs and lyECs remained relatively scarce throughout the repair timeline.\u003c/p\u003e\u003cp\u003eEC subtypes exhibited distinct temporal patterns and functional enrichments during nerve repair (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e5\u003c/span\u003ec,g). In the uninjured nerve, ECs were rare, and no ECs were detected at Day 1 post − injury. From Day 3 to Day 10, EC0 and EC1 constituted the predominant endothelial populations contributing to repair. By Day 14, EC2 became the dominant subtype. GO analysis revealed functional specialization among these subpopulations (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). EC0 was enriched in pathways associated with epithelial tube morphogenesis, negative regulation of cell migration, and response to reactive oxygen species, highlighting its role in early − stage tissue remodeling. EC1 displayed enrichment in inflammatory response regulation, nitric oxide metabolism, and maintenance of blood vessel diameter, indicating its involvement in modulating vascular tone and oxidative stress response. EC2, emerging in the later stages, was linked to apoptotic signaling regulation, nucleic acid catabolism, and toll − like receptor signaling, suggesting a role in resolving inflammation and tissue remodeling.\u003c/p\u003e\u003cp\u003elyECs were scarcely present in the normal sciatic nerve, with lyEC1 constituting the primary population (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e5\u003c/span\u003ef,i). Following injury, lyECs began to emerge at Day 5, predominantly consisting of lyEC0. Functional analysis of lyEC subtypes revealed that lyEC0 was enriched in apoptotic signaling regulation, \u003cem\u003eTGF−\u003c/em\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\beta\\:\\)\u003c/span\u003e\u003c/span\u003e receptor signaling, and nucleic acid catabolic processes, indicating its potential role in immune modulation and extracellular remodeling (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). lyEC1, on the other hand, shared enrichment in \u003cem\u003eTGF−\u003c/em\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\beta\\:\\)\u003c/span\u003e\u003c/span\u003e receptor signaling but was also linked to protein catabolism, epigenetic regulation of gene expression, and mitochondrial import, suggesting its involvement in cellular adaptation and metabolic regulation during the later stages of repair.\u003c/p\u003e\u003cp\u003ePericytes (PCs) exhibited a distinct temporal pattern, with minimal presence in the uninjured nerve (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e5\u003c/span\u003ee,h). PC populations emerged at Day 5 and followed a dynamic shift in subtypes over time. In the early repair phase (Day 5–Day 10), PC0 was the predominant PC subtype, while by Day 14, PC2 became the dominant contributor. Functional analysis of PC subtypes highlighted PC0's role in extracellular matrix organization, regulation of blood circulation, and \u003cem\u003eTGF−\u003c/em\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\beta\\:\\)\u003c/span\u003e\u003c/span\u003e signaling, reflecting its involvement in early vascular stabilization and tissue remodeling (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). PC1 was enriched in muscle cell differentiation, cholesterol metabolism, and viral release pathways, indicating a role in metabolic support and immune response. PC2 exhibited enrichment in apoptotic signaling regulation, nuclear protein import, and pigmentation regulation, suggesting its involvement in later−stage tissue homeostasis. A minor population of PC3, detected at later stages, was enriched in chromosome segregation, oxidative stress response, and viral life cycle regulation, indicating a potential role in cellular proliferation and stress adaptation.\u003c/p\u003e\u003cp\u003eSMCs were present in very low numbers throughout the repair process, appearing only at Day 7 post − injury (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e5\u003c/span\u003ed,j). SMC0 and SMC1 were identified as the primary subtypes, each displaying distinct functional properties. SMC0 exhibited enrichment in extracellular matrix organization, muscle cell proliferation, nucleocytoplasmic transport, and calcium ion transmembrane transport, suggesting its involvement in vascular support and contractile function (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). SMC1 shared enrichment in extracellular matrix organization and \u003cem\u003eTGF−\u003c/em\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\beta\\:\\)\u003c/span\u003e\u003c/span\u003e signaling but also displayed enrichment in amino acid metabolic processes, potentially linking it to metabolic regulation within the vascular microenvironment during nerve repair.\u003c/p\u003e\u003cp\u003eOverall, vascular cell dynamics during sciatic nerve repair revealed a highly coordinated response, with ECs playing a central role in early repair, followed by the recruitment of PCs and the late emergence of SMCs. The functional diversity of these subtypes suggests specialized roles in angiogenesis, inflammatory modulation, and extracellular matrix remodeling, highlighting their critical contributions to the regeneration process.\u003c/p\u003e\u003ch2\u003e5. Dynamic Shifts in NF Subpopulations Reveal Phase − Specific Roles in Nerve Regeneration\u003c/h2\u003e\u003cp\u003eDynamic changes in NF subpopulations were observed throughout the sciatic nerve repair process, highlighting their diverse functional roles in different phases of regeneration (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea − c). At Day 0, NF2 cells, identified by marker genes \u003cem\u003eCrispld2, Sqle, Aldh1a1, Idi1, Ralgps2, Kcnk2, Myoc, Cttnbp2, Col9a1\u003c/em\u003e, and \u003cem\u003eCol9a2\u003c/em\u003e, were the predominant population (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ed). These cells were classified as endoneurial mesenchymal cells and were primarily involved in extracellular matrix organization and transforming growth factor beta (\u003cem\u003eTGF−\u003c/em\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\beta\\:\\)\u003c/span\u003e\u003c/span\u003e) receptor superfamily signaling pathways, supporting the structural integrity of the nerve and facilitating initial cellular responses post − injury (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ee and Additional file 2: Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eb).\u003c/p\u003e\u003cp\u003eIn the early inflammatory phase (Day 1 to Day 3), NF5 cells, characterized by \u003cem\u003eNcapg, Tpx2, Hmmr, Ect2, Kif4a, Cenpu, Cenpf, Sgo2, Pclaf\u003c/em\u003e, and \u003cem\u003eKif20b\u003c/em\u003e, exhibited significant expansion (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec,d). These cells, categorized as proliferating mesenchymal cells, played a critical role in chromosome segregation and non − membrane − bounded organelle assembly (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ee and Additional file 2: Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eb) [38, 39]. Their increased activity suggested a surge in cell proliferation, likely contributing to the rapid remodeling of the extracellular environment to accommodate immune cell infiltration and debris clearance [40, 41].\u003c/p\u003e\u003cp\u003eBy Day 5, NF0 cells, defined by \u003cem\u003eSpon1\u003c/em\u003e, displayed a notable peak, surpassing their levels at Day 3 and Day 7 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec,d). NF0 cells, along with NF1 and NF3, were classified as differentiating mesenchymal cells (Additional file 2: Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eb). NF0 cells were specifically enriched in pathways related to extracellular matrix organization, \u003cem\u003eTGF−\u003c/em\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\beta\\:\\)\u003c/span\u003e\u003c/span\u003e receptor signaling and nuclear receptor−mediated signaling (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ee). The increased presence of NF0 at this stage indicated their crucial role in transitioning from the inflammatory to the regenerative phase, facilitating extracellular matrix remodeling and tissue stabilization. Simultaneously, NF4 cells, characterized by the expression of \u003cem\u003eLrg1\u003c/em\u003e and \u003cem\u003ePlvap\u003c/em\u003e—genes commonly implicated in vascular biology [42, 43]—were enriched in collagen fibril organization and glycolytic pathways, indicating a role in matrix remodeling and metabolic adaptation essential for repair (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec−e) [44].\u003c/p\u003e\u003cp\u003eDuring the mid − to − late regenerative phase (Day 7 to Day 14), NF0 cells continued to increase gradually, whereas NF3 cells, marked by \u003cem\u003eSbsn, Cdkn2a, Bhlhe22, Apod, Rdh10, Slc16a11, A2m, Scn3b, Plcxd3\u003c/em\u003e, and \u003cem\u003eMrap2\u003c/em\u003e, showed a progressive decline (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec,d). NF3 cells were involved in epithelial cell proliferation regulation and \u003cem\u003eTGF−\u003c/em\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\beta\\:\\)\u003c/span\u003e\u003c/span\u003e receptor signaling, suggesting their involvement in early repair mechanisms that diminished as regeneration progressed (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ee).\u003c/p\u003e\u003cp\u003eNF6 cells, identified by \u003cem\u003eFst, Mpzl2, Nkain4, Dpep1, Cntfr, Aox3, Inmt, Mmp27, Prlr\u003c/em\u003e, and \u003cem\u003eSpock2\u003c/em\u003e, were primarily fibroblasts involved in connective tissue development and nuclear receptor − mediated signaling pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ee). These cells played a key role in the final stages of repair by contributing to the reconstruction of the extracellular matrix and supporting the structural and functional restoration of the nerve tissue [45, 46].\u003c/p\u003e\u003cp\u003eOverall, the dynamic shifts in NF subpopulations underscore their essential contributions to different phases of nerve repair. The transition from NF5 − driven proliferative responses to NF4 − mediated metabolic transformation and NF0 − associated extracellular matrix remodeling highlights the coordinated interplay of these NF subsets. Their involvement in key signaling pathways, particularly \u003cem\u003eTGF−\u003c/em\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\beta\\:\\)\u003c/span\u003e\u003c/span\u003e receptor superfamily signaling [47], suggests that modulating these pathways could be a potential therapeutic strategy to enhance nerve regeneration.\u003c/p\u003e\u003ch2\u003e6. Dynamic Heterogeneity and Temporal Fate Transitions of Glis during Sciatic Nerve Regeneration\u003c/h2\u003e\u003cp\u003eDynamic changes in Gli populations were observed during the repair process following sciatic nerve transection (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ea − c). In the uninjured state, the predominant Gli subtype was Gli5, which represents myelinating Schwann cells (Additional file 2: Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003ec). Following nerve injury, Glis underwent significant phenotypic transitions, with Gli0, identified as repair − associated Schwann cells, emerging as the dominant subtype during the early phase of regeneration (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ec and Additional file 2: Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003ec). As the repair process progressed, Gli0 cells gradually declined, while Gli2 cells increased, suggesting a shift toward remyelination and structural recovery (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ec). Gli0 − 4 contribute to nerve regeneration by engaging the \u003cem\u003eTGF−\u003c/em\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\beta\\:\\)\u003c/span\u003e\u003c/span\u003e receptor superfamily signaling pathway to modulate cellular responses, orchestrating extracellular matrix organization to reshape the tissue microenvironment, and regulating axon ensheathment to support the restoration of nerve function (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ef).\u003c/p\u003e\u003cp\u003eSingle − cell transcriptomic analysis identified distinct marker genes associated with different Gli subtypes (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ee–h and Additional file 7: Fig. \u003cspan refid=\"MOESM7\" class=\"InternalRef\"\u003eS7\u003c/span\u003ea, b). Gli5, characteristic of myelinating Schwann cells, expressed markers such as \u003cem\u003eNcmap, Sema5a, Mt1 and Kcna1\u003c/em\u003e. In contrast, Gli0 (repair Schwann cells) exhibited increased expression of genes such as \u003cem\u003eClcf1, Met, Artn, and Runx2\u003c/em\u003e, which are associated with regeneration and extracellular matrix reorganization (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ef). Gli3 (proliferating Schwann cells) exhibited high expression of \u003cem\u003eUbe2c, Kif14, Kif4a\u003c/em\u003e and \u003cem\u003ePlk1\u003c/em\u003e, indicating active cell cycle progression and proliferation. The transition toward remyelination was marked by the emergence of Gli2, which expressed genes such as \u003cem\u003eNefm, Cuedc2, Cldn19\u003c/em\u003e, and \u003cem\u003eMag\u003c/em\u003e, indicating a functional shift toward axon ensheathment and nerve fiber stabilization [48, 49].\u003c/p\u003e\u003cp\u003eFunctional enrichment analysis further elucidated the biological roles of different Gli subtypes during the repair process (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ef). Gli0 was associated with pathways involved in \u003cem\u003eTGF−\u003c/em\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\beta\\:\\)\u003c/span\u003e\u003c/span\u003e receptor signaling, neuron projection guidance, and glycoprotein metabolism, all of which are critical for early nerve repair. Gli1, which plays a role in extracellular matrix remodeling, was enriched for pathways related to \u003cem\u003eTGF−\u003c/em\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\beta\\:\\)\u003c/span\u003e\u003c/span\u003e signaling, peptide cross−linking, and interferon−mediated signaling. Gli2 exhibited functional enrichment in myelination−related processes, including amine transport and apical protein localization, consistent with its role in late−stage nerve repair. Gli3 was primarily associated with cell cycle regulation, with enrichment in chromosome segregation and organelle assembly. Gli4 was linked to extracellular matrix organization and nuclear receptor−mediated signaling, suggesting involvement in tissue remodeling. Finally, Gli5, as the mature myelinating Schwann cell population, was enriched for pathways regulating axon ensheathment, potassium ion transport, and sterol biosynthesis, all of which are crucial for maintaining functional nerve architecture.\u003c/p\u003e\u003cp\u003ePseudotime trajectory analysis revealed two major regenerative pathways governing Gli fate transitions (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ee − h and Additional file 7: Fig. \u003cspan refid=\"MOESM7\" class=\"InternalRef\"\u003eS7\u003c/span\u003ea − b). The first trajectory was characterized by increased expression of genes associated with myelination, proliferation, autophagy, and metabolic regulation, including \u003cem\u003eCol3a1, Csrp2, Mbp, Mpz, posten\u003c/em\u003e, and \u003cem\u003ePmp22\u003c/em\u003e (Additional file 7: Fig. \u003cspan refid=\"MOESM7\" class=\"InternalRef\"\u003eS7\u003c/span\u003eb). These genes were upregulated during later stages of repair, highlighting their role in structural restoration [50–52]. The second trajectory was primarily associated with growth factor secretion and extracellular matrix modulation, with early upregulation of genes such as \u003cem\u003eApod, Apoe, Col1a1\u003c/em\u003e, and \u003cem\u003eFn1\u003c/em\u003e (Additional file 7: Fig. \u003cspan refid=\"MOESM7\" class=\"InternalRef\"\u003eS7\u003c/span\u003eb) [53, 54]. The dynamic interplay between these two pathways underscores the complex cellular mec7anisms governing Schwann cell function during nerve regeneration.\u003c/p\u003e\u003cp\u003eAdditionally, cell − cell communication analysis indicated that Glis actively interacted with NFs during repair, particularly through enhanced PTN signaling (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ei and Additional file 7: Fig. \u003cspan refid=\"MOESM7\" class=\"InternalRef\"\u003eS7\u003c/span\u003ec). This interaction was not only observed during nerve regeneration but has also been implicated in pathological conditions such as neurofibromatosis, where Gli − NF communication is dysregulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ej and Additional file 8: Fig. \u003cspan refid=\"MOESM8\" class=\"InternalRef\"\u003eS8\u003c/span\u003e) [55]. Collectively, these findings provide a comprehensive understanding of Gli heterogeneity, their temporal dynamics, and their critical roles in coordinating nerve repair following injury.\u003c/p\u003e\u003cp\u003e \u003cb\u003e7. Divergent Cellular and Molecular Repair Programs in Crush Versus Transection Models of Sciatic Nerve Injury\u003c/b\u003e \u003c/p\u003e\u003cp\u003eThe comparative analysis of crush and transection injuries revealed distinct cellular responses and molecular pathways involved in the repair process (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). In the early stages following crush injury, Macs were the predominant immune cell type, maintaining a stable presence from Day 1 to Day 7 (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ea). In contrast, in transection injury, Mac presence was more pronounced in the early phases, with a higher proportion on Day 1 and Day 3 compared to crush injury. However, by Day 7, the proportion of Macs had significantly declined. Additionally, Gran were more abundant in the early stages of transection injury (Day 1 and Day 3) than in crush injury, but their presence diminished markedly by Day 7.\u003c/p\u003e\u003cp\u003eSchwann cell dynamics also varied between the two injury models (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eb − e). In crush injury, Schwann cells progressively increased from Day 1 to Day 7, contributing to nerve regeneration. Conversely, in transection injury, Schwann cells were scarce in the early stages (Day 1 and Day 3), with a notable presence only emerging by Day 7. These findings suggest that Schwann cell recruitment and proliferation are delayed in transection injury, potentially affecting the overall repair process.\u003c/p\u003e\u003cp\u003eMesenchymal stem cells exhibited a distinct pattern in both models (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eb − e). In crush injury, they were continuously present throughout the repair timeline, suggesting a sustained role in tissue remodeling and repair. In contrast, in transection injury, mesenchymal stem cells were present at lower levels during the early stages (Day 1 and Day 3) but showed a substantial increase by Day 7, indicating a delayed yet significant involvement in the repair process.\u003c/p\u003e\u003cp\u003eThe enrichment analysis of shared upregulated genes between crush and transection injuries highlighted several key pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eg). On Day 1, common pathways included leukocyte migration, positive regulation of cytosolic calcium ion concentration, interferon − mediated signaling, and toll − like receptor signaling. By Day 3, pathways related to chromosome segregation, multicellular homeostasis, and M phase regulation were enriched, reflecting active cell division and immune responses. On Day 7, pathways such as chromosome segregation and structural disruption in another organism remained prevalent, indicating ongoing cellular proliferation and inflammatory responses.\u003c/p\u003e\u003cp\u003eIn contrast, pathways specifically enriched in transection injury revealed distinct molecular mechanisms (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eh). On Day 1, pathways such as regulation of angiogenesis, extracellular matrix organization, inflammatory response modulation, and neuron apoptotic processes were significantly enriched, suggesting a robust early vascular and immune response. By Day 3, \u003cem\u003eTGF−\u003c/em\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\beta\\:\\)\u003c/span\u003e\u003c/span\u003e receptor signaling and potassium ion transport were notably active, highlighting their role in tissue remodeling. On Day 7, pathways involved in cytosolic calcium ion regulation, interleukin−1 response, and fatty acid biosynthesis were enriched, indicating a shift toward metabolic and immune modulation during later repair phases.\u003c/p\u003e\u003cp\u003eThese findings underscore the fundamental differences between crush and transection injuries in terms of immune cell recruitment, Schwann cell involvement, and mesenchymal stem cell dynamics. The enrichment analysis further elucidates the distinct molecular pathways governing nerve repair in each model, with transection injury demonstrating a more complex and delayed regenerative process. The differential activation of immune and metabolic pathways suggests potential therapeutic targets to enhance nerve regeneration in severe injury conditions.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present study integrates our single\u0026thinsp;\u0026minus;\u0026thinsp;cell transcriptomic findings into a comprehensive narrative of sciatic nerve repair post\u0026thinsp;\u0026minus;\u0026thinsp;transection, revealing a dynamic, multi\u0026thinsp;\u0026minus;\u0026thinsp;phasic process that diverges from traditional theories of nerve regeneration. In contrast to classical views that primarily emphasize Wallerian degeneration and subsequent axonal regrowth, our results demonstrate that multiple cell types participate in a temporally coordinated response. Notably, the immediate post\u0026thinsp;\u0026minus;\u0026thinsp;injury phase (Day 1\u0026ndash;3) is marked by a significant influx of immune cells. During this critical window, distinct Mac subsets emerge\u0026mdash;characterized by the expression of \u003cem\u003eSlpi\u003c/em\u003e [56] and Ass\u003cem\u003e1\u003c/em\u003e [57]\u0026mdash;alongside Gran identifiable by markers such as \u003cem\u003eS100a8/\u003c/em\u003e9 [58], \u003cem\u003eAnxa1\u003c/em\u003e [59], \u003cem\u003eMmp8\u003c/em\u003e [60], and \u003cem\u003ePglyrp1\u003c/em\u003e [61]. These observations concur with earlier studies indicating that early immune activation is indispensable for efficient debris clearance via phagocytosis and proteolytic mechanisms.\u003c/p\u003e \u003cp\u003eAs repair commences, the inflammatory landscape gradually shifts. Immune cells, which dominate the early stages with over 95% of the cell population by Day 1, give way to a robust re\u0026thinsp;\u0026minus;\u0026thinsp;emergence of NFs and Schwann cells. Detailed subclustering of NF populations reveals that the NFs, which are predominant in the uninjured nerve, begin to undergo significant reprogramming post\u0026thinsp;\u0026minus;\u0026thinsp;injury. By Day 5, a specialized NF subpopulation\u0026mdash;NF4, distinguished by high \u003cem\u003eLrg1\u003c/em\u003e [43, 44] and \u003cem\u003ePlvap\u003c/em\u003e [42, 62] expression\u0026mdash;appears. This NF4 subset is hypothesized to be a pivotal intermediary, potentially serving as a key transitional cell type that integrates signals from the early immune response and fosters the subsequent emergence of mature fibroblasts, Glis, and vascular cells. Such a role is consistent with the observation that NF4 marks the transformation of NFs from a proliferative state towards a more differentiated, mature phenotype as part of the evolving tissue repair process.\u003c/p\u003e \u003cp\u003eConcurrently, Schwann cells exhibit significant phenotypic transitions that underscore their essential role in restoring nerve function. In uninjured tissue, myelinating Schwann cells dominate; however, following injury, repair\u0026thinsp;\u0026minus;\u0026thinsp;associated subtypes rapidly emerge. The initial surge of Gli0 cells, characterized by the upregulation of regeneration\u0026thinsp;\u0026minus;\u0026thinsp;associated genes such as \u003cem\u003eClcf1\u003c/em\u003e [63] and \u003cem\u003eRunx\u003c/em\u003e [64, 65], facilitates early axonal guidance and extracellular matrix reorganization. As regeneration progresses, a shift occurs with the increase of Gli2 cells, which express markers including \u003cem\u003eMag\u003c/em\u003e [66] and \u003cem\u003eCldn19\u003c/em\u003e [67], signifying a transition towards remyelination and stabilization of the axonal architecture. This sequential switch\u0026mdash;from an initial repair\u0026thinsp;\u0026minus;\u0026thinsp;focused profile to one committed to remyelination\u0026mdash;underscores a dynamic process in which Schwann cell functionality is finely tuned to meet the evolving requirements of the regenerating nerve.\u003c/p\u003e \u003cp\u003eVascular remodeling also plays a critical role in the repair process. Although vascular cells are relatively sparse in the immediate aftermath of injury, ECs begin to accumulate from Day 3 onward, and by Day 7, there is a dramatic surge in PC numbers. The robust intercellular communication between Schwann cells and vascular cells, coupled with the emergence of PC subsets involved in \u003cem\u003eTGF\u0026minus;\u003c/em\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\beta\\:\\)\u003c/span\u003e\u003c/span\u003e signaling, establishes a supportive microenvironment essential for angiogenesis. Such vascular expansion not only restores blood flow but also creates a niche that is vital for sustaining axonal regrowth and overall tissue homeostasis.\u003c/p\u003e \u003cp\u003eMoreover, the trajectory analysis of Mo\u0026thinsp;\u0026minus;\u0026thinsp;derived Macs reveals distinct differentiation pathways that are intimately linked with the regenerative process. Early Mac subsets engaged in inflammatory clearance gradually give way to populations involved in tissue remodeling and extracellular matrix deposition. The interplay between these immune cells and NFs, mediated by enhanced collagen signaling, highlights an integrated network that regulates both inflammation resolution and tissue repair.\u003c/p\u003e \u003cp\u003eIn summary, our time\u0026thinsp;\u0026minus;\u0026thinsp;resolved single\u0026thinsp;\u0026minus;\u0026thinsp;cell atlas of sciatic nerve transection injury reveals a coordinated, multi\u0026thinsp;\u0026minus;\u0026thinsp;phasic repair program that progresses through three principal biological phases: early immune activation, extracellular matrix remodeling, and Schwann cell\u0026thinsp;\u0026minus;\u0026thinsp;driven remyelination. Initially, the robust infiltration of specialized macrophages and granulocytes not only facilitates debris clearance but also establishes a pro\u0026thinsp;\u0026minus;\u0026thinsp;regenerative cytokine environment. This is followed by a transitional phase marked by the emergence of NF4 fibroblasts and proliferative mesenchymal subsets, which remodel the extracellular matrix through \u003cem\u003eTGF\u0026minus;\u003c/em\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\beta\\:\\)\u003c/span\u003e\u003c/span\u003e and collagen\u0026minus;related signaling, setting the foundation for tissue repair. In the later phase, Schwann cells exhibit dynamic fate transitions\u0026mdash;from repair\u0026minus;associated Gli0 to myelinating Gli2 subtypes\u0026mdash;underscoring their essential role in axonal ensheathment and functional restoration.\u003c/p\u003e \u003cp\u003eImportantly, our cross\u0026thinsp;\u0026minus;\u0026thinsp;species integration with human neurofibroma data highlights conserved PTN signaling between neurofibroblasts and Schwann cells, implicating a broader relevance of the NF\u0026ndash;Gli axis in both regenerative and pathological contexts. Moreover, by comparing crush and transection injury models, we demonstrate that the delayed engagement of Schwann cells and mesenchymal subtypes in transection injury likely contributes to impaired regeneration, pointing to a narrower therapeutic window for intervention.\u003c/p\u003e \u003cp\u003eTogether, these findings provide not only a granular understanding of the cellular and molecular mechanisms orchestrating nerve repair, but also identify temporal checkpoints\u0026mdash;such as early macrophage heterogeneity, mid\u0026thinsp;\u0026minus;\u0026thinsp;phase NF4 activation, and late Schwann cell remyelination\u0026mdash;as actionable targets for stage\u0026thinsp;\u0026minus;\u0026thinsp;specific therapeutic modulation. This work lays a foundation for developing time\u0026thinsp;\u0026minus;\u0026thinsp;tuned regenerative therapies tailored to the specific needs of each phase of peripheral nerve repair.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, we present a high-resolution, time\u0026thinsp;\u0026minus;\u0026thinsp;resolved single\u0026thinsp;\u0026minus;\u0026thinsp;cell atlas of rat sciatic nerve transection injury that reveals three successive, tightly coordinated phases of regeneration. First, within 24 h of injury there is massive infiltration of pro\u0026thinsp;\u0026minus;\u0026thinsp;inflammatory macrophages and granulocytes, together with expansion of proliferative mesenchymal fibroblasts (NF5), which together clear debris and establish a pro\u0026thinsp;\u0026minus;\u0026thinsp;regenerative cytokine milieu​​. Second, between Days 3\u0026ndash;7, proliferative NF4 and NF0 subsets drive extracellular\u0026thinsp;\u0026minus;\u0026thinsp;matrix remodeling via \u003cem\u003eTGF\u0026minus;\u003c/em\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\beta\\:\\)\u003c/span\u003e\u003c/span\u003e and collagen signaling, while repair Schwann cells (Gli0) emerge to guide axon outgrowth and re\u0026minus;establish cell\u0026ndash;cell communication with fibroblasts and endothelium​​. Third, from Day 7 onward, Schwann cells transition into myelinating states (Gli2/5), vascular cells (ECs, PCs) expand to rebuild blood supply, and immune populations shift toward tissue\u0026minus;remodeling and resolution phenotypes (Mac3/4), culminating in restoration of nerve architecture by Day 14​​. Compared to crush injury, transection elicits a stronger early immune response and delays Schwann cell\u0026minus;driven remyelination, identifying a narrowed therapeutic window for intervention​​. Together, these data define phase\u0026minus;specific cellular and molecular targets\u0026mdash;early macrophage heterogeneity, mid\u0026minus;phase NF4 activation, and late Schwann cell remyelination\u0026mdash;for the development of time\u0026minus;tuned therapies to enhance peripheral nerve repair.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"0\" cellspacing=\"3\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003escRNA\u0026minus;seq\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 255px;\"\u003e\n \u003cp\u003esingle\u0026minus;cell RNA sequencing\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003eNF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 255px;\"\u003e\n \u003cp\u003eneurofibroblasts\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003eGli\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 255px;\"\u003e\n \u003cp\u003eglial cells\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003eMac\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 255px;\"\u003e\n \u003cp\u003emacrophages\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003eMo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 255px;\"\u003e\n \u003cp\u003emonocytes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003eGran\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 255px;\"\u003e\n \u003cp\u003egranulocytes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003eDC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 255px;\"\u003e\n \u003cp\u003edendritic cells\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 255px;\"\u003e\n \u003cp\u003eT cells\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003eNK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 255px;\"\u003e\n \u003cp\u003enatural killer cells\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 255px;\"\u003e\n \u003cp\u003eB cells\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003ePC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 255px;\"\u003e\n \u003cp\u003epericytes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003eSMC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 255px;\"\u003e\n \u003cp\u003esmooth muscle cells\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003eEC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 255px;\"\u003e\n \u003cp\u003eendothelial cells\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003elyEC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 255px;\"\u003e\n \u003cp\u003elymphatic endothelial cells\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003eDEGs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 255px;\"\u003e\n \u003cp\u003edifferentially expressed genes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003eDGE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 255px;\"\u003e\n \u003cp\u003edifferential gene expression\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003eUMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 255px;\"\u003e\n \u003cp\u003eunique molecular identifier\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003ePCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 255px;\"\u003e\n \u003cp\u003eprincipal component analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003eUMAP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 255px;\"\u003e\n \u003cp\u003euniform manifold approximation and projection\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003eSNN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 255px;\"\u003e\n \u003cp\u003eshared nearest\u0026minus;neighbor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e\u003cem\u003eTGF\u003c/em\u003e\u003cem\u003e\u0026minus;\u003c/em\u003e\u003cimg width=\"10\" height=\"19\" src=\"data:image/png;base64,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\" alt=\"image\"\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 255px;\"\u003e\n \u003cp\u003etransforming growth factor\u0026minus;beta\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003ePBS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 255px;\"\u003e\n \u003cp\u003ephosphate\u0026minus;buffered saline\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003eBSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 255px;\"\u003e\n \u003cp\u003ebovine serum albumin\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003eDAPI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 255px;\"\u003e\n \u003cp\u003e4\u0026rsquo;,6\u0026minus;diamidino\u0026minus;2\u0026minus;phenylindole\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003eGO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 255px;\"\u003e\n \u003cp\u003eGene Ontology\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003eKEGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 255px;\"\u003e\n \u003cp\u003eKyoto Encyclopedia of Genes and Genomes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank Beijing Capital Biotechnology Co., Ltd. for assistance with single\u0026minus;cell RNA sequencing. We also acknowledge the use of publicly available datasets (GSE198582, human neurofibroma scRNA\u0026minus;seq data) which greatly supported the cross\u0026minus;validation and annotation processes. The authors thank the developers of CellChat, Monocle, Seurat, and related tools for their contributions to open science.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYiben Ouyang, Mingqian Yu, Haolin Liu and Haofeng Cheng contributed equally to this work. The study was conceptualized and designed by Yiben Ouyang, Jiang Peng and Yu Wang. Methodology development and animal experiments were performed by Tieyuan Zhang, Haolin Liu, Haofeng Cheng, Liang Zuo, and Yiben Ouyang, while single\u0026minus;cell library preparation and sequencing were carried out by Yanjun Guan and Sice Wang. Data processing and formal analysis were conducted by Mingqian Yu, Ao Liu, Ruichao He,\u0026nbsp;Xiaoyang Fu, and Jiajie Chen, with bioinformatics and pseudotime trajectory analyses by Mingqian Yu, Yixiao Tan, Yuhui\u0026nbsp;Cui, Junli Wang, and Yiben Ouyang. Cell\u0026ndash;cell communication and enrichment analyses were performed by Jinjuan Zhao, Ao Liu, Xiaochun Zhang and Tianqi\u0026nbsp;Su, and visualization was produced by Mingqian Yu and Yiben Ouyang. The original draft was written by Yiben Ouyang and Mingqian Yu, and all authors contributed to review and editing. Supervision and funding acquisition were provided by Jiang Peng and Yu Wang.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by the Key Technologies Research and Development Program (2024YFC3406806) and the National Natural Science Foundation of China (32171356).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe raw sequencing data generated in this study have been deposited in the NCBI Sequence Read Archive (SRA) under BioProject accession number SAMN48188757. These data will be made publicly available upon acceptance of the manuscript. Prior to publication, the data are accessible to editors and reviewers upon request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll experimental procedures involving animals were approved by the International Council for Laboratory Animal Science and conducted in accordance with guidelines for the care and use of laboratory animals. Efforts were made to minimize animal suffering and reduce the number of animals used in the study. All procedures were approved by the Institutional Animal\u0026nbsp;Care and Use Committee of PLA General Hospital (approval number: 2016‑x9‑07) and conformed to national guidelines for animal care.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u0026nbsp;\u003c/sup\u003eSchool of Medicine, Nankai University, No. 94, Weijin Road, Nankai District, Tianjin, 300071, PR China. \u003csup\u003e2\u003c/sup\u003e Institute of Orthopedics, The Fourth Medical Center of Chinese PLA General Hospital, Beijing Key Lab of Regenerative Medicine in Orthopedics, Key Laboratory of Musculoskeletal Trauma \u0026amp; War Injuries PLA, No. 51 Fucheng Road, Beijing, 100048, PR China.\u003csup\u003e\u0026nbsp;3\u0026nbsp;\u003c/sup\u003eCo-innovation Center of Neuroregeneration, Nantong University Nantong, Jiangsu Province, 226007, PR China. \u003csup\u003e4\u0026nbsp;\u003c/sup\u003eCheeloo College of Medicine, Shandong University, 44 West Wenhua Road, Lixia District, Jinan, Shandong 250012, P.R. China.\u003csup\u003e\u0026nbsp;5\u0026nbsp;\u003c/sup\u003eJinzhou Medical University, No. 40, Songpo Road, San Duan, Linghe District, Jinzhou, Liaoning, China.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eArbash M, Alzobi OZ, Salameh M, Alkhayarin M, Ahmed G: \u003cstrong\u003eIncidence, risk factors, and prognosis of sciatic nerve injury in acetabular fractures: a retrospective cross-sectional study.\u003c/strong\u003e \u003cem\u003eInt Orthop \u003c/em\u003e2024, \u003cstrong\u003e48:\u003c/strong\u003e849-856.\u003c/li\u003e\n\u003cli\u003eLiu Z, Fu B, Xu W, Liu F, Dong J, Li L, Zhou D, Hao Z, Lu S: \u003cstrong\u003eIncidence of Traumatic Sciatic Nerve Injury in Association with Acetabular Fracture: A Retrospective Observational Single-Center Study.\u003c/strong\u003e \u003cem\u003eInt J Gen Med \u003c/em\u003e2022, \u003cstrong\u003e15:\u003c/strong\u003e7417-7425.\u003c/li\u003e\n\u003cli\u003eCallaghan BC, Cheng HT, Stables CL, Smith AL, 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polyneuropathy with claudin-12 deficiency.\u003c/strong\u003e \u003cem\u003eNeurobiol Dis \u003c/em\u003e2023, \u003cstrong\u003e185:\u003c/strong\u003e106246.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-neuroinflammation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jneu","sideBox":"Learn more about [Journal of Neuroinflammation](http://jneuroinflammation.biomedcentral.com)","snPcode":"12974","submissionUrl":"https://submission.nature.com/new-submission/12974/3","title":"Journal of Neuroinflammation","twitterHandle":"@bmc","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Sciatic nerve transection, Single − cell RNA − seq, Immune microenvironment, Macrophage heterogeneity, Schwann cell remyelination, Neurofibroblast activation","lastPublishedDoi":"10.21203/rs.3.rs-6582223/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6582223/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePeripheral nerve injuries, especially those involving the sciatic nerve, often result in persistent deficits with limited treatment options. To dissect the cellular mechanisms of nerve repair, we performed single\u0026thinsp;\u0026minus;\u0026thinsp;cell RNA sequencing on rat sciatic nerves at seven time points (Day 0, 1, 3, 5, 7, 10, 14) following transection injury. We identified dynamic changes in four major cell compartments\u0026mdash;neurofibroblasts (NFs), glial cells (Glis), immune cells, and vascular cells. Early responses (Day 1) featured immune cell infiltration, followed by expansion of proliferative mesenchymal cells (NF5) and repair Schwann cells (Gli0) by Day 3\u0026ndash;5. Vascular cells expanded from Day 7 to Day 10, and by Day 14, Glis transitioned into mature myelinating states while NF and immune cells stabilized. Compared to crush injury, transection induced a stronger early immune response and delayed Schwann cell recovery. These findings provide a time\u0026thinsp;\u0026minus;\u0026thinsp;resolved atlas of sciatic nerve regeneration and highlight stage\u0026thinsp;\u0026minus;\u0026thinsp;specific therapeutic targets, particularly macrophage activation and NF\u0026ndash;Gli signaling.\u003c/p\u003e","manuscriptTitle":"Single − Cell Transcriptomic Landscape of Sciatic Nerve After Transection Injury","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-12 10:50:06","doi":"10.21203/rs.3.rs-6582223/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-05-19T19:21:15+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-19T17:35:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"310586179988948582444147655546553083694","date":"2025-05-19T02:03:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"252556556630364542323809788089165542691","date":"2025-05-17T12:41:14+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-15T18:21:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"22546927361120343650991954553760698189","date":"2025-05-07T10:38:41+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-07T00:21:27+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-05T05:45:12+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-03T07:01:45+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Neuroinflammation","date":"2025-05-03T06:01:26+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-neuroinflammation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jneu","sideBox":"Learn more about [Journal of Neuroinflammation](http://jneuroinflammation.biomedcentral.com)","snPcode":"12974","submissionUrl":"https://submission.nature.com/new-submission/12974/3","title":"Journal of Neuroinflammation","twitterHandle":"@bmc","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0fe5a1f4-d5ec-4bf5-96d3-0a3f9b56bc6c","owner":[],"postedDate":"May 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-08-25T16:37:01+00:00","versionOfRecord":{"articleIdentity":"rs-6582223","link":"https://doi.org/10.1186/s12974-025-03514-3","journal":{"identity":"journal-of-neuroinflammation","isVorOnly":false,"title":"Journal of Neuroinflammation"},"publishedOn":"2025-08-23 16:29:38","publishedOnDateReadable":"August 23rd, 2025"},"versionCreatedAt":"2025-05-12 10:50:06","video":"","vorDoi":"10.1186/s12974-025-03514-3","vorDoiUrl":"https://doi.org/10.1186/s12974-025-03514-3","workflowStages":[]},"version":"v1","identity":"rs-6582223","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6582223","identity":"rs-6582223","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}