A high-resolution single-nucleus RNA sequencing reveals conserved macrophage activation trajectories across muscle regeneration and dystrophy.

A high-resolution single-nucleus RNA sequencing reveals conserved macrophage activation trajectories across muscle regeneration and dystrophy. | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article A high-resolution single-nucleus RNA sequencing reveals conserved macrophage activation trajectories across muscle regeneration and dystrophy. Jea-Hyun Baek, Woo Seok Byun, Eun-Chong Bang, Yoon-Gyu Shim This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9129034/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Macrophages are dominant leukocytes in skeletal muscle, yet their functional heterogeneity, particularly in muscular dystrophies, remains poorly defined. To reconcile this, we generated a high-resolution single-nucleus RNA sequencing (snRNA-seq) atlas of skeletal muscle. By bypassing dissociation-induced stress, we achieved superior resolution over standard single-cell clustering, capturing multinucleated myocytes and resolving cell-cell interactomes. In regenerating muscle, we crisply delineated macrophage trajectories, identifying three parallel and co-existing activation states, along with a G2/M proliferative population. Integrating these trajectories with parabiosis-based fate mapping, we provide the first evidence that recruited monocytes undergo synchronous differentiation without local proliferation, exhibiting pyroptosis before population contraction. While this recruited inflammatory population is prominent during muscular dystrophy, it surprisingly retains transcriptional programs conserved with regeneration. In parallel, tissue-resident macrophages adapt to the local environment without initiating de novo pathogenic programs. These findings suggest that at the onset of muscular dystrophy, macrophages act primarily as adaptive bystanders rather than destroyers, providing a framework for immune regulation in muscle degeneration. Biological sciences/Immunology/Innate immune cells/Monocytes and macrophages Biological sciences/Immunology/Gene regulation in immune cells/Immunogenetics Biological sciences/Cell biology/Mechanisms of disease Macrophage fate intra-muscle macrophages muscle regeneration single-nucleus analysis muscle immunology Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Skeletal muscle possesses a remarkable capacity for regeneration, a process tightly regulated by intricate crosstalk between resident stem cells and the immune system [ 1 , 2 ]. Following injury, a coordinated infiltration of immune cells is thought to drive debris clearance and establishes a pro-regenerative environment [ 3 , 4 ]. Among these, macrophages are the most abundant immune population in both regenerating and diseased muscles, far outnumbering other leukocyte subsets [ 5 , 6 ]. The prevailing paradigm suggests that these cells display pronounced plasticity, initially adopting inflammatory states before transitioning toward reparative phenotypes that promote tissue repair [ 7 , 8 ]. However, whether this linear transition sufficiently captures the complexity of macrophage behavior—especially within the chronic, non-resolving environment of muscular dystrophy—remains a subject of debate [ 9 – 11 ]. The survival, differentiation, and proliferation of macrophages are strictly dependent on colony-stimulating factor 1 receptor (CSF-1R) signaling [ 12 , 13 ]. While CSF-1R is activated by two distinct ligands—colony-stimulating factor 1 (CSF-1) and interleukin-34 (IL-34) [ 14 , 15 ]—their specific roles and cellular sources within skeletal muscle also remain poorly defined. Although these ligands share a common receptor, they often exhibit a complex balance of functional redundancy and tissue-specific signaling, dictated by their differing binding affinities and distinct expression patterns across various physiological contexts [ 16 – 18 ]. Given this complex regulatory landscape, a fundamental question arises: does macrophage-associated pathology in muscle result from the emergence of de novo , disease-specific transcriptional programs or merely the pathological expansion of conserved activation states [ 19 ]? While single-cell RNA sequencing (scRNA-seq) have provided valuable insights into the cellular landscape of skeletal muscle, these datasets failed to crisply delineate macrophage activation states due to inherent technical limitations [ 20 ]. Specifically, the enzymatic dissociation required for scRNA-seq induces cellular stress that can distort transcriptional profiles of macrophages, which are highly plastic [ 21 ]. Moreover, multinucleated myocytes (e.g., myotubes, myofibers) are typically lost during scRNA-seq processing, precluding direct analysis of myocyte–immune interactions [ 20 ]. To overcome these challenges, we employed single-nucleus RNA sequencing (snRNA-seq), which enables unbiased profiling of transcripts from all cell types in muscle while minimizing dissociation-induced artifacts. By integrating this high-resolution approach with trajectory inference, we were able to define macrophage activation states and resolve their temporal dynamics with unprecedented clarity. Our analysis reveals a high-resolution map of macrophage functional diversity, delineating three distinct activation trajectories characterized by the expression of Ly6C, IL-7R, and CD163. Beyond these transcriptional states, we uncover the structural basis of macrophage maintenance, identifying mural cells as the primary niche providing the macrophage growth factor IL-34 to sustain homeostatic, tissue-resident populations. We further investigated the origin and fate of these cells through parabiosis-based fate mapping. These experiments demonstrate that recruited monocytes undergo a synchronous differentiation into inflammatory macrophages that do not self-renew and undergo a programmed contraction via pyroptosis/hyperactivation. Crucially, our comparative analysis across models of muscular dystrophy reveals that macrophages do not initiate de novo pathogenic programs; instead, they act as adaptive bystanders, retaining highly conserved regenerative programs while responding to the chronic tissue stress of a dystrophic environment. Together, these findings establish a new framework for understanding immune regulation in muscle degeneration and clarify the role of macrophages as responders rather than active drivers of pathology during the onset of the disease. Results Single-nucleus RNA sequencing provides a comprehensive cellular atlas of regenerating muscle. To generate a high-resolution cellular atlas of skeletal muscle macrophages, we performed snRNA-seq on nuclei isolated from uninjured and injured C57BL/6J tibialis anterior (TA) at 3, 4, 5, and 10 days after intramuscular cardiotoxin (CTx) injection, capturing the period of inflammatory resolution and muscle regeneration [ 22 , 23 ] (Fig. 1 A). Histological analysis (H&E) at 3 days post-injury (dpi) revealed extensive tissue damage, with widespread necrotic myofibers and dense infiltration of mononucleated cells. This infiltration then markedly declined between 3 and 5 dpi, as previously reported [ 24 ] (Fig. 1 B). Immunohistochemistry (IHC) for macrophage markers (CD68, F4/80) confirmed that the infiltrating population was dominated by macrophages, which persisted throughout regeneration (Supp-1A, B). Unsupervised clustering of the aggregated dataset identified 19 distinct cell populations representing the major myogenic, immune, and stromal lineages (Fig. 1 C). We validated these cluster identities using canonical marker genes: Pax7 for quiescent muscle stem cells (QuSCs), Dmd for myofibers, Ptprc (CD45) for immune cells, Pdgfra for fibroblasts, Myh11 for smooth muscle cells (SMCs), and Ptprb for endothelial cells (Fig. 1 D). Tracking cellular composition over time revealed dynamic immune infiltration peaking at 3 dpi, followed by expansion of myogenic and stromal populations during the regenerative phase (Fig. 1 E). Given the complexity of the immune response, we performed sub-clustering of the immune compartment ( Ptprc ), which resolved into macrophages ( Csf1r , Adgre1 , Mertk ), T cells ( Cd247 , Skap1 ), and several dendritic cell (DC) subsets (Fig. 1 F-I). These included inflammatory conventional type 2 DCs (inflam cDC2; Ccr2 , Cd209a ), conventional type 1 DCs (cDC1; Clec9a , Xcr1 ), plasmacytoid DCs (pDC; Siglech , Tcf4 ), and mature regulatory DCs (Mreg DC; Mreg , Relb , Ly75 ). In addition, osteoclasts (Ocl; Ctsk , Acp5 ) and proliferative immune populations ( Mki67 , Top2a ) were identified (Fig. 1 H, I). Across all time points, macrophages were the dominant immune population (Fig. 1 J). Trajectory analysis identifies distinct macrophage transcriptional states and a transient proliferative population. To further resolve macrophage heterogeneity, we performed trajectory analysis using potential of heat-diffusion for affinity-based transition embedding (PHATE) on macrophage nuclei (n = 12,366) (Fig. 1 J). This analysis revealed three macrophage branches, designated Branches 1, 2, and 3, each subdivided into three subsegments (e.g., 1–1, 1–2, 1–3). Additionally, we identified a distinct population of proliferative macrophages (ProlifM) that formed a separate cluster adjacent to the main macrophage trajectory and aligned with the early region of Branch 2 (Fig. 2 A). In uninjured muscle, macrophages mapped exclusively to Branch 3, indicating that this branch represents homeostatic tissue-resident macrophages. Following injury, macrophage proliferation was most pronounced at 3 dpi, as reported previously [ 24 ]. At this peak of infiltration, the macrophage compartment was dominated by Branch 2, followed by Branch 1 (Fig. 2 B, C). The Branch 1 population peaked at 4 dpi, whereas by 5 and 10 dpi the composition progressively shifted toward Branch 3, accompanied by a gradual decline in Branch 2 representation (Fig. 2 C). Expression of cell-cycle markers ( Mki67 , Top2a , Diaph3 ) and cell-cycle scoring confirmed that ProlifM cells were in the G2/M phase (Fig. 2 D). These cells were transcriptionally associated with Branches 2 and 3, showing the highest transition probability toward Branch 2 (Fig. 2 D, E). Gene expression and functional analysis revealed marked divergence among branches. Branch 1 showed elevated expression of inflammatory and interferon-stimulated genes (ISGs), identifying it as an inflammatory macrophage state (M1-like). Conversely, Branch 2 preferentially upregulated genes associated with tissue repair and resolution (M2-like) (Fig. 2 F-I, Suppl-1C). Single-cell regulatory network inference and clustering (SCENIC) analysis further distinguished these states by regulon activity, with Branch 1 enriched for inflammatory regulators such as Irf7 and Stat1 , and Branch 2 characterized by activity of Mafg , Bhlhe40 , and Pparg (Fig. 2 J and Suppl-1D). Single-nucleus trajectory better resolves the identity of all macrophage subpopulations in single-cell data. To assess the compatibility of our snRNA-seq trajectory with single-cell datasets, we integrated our data with a recently published scRNA-seq dataset of regenerating muscle [ 25 ]. In whole-muscle clustering, immune populations were first separated from myogenic and stromal lineages (Suppl-2A). Although scRNA-seq yielded higher counts of unique molecular identifiers (UMIs) and genes, as well as higher levels of protein-coding and cell membrane-associated transcripts, snRNA-seq showed superior enrichment for transcription factors (TFs) and long non-coding RNAs (lncRNAs), as reported previously [ 26 ]. Importantly, snRNA-seq exhibited markedly lower expression of core stress-response genes than scRNA-seq, indicating that nuclear profiling reduces dissociation-induced transcriptional artifacts [ 21 ] (Fig. 3 A). In a previous scRNA-seq study, myeloid cells were classified into multiple groups: cycling, patrolling, Ccr2 + , Cx3cr1 + , Cxcl10 + , and Mrc1 + populations, along with DC subsets (GSE143437; GSE159500; GSE162172; GSE232106 [ 25 ]) (Fig. 3 B). However, examination of marker expression revealed substantial overlap among these groups, and boundaries between them were not crisply delineated (Fig. 3 C). Projecting the scRNA-seq dataset onto our snRNA-seq trajectory revealed strong concordance between modalities (ρ = 0.79–0.88) across the three main branches and the proliferative population (Suppl-2B). Projected cells aligned with macrophage identities within the trajectory embeddings: Cxcl10 + inflammatory monocytes/macrophages mapped predominantly to Branch 1, patrolling macrophages to Branch 2, and Mrc1 + macrophages to Branch 3. Ccr2 + and Cx3cr1 + macrophages were broadly distributed across all macrophage subpopulations (Fig. 3 E). Using the whole-cell data to bolster surface marker detection, we identified Ly6c2 , Il7r and Cd163 as a robust marker set enabling discrimination of Branches 1, 2, and 3, respectively (Fig. 3 A, F, G). We further examined macrophage dynamics by quantifying branch occupancy over time for scRNA-seq macrophages projected onto the snRNA-seq–derived trajectory. At early time points (1 and 2 dpi), the projected cells were dominated by Branch 2. Branch 1 cells increased sharply, peaking at 3.5 dpi, then declined, indicating a transient expansion in the trajectory analysis. When normalized to total nuclei, monocytes/macrophages peaked between 2 and 3.5 dpi (Fig. 3 H). In contrast, analysis of relative composition within the macrophage compartment revealed a gradual recovery of Branch 3 at later time points, consistent with the progressive shift toward this branch observed during regenerative phases in the snRNA-seq trajectory (Fig. 3 H and 2 C). To validate these branch-associated markers at the protein level, we performed multiplex immunofluorescence staining for Ly6C, IL-7R, CD163, and DAPI across the regenerative time course (Fig. 4 A). This analysis revealed no-to-low overlap among Ly6C + , IL-7R + , and CD163 + macrophages, indicating mutually exclusive macrophage populations in vivo. We next assessed proliferative activity within these marker-defined populations by co-staining for Ki67 with Ly6C, IL-7R, or CD163 (Fig. 4 B). Quantification showed that a substantial fraction of IL-7R⁺ and CD163⁺ macrophages were Ki67⁺, consistent with active proliferation within these subsets, whereas Ly6C⁺ macrophages showed minimal proliferation (Fig. 4 C). Together, these observations align with trajectory analysis indicating that ProlifM represents a transient proliferative macrophage state derived primarily from Branches 2 and 3 rather than the Ly6C⁺ inflammatory branch (Fig. 2 D, Suppl-3C). Distinct cytokine signals characterize the intercellular communication of macrophage subpopulations. To investigate the intercellular signals governing distinct macrophage subpopulations, we leveraged our snRNA-seq dataset, which enables comprehensive analysis of cell–cell communication within skeletal muscle tissue. Unlike conventional scRNA-seq approaches, which systematically underrepresent or exclude multinucleated cells (e.g., myotubes, myofibers, osteoclasts, and other syncytial cells), snRNA-seq preserves nuclear transcripts from all major tissue compartments. Consequently, the CellChat inference framework using snRNA-seq data recovered receptor-ligand interactions that are typically missed in scRNA-seq database analyses, particularly those where one interactor (ligand or receptor) is expressed in multinucleated cells. Using our snRNA-seq data, CellChat analysis revealed that connectivity between fibroblasts and Branch 2 macrophages markedly increased during the inflammatory phase (3 dpi) (Fig. 5 A). To elucidate the molecular basis of these interactions, we examined significant incoming signals targeted to macrophage populations within the intercellular communication networks. This analysis uncovered a complex signaling landscape involving chemokines ( Ccl2, Ccl6, Cxcl12 ) and growth factors, including Igf1, Igf2 , Pdgfa , as well as prominent reparative cues targeting Branch 2 macrophages, including Spp1 , Tgfb1 , Lgals9 , and Gas6 . In addition, Bmp5 and Bmp6 emerged as putative regulators of proliferative macrophages. BMP6 inhibits macrophage proliferation and modulates their activation state [ 27 ]. BMP5 is hypothesized to have similar effects based on pathway homology [ 28 ]. Among the incoming signals received by macrophages, IL-34 and CSF-1 exhibited divergent expression dynamics. While Il34 was most prominent in resting muscle (0 dpi) and toward the end of resolution (10 dpi), Csf1 expression increased during inflammation (Fig. 5 B). Notably, the two ligands displayed distinct cellular origins: Il34 was expressed by mesenchymal mural cells (SMCs, pericytes) in the steady state and by myotubes during muscle regeneration, whereas Csf1 was predominantly expressed by fibroblasts (Fib) and fibro-adipogenic progenitors (FAPs). Csf1r was expressed across all branches, as well as within the ProlifM subpopulation (Fig. 5 C). This cell-specific ligand expression, together with the interaction patterns, supports a model in which IL-34 maintains the resident Branch 3 population within the perimysial niche. In contrast, fibroblast-derived CSF-1 appears to drive the expansion and maintenance of inflammatory and reparative Branch 1 and 2 macrophages during active tissue repair. Consistent with this, Branch 3 macrophages, which are abundant in the uninjured state (0 dpi) and redominate toward the end of resolution (10 dpi) (Fig. 2 B). Distinct cell surface marker profiles define non-overlapping macrophage subpopulations. Immunofluorescence analysis revealed that CD163 + macrophages are enriched in the perimysial regions of uninjured TA muscles (0 dpi) and closely associate with α-SMA⁺ mural cells (Suppl-3A). This spatial relationship aligns with our identification of Branch 3 macrophages as a homeostatic, muscle-resident population supported by IL-34-producing mural cells. To determine whether Branch 3 macrophages maintain spatial confinement during inflammation, we examined their localization during injury. At 0 dpi, CD163⁺ macrophages remain tightly associated with α-SMA⁺ mural cells within well-defined perimysial regions (Suppl-3A). In contrast, between 3–5 dpi, CD163 + macrophages became broadly distributed throughout the tissue, coinciding with the disruption of the perimysial architecture and reduced association with mural cells (Suppl-3B). Consistent with this spatial redistribution, Palantir analysis indicates that Branch 3 macrophages possess a strong potential to transition toward Branch 2, with a lower probability of transitioning toward the inflammatory Branch 1 (Suppl-3C). Finally, to relate ligand-responsive states to the trajectory-defined populations, we compared the IL-34– and CSF-1–derived macrophage signatures with the branch-specific transcriptomic profiles (GSE151194 [ 29 ]). IL-34–stimulated macrophages were transcriptionally similar to Branch 3, whereas CSF-1–stimulated macrophages closely resembled the inflammatory Branch 1 population (Suppl-3D). These data indicate that niche-specific growth factors not only maintain macrophage pool but also actively drive macrophage functional phenotype. Monocyte-derived macrophages exhibit dynamic phenotypic shifts during regeneration. To better resolve macrophage dynamics in the early inflammatory phase, we applied our snRNA-seq-derived trajectory framework to a scRNA-seq dataset from parabiosis-based fate mapping (GSE198055 [ 30 ]) (Fig. 6 A). From flow-sorted GFP + parabiont cells, only monocytes/macrophages were included in the downstream analyses (Suppl-4A-C). Upon entry into the injured muscle, monocyte-derived cells initially occupied early branch states proximal to the bifurcation point (states 2–2 and 2–3), corresponding to the regions occupied by Cx3cr1 + cells (Fig. 3 D, upper panel). These cells then converged toward the Branch 1 trajectory in a coordinated manner (Fig. 6 B). As expected, spiked-in GFP − TIMD-4 + cells were detected in Branch 3, reflecting tissue-resident macrophages [ 30 ] (Fig. 6 B). Notably, monocyte-derived macrophages did not overlap with the ProlifM population; GFP⁺ parabiont-derived cells lacked proliferative signatures from 6 hours to 8 dpi, indicating that recruited monocytes do not undergo local proliferation after tissue entry (Fig. 6 B, C). At 6 hours post-injury, Gfp ⁺ cells exhibited a circulating inflammatory monocyte signature (e.g., Cd14 , Cd44 , Cxcl2 , Cxcl3 , Il1rn , Ly6c2 , Plaur , S100a6 , S1008a8 , and Sell ), consistent with inflammatory monocyte genes described in the mononuclear phagocyte (MNP) single-cell reference atlas (“MNP-verse”) [ 31 ]. Gene ontology (GO) analysis revealed enrichment of pathways associated with monocyte chemotaxis and chemokine signaling (Suppl-4D). By 2 dpi, Gfp ⁺ macrophages retained a largely monocyte-associated transcriptional profile, with enrichment of programs related to myeloid cell development and chemotaxis, indicating an incomplete transition to terminal macrophage activation states (Fig. 6 D, E). At this stage, ISGs accounted for only ~ 26% of upregulated genes. At 4 dpi, Gfp + macrophages transitioned to a pronounced ISG-dominant transcriptional program, accounting for ~ 58% of upregulated genes. This shift coincided with induction of pathways associated with antigen processing and presentation, response to interferon, and regulation of T cell activation, consistent with progression toward inflammatory resolution (Fig. 6 D). To assess whether this ISG program is induced by paracrine interferon stimulation, we examined interferon ligand expression in injured muscle. Type I interferon transcripts were detected at no-to-low levels and were largely restricted to DCs and a subset of macrophages, suggesting that the robust ISG signature a response to damage-associated molecular patterns (DAMPs) rather than paracrine interferon signaling (Fig. 6 F, Suppl-4E). Critically, monocyte-derived cells do not re-transition from Branch 1 back to Branch 2 during resolution. However, resident macrophages may transition between these states with higher plasticity (fig. S3C). Branch 1 macrophages were transcriptionally primed for inflammasome activation and pyroptosis ( Gsdmd , Ilb , Il18 , Casp1 , Casp4 , Nlrp3 , and Aim2 ), whereas Branch 2 macrophages expressed intrinsic apoptotic regulators ( Bax , Bak1 , and Apaf1 ) (Suppl-5A). We confirmed these cell death gene expression using spatial transcriptomics in gastrocnemius muscle partially injected with CTx (GSE225766 [ 32 ]) (Suppl-5B). In situ analysis confirmed the presence of apoptotic Branch 2 and Branch 3 macrophages, whereas Ly6C + Branch 1 cells were TUNEL-negative, suggesting that these cells do not undergo classical apoptosis during the resolution phase (Suppl-5C). snRNA-seq reveals macrophage stress–response programs with limited evidence for pathology-driving transcriptional programs at the onset of muscle dystrophy. Next, we questioned whether macrophages act as pathological drivers at the onset of muscular dystrophy, we performed snRNA-seq on skeletal muscle from BLA/J mice at 12 months of age, focusing on the TA and gluteus maximus (GM) [ 33 ]. Histological analysis confirmed centronucleated myofibers and leukocyte infiltration, with the GM showing more advanced adipose deposition and remodeling as reported previously [ 33 ] (Fig. 7 A). Immunostaining for CD68 and F4/80 confirmed that macrophages are the prominent cellular component at disease onset (Fig. 7 B). Using our regenerative trajectory framework, we next examined branch-specific alterations in BLA/J TA and GM. Surprisingly, Branch 1 macrophages showed no meaningful gene or pathway enrichment in either muscle, arguing against a dominant pathology-driving transcriptional program at early disease stages. Instead, transcriptional alterations were largely confined to Branch 3 in both BLA/J TA and GM (Fig. 7 C, Suppl-6A). In BLA/J TA, the number of differentially expressed genes (DEGs) relative to B6 was minimal in Branches 1 and 2, providing an insufficient basis for pathway enrichment. In Branch 3, only 35 genes were found to be upregulated in BLA/J TA, which are associated with single-strand DNA binding, calcium, oxytocin and Phospholipase D signaling pathways, mRNA slicing. To our surprise, no immune-related pathway was observed (Suppl-6A). In the BLA/J GM, Branch 3 macrophages displayed signatures consistent with a cellular stress response rather than a pathogenic state, including mediators of mitochondrial quality control ( Prkn ), epigenetic reprogramming ( Jarid2 , Nfia ), and proteostatic stress responses ( Psme4 , Ube2e2 ). These signatures resemble stress-adaptive programs seem in chronically injured parenchymal cells, supporting our notion that macrophages at this stage are responding to tissue damage rather than initiating pathology (Fig. 7 C). Because dysferlinopathy onset does not substantially alter the genetic program of Branch 1 macrophages, we next asked whether increasing disease severity affects macrophage behavior. To this end, we extended our analysis to two murine Duchenne muscular dystrophy (DMD) models: ΔEx51 TA at 4 weeks of age (GSE156498 [ 34 ]) and mdx TA at 8 months of age (PRJNA771932 [ 35 ]), both of which display severe pathology prominent already at postnatal day 26 [ 36 ] (Fig. 7 D). In these models, we observed increased macrophage proliferation, indicating heightened immune engagement in response to tissue injury (Fig. 7 E, F). Despite the severe pathology, the macrophage subpopulation structure remained remarkably conserved across both DMD models (Fig. 7 G). Notably, macrophages from these two models clustered closely in principal component analysis (PCA) (Fig. 7 H). Given this high degree of concordance, subsequent analyses were performed using mdx TA data only due to overall higher data quality (Suppl-6B). In contrast to BLA/J muscle, mdx TA macrophages displayed DEGs shared across all three branches. These were genes related to lysosomal and degradative programs ( Ctsb , Ctss , Hexb , Psap , and Cd63 ) likely associated with enhanced debris clearance. Branch 3 was characterized by a substantially larger number of upregulated genes compared to Branches 1 and 2, reflecting a state of heightened transcriptional activity, with enrichment for antigen processing and MHC class II–mediated presentation pathways, indicative of macrophage activation in response to sustained tissue damage (Fig. 7 I). Furthermore, the proportion of Branch 1 macrophages was notably higher in DMD models, contributing to a persistent inflammatory environment (Fig. 2 G-I, Fig. 7 G, Suppl-5A). These findings suggest that while macrophage branch-specific identities remain intact across different dystrophies, the depth of the transcriptional response and population composition shifts significantly with disease severity. Whole-macrophage transcriptomic analysis reveals activation- and migration-associated programs in advanced muscular dystrophy. To capture the full scale of the transcriptional shift across disease states, we performed a DEG analysis of the entire macrophage population across BLA/J TA, BLA/J GM, and mdx TA, compared with B6 TA at 10 dpi (Fig. 8 A, B). Surprisingly, comparison across all three conditions revealed only nine commonly upregulated genes (Fig. 8 A). This small core set suggests that early dystrophic pathology does not impose a dominant, uniform macrophage activation program. In the TA muscle specifically, we identified 35 genes commonly upregulated in both BLA/J and mdx TA models, defining a signature of phenotypic plasticity. This set included components for sensing mechanical and TGF-β–related signals ( Tgfbi , Acvr1 ) and post-transcriptional fine-tuning via RNA splicing and microRNA processing ( Srsf6 , Dgcr8 ), as well as regulators of chromatin-mediated cell state stabilization ( Kmt5a , Pcgf3 ) (Fig. 8 A). Rather than overt inflammatory programs, these genes describe regulatory layers that enable macrophages to adapt and integrate environmental cues—a state consistent with a poised, adaptive bystander. In contrast, mdx TA macrophages exhibited a markedly expanded response, with 347 uniquely upregulated genes enriched for pathways including antigen presentation, extracellular matrix (ECM) organization, inflammatory responses, and superoxide anion generation (Fig. 8 A, B). This indicates the acquisition of oxidative effector programs in response to sustained, chronic tissue damage. To determine the drivers of Branch 1 macrophage accumulation in the mdx , we used CellChat to map macrophage-centered communication (Suppl-6C). We identified a strong upregulation of macrophage growth factors ( Csf1 , Il34 ) and chemokines ( Ccl5 , Ccl6 , Cxcl12 ) among signals received by macrophages (Suppl-6D). Notably, while Ccl6 acts as an autocrine factor (Suppl-6E), Cxcl12 (stromal-derived factor 1) was predominantly expressed by mural cells, endothelial cells, and fibroblasts (Fig. 8 C, D). Together, these analyses reveal a fundamental shift in macrophage behavior. At disease onset, macrophages act as reactive sensors, exhibiting highly constrained, stress-adaptive programs. As tissue damage becomes chronic, the dystrophic environment—specifically signals from the activated endothelium and surrounding mural cells—facilitates the recruitment of pro-inflammatory Branch 1 macrophages, eventually driving the system toward a pathology-perpetuating state. Discussion We present here the first high-resolution snRNA-seq trajectory of macrophages in regenerating and dystrophic skeletal muscle, providing a definitive map of nascent transcriptional programs while bypassing dissociation-induced artifacts. Our analysis identified three crisply distinguishable macrophage activation states and demonstrates for the first time that intra-muscle macrophage proliferation is restricted to a narrow time window between 2 and 4 dpi (Fig. 2 B and 3 C). Lever et al. previously reported the coexistence of multiple transcriptionally distinct macrophage subpopulations within the kidney following ischemia-reperfusion injury [ 37 ]. Building on our previous findings that these populations may play distinct roles in fibrogenesis [ 38 ], our current study provides novel insights by delineating how these subpopulations evolve over a discrete regenerative trajectory. By capturing these states in situ through nuclear signatures, we demonstrate that the macrophage response is not a binary transition but a highly regulated sequence of activation, proliferation, and maturation. We found that the spatial regulation of macrophage states is governed by distinct growth factor niches: Il34 is the predominant ligand in resting muscle—produced by SMCs and pericytes in the perimysium—whereas Csf1 is transiently induced by fibroblasts during active inflammation (Fig. 5 C). These findings refine the understanding of CSF-1R signaling in muscle [ 39 ] by identifying IL-34 as the specific homeostatic ligand that maintains Branch 3 macrophages within the peri- and epimysium [ 40 ] (Suppl-3A). By integrating parabiosis with injury models, we demonstrate that infiltrating monocytes enter the tissue at the early bifurcation point of Branch 2 (Fig. 6 C), aligning with the Cx3cr1 + populations identified in previous scRNA-seq studies [ 25 ] (Fig. 3 D, H). Crucially, our analysis reveals an intriguing developmental trajectory: these recruited cells remain non-proliferative and undergo a unidirectional differentiation program between 2 and 4 dpi to transition to Branch 1 inflammatory macrophages (Fig. 6 C-E). Unlike the reparative or resident branches, this population exhibits a transcriptomic signature of hyperactivation and pyroptosis, indicating a divergent and terminal functional fate rather than a transition back to a resting state (Suppl-5A). A central, unresolved question in muscle biology is whether the accumulating macrophages in muscular dystrophies represent primary “culprits” driving pathology or a secondary “bystander” responding to a chronic degenerative environment responding to a chronic degenerative environment. Our finding suggest that macrophages are primarily as adaptive responders [ 33 ]; rather than initiating de novo disease-promoting programs, they expand and modify conserved, homeostatic states in response to the local niche. Across models of both late-onset dysferlinopathy (BLA/J) and early-onset DMD ( mdx ), inflammatory Branch 1 macrophages expand in correlation with disease severity while retaining transcriptional programs highly conserved with acute regeneration (Fig. 7 C, I, Suppl-6A). Even when tissue-reparative macrophages upregulate fibrosis-associated genes in dystrophic muscle, our fate analyses reveal these states represent an adaptation to a pathological environment rather than a de novo disease-intrinsic program (Fig. 8 A). In this study, we analyzed and compared two different models of muscular dystrophy with distinct genetic background: dysferlinopathy and DMD. Although dysferlin is expressed in leukocytes, its specific functional role in monocytes—particularly in the context of dysferlinopathy—remains largely elusive. While some studies suggest that dysferlin may play role in cell adhesion and vesicular trafficking [ 41 , 42 ], how these specific defects contribute to the progressive muscle wasting observed in patients is not yet fully understood. Previous reports indicated that bone marrow transplantation in dysferlin-deficient mice yielded only a marginal improvement in muscle function and failed to halt overall disease progression [ 43 ]. In this context, innate memory might be a critical factor. Innate memory involves the epigenetic and metabolic reprogramming of innate immune cells, allowing them to mount a heightened or altered response upon secondary stimulation [ 44 ]. It is possible that the chronic inflammatory milieu in dystrophic muscle imprints a specific functional state on these cells that persist regardless of the immediate genetic rescue. However, our data showed that in branch-wise comparisons, the macrophage profiles of BLA/J mice were remarkably similar to those of healthy regenerating mice. Therefore, concerns regarding innate memory may not be critically relevant to the dysferlin-deficient model in this study. In summary, these findings establish that muscle macrophage heterogeneity is governed by the tissue niche and cellular origin. The persistent inflammatory presence in dystrophy reflects a quantitative expansion of conserved programs adapting to a maladaptive environment. Consequently, macrophages at disease onset act primarily as adaptive bystanders, providing a framework for immune regulation in chronic muscle degeneration. Methods Animals C57BL/6J (B6) mice were purchased from Hyochang Science, Inc. (Daegu, Republic of Korea). Dysf -deficient B6.A-Dysf prmd /GeneJ (BLA/J) mice were kindly provided by the Jain Foundation (Seattle, WA, USA). Unless otherwise stated, male mice aged 6–12 weeks were used in cardiotoxin (CTx)-induced muscle injury experiments. Our study examined male animals only. Given that DMD is an X-linked disorder primarily affecting males, male mice were utilized across all experimental groups—including the dysferlin-deficient models—to ensure experimental uniformity and allow for direct mechanistic comparisons between the two pathologies. Histological staining Hematoxylin and eosin (H&E) staining was performed on 5-µm-thick paraffin-embedded muscle sections using an H&E Staining Kit (Cat#: ab245880; Abcam, Cambridge, UK) according to the manufacturer’s instructions. Immunostaining Muscle cryosections (5 µm thick) were stained to detect macrophages and their subpopulations, as previously described [ 16 ]. The following antibodies were used: phycoerythrin (PE)-conjugated anti-mouse/human CD11b (clone: M1/70; Cat# 101207); allophycocyanin (APC)-conjugated anti-mouse CD163 (clone: S15049I; Cat# 155305); fluorescein isothiocyanate (FITC)-conjugated anti-mouse F4/80 (clone: BM8; Cat# 123107); APC-conjugated anti-IL-7R (clone: A7R34; Cat# 135011); PE-conjugated anti-IL-7R (clone: S18006K; Cat# 158203); FITC-conjugated anti-mouse/human Ki67 (clone: 11F6; Cat# 151211); FITC-conjugated anti-mouse Ly6C (clone: HK1.4; Cat# 128005); PE-conjugated anti-mouse Ly6C (clone: HK1.4; Cat# 128007). All antibodies listed above were purchased from BioLegend (San Diego, CA, USA). AlexaFluor®488-conjugated anti-α-smooth muscle actin (clone: 1A4; Cat# 53-9760-80) was purchased from Invitrogen (Waltham, MA, USA). The terminal deoxynucleotidyl transferase dUTP Nick End Labelling (TUNEL) assay was performed using an In Situ Apoptosis Detection Kit (Cat# MK500; Takara Bio, Kusatsu, Japan) according to the manufacturer’s instructions. Nuclei isolation For each sample, tissues from four mice were pooled, immediately frozen, and stored until processing. Tissues were homogenized in a lysis buffer containing 10 mM Tris-HCl, 10 mM NaCl, 3 mM MgCl 2 , and 0.1% IGEPAL CA-630 in nuclease-free water. After homogenization, nuclei were isolated and purified by density gradient centrifugation with 20% Percoll. Nuclei concentration was measured using a LUNA-FL™ Automated Fluorescence Cell Counter (Logos Biosystems, Anyang, Republic of Korea), and nuclear morphology was examined by light microscopy. Single-nucleus RNA sequencing (snRNA-seq) library preparation snRNA-seq libraries were prepared using the Chromium Controller (10x Genomics, Pleasanton, CA, USA) following the 10x Chromium Next GEM Single Cell 3’ v3.1 protocol (CG000315). Briefly, nuclei suspensions were diluted in nuclease-free water to target a recovery of 10,000 nuclei. The nuclei suspension was combined with the reverse transcription master mix and loaded with Single Cell 3’ v3.1 Gel Beads and Partitioning Oil into a Chromium Next GEM Chip G. RNA transcripts from individual nuclei were uniquely barcoded and reverse-transcribed within nanolitre-scale droplets. After reverse transcription, cDNA was pooled and subjected to end repair, A-tailing, and adaptor ligation. Libraries were purified and amplified by PCR to generate the final cDNA libraries. The purified libraries were quantified by qPCR with the KAPA Library Quantification Kit (Roche, Basel, Switzerland) and assessed for quality on an Agilent 4200 TapeStation (Agilent Technologies, Santa Clara, CA, USA). Libraries were sequenced on a NovaSeq platform (Illumina, San Diego, CA, USA) using read lengths specified in the manufacturer’s user guide. Public datasets The following publicly available datasets were obtained for reference-based integration and supplementary analyses: single-cell RNA sequencing (scRNA-seq) data on GFP + parabiont monocytes/macrophages from Babaeijandaghi et al. (GSE198055); scRNA-seq atlas of regenerating TA muscle from Walter et al. (GSE143437; GSE159500; GSE162172; GSE232106) [ 25 ]; snRNA-seq data of mdx TA muscle from Scripture-Adams et al. (PRJNA771932) [ 35 ]; snRNA-seq data of ΔEx51 TA muscle from Chemello et al . (GSE156498) [ 34 ]; and 10x Visium data of the injured gastrocnemius/plantaris complex from Coulis et al. (GSE225766) [ 45 ] snRNA-seq data preprocessing FASTQ reads were aligned to the mm10 reference genome with CellRanger (v7.1.0). Ambient RNA removal and empty droplet identification were performed on raw feature-barcode matrices with CellBender (v0.3.2), using the --expected-cells parameter derived from the barcode rank plot. Putative doublets were identified with scDblFinder (v1.21.2). The expected doublet rate was computed from CellBender cell counts using a linear regression model fit to the 10x Chromium doublet rate specifications. After doublet removal, quality control was performed using data-driven quality control (ddqc). Briefly, each sample was processed through the standard single-cell analysis workflow in Seurat (v4.4.0) with default parameters, including NormalizeData, ScaleData, FindVariableFeatures, RunPCA, FindNeighbors, and FindClusters (resolution = 1). For each Louvain cluster, outlier nuclei were removed using a ± 2 median absolute deviation (MAD) threshold for total unique molecular identifier (UMI) counts, number of detected genes, and mitochondrial gene percentage. To limit extreme outliers, the upper bound for UMI counts was capped at the 90th percentile. Finally, ddqc-derived clusters were excluded if they lacked significant marker genes (fold change [FC] > 1.5, pct.1 > 0.1, and Benjamini-Hochberg [BH]-adjusted p 80% of the cluster comprising nuclei (hereafter referred to as "cells") classified as empty droplets by CellRanger but retained by CellBender. For pseudobulk analysis, individual cells were aggregated into sample-level counts via UMI summation. Trimmed mean of M-values scaling factors were estimated using the calcNormFactors function from edgeR (v4.4.2). Counts per million were computed by normalizing the raw counts against the effective library sizes for cross-sample comparisons. Integration, subclustering, and annotation Batch effect correction and data integration were performed using scVI (v1.3.0). The time point of injury was used as the batch covariate, and 2,000 highly variable genes (HVGs) were selected by ranking genes by their median variability across batches. The scVI model was trained with a negative binomial likelihood and 30 latent dimensions. Using scVI-derived latent embeddings, initial clustering at a resolution of 1.2 identified major cell populations: myogenic, immune, stromal, endothelial, mural, neural, and adipocyte. Immune cells were subclustered at a resolution of 1.0 and annotated with SingleR (v2.8.0) using microarray profiles of sorted mouse immune cell populations from the ImmGen database. Trajectory and pseudo-time analysis Macrophages were subset and processed with SCTransform (v0.4.1). Pearson residuals from the top 1,000 HVGs were used to compute 20 principal components (PCs), which were then integrated with Harmony (v1.2.3). The harmonized PCs were visualized using the potential of heat diffusion for affinity-based transition embedding (PHATE; Python; v1.0.11) with parameters gamma = 0 and knn = 10. To define progression states, macrophages were partitioned into 10 clusters using k -means clustering on the PHATE potential distance matrix. Palantir (v1.4.1) was used to infer macrophage plasticity and state transitions. For each phenotypic extreme identified in the PHATE manifold, a separate Palantir model was constructed by designating the corresponding branch tip as the root state and the remaining branch tips as terminal states. Gene regulatory network analysis Gene regulatory networks underlying macrophage polarization were reconstructed using the Python implementation of single-cell regulatory network inference and clustering (pySCENIC; v0.12.1). Transcription factor (TF)-gene co-expression modules were first identified with GRNBoost2. These modules were refined into regulons with cisTarget by pruning target genes that lacked motif enrichment, using the mm10 10kb motif database. Regulon activity at the single-cell level was quantified with AUCell, and the resulting network topology was visualized with Cytoscape (v3.10.2). To identify lineage-driving TFs, pseudotime ordering was performed with Slingshot (v2.14.0), followed by differential regulon activity analysis with tradeSeq (v1.20.0). A generalized additive model was fitted to the SCENIC AUC scores using the fitGAM function with a Beta regression family (family="betar"). Regulons upregulated at each branch terminus were identified with the startVsEndTest function, using an FC threshold of > 1.5. Intercellular signaling network analysis Cell–cell communication analysis was performed on snRNA-seq datasets from regenerating muscle using CellChat (v2.1.2) and the mouse CellChatDB database. Macrophage identities were defined based on their branch assignments, while all other cell populations retained their original population- or subcluster-level annotations. CellChat analyses were conducted independently for each time point using the standard workflow, beginning with the identification of overexpressed genes and interactions (identifyOverExpressedGenes, identifyOverExpressedInteractions). Communication probabilities were computed using the truncated mean method (computeCommunProb with type="truncatedMean" and population.size=TRUE). Finally, after filtering each cell-cell communication network (filterCommunication), ligand-receptor interaction probabilities were aggregated by ligand family to visualize pathway-level interaction strengths targeting distinct macrophage branches. Reference-based mapping and label transfer Single-nucleus query datasets were independently mapped onto the regenerating muscle reference atlas using scArches, which applies architectural surgery to integrate query-specific parameters into the pretrained scVI model. The reference scVI model was further trained for 500 additional epochs with early stopping and an adaptive learning rate scheduler (early_stopping=True, early_stopping_patience=10, reduce_lr_on_plateau=True). During mapping, the reference model weights were frozen, and weight decay was set to zero (weight_decay=0.0) to preserve the reference latent space. Following mapping, cell-type annotations were transferred from the reference to the query datasets using k -nearest neighbour classification in the scVI latent space. Query nuclei were assigned labels by majority vote among the 15 nearest reference neighbours, using the reference subcluster annotations as training labels. To align query macrophages with the reference trajectory framework, query datasets were first transformed with the pretrained reference SCTransform model. Query macrophages were then projected into the harmonized PC space using the mapQuery function from Symphony (v0.1.1). Mapping quality was assessed with a weighted Mahalanobis distance metric, as described by Kang et al. [46], and outliers were identified and removed using a ±2 MAD threshold. After quality control, query macrophages were projected onto the reference trajectory embedding using the pretrained PHATE model. To assign progression states, an affinity matrix from query cells to PHATE landmarks was computed and used to interpolate reference potential distances onto the query cells. Finally, query macrophages were assigned to one of the 10 reference progression states using a pretrained K-means model applied to the interpolated potential distance matrix. Spatial transcriptomics Filtered feature-barcode matrices from SpaceRanger (v1.3.1) were preprocessed with Seurat. Ribosomal and mitochondrial genes were excluded from the analysis. Spots with fewer than 200 UMIs were filtered out. For each slide, spots were partitioned into injured and uninjured clusters using the Louvain algorithm with the 10 PCs derived from 1,000 HVGs. Gene set analysis Differentially expressed genes (DEGs) were identified using MAST (v1.10). Analyses were conducted in a one-versus-rest manner unless otherwise stated. Gene set enrichment analysis (GSEA) was performed with clusterProfiler (v4.14.6) using a weighted gene list ranked by -sign(logFC) × -log(BH-adjusted p ) from MAST. GSEA was run with a weighting exponent of 0.5 and a one-sided tail test to focus on positive enrichment (exponent=0.5, scoreType="pos"). Over-representation analysis was performed via enrichr (v3.4) using the set of all genes that passed pct.1 = 0.1 filter in the preceding DEG analysis as the universal gene set. Gene sets from MSigDB, including GO:BP, GO:MF, REACTOME, and KEGG, were evaluated for significance. All gene set activities were quantified using the AddModuleScore function in Seurat. Cell cycle scores were computed using the standard G2/M and S phase marker sets provided by Seurat. Macrophage polarization states were assessed by computing module scores for M1 and M2 gene sets derived from Orecchioni et al. [47] Gene sets representing monocytes cultured with CSF-1 and IL-34 were obtained from Bézie et al. [29] Statistics Unless otherwise stated, data are presented as mean ± s.d. Statistical significance was assessed using the nonparametric Mann–Whitney U test. P values are reported as follows: * P < 0.05, ** P < 0.01, *** P < 0.001, and **** P < 0.0001. Study approval All animal experiments were conducted in accordance with the guidelines and regulations of the Institutional Animal Care and Use Committee (IACUC) at Handong Global University. Study protocols were approved under the following reference numbers: HGU-IACUC 20211123-14, HGU-IACUC 20221123-16, HGU-IACUC 20231024-19, HGU-IACUC 20240323-03, HGU-IACUC 20240904-16, and HGU-20250901-10. Nonstandard abbreviations used a-smooth muscle actin, a-SMA; cDC, conventional dendritic cells; CSF-1, colony-stimulating factor-1; CSF-1R, colony-stimulating factor-1 receptor; CTx, cardiotoxin; DAMPs, damage-associated molecular patterns; DAPI, 4′,6-diamidino-2-phenylindole; DC, dendritic cell; DEG, differentially expressed gene; DMD, Duchenne muscular dystrophy; dpi, day(s) post-injury; ECM, extracellular matrix; FAP, fibro-adipogenic progenitor; GM, gluteus maximus; GO, gene ontology; H&E , hematoxylin and eosin ; IHC, immunohistochemistry; lncRNA, long non-coding RNA; ISG, interferon-stimulated gene; MNP, mononuclear phagocyte; Mreg, mature regulatory; PCA, principal component analysis; pDC, plasmacytoid dendritic cells; PHATE, potential of heat-diffusion for affinity-based transition embedding; ProlifM, proliferative macrophage; QuSC, quiescence muscle stem cell; scRNA-seq, single-cell RNA sequencing; s.d., standard deviation; SCENIC, single-cell regulatory network inference and clustering; SMC, smooth muscle cell; snRNA-seq, single-nucleus RNA sequencing; UMI, unique molecular identifier; TA, tibialis anterior; TF, transcription factor; tSNE, t-distributed stochastic neighbour embedding; TUNEL, terminal deoxynucleotidyl transferase-mediated dUTP nick end labelling; UMAP, uniform manifold approximation and projection. Declarations Conflict of interest All authors declare no competing interests. Funding This research was supported by the National Research Foundation of Korea (NRF) through the Ministry of Education (2021R1I1A3059820) (to Jea-Hyun Baek). Author Contributions This study was conceived and supervised by J.-H.B.; E.-C.B., Y.-G.S. and W.S.B. performed experiments and data analysis; W.S.B. and J.-H.B. prepared the original draft; J.H.B. reviewed and edited. All authors have read and agreed to the published version of the manuscript. Acknowledgments The authors thank the Jain Foundation (Seattle, WA, USA) for providing us with BLA/J mice and all Baek laboratory members for technical assistance and discussions. Data availability All raw and pre-processed snRNA-seq data have been deposited in the National Center for Biotechnology Information Gene Expression Omnibus under accession . The fully processed Seurat and serialized objects are publicly available for download on Dryad (). Values for all data points in graphs are reported in the Supporting Data Values file. All code and instructions for reproducing the single-cell and spatial analyses for this study are available on GitHub ( https://github.com/wsk-byun/SkM-Mac-Traj ). References Fang J, Feng C, Chen W, Hou P, Liu Z, Zuo M, et al. Redressing the interactions between stem cells and immune system in tissue regeneration. Biol Direct. 2021;16:18. doi: 10.1186/s13062-021-00306-6 PubMed PMID: 34670590; PubMed Central PMCID: PMC8527311. Xu HR, Le VV, Oprescu SN, Kuang S. Muscle stem cells as immunomodulator during regeneration. Curr Top Dev Biol. 2024;158:221–38. doi: 10.1016/bs.ctdb. 2024.01.010 PubMed PMID: 38670707; PubMed Central PMCID: PMC11801201. Tidball JG, Villalta SA. Regulatory interactions between muscle and the immune system during muscle regeneration. Am J Physiol Regul Integr Comp Physiol. 2010;298(5):R1173-1187. doi: 10.1152/ajpregu.00735 .2009 PubMed PMID: 20219869; PubMed Central PMCID: PMC2867520. Tidball JG. Regulation of muscle growth and regeneration by the immune system. Nat Rev Immunol. 2017;17(3):165–78. doi: 10.1038/nri .2016.150 PubMed PMID: 28163303; PubMed Central PMCID: PMC5452982. Bansal D, Miyake K, Vogel SS, Groh S, Chen CC, Williamson R, et al. Defective membrane repair in dysferlin-deficient muscular dystrophy. Nature. 2003;423(6936):168–72. doi: 10.1038/nature01573 PubMed PMID: 12736685. Oishi Y, Manabe I. Macrophages in inflammation, repair and regeneration. Int Immunol. 2018;30(11):511–28. doi: 10.1093/intimm/dxy 054 PubMed PMID: 30165385. Wynn TA, Vannella KM. Macrophages in Tissue Repair, Regeneration, and Fibrosis. Immunity. 2016;44(3):450–62. doi: 10.1016/j.immuni .2016.02.015 PubMed PMID: 26982353; PubMed Central PMCID: PMC4794754. Kim JH, Baek JH. Macrophages in the pathogenesis of monogenic muscular dystrophies: inflammation, fibrosis, and therapeutic implications. EI. 2025;5:1003192. doi: 10.37349/ei.2025.1003192 Spencer CG, Hamilton M, Bedsole E, Wei YN, Rojas AM, Burciu A, et al. Chronic macrophage activation derails muscle repair by disrupting mannose-receptor-linked plasticity revealed by endogenous irg1/acod1 tracking. Nat Commun. 2026;17:1466. doi: 10.1038/s41467-025-68204-3 PubMed PMID: 41501052; PubMed Central PMCID: PMC12886890. Theret M, Saclier M, Messina G, Rossi FMV. Macrophages in Skeletal Muscle Dystrophies, An Entangled Partner. J Neuromuscul Dis. 9(1):1–23. doi:10.3233/JND-210737 PubMed PMID: 34542080; PubMed Central PMCID: PMC8842758. Rodríguez-Morales P, Franklin RA. Macrophage phenotypes and functions: resolving inflammation and restoring homeostasis. Trends Immunol. 2023;44(12):986–98. doi: 10.1016/j.it.2023.10.004 PubMed PMID: 37940394; PubMed Central PMCID: PMC10841626. Stanley ER, Heard PM. Factors regulating macrophage production and growth. Purification and some properties of the colony stimulating factor from medium conditioned by mouse L cells. J Biol Chem. 1977;252(12):4305–12. PubMed PMID: 301140. Stanley ER, Cifone M, Heard PM, Defendi V. Factors regulating macrophage production and growth: identity of colony-stimulating factor and macrophage growth factor. J Exp Med. 1976;143(3):631–47. doi: 10.1084/jem.143.3.631 PubMed PMID: 1082493; PubMed Central PMCID: PMC2190132. Greter M, Lelios I, Pelczar P, Hoeffel G, Price J, Leboeuf M, et al. Stroma-derived interleukin-34 controls the development and maintenance of langerhans cells and the maintenance of microglia. Immunity. 2012;37(6):1050–60. doi:10.1016/j.immuni.2012.11.001 PubMed PMID: 23177320; PubMed Central PMCID: PMC4291117. Wang Y, Szretter KJ, Vermi W, Gilfillan S, Rossini C, Cella M, et al. IL-34 is a tissue-restricted ligand of CSF1R required for the development of Langerhans cells and microglia. Nat Immunol. 2012;13(8):753–60. doi: 10.1038/ni.2360 PubMed PMID: 22729249; PubMed Central PMCID: PMC3941469. Baek JH, Zeng R, Weinmann-Menke J, Valerius MT, Wada Y, Ajay AK, et al. IL-34 mediates acute kidney injury and worsens subsequent chronic kidney disease. J Clin Invest. 2015;125(8):3198–214. doi: 10.1172/JCI81166 PubMed PMID: 26121749; PubMed Central PMCID: PMC4563757. Wei S, Nandi S, Chitu V, Yeung YG, Yu W, Huang M, et al. Functional overlap but differential expression of CSF-1 and IL-34 in their CSF-1 receptor-mediated regulation of myeloid cells. J Leukoc Biol. 2010;88(3):495–505. doi: 10.1189/jlb.1209822 PubMed PMID: 20504948; PubMed Central PMCID: PMC2924605. Felix J, Elegheert J, Gutsche I, Shkumatov AV, Wen Y, Bracke N, et al. Human IL-34 and CSF-1 establish structurally similar extracellular assemblies with their common hematopoietic receptor. Structure. 2013;21(4):528–39. doi: 10.1016/j.str .2013.01.018 PubMed PMID: 23478061. Mitsui Y, Satoh T. Functional diversity of disorder-specific macrophages involved in various diseases. Inflamm Regen. 2025;45:29. doi: 10.1186/s41232- 025-00390-5 PubMed PMID: 41035106; PubMed Central PMCID: PMC12487080. Byun WS, Lee J, Baek JH. Beyond the bulk: overview and novel insights into the dynamics of muscle satellite cells during muscle regeneration. Inflamm Regen. 2024;44(1):39. doi: 10.1186/s41232-024-00354-1 PubMed PMID: 39327631; PubMed Central PMCID: PMC11426090. Machado L, Geara P, Camps J, Dos Santos M, Teixeira-Clerc F, Van Herck J, et al. Tissue damage induces a conserved stress response that initiates quiescent muscle stem cell activation. Cell Stem Cell. 2021;28(6):1125–1135.e7. doi: 10.1016/j.stem .2021.01.017 PubMed PMID: 33609440. Saini J, McPhee JS, Al-Dabbagh S, Stewart CE, Al-Shanti N. Regenerative function of immune system: Modulation of muscle stem cells. Ageing Res Rev. 2016;27:67–76. doi: 10.1016/j.arr.2016.03.006 PubMed PMID: 27039885. Wang Y, Lu J, Liu Y. Skeletal Muscle Regeneration in Cardiotoxin-Induced Muscle Injury Models. Int J Mol Sci. 2022;23(21):13380. doi:10.3390/ijms232113380 PubMed PMID: 36362166; PubMed Central PMCID: PMC9657523. Castiglioni A, Corna G, Rigamonti E, Basso V, Vezzoli M, Monno A, et al. FOXP3 + T Cells Recruited to Sites of Sterile Skeletal Muscle Injury Regulate the Fate of Satellite Cells and Guide Effective Tissue Regeneration. PLoS One. 2015;10(6):e0128094. doi: 10.1371/journal.pone.0128094 PubMed PMID: 26039259; PubMed Central PMCID: PMC4454513. Walter LD, Orton JL, Ntekas I, Fong EHH, Maymi VI, Rudd BD, et al. Transcriptomic analysis of skeletal muscle regeneration across mouse lifespan identifies altered stem cell states. Nat Aging. 2024;4(12):1862–81. doi: 10.1038/s43587-024-00756-3 McKellar DW, Walter LD, Song LT, Mantri M, Wang MFZ, De Vlaminck I, et al. Large-scale integration of single-cell transcriptomic data captures transitional progenitor states in mouse skeletal muscle regeneration. Commun Biol. 2021;4(1):1280. doi: 10.1038/s42003-021-02810-x PubMed PMID: 34773081; PubMed Central PMCID: PMC8589952. Hong JH, Lee GT, Lee JH, Kwon SJ, Park SH, Kim SJ, et al. Effect of bone morphogenetic protein-6 on macrophages. Immunology. 2009;128(1 Pt 2):e442–50. doi: 10.1111/j.1365-2567.2008.02998.x PubMed PMID: 19191909; PubMed Central PMCID: PMC2753950. Xiao X, Xu Y, Moschetta GA, Yu Y, Fisher AL, Alfaro-Magallanes VM, et al. BMP5 contributes to hepcidin regulation and systemic iron homeostasis in mice. Blood. 2023;142(15):1312–22. doi: 10.1182/blood.2022019195 PubMed PMID: 37478395; PubMed Central PMCID: PMC10613724. Bézie S, Freuchet A, Sérazin C, Salama A, Vimond N, Anegon I, et al. IL-34 Actions on FOXP3 + Tregs and CD14 + Monocytes Control Human Graft Rejection. Front Immunol. 2020;11:1496. doi: 10.3389/fimmu.2020.01496 Babaeijandaghi F, Cheng R, Kajabadi N, Soliman H, Chang CK, Smandych J, et al. Metabolic reprogramming of skeletal muscle by resident macrophages points to CSF1R inhibitors as muscular dystrophy therapeutics. Sci Transl Med. 2022;14(651):eabg7504. doi: 10.1126/scitranslmed.abg 7504 PubMed PMID: 35767650. Mulder K, Patel AA, Kong WT, Piot C, Halitzki E, Dunsmore G, et al. Cross-tissue single-cell landscape of human monocytes and macrophages in health and disease. Immunity. 2021;54(8):1883–1900.e5. doi: 10.1016/j.immuni.2021.07.007 Stec MJ, Su Q, Adler C, Zhang L, Golann DR, Khan NP, et al. A cellular and molecular spatial atlas of dystrophic muscle. Proc Natl Acad Sci U S A. 2023;120(29):e2221249120. doi: 10.1073/pnas.2221249120 PubMed PMID: 37410813; PubMed Central PMCID: PMC10629561. Baek JH, Many GM, Evesson FJ, Kelley VR. Dysferlinopathy Promotes an Intramuscle Expansion of Macrophages with a Cyto-Destructive Phenotype. The American Journal of Pathology. 2017;187(6):1245–57. doi: 10.1016/j.ajpath.2017.02.011 Chemello F, Wang Z, Li H, McAnally JR, Liu N, Bassel-Duby R, et al. Degenerative and regenerative pathways underlying Duchenne muscular dystrophy revealed by single-nucleus RNA sequencing. Proc Natl Acad Sci U S A. 2020;117(47):29691–701. doi: 10.1073/pnas.2018391117 PubMed PMID: 33148801; PubMed Central PMCID: PMC7703557. Scripture-Adams DD, Chesmore KN, Barthélémy F, Wang RT, Nieves-Rodriguez S, Wang DW, et al. Single nuclei transcriptomics of muscle reveals intra-muscular cell dynamics linked to dystrophin loss and rescue. Commun Biol. 2022;5:989. doi: 10.1038/s42003-022-03938-0 PubMed PMID: 36123393; PubMed Central PMCID: PMC9485160. Geissinger HD, Rao PV, McDonald-Taylor CK. “mdx” mouse myopathy: histopathological, morphometric and histochemical observations on young mice. J Comp Pathol. 1990;102(3):249–63. doi: 10.1016/s0021-9975(08)80015-1 PubMed PMID: 2365843. Lever JM, Hull TD, Boddu R, Pepin ME, Black LM, Adedoyin OO, et al. Resident macrophages reprogram toward a developmental state after acute kidney injury. JCI Insight. 2019;4(2):e125503. doi: 10.1172/jci.insight.125503 PubMed PMID: 30674729; PubMed Central PMCID: PMC6413788. Oh H, Kwon O, Kong MJ, Park KM, Baek JH. Macrophages promote Fibrinogenesis during kidney injury. Front Med (Lausanne). 2023;10:1206362. doi: 10.3389/fmed.2023.1206362 PubMed PMID: 37425313; PubMed Central PMCID: PMC10325639. Babaeijandaghi F, Kajabadi N, Long R, Tung LW, Cheung CW, Ritso M, et al. DPPIV+ fibro-adipogenic progenitors form the niche of adult skeletal muscle self-renewing resident macrophages. Nat Commun. 2023;14(1):8273. doi: 10.1038/s41467-023-43579-3 PubMed PMID: 38092736; PubMed Central PMCID: PMC10719395. Saclier M, Cuvellier S, Magnan M, Mounier R, Chazaud B. Monocyte/macrophage interactions with myogenic precursor cells during skeletal muscle regeneration. FEBS J. 2013;280(17):4118–30. doi: 10.1111/febs.12166 PubMed PMID: 23384231. Nagaraju K, Rawat R, Veszelovszky E, Thapliyal R, Kesari A, Sparks S, et al. Dysferlin deficiency enhances monocyte phagocytosis: a model for the inflammatory onset of limb-girdle muscular dystrophy 2B. Am J Pathol. 2008;172(3):774–85. doi: 10.2353/ajpath.2008.070327 PubMed PMID: 18276788; PubMed Central PMCID: PMC2258254. de Morrée A, Flix B, Bagaric I, Wang J, van den Boogaard M, Grand Moursel L, et al. Dysferlin Regulates Cell Adhesion in Human Monocytes. J Biol Chem. 2013;288(20):14147–57. doi: 10.1074/jbc.M112.448589 PubMed PMID: 23558685; PubMed Central PMCID: PMC3656271. Flix B, Suárez-Calvet X, Díaz-Manera J, Santos-Nogueira E, Mancuso R, Barquinero J, et al. Bone Marrow Transplantation in Dysferlin-Deficient Mice Results in a Mild Functional Improvement. Stem Cells Dev. 2013;22(21):2885–94. doi: 10.1089/scd.2013.0049 PubMed PMID: 23777246; PubMed Central PMCID: PMC3804083. Netea MG, Joosten LAB, Latz E, Mills KHG, Natoli G, Stunnenberg HG, et al. Trained immunity: A program of innate immune memory in health and disease. Science. 2016;352(6284):aaf1098. doi: 10.1126/science.aaf1098 PubMed PMID: 27102489; PubMed Central PMCID: PMC5087274. Coulis G, Jaime D, Guerrero-Juarez C, Kastenschmidt JM, Farahat PK, Nguyen Q, et al. Single-cell and spatial transcriptomics identify a macrophage population associated with skeletal muscle fibrosis. Sci Adv. 2023;9(27):eadd9984. doi: 10.1126/sciadv.add9984 PubMed PMID: 37418531; PubMed Central PMCID: PMC10328414. Kang JB, Nathan A, Weinand K, Zhang F, Millard N, Rumker L, et al. Efficient and precise single-cell reference atlas mapping with Symphony. Nat Commun. 2021;12(1):5890. doi: 10.1038/s41467-021-25957-x PubMed PMID: 34620862; PubMed Central PMCID: PMC8497570. Orecchioni M, Ghosheh Y, Pramod AB, Ley K. Macrophage Polarization: Different Gene Signatures in M1(LPS+) vs. Classically and M2(LPS-) vs. Alternatively Activated Macrophages. Front Immunol. 2019;10:1084. doi: 10.3389/fimmu.2019.01084 PubMed PMID: 31178859; PubMed Central PMCID: PMC6543837. Rusinova I, Forster S, Yu S, Kannan A, Masse M, Cumming H, et al. Interferome v2.0: an updated database of annotated interferon-regulated genes. Nucleic Acids Res. 2013;41(Database issue):D1040-1046. doi: 10.1093/nar/gks1215 PubMed PMID: 23203888; PubMed Central PMCID: PMC3531205. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9129034","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":613988336,"identity":"d568278a-51dc-488e-ad11-3dd04fd7ba2a","order_by":0,"name":"Jea-Hyun Baek","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzElEQVRIiWNgGAWjYBACxhlA3FBxAMhMgAg0EKflDClaGCSAqhrbSNHCPLv54cOZ8+7Im7MnH3v4hcFGdsMBQg6bc8zYcOO2Z4Y7e56lG8swpBkT1jIjwUzy4bbDjBtu5JhJSzAcTiRCS/r3nw/nHLaHavlPjJYcM8aNDUDDgVokPzAcIELLnDPFkjOOHU7ecOZZmjSDQbLxTEJaDGe3b/zYU3PYdsPx5GOSPyrsZPsIamlA4jDzGBBQDgLyKK78QYSOUTAKRsEoGHkAAENcT5xJ7pbPAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0003-4439-5545","institution":"Handong Global University","correspondingAuthor":true,"prefix":"","firstName":"Jea-Hyun","middleName":"","lastName":"Baek","suffix":""},{"id":613988337,"identity":"eb9b46d5-faba-4089-a5de-8a9095da2a43","order_by":1,"name":"Woo Seok Byun","email":"","orcid":"https://orcid.org/0000-0002-4837-6563","institution":"Handong Global University","correspondingAuthor":false,"prefix":"","firstName":"Woo","middleName":"Seok","lastName":"Byun","suffix":""},{"id":613988338,"identity":"d3347202-1511-4744-b3f3-d73e4c1ae84a","order_by":2,"name":"Eun-Chong Bang","email":"","orcid":"","institution":"Handong Global University","correspondingAuthor":false,"prefix":"","firstName":"Eun-Chong","middleName":"","lastName":"Bang","suffix":""},{"id":613988339,"identity":"e164dd48-b699-4118-b7e5-05be07c63df5","order_by":3,"name":"Yoon-Gyu Shim","email":"","orcid":"","institution":"Handong Global University","correspondingAuthor":false,"prefix":"","firstName":"Yoon-Gyu","middleName":"","lastName":"Shim","suffix":""}],"badges":[],"createdAt":"2026-03-15 13:45:14","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9129034/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9129034/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105904827,"identity":"9ea76078-3ba3-478e-8ea2-75efa17e5d0b","added_by":"auto","created_at":"2026-04-01 10:10:38","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2920378,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003esnRNA-seq defines the immune landscape of regenerating skeletal muscle regeneration. \u003c/strong\u003e(A) Experimental design for snRNA-seq of TA muscles from C57BL/6 mice following intramuscular CTx injection. (B) Representative H\u0026amp;E–stained TA sections at 0, 3, 5, and 10 dpi. Scale bar: 100 µm. (C) tSNE embedding of all nuclei revealing 19 transcriptionally distinct cell populations. (D) Expression of canonical markers used to annotate major lineages, including QuSCs (\u003cem\u003ePax7\u003c/em\u003e), myofibres (\u003cem\u003eDmd\u003c/em\u003e), immune cells (\u003cem\u003ePtprc\u003c/em\u003e), FAPs (\u003cem\u003ePdgfra\u003c/em\u003e), SMCs (\u003cem\u003eMyh11\u003c/em\u003e), and endothelial cells (\u003cem\u003ePtprb\u003c/em\u003e). (E) Temporal changes in cellular composition across regeneration.\u003cstrong\u003e \u003c/strong\u003e(F) Sub-clustering of \u003cem\u003ePtprc\u003c/em\u003e⁺ nuclei resolving distinct immune populations.\u003cstrong\u003e \u003c/strong\u003e(G)\u003cstrong\u003e \u003c/strong\u003eAnnotation of immune clusters referenced using ImmGen database. (H)\u003cstrong\u003e \u003c/strong\u003eFeature plots showing the 2-dimentional density of macrophage markers (\u003cem\u003eCsf1r\u003c/em\u003e, \u003cem\u003eAdgre1\u003c/em\u003e, \u003cem\u003eMertk\u003c/em\u003e) and proliferative markers (\u003cem\u003eMki67\u003c/em\u003e, \u003cem\u003eTop2a\u003c/em\u003e) overlaid on the tSNE embedding. (I) Heatmap showing the expression profile of non-macrophage immune subtypes, including inflammatory cDC2 (Inflam cDC2), T cells (T), cDC1, osteoclasts (Ocl), Mreg, and pDCs.\u003cstrong\u003e \u003c/strong\u003e(J) Relative abundance of immune cell types across all time points, demonstrating macrophages as the dominant immune population throughout muscle regeneration.\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-9129034/v1/75f5a2e245687dd95a01d65f.png"},{"id":105904411,"identity":"7dbbcfda-0ca1-4955-8254-4b66c505087c","added_by":"auto","created_at":"2026-04-01 10:08:15","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2273249,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTrajectory analysis identifies distinct macrophage transcriptional states and a transient proliferative population.\u003c/strong\u003e (A) PHATE embedding of macrophage nuclei (n = 12,366) revealing three major macrophage branches (Branches 1–3), and a distinct proliferative macrophage population (ProlifM). (B)\u003cstrong\u003e \u003c/strong\u003ePHATE embedding colored by time point showing dynamic redistribution of macrophage states during regeneration. (C) Relative proportions of macrophage branches and ProlifM across time, shown as percentages of total nuclei (left) and within the macrophage compartment (right). (D) Expression of cell-cycle markers (\u003cem\u003eMki67\u003c/em\u003e, \u003cem\u003eTop2a\u003c/em\u003e, \u003cem\u003eDiaph3\u003c/em\u003e), S score, G2/M score as well as cell-cycle phase (G0/G1, S, G2/M) inferred from S and G2/M scores. (E) Palantir analysis showing pseudo-time ordering, and fate probabilities with ProlifM defined as the root state. (F)\u003cstrong\u003e \u003c/strong\u003eDEGs across branches and ProlifM, highlighting branch-specific transcriptional programs.\u003cstrong\u003e \u003c/strong\u003e(G) Gene set enrichment analysis of branch-specific differentially expressed genes.\u003cstrong\u003e \u003c/strong\u003e(H) Proportion of ISGs among branch-specific DEGs, defined using the Interferome database[48]. (I) Heatmap of branch-specific transcription factor regulon activity inferred by SCENIC, (J) Summary of inferred macrophage state transitions and regulatory programs underlying inflammatory, reparative, and proliferative macrophage phenotypes during muscle regeneration.\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-9129034/v1/35c351f818ba616ad46ddc0c.png"},{"id":105904571,"identity":"5c43fa21-864b-4468-b1f9-554a00201bfd","added_by":"auto","created_at":"2026-04-01 10:09:37","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1969963,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIntegration of snRNA- and scRNA-seq datasets validates macrophage trajectory and branch identities.\u003c/strong\u003e (A) Comparison of sequencing characteristics between snRNA-seq (nuclei) and scRNA-seq (whole cells), including numbers of detected UMIs and genes, and proportions of transcripts belonging to stress-response, protein-coding, lncRNA, cell membrane-associated, and TFs gene sets. (B) tSNE embedding of myeloid populations, annotated into cycling, patrolling, \u003cem\u003eCcr2\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e, \u003cem\u003eCx3cr1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e, \u003cem\u003eCxcl10\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e, and \u003cem\u003eMrc1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e subsets as defined by Walter \u003cem\u003eet al\u003c/em\u003e.[25] (C)\u003cstrong\u003e \u003c/strong\u003eFeature plots showing expression of subset-defining markers (\u003cem\u003eCcr2\u003c/em\u003e, \u003cem\u003eCx3cr1\u003c/em\u003e, \u003cem\u003eCxcl10\u003c/em\u003e, and \u003cem\u003eMrc1\u003c/em\u003e), along with \u003cem\u003eGpnmb\u003c/em\u003e and proliferation marker \u003cem\u003eMki67\u003c/em\u003e. (D) Projection of scRNA-seq macrophages onto the snRNA-seq-derived PHATE trajectory. (E) Mapping of scRNA-seq-defined macrophage identities onto the snRNA-seq trajectory. (F) Comparison of cell membrane protein-encoding transcripts detected by scRNA- and snRNA-seq. (G) Comparison of \u003cem\u003eLy6c2\u003c/em\u003e, \u003cem\u003eIl7r\u003c/em\u003e, and \u003cem\u003eCd163\u003c/em\u003e expression between scRNA- and snRNA-seq. (H) Temporal dynamics of scRNA-seq macrophages projected onto the snRNA-seq trajectory, shown as proportions relative to total cells and within the macrophage compartment.\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-9129034/v1/9f048c6aec55c0f98e1cd0dd.png"},{"id":105797656,"identity":"28dbbf07-debf-4f96-a147-385a5ca538d7","added_by":"auto","created_at":"2026-03-31 09:06:03","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2183865,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eIn situ\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e validation of branch-associated macrophage markers and proliferation states. \u003c/strong\u003e(A) Representative multiplex immunofluorescence images of TA sections stained for Ly6C, IL-7R, CD163, and DAPI. Scale bar: 100 µm. (B)\u003cstrong\u003e \u003c/strong\u003eRepresentative immunofluorescence images of Ki67 co-staining with Ly6C, IL-7R, or CD163. Arrow: Ki67\u003csup\u003e+\u003c/sup\u003eIL-7R\u003csup\u003e+ \u003c/sup\u003ecells (proliferating). Arrowhead: Ki67\u003csup\u003e+\u003c/sup\u003eIL-7R\u003csup\u003e+\u003c/sup\u003e cells (non-proliferating). Scale bar: 100 µm. (C) Quantification of Ki67\u003csup\u003e+\u003c/sup\u003e and Ki67\u003csup\u003e-\u003c/sup\u003e cells within Ly6C\u003csup\u003e+\u003c/sup\u003e, IL-7R\u003csup\u003e+\u003c/sup\u003e, or CD163\u003csup\u003e+\u003c/sup\u003e macrophage populations. Data represents mean ± s.d. Statistics by Mann-Whiteney U, separately for total macrophage counts (green) and proliferating macrophages (Ki67⁺; blue). \u003cem\u003en\u003c/em\u003e = 10 (for all combinations). *\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.05, **\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.01, ***\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.001, ****\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.0001.\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-9129034/v1/e7fbd22eb02cb73f1c4a1864.png"},{"id":105797658,"identity":"5b085797-166e-4a13-b3cc-f0a9b57bea85","added_by":"auto","created_at":"2026-03-31 09:06:03","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1715062,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003esnRNA-seq-based analysis of cell-cell communication involving macrophages during muscle regeneration\u003c/strong\u003e. (A) Cell-cell communication networks inferred by CellChat from snRNA-seq data. (B) Dot plot summarizing aggregated ligand signaling received by macrophage branches and proliferative macrophages. (C) Expression of macrophage growth factor ligands (\u003cem\u003eIl34\u003c/em\u003e, \u003cem\u003eCsf1\u003c/em\u003e, and \u003cem\u003eCsf2\u003c/em\u003e) across cell types and time points.\u003c/p\u003e","description":"","filename":"Fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-9129034/v1/a0f4aa3864a0c3de92764dce.png"},{"id":105797660,"identity":"4010305c-4889-4de5-b65b-0f1a683df5d6","added_by":"auto","created_at":"2026-03-31 09:06:03","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":7573133,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMonocyte-derived macrophages undergo dynamic transcriptional transitions during muscle regeneration.\u003c/strong\u003e (A) Schematic of the parabiosis-based fate-tracing strategy (GSE198055). (B) Projection of GFP\u003csup\u003e+\u003c/sup\u003e parabiont-derived monocytes/macrophages onto the snRNA-seq-derived macrophage trajectory. Spiked-in GFP\u003csup\u003e-\u003c/sup\u003eTIMD-4\u003csup\u003e+\u003c/sup\u003e cells were detected in Branch 3. (C) Feature plots showing expression of \u003cem\u003eGfp\u003c/em\u003e, \u003cem\u003eLy6c\u003c/em\u003e, \u003cem\u003eIl7r\u003c/em\u003e, \u003cem\u003eCd163\u003c/em\u003e, and \u003cem\u003eMki67\u003c/em\u003e in sorted exudate (GFP\u003csup\u003e+\u003c/sup\u003e) cells. (D) DEG and GO analysis of \u003cem\u003eGfp\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e cells at 2 versus 4 dpi. (E)\u003cstrong\u003e \u003c/strong\u003eHeatmap showing temporal changes in monocyte- and macrophage-associated gene signatures in \u003cem\u003eGfp\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e cells from 6 hours to 8 dpi. (F) Interferome-based classification of DEGs in \u003cem\u003eGfp\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e cells, showing the proportion and type of ISGs induced at 2 and 4 dpi. (G)\u003cstrong\u003e \u003c/strong\u003eSingle-cell feature plots showing expression of type I and II interferon ligands across cell types within injured muscle.\u003c/p\u003e","description":"","filename":"Fig6.png","url":"https://assets-eu.researchsquare.com/files/rs-9129034/v1/c4be55430049b10028493bce.png"},{"id":105797659,"identity":"33665115-3480-4c46-84ec-b99ea7e8c647","added_by":"auto","created_at":"2026-03-31 09:06:03","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":3101480,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMacrophage stress-response programs dominate at the onset of muscle dystrophy. \u003c/strong\u003e(A) Representative H\u0026amp;E-stained sections of TA and GM muscles from age-matched C57BL/6 (B6) and BLA/J mice at disease onset. (B) IHC for CD68 and F4/80 in BLA/J TA and GM. (C) snRNA-seq-based branch-specific differential gene expression and over-representation analysis of macrophages from BLA/J GM projected onto the regenerative muscle-derived macrophage trajectory. (D, E) Overview of cellular composition in dystrophic muscle models (BLA/J, ΔEx51, and \u003cem\u003emdx\u003c/em\u003e), visualized by (D) tSNE embedding and (E) bar plots of cell type proportions. (F) Projection of macrophages from B6, BLA/J, ΔEx51 and \u003cem\u003emdx\u003c/em\u003e muscles onto the regenerative macrophage trajectory. (G)\u003cstrong\u003e \u003c/strong\u003eQuantification of macrophage branch composition across dystrophic models, shown as proportions relative to total nuclei (left) and within the macrophage compartment (right). (H)\u003cstrong\u003e \u003c/strong\u003ePseudobulk-derived\u003cstrong\u003e \u003c/strong\u003ePCA of macrophage transcriptional profiles from B6, BLA/J TA and GM, ΔEx51 and \u003cem\u003emdx\u003c/em\u003eTA (last two: DMD models). (I) Branch-specific differential gene expression and pathway enrichment analysis of macrophages from \u003cem\u003emdx\u003c/em\u003e TA.\u003c/p\u003e","description":"","filename":"Fig7.png","url":"https://assets-eu.researchsquare.com/files/rs-9129034/v1/352bb0337c2e4ccbc7089ec4.png"},{"id":105797661,"identity":"1f63d28a-a8b9-41f7-bb73-9eeb0a080b98","added_by":"auto","created_at":"2026-03-31 09:06:03","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":1657553,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eWhole-macrophage transcriptomic analysis reveals activation- and migration-associated programs in advanced muscular dystrophy. \u003c/strong\u003e(A) Venn diagram showing overlap of DEGs identified by snRNA-seq in macrophages from BLA/J TA, BLA/J GM, and \u003cem\u003emdx\u003c/em\u003e TA muscles. Numbers indicate genes commonly or uniquely upregulated across conditions. (B) Heatmap summarizing functional categories and pathway enrichment of genes uniquely upregulated in \u003cem\u003emdx\u003c/em\u003e TA macrophages, including antigen processing and presentation, ECM organization, inflammatory responses, and oxidative stress–associated programs. (C) Feature plots showing expression of macrophage growth factors and chemokines (\u003cem\u003eCsf1\u003c/em\u003e, \u003cem\u003eIl34\u003c/em\u003e, \u003cem\u003eCcl5\u003c/em\u003e, \u003cem\u003eCcl6\u003c/em\u003e, \u003cem\u003eCcl20\u003c/em\u003e, and \u003cem\u003eCxcl12\u003c/em\u003e) across macrophages (\u003cem\u003ePtprc\u003c/em\u003e\u003csup\u003e\u003cem\u003e+\u003c/em\u003e\u003c/sup\u003e, \u003cem\u003eCsf1r\u003c/em\u003e\u003csup\u003e\u003cem\u003e+\u003c/em\u003e\u003c/sup\u003e) from BLA/J TA, BLA/J GM, and \u003cem\u003emdx\u003c/em\u003e TA. (D) Violin plots showing expression levels of macrophage-relevant ligands and receptors across cell types, highlighting stromal- and macrophage-derived contributions to macrophage-centered signaling networks.\u003c/p\u003e","description":"","filename":"Fig8.png","url":"https://assets-eu.researchsquare.com/files/rs-9129034/v1/b2de092decc10ded59c4579a.png"},{"id":106401649,"identity":"ab0099da-b87d-42ed-9330-4800e9030a6c","added_by":"auto","created_at":"2026-04-08 09:08:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":25022032,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9129034/v1/c7a945af-ee98-428e-b030-b18fb5e12c90.pdf"},{"id":105797652,"identity":"3edf953f-d82b-4b10-8e5b-0925549e67f1","added_by":"auto","created_at":"2026-03-31 09:06:03","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":7202828,"visible":true,"origin":"","legend":"Supplemental Figures","description":"","filename":"SupplementalFiguresCMI.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9129034/v1/a1bdb74dd0e0039eabe5ffe0.pdf"},{"id":105904549,"identity":"b24c25c6-18fd-4ed9-b219-e8c71c99aafa","added_by":"auto","created_at":"2026-04-01 10:09:29","extension":"zip","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":142938567,"visible":true,"origin":"","legend":"Unprocessed original figures","description":"","filename":"Unprocessedoriginalfigures.zip","url":"https://assets-eu.researchsquare.com/files/rs-9129034/v1/6a4111cc20aa820362180382.zip"},{"id":105797654,"identity":"46c92354-58b7-4080-afdd-d8bb97ed3a99","added_by":"auto","created_at":"2026-03-31 09:06:03","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":19359358,"visible":true,"origin":"","legend":"Unprocessed original images","description":"","filename":"Unprocessedoriginalimages.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9129034/v1/a707c45f6b33c0f23781c465.pdf"},{"id":105797657,"identity":"a139c63a-46c3-4ceb-b4a1-4d4e2d2a3971","added_by":"auto","created_at":"2026-03-31 09:06:03","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":14275,"visible":true,"origin":"","legend":"","description":"","filename":"ListofSupplementalFigures.docx","url":"https://assets-eu.researchsquare.com/files/rs-9129034/v1/100462f10f404804c63ee513.docx"}],"financialInterests":"(Not answered)","formattedTitle":"A high-resolution single-nucleus RNA sequencing reveals conserved macrophage activation trajectories across muscle regeneration and dystrophy.","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSkeletal muscle possesses a remarkable capacity for regeneration, a process tightly regulated by intricate crosstalk between resident stem cells and the immune system [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Following injury, a coordinated infiltration of immune cells is thought to drive debris clearance and establishes a pro-regenerative environment [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Among these, macrophages are the most abundant immune population in both regenerating and diseased muscles, far outnumbering other leukocyte subsets [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The prevailing paradigm suggests that these cells display pronounced plasticity, initially adopting inflammatory states before transitioning toward reparative phenotypes that promote tissue repair [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. However, whether this linear transition sufficiently captures the complexity of macrophage behavior\u0026mdash;especially within the chronic, non-resolving environment of muscular dystrophy\u0026mdash;remains a subject of debate [\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe survival, differentiation, and proliferation of macrophages are strictly dependent on colony-stimulating factor 1 receptor (CSF-1R) signaling [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. While CSF-1R is activated by two distinct ligands\u0026mdash;colony-stimulating factor 1 (CSF-1) and interleukin-34 (IL-34) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u0026mdash;their specific roles and cellular sources within skeletal muscle also remain poorly defined. Although these ligands share a common receptor, they often exhibit a complex balance of functional redundancy and tissue-specific signaling, dictated by their differing binding affinities and distinct expression patterns across various physiological contexts [\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGiven this complex regulatory landscape, a fundamental question arises: does macrophage-associated pathology in muscle result from the emergence of \u003cem\u003ede novo\u003c/em\u003e, disease-specific transcriptional programs or merely the pathological expansion of conserved activation states [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]? While single-cell RNA sequencing (scRNA-seq) have provided valuable insights into the cellular landscape of skeletal muscle, these datasets failed to crisply delineate macrophage activation states due to inherent technical limitations [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Specifically, the enzymatic dissociation required for scRNA-seq induces cellular stress that can distort transcriptional profiles of macrophages, which are highly plastic [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Moreover, multinucleated myocytes (e.g., myotubes, myofibers) are typically lost during scRNA-seq processing, precluding direct analysis of myocyte\u0026ndash;immune interactions [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. To overcome these challenges, we employed single-nucleus RNA sequencing (snRNA-seq), which enables unbiased profiling of transcripts from all cell types in muscle while minimizing dissociation-induced artifacts. By integrating this high-resolution approach with trajectory inference, we were able to define macrophage activation states and resolve their temporal dynamics with unprecedented clarity.\u003c/p\u003e \u003cp\u003eOur analysis reveals a high-resolution map of macrophage functional diversity, delineating three distinct activation trajectories characterized by the expression of Ly6C, IL-7R, and CD163. Beyond these transcriptional states, we uncover the structural basis of macrophage maintenance, identifying mural cells as the primary niche providing the macrophage growth factor IL-34 to sustain homeostatic, tissue-resident populations.\u003c/p\u003e \u003cp\u003eWe further investigated the origin and fate of these cells through parabiosis-based fate mapping. These experiments demonstrate that recruited monocytes undergo a synchronous differentiation into inflammatory macrophages that do not self-renew and undergo a programmed contraction \u003cem\u003evia\u003c/em\u003e pyroptosis/hyperactivation.\u003c/p\u003e \u003cp\u003eCrucially, our comparative analysis across models of muscular dystrophy reveals that macrophages do not initiate \u003cem\u003ede novo\u003c/em\u003e pathogenic programs; instead, they act as adaptive bystanders, retaining highly conserved regenerative programs while responding to the chronic tissue stress of a dystrophic environment. Together, these findings establish a new framework for understanding immune regulation in muscle degeneration and clarify the role of macrophages as responders rather than active drivers of pathology during the onset of the disease.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cb\u003eSingle-nucleus RNA sequencing provides a comprehensive cellular atlas of regenerating muscle.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo generate a high-resolution cellular atlas of skeletal muscle macrophages, we performed snRNA-seq on nuclei isolated from uninjured and injured C57BL/6J tibialis anterior (TA) at 3, 4, 5, and 10 days after intramuscular cardiotoxin (CTx) injection, capturing the period of inflammatory resolution and muscle regeneration [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Histological analysis (H\u0026amp;E) at 3 days post-injury (dpi) revealed extensive tissue damage, with widespread necrotic myofibers and dense infiltration of mononucleated cells. This infiltration then markedly declined between 3 and 5 dpi, as previously reported [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Immunohistochemistry (IHC) for macrophage markers (CD68, F4/80) confirmed that the infiltrating population was dominated by macrophages, which persisted throughout regeneration (Supp-1A, B).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eUnsupervised clustering of the aggregated dataset identified 19 distinct cell populations representing the major myogenic, immune, and stromal lineages (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). We validated these cluster identities using canonical marker genes: \u003cem\u003ePax7\u003c/em\u003e for quiescent muscle stem cells (QuSCs), \u003cem\u003eDmd\u003c/em\u003e for myofibers, \u003cem\u003ePtprc\u003c/em\u003e (CD45) for immune cells, \u003cem\u003ePdgfra\u003c/em\u003e for fibroblasts, \u003cem\u003eMyh11\u003c/em\u003e for smooth muscle cells (SMCs), and \u003cem\u003ePtprb\u003c/em\u003e for endothelial cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). Tracking cellular composition over time revealed dynamic immune infiltration peaking at 3 dpi, followed by expansion of myogenic and stromal populations during the regenerative phase (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003eGiven the complexity of the immune response, we performed sub-clustering of the immune compartment (\u003cem\u003ePtprc\u003c/em\u003e), which resolved into macrophages (\u003cem\u003eCsf1r\u003c/em\u003e, \u003cem\u003eAdgre1\u003c/em\u003e, \u003cem\u003eMertk\u003c/em\u003e), T cells (\u003cem\u003eCd247\u003c/em\u003e, \u003cem\u003eSkap1\u003c/em\u003e), and several dendritic cell (DC) subsets (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF-I). These included inflammatory conventional type 2 DCs (inflam cDC2; \u003cem\u003eCcr2\u003c/em\u003e, \u003cem\u003eCd209a\u003c/em\u003e), conventional type 1 DCs (cDC1; \u003cem\u003eClec9a\u003c/em\u003e, \u003cem\u003eXcr1\u003c/em\u003e), plasmacytoid DCs (pDC; \u003cem\u003eSiglech\u003c/em\u003e, \u003cem\u003eTcf4\u003c/em\u003e), and mature regulatory DCs (Mreg DC; \u003cem\u003eMreg\u003c/em\u003e, \u003cem\u003eRelb\u003c/em\u003e, \u003cem\u003eLy75\u003c/em\u003e). In addition, osteoclasts (Ocl; \u003cem\u003eCtsk\u003c/em\u003e, \u003cem\u003eAcp5\u003c/em\u003e) and proliferative immune populations (\u003cem\u003eMki67\u003c/em\u003e, \u003cem\u003eTop2a\u003c/em\u003e) were identified (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eH, I). Across all time points, macrophages were the dominant immune population (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eJ).\u003c/p\u003e \u003cp\u003e \u003cb\u003eTrajectory analysis identifies distinct macrophage transcriptional states and a transient proliferative population.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo further resolve macrophage heterogeneity, we performed trajectory analysis using potential of heat-diffusion for affinity-based transition embedding (PHATE) on macrophage nuclei (n\u0026thinsp;=\u0026thinsp;12,366) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eJ). This analysis revealed three macrophage branches, designated Branches 1, 2, and 3, each subdivided into three subsegments (e.g., 1\u0026ndash;1, 1\u0026ndash;2, 1\u0026ndash;3). Additionally, we identified a distinct population of proliferative macrophages (ProlifM) that formed a separate cluster adjacent to the main macrophage trajectory and aligned with the early region of Branch 2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn uninjured muscle, macrophages mapped exclusively to Branch 3, indicating that this branch represents homeostatic tissue-resident macrophages. Following injury, macrophage proliferation was most pronounced at 3 dpi, as reported previously [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. At this peak of infiltration, the macrophage compartment was dominated by Branch 2, followed by Branch 1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB, C). The Branch 1 population peaked at 4 dpi, whereas by 5 and 10 dpi the composition progressively shifted toward Branch 3, accompanied by a gradual decline in Branch 2 representation (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003eExpression of cell-cycle markers (\u003cem\u003eMki67\u003c/em\u003e, \u003cem\u003eTop2a\u003c/em\u003e, \u003cem\u003eDiaph3\u003c/em\u003e) and cell-cycle scoring confirmed that ProlifM cells were in the G2/M phase (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). These cells were transcriptionally associated with Branches 2 and 3, showing the highest transition probability toward Branch 2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD, E).\u003c/p\u003e \u003cp\u003eGene expression and functional analysis revealed marked divergence among branches. Branch 1 showed elevated expression of inflammatory and interferon-stimulated genes (ISGs), identifying it as an inflammatory macrophage state (M1-like). Conversely, Branch 2 preferentially upregulated genes associated with tissue repair and resolution (M2-like) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF-I, Suppl-1C). Single-cell regulatory network inference and clustering (SCENIC) analysis further distinguished these states by regulon activity, with Branch 1 enriched for inflammatory regulators such as \u003cem\u003eIrf7\u003c/em\u003e and \u003cem\u003eStat1\u003c/em\u003e, and Branch 2 characterized by activity of \u003cem\u003eMafg\u003c/em\u003e, \u003cem\u003eBhlhe40\u003c/em\u003e, and \u003cem\u003ePparg\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eJ and Suppl-1D).\u003c/p\u003e \u003cp\u003e \u003cb\u003eSingle-nucleus trajectory better resolves the identity of all macrophage subpopulations in single-cell data.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo assess the compatibility of our snRNA-seq trajectory with single-cell datasets, we integrated our data with a recently published scRNA-seq dataset of regenerating muscle [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. In whole-muscle clustering, immune populations were first separated from myogenic and stromal lineages (Suppl-2A). Although scRNA-seq yielded higher counts of unique molecular identifiers (UMIs) and genes, as well as higher levels of protein-coding and cell membrane-associated transcripts, snRNA-seq showed superior enrichment for transcription factors (TFs) and long non-coding RNAs (lncRNAs), as reported previously [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Importantly, snRNA-seq exhibited markedly lower expression of core stress-response genes than scRNA-seq, indicating that nuclear profiling reduces dissociation-induced transcriptional artifacts [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn a previous scRNA-seq study, myeloid cells were classified into multiple groups: cycling, patrolling, \u003cem\u003eCcr2\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e, \u003cem\u003eCx3cr1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e, \u003cem\u003eCxcl10\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e, and \u003cem\u003eMrc1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e populations, along with DC subsets (GSE143437; GSE159500; GSE162172; GSE232106 [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). However, examination of marker expression revealed substantial overlap among these groups, and boundaries between them were not crisply delineated (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). Projecting the scRNA-seq dataset onto our snRNA-seq trajectory revealed strong concordance between modalities (ρ\u0026thinsp;=\u0026thinsp;0.79\u0026ndash;0.88) across the three main branches and the proliferative population (Suppl-2B).\u003c/p\u003e \u003cp\u003eProjected cells aligned with macrophage identities within the trajectory embeddings: \u003cem\u003eCxcl10\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e inflammatory monocytes/macrophages mapped predominantly to Branch 1, patrolling macrophages to Branch 2, and \u003cem\u003eMrc1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e macrophages to Branch 3. \u003cem\u003eCcr2\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e and \u003cem\u003eCx3cr1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e macrophages were broadly distributed across all macrophage subpopulations (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE). Using the whole-cell data to bolster surface marker detection, we identified \u003cem\u003eLy6c2\u003c/em\u003e, \u003cem\u003eIl7r\u003c/em\u003e and \u003cem\u003eCd163\u003c/em\u003e as a robust marker set enabling discrimination of Branches 1, 2, and 3, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, F, G).\u003c/p\u003e \u003cp\u003eWe further examined macrophage dynamics by quantifying branch occupancy over time for scRNA-seq macrophages projected onto the snRNA-seq\u0026ndash;derived trajectory. At early time points (1 and 2 dpi), the projected cells were dominated by Branch 2. Branch 1 cells increased sharply, peaking at 3.5 dpi, then declined, indicating a transient expansion in the trajectory analysis. When normalized to total nuclei, monocytes/macrophages peaked between 2 and 3.5 dpi (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eH). In contrast, analysis of relative composition within the macrophage compartment revealed a gradual recovery of Branch 3 at later time points, consistent with the progressive shift toward this branch observed during regenerative phases in the snRNA-seq trajectory (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eH and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003eTo validate these branch-associated markers at the protein level, we performed multiplex immunofluorescence staining for Ly6C, IL-7R, CD163, and DAPI across the regenerative time course (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). This analysis revealed no-to-low overlap among Ly6C\u003csup\u003e+\u003c/sup\u003e, IL-7R\u003csup\u003e+\u003c/sup\u003e, and CD163\u003csup\u003e+\u003c/sup\u003e macrophages, indicating mutually exclusive macrophage populations \u003cem\u003ein vivo.\u003c/em\u003e We next assessed proliferative activity within these marker-defined populations by co-staining for Ki67 with Ly6C, IL-7R, or CD163 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Quantification showed that a substantial fraction of IL-7R⁺ and CD163⁺ macrophages were Ki67⁺, consistent with active proliferation within these subsets, whereas Ly6C⁺ macrophages showed minimal proliferation (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). Together, these observations align with trajectory analysis indicating that ProlifM represents a transient proliferative macrophage state derived primarily from Branches 2 and 3 rather than the Ly6C⁺ inflammatory branch (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD, Suppl-3C).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eDistinct cytokine signals characterize the intercellular communication of macrophage subpopulations.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo investigate the intercellular signals governing distinct macrophage subpopulations, we leveraged our snRNA-seq dataset, which enables comprehensive analysis of cell\u0026ndash;cell communication within skeletal muscle tissue. Unlike conventional scRNA-seq approaches, which systematically underrepresent or exclude multinucleated cells (e.g., myotubes, myofibers, osteoclasts, and other syncytial cells), snRNA-seq preserves nuclear transcripts from all major tissue compartments. Consequently, the CellChat inference framework using snRNA-seq data recovered receptor-ligand interactions that are typically missed in scRNA-seq database analyses, particularly those where one interactor (ligand or receptor) is expressed in multinucleated cells.\u003c/p\u003e \u003cp\u003eUsing our snRNA-seq data, CellChat analysis revealed that connectivity between fibroblasts and Branch 2 macrophages markedly increased during the inflammatory phase (3 dpi) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). To elucidate the molecular basis of these interactions, we examined significant incoming signals targeted to macrophage populations within the intercellular communication networks. This analysis uncovered a complex signaling landscape involving chemokines (\u003cem\u003eCcl2, Ccl6, Cxcl12\u003c/em\u003e) and growth factors, including \u003cem\u003eIgf1, Igf2\u003c/em\u003e, \u003cem\u003ePdgfa\u003c/em\u003e, as well as prominent reparative cues targeting Branch 2 macrophages, including \u003cem\u003eSpp1\u003c/em\u003e, \u003cem\u003eTgfb1\u003c/em\u003e, \u003cem\u003eLgals9\u003c/em\u003e, and \u003cem\u003eGas6\u003c/em\u003e. In addition, \u003cem\u003eBmp5\u003c/em\u003e and \u003cem\u003eBmp6\u003c/em\u003e emerged as putative regulators of proliferative macrophages. BMP6 inhibits macrophage proliferation and modulates their activation state [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. BMP5 is hypothesized to have similar effects based on pathway homology [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAmong the incoming signals received by macrophages, IL-34 and CSF-1 exhibited divergent expression dynamics. While \u003cem\u003eIl34\u003c/em\u003e was most prominent in resting muscle (0 dpi) and toward the end of resolution (10 dpi), \u003cem\u003eCsf1\u003c/em\u003e expression increased during inflammation (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Notably, the two ligands displayed distinct cellular origins: \u003cem\u003eIl34\u003c/em\u003e was expressed by mesenchymal mural cells (SMCs, pericytes) in the steady state and by myotubes during muscle regeneration, whereas \u003cem\u003eCsf1\u003c/em\u003e was predominantly expressed by fibroblasts (Fib) and fibro-adipogenic progenitors (FAPs). \u003cem\u003eCsf1r\u003c/em\u003e was expressed across all branches, as well as within the ProlifM subpopulation (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003eThis cell-specific ligand expression, together with the interaction patterns, supports a model in which IL-34 maintains the resident Branch 3 population within the perimysial niche. In contrast, fibroblast-derived CSF-1 appears to drive the expansion and maintenance of inflammatory and reparative Branch 1 and 2 macrophages during active tissue repair. Consistent with this, Branch 3 macrophages, which are abundant in the uninjured state (0 dpi) and redominate toward the end of resolution (10 dpi) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003cb\u003eDistinct cell surface marker profiles define non-overlapping macrophage subpopulations.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eImmunofluorescence analysis revealed that CD163\u003csup\u003e+\u003c/sup\u003e macrophages are enriched in the perimysial regions of uninjured TA muscles (0 dpi) and closely associate with α-SMA⁺ mural cells (Suppl-3A). This spatial relationship aligns with our identification of Branch 3 macrophages as a homeostatic, muscle-resident population supported by IL-34-producing mural cells.\u003c/p\u003e \u003cp\u003eTo determine whether Branch 3 macrophages maintain spatial confinement during inflammation, we examined their localization during injury. At 0 dpi, CD163⁺ macrophages remain tightly associated with α-SMA⁺ mural cells within well-defined perimysial regions (Suppl-3A). In contrast, between 3\u0026ndash;5 dpi, CD163\u003csup\u003e+\u003c/sup\u003e macrophages became broadly distributed throughout the tissue, coinciding with the disruption of the perimysial architecture and reduced association with mural cells (Suppl-3B). Consistent with this spatial redistribution, Palantir analysis indicates that Branch 3 macrophages possess a strong potential to transition toward Branch 2, with a lower probability of transitioning toward the inflammatory Branch 1 (Suppl-3C).\u003c/p\u003e \u003cp\u003eFinally, to relate ligand-responsive states to the trajectory-defined populations, we compared the IL-34\u0026ndash; and CSF-1\u0026ndash;derived macrophage signatures with the branch-specific transcriptomic profiles (GSE151194 [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]). IL-34\u0026ndash;stimulated macrophages were transcriptionally similar to Branch 3, whereas CSF-1\u0026ndash;stimulated macrophages closely resembled the inflammatory Branch 1 population (Suppl-3D). These data indicate that niche-specific growth factors not only maintain macrophage pool but also actively drive macrophage functional phenotype.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMonocyte-derived macrophages exhibit dynamic phenotypic shifts during regeneration.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo better resolve macrophage dynamics in the early inflammatory phase, we applied our snRNA-seq-derived trajectory framework to a scRNA-seq dataset from parabiosis-based fate mapping (GSE198055 [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). From flow-sorted GFP\u003csup\u003e+\u003c/sup\u003e parabiont cells, only monocytes/macrophages were included in the downstream analyses (Suppl-4A-C).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eUpon entry into the injured muscle, monocyte-derived cells initially occupied early branch states proximal to the bifurcation point (states 2\u0026ndash;2 and 2\u0026ndash;3), corresponding to the regions occupied by \u003cem\u003eCx3cr1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD, upper panel). These cells then converged toward the Branch 1 trajectory in a coordinated manner (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). As expected, spiked-in GFP\u003csup\u003e\u0026minus;\u003c/sup\u003eTIMD-4\u003csup\u003e+\u003c/sup\u003e cells were detected in Branch 3, reflecting tissue-resident macrophages [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). Notably, monocyte-derived macrophages did not overlap with the ProlifM population; GFP⁺ parabiont-derived cells lacked proliferative signatures from 6 hours to 8 dpi, indicating that recruited monocytes do not undergo local proliferation after tissue entry (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB, C).\u003c/p\u003e \u003cp\u003eAt 6 hours post-injury, \u003cem\u003eGfp\u003c/em\u003e⁺ cells exhibited a circulating inflammatory monocyte signature (e.g., \u003cem\u003eCd14\u003c/em\u003e, \u003cem\u003eCd44\u003c/em\u003e, \u003cem\u003eCxcl2\u003c/em\u003e, \u003cem\u003eCxcl3\u003c/em\u003e, \u003cem\u003eIl1rn\u003c/em\u003e, \u003cem\u003eLy6c2\u003c/em\u003e, \u003cem\u003ePlaur\u003c/em\u003e, \u003cem\u003eS100a6\u003c/em\u003e, \u003cem\u003eS1008a8\u003c/em\u003e, and \u003cem\u003eSell\u003c/em\u003e), consistent with inflammatory monocyte genes described in the mononuclear phagocyte (MNP) single-cell reference atlas (\u0026ldquo;MNP-verse\u0026rdquo;) [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Gene ontology (GO) analysis revealed enrichment of pathways associated with monocyte chemotaxis and chemokine signaling (Suppl-4D).\u003c/p\u003e \u003cp\u003eBy 2 dpi, \u003cem\u003eGfp\u003c/em\u003e⁺ macrophages retained a largely monocyte-associated transcriptional profile, with enrichment of programs related to myeloid cell development and chemotaxis, indicating an incomplete transition to terminal macrophage activation states (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD, E). At this stage, ISGs accounted for only\u0026thinsp;~\u0026thinsp;26% of upregulated genes. At 4 dpi, \u003cem\u003eGfp\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e macrophages transitioned to a pronounced ISG-dominant transcriptional program, accounting for ~\u0026thinsp;58% of upregulated genes. This shift coincided with induction of pathways associated with antigen processing and presentation, response to interferon, and regulation of T cell activation, consistent with progression toward inflammatory resolution (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003eTo assess whether this ISG program is induced by paracrine interferon stimulation, we examined interferon ligand expression in injured muscle. Type I interferon transcripts were detected at no-to-low levels and were largely restricted to DCs and a subset of macrophages, suggesting that the robust ISG signature a response to damage-associated molecular patterns (DAMPs) rather than paracrine interferon signaling (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF, Suppl-4E).\u003c/p\u003e \u003cp\u003eCritically, monocyte-derived cells do not re-transition from Branch 1 back to Branch 2 during resolution. However, resident macrophages may transition between these states with higher plasticity (fig. S3C). Branch 1 macrophages were transcriptionally primed for inflammasome activation and pyroptosis (\u003cem\u003eGsdmd\u003c/em\u003e, \u003cem\u003eIlb\u003c/em\u003e, \u003cem\u003eIl18\u003c/em\u003e, \u003cem\u003eCasp1\u003c/em\u003e, \u003cem\u003eCasp4\u003c/em\u003e, \u003cem\u003eNlrp3\u003c/em\u003e, and \u003cem\u003eAim2\u003c/em\u003e), whereas Branch 2 macrophages expressed intrinsic apoptotic regulators (\u003cem\u003eBax\u003c/em\u003e, \u003cem\u003eBak1\u003c/em\u003e, and \u003cem\u003eApaf1\u003c/em\u003e) (Suppl-5A). We confirmed these cell death gene expression using spatial transcriptomics in gastrocnemius muscle partially injected with CTx (GSE225766 [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]) (Suppl-5B). \u003cem\u003eIn situ\u003c/em\u003e analysis confirmed the presence of apoptotic Branch 2 and Branch 3 macrophages, whereas Ly6C\u003csup\u003e+\u003c/sup\u003e Branch 1 cells were TUNEL-negative, suggesting that these cells do not undergo classical apoptosis during the resolution phase (Suppl-5C).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003esnRNA-seq reveals macrophage stress\u0026ndash;response programs with limited evidence for pathology-driving transcriptional programs at the onset of muscle dystrophy.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eNext, we questioned whether macrophages act as pathological drivers at the onset of muscular dystrophy, we performed snRNA-seq on skeletal muscle from BLA/J mice at 12 months of age, focusing on the TA and gluteus maximus (GM) [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Histological analysis confirmed centronucleated myofibers and leukocyte infiltration, with the GM showing more advanced adipose deposition and remodeling as reported previously [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). Immunostaining for CD68 and F4/80 confirmed that macrophages are the prominent cellular component at disease onset (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e7\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eUsing our regenerative trajectory framework, we next examined branch-specific alterations in BLA/J TA and GM. Surprisingly, Branch 1 macrophages showed no meaningful gene or pathway enrichment in either muscle, arguing against a dominant pathology-driving transcriptional program at early disease stages. Instead, transcriptional alterations were largely confined to Branch 3 in both BLA/J TA and GM (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e7\u003c/span\u003eC, Suppl-6A). In BLA/J TA, the number of differentially expressed genes (DEGs) relative to B6 was minimal in Branches 1 and 2, providing an insufficient basis for pathway enrichment. In Branch 3, only 35 genes were found to be upregulated in BLA/J TA, which are associated with single-strand DNA binding, calcium, oxytocin and Phospholipase D signaling pathways, mRNA slicing. To our surprise, no immune-related pathway was observed (Suppl-6A). In the BLA/J GM, Branch 3 macrophages displayed signatures consistent with a cellular stress response rather than a pathogenic state, including mediators of mitochondrial quality control (\u003cem\u003ePrkn\u003c/em\u003e), epigenetic reprogramming (\u003cem\u003eJarid2\u003c/em\u003e, \u003cem\u003eNfia\u003c/em\u003e), and proteostatic stress responses (\u003cem\u003ePsme4\u003c/em\u003e, \u003cem\u003eUbe2e2\u003c/em\u003e). These signatures resemble stress-adaptive programs seem in chronically injured parenchymal cells, supporting our notion that macrophages at this stage are responding to tissue damage rather than initiating pathology (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e7\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003eBecause dysferlinopathy onset does not substantially alter the genetic program of Branch 1 macrophages, we next asked whether increasing disease severity affects macrophage behavior. To this end, we extended our analysis to two murine Duchenne muscular dystrophy (DMD) models: ΔEx51 TA at 4 weeks of age (GSE156498 [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]) and \u003cem\u003emdx\u003c/em\u003e TA at 8 months of age (PRJNA771932 [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]), both of which display severe pathology prominent already at postnatal day 26 [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e7\u003c/span\u003eD). In these models, we observed increased macrophage proliferation, indicating heightened immune engagement in response to tissue injury (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e7\u003c/span\u003eE, F). Despite the severe pathology, the macrophage subpopulation structure remained remarkably conserved across both DMD models (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e7\u003c/span\u003eG). Notably, macrophages from these two models clustered closely in principal component analysis (PCA) (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e7\u003c/span\u003eH). Given this high degree of concordance, subsequent analyses were performed using \u003cem\u003emdx\u003c/em\u003e TA data only due to overall higher data quality (Suppl-6B).\u003c/p\u003e \u003cp\u003eIn contrast to BLA/J muscle, \u003cem\u003emdx\u003c/em\u003e TA macrophages displayed DEGs shared across all three branches. These were genes related to lysosomal and degradative programs (\u003cem\u003eCtsb\u003c/em\u003e, \u003cem\u003eCtss\u003c/em\u003e, \u003cem\u003eHexb\u003c/em\u003e, \u003cem\u003ePsap\u003c/em\u003e, and \u003cem\u003eCd63\u003c/em\u003e) likely associated with enhanced debris clearance. Branch 3 was characterized by a substantially larger number of upregulated genes compared to Branches 1 and 2, reflecting a state of heightened transcriptional activity, with enrichment for antigen processing and MHC class II\u0026ndash;mediated presentation pathways, indicative of macrophage activation in response to sustained tissue damage (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e7\u003c/span\u003eI). Furthermore, the proportion of Branch 1 macrophages was notably higher in DMD models, contributing to a persistent inflammatory environment (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG-I, Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e7\u003c/span\u003eG, Suppl-5A). These findings suggest that while macrophage branch-specific identities remain intact across different dystrophies, the depth of the transcriptional response and population composition shifts significantly with disease severity.\u003c/p\u003e \u003cp\u003e \u003cb\u003eWhole-macrophage transcriptomic analysis reveals activation- and migration-associated programs in advanced muscular dystrophy.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo capture the full scale of the transcriptional shift across disease states, we performed a DEG analysis of the entire macrophage population across BLA/J TA, BLA/J GM, and \u003cem\u003emdx\u003c/em\u003e TA, compared with B6 TA at 10 dpi (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e8\u003c/span\u003eA, B). Surprisingly, comparison across all three conditions revealed only nine commonly upregulated genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e8\u003c/span\u003eA). This small core set suggests that early dystrophic pathology does not impose a dominant, uniform macrophage activation program.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn the TA muscle specifically, we identified 35 genes commonly upregulated in both BLA/J and \u003cem\u003emdx\u003c/em\u003e TA models, defining a signature of phenotypic plasticity. This set included components for sensing mechanical and TGF-β\u0026ndash;related signals (\u003cem\u003eTgfbi\u003c/em\u003e, \u003cem\u003eAcvr1\u003c/em\u003e) and post-transcriptional fine-tuning \u003cem\u003evia\u003c/em\u003e RNA splicing and microRNA processing (\u003cem\u003eSrsf6\u003c/em\u003e, \u003cem\u003eDgcr8\u003c/em\u003e), as well as regulators of chromatin-mediated cell state stabilization (\u003cem\u003eKmt5a\u003c/em\u003e, \u003cem\u003ePcgf3\u003c/em\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e8\u003c/span\u003eA). Rather than overt inflammatory programs, these genes describe regulatory layers that enable macrophages to adapt and integrate environmental cues\u0026mdash;a state consistent with a poised, adaptive bystander.\u003c/p\u003e \u003cp\u003eIn contrast, \u003cem\u003emdx\u003c/em\u003e TA macrophages exhibited a markedly expanded response, with 347 uniquely upregulated genes enriched for pathways including antigen presentation, extracellular matrix (ECM) organization, inflammatory responses, and superoxide anion generation (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e8\u003c/span\u003eA, B). This indicates the acquisition of oxidative effector programs in response to sustained, chronic tissue damage.\u003c/p\u003e \u003cp\u003eTo determine the drivers of Branch 1 macrophage accumulation in the \u003cem\u003emdx\u003c/em\u003e, we used CellChat to map macrophage-centered communication (Suppl-6C). We identified a strong upregulation of macrophage growth factors (\u003cem\u003eCsf1\u003c/em\u003e, \u003cem\u003eIl34\u003c/em\u003e) and chemokines (\u003cem\u003eCcl5\u003c/em\u003e, \u003cem\u003eCcl6\u003c/em\u003e, \u003cem\u003eCxcl12\u003c/em\u003e) among signals received by macrophages (Suppl-6D). Notably, while \u003cem\u003eCcl6\u003c/em\u003e acts as an autocrine factor (Suppl-6E), \u003cem\u003eCxcl12\u003c/em\u003e (stromal-derived factor 1) was predominantly expressed by mural cells, endothelial cells, and fibroblasts (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e8\u003c/span\u003eC, D).\u003c/p\u003e \u003cp\u003eTogether, these analyses reveal a fundamental shift in macrophage behavior. At disease onset, macrophages act as reactive sensors, exhibiting highly constrained, stress-adaptive programs. As tissue damage becomes chronic, the dystrophic environment\u0026mdash;specifically signals from the activated endothelium and surrounding mural cells\u0026mdash;facilitates the recruitment of pro-inflammatory Branch 1 macrophages, eventually driving the system toward a pathology-perpetuating state.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe present here the first high-resolution snRNA-seq trajectory of macrophages in regenerating and dystrophic skeletal muscle, providing a definitive map of nascent transcriptional programs while bypassing dissociation-induced artifacts. Our analysis identified three crisply distinguishable macrophage activation states and demonstrates for the first time that intra-muscle macrophage proliferation is restricted to a narrow time window between 2 and 4 dpi (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003eLever et al. previously reported the coexistence of multiple transcriptionally distinct macrophage subpopulations within the kidney following ischemia-reperfusion injury [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Building on our previous findings that these populations may play distinct roles in fibrogenesis [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], our current study provides novel insights by delineating how these subpopulations evolve over a discrete regenerative trajectory. By capturing these states \u003cem\u003ein situ\u003c/em\u003e through nuclear signatures, we demonstrate that the macrophage response is not a binary transition but a highly regulated sequence of activation, proliferation, and maturation.\u003c/p\u003e \u003cp\u003eWe found that the spatial regulation of macrophage states is governed by distinct growth factor niches: \u003cem\u003eIl34\u003c/em\u003e is the predominant ligand in resting muscle\u0026mdash;produced by SMCs and pericytes in the perimysium\u0026mdash;whereas \u003cem\u003eCsf1\u003c/em\u003e is transiently induced by fibroblasts during active inflammation (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). These findings refine the understanding of CSF-1R signaling in muscle [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] by identifying IL-34 as the specific homeostatic ligand that maintains Branch 3 macrophages within the peri- and epimysium [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] (Suppl-3A).\u003c/p\u003e \u003cp\u003eBy integrating parabiosis with injury models, we demonstrate that infiltrating monocytes enter the tissue at the early bifurcation point of Branch 2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC), aligning with the \u003cem\u003eCx3cr1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e populations identified in previous scRNA-seq studies [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD, H). Crucially, our analysis reveals an intriguing developmental trajectory: these recruited cells remain non-proliferative and undergo a unidirectional differentiation program between 2 and 4 dpi to transition to Branch 1 inflammatory macrophages (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC-E). Unlike the reparative or resident branches, this population exhibits a transcriptomic signature of hyperactivation and pyroptosis, indicating a divergent and terminal functional fate rather than a transition back to a resting state (Suppl-5A).\u003c/p\u003e \u003cp\u003eA central, unresolved question in muscle biology is whether the accumulating macrophages in muscular dystrophies represent primary \u0026ldquo;culprits\u0026rdquo; driving pathology or a secondary \u0026ldquo;bystander\u0026rdquo; responding to a chronic degenerative environment responding to a chronic degenerative environment. Our finding suggest that macrophages are primarily as adaptive responders [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]; rather than initiating \u003cem\u003ede novo\u003c/em\u003e disease-promoting programs, they expand and modify conserved, homeostatic states in response to the local niche. Across models of both late-onset dysferlinopathy (BLA/J) and early-onset DMD (\u003cem\u003emdx\u003c/em\u003e), inflammatory Branch 1 macrophages expand in correlation with disease severity while retaining transcriptional programs highly conserved with acute regeneration (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e7\u003c/span\u003eC, I, Suppl-6A). Even when tissue-reparative macrophages upregulate fibrosis-associated genes in dystrophic muscle, our fate analyses reveal these states represent an adaptation to a pathological environment rather than a \u003cem\u003ede novo\u003c/em\u003e disease-intrinsic program (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e8\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003eIn this study, we analyzed and compared two different models of muscular dystrophy with distinct genetic background: dysferlinopathy and DMD. Although dysferlin is expressed in leukocytes, its specific functional role in monocytes\u0026mdash;particularly in the context of dysferlinopathy\u0026mdash;remains largely elusive. While some studies suggest that dysferlin may play role in cell adhesion and vesicular trafficking [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], how these specific defects contribute to the progressive muscle wasting observed in patients is not yet fully understood. Previous reports indicated that bone marrow transplantation in dysferlin-deficient mice yielded only a marginal improvement in muscle function and failed to halt overall disease progression [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. In this context, innate memory might be a critical factor. Innate memory involves the epigenetic and metabolic reprogramming of innate immune cells, allowing them to mount a heightened or altered response upon secondary stimulation [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. It is possible that the chronic inflammatory milieu in dystrophic muscle imprints a specific functional state on these cells that persist regardless of the immediate genetic rescue. However, our data showed that in branch-wise comparisons, the macrophage profiles of BLA/J mice were remarkably similar to those of healthy regenerating mice. Therefore, concerns regarding innate memory may not be critically relevant to the dysferlin-deficient model in this study.\u003c/p\u003e \u003cp\u003eIn summary, these findings establish that muscle macrophage heterogeneity is governed by the tissue niche and cellular origin. The persistent inflammatory presence in dystrophy reflects a quantitative expansion of conserved programs adapting to a maladaptive environment. Consequently, macrophages at disease onset act primarily as adaptive bystanders, providing a framework for immune regulation in chronic muscle degeneration.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eAnimals\u003c/h2\u003e \u003cp\u003eC57BL/6J (B6) mice were purchased from Hyochang Science, Inc. (Daegu, Republic of Korea). \u003cem\u003eDysf\u003c/em\u003e-deficient B6.A-Dysf\u003csup\u003eprmd\u003c/sup\u003e/GeneJ (BLA/J) mice were kindly provided by the Jain Foundation (Seattle, WA, USA). Unless otherwise stated, male mice aged 6\u0026ndash;12 weeks were used in cardiotoxin (CTx)-induced muscle injury experiments. Our study examined male animals only. Given that DMD is an X-linked disorder primarily affecting males, male mice were utilized across all experimental groups\u0026mdash;including the dysferlin-deficient models\u0026mdash;to ensure experimental uniformity and allow for direct mechanistic comparisons between the two pathologies.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eHistological staining\u003c/h3\u003e\n\u003cp\u003eHematoxylin and eosin (H\u0026amp;E) staining was performed on 5-\u0026micro;m-thick paraffin-embedded muscle sections using an H\u0026amp;E Staining Kit (Cat#: ab245880; Abcam, Cambridge, UK) according to the manufacturer\u0026rsquo;s instructions.\u003c/p\u003e\n\u003ch3\u003eImmunostaining\u003c/h3\u003e\n\u003cp\u003eMuscle cryosections (5 \u0026micro;m thick) were stained to detect macrophages and their subpopulations, as previously described [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The following antibodies were used: phycoerythrin (PE)-conjugated anti-mouse/human CD11b (clone: M1/70; Cat# 101207); allophycocyanin (APC)-conjugated anti-mouse CD163 (clone: S15049I; Cat# 155305); fluorescein isothiocyanate (FITC)-conjugated anti-mouse F4/80 (clone: BM8; Cat# 123107); APC-conjugated anti-IL-7R (clone: A7R34; Cat# 135011); PE-conjugated anti-IL-7R (clone: S18006K; Cat# 158203); FITC-conjugated anti-mouse/human Ki67 (clone: 11F6; Cat# 151211); FITC-conjugated anti-mouse Ly6C (clone: HK1.4; Cat# 128005); PE-conjugated anti-mouse Ly6C (clone: HK1.4; Cat# 128007). All antibodies listed above were purchased from BioLegend (San Diego, CA, USA). AlexaFluor\u0026reg;488-conjugated anti-α-smooth muscle actin (clone: 1A4; Cat# 53-9760-80) was purchased from Invitrogen (Waltham, MA, USA).\u003c/p\u003e \u003cp\u003eThe terminal deoxynucleotidyl transferase dUTP Nick End Labelling (TUNEL) assay was performed using an \u003cem\u003eIn Situ\u003c/em\u003e Apoptosis Detection Kit (Cat# MK500; Takara Bio, Kusatsu, Japan) according to the manufacturer\u0026rsquo;s instructions.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eNuclei isolation\u003c/h2\u003e \u003cp\u003eFor each sample, tissues from four mice were pooled, immediately frozen, and stored until processing. Tissues were homogenized in a lysis buffer containing 10 mM Tris-HCl, 10 mM NaCl, 3 mM MgCl\u003csub\u003e2\u003c/sub\u003e, and 0.1% IGEPAL CA-630 in nuclease-free water. After homogenization, nuclei were isolated and purified by density gradient centrifugation with 20% Percoll. Nuclei concentration was measured using a LUNA-FL\u0026trade; Automated Fluorescence Cell Counter (Logos Biosystems, Anyang, Republic of Korea), and nuclear morphology was examined by light microscopy.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSingle-nucleus RNA sequencing (snRNA-seq) library preparation\u003c/h3\u003e\n\u003cp\u003esnRNA-seq libraries were prepared using the Chromium Controller (10x Genomics, Pleasanton, CA, USA) following the 10x Chromium Next GEM Single Cell 3\u0026rsquo; v3.1 protocol (CG000315). Briefly, nuclei suspensions were diluted in nuclease-free water to target a recovery of 10,000 nuclei. The nuclei suspension was combined with the reverse transcription master mix and loaded with Single Cell 3\u0026rsquo; v3.1 Gel Beads and Partitioning Oil into a Chromium Next GEM Chip G. RNA transcripts from individual nuclei were uniquely barcoded and reverse-transcribed within nanolitre-scale droplets.\u003c/p\u003e \u003cp\u003eAfter reverse transcription, cDNA was pooled and subjected to end repair, A-tailing, and adaptor ligation. Libraries were purified and amplified by PCR to generate the final cDNA libraries. The purified libraries were quantified by qPCR with the KAPA Library Quantification Kit (Roche, Basel, Switzerland) and assessed for quality on an Agilent 4200 TapeStation (Agilent Technologies, Santa Clara, CA, USA). Libraries were sequenced on a NovaSeq platform (Illumina, San Diego, CA, USA) using read lengths specified in the manufacturer\u0026rsquo;s user guide.\u003c/p\u003e\n\u003ch3\u003ePublic datasets\u003c/h3\u003e\n\u003cp\u003eThe following publicly available datasets were obtained for reference-based integration and supplementary analyses: single-cell RNA sequencing (scRNA-seq) data on GFP\u003csup\u003e+\u003c/sup\u003e parabiont monocytes/macrophages from Babaeijandaghi \u003cem\u003eet al.\u003c/em\u003e (GSE198055); scRNA-seq atlas of regenerating TA muscle from Walter \u003cem\u003eet al.\u003c/em\u003e (GSE143437; GSE159500; GSE162172; GSE232106) [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]; snRNA-seq data of \u003cem\u003emdx\u003c/em\u003e TA muscle from Scripture-Adams \u003cem\u003eet al.\u003c/em\u003e (PRJNA771932) [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]; snRNA-seq data of ΔEx51 TA muscle from Chemello \u003cem\u003eet al\u003c/em\u003e. (GSE156498) [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]; and 10x Visium data of the injured gastrocnemius/plantaris complex from Coulis \u003cem\u003eet al.\u003c/em\u003e (GSE225766) [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003esnRNA-seq data preprocessing\u003c/h2\u003e \u003cp\u003eFASTQ reads were aligned to the \u003cem\u003emm10\u003c/em\u003e reference genome with CellRanger (v7.1.0). Ambient RNA removal and empty droplet identification were performed on raw feature-barcode matrices with CellBender (v0.3.2), using the --expected-cells parameter derived from the barcode rank plot. Putative doublets were identified with scDblFinder (v1.21.2). The expected doublet rate was computed from CellBender cell counts using a linear regression model fit to the 10x Chromium doublet rate specifications.\u003c/p\u003e \u003cp\u003eAfter doublet removal, quality control was performed using data-driven quality control (ddqc). Briefly, each sample was processed through the standard single-cell analysis workflow in Seurat (v4.4.0) with default parameters, including NormalizeData, ScaleData, FindVariableFeatures, RunPCA, FindNeighbors, and FindClusters (resolution\u0026thinsp;=\u0026thinsp;1). For each Louvain cluster, outlier nuclei were removed using a\u0026thinsp;\u0026plusmn;\u0026thinsp;2 median absolute deviation (MAD) threshold for total unique molecular identifier (UMI) counts, number of detected genes, and mitochondrial gene percentage. To limit extreme outliers, the upper bound for UMI counts was capped at the 90th percentile.\u003c/p\u003e \u003cp\u003eFinally, ddqc-derived clusters were excluded if they lacked significant marker genes (fold change [FC]\u0026thinsp;\u0026gt;\u0026thinsp;1.5, pct.1\u0026thinsp;\u0026gt;\u0026thinsp;0.1, and Benjamini-Hochberg [BH]-adjusted \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), exhibited doublet-associated expression signatures, or had\u0026thinsp;\u0026gt;\u0026thinsp;80% of the cluster comprising nuclei (hereafter referred to as \"cells\") classified as empty droplets by CellRanger but retained by CellBender.\u003c/p\u003e \u003cp\u003eFor pseudobulk analysis, individual cells were aggregated into sample-level counts via UMI summation. Trimmed mean of M-values scaling factors were estimated using the calcNormFactors function from edgeR (v4.4.2). Counts per million were computed by normalizing the raw counts against the effective library sizes for cross-sample comparisons.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eIntegration, subclustering, and annotation\u003c/h2\u003e \u003cp\u003eBatch effect correction and data integration were performed using scVI (v1.3.0). The time point of injury was used as the batch covariate, and 2,000 highly variable genes (HVGs) were selected by ranking genes by their median variability across batches. The scVI model was trained with a negative binomial likelihood and 30 latent dimensions.\u003c/p\u003e \u003cp\u003eUsing scVI-derived latent embeddings, initial clustering at a resolution of 1.2 identified major cell populations: myogenic, immune, stromal, endothelial, mural, neural, and adipocyte. Immune cells were subclustered at a resolution of 1.0 and annotated with SingleR (v2.8.0) using microarray profiles of sorted mouse immune cell populations from the ImmGen database.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eTrajectory and pseudo-time analysis\u003c/h2\u003e \u003cp\u003eMacrophages were subset and processed with SCTransform (v0.4.1). Pearson residuals from the top 1,000 HVGs were used to compute 20 principal components (PCs), which were then integrated with Harmony (v1.2.3). The harmonized PCs were visualized using the potential of heat diffusion for affinity-based transition embedding (PHATE; Python; v1.0.11) with parameters gamma\u0026thinsp;=\u0026thinsp;0 and knn\u0026thinsp;=\u0026thinsp;10.\u003c/p\u003e \u003cp\u003eTo define progression states, macrophages were partitioned into 10 clusters using \u003cem\u003ek\u003c/em\u003e-means clustering on the PHATE potential distance matrix. Palantir (v1.4.1) was used to infer macrophage plasticity and state transitions. For each phenotypic extreme identified in the PHATE manifold, a separate Palantir model was constructed by designating the corresponding branch tip as the root state and the remaining branch tips as terminal states.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eGene regulatory network analysis\u003c/h2\u003e \u003cp\u003eGene regulatory networks underlying macrophage polarization were reconstructed using the Python implementation of single-cell regulatory network inference and clustering (pySCENIC; v0.12.1). Transcription factor (TF)-gene co-expression modules were first identified with GRNBoost2. These modules were refined into regulons with cisTarget by pruning target genes that lacked motif enrichment, using the \u003cem\u003emm10\u003c/em\u003e 10kb motif database. Regulon activity at the single-cell level was quantified with AUCell, and the resulting network topology was visualized with Cytoscape (v3.10.2).\u003c/p\u003e \u003cp\u003eTo identify lineage-driving TFs, pseudotime ordering was performed with Slingshot (v2.14.0), followed by differential regulon activity analysis with tradeSeq (v1.20.0). A generalized additive model was fitted to the SCENIC AUC scores using the fitGAM function with a Beta regression family (family=\"betar\"). Regulons upregulated at each branch terminus were identified with the startVsEndTest function, using an FC threshold of \u0026gt;\u0026thinsp;1.5.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eIntercellular signaling network analysis\u003c/h2\u003e \u003cp\u003eCell\u0026ndash;cell communication analysis was performed on snRNA-seq datasets from regenerating muscle using CellChat (v2.1.2) and the mouse CellChatDB database. Macrophage identities were defined based on their branch assignments, while all other cell populations retained their original population- or subcluster-level annotations. CellChat analyses were conducted independently for each time point using the standard workflow, beginning with the identification of overexpressed genes and interactions (identifyOverExpressedGenes, identifyOverExpressedInteractions).\u003c/p\u003e \u003cp\u003eCommunication probabilities were computed using the truncated mean method (computeCommunProb with type=\"truncatedMean\" and population.size=TRUE). Finally, after filtering each cell-cell communication network (filterCommunication), ligand-receptor interaction probabilities were aggregated by ligand family to visualize pathway-level interaction strengths targeting distinct macrophage branches.\u003c/p\u003e \u003c/div\u003e\u003cp\u003e\u003cstrong\u003eReference-based mapping and label transfer\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSingle-nucleus query datasets were independently mapped onto the regenerating muscle reference atlas using scArches, which applies architectural surgery to integrate query-specific parameters into the pretrained scVI model. The reference scVI model was further trained for 500 additional epochs with early stopping and an adaptive learning rate scheduler (early_stopping=True,\u0026nbsp;early_stopping_patience=10,\u0026nbsp;reduce_lr_on_plateau=True). During mapping, the reference model weights were frozen, and weight decay was set to zero\u0026nbsp;(weight_decay=0.0)\u0026nbsp;to preserve the reference latent space.\u003c/p\u003e\n\u003cp\u003eFollowing mapping, cell-type annotations were transferred from the reference to the query datasets using \u003cem\u003ek\u003c/em\u003e-nearest neighbour classification in the scVI latent space. Query nuclei were assigned labels by majority vote among the 15 nearest reference neighbours, using the reference subcluster annotations as training labels.\u003c/p\u003e\n\u003cp\u003eTo align query macrophages with the reference trajectory framework, query datasets were first transformed with the pretrained reference SCTransform model. Query macrophages were then projected into the harmonized PC space using the\u0026nbsp;mapQuery\u0026nbsp;function from Symphony (v0.1.1). Mapping quality was assessed with a weighted Mahalanobis distance metric, as described by Kang \u003cem\u003eet al.\u003c/em\u003e [46], and outliers were identified and removed using a ±2 MAD threshold. After quality control, query macrophages were projected onto the reference trajectory embedding using the pretrained PHATE model.\u003c/p\u003e\n\u003cp\u003eTo assign progression states, an affinity matrix from query cells to PHATE landmarks was computed and used to interpolate reference potential distances onto the query cells. Finally, query macrophages were assigned to one of the 10 reference progression states using a pretrained K-means model applied to the interpolated potential distance matrix.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSpatial transcriptomics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFiltered feature-barcode matrices from SpaceRanger (v1.3.1) were preprocessed with Seurat. Ribosomal and mitochondrial genes were excluded from the analysis. Spots with fewer than 200 UMIs were filtered out. For each slide, spots were partitioned into injured and uninjured clusters using the Louvain algorithm with the 10 PCs derived from 1,000 HVGs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGene set analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDifferentially expressed genes (DEGs) were identified using MAST (v1.10). Analyses were conducted in a one-versus-rest manner unless otherwise stated. Gene set enrichment analysis (GSEA) was performed with clusterProfiler (v4.14.6) using a weighted gene list ranked by -sign(logFC) × -log(BH-adjusted \u003cem\u003ep\u003c/em\u003e) from MAST. GSEA was run with a weighting exponent of 0.5 and a one-sided tail test to focus on positive enrichment (exponent=0.5,\u0026nbsp;scoreType=\"pos\"). Over-representation analysis was performed via enrichr (v3.4) using the set of all genes that passed pct.1 = 0.1 filter in the preceding DEG analysis as the universal gene set. Gene sets from MSigDB, including GO:BP, GO:MF, REACTOME, and KEGG, were evaluated for significance.\u003c/p\u003e\n\u003cp\u003eAll gene set activities were quantified using the\u0026nbsp;AddModuleScore function in Seurat. Cell cycle scores were computed using the standard G2/M and S phase marker sets provided by Seurat. Macrophage polarization states were assessed by computing module scores for M1 and M2 gene sets derived from Orecchioni \u003cem\u003eet al.\u003c/em\u003e[47]\u0026nbsp;Gene sets representing monocytes cultured with CSF-1 and IL-34 were obtained from Bézie \u003cem\u003eet al.\u003c/em\u003e[29]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUnless otherwise stated, data are presented as mean ± s.d. Statistical significance was assessed using the nonparametric Mann–Whitney U test. \u003cem\u003eP\u003c/em\u003e values are reported as follows: *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001, and ****\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.0001.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll animal experiments were conducted in accordance with the guidelines and regulations of the Institutional Animal Care and Use Committee (IACUC) at Handong Global University. Study protocols were approved under the following reference numbers: HGU-IACUC 20211123-14, HGU-IACUC 20221123-16, HGU-IACUC 20231024-19, HGU-IACUC 20240323-03, HGU-IACUC 20240904-16, and HGU-20250901-10.\u003c/p\u003e"},{"header":"Nonstandard abbreviations used","content":"\u003cp\u003ea-smooth muscle actin,\u0026nbsp;a-SMA; cDC, conventional dendritic cells; CSF-1, colony-stimulating factor-1; CSF-1R, colony-stimulating factor-1 receptor; CTx, cardiotoxin; DAMPs, damage-associated molecular patterns; DAPI, 4\u0026prime;,6-diamidino-2-phenylindole; DC, dendritic cell; DEG, differentially expressed gene; DMD, Duchenne muscular dystrophy; dpi, day(s) post-injury; ECM, extracellular matrix; FAP, fibro-adipogenic progenitor; GM, gluteus maximus; GO, gene ontology;\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eH\u0026amp;E\u003cstrong\u003e,\u0026nbsp;\u003c/strong\u003ehematoxylin and eosin\u003cstrong\u003e;\u0026nbsp;\u003c/strong\u003eIHC, immunohistochemistry; lncRNA, long non-coding RNA; ISG, interferon-stimulated gene; MNP, mononuclear phagocyte; Mreg, mature regulatory; PCA, principal component analysis; pDC, plasmacytoid dendritic cells; PHATE, potential of heat-diffusion for affinity-based transition embedding; ProlifM, proliferative macrophage; QuSC, quiescence muscle stem cell; scRNA-seq, single-cell RNA sequencing; s.d., standard deviation; SCENIC, single-cell regulatory network inference and clustering; SMC, smooth muscle cell; snRNA-seq, single-nucleus RNA sequencing; UMI, unique molecular identifier; TA, tibialis anterior; TF, transcription factor; tSNE, t-distributed stochastic neighbour embedding; TUNEL, terminal deoxynucleotidyl transferase-mediated dUTP nick end labelling; UMAP, uniform manifold approximation and projection.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eConflict of interest\u003c/h2\u003e \u003cp\u003eAll authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis research was supported by the National Research Foundation of Korea (NRF) through the Ministry of Education (2021R1I1A3059820) (to Jea-Hyun Baek).\u003c/p\u003e\u003ch2\u003eAuthor Contributions\u003c/h2\u003e\u003cp\u003eThis study was conceived and supervised by J.-H.B.; E.-C.B., Y.-G.S. and W.S.B. performed experiments and data analysis; W.S.B. and J.-H.B. prepared the original draft; J.H.B. reviewed and edited. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e\u003cp\u003eThe authors thank the Jain Foundation (Seattle, WA, USA) for providing us with BLA/J mice and all Baek laboratory members for technical assistance and discussions.\u003c/p\u003e\u003ch2\u003eData availability\u003c/h2\u003e\u003cp\u003eAll raw and pre-processed snRNA-seq data have been deposited in the National Center for Biotechnology Information Gene Expression Omnibus under accession\u0026thinsp;\u0026lt;\u0026thinsp;ACC NUMBER\u0026gt;. The fully processed Seurat and serialized objects are publicly available for download on Dryad (\u0026lt;\u0026thinsp;DRYAD LINK\u0026gt;). Values for all data points in graphs are reported in the Supporting Data Values file. All code and instructions for reproducing the single-cell and spatial analyses for this study are available on GitHub (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/wsk-byun/SkM-Mac-Traj\u003c/span\u003e\u003cspan address=\"https://github.com/wsk-byun/SkM-Mac-Traj\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eFang J, Feng C, Chen W, Hou P, Liu Z, Zuo M, et al. Redressing the interactions between stem cells and immune system in tissue regeneration. Biol Direct. 2021;16:18. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s13062-021-00306-6\u003c/span\u003e\u003cspan address=\"10.1186/s13062-021-00306-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e PubMed PMID: 34670590; PubMed Central PMCID: PMC8527311.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXu HR, Le VV, Oprescu SN, Kuang S. Muscle stem cells as immunomodulator during regeneration. Curr Top Dev Biol. 2024;158:221\u0026ndash;38. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/bs.ctdb.\u003c/span\u003e\u003cspan address=\"10.1016/bs.ctdb.\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e2024.01.010 PubMed PMID: 38670707; PubMed Central PMCID: PMC11801201.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTidball JG, Villalta SA. Regulatory interactions between muscle and the immune system during muscle regeneration. Am J Physiol Regul Integr Comp Physiol. 2010;298(5):R1173-1187. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1152/ajpregu.00735\u003c/span\u003e\u003cspan address=\"10.1152/ajpregu.00735\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.2009 PubMed PMID: 20219869; PubMed Central PMCID: PMC2867520.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTidball JG. Regulation of muscle growth and regeneration by the immune system. Nat Rev Immunol. 2017;17(3):165\u0026ndash;78. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/nri\u003c/span\u003e\u003cspan address=\"10.1038/nri\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.2016.150 PubMed PMID: 28163303; PubMed Central PMCID: PMC5452982.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBansal D, Miyake K, Vogel SS, Groh S, Chen CC, Williamson R, et al. Defective membrane repair in dysferlin-deficient muscular dystrophy. Nature. 2003;423(6936):168\u0026ndash;72. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/nature01573\u003c/span\u003e\u003cspan address=\"10.1038/nature01573\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e PubMed PMID: 12736685.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOishi Y, Manabe I. Macrophages in inflammation, repair and regeneration. Int Immunol. 2018;30(11):511\u0026ndash;28. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/intimm/dxy\u003c/span\u003e\u003cspan address=\"10.1093/intimm/dxy\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e054 PubMed PMID: 30165385.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWynn TA, Vannella KM. Macrophages in Tissue Repair, Regeneration, and Fibrosis. Immunity. 2016;44(3):450\u0026ndash;62. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.immuni\u003c/span\u003e\u003cspan address=\"10.1016/j.immuni\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.2016.02.015 PubMed PMID: 26982353; PubMed Central PMCID: PMC4794754.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKim JH, Baek JH. Macrophages in the pathogenesis of monogenic muscular dystrophies: inflammation, fibrosis, and therapeutic implications. EI. 2025;5:1003192. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.37349/ei.2025.1003192\u003c/span\u003e\u003cspan address=\"10.37349/ei.2025.1003192\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSpencer CG, Hamilton M, Bedsole E, Wei YN, Rojas AM, Burciu A, et al. Chronic macrophage activation derails muscle repair by disrupting mannose-receptor-linked plasticity revealed by endogenous irg1/acod1 tracking. Nat Commun. 2026;17:1466. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41467-025-68204-3\u003c/span\u003e\u003cspan address=\"10.1038/s41467-025-68204-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e PubMed PMID: 41501052; PubMed Central PMCID: PMC12886890.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTheret M, Saclier M, Messina G, Rossi FMV. Macrophages in Skeletal Muscle Dystrophies, An Entangled Partner. J Neuromuscul Dis. 9(1):1\u0026ndash;23. doi:10.3233/JND-210737 PubMed PMID: 34542080; PubMed Central PMCID: PMC8842758.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRodr\u0026iacute;guez-Morales P, Franklin RA. Macrophage phenotypes and functions: resolving inflammation and restoring homeostasis. Trends Immunol. 2023;44(12):986\u0026ndash;98. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.it.2023.10.004\u003c/span\u003e\u003cspan address=\"10.1016/j.it.2023.10.004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e PubMed PMID: 37940394; PubMed Central PMCID: PMC10841626.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eStanley ER, Heard PM. Factors regulating macrophage production and growth. Purification and some properties of the colony stimulating factor from medium conditioned by mouse L cells. J Biol Chem. 1977;252(12):4305\u0026ndash;12. PubMed PMID: 301140.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eStanley ER, Cifone M, Heard PM, Defendi V. Factors regulating macrophage production and growth: identity of colony-stimulating factor and macrophage growth factor. J Exp Med. 1976;143(3):631\u0026ndash;47. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1084/jem.143.3.631\u003c/span\u003e\u003cspan address=\"10.1084/jem.143.3.631\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e PubMed PMID: 1082493; PubMed Central PMCID: PMC2190132.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGreter M, Lelios I, Pelczar P, Hoeffel G, Price J, Leboeuf M, et al. Stroma-derived interleukin-34 controls the development and maintenance of langerhans cells and the maintenance of microglia. Immunity. 2012;37(6):1050\u0026ndash;60. doi:10.1016/j.immuni.2012.11.001 PubMed PMID: 23177320; PubMed Central PMCID: PMC4291117.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang Y, Szretter KJ, Vermi W, Gilfillan S, Rossini C, Cella M, et al. IL-34 is a tissue-restricted ligand of CSF1R required for the development of Langerhans cells and microglia. Nat Immunol. 2012;13(8):753\u0026ndash;60. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/ni.2360\u003c/span\u003e\u003cspan address=\"10.1038/ni.2360\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e PubMed PMID: 22729249; PubMed Central PMCID: PMC3941469.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBaek JH, Zeng R, Weinmann-Menke J, Valerius MT, Wada Y, Ajay AK, et al. IL-34 mediates acute kidney injury and worsens subsequent chronic kidney disease. J Clin Invest. 2015;125(8):3198\u0026ndash;214. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1172/JCI81166\u003c/span\u003e\u003cspan address=\"10.1172/JCI81166\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e PubMed PMID: 26121749; PubMed Central PMCID: PMC4563757.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWei S, Nandi S, Chitu V, Yeung YG, Yu W, Huang M, et al. Functional overlap but differential expression of CSF-1 and IL-34 in their CSF-1 receptor-mediated regulation of myeloid cells. J Leukoc Biol. 2010;88(3):495\u0026ndash;505. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1189/jlb.1209822\u003c/span\u003e\u003cspan address=\"10.1189/jlb.1209822\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e PubMed PMID: 20504948; PubMed Central PMCID: PMC2924605.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFelix J, Elegheert J, Gutsche I, Shkumatov AV, Wen Y, Bracke N, et al. Human IL-34 and CSF-1 establish structurally similar extracellular assemblies with their common hematopoietic receptor. Structure. 2013;21(4):528\u0026ndash;39. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.str\u003c/span\u003e\u003cspan address=\"10.1016/j.str\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.2013.01.018 PubMed PMID: 23478061.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMitsui Y, Satoh T. Functional diversity of disorder-specific macrophages involved in various diseases. Inflamm Regen. 2025;45:29. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s41232-\u003c/span\u003e\u003cspan address=\"10.1186/s41232-\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e025-00390-5 PubMed PMID: 41035106; PubMed Central PMCID: PMC12487080.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eByun WS, Lee J, Baek JH. Beyond the bulk: overview and novel insights into the dynamics of muscle satellite cells during muscle regeneration. Inflamm Regen. 2024;44(1):39. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s41232-024-00354-1\u003c/span\u003e\u003cspan address=\"10.1186/s41232-024-00354-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e PubMed PMID: 39327631; PubMed Central PMCID: PMC11426090.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMachado L, Geara P, Camps J, Dos Santos M, Teixeira-Clerc F, Van Herck J, et al. Tissue damage induces a conserved stress response that initiates quiescent muscle stem cell activation. Cell Stem Cell. 2021;28(6):1125\u0026ndash;1135.e7. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.stem\u003c/span\u003e\u003cspan address=\"10.1016/j.stem\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.2021.01.017 PubMed PMID: 33609440.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSaini J, McPhee JS, Al-Dabbagh S, Stewart CE, Al-Shanti N. Regenerative function of immune system: Modulation of muscle stem cells. Ageing Res Rev. 2016;27:67\u0026ndash;76. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.arr.2016.03.006\u003c/span\u003e\u003cspan address=\"10.1016/j.arr.2016.03.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e PubMed PMID: 27039885.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang Y, Lu J, Liu Y. Skeletal Muscle Regeneration in Cardiotoxin-Induced Muscle Injury Models. Int J Mol Sci. 2022;23(21):13380. doi:10.3390/ijms232113380 PubMed PMID: 36362166; PubMed Central PMCID: PMC9657523.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCastiglioni A, Corna G, Rigamonti E, Basso V, Vezzoli M, Monno A, et al. FOXP3\u0026thinsp;+\u0026thinsp;T Cells Recruited to Sites of Sterile Skeletal Muscle Injury Regulate the Fate of Satellite Cells and Guide Effective Tissue Regeneration. PLoS One. 2015;10(6):e0128094. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pone.0128094\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0128094\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e PubMed PMID: 26039259; PubMed Central PMCID: PMC4454513.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWalter LD, Orton JL, Ntekas I, Fong EHH, Maymi VI, Rudd BD, et al. Transcriptomic analysis of skeletal muscle regeneration across mouse lifespan identifies altered stem cell states. Nat Aging. 2024;4(12):1862\u0026ndash;81. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s43587-024-00756-3\u003c/span\u003e\u003cspan address=\"10.1038/s43587-024-00756-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMcKellar DW, Walter LD, Song LT, Mantri M, Wang MFZ, De Vlaminck I, et al. Large-scale integration of single-cell transcriptomic data captures transitional progenitor states in mouse skeletal muscle regeneration. Commun Biol. 2021;4(1):1280. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s42003-021-02810-x\u003c/span\u003e\u003cspan address=\"10.1038/s42003-021-02810-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e PubMed PMID: 34773081; PubMed Central PMCID: PMC8589952.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHong JH, Lee GT, Lee JH, Kwon SJ, Park SH, Kim SJ, et al. Effect of bone morphogenetic protein-6 on macrophages. Immunology. 2009;128(1 Pt 2):e442\u0026ndash;50. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/j.1365-2567.2008.02998.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1365-2567.2008.02998.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e PubMed PMID: 19191909; PubMed Central PMCID: PMC2753950.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXiao X, Xu Y, Moschetta GA, Yu Y, Fisher AL, Alfaro-Magallanes VM, et al. BMP5 contributes to hepcidin regulation and systemic iron homeostasis in mice. Blood. 2023;142(15):1312\u0026ndash;22. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1182/blood.2022019195\u003c/span\u003e\u003cspan address=\"10.1182/blood.2022019195\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e PubMed PMID: 37478395; PubMed Central PMCID: PMC10613724.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eB\u0026eacute;zie S, Freuchet A, S\u0026eacute;razin C, Salama A, Vimond N, Anegon I, et al. IL-34 Actions on FOXP3\u0026thinsp;+\u0026thinsp;Tregs and CD14\u0026thinsp;+\u0026thinsp;Monocytes Control Human Graft Rejection. Front Immunol. 2020;11:1496. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fimmu.2020.01496\u003c/span\u003e\u003cspan address=\"10.3389/fimmu.2020.01496\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBabaeijandaghi F, Cheng R, Kajabadi N, Soliman H, Chang CK, Smandych J, et al. Metabolic reprogramming of skeletal muscle by resident macrophages points to CSF1R inhibitors as muscular dystrophy therapeutics. Sci Transl Med. 2022;14(651):eabg7504. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1126/scitranslmed.abg\u003c/span\u003e\u003cspan address=\"10.1126/scitranslmed.abg\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e7504 PubMed PMID: 35767650.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMulder K, Patel AA, Kong WT, Piot C, Halitzki E, Dunsmore G, et al. Cross-tissue single-cell landscape of human monocytes and macrophages in health and disease. Immunity. 2021;54(8):1883\u0026ndash;1900.e5. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.immuni.2021.07.007\u003c/span\u003e\u003cspan address=\"10.1016/j.immuni.2021.07.007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eStec MJ, Su Q, Adler C, Zhang L, Golann DR, Khan NP, et al. A cellular and molecular spatial atlas of dystrophic muscle. Proc Natl Acad Sci U S A. 2023;120(29):e2221249120. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1073/pnas.2221249120\u003c/span\u003e\u003cspan address=\"10.1073/pnas.2221249120\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e PubMed PMID: 37410813; PubMed Central PMCID: PMC10629561.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBaek JH, Many GM, Evesson FJ, Kelley VR. Dysferlinopathy Promotes an Intramuscle Expansion of Macrophages with a Cyto-Destructive Phenotype. The American Journal of Pathology. 2017;187(6):1245\u0026ndash;57. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ajpath.2017.02.011\u003c/span\u003e\u003cspan address=\"10.1016/j.ajpath.2017.02.011\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChemello F, Wang Z, Li H, McAnally JR, Liu N, Bassel-Duby R, et al. Degenerative and regenerative pathways underlying Duchenne muscular dystrophy revealed by single-nucleus RNA sequencing. Proc Natl Acad Sci U S A. 2020;117(47):29691\u0026ndash;701. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1073/pnas.2018391117\u003c/span\u003e\u003cspan address=\"10.1073/pnas.2018391117\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e PubMed PMID: 33148801; PubMed Central PMCID: PMC7703557.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eScripture-Adams DD, Chesmore KN, Barth\u0026eacute;l\u0026eacute;my F, Wang RT, Nieves-Rodriguez S, Wang DW, et al. Single nuclei transcriptomics of muscle reveals intra-muscular cell dynamics linked to dystrophin loss and rescue. Commun Biol. 2022;5:989. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s42003-022-03938-0\u003c/span\u003e\u003cspan address=\"10.1038/s42003-022-03938-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e PubMed PMID: 36123393; PubMed Central PMCID: PMC9485160.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGeissinger HD, Rao PV, McDonald-Taylor CK. \u0026ldquo;mdx\u0026rdquo; mouse myopathy: histopathological, morphometric and histochemical observations on young mice. J Comp Pathol. 1990;102(3):249\u0026ndash;63. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/s0021-9975(08)80015-1\u003c/span\u003e\u003cspan address=\"10.1016/s0021-9975(08)80015-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e PubMed PMID: 2365843.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLever JM, Hull TD, Boddu R, Pepin ME, Black LM, Adedoyin OO, et al. Resident macrophages reprogram toward a developmental state after acute kidney injury. JCI Insight. 2019;4(2):e125503. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1172/jci.insight.125503\u003c/span\u003e\u003cspan address=\"10.1172/jci.insight.125503\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e PubMed PMID: 30674729; PubMed Central PMCID: PMC6413788.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOh H, Kwon O, Kong MJ, Park KM, Baek JH. Macrophages promote Fibrinogenesis during kidney injury. Front Med (Lausanne). 2023;10:1206362. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fmed.2023.1206362\u003c/span\u003e\u003cspan address=\"10.3389/fmed.2023.1206362\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e PubMed PMID: 37425313; PubMed Central PMCID: PMC10325639.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBabaeijandaghi F, Kajabadi N, Long R, Tung LW, Cheung CW, Ritso M, et al. DPPIV+ fibro-adipogenic progenitors form the niche of adult skeletal muscle self-renewing resident macrophages. Nat Commun. 2023;14(1):8273. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41467-023-43579-3\u003c/span\u003e\u003cspan address=\"10.1038/s41467-023-43579-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e PubMed PMID: 38092736; PubMed Central PMCID: PMC10719395.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSaclier M, Cuvellier S, Magnan M, Mounier R, Chazaud B. Monocyte/macrophage interactions with myogenic precursor cells during skeletal muscle regeneration. FEBS J. 2013;280(17):4118\u0026ndash;30. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/febs.12166\u003c/span\u003e\u003cspan address=\"10.1111/febs.12166\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e PubMed PMID: 23384231.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNagaraju K, Rawat R, Veszelovszky E, Thapliyal R, Kesari A, Sparks S, et al. Dysferlin deficiency enhances monocyte phagocytosis: a model for the inflammatory onset of limb-girdle muscular dystrophy 2B. Am J Pathol. 2008;172(3):774\u0026ndash;85. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2353/ajpath.2008.070327\u003c/span\u003e\u003cspan address=\"10.2353/ajpath.2008.070327\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e PubMed PMID: 18276788; PubMed Central PMCID: PMC2258254.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ede Morr\u0026eacute;e A, Flix B, Bagaric I, Wang J, van den Boogaard M, Grand Moursel L, et al. Dysferlin Regulates Cell Adhesion in Human Monocytes. J Biol Chem. 2013;288(20):14147\u0026ndash;57. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1074/jbc.M112.448589\u003c/span\u003e\u003cspan address=\"10.1074/jbc.M112.448589\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e PubMed PMID: 23558685; PubMed Central PMCID: PMC3656271.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFlix B, Su\u0026aacute;rez-Calvet X, D\u0026iacute;az-Manera J, Santos-Nogueira E, Mancuso R, Barquinero J, et al. Bone Marrow Transplantation in Dysferlin-Deficient Mice Results in a Mild Functional Improvement. Stem Cells Dev. 2013;22(21):2885\u0026ndash;94. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1089/scd.2013.0049\u003c/span\u003e\u003cspan address=\"10.1089/scd.2013.0049\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e PubMed PMID: 23777246; PubMed Central PMCID: PMC3804083.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNetea MG, Joosten LAB, Latz E, Mills KHG, Natoli G, Stunnenberg HG, et al. Trained immunity: A program of innate immune memory in health and disease. Science. 2016;352(6284):aaf1098. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1126/science.aaf1098\u003c/span\u003e\u003cspan address=\"10.1126/science.aaf1098\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e PubMed PMID: 27102489; PubMed Central PMCID: PMC5087274.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCoulis G, Jaime D, Guerrero-Juarez C, Kastenschmidt JM, Farahat PK, Nguyen Q, et al. Single-cell and spatial transcriptomics identify a macrophage population associated with skeletal muscle fibrosis. Sci Adv. 2023;9(27):eadd9984. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1126/sciadv.add9984\u003c/span\u003e\u003cspan address=\"10.1126/sciadv.add9984\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e PubMed PMID: 37418531; PubMed Central PMCID: PMC10328414.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKang JB, Nathan A, Weinand K, Zhang F, Millard N, Rumker L, et al. Efficient and precise single-cell reference atlas mapping with Symphony. Nat Commun. 2021;12(1):5890. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41467-021-25957-x\u003c/span\u003e\u003cspan address=\"10.1038/s41467-021-25957-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e PubMed PMID: 34620862; PubMed Central PMCID: PMC8497570.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOrecchioni M, Ghosheh Y, Pramod AB, Ley K. Macrophage Polarization: Different Gene Signatures in M1(LPS+) vs. Classically and M2(LPS-) vs. Alternatively Activated Macrophages. Front Immunol. 2019;10:1084. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fimmu.2019.01084\u003c/span\u003e\u003cspan address=\"10.3389/fimmu.2019.01084\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e PubMed PMID: 31178859; PubMed Central PMCID: PMC6543837.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRusinova I, Forster S, Yu S, Kannan A, Masse M, Cumming H, et al. Interferome v2.0: an updated database of annotated interferon-regulated genes. Nucleic Acids Res. 2013;41(Database issue):D1040-1046. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/nar/gks1215\u003c/span\u003e\u003cspan address=\"10.1093/nar/gks1215\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e PubMed PMID: 23203888; PubMed Central PMCID: PMC3531205.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Macrophage fate, intra-muscle macrophages, muscle regeneration, single-nucleus analysis, muscle immunology","lastPublishedDoi":"10.21203/rs.3.rs-9129034/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9129034/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMacrophages are dominant leukocytes in skeletal muscle, yet their functional heterogeneity, particularly in muscular dystrophies, remains poorly defined. To reconcile this, we generated a high-resolution single-nucleus RNA sequencing (snRNA-seq) atlas of skeletal muscle. By bypassing dissociation-induced stress, we achieved superior resolution over standard single-cell clustering, capturing multinucleated myocytes and resolving cell-cell interactomes. In regenerating muscle, we crisply delineated macrophage trajectories, identifying three parallel and co-existing activation states, along with a G2/M proliferative population. Integrating these trajectories with parabiosis-based fate mapping, we provide the first evidence that recruited monocytes undergo synchronous differentiation without local proliferation, exhibiting pyroptosis before population contraction. While this recruited inflammatory population is prominent during muscular dystrophy, it surprisingly retains transcriptional programs conserved with regeneration. In parallel, tissue-resident macrophages adapt to the local environment without initiating de novo pathogenic programs. These findings suggest that at the onset of muscular dystrophy, macrophages act primarily as adaptive bystanders rather than destroyers, providing a framework for immune regulation in muscle degeneration.\u003c/p\u003e","manuscriptTitle":"A high-resolution single-nucleus RNA sequencing reveals conserved macrophage activation trajectories across muscle regeneration and dystrophy.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-31 09:05:55","doi":"10.21203/rs.3.rs-9129034/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4b725300-afca-4499-87f7-3cc7d1e5e64f","owner":[],"postedDate":"March 31st, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":65333237,"name":"Biological sciences/Immunology/Innate immune cells/Monocytes and macrophages"},{"id":65333238,"name":"Biological sciences/Immunology/Gene regulation in immune cells/Immunogenetics"},{"id":65333239,"name":"Biological sciences/Cell biology/Mechanisms of disease"}],"tags":[],"updatedAt":"2026-03-31T09:05:55+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-31 09:05:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9129034","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9129034","identity":"rs-9129034","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}