Aging-enhanced accumulation of fibroblasts excludes oligodendrocytes in demyelinated lesions

Aging-enhanced accumulation of fibroblasts excludes oligodendrocytes in demyelinated lesions | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Aging-enhanced accumulation of fibroblasts excludes oligodendrocytes in demyelinated lesions Brian M. Lozinski, Charlotte D’Mello, Rianne P. Gorter, Yifei Dong, and 12 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9327007/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 9 You are reading this latest preprint version Abstract Fibroblast dysregulation contributes to pathological fibrosis and aberrant repair. Emerging evidence suggest that fibroblasts accumulate in lesions following central nervous system injury, but whether and how they influence oligodendrocyte repair responses, including in aging, is uncertain. Here we report that fibroblasts accumulate in the parenchyma of spinal cord white matter lesions of 6–10 week old young mice after lysolecithin-induced demyelination. This was first observed through immunofluorescence microscopy that employed several markers attributed to fibroblasts, including platelet-derived growth factor-β, collagen type 1α1, α-smooth muscle actin, periostin and fibronectin; and by the use of platelet-derived growth factor-β TdTomato reporter transgenic mice. Spatial transcriptomics and single-nucleus RNA sequencing of lysolecithin lesions established the presence of fibroblasts in lysolecithin lesions and delineated them from closely related pericytes. CellChat ligand – receptor analyses highlight fibroblasts in the lysolecithin environment as a major source of input of signals for microglia/macrophages and oligodendrocyte precursor cells, with numerous reciprocal interactions. The infiltration of fibroblasts was promoted by microglia/macrophages, as anticipated by their temporal representation in lysolecithin lesions, and by tissue culture experiments where the migration of fibroblasts was enhanced by macrophages. Particularly relevant to regenerative events that occur spontaneously after lysolecithin demyelination, the areas of fibroblast accumulation were devoid of oligodendrocyte precursor cells. In tissue culture, oligodendrocyte precursor cells were excluded from fibroblast domains. Moreover, fibroblast accumulation after lysolecithin injury was enhanced with increasing age, a known detriment to the capacity to remyelinate after injury, and exclusion of oligodendrocyte precursor cells from fibroblast areas of 48–52 week mice exceed that occurring in younger 6–10 weeks animals. Finally, by mining a publicly available single-nucleus RNA database of multiple sclerosis, we found fibroblasts in the edge of chronic active and chronic inactive lesions and in lesion core, and fewer in periplaque or normal white matter. There were several communication networks between fibroblasts, microglia/macrophages and oligodendrocyte precursor cells in these MS lesions. Our collective results demonstrate a role of fibroblasts in demyelination-associated neuropathology, which is exacerbated by aging, and highlight the importance of regulating fibroblasts to promote effective CNS repair. Impediments to repair Lysolecithin Neurofibrosis Remyelination in aging Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Introduction Fibroblasts are a heterogeneous cell population important for synthesis of extracellular matrix (ECM) components, maintenance of cellular niches, tissue regeneration and wound healing 1 . In these roles, fibroblasts communicate with immune cells such as macrophages. Fibroblasts associated with the CNS primarily reside in the meninges, perivascular space, and choroid plexus where they contribute to tissue homeostasis 2 and immune regulation 3 , 4 . Several recent studies suggest that in response to neural injury, these border-associated fibroblasts accumulate in the CNS parenchyma and contribute to chronic fibrotic scaring 3 , 5 – 11 . Nonetheless, beneficial roles of fibroblasts may manifest in the acute phase after neural injury, and include regenerative functions and limiting the expansion of lesions 9 , 12 . Overall, while fibroblast activation is necessary to maintain tissue integrity, it may also ultimately impair regenerative processes 3 , 13 , 14 . Remyelination is the most common and robust form of CNS regeneration 15 – 17 , involving the migration, proliferation, and differentiation of oligodendrocyte progenitor cells (OPCs) into mature myelinating oligodendrocytes 18 , 19 . Restoring myelin to denuded axons reconstitutes saltatory conduction, axonal trophic support, and produces a mechanical barrier to injury leading to axonal preservation and functional improvement 15 , 20 , 21 . However, remyelination often fails due to extrinsic variables in the environment, such as inhibitory ECM, and intrinsic factors including age-related cellular dysfunctions 21 – 23 . Age contributes to disease worsening across a range of conditions, including MS and fibrotic disorders 24 – 26 . OPCs also show signs of age-related impairment, including upregulation of senescence markers, epigenetic dysfunction, reduced responsiveness to growth factors, and impaired differentiation and remyelination capacity 23 , 27 – 29 . While age-related decline in remyelination is well recognized, the impact of aging on fibroblast responses to CNS injury remains poorly understood. In this study, we characterized the response of fibroblasts in lesions following focal lysolecithin (LPC)-mediated demyelination of the spinal cord white matter, and further evaluated the influence of increasing age on the fibrotic response. We compared middle-aged to young mice, as middle-age is a period when many people with MS transition to progressive MS. With spatial transcriptomics of LPC tissue and cells in culture, we interrogated interactions between fibroblasts, microglia/macrophages and OPCs. Finally, we utilized a publicly available transcriptomic dataset to show that fibroblasts are present in MS lesions, and describe communication patterns between fibroblasts, microglia/macrophages and OPCs in MS. Our findings highlight the necessity of considering invasion of fibroblasts into the CNS parenchyma in lesion evolution and recovery. Materials and methods MS specimens Postmortem frozen brain tissues from people with MS were obtained from The Montreal Brain Bank courtesy of Dr. Alexandre Prat, with informed consent approved by the CRCHUM and University of Montreal research ethics committee. Additional autopsied frozen MS brain tissues were from The Multiple Sclerosis and Parkinson’s Tissue Bank situated at Imperial College, London. The use of these human tissue in Calgary for research was approved by the Conjoint Health Research Ethics Board at the University of Calgary (Ethics ID REB15-0444). MS sections were characterized using luxol fast blue (LFB) and hematoxylin & eosin (H&E) to identify lesions as previously described 30 . Mice All experiments were conducted with ethics approval from the Animal Care Committee at the University of Calgary under regulations of the Canadian Council of Animal Care. Female C57BL/6J mice were from Jackson laboratories. CX3CR1-CreER (JAX 021160), iDTR [Rosa26iDTR (JAX 007900)], PDGFRβ-P2A-CreERT2 (JAX 030201), Ai9 [Rosa26-TdTomato (JAX 007905)], NG2-CreER (JAX 008538), and Tau-mGFP (JAX 021162) mice were from The Jackson Laboratory. All mouse strains were bred in the single barrier mouse unit at the University of Calgary. All young female mice were 6-10 weeks of age, and all middle-aged female mice were 48-52 weeks of age. Male and female CD1 pups P0-2 were used for OPC and meningeal fibroblast cultures. C57BL6 female mice 6-10 weeks of age were used for bone marrow-derived macrophages (BMDM) cultures. All mice were maintained on a 12-h light/dark cycle with food (Pico-Vac Mouse Diet 20) and water given ad libitum. Spinal cord surgery Lysolecithin/lysophosphatidylcholine (LPC) demyelination was accomplished as previously described 31 . Mice were anaesthetized with intraperitoneal injections of ketamine (100 mgkg-1) and xylazine (10 mgkg-1). Buprenorphine (0.05 mgkg-1) was given as an analgesic. Mice were positioned on a stereotaxic frame and a 2-3 cm incision was made between the shoulder blades. The intervertebral space between T3 and T4 was identified, and a 32-gauge needle attached to a 10 μL Hamilton syringe was used to inject 0.5 μL of 1% w/v LPC (Sigma-Aldrich, L1381) into the ventral column at a rate of 0.25 μLmin-1 for 2 min. The needle was left for 2 min following the injection to avoid back flow. After suturing, mice were placed in a thermally controlled environment for recovery. Spinal cord tissue isolation Anaesthetized mice were transcardially perfused with 15 mL of phosphate-buffered saline (PBS). Dissected spinal cords were fixed in 4% paraformaldehyde (PFA) at 4 o C overnight. Tissue was cryoprotected in 30% w/v sucrose solution for 72h then frozen in FSC 22 frozen section media (Leica). Coronal sections were cut with a cryostat and stored at -20 o C until analysis. Immunofluorescence staining Slides were permeabilized with 0.2% TritonX-100 and blocked with a 10% horse serum-containing solution. Primary antibody incubation was conducted overnight in phosphate-buffered saline (PBS) containing 0.1% cold fish stain gelatin and 0.1% Triton X-100. Slides were then stained with TrueBlack Lipofuscin Autofluorescence Quencher (Biotium) for 2 min, and incubated with secondary antibody and 1 µgmL-1 of DAPI for 1h before mounting. Antibodies used were anti-mouse myelin basic protein (MBP, BioLegend, PA1-10008), anti-mouse periostin (POSTN, R&D Systems AF2955), anti-mouse α smooth muscle actin (αSMA-Cy3, Millipore, C6198), anti-mouse fibronectin (FN1, Millipore, AB2033), anti-CD45 (Thermofisher, MA5-17687), rabbit anti-mouse pan-laminin (gift from L. Sorokin, University of Münster), anti-mouse/human PDGFRβ (R&D Systems, AF1042), anti-mouse Iba1 (Wako, 019-19741), anti-mouse CD45-AF488 (Biolegend, 103122), rabbit anti-collagen 1a1 (Col1a1, Invitrogen, PA1-26204), anti-mouse GFAP (Biolegend, PCK-591P), anti-mouse NFH (RPCA-NF-H), anti-mouse PDGFRα (R&D Systems, AF1062), anti-mouse Olig2 (Millipore, ab9610), anti-green fluorescence protein (GFP, Aveslab, GFP-1020), anti-mouse sulfatide O4 (R&D Systems, MAB1326), anti-mouse arginase-1 (Arg1, BioLegend, 678802), and anti-mouse MHCII (Thermofisher, 13-5321-82). Secondary antibodies from Jackson ImmunoResearch were AF488 donkey anti-mouse IgM, AF488 donkey anti-rabbit IgG, AF488 donkey anti-rat IgG, AF488 donkey anti-goat IgG, cyanine Cy3 donkey anti-goat IgG, cyanine Cy3 donkey anti-chicken IgY, AF647 donkey anti-rat IgG, and AF647 donkey anti-rabbit IgG. Confocal immunofluorescence microscopy and analysis Images were collected with a Leica TCS Sp8 laser confocal microscope. Equal laser, gain and offset settings to maximize contrast and minimize saturation were consistently used for all samples within each set of experiments. Leica Application Suite X, ImageJ, and QuPath 0.3.1 were used for image acquisition and analysis. Maximum-intensity projections were created for each channel/marker. All images were blinded prior to analysis. ROI for lesions were drawn using DAPI or MBP. Volume analysis of ventral white matter lesions was done using MBP immunostaining of serial sections to manually trace the region of demyelination largely devoid of myelin, or containing intense fragmented MBP signal, per section, across the coronal sections from each mouse. Fibroblast regions were defined using threshold of PDGFRβ-Tdtomato or PDGFRβ immunostaining. Proportion of lesion occupied by fibroblasts was calculated by dividing the PDGFRβ-Tdtomato or PDGFRβ immunostained area by the MBP derived lesion area for the respective sections. Threshold intensity was determined using at least 3 images. The Analyze Particles function was then used to create a mask and to quantify the positive signals in each ROI using size exclusion of 2-infinity. Threshold intensity, size exclusion, and circularity settings for particle analysis were kept constant across all samples for each experimental set. Oligodendrocytes were counted manually using Olig2 and DAPI as a guide. When analyzing lesion spread, samples were excluded if more than 20% of sections were unable to be analysed due to tearing, folding, or other loss. Cell cultures and analyses For fibroblasts, meninges from P0-2 CD1 pups were collected in RPMI and digested for 18 minutes at 37 o C in 1 μgmL-1 DNase I (Sigma) and 3.7 μgmL-1 collagenase D (Sigma) triturating every 6 minutes. Meninges were centrifuged at 300xg for 3 minutes and cells were resuspended in growth medium (DMEM (Gibco) supplemented with 10% FBS, 1% non-essential amino-acids (NEAA), 1% GlutaMAX, 1% sodium pyruvate, and 1% penicillin-streptomycin (all from Gibco). Cells were incubated in T-75 flasks at 37 o C with 5% CO2. Medium was replaced every 3 days and cells were passaged when 90% confluent. Mouse OPCs were cultured from cortices of P0 CD1 pups as previously described 21 . Mouse bone marrow-derived macrophages (BMDMs) were generated as detailed elsewhere 32 . For co-cultures, OPCs were added at a density of 31,250 cells/cm 2 to poly-L-lysine coated 96-well plates. Meningeal fibroblasts were added at particular densities as listed in Results. OPCs and meningeal fibroblasts were added together, or one added 2h before the other as defined in the experiment, in 100 µL of OPC differentiating medium. Cocultures were incubated at 37 o C with 8.5% CO 2 for 72 hours. Cells were fixed and anti-O4 primary antibody was added in Licor antibody dilution buffer (1:250) overnight at 4 o C. Donkey anti-mouse IgM AF488 secondary antibody was then added (1:200) followed by permeabilization with 0.02% Triton-X100 in PBS. Cells were further incubasted overnight at 4 o C with anti-PDGFRβ (1:50; R&D systems;AF1042) and anti-MBP (1:100; Abcam; ab7349). After secondary antibodies (1:200) exposure, cells were suspended in PBS with 1 μg/mL DAPI until imaging. For analyses of stained plates, these were imaged using a ×10/0.5 NA air objective on an ImageXpress Micro XLS High-Content Analysis system (Molecular Devices) as previously described 24,30 . Nine–12 fields of views (FOVs) were imaged per well. Multiwavelength cell scoring analysis in the MetaXpress High-Content Image Acquisition and Analysis software (Molecular Devices) was used to quantify cell survival from the fluorescence microscopy images gathered. For fold change values, all numbers were divided by the mean of the control samples. For the representative images shown, each channel/marker for the sample was merged and displayed using pseudocolours. Transwell migration assays BMDMs were plated in 24 well plates at a density of 260,000 cells/cm 2 overnight at 37 o C with 8.5% CO 2 . Medium with 1% FBS was added for 24h prior to experiments. Meningeal fibroblasts (150,000 cells/cm 2 ) were added to transwell inserts (Corning, 29442-120) for 16h, then fixed and stained. The upper chamber was cleaned using a cotton swab, inserts were transferred to hematoxylin for 15 minutes, washed, mounted and imaged on an Olympus VS110 slide scanner on a 10x/0.4 NA objective and VS-ASW-S5 (V2.9) with Batch Converter software. CosMx spatial molecular imaging and data analysis FFPE sections as prepared above were sent to NanoString for CosMx platform analysis. Sample processing, staining, imaging and cell segmentation were performed as previously described 33 . The 1000-plex CosMx Mouse Neuro probe panel was used along with markers for morphology and cell segmentation. The CosMx optical system has an epifluorescent configuration based on a customized water objective (13×, NA 0.82), and uses widefield illumination, with a mix of lasers and light-emitting diodes to image DAPI, Alexa Fluor-488, Atto-532, Dyomics Dy-605 and Alexa Fluor-647, as well as removal of photocleavable dye components. Nine 0.8 um Z-stack images of each FOV were acquired and then fluorophores on the reporter probes were UV cleaved and washed off with Strip Wash buffer. This procedure was repeated for the remaining 15 reporter pools, and the 16 cycles of reporter hybridization-imaging was repeated 8 times to increase RNA detection sensitivity. Raw image processing and feature extraction were performed using an in-house SMI data processing pipeline which includes registration, feature detection, and localization 33 . A machine learning algorithm cell segmentation pipeline was used to assign transcripts to cell locations and subcellular compartments as previously described 34 . Data from the CosMx platform was formulated into an object for Seurat v5 35 in R. Data from 2 LPC and 3 Sham brain sections with FOVs from the right hemisphere were used. Data was normalized using SCTransform v2. Unsupervised analysis and generation of a final UMAP with 20 PCs and resolution = 0.7 was done. Harmony 36 was used to integrate ‘Group’ data (LPS or Sham). Robust Cell Type Decomposition (RCTD) was used to map the cluster profiles to the Azimuth mouse motor cortex reference map (azimuth.hubmapconsortium.org). The following cell types were identified: astrocytes, endothelial cells, neurons, macrophages, oligodendrocytes, oligodendrocyte precursor cells (OPCs), vascular leptomeningeal cells (VLMCs) and pericytes. A separate fibroblast identity was created expressing Col1a2 and Pdgfrb , and for OPCs expressing Pdgfra and Sox6 . FindAllMarkers with default Wilcoxon rank sum test was used to identify distinguishing markers for each cluster. DoHeatmap with downsampling to 1000 was used to generate a heatmap with top 5 genes for each cluster. Data from macrophages, fibroblasts and OPCs from LPC sections were subset from the main dataset and used with the package CellChat v2 to assess ligand-receptor interactions 37 . To assess significant ligand-receptor interactions the computeCommunProb command was used with the following parameters (type=”truncatedMean”, distance.use=TRUE, contact.dependent =TRUE, contact.range=50). Analysis of mouse single nuclei dataset from LPC lesions Fibroblast expansion was validated in our publicly available LPC-lesioned tissue dataset (Melchor et al. accession number GSE278643). Briefly, processed data were directly downloaded, and all quality control, dimensionality reduction, and clustering were performed using Seurat (v5.1.0) in R (v4.4.1) as per original publication. All samples (naïve spinal cord tissue (n=2), LPC and sham 5dpl (n=3, n=1), 10dpl (n=2, n=1), and 20dpl (n=3, n=1) were utilized. To cluster the data, we used the SCTransform v2 function 38,39 . The count matrix was log normalized using the NormalizeData function to identify cluster gene markers. All cell populations were annotated based off of well-known and calculated marker genes through the FindAllMarkers function. Vascular and mesenchymal cells ( Pdgfrb, Cfap43, Pecam1, Cspg4, Col1a2 ) were subset based off marker genes. The subset vascular/mesenchymal dataset was reclustered (65 PCs and resolution = 2.2) and filtered following the original publication, count matrices were renormalized using the NormalizeData function, and clusters were manually annotated based on the expression of cell type-specific genes. Analysis of human single nucleus RNA dataset from MS patients Fastq files from Absinta et al. (accession number GSE180759) were downloaded and demultiplexed using 10x Genomics Cellranger version 5.0 pipeline. The final expression matrix with gene counts from n=3 controls and n=5 patients was analysed using the bioinformatics package Seurat v5 35 . Metadata defining tissue area was included in the object. Before running QC metrics the expression matrix comprised 34,131 features and 134,932 cells. Data was filtered for parameters: gene present in > 3 cells and cells with < 150,000 nCount_RNA and percent of mitochondrial genes < 10%. Post filtering, the expression matrix contained 33,454 features and 128,343 cells. Data was normalized and scaled using the SCTransform v2 function with percent.mt being regressed out. A PCA-reduction was performed and 30 significant dimensions were considered to generate a UMAP with all cells in the dataset. Clusters were determined using the FindClusters function which implements the Louvain algorithm for modularity optimization and with resolution of 0.3. Cluster annotation was done manually based on the expression of lineage specific hallmark genes. Differentially expressed genes for one cluster (versus all cells in other clusters) was determined by the default Wilcoxon rank sum test. PDGFRB + cells from the initial fibroblast cluster were then subset and re-clustered. Data from microglia/macrophages, OPCs and PDGFRB + fibroblasts were subset from the main data and used with the package CellChat2 v2 to assess ligand-receptor interactions 37 . To assess significant ligand-receptor interactions the computeCommunProb command was used with the following parameters (type=”truncatedMean”, trim = 0.05, population.size=TRUE). Statistical analysis Data was collated using Microsoft Excel. Graphs were generated using GraphPad Prism 8. Data shown are the individual data points, each point on a bar graph represents a biological (in vivo) or technical replicate (in vitro), and the mean. Sample sizes were similar to those previously reported 30,40,41 . Experimental groups were randomly assigned. Data collection and analysis were not performed blind to the conditions of the experiment (unless otherwise stated), as all image and data analyses were completed with the same acquisition conditions and analysis thresholds. Statistical tests are listed in figure legends. Data availability All data are available in the main text or the supplementary materials. Results Fibroblasts are localized within demyelinated lesions Although restricted to CNS borders in homeostasis, fibroblasts are found in the parenchyma after neural injury 3,5–11 . However, their functional connectivity and contribution to remyelination remain poorly understood. The LPC model of demyelination allows precise spatial and temporal dissection of pathophysiology in lesions 41,42 . Injection of LPC into the ventral spinal cord white matter of mice results in a focal lesion characterized by loss or disrupted MBP stain, swollen axons based on neurofilament heavy chain staining (NFH), and GFAP-positive reactive astrocytes (Fig 1a). In PDGFRβTdTomato transgenic mice commonly used to study fibroblast responses in peripheral organs 43–45 , we found TdTomato within disrupted MBP-positive debris and in proximity to Iba1-positive microglia/macrophages (Fig 1b). As well, TdTomato and PDGFRβ immunofluorescence overlapped in LPC lesions giving confidence to the specificity of expression (Fig 1c). PDGFRβ was correspondent with a number of other markers of fibroblasts including collagen type 1 alpha 1 (Col1a1) and SMA for activated fibroblasts (Fig 1d). Fibroblast responses were studied 7 and 21 days post injury (dpi), periods when tissue regenerative responses are robust and when they are concluding, respectively 22,41 (Fig 1e-j). While the total lesion volume was reduced from day 7 to 21 (Fig 1e, h), the total TdTomato-positive volume was comparable between 7 to 21 dpi (Fig 1f, i). Thus, the proportion of the lesion occupied by fibroblasts was relatively greater at 21dpi (Fig 1g, j). We then investigated if fibroblasts differentiate into contractile ECM producing myofibroblasts using markers of ECM expression (fibronectin and periostin) and cell contractility (SMA) (Fig 1k). We found that around 40% of TdTomato-positive fibroblasts overlapped with these markers at 7 dpi (Fig 1l-n), and increased co-expression of SMA and periostin while decreasing fibronectin at 21 dpi (Fig 1l-n). We validated the fibroblast response in LPC lesions using single-nuclear RNA sequencing (snRNAseq) dataset at 5, 10, and 20 dpi (Fig 2a-c). Vascular and mesenchymal cells in LPC lesions were clustered and annotated into five groups: fibroblasts ( Col3a1, Col1a2, Col1a1, Bnc2, Dcn , and Foxp2 ), endothelial cells ( Flt1, Pecam1, Ly6c1, Adgrl4 ), pericytes ( Abcc9, Cspg4, Lin7a ), smooth muscle cell (SMC) ( Acta2, Myh11, Pdlim3, Lmod1 ), and ependymal cells ( Adamts20, Cfap54, Dnah12, Agbl4, Cfap299 ) (Fig 2a, b). Pdgfrb was variably expressed in fibroblasts, pericytes, and SMCs (Fig 2a). Fibroblasts were confirmed as significantly expanded in LPC lesions at all timepoints compared to naïve and non-lesioned samples (Fig 2b, c). Fibroblasts constituted ~75% of the vascular and mesenchymal cells 5 and 10 dpi, and approximately 60% at 20 dpi (Fig 2d). Further evaluation of the vascular and mesenchymal cell populations can be found elsewhere 46 . Collectively, the data show that activated fibroblasts expand rapidly and persist into late stages of LPC injury. Spatial transcriptomics of fibroblasts in LPC lesions To address spatial distribution, we used the 1000-plex Mouse Neuroscience Panel with the in-situ single cell CosMx spatial platform from Nanostring. We analysed sham and LPC sections 14 dpi, after peak fibroblast response and when remyelination is ongoing 41,47 (Fig 3a, b). We identified 1828 cells from LPC and 1126 cells from sham controls in 12 clusters which were mapped to the Azimuth mouse motor cortex reference map (Fig 3c, d). This identified astrocytes ( Gja1, Slc4a4, Slc6a1), oligodendrocytes ( Mag, Mog, Myrf ), OPCs ( Pdgfra, Cspg5, Vcan ), microglia/macrophages ( Csf1r, Hexb, Csf3r ), vascular leptomeningeal cells (VLMCs) ( Dcn, Vtn, Igfbp7 ), endothelial cells ( Cldn5, Pecam1, Flt1 ) and pericytes ( Rgs5, Vtn, Myl9 ) (Fig 3d, e). A separate fibroblast identity was created for cells expressing Col1a2 and Pdgfrb (Fig 3d, e). We identified a total of 84 Col1a2 and Pdgfrb double-positive fibroblasts, 5 in sham controls and 79 in LPC lesions (Fig 3f, g). The top genes for fibroblasts aside from Pdgfrb and Col1a2 were Col3a1, Col1a1, Vtn and Dcn (Fig 3e). Importantly, fibroblasts did not express the pericyte marker ( Rgs5 ) or smooth muscle cell marker ( Tagln ) (Supplementary Fig 1a, b). Fibroblasts were 16 times more abundant in the parenchyma of LPC lesions at 14 dpi compared to sham (Fig 3g), and were in close proximity to macrophages and OPCs (Fig 3b). Fibroblast interactions in LPC lesions Cellchat analysis 37 of ligand-receptor interactions in our CosMx data between fibroblasts, microglia/macrophages and OPCs identified a robust fibroblast communication network in LPC lesions (Supplementary Fig 1c-g). Fibroblasts-OPC interactions were most common, followed by fibroblast-fibroblast interactions. However, the strongest interactions were between fibroblasts and microglia/macrophages (Supplementary Fig 1d, f). Fibroblast derived ligands affected receptors involved in signaling for phagocytosis ( Pros1-Axl, Pros1-Mertk ), macrophage polarization ( Spp1-Cd44, Apoe-Trem2/Tyrob p), cell adhesion ( Col1a1-Itgav/Itgb8 ) and OPC survival ( Ptn-Ptprz1, Bdnf-Ntrk2 ) (Supplementary Fig 1h; Supplementary Fig 2a). The signals that fibroblasts were predicted to respond to influenced survival, proliferation, differentiation, and migration such as Pdgfd-Pdgfrb, Fgf2-Fgfr2 and Ptprs-Ntrk3 (Supplementary Fig 1i; Supplementary Fig 2b). Categorization of ligand-receptor interactions into functionally related signaling pathways showed fibroblasts were the major driver of outgoing signaling in LPC lesions (Supplementary Fig 1g; Supplementary Fig 3a). Fibroblasts contributed to pathways such as apolipoprotein E (APOE), COLLAGEN, and FN1; microglia/macrophages to APOE, osteopontin (SPP1) and colony stimulating factor (CSF) pathways; and OPCs to pleiotrophin (PTN), Glutamate, and PDGF pathways (Supplementary Fig 3a; Supplementary Fig 4a). Microglia/macrophages were the primary recipient of ligand-receptor signaling through pathways such as APOE and SPP1, and OPCs responded to PTN, and FN1 (Supplementary Fig 3b; Supplementary Fig 4b). Although fibroblasts were not identified as a dominant receiver of signaling through the cell types assessed, they were inferred to receive signaling predominantly from COLLAGEN and PDGF signaling pathways (Supplementary Fig 3b; Supplementary Fig 1g). Analysing global communication patterns we identified three outgoing and three incoming signaling patterns dominated by individual cell groups (Supplementary Fig 4c-f). Altogether this highlights the involvement of fibroblasts in the LPC environment as a major source of input for microglia/macrophages and OPCs. Fibroblasts respond to microglia/macrophages As fibroblasts appear to engage in reciprocal communication with microglia/macrophages in the 14 dpi LPC lesion, we hypothesized that this may affect recruitment to the area of injury. Indeed, Iba1+ microglia/macrophages and PDGFRβ+ fibroblasts were closely associated in day 7 LPC lesions (Fig 1b). Also, we found that microglia/macrophages began accumulating in the LPC lesion at day 3 before peaking at 7 days (Fig 4a, c). Fibroblasts also peaked at day 7, but there was no observable PDGFRβ+ cells at day 3 indicating they may respond to migratory signals from microglia/macrophages (Fig 4b, d). To support this, we added meningeal fibroblasts to the upper compartment of a Boyden chamber with bone marrow-derived macrophages or medium alone in the lower chamber (Fig 4e). After 24 hours there was a 3-fold increase in the number of fibroblasts that migrated across the membrane when cultured with macrophages compared to the basal rate of migration (Fig 4f, g). These results suggest a role for macrophages in the promotion of fibroblast elevation in CNS lesions. Fibroblasts spatially exclude OPCs In contrast to an affinity of fibroblasts for macrophages (Fig 4), we found a separation of fibroblasts for OPCs. In LPC lesions, Olig2+PDGFRα+ OPCs were restricted from regions occupied by PDGFRβ+ fibroblasts (Fig 5a). This was corroborated by the use of NG2CreER:MAPTmGFP mice where newly differentiated oligodendrocytes expressed a membrane bound green fluorescent protein (GFP) 20,21 . Areas occupied by fibroblasts, based on PDGFRβ immunoreactivity, in 14 dpi lesions contained little GFP (Fig 5b, c). This was not due to axonal preservation as no difference in NFH-positive axon density was seen in the fibroblast occupied region compared to the rest of the LPC lesion (Supplementary Fig. 5a, b). These results suggest that fibroblasts regionally restrict OPC localization in LPC lesions. To test this further we seeded cultures of meningeal fibroblasts with OPCs (Fig 5d). No change in total O4+ OPCs was observed (Fig 5e) but there was a significant decrease in the number of mature O4+MBP+ oligodendrocytes in co-cultured wells (Fig 5f). Oligodendrocytes also had less complex morphological phenotypes in co-cultures only occupying regions without fibroblasts (Fig 5d, g). Collectively, this data highlights the inhibitory effect of fibroblasts on OPC differentiation. Aging enhances fibroblast properties in LPC lesions Age affects many biological processes including tissue regeneration 48 . Age is known to affect OPC and microglia/macrophage dynamics, and is a critical factor in remyelination potential 22,24,49,50 . To test how age affects the fibroblast response to CNS injury and the downstream effects, young (6-10 week) and middle-aged (48-52 week) mice were injected with LPC in the ventral column of the spinal cord. Tissue was collected 7, 14 and 21 dpi and immunostained for MBP and PDGFRβ (Fig 6a). Age did not affect lesion volume at 7 dpi but LPC lesions in middle-aged mice were significantly larger at 14 and 21 dpi (Fig 6b, d). Age also did not affect the PDGFRβ+ volume at 7 and 14 dpi, but did cause a significant increase at 21dpi in middle-aged lesions (Fig 6c, e). As well, a greater proportion of the middle-aged LPC lesion was positive for fibroblasts than young lesions at 7 and 21 dpi (Fig 6f). We investigated the effect of age on the expression of phenotypic markers in fibroblasts in LPC lesions. Middle-aged LPC fibroblasts at day 7 displayed elevated levels of activated myofibroblast marker SMA compared to young (Fig 7a). While no differences in Col1a1 or laminin were seen at 7 dpi, these were elevated at 21 dpi (Fig 7b, c). Finally, we found fewer Olig2+ oligodendrocyte lineage cells within the fibroblast-occupied regions of middle-aged compared to young mice (Fig 7d). Overall, we found elevated ECM levels in middle-aged mice that corresponded with a reduction in oligodendrocyte lineage cells in lysolecithin injury. Fibroblasts are present in MS lesions We extended from murine data into the human demyelinating condition, MS. Frozen brain sections from autopsied MS cases collected in Montreal were stained for PDGFRβ as well as CD45 to identify leukocytes, and DAPI for cell nuclei (Fig 8a). We found that PDGFRβ+ cells were found in close proximity to CD45+ leukocytes (Fig 8a). From the Imperial College autopsied specimens, active and chronic active lesions were identified, and PDGFRβ+Col1a1+ double positive cells were identified to increase confidence of their fibroblast nature. Figure 9 shows that these double positive cells could be found in active and chronic active lesions, in structures that were perivascular as well as sparsely distributed in the parenchyma. Next, we reanalysed previously published snRNAseq datasets from human brain tissues of MS lesion rim, core and periplaque; and white matter tissue from neurologically healthy controls 51 . Unsupervised clustering identified 25 clusters from the 66,432 total cells including leukocytes, Bergmann glia, dendritic cells, vascular endothelial cells, OPCs, oligodendrocytes, astrocytes, ependymal cells, microglia/macrophages, and neurons (Fig 8b). We highlighted a cluster of 174 cells that expressed fibroblast related genes 52 such as PDGFRB, COL1A2, COL15A1, and DCN (Fig 8c). From this cluster we focused on PDGFRB+ cells as our population of interest that were positive for the markers PDGFRA, COL1A2, TGFBR3, COL15A1 , and LAMC3 (Fig 8d; Supplementary Fig 7). Importantly, genes associated with pericytes ( RGS5 ), and smooth muscle cells ( TAGLN ) were not appreciably expressed in our population of interest (Supplementary Fig 7a) 53 . The PDGFRB+ fibroblasts were separated from the other cells in the dataset and re-clustered. This delineated 2 subpopulations (Fig 8e). Subcluster 1 expressed genes associated with stress response ( HSPB1, HMGB1 ) while subcluster 0 expressed genes associated with ECM, cell proliferation, and migration ( LAMA2, NTRK3 ) (Fig 8e; Supplementary Fig 7b, c). Assessment of these clusters via region showed that subcluster 0 was present in chronic active and chronic inactive edges and also the lesion core, but only minimally observed in control white matter or periplaque area; subcluster 1 was elevated only in the lesion core (Fig 8f). Fibroblast interactions in MS lesions We interrogated the interactions of fibroblasts with other cell types in MS lesions using CellChat ligand-receptor interaction analysis. Similar to LPC lesions, fibroblasts, microglia/macrophages and OPCs all communicated with each other (Supplementary Fig 8a-d). Fibroblast outgoing signals primarily involved ECM-receptor interactions while most incoming signals were via secreted signaling (Supplementary Fig 8e; Supplementary Fig 9a, b). This is seen in the ligand-receptor pairings engaged by fibroblast derived ligands such as COL1A2-ITGAV/ITGB9 and LAMA2-CD44 (Supplementary Fig 8f; Supplementary Fig 9a, b). As well, incoming secreted ligand-receptor interactions that fibroblasts were predicted to respond to included FGF1-FGFR4 and PDGFB-PDGFRA (Supplementary Fig 8g; Supplementary Fig 9a, b). Fibroblast ligand-receptor pairs were further categorized into 76 signaling pathways such as transforming growth factor beta (TGFB), PDGF, FN1, and COLLAGEN (Supplementary Fig 10a). Fibroblasts contributed to pathways that drove microglia/macrophage signaling including COLLAGEN, LAMININ, CSF, and CXC motif chemokine ligand (CXCL) (Supplementary Fig 9c; Supplementary Fig 10a; Supplementary Fig 11a). Additionally, fibroblasts participated in signaling pathways that signaled at OPCs including WNT, FGF, COLLAGEN, and LAMININ (Supplementary Fig 9c; Supplementary Fig 10a; Supplementary Fig 11a). As previously done, we analysed communication patterns between cell populations and found three outgoing and three incoming patterns (Supplementary Fig 10a, b). Outgoing pattern three was characterized primarily by fibroblast signaling and was driven by pathways such as GAP, KIT, and plasminogen activator urokinase (PLAU) pathways (Supplementary Fig 10a). In the reverse, fibroblasts responded to incoming pattern three represented by VCAM, neurotrophin (NT) and adhesion G protein-coupled receptor A2 (ADGRA) pathways (Supplementary Fig 10b). Comparison of the signaling pathways in LPC and MS lesions revealed 16.3% overlap of outgoing fibroblast pathways and 15.3% of shared incoming pathways (Supplementary Fig 10c, d). Shared outgoing pathways included PDGF, PTN and COL1A1, and shared incoming pathways included FGF, COL1A1 and VEGF (Supplementary Fig 10c, d). Fibroblast specific signaling pathways in MS included outgoing PLAU and TGFB and incoming WNT and TGFB (Supplementary Fig 10c, d). Within these pathways we identified 16 conserved outgoing (FGF1-FGFR2 and PTN-PTPRZ1) and 10 conserved incoming (COL1A1-CD44 and PTPRS-NTRK3) fibroblast ligand-receptor interactions (Supplementary Fig 11c, d). Similarities between ligand-receptor interactions of fibroblasts in MS and LPC lesions include the outgoing ligand-receptor interactions (FGF1-FGFR2, and COL1A1-CD44), and incoming interactions (FGF2-FGFR2, COL1A1-ITGAV) (Supplementary Fig 10c, d; Supplementary Fig 2; Supplementary Fig 4; Supplementary Fig 9a, b; Supplementary Fig 11c, d). Overall, it appears that microglia/macrophages and OPCs employ multiple signaling pathways compared to fibroblasts. Furthermore, cross-referencing the outgoing and incoming patterns highlighted that some pathways such as VCAM are shared by all three cell groups assessed with microglia/macrophages and OPCs both contributing to outgoing VCAM signaling and fibroblasts as sole responder (Supplementary Fig 11a, c). Taken together these data highlight not only the presence of fibroblasts in MS lesions but also their prominent interactions with microglia/macrophages and OPCs in their environment. Discussion The presence of parenchymal fibroblasts in both neuroinflammatory and traumatic CNS injury is more appreciable than previously thought 3,9 . Here, we report that the expansion of fibroblasts in the lysolecithin (LPC)-injured spinal cord white matter is correspondent with lowered oligodendrocyte density and exacerbated with age. We verify the identity of fibroblasts using PDGFRβ immunoreactivity and a PDGFRβ-TdTomato reporter mouse line 43,44,54 , and by additional markers including fibronectin and periostin. Moreover, transcriptomics data identified additional markers of fibroblasts, including several collagens, decorin and fibulin-1, and their absence of the pericyte marker Rgs5 and smooth muscle cell marker Talgn. Analysis of a snRNAseq dataset of LPC lesions shows that while fibroblast counts expand significantly at 5, 10, and 20 dpi, pericytes are scarce in the LPC lesion environment. Highly multiplexed RNA in situ hybridization (RNA-ISH, CosMx) further supports that fibroblasts are expanded in the LPC lesions as we found a 16-fold increase in fibroblasts in lesion compared to sham controls. Moreover, the absence of Rgs5 , a commonly used pericyte marker, lends further support to the identification of fibroblasts. In MS lesions, we identified PDGFRβ+Col1a1+ cells by immunohistochemistry in close proximity to CD45 positive leukocytes, in both active and chronic active lesions. We analysed a single-nucleus RNA sequencing dataset of MS 51 and found a small population of presumptive fibroblasts. We identified a population of PDGFRβ positive cells that expressed genes associated with fibroblasts, and similar to those found in LPC lesions, such as COL1A2, COL15A1, DCN and FBLN1 . It is noteworthy that these cells are found in lesions but minimally in the periplaque area or neurologically normal white matter, indicating their association with pathology in MS. This is consistent with findings from other groups that have shown greater presentation of PDGFRβ cells in chronic active MS lesions 14 . Cellchat analysis of both LPC and MS lesions indicates that fibroblasts possess immense potential to interact with a range of cells including microglia/macrophages and oligodendrocyte lineage cells in the lesion environments. The potential effect of fibroblasts on CNS injuries has received scant attention, and little is known regarding their effect on the most common form of CNS regeneration, remyelination 3,6 . Others have suggested that fibroblasts have the potential to influence oligodendrocyte lineage cells that are responsible for remyelination 3,6 . Our analysis of predicted ligand-receptor interactions from CosMx suggests that fibroblasts are a potent driver of signaling in LPC demyelination including many fibroblast-derived ligands that can affect OPCs including Fn1 and Col1a1 55 . Accumulation of inhibitory ECM components, such as fibronectin aggregates 21,56,57 , and collagen 1 58 can impair OPC function, maturation, and reduce remyelination. ECM components can also impede OPC function through indirect effects including by stimulating microglia/macrophages and lymphocytes that kill OPCs 57 . Whether fibroblast phenotypes and their communication with other cells in LPC and MS lesions changes over time is unclear and requires further investigation. However, recent findings in traumatic brain injury have shown that fibroblasts do have dynamic responses over the development of the injury 9 . Furthermore, how potential fibroblast-led signaling interactions continue to influence remyelination requires further investigation, as fibroblasts are transcriptionally dynamic throughout remyelination 46 . It is feasible that fibroblasts provide a physical obstacle to oligodendrocyte processes contacting axons for remyelination. Indeed, we show in vivo that few OPCs or newly formed myelin are present in areas occupied by fibroblasts despite persistence of axons. Our results in culture show that OPCs avoid fibroblast areas. Future experiments should be designed to provide clarity to the mechanisms regulating the inhibition of oligodendrocyte lineage cells by fibroblasts. Although fibroblasts are not present in large numbers following injury, their broad surface area, potent capacity to exclude oligodendrocytes from lesions, and their many potential interactions with microglia/macrophages position them to be important regulators of pathophysiology and recovery. Age contributes to overall morbidity and the reduced efficiency of many biological processes 48 including the formation of oligodendrocytes 59 and remyelination 22,60,61 . We found a greater number of fibroblasts in LPC lesions of middle-aged compared to young mice at both early and late timepoints with a greater accumulation of ECM at later timepoints. It has been well described that the rate of accumulation of microglia/macrophages is reduced within lesions of aging mice compared to young mice. We have previously shown that there is an expansion of microglia/macrophages in aging lesions that resembles pro-fibrotic scar-associated macrophages that express Spp1 , Trem2 , Cd63 , and Fabp5 9,24,30,62,63 . Importantly, osteopontin ( Spp1 ) stimulates inflammatory microglial states and exacerbates CNS injury, and Spp1-positive macrophages have been shown to drive the activation of myofibroblasts leading to increased tissue fibrosis 64 . Interestingly, this same population has been described in chronic CNS lesions including chronic active MS lesions 65 . This is consistent with our data in which we found that Spp1-positive microglia/macrophages are present in both our LPC dataset and the reanalyzed MS lesion results. Further interrogation of the connection between aging microglia phenotypes and fibrosis will provide more direct connection between this aging associated microglia/macrophage population and fibroblast expansion. The consequences of the age associated expansion of fibroblasts are also of great interest. We found a reduction in the density of oligodendroglia in the fibroblast occupied areas. However, it has been known for some time that age impacts OPCs and remyelination efficiency. Without targeting the fibroblast response in the aging lesion, it is not certain that the reduction is a fibroblast driven phenomena rather than an OPC or some other age-related mechanism such as immune responses. CNS lesions are known to become stiffer with age and contribute to OPC dysfunction 50 . We also found that the expansion of fibroblasts was associated with increased ECM levels. It is not clear if fibroblasts act directly or indirectly on OPCs in the aging lesion, but CellChat analysis suggests fibroblast-OPC interactions are primarily ECM mediated lending to a more indirect effect. Thus, the increased presence of fibroblasts in middle-aged LPC lesions may contribute to worse remyelination outcomes noted with aging. In conclusion, we provide evidence that fibroblasts accumulate in demyelinated lesions including in MS. We show that fibroblast-occupied areas lack new myelinating cells, and that fibroblasts inhibit OPC differentiation in vitro. Importantly, this process is exacerbated by age leading to greater fibrosis of the lesion. These results highlight the necessity of targeting fibroblasts to mediate regenerative processes and improve outcomes in neurological conditions. Declarations Acknowledgements We thank the Hotchkiss Brain Institute AMP core facility, and the NeuroOmics core facility, for technical support. Funding We acknowledge operating grant support from MS Canada (number 1192262), the Canadian Institutes of Health Research (CIHR, FDN 167270) and National Natural Science Foundation of China (W2541024). BML and DM received PhD studentship awards, and RJ and RPG postdoctoral fellowships, from MS Canada. RPG also acknowledges the Dutch MS Society for the Gemmy and Mibeth Tichelaar Award. YD and SG were recipients of postdoctoral fellowships from CIHR. MM received an Internationalisation fellowship from the Carlsberg Foundation, Denmark. Competing interests The authors report no competing interests. Data availability All data are available from VWY upon reasonable requests. Author contributions BML produced the majority of the datasets and wrote the initial drafts of the manuscript. CDM, RPG, YD, GSM, SG, CL, MM, DM, RJ, PEM, CC Ling, JKH, CC-Lemarroy and RL provided data or experimental samples. VWY supervised and completed the final version of this manuscript. All authors read, edited and approved the final manuscript. References Plikus MV, Wang X, Sinha S, et al. Fibroblasts: Origins, definitions, and functions in health and disease. Cell . 2021;184(15):3852-3872. doi:10.1016/j.cell.2021.06.024 Zhou X, Franklin RA, Adler M, et al. Microenvironmental sensing by fibroblasts controls macrophage population size. Proc Natl Acad Sci U S A . 2022;119(32):e2205360119. doi:10.1073/pnas.2205360119 Dorrier CE, Aran D, Haenelt EA, et al. CNS fibroblasts form a fibrotic scar in response to immune cell infiltration. Nat Neurosci . 2021;24(2):234-244. doi:10.1038/s41593-020-00770-9 Plikus MV, Guerrero-Juarez CF, Ito M, et al. Regeneration of fat cells from myofibroblasts during wound healing. Science . 2017;355(6326):748-752. doi:10.1126/science.aai8792 Bolte AC, Shapiro DA, Dutta AB, et al. The meningeal transcriptional response to traumatic brain injury and aging. eLife . 2023;12:1-38. doi:10.7554/eLife.81154 Yahn SL, Li J, Goo I, Gao H, Brambilla R, Lee JK. Neurobiology of Disease Fibrotic scar after experimental autoimmune encephalomyelitis inhibits oligodendrocyte di ff erentiation. Neurobiol Dis . 2020;134(October 2019):104674. doi:10.1016/j.nbd.2019.104674 Liu X, Liu Y, Jin H, et al. Reactive Fibroblasts in Response to Optic Nerve Crush Injury. Mol Neurobiol . 2021;58(4):1392-1403. doi:10.1007/s12035-020-02199-4 Şekerdağ-Kılıç E, Ulusoy C, Atak D, et al. Perivascular PDGFRB+ cells accompany lesion formation and clinical evolution differentially in two different EAE models. Mult Scler Relat Disord . 2023;69:104428. doi:10.1016/j.msard.2022.104428 Ewing-Crystal NA, Mroz NM, Larpthaveesarp A, et al. Dynamic fibroblast–immune interactions shape recovery after brain injury. Nature . 2025;646(8086):934-944. doi:10.1038/s41586-025-09449-2 Protzmann J, Zeitelhofer M, Stefanitsch C, et al. PDGFRα inhibition reduces myofibroblast expansion in the fibrotic rim and enhances recovery after ischemic stroke. J Clin Invest . 2025;135(5):e171077. doi:10.1172/JCI171077 Goritz C, Dias D, Tomilin N, Barbacid M, Shupliakov O, Frisen J. A Pericyte Origin of Spinal Cord Scar Tissue. Science . 2011;333(July):238-243. doi:10.1126/science.1203165 Bernier LP, Hefendehl JK, Scott RW, et al. Brain pericytes and perivascular fibroblasts are stromal progenitors with dual functions in cerebrovascular regeneration after stroke. Nat Neurosci . 2025;28(3):517-535. doi:10.1038/s41593-025-01872-y Dias DO, Kim H, Holl D, et al. Reducing Pericyte-Derived Scarring Promotes Recovery after Spinal Cord Injury. Cell . 2018;173(1):153-165. doi:10.1016/j.cell.2018.02.004 Dias DO, Kalkitsas J, Kelahmetoglu Y, et al. Pericyte-derived fibrotic scarring is conserved across diverse central nervous system lesions. Nat Commun . 2021;12(1):5501. doi:10.1038/s41467-021-25585-5 Franklin RJM, Simons M. CNS remyelination and inflammation: From basic mechanisms to therapeutic opportunities. Neuron . 2022;110(21):3549-3565. doi:10.1016/j.neuron.2022.09.023 Gluck L, Gerstein B, Kaunzner UW. Repair mechanisms of the central nervous system: From axon sprouting to remyelination. Neurotherapeutics . 2025;22(4):e00583. doi:10.1016/j.neurot.2025.e00583 Bergner CG, Van Der Meer F, Franz J, et al. BCAS1-positive oligodendrocytes enable efficient cortical remyelination in multiple sclerosis. Brain . 2025;148(3):908-920. doi:10.1093/brain/awae293 Lubetzki C, Zalc B, Williams A, Stadelmann C, Stankoff B. Remyelination in multiple sclerosis : from basic science to clinical translation. Lancet Neurol . 2020;19(8):678-688. doi:10.1016/S1474-4422(20)30140-X Niu J, Verkhratsky A, Butt A, Yi C. Demyelination and Remyelination: General Principles. Adv Neurobiol . 2025;43:207-255. doi:10.1007/978-3-031-87919-7_9 Mei F, Lehmann-Horn K, Shen YAA, et al. Accelerated remyelination during inflammatory demyelination prevents axonal loss and improves functional recovery. eLife . 2016;5(September):1-21. doi:10.7554/eLife.18246 Ghorbani S, Jelinek E, Jain R, et al. Versican promotes T helper 17 cytotoxic inflammation and impedes oligodendrocyte precursor cell remyelination. Nat Commun . 2022;13(1):1-18. doi:10.1038/s41467-022-30032-0 Rawji KS, Young AMH, Ghosh T, et al. Niacin-mediated rejuvenation of macrophage/microglia enhances remyelination of the aging central nervous system. Acta Neuropathol (Berl) . 2020;139(5):893-909. doi:10.1007/s00401-020-02129-7 Mukherjee T, McMurran CE, Holland J, et al. Ageing and remyelination failure in people with multiple sclerosis. Brain J Neurol . Published online October 6, 2025:awaf373. doi:10.1093/brain/awaf373 Dong Y, Jain RW, Lozinski BM, et al. Single-cell and spatial RNA sequencing identify perturbators of microglial functions with aging. Nat Aging . Published online 2022. doi:10.1038/s43587-022-00205-z Koch M, Mostert J, Heersema D, De Keyser J. Progression in multiple sclerosis: Further evidence of an age dependent process. J Neurol Sci . 2007;255(1-2):35-41. doi:10.1016/j.jns.2007.01.067 Brack AS, Conboy MJ, Roy S, et al. Increased Wnt signaling during aging alters muscle stem cell fate and increases fibrosis. Science . 2007;317(5839):807-810. doi:10.1126/science.1144090 Shen S, Sandoval J, Swiss VA, et al. Age-dependent epigenetic control of differentiation inhibitors is critical for remyelination efficiency. Nat Neurosci . 2008;11(9):1024-1034. doi:10.1038/nn.2172 Nicaise AM, Wagstaff LJ, Willis CM, et al. Cellular senescence in progenitor cells contributes to diminished remyelination potential in progressive multiple sclerosis. Proc Natl Acad Sci U S A . 2019;116(18):9030-9039. doi:10.1073/pnas.1818348116 Neumann B, Baror R, Zhao C, et al. Metformin Restores CNS Remyelination Capacity by Rejuvenating Aged Stem Cells. Cell Stem Cell . 2019;25(4):473-485.e8. doi:10.1016/j.stem.2019.08.015 Dong Y, D’Mello C, Pinsky W, et al. Oxidized phosphatidylcholines found in multiple sclerosis lesions mediate neurodegeneration and are neutralized by microglia. Nat Neurosci . 2021;24(4):489-503. doi:10.1038/s41593-021-00801-z Keough MB, Jensen SK, Wee Yong V. Experimental demyelination and remyelination of murine spinal cord by focal injection of lysolecithin. J Vis Exp . 2015;2015(97):1-8. doi:10.3791/52679 Mishra MK, Rawji KS, Keough MB, et al. Harnessing the Benefits of Neuroinflammation: Generation of Macrophages/Microglia with Prominent Remyelinating Properties. J Neurosci Off J Soc Neurosci . 2021;41(15):3366-3385. doi:10.1523/JNEUROSCI.1948-20.2021 He S, Bhatt R, Brown C, et al. High-plex imaging of RNA and proteins at subcellular resolution in fixed tissue by spatial molecular imaging. Nat Biotechnol . 2022;40(12):1794-1806. doi:10.1038/s41587-022-01483-z Stringer C, Wang T, Michaelos M, Pachitariu M. Cellpose: a generalist algorithm for cellular segmentation. Nat Methods . 2021;18(1):100-106. doi:10.1038/s41592-020-01018-x Hao Y, Stuart T, Kowalski MH, et al. Dictionary learning for integrative, multimodal and scalable single-cell analysis. Nat Biotechnol . 2024;42(2):293-304. doi:10.1038/s41587-023-01767-y Korsunsky I, Millard N, Fan J, et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat Methods . 2019;16(12):1289-1296. doi:10.1038/s41592-019-0619-0 Jin S, Guerrero-Juarez CF, Zhang L, et al. Inference and analysis of cell-cell communication using CellChat. Nat Commun . 2021;12:1088. doi:10.1038/s41467-021-21246-9 Choudhary S, Satija R. Comparison and evaluation of statistical error models for scRNA-seq. Genome Biol . 2022;23:27. doi:10.1186/s13059-021-02584-9 Hafemeister C, Satija R. Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression. Genome Biol . 2019;20:296. doi:10.1186/s13059-019-1874-1 Jain RW, Elliott DA, Yong VW. Single Cell Analysis of High-Parameter Histology Images Using Histoflow Cytometry. J Immunol . 2023;210(12):2038-2049. doi:10.4049/jimmunol.2200700 Plemel JR, Stratton JA, Michaels NJ, et al. Microglia response following acute demyelination is heterogeneous and limits infiltrating macrophage dispersion. Sci Adv . 2020;6(3):eaay6324. doi:10.1126/sciadv.aay6324 Lozinski BM, de Almeida LGN, Silva C, et al. Exercise rapidly alters proteomes in mice following spinal cord demyelination. Sci Rep . 2021;11(1). doi:10.1038/s41598-021-86593-5 Buhl EM, Djudjaj S, Klinkhammer BM, et al. Dysregulated mesenchymal PDGFR‐β drives kidney fibrosis. EMBO Mol Med . 2020;12. doi:10.15252/emmm.201911021 Henderson NC, Arnold TD, Katamura Y, et al. Targeting of αv integrin identifies a core molecular pathway that regulates fibrosis in several organs. Nat Med . 2013;19(12):1617-1624. doi:10.1038/nm.3282 Holl D, Hau WF, Julien A, et al. Distinct origin and region-dependent contribution of stromal fibroblasts to fibrosis following traumatic injury in mice. Nat Neurosci . Published online June 7, 2024. doi:10.1038/s41593-024-01678-4 Melchor GS, Baydyuk M, Manavi Z, Hu J, Huang JK. Dissecting the evolving cellular landscape of a remyelinating microenvironment. bioRxiv . Preprint posted online December 25, 2024:2024.12.25.630253. doi:10.1101/2024.12.25.630253 Jensen SK, Michaels NJ, Ilyntskyy S, Keough MB, Kovalchuk O, Yong VW. Multimodal Enhancement of Remyelination by Exercise with a Pivotal Role for Oligodendroglial PGC1α. Cell Rep . 2018;24(12):3167-3179. doi:10.1016/j.celrep.2018.08.060 López-Otín C, Blasco MA, Partridge L, Serrano M, Kroemer G. Hallmarks of aging: An expanding universe. Cell . 2023;186(2):243-278. doi:10.1016/j.cell.2022.11.001 Goldschmidt T, Antel J, Konig FB, Bruck W, Kuhlmann T. Remyelination capacity of the MS brain decreases with disease chronicity. Neurology . 2009;72(22):1914-1921. Segel M, Neumann B, Hill MFE, et al. Niche stiffness underlies the ageing of central nervous system progenitor cells. Nature . 2019;573(7772):130-134. doi:10.1038/s41586-019-1484-9 Absinta M, Maric D, Gharagozloo M, et al. A lymphocyte – microglia – astrocyte axis in chronic active multiple sclerosis. Nature . 2021;(November 2020). doi:10.1038/s41586-021-03892-7 Yang AC, Vest RT, Kern F, et al. A human brain vascular atlas reveals diverse mediators of Alzheimer’s risk. Nature . 2022;603(7903):885-892. doi:10.1038/s41586-021-04369-3 Vanlandewijck M, He L, Mäe MA, et al. A molecular atlas of cell types and zonation in the brain vasculature. Nature . 2018;554(7693):475-480. doi:10.1038/nature25739 Rustenhoven J, Drieu A, Mamuladze T, et al. Functional characterization of the dural sinuses as a neuroimmune interface. Cell . 2021;184(4):1000-1016.e27. doi:10.1016/j.cell.2020.12.040 Bernier LP, Hefendehl JK, Scott RW, et al. Brain pericytes and perivascular fibroblasts are stromal progenitors with dual functions in cerebrovascular regeneration after stroke. Nat Neurosci . 2025;28(3):517-535. doi:10.1038/s41593-025-01872-y Stephenson EL, Mishra MK, Moussienko D, et al. Chondroitin sulfate proteoglycans as novel drivers of leucocyte infiltration in multiple sclerosis. Brain . Published online 2018:1094-1110. doi:10.1093/brain/awy033 Ghorbani S, Yong VW. The extracellular matrix as modifier of neuroinflammation and remyelination in multiple sclerosis. Brain . 2021;144(7):1958-1973. doi:10.1093/brain/awab059 Yamazaki R, Azuma M, Osanai Y, et al. Type I collagen secreted in white matter lesions inhibits remyelination and functional recovery. Cell Death Dis . 2025;16(1):285. doi:10.1038/s41419-025-07633-w Wang F, Ren SY, Chen JF, et al. Myelin degeneration and diminished myelin renewal contribute to age-related deficits in memory. Nat Neurosci . 2020;23(4):481-486. doi:10.1038/s41593-020-0588-8 Shields SA, Gilson JM, Blakemore WF, Franklin RJM. Remyelination occurs as extensively but more slowly in old rats compared to young rats following gliotoxin-induced CNS demyelination. Glia . 1999;28(1):77-83. doi:10.1002/(SICI)1098-1136(199910)28:1%3C77::AID-GLIA9%3E3.0.CO;2-F Gross PS, Durán-Laforet V, Ho LT, et al. Senescent-like microglia limit remyelination through the senescence associated secretory phenotype. Nat Commun . 2025;16(1):2283. doi:10.1038/s41467-025-57632-w Müller L, Di Benedetto S. Aging brain: exploring the interplay between bone marrow aging, immunosenescence, and neuroinflammation. Front Immunol . 2024;15:1393324. doi:10.3389/fimmu.2024.1393324 Rawji KS, Mishra MK, Michaels NJ, Rivest S, Stys PK, Yong VW. Immunosenescence of microglia and macrophages: Impact on the ageing central nervous system. Brain . 2016;139(3):653-661. doi:10.1093/brain/awv395 Hoeft K, Schaefer GJL, Kim H, et al. Platelet-instructed SPP1+ macrophages drive myofibroblast activation in fibrosis in a CXCL4-dependent manner. Cell Rep . 2023;42(2):112131. doi:10.1016/j.celrep.2023.112131 Yu R, Lozinski BM, Seifert A, et al. Oxidized phosphatidylcholines deposition drives chronic neurodegeneration in a mouse model of progressive multiple sclerosis via IL-1β signaling. Nat Neurosci . 2026;29(1):67-80. doi:10.1038/s41593-025-02113-y Additional Declarations No competing interests reported. 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Melchor","email":"","orcid":"","institution":"Georgetown University","correspondingAuthor":false,"prefix":"","firstName":"George","middleName":"S.","lastName":"Melchor","suffix":""},{"id":623831267,"identity":"4daeac38-6568-4140-9df3-ff0d740b22ba","order_by":5,"name":"Samira Ghorbani","email":"","orcid":"","institution":"University of Calgary","correspondingAuthor":false,"prefix":"","firstName":"Samira","middleName":"","lastName":"Ghorbani","suffix":""},{"id":623831268,"identity":"a4fb5680-97ce-4646-8e27-261444c58089","order_by":6,"name":"Cenxiao Li","email":"","orcid":"","institution":"University of Calgary","correspondingAuthor":false,"prefix":"","firstName":"Cenxiao","middleName":"","lastName":"Li","suffix":""},{"id":623831269,"identity":"f558e2cd-926f-45b0-90b9-d7eb3e5062c6","order_by":7,"name":"Marlene Morch","email":"","orcid":"","institution":"University of Calgary","correspondingAuthor":false,"prefix":"","firstName":"Marlene","middleName":"","lastName":"Morch","suffix":""},{"id":623831270,"identity":"8335002f-1227-4cea-bfa3-8e899c85555f","order_by":8,"name":"Dorsa Moezzi","email":"","orcid":"","institution":"University of Calgary","correspondingAuthor":false,"prefix":"","firstName":"Dorsa","middleName":"","lastName":"Moezzi","suffix":""},{"id":623831271,"identity":"057173aa-0420-4efa-952b-b27e68d74b52","order_by":9,"name":"Rajiv Jain","email":"","orcid":"","institution":"University of Calgary","correspondingAuthor":false,"prefix":"","firstName":"Rajiv","middleName":"","lastName":"Jain","suffix":""},{"id":623831272,"identity":"5378c386-fc20-445d-ac35-119c01f37d7d","order_by":10,"name":"Parisa Etemadi Nezhad","email":"","orcid":"","institution":"University of Calgary","correspondingAuthor":false,"prefix":"","firstName":"Parisa","middleName":"Etemadi","lastName":"Nezhad","suffix":""},{"id":623831273,"identity":"ca4e8fcd-eb3d-4c2f-bc22-3a585b0e9416","order_by":11,"name":"Chang-Chun Ling","email":"","orcid":"","institution":"University of Calgary","correspondingAuthor":false,"prefix":"","firstName":"Chang-Chun","middleName":"","lastName":"Ling","suffix":""},{"id":623831274,"identity":"6faea90b-5fb7-41bf-abd5-21e311733438","order_by":12,"name":"Jeffrey K. Huang","email":"","orcid":"","institution":"Georgetown University","correspondingAuthor":false,"prefix":"","firstName":"Jeffrey","middleName":"K.","lastName":"Huang","suffix":""},{"id":623831275,"identity":"1a7c580a-e93c-448f-a447-1afaa9490cb2","order_by":13,"name":"Carlos Camara-Lemarroy","email":"","orcid":"","institution":"University of Calgary","correspondingAuthor":false,"prefix":"","firstName":"Carlos","middleName":"","lastName":"Camara-Lemarroy","suffix":""},{"id":623831276,"identity":"7ffded30-72c2-4cff-bf8a-da4b0907d176","order_by":14,"name":"Rui Li","email":"","orcid":"","institution":"Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Rui","middleName":"","lastName":"Li","suffix":""},{"id":623831277,"identity":"ac1d4945-4502-4853-8ca2-26aad5ac2303","order_by":15,"name":"V. Wee Yong","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA10lEQVRIiWNgGAWjYBACgwMgsgKI2UnTcgaImUnSwthGihbJGcnHHn6dd1iev5n94ufKNgZ5/gaCWtLSjWW3HTaccZinWPJsG4PhjAMEteSYSUtuO5zAcJgnQbKxjSGBgZAWfgmQljmHE+QP8yT/BGmRJ0aL5MeGwwkGh9mPgW0xIKSFjedZmjTDsXTDjYd52CwbzkkYbiSohT35mOSPGmt5uePtj282lNnIyxHSwiCQwMDMA2bxGDAwskkQUg/yzAEGxh9gFvsDBoY/ROgYBaNgFIyCEQcAgWZAgiYAHKEAAAAASUVORK5CYII=","orcid":"","institution":"University of Calgary","correspondingAuthor":true,"prefix":"","firstName":"V.","middleName":"Wee","lastName":"Yong","suffix":""}],"badges":[],"createdAt":"2026-04-05 14:53:45","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9327007/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9327007/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107481086,"identity":"c3811f38-6b00-415c-9dbc-98c3fef4d2be","added_by":"auto","created_at":"2026-04-22 02:15:46","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2398754,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFibroblasts localize in LPC demyelinated spinal cord white matter.\u003c/strong\u003e (\u003cstrong\u003ea-d\u003c/strong\u003e) Immunofluorescence images of day 7 LPC lesions of 6-10 weeks old mice labeled with: (\u003cstrong\u003ea\u003c/strong\u003e) DAPI, MBP, NFH and GFAP in wildtype mice; (\u003cstrong\u003eb\u003c/strong\u003e) DAPI, Iba1 and TdTomato (TdTom) in PDGFRβ-TdTom mice; (\u003cstrong\u003ec\u003c/strong\u003e) DAPI, TdTom and PDGFRβ in PDGFRβ-TdTom mice; (\u003cstrong\u003ed\u003c/strong\u003e) DAPI, PDGFRβ, COL1A1 and SMA in wildtype mice. Scale bar = 100 μm, inset = 25 μm. (\u003cstrong\u003ee-j\u003c/strong\u003e) Comparison of day 7 and day 21 lesion, where each dot represents data per section per animal from the beginning of the lesion (zero distance) for (\u003cstrong\u003ee\u003c/strong\u003e) lesion area, (\u003cstrong\u003ef\u003c/strong\u003e) TdTom+ area, or (\u003cstrong\u003eg\u003c/strong\u003e) the % of lesion covered by TdT; their area under the curve (AUC) across the sections (thus volume) for individual mice are shown for (\u003cstrong\u003eh\u003c/strong\u003e) lesion size, (\u003cstrong\u003ei\u003c/strong\u003e) TdTom, and (\u003cstrong\u003ej\u003c/strong\u003e) Tdtom/lesion plots. (\u003cstrong\u003ek\u003c/strong\u003e) Representative images of day 7 and 21 LPC lesions labeling for PDGFRβ-TdTom and (left) periostin (POSTN), (middle) fibronectin (FN1), and (right) SMA. Scale bar = 100 μm. (\u003cstrong\u003el-n\u003c/strong\u003e) Graphs comparing the proportion of day 7 and 21 lesions positive for (\u003cstrong\u003el\u003c/strong\u003e) POSTN, (\u003cstrong\u003em\u003c/strong\u003e) FN1, (\u003cstrong\u003en\u003c/strong\u003e) SMA. Data was acquired from two experiments; \u003cem\u003en\u003c/em\u003e=8 per experimental group. Significance is indicated as *\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; unpaired \u003cem\u003et\u003c/em\u003e-test, comparing day 7 and day 21 LPC lesions. Data presented as the mean.\u003c/p\u003e","description":"","filename":"Figure1to9April520261.png","url":"https://assets-eu.researchsquare.com/files/rs-9327007/v1/c7b70f473475d0f32db388b0.png"},{"id":107099676,"identity":"a0daa60f-f5a0-4142-b194-52b5fa8d6a74","added_by":"auto","created_at":"2026-04-16 18:37:46","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":304312,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSingle-nucleus RNA sequencing confirms the prominence of fibroblasts in LPC lesions.\u003c/strong\u003e (\u003cstrong\u003ea\u003c/strong\u003e) Dotplot of broadly annotated vascular and mesenchymal cell populations highlighting marker genes used for annotation. (\u003cstrong\u003eb\u003c/strong\u003e) UMAP plot of broadly annotated vascular and mesenchymal cell populations (left), and at the different timepoints investigated post-demyelination (\u003cstrong\u003ec\u003c/strong\u003e). (\u003cstrong\u003ed\u003c/strong\u003e) Bar plot showing the relative frequencies of broadly annotated vascular/mesenchymal subpopulations in each experimental timepoint, highlighting the prominence of fibroblasts in lesioned samples.\u003c/p\u003e","description":"","filename":"Figure1to9April520262.png","url":"https://assets-eu.researchsquare.com/files/rs-9327007/v1/bb8ea8c0dbb9ef7fe5a51c2d.png"},{"id":107483040,"identity":"ba3c306d-d118-42af-af87-ffc149855219","added_by":"auto","created_at":"2026-04-22 02:26:00","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":894616,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSpatial transcriptomics of fibroblasts in demyelinated lesions.\u003c/strong\u003e (\u003cstrong\u003ea, b\u003c/strong\u003e) Representative images of annotated cells in (\u003cstrong\u003ea\u003c/strong\u003e) sham and (\u003cstrong\u003eb\u003c/strong\u003e) LPC demyelinated spinal cords at 14 dpi. Lesion edge indicated by white dashed line. (\u003cstrong\u003ec, d\u003c/strong\u003e) UMAP visualization of 2954 cells from LPC and sham colored by (\u003cstrong\u003ec\u003c/strong\u003e) experimental group and (\u003cstrong\u003ed\u003c/strong\u003e) mapped cell clusters (20 principal components, resolution=0.7). (\u003cstrong\u003ee\u003c/strong\u003e) Heatmap of top 5 differentially expressed genes across annotated cell groups as determined using FindAllMarkers (default Wilcoxon Rank Sum test, min. logfc threshold = 0.25). (\u003cstrong\u003ef, g\u003c/strong\u003e) Bar graph showing (\u003cstrong\u003ef\u003c/strong\u003e) the total number of cells, and (\u003cstrong\u003eg\u003c/strong\u003e) the fold change in cell groups between LPC and sham groups. Abbreviations: M/M – microglia/macrophages; OL – oligodendrocytes; OPCs – oligodendrocyte precursor cells; EC – endothelial cells; VLMC – vascular leptomeningeal cells.\u003c/p\u003e","description":"","filename":"Figure1to9April520263.png","url":"https://assets-eu.researchsquare.com/files/rs-9327007/v1/9b729531483d3cfb00d216b0.png"},{"id":107099679,"identity":"94957537-1442-4907-bafb-ea4e3e732c10","added_by":"auto","created_at":"2026-04-16 18:37:47","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1614546,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePost inflammation fibroblast response stimulated by microglia/macrophages.\u003c/strong\u003e (\u003cstrong\u003ea, b\u003c/strong\u003e) Representative image of (\u003cstrong\u003ea\u003c/strong\u003e) day 3 and (\u003cstrong\u003eb\u003c/strong\u003e) day 7 LPC lesion labelling for DAPI, PDGFRβ, and Iba1. Scale bar = 100 μm. (\u003cstrong\u003ec, d\u003c/strong\u003e) Graphs indicating day 3, 7, 14, and 21 day (\u003cstrong\u003ec\u003c/strong\u003e) Iba1 positive % area of lesion and (\u003cstrong\u003ed\u003c/strong\u003e) PDGFRβ positive % area of lesion. (\u003cstrong\u003ee\u003c/strong\u003e) Schematic of experimental design. Meningeal fibroblasts were added to upper compartment and BMDMs or medium alone added to the lower compartment. (\u003cstrong\u003ef\u003c/strong\u003e) Representative images of hematoxylin stained transwell inserts from BMDM and medium control groups. (\u003cstrong\u003eg\u003c/strong\u003e) Graph comparing the amount of hematoxylin stained meningeal fibroblasts migrated across the transwell membrane in BMDM and medium alone control. (\u003cstrong\u003ec, d\u003c/strong\u003e) \u003cem\u003en\u003c/em\u003e=5 (D3), 10 (D7), 7 (D14), 5 (D21); (\u003cstrong\u003eg\u003c/strong\u003e) \u003cem\u003en\u003c/em\u003e = 4 for each experimental group; Significance indicated as *\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05, **\u003cem\u003eP\u003c/em\u003e\u0026lt;0.01, one-way ANOVA comparing time points (D3, 7, 14, or 21) (\u003cstrong\u003ec, d\u003c/strong\u003e); or two-tailed unpaired \u003cem\u003et\u003c/em\u003e-test (\u003cstrong\u003eg\u003c/strong\u003e). All data presented as the mean.\u003c/p\u003e","description":"","filename":"Figure1to9April520264.png","url":"https://assets-eu.researchsquare.com/files/rs-9327007/v1/94aac0f419595915e6347108.png"},{"id":107099677,"identity":"d5fc4144-bc90-42c7-88ba-7f3e572d4b9c","added_by":"auto","created_at":"2026-04-16 18:37:47","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2160325,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFibroblasts spatially exclude OPCs in LPC lesions and in vitro.\u003c/strong\u003e(\u003cstrong\u003ea\u003c/strong\u003e) Representative images of day 14 LPC lesion stained for PDGFRβ, Olig2, and PDGFRα. (\u003cstrong\u003eb\u003c/strong\u003e) Representative images of day 14 LPC lesion in NG2CreER:MAPTmGFP mice labelling for DAPI, PDGFRβ, NG2-GFP. (\u003cstrong\u003ec\u003c/strong\u003e) Graph comparing GFP positive area in day 14 LPC lesions. PDGFRβ positive area of LPC lesion (red) and PDGFRβ negative area of LPC lesion (white). \u003cem\u003en\u003c/em\u003e = 9 , Significance indicated ****\u003cem\u003eP\u003c/em\u003e\u0026lt;0.0001, two-tailed unpaired \u003cem\u003et\u003c/em\u003e-test. Data presented as mean. (\u003cstrong\u003ed\u003c/strong\u003e) Representative image of OPC alone (top, with magnified image of box at right) and OPC-fibroblast co-culture (bottom, with magnified image of box displayed at right) labelled for nuclei (blue), O4 (green), MBP (red), and PDGFRβ (white). (\u003cstrong\u003ee-g\u003c/strong\u003e) Graphs indicating fold change in (\u003cstrong\u003ee\u003c/strong\u003e) O4 positive OPCs, (\u003cstrong\u003ef\u003c/strong\u003e) O4 positive MBP positive mature oligodendrocytes, and (\u003cstrong\u003eg\u003c/strong\u003e) mean processes per cell comparing co-cultures with OPC alone controls. \u003cem\u003en\u003c/em\u003e = 2 independent experiments with 4 replicates for \u003cstrong\u003ee-g\u003c/strong\u003e. Significance indicated as *\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05, **\u003cem\u003eP\u003c/em\u003e\u0026lt;0.01, two-tailed, unpaired \u003cem\u003et\u003c/em\u003e-test, Data presented as mean.\u003c/p\u003e","description":"","filename":"Figure1to9April520265.png","url":"https://assets-eu.researchsquare.com/files/rs-9327007/v1/6d3ffa29ce9ca19916e51612.png"},{"id":107099678,"identity":"cd205fb2-01fc-4703-b9e0-da561b973d87","added_by":"auto","created_at":"2026-04-16 18:37:47","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1964783,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAge exacerbates fibroblast response to demyelination.\u003c/strong\u003e (\u003cstrong\u003ea\u003c/strong\u003e) Representative immunofluorescence images of LPC lesions in young (6-10 week old) and middle-aged (48-52 weeks) mice labeled with DAPI and MBP (red) or PDGFRβ (white). Lesion is indicated with white dashed line. (\u003cstrong\u003eb-f\u003c/strong\u003e) Graphs comparing the extent of demyelination (MBP-minus) and PDGFRβ+ fibroblasts in LPC-spinal cord lesions at days 7, 14 and 21 in young and middle-aged mice, where each dot in panels \u003cstrong\u003eb\u003c/strong\u003e and \u003cstrong\u003ec\u003c/strong\u003e represents data per section per animal from the beginning of the lesion (zero distance); the data across each animal is then used to generate the corresponding volume of lesion (\u003cstrong\u003ed\u003c/strong\u003e) and PDGFRβ+ cells (\u003cstrong\u003ee\u003c/strong\u003e), and the % of lesion covered by PDGFRβ+ immunoreactivity. For young group, \u003cem\u003en\u003c/em\u003e = 4 (D7), 4 (D14), 5 (D21; for middle-aged group \u003cem\u003en\u003c/em\u003e = 5 for each time point. Significance is indicated as *\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05, **P\u0026lt;0.01; unpaired, \u003cem\u003et\u003c/em\u003e-test, comparing young and middle-aged groups. Data is presented as the mean.\u003c/p\u003e","description":"","filename":"Figure1to9April520266.png","url":"https://assets-eu.researchsquare.com/files/rs-9327007/v1/6ace72b6d96a8fca9dbba314.png"},{"id":107099680,"identity":"80deb78b-b5c7-4e65-a7cf-85f44984e4c5","added_by":"auto","created_at":"2026-04-16 18:37:47","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":2137282,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAge enhances fibroblast properties in demyelinated lesions.\u003c/strong\u003e (\u003cstrong\u003ea)\u003c/strong\u003eRepresentative immunofluorescence images of day 7 LPC lesions in young and middle-aged mice labeled for DAPI, PDGFRβ and SMA; and quantification of the proportion of PDGFRβ overlapping with SMA at 7 and 21 dpi in young and middle-aged mice. (\u003cstrong\u003eb)\u003c/strong\u003e Representative immunofluorescence images of day 7 LPC lesion of young mice labeled for DAPI, PDGFRβ, and Col1a1; and quantification of Col1a1 positive area at 7 and 21 dpi in young and middle-aged mice. (\u003cstrong\u003ec) \u003c/strong\u003eRepresentative immunofluorescence images of day 7 LPC lesion in young mice labeled for DAPI and LAMA (laminin); and quantification of LAMA positive area at 7 and 21 dpi in young and middle-aged mice. (\u003cstrong\u003ed)\u003c/strong\u003eRepresentative immunofluorescence images of day 7 LPC lesion in young mice labeled for DAPI, Olig2, and PDGFRβ; graphs comparing the number of Olig2 positive cells within PDGFRβ positive regions of the LPC lesion at 7 and 21 days post LPC injury. In the graphs, each dot is an individual mouse. Significance is indicated as *\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05, **\u003cem\u003eP\u003c/em\u003e\u0026lt;0.01, ***P\u0026lt;0.001; unpaired, \u003cem\u003et\u003c/em\u003e-test, comparing young and middle-aged groups. Data presented as mean.\u003c/p\u003e","description":"","filename":"Figure1to9April520267.png","url":"https://assets-eu.researchsquare.com/files/rs-9327007/v1/60df17b7b3d45fa88fd899a4.png"},{"id":107481585,"identity":"d871f56b-c795-400b-b075-efcf42ad345d","added_by":"auto","created_at":"2026-04-22 02:19:16","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":1433128,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFibroblasts in MS lesions.\u003c/strong\u003e (\u003cstrong\u003ea)\u003c/strong\u003e Representative immunofluorescence images of MS lesions labeled for DAPI, CD45, and PDGFRβ. This is observed in 3 different MS cases (not shown) from the Montreal Bank. (\u003cstrong\u003eb) \u003c/strong\u003eUMAP visualization of 66,432 cells from MS lesions and their cell annotation\u003csup\u003e51\u003c/sup\u003e. (\u003cstrong\u003ec)\u003c/strong\u003e Dot plot indicating markers used to identify fibroblast population. Size of the dot indicates the percentage of cells while the color encodes the average expression levels across all cells. (\u003cstrong\u003ed)\u003c/strong\u003e Violin plots depicting log normalized gene expression of fibroblast markers comparing PDGFRβ positive fibroblast population to other cell groups. (\u003cstrong\u003ee, f)\u003c/strong\u003e. UMAP visualization of (\u003cstrong\u003ee\u003c/strong\u003e) subclusters of PDGFRβ positive fibroblast population with top 10 DEGs, and (\u003cstrong\u003ef\u003c/strong\u003e) distribution of PDGFRβ positive fibroblast subclusters across MS lesion types and frontal white matter from neurologically normal controls.\u003c/p\u003e","description":"","filename":"Figure1to9April520268.png","url":"https://assets-eu.researchsquare.com/files/rs-9327007/v1/8d7fdd72f038718bdcd53971.png"},{"id":107480707,"identity":"ea853233-fd39-4825-add3-5a5d9075cb0a","added_by":"auto","created_at":"2026-04-22 02:13:12","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":3121347,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFibroblasts are found in active and chronic active MS lesions.\u003c/strong\u003e The nature of demyelinated (PLP-negative) lesions was identified by HLADR+ microglia/macrophages uniformly throughout a plaque (active) (\u003cstrong\u003ea\u003c/strong\u003e), or at the rim (chronic active) (\u003cstrong\u003eb\u003c/strong\u003e). Panels 1-3 show higher magnification inserts. PDGFRβ+Col1a1+ cells were detected in both lesion types, associated with vascular structures (e.g. 1 in a, 3 in b) or in parenchyma (e.g. 2 in a). These results were observed in 5 of 5 active, and 5 of 5 chronic active lesions. Panel 4 presents an Imaris 3D reconstruction demonstrating close spatial localization and partial overlap of PDGFRβ and Col1a1. Scale bars: 50μm.\u003c/p\u003e","description":"","filename":"Figure1to9April520269.png","url":"https://assets-eu.researchsquare.com/files/rs-9327007/v1/15680e45450b44ebed6068ae.png"},{"id":108005847,"identity":"0ee056c8-f4eb-466e-906d-3ceac7cb2ac7","added_by":"auto","created_at":"2026-04-28 12:49:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":15359682,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9327007/v1/4e33e41a-d50d-4d92-acac-7b63f35c1703.pdf"},{"id":107099674,"identity":"97d71e49-ccc5-4e88-85bf-8e961d3ceabf","added_by":"auto","created_at":"2026-04-16 18:37:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1434948,"visible":true,"origin":"","legend":"","description":"","filename":"SuppFig1to11April52026reduced.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9327007/v1/239a03d35f5e32858e081276.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Aging-enhanced accumulation of fibroblasts excludes oligodendrocytes in demyelinated lesions","fulltext":[{"header":"Introduction","content":"\u003cp\u003eFibroblasts are a heterogeneous cell population important for synthesis of extracellular matrix (ECM) components, maintenance of cellular niches, tissue regeneration and wound healing\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. In these roles, fibroblasts communicate with immune cells such as macrophages. Fibroblasts associated with the CNS primarily reside in the meninges, perivascular space, and choroid plexus where they contribute to tissue homeostasis\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e and immune regulation\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Several recent studies suggest that in response to neural injury, these border-associated fibroblasts accumulate in the CNS parenchyma and contribute to chronic fibrotic scaring\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan additionalcitationids=\"CR6 CR7 CR8 CR9 CR10\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Nonetheless, beneficial roles of fibroblasts may manifest in the acute phase after neural injury, and include regenerative functions and limiting the expansion of lesions\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Overall, while fibroblast activation is necessary to maintain tissue integrity, it may also ultimately impair regenerative processes\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eRemyelination is the most common and robust form of CNS regeneration\u003csup\u003e\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e, involving the migration, proliferation, and differentiation of oligodendrocyte progenitor cells (OPCs) into mature myelinating oligodendrocytes\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Restoring myelin to denuded axons reconstitutes saltatory conduction, axonal trophic support, and produces a mechanical barrier to injury leading to axonal preservation and functional improvement\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. However, remyelination often fails due to extrinsic variables in the environment, such as inhibitory ECM, and intrinsic factors including age-related cellular dysfunctions\u003csup\u003e\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAge contributes to disease worsening across a range of conditions, including MS and fibrotic disorders\u003csup\u003e\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. OPCs also show signs of age-related impairment, including upregulation of senescence markers, epigenetic dysfunction, reduced responsiveness to growth factors, and impaired differentiation and remyelination capacity\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. While age-related decline in remyelination is well recognized, the impact of aging on fibroblast responses to CNS injury remains poorly understood.\u003c/p\u003e \u003cp\u003eIn this study, we characterized the response of fibroblasts in lesions following focal lysolecithin (LPC)-mediated demyelination of the spinal cord white matter, and further evaluated the influence of increasing age on the fibrotic response. We compared middle-aged to young mice, as middle-age is a period when many people with MS transition to progressive MS. With spatial transcriptomics of LPC tissue and cells in culture, we interrogated interactions between fibroblasts, microglia/macrophages and OPCs. Finally, we utilized a publicly available transcriptomic dataset to show that fibroblasts are present in MS lesions, and describe communication patterns between fibroblasts, microglia/macrophages and OPCs in MS. Our findings highlight the necessity of considering invasion of fibroblasts into the CNS parenchyma in lesion evolution and recovery.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e\u003cstrong\u003eMS specimens\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePostmortem frozen brain tissues from people with MS were obtained from The Montreal Brain Bank courtesy of Dr. Alexandre Prat, with informed consent approved by the CRCHUM and University of Montreal research ethics committee. Additional autopsied\u0026nbsp;frozen MS brain tissues were from The Multiple Sclerosis and Parkinson\u0026rsquo;s Tissue Bank situated at Imperial College, London. The use of these human tissue in Calgary for research was approved by the Conjoint Health Research Ethics Board at the University of Calgary (Ethics ID REB15-0444). MS sections were characterized using luxol fast blue (LFB) and hematoxylin \u0026amp; eosin (H\u0026amp;E) to identify lesions as previously described\u003csup\u003e30\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMice\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll experiments were conducted with ethics approval from the Animal Care Committee at the University of Calgary under regulations of the Canadian Council of Animal Care. Female C57BL/6J mice were from Jackson laboratories. CX3CR1-CreER (JAX 021160), iDTR [Rosa26iDTR (JAX 007900)], PDGFR\u0026beta;-P2A-CreERT2 (JAX 030201), Ai9 [Rosa26-TdTomato (JAX 007905)], NG2-CreER (JAX 008538), and Tau-mGFP (JAX 021162) mice were from The Jackson Laboratory. All mouse strains were bred in the single barrier mouse unit at the University of Calgary. All young female mice were 6-10 weeks of age, and all middle-aged female mice were 48-52 weeks of age. Male and female CD1 pups P0-2 were used for OPC and meningeal fibroblast cultures. C57BL6 female mice 6-10 weeks of age were used for bone marrow-derived macrophages (BMDM) cultures. All mice were maintained on a 12-h light/dark cycle with food (Pico-Vac Mouse Diet 20) and water given ad libitum.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSpinal cord surgery\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLysolecithin/lysophosphatidylcholine (LPC) demyelination was accomplished as previously described\u003csup\u003e31\u003c/sup\u003e. Mice were anaesthetized with intraperitoneal injections of ketamine (100 mgkg-1) and xylazine (10 mgkg-1). Buprenorphine (0.05 mgkg-1) was given as an analgesic. Mice were positioned on a stereotaxic frame and a 2-3 cm incision was made between the shoulder blades. The intervertebral space between T3 and T4 was identified, and a 32-gauge needle attached to a 10 \u0026mu;L Hamilton syringe was used to inject 0.5 \u0026mu;L of 1% w/v LPC (Sigma-Aldrich, L1381) into the ventral column at a rate of 0.25 \u0026mu;Lmin-1 for 2 min. The needle was left for 2 min following the injection to avoid back flow. After \u0026nbsp;suturing, mice were placed in a thermally controlled environment for recovery.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSpinal cord tissue isolation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnaesthetized mice were transcardially perfused with 15 mL of phosphate-buffered saline (PBS). Dissected spinal cords were fixed in 4% paraformaldehyde (PFA) at 4\u003csup\u003eo\u003c/sup\u003eC overnight. Tissue was cryoprotected in 30% w/v sucrose solution for 72h then frozen in FSC 22 frozen section media (Leica). Coronal sections were cut with a cryostat and stored at -20\u003csup\u003eo\u003c/sup\u003eC until analysis. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImmunofluorescence staining\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSlides were permeabilized with 0.2% TritonX-100 and blocked with a 10% horse serum-containing solution. Primary antibody incubation was conducted overnight in phosphate-buffered saline (PBS) containing 0.1% cold fish stain gelatin and 0.1% Triton X-100. Slides were then stained with TrueBlack Lipofuscin Autofluorescence Quencher (Biotium) for 2 min, and incubated with secondary antibody and 1 \u0026micro;gmL-1 of DAPI for 1h before mounting. Antibodies used were anti-mouse myelin basic protein (MBP, BioLegend, PA1-10008), anti-mouse periostin (POSTN, R\u0026amp;D Systems AF2955), anti-mouse \u0026alpha; smooth muscle actin (\u0026alpha;SMA-Cy3, Millipore, C6198), anti-mouse fibronectin (FN1, Millipore, AB2033), anti-CD45 (Thermofisher, MA5-17687), rabbit anti-mouse pan-laminin (gift from L. Sorokin, University of M\u0026uuml;nster), anti-mouse/human PDGFR\u0026beta; (R\u0026amp;D Systems, AF1042), anti-mouse \u0026nbsp;Iba1 (Wako, 019-19741), anti-mouse CD45-AF488 (Biolegend, 103122), rabbit anti-collagen 1a1 (Col1a1, Invitrogen, PA1-26204), anti-mouse GFAP (Biolegend, PCK-591P), anti-mouse NFH (RPCA-NF-H), anti-mouse PDGFR\u0026alpha; (R\u0026amp;D Systems, AF1062), anti-mouse Olig2 (Millipore, ab9610), anti-green fluorescence protein (GFP, Aveslab, GFP-1020), anti-mouse sulfatide O4 (R\u0026amp;D Systems, MAB1326), anti-mouse arginase-1 (Arg1, BioLegend, 678802), and anti-mouse MHCII (Thermofisher, 13-5321-82). \u0026nbsp; Secondary antibodies from Jackson ImmunoResearch were AF488 donkey anti-mouse IgM, AF488 donkey anti-rabbit IgG, AF488 donkey anti-rat IgG, AF488 donkey anti-goat IgG, cyanine Cy3 donkey anti-goat IgG, cyanine Cy3 donkey anti-chicken IgY, AF647 donkey anti-rat IgG, and AF647 donkey anti-rabbit IgG.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConfocal immunofluorescence microscopy and analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eImages were collected with a Leica TCS Sp8 laser confocal microscope. Equal laser, gain and offset settings to maximize contrast and minimize saturation were consistently used for all samples within each set of experiments. Leica Application Suite X, ImageJ, and QuPath 0.3.1 were used for image acquisition and analysis. Maximum-intensity projections were created for each channel/marker. All images were blinded prior to analysis. ROI for lesions were drawn using DAPI or MBP. Volume analysis of ventral white matter lesions was done using MBP immunostaining of serial sections to manually trace the region of demyelination largely devoid of myelin, or containing intense fragmented MBP signal, per section, across the coronal sections from each mouse. Fibroblast regions were defined using threshold of PDGFR\u0026beta;-Tdtomato or PDGFR\u0026beta; immunostaining. Proportion of lesion occupied by fibroblasts was calculated by dividing the PDGFR\u0026beta;-Tdtomato or PDGFR\u0026beta; immunostained area by the MBP derived lesion area for the respective sections. Threshold intensity was determined using at least 3 images. The Analyze Particles function was then used to create a mask and to quantify the positive signals in each ROI using size exclusion of 2-infinity. Threshold intensity, size exclusion, and circularity settings for particle analysis were kept constant across all samples for each experimental set. Oligodendrocytes were counted manually using Olig2 and DAPI as a guide. When analyzing lesion spread, samples were excluded if more than 20% of sections were unable to be analysed due to tearing, folding, or other loss.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCell cultures and analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor fibroblasts, meninges from P0-2 CD1 pups were collected in RPMI and digested for 18 minutes at 37\u003csup\u003eo\u003c/sup\u003eC in 1 \u0026mu;gmL-1 DNase I (Sigma) and 3.7 \u0026mu;gmL-1 collagenase D (Sigma) triturating every 6 minutes. Meninges were centrifuged at 300xg for 3 minutes and cells were resuspended in growth medium (DMEM (Gibco) supplemented with 10% FBS, 1% non-essential amino-acids (NEAA), 1% GlutaMAX, 1% sodium pyruvate, and 1% penicillin-streptomycin (all from Gibco). Cells were incubated in T-75 flasks at 37\u003csup\u003eo\u003c/sup\u003eC with 5% CO2. Medium was replaced every 3 days and cells were passaged when 90% confluent.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMouse OPCs were cultured from cortices of P0 CD1 pups as previously described\u003csup\u003e21\u003c/sup\u003e. Mouse bone marrow-derived macrophages (BMDMs) were generated as detailed elsewhere\u003csup\u003e32\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eFor co-cultures, OPCs were added at a density of 31,250 cells/cm\u003csup\u003e2\u003c/sup\u003e to poly-L-lysine coated 96-well plates. Meningeal fibroblasts were added at particular densities as listed in Results. OPCs and meningeal fibroblasts were added together, or one added 2h before the other as defined in the experiment, in 100 \u0026micro;L of OPC differentiating medium. Cocultures were incubated at 37\u003csup\u003eo\u003c/sup\u003eC with 8.5% CO\u003csub\u003e2\u003c/sub\u003e for 72 hours. Cells were fixed and anti-O4 primary antibody was added in Licor antibody dilution buffer (1:250) overnight at 4\u003csup\u003eo\u003c/sup\u003eC. Donkey anti-mouse IgM AF488 secondary antibody was then added (1:200) followed by permeabilization with 0.02% Triton-X100 in PBS. Cells were further incubasted overnight at 4\u003csup\u003eo\u003c/sup\u003eC with anti-PDGFR\u0026beta; (1:50; R\u0026amp;D systems;AF1042) and anti-MBP (1:100; Abcam; ab7349). After secondary antibodies (1:200) exposure, cells were suspended in PBS with 1 \u0026mu;g/mL DAPI until imaging. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor analyses of stained plates, these were imaged using a \u0026times;10/0.5 NA air objective on an ImageXpress Micro XLS High-Content Analysis system (Molecular Devices) as previously described\u003csup\u003e24,30\u003c/sup\u003e. Nine\u0026ndash;12 fields of views (FOVs) were imaged per well. Multiwavelength cell scoring analysis in the MetaXpress High-Content Image Acquisition and Analysis software (Molecular Devices) was used to quantify cell survival from the fluorescence microscopy images gathered. For fold change values, all numbers were divided by the mean of the control samples. \u0026nbsp;For the representative images shown, each channel/marker for the sample was merged and displayed using pseudocolours.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTranswell migration assays\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBMDMs were plated in 24 well plates at a density of 260,000 cells/cm\u003csup\u003e2\u003c/sup\u003e overnight at 37\u003csup\u003eo\u003c/sup\u003eC with 8.5% CO\u003csub\u003e2\u003c/sub\u003e. Medium with 1% FBS was added for 24h prior to experiments. Meningeal fibroblasts (150,000 cells/cm\u003csup\u003e2\u003c/sup\u003e) were added to transwell inserts (Corning, 29442-120) for 16h, then fixed and stained. The upper chamber was cleaned using a cotton swab, inserts were transferred to hematoxylin for 15 minutes, washed, mounted and imaged on an Olympus VS110 slide scanner on a 10x/0.4 NA objective and VS-ASW-S5 (V2.9) with Batch Converter software.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCosMx spatial molecular imaging and data analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFFPE sections as prepared above were sent to NanoString for CosMx platform analysis. Sample processing, staining, imaging and cell segmentation were performed as previously described\u003csup\u003e33\u003c/sup\u003e. The 1000-plex CosMx Mouse Neuro probe panel was used along with markers for morphology and cell segmentation. The CosMx optical system has an epifluorescent configuration based on a customized water objective (13\u0026times;, NA 0.82), and uses widefield illumination, with a mix of lasers and light-emitting diodes to image DAPI, Alexa Fluor-488, Atto-532, Dyomics Dy-605 and Alexa Fluor-647, as well as removal of photocleavable dye components. Nine 0.8 um Z-stack images of each FOV were acquired and then fluorophores on the reporter probes were UV cleaved and washed off with Strip Wash buffer. This procedure was repeated for the remaining 15 reporter pools, and the 16 cycles of reporter hybridization-imaging was repeated 8 times to increase RNA detection sensitivity. Raw image processing and feature extraction were performed using an in-house SMI data processing pipeline which includes registration, feature detection, and localization\u003csup\u003e33\u003c/sup\u003e. A machine learning algorithm cell segmentation pipeline was used to assign transcripts to cell locations and subcellular compartments as previously described\u003csup\u003e34\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eData from the CosMx platform was formulated into an object for Seurat v5\u003csup\u003e35\u003c/sup\u003e in R. Data from 2 LPC and 3 Sham brain sections with FOVs from the right hemisphere were used. Data was normalized using SCTransform v2. \u0026nbsp; Unsupervised analysis and generation of a final UMAP with 20 PCs and resolution = 0.7 was done. Harmony\u003csup\u003e36\u003c/sup\u003e was used to integrate \u0026lsquo;Group\u0026rsquo; data (LPS or Sham). Robust Cell Type Decomposition (RCTD) was used to map the cluster profiles to the Azimuth mouse motor cortex reference map (azimuth.hubmapconsortium.org). The following cell types were identified: astrocytes, endothelial cells, neurons, macrophages, oligodendrocytes, oligodendrocyte precursor cells (OPCs), vascular leptomeningeal cells (VLMCs) and pericytes. A separate fibroblast identity was created expressing \u003cem\u003eCol1a2\u003c/em\u003e and \u003cem\u003ePdgfrb\u003c/em\u003e, and for OPCs expressing \u003cem\u003ePdgfra\u003c/em\u003e and \u003cem\u003eSox6\u003c/em\u003e. FindAllMarkers with default Wilcoxon rank sum test was used to identify distinguishing markers for each cluster. DoHeatmap with downsampling to 1000 was used to generate a heatmap with top 5 genes for each cluster. Data from macrophages, fibroblasts and OPCs from LPC sections were subset from the main dataset and used with the package CellChat v2 to assess ligand-receptor interactions\u003csup\u003e37\u003c/sup\u003e. To assess significant ligand-receptor interactions the computeCommunProb command was used with the following parameters (type=\u0026rdquo;truncatedMean\u0026rdquo;, distance.use=TRUE, contact.dependent =TRUE, contact.range=50). \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnalysis of mouse single nuclei dataset from LPC lesions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFibroblast expansion was validated in our publicly available LPC-lesioned tissue dataset (Melchor et al. accession number GSE278643). Briefly, processed data were directly downloaded, and all quality control, dimensionality reduction, and clustering were performed using Seurat (v5.1.0) in R (v4.4.1) as per original publication. All samples (na\u0026iuml;ve spinal cord tissue (n=2), LPC and sham 5dpl (n=3, n=1), 10dpl (n=2, n=1), and 20dpl (n=3, n=1) were utilized. To cluster the data, we used the SCTransform v2 function\u003csup\u003e38,39\u003c/sup\u003e. The count matrix was log normalized using the NormalizeData function to identify cluster gene markers. All cell populations were annotated based off of well-known and calculated marker genes through the FindAllMarkers function. Vascular and mesenchymal cells (\u003cem\u003ePdgfrb, Cfap43, Pecam1, Cspg4, Col1a2\u003c/em\u003e) were subset based off marker genes. The subset vascular/mesenchymal dataset was reclustered (65 PCs and resolution = 2.2) and filtered following the original publication, count matrices were renormalized using the NormalizeData function, and clusters were manually annotated based on the expression of cell type-specific genes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnalysis of human single nucleus RNA dataset from MS patients\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFastq files from Absinta et al. (accession number GSE180759) were downloaded and demultiplexed using 10x Genomics Cellranger version 5.0 pipeline. The final expression matrix with gene counts from n=3 controls and n=5 patients was analysed using the bioinformatics package Seurat v5\u003csup\u003e35\u003c/sup\u003e. Metadata defining tissue area was included in the object. Before running QC metrics the expression matrix comprised 34,131 features and 134,932 cells. Data was filtered for parameters: gene present in \u0026gt; 3 cells and cells with \u0026lt; 150,000 nCount_RNA and percent of mitochondrial genes \u0026lt; 10%. Post filtering, the expression matrix contained 33,454 features and 128,343 cells. Data was normalized and scaled using the SCTransform v2 function with percent.mt being regressed out. A PCA-reduction was performed and 30 significant dimensions were considered to generate a UMAP with all cells in the dataset. Clusters were determined using the FindClusters function which implements the Louvain algorithm for modularity optimization and with resolution of 0.3. Cluster annotation was done manually based on the expression of lineage specific hallmark genes. Differentially expressed genes for one cluster (versus all cells in other clusters) was determined by the default Wilcoxon rank sum test. \u003cem\u003ePDGFRB\u003c/em\u003e+ cells from the initial fibroblast cluster were then subset and re-clustered. Data from microglia/macrophages, OPCs and \u003cem\u003ePDGFRB\u003c/em\u003e+ fibroblasts were subset from the main data and used with the package CellChat2 v2 to assess ligand-receptor interactions\u003csup\u003e37\u003c/sup\u003e. To assess significant ligand-receptor interactions the computeCommunProb command was used with the following parameters (type=\u0026rdquo;truncatedMean\u0026rdquo;, trim = 0.05, population.size=TRUE). \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData was collated using Microsoft Excel. Graphs were generated using GraphPad Prism 8. Data shown are the individual data points, each point on a bar graph represents a biological (in vivo) or technical replicate (in vitro), and the mean. Sample sizes were similar to those previously reported\u003csup\u003e30,40,41\u003c/sup\u003e. Experimental groups were randomly assigned. Data collection and analysis were not performed blind to the conditions of the experiment (unless otherwise stated), as all image and data analyses were completed with the same acquisition conditions and analysis thresholds. Statistical tests are listed in figure legends.\u003c/p\u003e\n\u003ch2\u003eData availability\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eAll data are available in the main text or the supplementary materials.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eFibroblasts are localized within demyelinated lesions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAlthough restricted to CNS borders in homeostasis, fibroblasts are found in the parenchyma after neural injury\u003csup\u003e3,5\u0026ndash;11\u003c/sup\u003e. However, their functional connectivity and contribution to remyelination remain poorly understood. The LPC model of demyelination allows precise spatial and temporal dissection of pathophysiology in lesions\u003csup\u003e41,42\u003c/sup\u003e. Injection of LPC into the ventral spinal cord white matter of mice results in a focal lesion characterized by loss or disrupted MBP stain, swollen axons based on neurofilament heavy chain staining (NFH), and GFAP-positive reactive astrocytes (Fig 1a). In PDGFR\u0026beta;TdTomato transgenic mice commonly used to study fibroblast responses in peripheral organs\u003csup\u003e43\u0026ndash;45\u003c/sup\u003e, we found TdTomato within disrupted MBP-positive debris and in proximity to Iba1-positive microglia/macrophages (Fig 1b). As well, TdTomato and PDGFR\u0026beta; immunofluorescence overlapped in LPC lesions giving confidence to the specificity of expression (Fig 1c). PDGFR\u0026beta; was correspondent with a number of other markers of fibroblasts including collagen type 1 alpha 1 (Col1a1) and SMA for activated fibroblasts (Fig 1d).\u003c/p\u003e\n\u003cp\u003eFibroblast responses were studied 7 and 21 days post injury (dpi), periods when tissue regenerative responses are robust and when they are concluding, respectively\u003csup\u003e22,41\u003c/sup\u003e (Fig 1e-j). While the total lesion volume was reduced from day 7 to 21 (Fig 1e, h), the total TdTomato-positive volume was comparable between 7 to 21 dpi (Fig 1f, i). Thus, the proportion of the lesion occupied by fibroblasts was relatively greater at 21dpi (Fig 1g, j). We then investigated if fibroblasts differentiate into contractile ECM producing myofibroblasts using markers of ECM expression (fibronectin and periostin) and cell contractility (SMA) (Fig 1k). We found that around 40% of TdTomato-positive fibroblasts overlapped with these markers at 7 dpi (Fig 1l-n), and increased co-expression of SMA and periostin while decreasing fibronectin at 21 dpi (Fig 1l-n).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe validated the fibroblast response in LPC lesions using single-nuclear RNA sequencing (snRNAseq) dataset at 5, 10, and 20 dpi (Fig 2a-c). Vascular and mesenchymal cells in LPC lesions were clustered and annotated into five groups: fibroblasts (\u003cem\u003eCol3a1, Col1a2, Col1a1, Bnc2, Dcn\u003c/em\u003e, and \u003cem\u003eFoxp2\u003c/em\u003e), endothelial cells (\u003cem\u003eFlt1, Pecam1, Ly6c1, Adgrl4\u003c/em\u003e), pericytes (\u003cem\u003eAbcc9, Cspg4, Lin7a\u003c/em\u003e), smooth muscle cell (SMC) (\u003cem\u003eActa2, Myh11, Pdlim3, Lmod1\u003c/em\u003e), and ependymal cells (\u003cem\u003eAdamts20, Cfap54, Dnah12, Agbl4, Cfap299\u003c/em\u003e) (Fig 2a, b). \u003cem\u003ePdgfrb\u0026nbsp;\u003c/em\u003ewas variably expressed in fibroblasts, pericytes, and SMCs (Fig 2a). Fibroblasts were confirmed as significantly expanded in LPC lesions at all timepoints compared to na\u0026iuml;ve and non-lesioned samples (Fig 2b, c). Fibroblasts constituted ~75% of the vascular and mesenchymal cells 5 and 10 dpi, and approximately 60% at 20 dpi (Fig 2d). Further evaluation of the vascular and mesenchymal cell populations can be found elsewhere\u003csup\u003e46\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCollectively, the data show that activated fibroblasts expand rapidly and persist into late stages of LPC injury.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSpatial transcriptomics of fibroblasts in LPC lesions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo address spatial distribution, we used the 1000-plex Mouse Neuroscience Panel with the in-situ single cell CosMx spatial platform from Nanostring. We analysed sham and LPC sections 14 dpi, after peak fibroblast response and when remyelination is ongoing\u003csup\u003e41,47\u003c/sup\u003e (Fig 3a, b). We identified 1828 cells from LPC and 1126 cells from sham controls in 12 clusters which were mapped to the Azimuth mouse motor cortex reference map (Fig 3c, d). This identified astrocytes (\u003cem\u003eGja1, Slc4a4, Slc6a1),\u003c/em\u003e oligodendrocytes (\u003cem\u003eMag, Mog, Myrf\u003c/em\u003e), OPCs (\u003cem\u003ePdgfra, Cspg5, Vcan\u003c/em\u003e), microglia/macrophages (\u003cem\u003eCsf1r, Hexb, Csf3r\u003c/em\u003e), vascular leptomeningeal cells (VLMCs) (\u003cem\u003eDcn, Vtn, Igfbp7\u003c/em\u003e), endothelial cells (\u003cem\u003eCldn5, Pecam1, Flt1\u003c/em\u003e) and pericytes (\u003cem\u003eRgs5, Vtn, Myl9\u003c/em\u003e) (Fig 3d, e). A separate fibroblast identity was created for cells expressing \u003cem\u003eCol1a2\u003c/em\u003e and \u003cem\u003ePdgfrb\u003c/em\u003e (Fig 3d, e). We identified a total of 84 \u003cem\u003eCol1a2\u003c/em\u003e and \u003cem\u003ePdgfrb\u003c/em\u003e double-positive fibroblasts, 5 in sham controls and 79 in LPC lesions (Fig 3f, g). The top genes for fibroblasts aside from \u003cem\u003ePdgfrb\u003c/em\u003e and \u003cem\u003eCol1a2\u003c/em\u003e were \u003cem\u003eCol3a1, Col1a1, Vtn\u003c/em\u003e and \u003cem\u003eDcn\u003c/em\u003e (Fig 3e). Importantly, fibroblasts did not express the pericyte marker (\u003cem\u003eRgs5\u003c/em\u003e) or smooth muscle cell marker (\u003cem\u003eTagln\u003c/em\u003e) (Supplementary Fig 1a, b). Fibroblasts were 16 times more abundant in the parenchyma of LPC lesions at 14 dpi compared to sham (Fig 3g), and were in close proximity to macrophages and OPCs (Fig 3b). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFibroblast interactions in LPC lesions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCellchat analysis\u003csup\u003e37\u003c/sup\u003e of ligand-receptor interactions in our CosMx data between fibroblasts, microglia/macrophages and OPCs identified a robust fibroblast communication network in LPC lesions (Supplementary Fig 1c-g). Fibroblasts-OPC interactions were most common, followed by fibroblast-fibroblast interactions. However, the strongest interactions were between fibroblasts and microglia/macrophages (Supplementary Fig 1d, f). Fibroblast derived ligands affected receptors involved in signaling for phagocytosis (\u003cem\u003ePros1-Axl, Pros1-Mertk\u003c/em\u003e), macrophage polarization (\u003cem\u003eSpp1-Cd44, Apoe-Trem2/Tyrob\u003c/em\u003ep), cell adhesion (\u003cem\u003eCol1a1-Itgav/Itgb8\u003c/em\u003e) and OPC survival (\u003cem\u003ePtn-Ptprz1, Bdnf-Ntrk2\u003c/em\u003e) (Supplementary Fig 1h; Supplementary Fig 2a). The signals that fibroblasts were predicted to respond to influenced survival, proliferation, differentiation, and migration such as \u003cem\u003ePdgfd-Pdgfrb, Fgf2-Fgfr2\u003c/em\u003e and \u003cem\u003ePtprs-Ntrk3\u003c/em\u003e (Supplementary Fig 1i; Supplementary Fig 2b).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCategorization of ligand-receptor interactions into functionally related signaling pathways showed fibroblasts were the major driver of outgoing signaling in LPC lesions (Supplementary Fig 1g; Supplementary Fig 3a). Fibroblasts contributed to pathways such as apolipoprotein E (APOE), COLLAGEN, and FN1; microglia/macrophages to APOE, osteopontin (SPP1) and colony stimulating factor (CSF) pathways; and OPCs to pleiotrophin (PTN), Glutamate, and PDGF pathways (Supplementary Fig 3a; Supplementary Fig 4a). Microglia/macrophages were the primary recipient of ligand-receptor signaling through pathways such as APOE and SPP1, and OPCs responded to PTN, and FN1 (Supplementary Fig 3b; Supplementary Fig 4b). Although fibroblasts were not identified as a dominant receiver of signaling through the cell types assessed, they were inferred to receive signaling predominantly from COLLAGEN and PDGF signaling pathways (Supplementary Fig 3b; Supplementary Fig 1g). Analysing global communication patterns we identified three outgoing and three incoming signaling patterns dominated by individual cell groups (Supplementary Fig 4c-f). Altogether this highlights the involvement of fibroblasts in the LPC environment as a major source of input for microglia/macrophages and OPCs. \u0026nbsp; \u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFibroblasts respond to microglia/macrophages\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs fibroblasts appear to engage in reciprocal communication with microglia/macrophages in the 14 dpi LPC lesion, we hypothesized that this may affect recruitment to the area of injury. Indeed, Iba1+ microglia/macrophages and PDGFR\u0026beta;+ fibroblasts were closely associated in day 7 LPC lesions (Fig 1b). Also, we found that microglia/macrophages began accumulating in the LPC lesion at day 3 before peaking at 7 days (Fig 4a, c). Fibroblasts \u0026nbsp;also peaked at day 7, but there was no observable PDGFR\u0026beta;+ cells at day 3 indicating they may respond to migratory signals from microglia/macrophages (Fig 4b, d). To support this, we added meningeal fibroblasts to the upper compartment of a Boyden chamber with bone marrow-derived macrophages or medium alone in the lower chamber (Fig 4e). After 24 hours there was a 3-fold increase in the number of fibroblasts that migrated across the membrane when cultured with macrophages compared to the basal rate of migration (Fig 4f, g). These results suggest a role for macrophages in the promotion of fibroblast elevation in CNS lesions.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFibroblasts spatially exclude OPCs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn contrast to an affinity of fibroblasts for macrophages (Fig 4), we found a separation of fibroblasts for OPCs. In LPC lesions, Olig2+PDGFR\u0026alpha;+ OPCs were restricted from regions occupied by PDGFR\u0026beta;+ fibroblasts (Fig 5a). This was corroborated by the use of NG2CreER:MAPTmGFP mice where newly differentiated oligodendrocytes expressed a membrane bound green fluorescent protein (GFP)\u003csup\u003e20,21\u003c/sup\u003e. Areas occupied by fibroblasts, based on PDGFR\u0026beta; immunoreactivity, in 14 dpi lesions contained little GFP (Fig 5b, c). This was not due to axonal preservation as no difference in NFH-positive axon density was seen in the fibroblast occupied region compared to the rest of the LPC lesion (Supplementary Fig. 5a, b). These results suggest that fibroblasts regionally restrict OPC localization in LPC lesions. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo test this further we seeded cultures of meningeal fibroblasts with OPCs (Fig 5d). No change in total O4+ OPCs was observed (Fig 5e) but there was a significant decrease in the number of mature O4+MBP+ oligodendrocytes in co-cultured wells (Fig 5f). Oligodendrocytes also had less complex morphological phenotypes in co-cultures only occupying regions without fibroblasts (Fig 5d, g). Collectively, this data highlights the inhibitory effect of fibroblasts on OPC differentiation. \u0026nbsp;\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAging enhances fibroblast properties in LPC lesions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAge affects many biological processes including tissue regeneration\u003csup\u003e48\u003c/sup\u003e. Age is known to affect OPC and microglia/macrophage dynamics, and is a critical factor in remyelination potential\u003csup\u003e22,24,49,50\u003c/sup\u003e. To test how age affects the fibroblast response to CNS injury and the downstream effects, young (6-10 week) and middle-aged (48-52 week) mice were injected with LPC in the ventral column of the spinal cord. Tissue was collected 7, 14 and 21 dpi and immunostained for MBP and PDGFR\u0026beta; (Fig 6a). Age did not affect lesion volume at 7 dpi but LPC lesions in middle-aged mice were significantly larger at 14 and 21 dpi (Fig 6b, d). Age also did not affect the PDGFR\u0026beta;+ volume at 7 and 14 dpi, but did cause a significant increase at 21dpi in middle-aged lesions (Fig 6c, e). As well, a greater proportion of the middle-aged LPC lesion was positive for fibroblasts than young lesions at 7 and 21 dpi (Fig 6f).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe investigated the effect of age on the expression of phenotypic markers in fibroblasts in LPC lesions. Middle-aged LPC fibroblasts at day 7 displayed elevated levels of activated myofibroblast marker SMA compared to young (Fig 7a). While no differences in Col1a1 or laminin were seen at 7 dpi, these were elevated at 21 dpi (Fig 7b, c). Finally, we found fewer Olig2+ oligodendrocyte lineage cells within the fibroblast-occupied regions of middle-aged compared to young mice (Fig 7d). Overall, we found elevated ECM levels in middle-aged mice that corresponded with a reduction in oligodendrocyte lineage cells in lysolecithin injury. \u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFibroblasts are present in MS lesions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe extended from murine data into the human demyelinating condition, MS. Frozen brain sections from autopsied MS cases collected in Montreal were stained for PDGFR\u0026beta; as well as CD45 to identify leukocytes, and DAPI for cell nuclei (Fig 8a). We found that PDGFR\u0026beta;+ cells were found in close proximity to CD45+ leukocytes (Fig 8a). From the Imperial College autopsied specimens, active and chronic active lesions were identified, and PDGFR\u0026beta;+Col1a1+ double positive cells were identified to increase confidence of their fibroblast nature. Figure 9 shows that these double positive cells could be found in active and chronic active lesions, in structures that were perivascular as well as sparsely distributed in the parenchyma. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNext, we reanalysed previously published snRNAseq datasets from human brain tissues of MS lesion rim, core and periplaque; and white matter tissue from neurologically healthy controls\u003csup\u003e51\u003c/sup\u003e. Unsupervised clustering identified 25 clusters from the 66,432 total cells including leukocytes, Bergmann glia, dendritic cells, vascular endothelial cells, OPCs, oligodendrocytes, astrocytes, ependymal cells, microglia/macrophages, and neurons (Fig 8b). We highlighted a cluster of 174 cells that expressed fibroblast related genes\u003csup\u003e52\u003c/sup\u003e such as \u003cem\u003ePDGFRB, COL1A2, COL15A1,\u0026nbsp;\u003c/em\u003eand\u003cem\u003e\u0026nbsp;DCN\u003c/em\u003e (Fig 8c). From this cluster we focused on \u003cem\u003ePDGFRB+\u003c/em\u003e cells as our population of interest that were positive for the markers \u003cem\u003ePDGFRA, COL1A2, TGFBR3, COL15A1\u003c/em\u003e, and \u003cem\u003eLAMC3\u003c/em\u003e (Fig 8d; Supplementary Fig 7). Importantly, genes associated with pericytes (\u003cem\u003eRGS5\u003c/em\u003e), and smooth muscle cells (\u003cem\u003eTAGLN\u003c/em\u003e) were not appreciably expressed in our population of interest (Supplementary Fig 7a)\u003csup\u003e53\u003c/sup\u003e. The \u003cem\u003ePDGFRB+\u003c/em\u003e fibroblasts were separated from the other cells in the dataset and re-clustered. This delineated 2 subpopulations (Fig 8e). Subcluster 1 expressed genes associated with stress response (\u003cem\u003eHSPB1, HMGB1\u003c/em\u003e) while subcluster 0 expressed genes associated with ECM, cell proliferation, and migration (\u003cem\u003eLAMA2, NTRK3\u003c/em\u003e) (Fig 8e; Supplementary Fig 7b, c). Assessment of these clusters via region showed that subcluster 0 was present in chronic active and chronic inactive edges and also the lesion core, but only minimally observed in control white matter or periplaque area; subcluster 1 was elevated only in the lesion core (Fig 8f). \u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFibroblast interactions in MS lesions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe interrogated the interactions of fibroblasts with other cell types in MS lesions using CellChat ligand-receptor interaction analysis. Similar to LPC lesions, fibroblasts, microglia/macrophages and OPCs all communicated with each other (Supplementary Fig 8a-d). Fibroblast outgoing signals primarily involved ECM-receptor interactions while most incoming signals were via secreted signaling (Supplementary Fig 8e; Supplementary Fig 9a, b). This is seen in the ligand-receptor pairings engaged by fibroblast derived ligands such as COL1A2-ITGAV/ITGB9 and LAMA2-CD44 (Supplementary Fig 8f; Supplementary Fig 9a, b). As well, incoming secreted ligand-receptor interactions that fibroblasts were predicted to respond to included FGF1-FGFR4 and PDGFB-PDGFRA (Supplementary Fig 8g; Supplementary Fig 9a, b).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFibroblast ligand-receptor pairs were further categorized into 76 signaling pathways such as transforming growth factor beta (TGFB), PDGF, FN1, and COLLAGEN (Supplementary Fig 10a). Fibroblasts contributed to pathways that drove microglia/macrophage signaling including COLLAGEN, LAMININ, CSF, and CXC motif chemokine ligand (CXCL) (Supplementary Fig 9c; Supplementary Fig 10a; Supplementary Fig 11a). Additionally, fibroblasts participated in signaling pathways that signaled at OPCs including WNT, FGF, COLLAGEN, and LAMININ (Supplementary Fig 9c; Supplementary Fig 10a; Supplementary Fig 11a).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAs previously done, we analysed communication patterns between cell populations and found three outgoing and three incoming patterns (Supplementary\u0026nbsp;Fig 10a, b). Outgoing pattern three was characterized primarily by fibroblast signaling and was driven by pathways such as GAP, KIT, and plasminogen activator urokinase (PLAU) pathways (Supplementary\u0026nbsp;Fig 10a). In the reverse, fibroblasts responded to incoming pattern three represented by VCAM, neurotrophin (NT) and adhesion G protein-coupled receptor A2 (ADGRA) pathways (Supplementary\u0026nbsp;Fig 10b).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eComparison of the signaling pathways in LPC and MS lesions revealed 16.3% overlap of outgoing fibroblast pathways and 15.3% of shared incoming pathways (Supplementary Fig 10c, d). Shared outgoing pathways included PDGF, PTN and COL1A1, and shared incoming pathways included FGF, COL1A1 and VEGF (Supplementary Fig 10c, d). Fibroblast specific signaling pathways in MS included outgoing PLAU and TGFB and incoming WNT and TGFB (Supplementary Fig 10c, d). Within these pathways we identified 16 conserved outgoing (FGF1-FGFR2 and PTN-PTPRZ1) and 10 conserved incoming (COL1A1-CD44 and PTPRS-NTRK3) fibroblast ligand-receptor interactions (Supplementary Fig 11c, d). Similarities between ligand-receptor interactions of fibroblasts in MS and LPC lesions include the outgoing ligand-receptor interactions (FGF1-FGFR2, and COL1A1-CD44), and incoming interactions (FGF2-FGFR2, COL1A1-ITGAV) (Supplementary Fig 10c, d; Supplementary Fig 2; Supplementary Fig 4; Supplementary Fig 9a, b; Supplementary Fig 11c, d).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOverall, it appears that microglia/macrophages and OPCs employ multiple signaling pathways compared to fibroblasts. Furthermore, cross-referencing the outgoing and incoming patterns highlighted that some pathways such as VCAM are shared by all three cell groups assessed with microglia/macrophages and OPCs both contributing to outgoing VCAM signaling and fibroblasts as sole responder (Supplementary Fig 11a, c). Taken together these data highlight not only the presence of fibroblasts in MS lesions but also their prominent interactions with microglia/macrophages and OPCs in their environment.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe presence of parenchymal fibroblasts in both neuroinflammatory and traumatic CNS injury is more appreciable than previously thought\u003csup\u003e3,9\u003c/sup\u003e. Here, we report that the expansion of fibroblasts in the lysolecithin (LPC)-injured spinal cord white matter is correspondent with lowered oligodendrocyte density and exacerbated with age. We verify the identity of fibroblasts using PDGFR\u0026beta; immunoreactivity and a PDGFR\u0026beta;-TdTomato reporter mouse line\u003csup\u003e43,44,54\u003c/sup\u003e, and by additional markers including fibronectin and periostin. Moreover, transcriptomics data identified additional markers of fibroblasts, including several collagens, decorin and fibulin-1, and their absence of the pericyte marker Rgs5 and smooth muscle cell marker Talgn. Analysis of a snRNAseq dataset of LPC lesions shows that while fibroblast counts expand significantly at 5, 10, and 20 dpi, pericytes are scarce in the LPC lesion environment. Highly multiplexed RNA in situ hybridization (RNA-ISH, CosMx) further supports that fibroblasts are expanded in the LPC lesions as we found a 16-fold increase in fibroblasts in lesion compared to sham controls. Moreover, the absence of \u003cem\u003eRgs5\u003c/em\u003e, a commonly used pericyte marker, lends further support to the identification of fibroblasts.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn MS lesions, we identified PDGFR\u0026beta;+Col1a1+ cells by immunohistochemistry in close proximity to CD45 positive leukocytes, in both active and chronic active lesions. We analysed a single-nucleus RNA sequencing dataset of MS\u003csup\u003e51\u003c/sup\u003e and found a small population of presumptive fibroblasts. We identified a population of PDGFR\u0026beta; positive cells that expressed genes associated with fibroblasts, and similar to those found in LPC lesions, such as \u003cem\u003eCOL1A2, COL15A1, DCN\u003c/em\u003e and \u003cem\u003eFBLN1\u003c/em\u003e. It is noteworthy that these cells are found in lesions but minimally in the periplaque area or neurologically normal white matter, indicating their association with pathology in MS. This is consistent with findings from other groups that have shown greater presentation of PDGFR\u0026beta; cells in chronic active MS lesions\u003csup\u003e14\u003c/sup\u003e. Cellchat analysis of both LPC and MS lesions indicates that fibroblasts possess immense potential to interact with a range of cells including microglia/macrophages and oligodendrocyte lineage cells in the lesion environments.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe potential effect of fibroblasts on CNS injuries has received scant attention, and little is known regarding their effect on the most common form of CNS regeneration, remyelination\u003csup\u003e3,6\u003c/sup\u003e. Others have suggested that fibroblasts have the potential to influence oligodendrocyte lineage cells that are responsible for remyelination\u003csup\u003e3,6\u003c/sup\u003e. Our analysis of predicted ligand-receptor interactions from CosMx suggests that fibroblasts are a potent driver of signaling in LPC demyelination including many fibroblast-derived ligands that can affect OPCs including Fn1 and Col1a1\u003csup\u003e55\u003c/sup\u003e. Accumulation of inhibitory ECM components, such as fibronectin aggregates\u003csup\u003e21,56,57\u003c/sup\u003e, and collagen 1\u003csup\u003e58\u003c/sup\u003e can impair OPC function, maturation, and reduce remyelination. ECM components can also impede OPC function through indirect effects including by stimulating microglia/macrophages and lymphocytes that kill OPCs\u003csup\u003e57\u003c/sup\u003e. Whether fibroblast phenotypes and their communication with other cells in LPC and MS lesions changes over time is unclear and requires further investigation. However, recent findings in traumatic brain injury have shown that fibroblasts do have dynamic responses over the development of the injury\u003csup\u003e9\u003c/sup\u003e. Furthermore, how potential fibroblast-led signaling interactions continue to influence remyelination requires further investigation, as fibroblasts are transcriptionally dynamic throughout remyelination\u003csup\u003e46\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIt is feasible that fibroblasts provide a physical obstacle to oligodendrocyte processes contacting axons for remyelination. Indeed, we show in vivo that few OPCs or newly formed myelin are present in areas occupied by fibroblasts despite persistence of axons. Our results in culture show that OPCs avoid fibroblast areas. Future experiments should be designed to provide clarity to the mechanisms regulating the inhibition of oligodendrocyte lineage cells by fibroblasts. Although fibroblasts are not present in large numbers following injury, their broad surface area, potent capacity to exclude oligodendrocytes from lesions, and their many potential interactions with microglia/macrophages position them to be important regulators of pathophysiology and recovery.\u003c/p\u003e\n\u003cp\u003eAge contributes to overall morbidity and the reduced efficiency of many biological processes\u003csup\u003e48\u003c/sup\u003e including the formation of oligodendrocytes\u003csup\u003e59\u003c/sup\u003e and remyelination\u003csup\u003e22,60,61\u003c/sup\u003e. We found a greater number of fibroblasts in LPC lesions of middle-aged compared to young mice at both early and late timepoints with a greater accumulation of ECM at later timepoints. It has been well described that the rate of accumulation of microglia/macrophages is reduced within lesions of aging mice compared to young mice. We have previously shown that there is an expansion of microglia/macrophages in aging lesions that resembles pro-fibrotic scar-associated macrophages that express \u003cem\u003eSpp1\u003c/em\u003e, \u003cem\u003eTrem2\u003c/em\u003e, \u003cem\u003eCd63\u003c/em\u003e, and \u003cem\u003eFabp5\u003c/em\u003e\u003csup\u003e9,24,30,62,63\u003c/sup\u003e. Importantly, osteopontin (\u003cem\u003eSpp1\u003c/em\u003e) stimulates inflammatory microglial states and exacerbates CNS injury, and Spp1-positive macrophages have been shown to drive the activation of myofibroblasts leading to increased tissue fibrosis\u003csup\u003e64\u003c/sup\u003e. Interestingly, this same population has been described in chronic CNS lesions including chronic active MS lesions\u003csup\u003e65\u003c/sup\u003e. This is consistent with our data in which we found that Spp1-positive microglia/macrophages are present in both our LPC dataset and the reanalyzed MS lesion results. Further interrogation of the connection between aging microglia phenotypes and fibrosis will provide more direct connection between this aging associated microglia/macrophage population and fibroblast expansion.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe consequences of the age associated expansion of fibroblasts are also of great interest. We found a reduction in the density of oligodendroglia in the fibroblast occupied areas. However, it has been known for some time that age impacts OPCs and remyelination efficiency. Without targeting the fibroblast response in the aging lesion, it is not certain that the reduction is a fibroblast driven phenomena rather than an OPC or some other age-related mechanism such as immune responses. CNS lesions are known to become stiffer with age and contribute to OPC dysfunction\u003csup\u003e50\u003c/sup\u003e. We also found that the expansion of fibroblasts was associated with increased ECM levels. It is not clear if fibroblasts act directly or indirectly on OPCs in the aging lesion, but CellChat analysis suggests fibroblast-OPC interactions are primarily ECM mediated lending to a more indirect effect. Thus, the increased presence of fibroblasts in middle-aged LPC lesions may contribute to worse remyelination outcomes noted with aging. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn conclusion, we provide evidence that fibroblasts accumulate in demyelinated lesions including in MS. We show that fibroblast-occupied areas lack new myelinating cells, and that fibroblasts inhibit OPC differentiation in vitro. Importantly, this process is exacerbated by age leading to greater fibrosis of the lesion. These results highlight the necessity of targeting fibroblasts to mediate regenerative processes and improve outcomes in neurological conditions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAcknowledgements\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eWe thank the Hotchkiss Brain Institute AMP core facility, and the NeuroOmics core facility, for technical support. \u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eFunding\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eWe acknowledge operating grant support from MS Canada (number 1192262), the Canadian Institutes of Health Research (CIHR, FDN 167270) and National Natural Science Foundation of China (W2541024). BML and DM received PhD studentship awards, and RJ and RPG postdoctoral fellowships, from MS Canada. RPG also acknowledges the Dutch MS Society for the Gemmy and Mibeth Tichelaar Award. YD and SG were recipients of postdoctoral fellowships from CIHR. MM received an Internationalisation fellowship from the Carlsberg Foundation, Denmark. \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThe authors report no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eAll data are available from VWY upon reasonable requests.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eBML produced the majority of the datasets and wrote the initial drafts of the manuscript. CDM, RPG, YD, GSM, SG, CL, MM, DM, RJ, PEM, CC Ling, JKH, CC-Lemarroy and RL provided data or experimental samples. VWY supervised and completed the final version of this manuscript. All authors read, edited and approved the final manuscript.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003ePlikus MV, Wang X, Sinha S, et al. Fibroblasts: Origins, definitions, and functions in health and disease. \u003cem\u003eCell\u003c/em\u003e. 2021;184(15):3852-3872. doi:10.1016/j.cell.2021.06.024\u003c/li\u003e\n\u003cli\u003eZhou X, Franklin RA, Adler M, et al. Microenvironmental sensing by fibroblasts controls macrophage population size. \u003cem\u003eProc Natl Acad Sci U S A\u003c/em\u003e. 2022;119(32):e2205360119. doi:10.1073/pnas.2205360119\u003c/li\u003e\n\u003cli\u003eDorrier CE, Aran D, Haenelt EA, et al. CNS fibroblasts form a fibrotic scar in response to immune cell infiltration. \u003cem\u003eNat Neurosci\u003c/em\u003e. 2021;24(2):234-244. doi:10.1038/s41593-020-00770-9\u003c/li\u003e\n\u003cli\u003ePlikus MV, Guerrero-Juarez CF, Ito M, et al. Regeneration of fat cells from myofibroblasts during wound healing. \u003cem\u003eScience\u003c/em\u003e. 2017;355(6326):748-752. doi:10.1126/science.aai8792\u003c/li\u003e\n\u003cli\u003eBolte AC, Shapiro DA, Dutta AB, et al. The meningeal transcriptional response to traumatic brain injury and aging. \u003cem\u003eeLife\u003c/em\u003e. 2023;12:1-38. doi:10.7554/eLife.81154\u003c/li\u003e\n\u003cli\u003eYahn SL, Li J, Goo I, Gao H, Brambilla R, Lee JK. Neurobiology of Disease Fibrotic scar after experimental autoimmune encephalomyelitis inhibits oligodendrocyte di ff erentiation. \u003cem\u003eNeurobiol Dis\u003c/em\u003e. 2020;134(October 2019):104674. doi:10.1016/j.nbd.2019.104674\u003c/li\u003e\n\u003cli\u003eLiu X, Liu Y, Jin H, et al. Reactive Fibroblasts in Response to Optic Nerve Crush Injury. \u003cem\u003eMol Neurobiol\u003c/em\u003e. 2021;58(4):1392-1403. doi:10.1007/s12035-020-02199-4\u003c/li\u003e\n\u003cli\u003eŞekerdağ-Kılı\u0026ccedil; E, Ulusoy C, Atak D, et al. Perivascular PDGFRB+ cells accompany lesion formation and clinical evolution differentially in two different EAE models. \u003cem\u003eMult Scler Relat Disord\u003c/em\u003e. 2023;69:104428. doi:10.1016/j.msard.2022.104428\u003c/li\u003e\n\u003cli\u003eEwing-Crystal NA, Mroz NM, Larpthaveesarp A, et al. Dynamic fibroblast\u0026ndash;immune interactions shape recovery after brain injury. \u003cem\u003eNature\u003c/em\u003e. 2025;646(8086):934-944. doi:10.1038/s41586-025-09449-2\u003c/li\u003e\n\u003cli\u003eProtzmann J, Zeitelhofer M, Stefanitsch C, et al. PDGFR\u0026alpha; inhibition reduces myofibroblast expansion in the fibrotic rim and enhances recovery after ischemic stroke. \u003cem\u003eJ Clin Invest\u003c/em\u003e. 2025;135(5):e171077. doi:10.1172/JCI171077\u003c/li\u003e\n\u003cli\u003eGoritz C, Dias D, Tomilin N, Barbacid M, Shupliakov O, Frisen J. A Pericyte Origin of Spinal Cord Scar Tissue. \u003cem\u003eScience\u003c/em\u003e. 2011;333(July):238-243. doi:10.1126/science.1203165\u003c/li\u003e\n\u003cli\u003eBernier LP, Hefendehl JK, Scott RW, et al. Brain pericytes and perivascular fibroblasts are stromal progenitors with dual functions in cerebrovascular regeneration after stroke. \u003cem\u003eNat Neurosci\u003c/em\u003e. 2025;28(3):517-535. doi:10.1038/s41593-025-01872-y\u003c/li\u003e\n\u003cli\u003eDias DO, Kim H, Holl D, et al. Reducing Pericyte-Derived Scarring Promotes Recovery after Spinal Cord Injury. \u003cem\u003eCell\u003c/em\u003e. 2018;173(1):153-165. doi:10.1016/j.cell.2018.02.004\u003c/li\u003e\n\u003cli\u003eDias DO, Kalkitsas J, Kelahmetoglu Y, et al. Pericyte-derived fibrotic scarring is conserved across diverse central nervous system lesions. \u003cem\u003eNat Commun\u003c/em\u003e. 2021;12(1):5501. doi:10.1038/s41467-021-25585-5\u003c/li\u003e\n\u003cli\u003eFranklin RJM, Simons M. CNS remyelination and inflammation: From basic mechanisms to therapeutic opportunities. \u003cem\u003eNeuron\u003c/em\u003e. 2022;110(21):3549-3565. doi:10.1016/j.neuron.2022.09.023\u003c/li\u003e\n\u003cli\u003eGluck L, Gerstein B, Kaunzner UW. Repair mechanisms of the central nervous system: From axon sprouting to remyelination. \u003cem\u003eNeurotherapeutics\u003c/em\u003e. 2025;22(4):e00583. doi:10.1016/j.neurot.2025.e00583\u003c/li\u003e\n\u003cli\u003eBergner CG, Van Der Meer F, Franz J, et al. BCAS1-positive oligodendrocytes enable efficient cortical remyelination in multiple sclerosis. \u003cem\u003eBrain\u003c/em\u003e. 2025;148(3):908-920. doi:10.1093/brain/awae293\u003c/li\u003e\n\u003cli\u003eLubetzki C, Zalc B, Williams A, Stadelmann C, Stankoff B. Remyelination in multiple sclerosis : from basic science to clinical translation. \u003cem\u003eLancet Neurol\u003c/em\u003e. 2020;19(8):678-688. doi:10.1016/S1474-4422(20)30140-X\u003c/li\u003e\n\u003cli\u003eNiu J, Verkhratsky A, Butt A, Yi C. Demyelination and Remyelination: General Principles. \u003cem\u003eAdv Neurobiol\u003c/em\u003e. 2025;43:207-255. doi:10.1007/978-3-031-87919-7_9\u003c/li\u003e\n\u003cli\u003eMei F, Lehmann-Horn K, Shen YAA, et al. Accelerated remyelination during inflammatory demyelination prevents axonal loss and improves functional recovery. \u003cem\u003eeLife\u003c/em\u003e. 2016;5(September):1-21. doi:10.7554/eLife.18246\u003c/li\u003e\n\u003cli\u003eGhorbani S, Jelinek E, Jain R, et al. Versican promotes T helper 17 cytotoxic inflammation and impedes oligodendrocyte precursor cell remyelination. \u003cem\u003eNat Commun\u003c/em\u003e. 2022;13(1):1-18. doi:10.1038/s41467-022-30032-0\u003c/li\u003e\n\u003cli\u003eRawji KS, Young AMH, Ghosh T, et al. Niacin-mediated rejuvenation of macrophage/microglia enhances remyelination of the aging central nervous system. \u003cem\u003eActa Neuropathol (Berl)\u003c/em\u003e. 2020;139(5):893-909. doi:10.1007/s00401-020-02129-7\u003c/li\u003e\n\u003cli\u003eMukherjee T, McMurran CE, Holland J, et al. Ageing and remyelination failure in people with multiple sclerosis. \u003cem\u003eBrain J Neurol\u003c/em\u003e. Published online October 6, 2025:awaf373. doi:10.1093/brain/awaf373\u003c/li\u003e\n\u003cli\u003eDong Y, Jain RW, Lozinski BM, et al. Single-cell and spatial RNA sequencing identify perturbators of microglial functions with aging. \u003cem\u003eNat Aging\u003c/em\u003e. Published online 2022. doi:10.1038/s43587-022-00205-z\u003c/li\u003e\n\u003cli\u003eKoch M, Mostert J, Heersema D, De Keyser J. Progression in multiple sclerosis: Further evidence of an age dependent process. \u003cem\u003eJ Neurol Sci\u003c/em\u003e. 2007;255(1-2):35-41. doi:10.1016/j.jns.2007.01.067\u003c/li\u003e\n\u003cli\u003eBrack AS, Conboy MJ, Roy S, et al. Increased Wnt signaling during aging alters muscle stem cell fate and increases fibrosis. \u003cem\u003eScience\u003c/em\u003e. 2007;317(5839):807-810. doi:10.1126/science.1144090\u003c/li\u003e\n\u003cli\u003eShen S, Sandoval J, Swiss VA, et al. Age-dependent epigenetic control of differentiation inhibitors is critical for remyelination efficiency. \u003cem\u003eNat Neurosci\u003c/em\u003e. 2008;11(9):1024-1034. doi:10.1038/nn.2172\u003c/li\u003e\n\u003cli\u003eNicaise AM, Wagstaff LJ, Willis CM, et al. Cellular senescence in progenitor cells contributes to diminished remyelination potential in progressive multiple sclerosis. \u003cem\u003eProc Natl Acad Sci U S A\u003c/em\u003e. 2019;116(18):9030-9039. doi:10.1073/pnas.1818348116\u003c/li\u003e\n\u003cli\u003eNeumann B, Baror R, Zhao C, et al. Metformin Restores CNS Remyelination Capacity by Rejuvenating Aged Stem Cells. \u003cem\u003eCell Stem Cell\u003c/em\u003e. 2019;25(4):473-485.e8. doi:10.1016/j.stem.2019.08.015\u003c/li\u003e\n\u003cli\u003eDong Y, D\u0026rsquo;Mello C, Pinsky W, et al. Oxidized phosphatidylcholines found in multiple sclerosis lesions mediate neurodegeneration and are neutralized by microglia. \u003cem\u003eNat Neurosci\u003c/em\u003e. 2021;24(4):489-503. doi:10.1038/s41593-021-00801-z\u003c/li\u003e\n\u003cli\u003eKeough MB, Jensen SK, Wee Yong V. Experimental demyelination and remyelination of murine spinal cord by focal injection of lysolecithin. \u003cem\u003eJ Vis Exp\u003c/em\u003e. 2015;2015(97):1-8. doi:10.3791/52679\u003c/li\u003e\n\u003cli\u003eMishra MK, Rawji KS, Keough MB, et al. Harnessing the Benefits of Neuroinflammation: Generation of Macrophages/Microglia with Prominent Remyelinating Properties. \u003cem\u003eJ Neurosci Off J Soc Neurosci\u003c/em\u003e. 2021;41(15):3366-3385. doi:10.1523/JNEUROSCI.1948-20.2021\u003c/li\u003e\n\u003cli\u003eHe S, Bhatt R, Brown C, et al. High-plex imaging of RNA and proteins at subcellular resolution in fixed tissue by spatial molecular imaging. \u003cem\u003eNat Biotechnol\u003c/em\u003e. 2022;40(12):1794-1806. doi:10.1038/s41587-022-01483-z\u003c/li\u003e\n\u003cli\u003eStringer C, Wang T, Michaelos M, Pachitariu M. Cellpose: a generalist algorithm for cellular segmentation. \u003cem\u003eNat Methods\u003c/em\u003e. 2021;18(1):100-106. doi:10.1038/s41592-020-01018-x\u003c/li\u003e\n\u003cli\u003eHao Y, Stuart T, Kowalski MH, et al. Dictionary learning for integrative, multimodal and scalable single-cell analysis. \u003cem\u003eNat Biotechnol\u003c/em\u003e. 2024;42(2):293-304. doi:10.1038/s41587-023-01767-y\u003c/li\u003e\n\u003cli\u003eKorsunsky I, Millard N, Fan J, et al. Fast, sensitive and accurate integration of single-cell data with Harmony. \u003cem\u003eNat Methods\u003c/em\u003e. 2019;16(12):1289-1296. doi:10.1038/s41592-019-0619-0\u003c/li\u003e\n\u003cli\u003eJin S, Guerrero-Juarez CF, Zhang L, et al. Inference and analysis of cell-cell communication using CellChat. \u003cem\u003eNat Commun\u003c/em\u003e. 2021;12:1088. doi:10.1038/s41467-021-21246-9\u003c/li\u003e\n\u003cli\u003eChoudhary S, Satija R. Comparison and evaluation of statistical error models for scRNA-seq. \u003cem\u003eGenome Biol\u003c/em\u003e. 2022;23:27. doi:10.1186/s13059-021-02584-9\u003c/li\u003e\n\u003cli\u003eHafemeister C, Satija R. Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression. \u003cem\u003eGenome Biol\u003c/em\u003e. 2019;20:296. doi:10.1186/s13059-019-1874-1\u003c/li\u003e\n\u003cli\u003eJain RW, Elliott DA, Yong VW. Single Cell Analysis of High-Parameter Histology Images Using Histoflow Cytometry. \u003cem\u003eJ Immunol\u003c/em\u003e. 2023;210(12):2038-2049. doi:10.4049/jimmunol.2200700\u003c/li\u003e\n\u003cli\u003ePlemel JR, Stratton JA, Michaels NJ, et al. Microglia response following acute demyelination is heterogeneous and limits infiltrating macrophage dispersion. \u003cem\u003eSci Adv\u003c/em\u003e. 2020;6(3):eaay6324. doi:10.1126/sciadv.aay6324\u003c/li\u003e\n\u003cli\u003eLozinski BM, de Almeida LGN, Silva C, et al. Exercise rapidly alters proteomes in mice following spinal cord demyelination. \u003cem\u003eSci Rep\u003c/em\u003e. 2021;11(1). doi:10.1038/s41598-021-86593-5\u003c/li\u003e\n\u003cli\u003eBuhl EM, Djudjaj S, Klinkhammer BM, et al. Dysregulated mesenchymal PDGFR‐\u0026beta; drives kidney fibrosis. \u003cem\u003eEMBO Mol Med\u003c/em\u003e. 2020;12. doi:10.15252/emmm.201911021\u003c/li\u003e\n\u003cli\u003eHenderson NC, Arnold TD, Katamura Y, et al. Targeting of \u0026alpha;v integrin identifies a core molecular pathway that regulates fibrosis in several organs. \u003cem\u003eNat Med\u003c/em\u003e. 2013;19(12):1617-1624. doi:10.1038/nm.3282\u003c/li\u003e\n\u003cli\u003eHoll D, Hau WF, Julien A, et al. Distinct origin and region-dependent contribution of stromal fibroblasts to fibrosis following traumatic injury in mice. \u003cem\u003eNat Neurosci\u003c/em\u003e. Published online June 7, 2024. doi:10.1038/s41593-024-01678-4\u003c/li\u003e\n\u003cli\u003eMelchor GS, Baydyuk M, Manavi Z, Hu J, Huang JK. Dissecting the evolving cellular landscape of a remyelinating microenvironment. \u003cem\u003ebioRxiv\u003c/em\u003e. Preprint posted online December 25, 2024:2024.12.25.630253. doi:10.1101/2024.12.25.630253\u003c/li\u003e\n\u003cli\u003eJensen SK, Michaels NJ, Ilyntskyy S, Keough MB, Kovalchuk O, Yong VW. Multimodal Enhancement of Remyelination by Exercise with a Pivotal Role for Oligodendroglial PGC1\u0026alpha;. \u003cem\u003eCell Rep\u003c/em\u003e. 2018;24(12):3167-3179. doi:10.1016/j.celrep.2018.08.060\u003c/li\u003e\n\u003cli\u003eL\u0026oacute;pez-Ot\u0026iacute;n C, Blasco MA, Partridge L, Serrano M, Kroemer G. Hallmarks of aging: An expanding universe. \u003cem\u003eCell\u003c/em\u003e. 2023;186(2):243-278. doi:10.1016/j.cell.2022.11.001\u003c/li\u003e\n\u003cli\u003eGoldschmidt T, Antel J, Konig FB, Bruck W, Kuhlmann T. Remyelination capacity of the MS brain decreases with disease chronicity. \u003cem\u003eNeurology\u003c/em\u003e. 2009;72(22):1914-1921.\u003c/li\u003e\n\u003cli\u003eSegel M, Neumann B, Hill MFE, et al. Niche stiffness underlies the ageing of central nervous system progenitor cells. \u003cem\u003eNature\u003c/em\u003e. 2019;573(7772):130-134. doi:10.1038/s41586-019-1484-9\u003c/li\u003e\n\u003cli\u003eAbsinta M, Maric D, Gharagozloo M, et al. A lymphocyte \u0026ndash; microglia \u0026ndash; astrocyte axis in chronic active multiple sclerosis. \u003cem\u003eNature\u003c/em\u003e. 2021;(November 2020). doi:10.1038/s41586-021-03892-7\u003c/li\u003e\n\u003cli\u003eYang AC, Vest RT, Kern F, et al. A human brain vascular atlas reveals diverse mediators of Alzheimer\u0026rsquo;s risk. \u003cem\u003eNature\u003c/em\u003e. 2022;603(7903):885-892. doi:10.1038/s41586-021-04369-3\u003c/li\u003e\n\u003cli\u003eVanlandewijck M, He L, M\u0026auml;e MA, et al. A molecular atlas of cell types and zonation in the brain vasculature. \u003cem\u003eNature\u003c/em\u003e. 2018;554(7693):475-480. doi:10.1038/nature25739\u003c/li\u003e\n\u003cli\u003eRustenhoven J, Drieu A, Mamuladze T, et al. Functional characterization of the dural sinuses as a neuroimmune interface. \u003cem\u003eCell\u003c/em\u003e. 2021;184(4):1000-1016.e27. doi:10.1016/j.cell.2020.12.040\u003c/li\u003e\n\u003cli\u003eBernier LP, Hefendehl JK, Scott RW, et al. Brain pericytes and perivascular fibroblasts are stromal progenitors with dual functions in cerebrovascular regeneration after stroke. \u003cem\u003eNat Neurosci\u003c/em\u003e. 2025;28(3):517-535. doi:10.1038/s41593-025-01872-y\u003c/li\u003e\n\u003cli\u003eStephenson EL, Mishra MK, Moussienko D, et al. Chondroitin sulfate proteoglycans as novel drivers of leucocyte infiltration in multiple sclerosis. \u003cem\u003eBrain\u003c/em\u003e. Published online 2018:1094-1110. doi:10.1093/brain/awy033\u003c/li\u003e\n\u003cli\u003eGhorbani S, Yong VW. The extracellular matrix as modifier of neuroinflammation and remyelination in multiple sclerosis. \u003cem\u003eBrain\u003c/em\u003e. 2021;144(7):1958-1973. doi:10.1093/brain/awab059\u003c/li\u003e\n\u003cli\u003eYamazaki R, Azuma M, Osanai Y, et al. Type I collagen secreted in white matter lesions inhibits remyelination and functional recovery. \u003cem\u003eCell Death Dis\u003c/em\u003e. 2025;16(1):285. doi:10.1038/s41419-025-07633-w\u003c/li\u003e\n\u003cli\u003eWang F, Ren SY, Chen JF, et al. Myelin degeneration and diminished myelin renewal contribute to age-related deficits in memory. \u003cem\u003eNat Neurosci\u003c/em\u003e. 2020;23(4):481-486. doi:10.1038/s41593-020-0588-8\u003c/li\u003e\n\u003cli\u003eShields SA, Gilson JM, Blakemore WF, Franklin RJM. Remyelination occurs as extensively but more slowly in old rats compared to young rats following gliotoxin-induced CNS demyelination. \u003cem\u003eGlia\u003c/em\u003e. 1999;28(1):77-83. doi:10.1002/(SICI)1098-1136(199910)28:1%3C77::AID-GLIA9%3E3.0.CO;2-F\u003c/li\u003e\n\u003cli\u003eGross PS, Dur\u0026aacute;n-Laforet V, Ho LT, et al. Senescent-like microglia limit remyelination through the senescence associated secretory phenotype. \u003cem\u003eNat Commun\u003c/em\u003e. 2025;16(1):2283. doi:10.1038/s41467-025-57632-w\u003c/li\u003e\n\u003cli\u003eM\u0026uuml;ller L, Di Benedetto S. Aging brain: exploring the interplay between bone marrow aging, immunosenescence, and neuroinflammation. \u003cem\u003eFront Immunol\u003c/em\u003e. 2024;15:1393324. doi:10.3389/fimmu.2024.1393324\u003c/li\u003e\n\u003cli\u003eRawji KS, Mishra MK, Michaels NJ, Rivest S, Stys PK, Yong VW. Immunosenescence of microglia and macrophages: Impact on the ageing central nervous system. \u003cem\u003eBrain\u003c/em\u003e. 2016;139(3):653-661. doi:10.1093/brain/awv395\u003c/li\u003e\n\u003cli\u003eHoeft K, Schaefer GJL, Kim H, et al. Platelet-instructed SPP1+ macrophages drive myofibroblast activation in fibrosis in a CXCL4-dependent manner. \u003cem\u003eCell Rep\u003c/em\u003e. 2023;42(2):112131. doi:10.1016/j.celrep.2023.112131\u003c/li\u003e\n\u003cli\u003eYu R, Lozinski BM, Seifert A, et al. Oxidized phosphatidylcholines deposition drives chronic neurodegeneration in a mouse model of progressive multiple sclerosis via IL-1\u0026beta; signaling. \u003cem\u003eNat Neurosci\u003c/em\u003e. 2026;29(1):67-80. doi:10.1038/s41593-025-02113-y\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-neuroinflammation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jneu","sideBox":"Learn more about [Journal of Neuroinflammation](http://jneuroinflammation.biomedcentral.com)","snPcode":"12974","submissionUrl":"https://submission.nature.com/new-submission/12974/3","title":"Journal of Neuroinflammation","twitterHandle":"@bmc","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Impediments to repair, Lysolecithin, Neurofibrosis, Remyelination in aging","lastPublishedDoi":"10.21203/rs.3.rs-9327007/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9327007/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eFibroblast dysregulation contributes to pathological fibrosis and aberrant repair. Emerging evidence suggest that fibroblasts accumulate in lesions following central nervous system injury, but whether and how they influence oligodendrocyte repair responses, including in aging, is uncertain. Here we report that fibroblasts accumulate in the parenchyma of spinal cord white matter lesions of 6\u0026ndash;10 week old young mice after lysolecithin-induced demyelination. This was first observed through immunofluorescence microscopy that employed several markers attributed to fibroblasts, including platelet-derived growth factor-β, collagen type 1α1, α-smooth muscle actin, periostin and fibronectin; and by the use of platelet-derived growth factor-β TdTomato reporter transgenic mice. Spatial transcriptomics and single-nucleus RNA sequencing of lysolecithin lesions established the presence of fibroblasts in lysolecithin lesions and delineated them from closely related pericytes. CellChat ligand \u0026ndash; receptor analyses highlight fibroblasts in the lysolecithin environment as a major source of input of signals for microglia/macrophages and oligodendrocyte precursor cells, with numerous reciprocal interactions. The infiltration of fibroblasts was promoted by microglia/macrophages, as anticipated by their temporal representation in lysolecithin lesions, and by tissue culture experiments where the migration of fibroblasts was enhanced by macrophages. Particularly relevant to regenerative events that occur spontaneously after lysolecithin demyelination, the areas of fibroblast accumulation were devoid of oligodendrocyte precursor cells. In tissue culture, oligodendrocyte precursor cells were excluded from fibroblast domains. Moreover, fibroblast accumulation after lysolecithin injury was enhanced with increasing age, a known detriment to the capacity to remyelinate after injury, and exclusion of oligodendrocyte precursor cells from fibroblast areas of 48\u0026ndash;52 week mice exceed that occurring in younger 6\u0026ndash;10 weeks animals. Finally, by mining a publicly available single-nucleus RNA database of multiple sclerosis, we found fibroblasts in the edge of chronic active and chronic inactive lesions and in lesion core, and fewer in periplaque or normal white matter. There were several communication networks between fibroblasts, microglia/macrophages and oligodendrocyte precursor cells in these MS lesions. Our collective results demonstrate a role of fibroblasts in demyelination-associated neuropathology, which is exacerbated by aging, and highlight the importance of regulating fibroblasts to promote effective CNS repair.\u003c/p\u003e","manuscriptTitle":"Aging-enhanced accumulation of fibroblasts excludes oligodendrocytes in demyelinated lesions","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-16 18:37:41","doi":"10.21203/rs.3.rs-9327007/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-29T17:14:36+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-29T16:08:55+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-22T11:50:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"273461861086869407312113102626400799076","date":"2026-04-15T17:58:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"108608309739710155683164807035847614603","date":"2026-04-14T07:25:42+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-08T13:54:56+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-08T12:25:04+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-08T05:38:17+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Neuroinflammation","date":"2026-04-05T14:46:41+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-neuroinflammation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jneu","sideBox":"Learn more about [Journal of Neuroinflammation](http://jneuroinflammation.biomedcentral.com)","snPcode":"12974","submissionUrl":"https://submission.nature.com/new-submission/12974/3","title":"Journal of Neuroinflammation","twitterHandle":"@bmc","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a785acd0-ed02-4001-aa0b-fb9705fa7afa","owner":[],"postedDate":"April 16th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-04-29T17:24:48+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-16 18:37:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9327007","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9327007","identity":"rs-9327007","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}