Single-Cell Transcriptome Analysis Reveals Dynamic Populations of Vascular Cells in Neointimal Hyperplasia

Background : Neointimal hyperplasia (NIH) is the pathological basis of vascular injury disease. Vascular cells are the dominant cells in the process of NIH, but the extent of heterogeneity amongst them is still unclear. Methods : A mouse model of NIH was constructed by inducing carotid artery ligation. Single-cell sequencing was then used to analyze the transcriptional profile of vascular cells. Cluster features were determined by functional enrichment analysis, gene set scoring, pseudo-time analysis, and cell-cell communication analysis. Additionally, immunofluorescencestainingwasconductedonvasculartissuesfromfibroblastlineage-traced(Pdgfra DreER -tdTomato)mice to validate the presence of Pecam1 + Pdgfra + tdTomato + cells. Results : The left carotid arteries (ligation) were compared to right carotid arteries (sham) from ligation-induced NIH C57BL/6 mice. Integrative analyses revealed a high level of heterogeneity amongst vascular cells, including fourteen clusters and seven cell types. We focused on three dominant cell types: endothelial cells (ECs), vascular smooth muscle cells (vSMCs), and fibroblasts. The major findings were: (1) four subpopulations of ECs, including ECs4, mesenchymal-like ECs (ECs1 and ECs2), and fibro-like ECs (ECs3); (2) four subpopulations of fibroblasts, including pro-inflammatory Fibs-1, Sca1 + Fibs-2, collagen-producing Fibs-3, and mesenchymal-like Fibs-4; (3) four subpopulations of vSMCs, including vSMCs-1, vSMCs-2, vSMCs-3, and vSMCs-3-derived vSMCs; (4) ECs3 express genes related to extracellular matrix (ECM) remodeling and cell migration, and fibro-like vSMCs showed strong chemokine secretion and relatively high levels of proteases; (5) fibro-like vSMCs that secrete Vegfa interact with ECs mainly through vascular endothelial growth factor receptor 2 (Vegfr2). Conclusions : This study presents the dynamic cellular landscape within NIH arteries and reveals potential relationships between several clusters, with a specific focus on ECs3 and fibro-like vSMCs. These two subpopulations may represent potential target cells for the treatment of NIH.


Introduction
Neointimal hyperplasia (NIH) develops after vascular damage caused by surgical operations such as balloon or stent angioplasty [1].The formation of NIH is mediated by both cellular and humoral factors.Previous findings highlighted the role of four major cell types and associated processes in NIH: endothelial cells (ECs), adventitial fibroblasts (Fibs), vascular smooth muscle cells (vSMCs), and monocytes and/or macrophages [1].Specific changes in these cell types during NIH include endothelial activation, monocyte accumulation, fibroblast migration, and vSMC phenotype switching [1].However, clinical prevention and therapy of NIH remain unsatisfactory, possibly due to vascular cell heterogeneity.
There is currently only a limited understanding of the heterogeneity of different vascular cells and the roles of various subpopulations in neointimal vessels.Single-cell RNA sequencing (scRNA-seq) is a tool used to study cellular heterogeneity and to identify rare cell subtypes [2].Recent studies have utilized scRNA-seq to profile the cellular landscape of aortic aneurysms [3], atherosclerosis [4], and endothelial denudation [5].However, there have been no reports on the use of scRNA-seq to study NIH induced by carotid ligation in mice.
Epithelial-to-mesenchymal transition (EMT) refers to the process by which epithelial cells lose their adhesion connections and polarization, and subsequently transform into mesenchymal cells [6].This phenomenon has been observed in blood vessels with disrupted blood flow, but has not been reported in the context of NIH [7].Adventitial fibroblasts contribute to NIH mainly by differentiating into myofibroblasts [8], and the conversion of human fibroblasts into ECs has been reported previously [9].
In response to various stimuli, vSMCs can undergo several phenotypic modulations including dedifferentiation, proliferation, and migration.This involves downregulation of the expression of vSMC marker genes such as Myh11 and Acta2, and upregulation of Ly6a expression [10,11].During vascular injury, vSMCs acquire characteristics of macrophages through dedifferentiation [12].In atherosclerosis, vSMCs dedifferentiate into cells with fibrotic features, thereby maintaining stability of the fibrous cap [13].However, this change in vSMCs has not yet been studied in NIH.
To better understand the underlying mechanism of NIH, the aim of this study was to examine the transcriptional landscape of vascular cells by using single-cell RNA sequencing coupled with contemporary analytical methods.

Animals
Eight-week-old C57BL/6 male mice were procured from Beijing Vital River Laboratory Animal Technology Co., Ltd.(Beijing, China).The Pdgfra DreER mouse line was described previously [14].Pdgfra DreER -tdTomato male mice were generated by crossing the Pdgfra DreER and R26R-rox-tdTomato reporter lines.All mice were housed in specific pathogen-free conditions in an animal room with a 12/12-hour day/night cycle and with ample access to water and food.Animal experiments in this study were conducted in accordance with the National Institutes of Health Guide for the Care and Use of Laboratory Animals.Approval was obtained from the Ethics Committee on Animal Experiments, Shanghai Jiao Tong University School of Medicine (SYXK-2003-0026).

Carotid Artery Ligation Injury Model
To induce vascular injury, mice underwent carotid artery ligation as previously described [15].In brief, mice were anesthetized using isoflurane, and the left carotid artery was completely ligated with 3-0 absorbable suture just proximal to the carotid bifurcation.

Single-Cell RNA Sequencing and Data Analysis
Carotid arteries from six C57BL/6 mice were harvested at 2-weeks post-ligation (Supplementary Fig. 1A).The vessels were enzymatically digested to obtain a singlecell suspension.scRNA-seq libraries were then constructed using the 10x Genomics platform.Raw, single-cell RNAsequencing data was processed using Cell Ranger software (version 6.0, https://github.com/10XGenomics/cellranger).Briefly, the "Cellranger mkfastq" command was employed to de-multiplex raw data and generate FASTQ files.These files were further processed by "Cellranger count" to align reads to the mouse reference, count the number of barcodes and unique molecular identifiers (UMIs), and generate feature-barcode matrices.The raw gene-expression matrix was converted into a Seurat object using the Seurat package (v4.0.5) of R (v4.1.1,University of Auckland, Auckland, New Zealand) [16].Harmony was utilized to mitigate batch effects across different groups (Supplementary Fig. 1B) [17].For quality control, only gene features expressed in at least three cells, and cells with at least 200 detected genes, were retained.Subsequently, cells displaying <200 gene features or having >15% mitochondrial counts were filtered out.Doublet cells were identified and excluded from the remaining cells using the R package Dou-bletFinder (v2.0.3) [18].A total of 17,605 cells remained after filtering.Data normalization was achieved through "LogNormalize".The top 2000 highly variable genes were selected and scaled using the "ScaleData" function.Principle Component Analysis (PCA) was performed on these selected, highly variable genes.The first 15 principal components with a resolution of 0.4 were employed for cell clustering and visualization via t-distributed stochastic neighbor embedding (t-SNE), or uniform manifold approximation and projection (UMAP).Marker genes upregulated in each cluster were identified through "FindAllMarkers" (min.pct = 0.25, logfc.threshold = 0.25).For sub-clustering of the major cell populations, the aforementioned procedure was repeated.To ascertain similarities between different clusters, correlation matrix heatmaps for all clusters were generated by Scpub [19].

SCENIC
The pySCENIC protocol (version 0.11.0, https://github.com/aertslab/pySCENIC)was used as part of a Python package set to default configurations in order to deduce the activity of transcription factors (TFs) and to identify their associated target genes within all sampled cells [22].This process was segmented into three primary phases.Initially, we refined the single-cell gene expression dataset by removing genes that appeared in <10 cells.The remainder were kept for the construction of co-expression modules with the GRNBoost2 algorithm, which operates on a regression per-target basis.Subsequently, these modules were refined by eliminating indirect targets via the implementation of cis-regulatory motif analysis (cisTarget).Finally, the operational status of these regulons was determined by using the AUCell method (version 3.18, https://bioconductor.org/packages/ release/bioc/html/AUCell.html) to calculate an enrichment score for the genes targeted by each regulon.

Scoring According to a Specific Gene Set
The "AddModuleScore" function built in the Seurat package was employed to quantify the activity of a specific gene set across different clusters.
For the analysis of RNA velocity within the subsets of fibroblasts and vSMCs-3, we applied the scvelo tool (version 0.2.4,https://github.com/theislab/scvelo)as recommended by its creators [24].This allowed investigation of the developmental trajectories from vSMCs-3 to fibro-like vSMCs using the trajectory analysis capabilities of the Monocle algorithm [25].

NicheNet
(version 2.0.4,https://github.com/saeyslab/nichenetr)was used for the identification of potential ligands secreted by fibrolike vSMCs.This is an advanced approach designed to predict ligands that may cause changes in the target cell transcriptome [26].Our analysis also incorporated CellChat (version 1.5.0,https://github.com/sqjin/CellChat)by using its comprehensive database, CellChatDB.mouse.This includes data on 1211 secreted signaling pairs, 432 extracellular matrix (ECM) receptor pairs, 378 cell-cell contact pairs, 229 families of signaling pathways (e.g., WNT, transforming growth factor-β (TGFβ), bone morphogenetic protein (BMP), epidermal growth factor (EGF), TGFα, fibroblast growth factor (FGF), platelet-derived growth factor (PDGF), vascular endothelial growth factors (VEGF), insulin-like growth factor (IGF)), and chemokine and cytokine signaling pathways (chemoattractant cytokine ligand (CCL), C-X-C motif chemokine ligand (CXCL), interleukins (IL), interferon (IFN)).CellChat assigns a probability of communication between ligand-receptor pairs based on the principle of mass action.It considers the average expression level of ligands in one cell population, and of receptors in another.The significance of these communication probabilities was determined using permutation tests, with p-values < 0.05 deemed significant.This analytical framework facilitates the assessment of interaction strengths between various cell clusters identified from the scRNA-seq data, with the results represented graphically as either circle or dot plots.
Tissue inflammation occurs in response to vascular injury and involves the infiltration and activation of immune cells [27].Two subgroups of macrophages and two subgroups of T cells are present in the carotid artery, and these cells are mainly found in NIH vessels.The elevated transcription levels of Foxp3 and Satb1 in TCs1 indicate this subpopulation possessed Treg (regulatory T cell) properties (Fig. 1C, Supplementary Fig. 1C) [28].TCs2 exhibit proliferative features due to high transcription levels of the MKI67 and TOP2A genes [29].The increased number of lymphatic endothelial cells (LECs) suggests that lymphangiogenesis occurs in NIH induced by vascular ligation.SWs are found in both healthy and diseased carotid arteries, which may be attributed to nerve fibers on the vascular wall.Additionally, subpopulations with different annotations but belonging to the same cell type showed high correlation (Fig. 1D).
These results demonstrate the heterogeneity of fibroblasts, ECs and vSMCs, which are the predominant cell populations within the artery.Our study therefore focused on these three cell types to gain a better understanding of their role in NIH.

Features of Different Endothelial Subtypes in NIH
To further characterize the heterogeneity of ECs in NIH, we performed a re-clustering of the EC cohort (C8).This identified four distinct subpopulations (ECs1, ECs2, ECs3, and ECs4) based on their transcriptomic profiles.Interestingly, the ECs4 subpopulation was found primarily in the sham group, whereas the frequency of cells from the other subpopulations was notably higher in the ligation group (Fig. 2A).A novel finding was the exclusive presence of ECs3 in the ligation group, characterized by the expression of genes typically associated with vascular fibroblasts, including Dcn and Col3a1 (Supplementary Fig. 2A).These cells express both Pdgfra and Pecam1 and are termed fibro-like ECs (Fig. 2B).Comparative analysis revealed up-regulation of mesenchymal markers (Prss23, Cd34, and Ly6a) in ECs1 and ECs2 relative to ECs4.This suggests that ECs1 and ECs2 have a mesenchymal-like phenotype, in contrast to the more endothelial-like phenotype of ECs4 (Fig. 2C).
Further exploration using the Monocle3 R package revealed a positional dichotomy between ECs2 and ECs4.ECs2 was centrally located, indicating a potential differentiation pathway from ECs4 to ECs1 and ECs2, but not to ECs3 (Fig. 2D).Analysis of an RNAseq dataset (GSE220512) revealed upregulation of feature genes for ECs1 and ECs2 in injured blood vessels at days 7 and 14 after vascular injury (Supplementary Fig. 2B).Dynamic expression of Teme252, Flt1, Nrp2 and Lrg1 showed an increasing trend along the pseudo-time trajectory (Supplementary Fig. 2C).Our analysis identified 5705 genes displaying four distinct expression patterns across the trajectory.Functional enrichment analysis found that genes associated with cell-substrate adhesion were highly expressed in ECs4, but were downregulated upon transition to ECs1 and ECs2.Conversely, genes implicated in cell proliferation and activation of the inflammasome complex were significantly upregulated in the later stages of pseudo-time (Fig. 2E).
Pathway analysis of ECs3 signature genes using Metascape provided insights into their biological functions, including ECM organization, elastic fiber formation, and cell motility.This suggests that ECs3 have ECM remodeling and cell migration activities in NIH (Fig. 2F).Previous studies have reported trans-differentiation of human fibroblasts into ECs [30].To investigate whether ECs3 represents a novel subpopulation derived from Pdgfra + vascular fibroblasts, we utilized Pdgfra DreER -tdTomato mice to trace fibroblast lineage cells within the carotid artery (Supplementary Fig. 3A) [31].Tamoxifen-induced Drerox recombination facilitated effective tdTomato labeling of vascular fibroblasts (Supplementary Fig. 3B).This was followed by vessel ligation to induce NIH in these mice (Supplementary Fig. 3C).Of note, intense tdTomato fluorescence within the neointima signified fibroblast migration from the vascular adventitia to the intima (Fig. 2G), with a subset of tdTomato + cells co-expressing Pecam1 and Pdgfra (Fig. 2G).Negative control observations are documented in Supplementary Fig. 3D.
Together, these findings revealed two subsets of ECs in carotid artery with NIH.These showed mesenchymal characteristics, markers for proliferation and the inflammasome complex, and existed alongside a subpopulation of fibro-like ECs.
We then analyzed the expression level of all chemokines, collagens and proteinase in all clusters.Of note, Fibs-1 and fibro-like vSMCs exhibited elevated levels of inflammatory chemokines (Cxcl14, Ccl11, Cxcl9, Ccl7 for Fibs-1 and Ccl4, Ccl22, Ccl6, Ccl25), while the latter cells also showed moderate collagen expression and elevated proteinase activity (Fig. 3C,D).Moreover, vSMCs-1 exhibited the highest scores in cell-cell and cell-ECM junctions, whereas fibro-like vSMCs had the lowest score for cell-ECM junctions, hinting at their migratory capability (Fig. 3E).
Fibs-1 emerge as pro-inflammatory, Fibs-3 as collagen-producing, and Fibs-4 as indicative of fibroblasts in homeostasis.This raises the question of the role of Fibs-2 in NIH.By conducting monocle analysis on Fibs-1, Fibs-2, Fibs-3 and Fibs-4, it was discovered that Fibs-3 likely originates from both Fibs-2 and Fibs-4, with Fibs-1 being derived from Fibs-2 (Supplementary Fig. 5A).We also examined the transcriptional level of stemness genes in different fibroblast subpopulations.Significant expression of the mesenchymal marker Gli1 was observed in Fibs-4, indicating this subpopulation was MSC-like fibroblasts (Supplementary Fig. 5B).The highest level of Sca1 transcription was seen in Fibs-2 (Supplementary Fig. 5B).The CytoTRACE algorithm revealed that Fibs-2 and Fibs-4 cells exhibited the highest level of stemness, followed by Fibs-1, and with Fibs-3 showing the least stemness (Supplementary Fig. 5C).The integration of monocle2 and CytoTRACE helps to differentiate the various fibroblast subpopulations (Supplementary Fig. 5D).
The above findings highlight the cellular heterogeneity present in NIH.The results identify fibro-like vSMCs as key players due to their robust chemokine secretion, moderate ECM production, relatively elevated proteinase levels, and diminished adhesion, thereby making them potential targets for NIH therapy.In addition, these results suggest that Fibs-2 are a minor, yet critical cell population within healthy blood vessels, and are capable of proliferation and differentiation into pro-inflammatory fibroblasts following vascular injury.

Fibro-Like vSMCs Originate from vSMCs-3 in NIH
Previous scRNA-seq analyses conducted on atherosclerotic lesions from murine and human arteries have shown that vSMCs tend to shift toward genuine  fibroblasts, while remaining distinct from these [13].We hypothesized that this transdifferentiated phenotype also exists in NIH.The transcriptional kinetics from vSMCs-3 to fibro-like vSMCs was revealed by RNA velocity, which is a high-dimensional vector that predicts the future state of individual cells over several hours (Fig. 4A) [33].The R package Monocle was used to construct the comprehensive lineage differentiation trajectory for vSMCs-3 to fibro-like vSMCs, highlighting the overlap between the two clusters on this trajectory (Fig. 4B).Subsequent analysis of gene expression patterns during the cell state transitions along this trajectory identified 6705 dynamically expressed genes, categorizing them into four distinct patterns.Functional enrichment analysis was carried out on three of these patterns (Fig. 4C).Canonical vSMC genes, such as Myh11 and Cnn1, were down-regulated, whereas fibroblast-specific genes such as Pdgfra and Dcn were up-regulated (Fig. 4D).Furthermore, SCENIC analysis found that Dlx5, Ets2, Rxrb, Nfya and Batf were the top five most active regulons in individual cells (Fig. 4E,F).
Taken together, the above results suggest that the key pathogenic cell subpopulation, fibro-like vSMCs, may be derived from vSMCs-3.

Fibro-Like vSMCs are the Primary Cells Responsible for Secreting Vegfa
CellChat was employed to build a cell-cellcommunication network based on known ligand-receptor pairs and their cofactors [34].Fibro-like vSMCs had distinct effects on different cell types, and interact mainly with ECs (Fig. 5A,B).NicheNet analysis [26] revealed that Vegfa was secreted predominantly by fibro-like vSMCs (Fig. 5C) and may drive biological functions in other cell types.In NIH, Vegfa influences ECs via Vegfr2 (Fig. 5D).Additionally, Vegfa expression was significantly higher in wire-induced or ligation-induced vessels, as shown by the RNA-seq datasets GSE70410 and GSE220512 (Fig. 5E).

Discussion
This study established a mouse model of NIH that was induced by carotid artery ligation.We subsequently performed scRNA sequencing on ligated and self-control arteries.This analysis revealed that the NIH microenvironment is more heterogeneous and complex than previously reported [35].Briefly, we analyzed 17,605 cells and identified 7 different cell types.These were comprised of subpopulations, thereby demonstrating their heterogeneity.The gene signatures of several of these cell subpopulations, not previously documented in earlier studies, were thoroughly elucidated.
Immune cell infiltration into vessels occurs postinjury.Our scRNA-seq data identified two distinct clusters of macrophages and T cells found predominantly in diseased vessels.Specifically, TCs1 showed significant expression of Foxp3, which is the hallmark gene of Tregs.Previous research has suggested that Tregs do not significantly affect the progression of NIH [36].TCs2 showed increased expression of genes associated with proliferation.However, the exact role of TCs2 in NIH remains to be elucidated, highlighting the need for further investigation.SWs were present in both healthy and diseased vessels, possibly due to the presence of pre-vascular nerve.Additionally, a distinct population of LECs was identified in vessels with NIH.Previous studies have demonstrated that fibroblastderived Vegfc promotes lymphangiogenesis in animal models of NIH induced by vascular transplantation.Moreover, the inhibition of lymphatic vessel formation has been shown to reduce neointimal thickness [37].This suggests that lymphangiogenesis may play a significant role in NIH following ligation, warranting further investigation to elucidate its impact.
ECs have the capacity to regulate the growth and regression of vSMCs through direct interaction or via the release of mediators.Additionally, ECs regulate the thickness of the ECM by secreting enzymes or their inhibitors [38].In the present study, a subpopulation of ECs expressing Pdgfra (fibroblast marker) and Pecam1 (endothelial cell marker) was found to play a role in ECM remodeling and cell migration in NIH.Additionally, cells coexpressing Pdgfra and Pecam1 were found in the neointima of Pdgfra DreER -tdTomato mice by immunofluorescence, leading us to hypothesize that these cells originate from adventitial fibroblasts.We plan to investigate the specific role and mechanism of Pdgfra and Pecam1 coexpressing cells in NIH in future studies.Additionally, we identified two subsets of ECs with mesenchymal features in carotid arteries with ligation-induced NIH.These ECs expressed genes involved in cell proliferation and in the inflammasome complex.
A previous study reported the presence of mesenchymal-like ECs in mouse carotid artery subjected to disturbed flow [7].In the current study, we evaluated the expression of the top 10 genes in mesenchymal-like ECs from the mouse carotid artery under disturbed flow conditions.This was carried out for three EC subsets: ECs1, ECs2, and ECs4.Our analysis revealed shared gene expression (Kit, Vcan, and Lamb1) between the two animal models, as well as genes with differential expression (Dkk2, Ngf, Col8a1, Ltbp2, Cdca1l, Dclkl, and Slc45a4) (Supplementary Fig. 6A).This suggests that ECs with mesenchymal characteristics and that arise from the two experimental methods possess distinct gene expression profiles.
Furthermore, mesenchymal-like ECs under disturbed flow were characterized by the production of TGF-β, a trait observed in both ECs1 and ECs2, indicating commonality in cellular function (Supplementary Fig. 6A).
Four distinct groups of vSMCs and fibroblasts were identified.vSMCs-2 exhibited elevated expression of genes associated with osteoblast differentiation, suggesting a propensity towards an osteochondrogenic-like phenotype.This observation is significant given that calcification and bone formation are commonly observed in advanced stages of NIH patients, and implicating vSMCs-2 as a potential contributory cell type to this pathology that warrants further experimental validation.Two fibroblast subpopulations, Sca1 + Fibs-2 and Gli1 + Fibs-4, showed the highest stemness scores, while Fibs-3 exhibited the lowest.Fibs-3 emerged as the primary cell type for collagen production and was derived from Fibs-2 and Fibs-4.Pro-inflammatory Fibs-1 was identified as the intermediary cell during this differentiation process.Consequently, inhibiting the differentiation of Fibs-2 or Fibs-4 into other subpopulations may represent a promising therapeutic strategy.
RNA velocity and Monocle analyses showed that vSMCs-3 transitioned toward fibro-like vSMCs in NIH, accompanied by a reduction in the expression of vSMCs markers.Fibro-like vSMCs have the ability to release inflammatory chemokines, which may contribute to the accumulation of immune cells and the subsequent reduction in lumen area [39].Moreover, fibro-like vSMCs showed minimal collagen release, but displayed high levels of protease expression and the lowest scores for cell-ECM junctions.This implies that fibro-like vSMCs are more likely to undergo cell migration [40].In atherosclerotic plaques, vSMCs undergo dedifferentiation to a fibroblast-like phe- notype.These were termed "fibromyocytes" by the authors, and are thought to play a critical role in stabilizing the plaque [13].Fibromyocytes are a protective cell type with distinct functions compared to the fibro-like vSMCs identified in NIH.Tcf21 was identified as a key transcription fac-tor for fibromyocytes.Fibro-like vSMCs in NIH have a relatively higher expression of Tcf21 compared to other vSMC subpopulations (Supplementary Fig. 6B).Therefore, the dedifferentiation of vSMCs-3 into fibro-like vSMCs in NIH may also be regulated by Tcf21.
Cell-communication analysis revealed that vascular injury shaped different signal-communication patterns.NicheNet analysis was used to predict ligands secreted by fibro-like vSMCs, with Vegfa notably among the top 10 active ligands.Upregulation of Vegfa has been observed consistently across different datasets, time points, and animal models of NIH.Cellchat analysis confirmed that fibrolike vSMCs are able to secrete Vegfa, which subsequently acts on different subpopulations of ECs and vascular lymphatic ECs.Previous research has indicated that inhibition of lentivirus-mediated Vegfa expression in injured vessels hinders neointimal formation [41].Fibro-like vSMCs may therefore play a key role in disease progression and serve as a new target for therapy.
The present study has several limitations.First, although the mouse model of carotid artery ligation injury is widely used to study NIH, there is nevertheless a translational gap with human conditions.Second, the procurement of vessels with NIH from humans is challenging, and significant differences exist between mice and humans.Third, the number of cells per cluster that were analyzed was limited.Fourth, scRNA-seq provides RNA-level information only, and inferred cellular functions (e.g., cell migration) based solely on gene signature may not be accurate.Fifth, the results of the analyses may be influenced by changes in the packages and in the parameter settings.Sixth, it is difficult to accurately distinguish between fibroblasts and mesenchymal stem cells using scRNA-seq.Finally, this study lacks in vivo animal experiments, particularly those involving lineage tracing of vSMCs.We plan to conduct further research to address this latter point.

Conclusions
Our study investigated the heterogeneity of vascular cells in NIH.We identified a total of four subpopulations each of ECs, fibroblasts, and vSMCs.These cell clusters were characterized at the mRNA level, with a particular focus on two notable clusters: ECs3 and fibroblastlike vSMC.ECs3 is derived from fibroblasts and is involved in ECM remodeling.The fibro-like vSMC is derived from vSMCs-3 and is characterized by the secretion of chemokines and Vegfa.These findings highlight the potential of ECs3 and fibro-like vSMC as targets for NIH treatment.

Fig. 1 .
Fig. 1.The lineage and characteristics of all cells in healthy (Sham) and ligated (Ligation) carotid artery.(A) Annotated UMAP of mouse carotid artery single-cell RNA sequencing (scRNA-Seq).(B) Relative expression of marker genes in all cells from all samples projected onto a Dotplot.(C) Heatmap showing the mean expression of highest specificity gene markers of each clusters.Color represents average normalized expression level within the denoted cluster.(D) The correlation between different clusters.vSMCs, vascular Smooth Muscle Cells; ECs, Endothelial Cells; LECs, Lymphatic Endothelial Cells; SWs, Schwann Cells; UMAP, Uniform Manifold Approximation and Projection.

Fig. 2 .
Fig. 2. Description of phenotype and function of ECs in NIH.(A) Re-clustering of ECs identified in carotid tissue before and after acute injury.(B) Gene expression of Pdgfra and Pecam1 for each clusters.(C) Violin plots on the expression of selected mesenchymal markers of ECs1, ECs2 and ECs4.(D) UMAP visualization showing the pseudo-time trajectory of the ECs1, ECs2 and ECs4.(E) Heatmap hierarchical clustering showing differentially expressed genes along with the pseudotime curve (left).Gene Ontology biological processes of each DEGs clusters (right).Color key from blue to red indicates relative expression levels from low to high.(F) Top-ranked enrichment analysis of feature genes of ECs3 on Metascape platform.Bars are discretely colored to encode p-values of increasing statistical significance.(G) The immunofluorescent staining of Pecam1 (green), tdTomato (red) and Pdgfra (purple, autofluorescence) in carotid artery from Pdgfra DreER -tdTomato mice.Nuclei were counterstained with 4 ′ ,6-diamidino-2-phenylindole (DAPI) (blue).Scale bar = 50 µm (n = 3).A, adventitia; M, media; N, neointimal hyperplasia; NIH, Neointimal Hyperplasia; DEGs, Differentially Expressed Genes; IGF, insulin like growth factor; ECM, extracellular matrix; MAPK, mitogen-activated protein kinase.

Fig. 3 .
Fig. 3. Functional analysis of different subtypes of fibroblasts.(A) A t-SNE plot of all fibroblasts and vSMCs colored according to cluster (left) and group (right).(B) Relative expression of Myh11 and Pdgfra in fibroblasts and vSMCs projected onto a t-SNE plot.(C) Chemokines, Collagens and Proteinase score calculated by the AddModuleScore function.(D) Heatmap showing chemokine genes, collagen genes, and proteinase genes in eight clusters.(E) The cell-cell junction scores between any two clusters (left) and cell matrix scores of each clusters (right).

Fig. 4 .
Fig. 4. The transdifferentiation of vSMCs-3 into fibro-like vSMCs.(A) RNA velocity overlaid on tSNE of the extracted vSMC and fibroblast subset.(B) The developmental trajectory of selected clusters, coloured-coded by the groups (upper) and associated cell subpopulations (lower).(C) Heatmap hierarchical clustering showing differentially expressed genes along with the pseudotime curve (left).Gene Ontology biological processes of each DEGs clusters (right).Color key from green to purple indicates relative expression levels from low to high.(D) Vlnplot showing the expression levels of Myh11, Cnn1, Pdgfra and Dcn in fibro-like vSMCs and vSMCs-3.(E) The regulatory activity of all clusters.(F) Scatter plot showing the specificity scores of regulons of fibro-like vSMCs.The top 6 regulons are highlighted.

Fig. 5 .
Fig. 5. Cell-cell communications of fibro-like vSMCs with other cells.(A) Arrow plot showing the interaction strength between fibro-like vSMCs with other cell populations, based on the interaction scores.(B) Heatmap showing the number of interactions between different structual cells in vessel.Blue block indicate that the displayed communication is decreased in ligated carotid artery (CA), whereas red cblock indicate that the displayed communication is increased in ligated CA compared with healthy carotid artery.The darker the color, the greater the communication probability between the two cell type.(C) Genes are ligand that predicted by Nichenet, secreted by fibro-like vSMCs, ranked by prioritized liand activity.The darker the color, the higher the ranking.Dot plot showing average expression of ligand in all clusters.(D) Dot plot showing vascular endothelial growth factor (VEGF) related chemokine-receptor interaction pairs between fibro-like vSMCs and three ECs subsets or LECs, based on the communication probability.(E) Barplots showing expression level of Vegfa in different group of different database.**, p ≤ 0.05; *, p ≤ 0.05; ns = not significant.Student's t test (left) and paired Student's t test (right).