Multi-Omic Integration of Single-Cell, Spatial, and Bulk RNA-Seq Deciphers Endothelial Heterogeneity and WWTR1-Mediated Vascular Reprogramming in Keloids

Multi-Omic Integration of Single-Cell, Spatial, and Bulk RNA-Seq Deciphers Endothelial Heterogeneity and WWTR1-Mediated Vascular Reprogramming in Keloids | 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 Multi-Omic Integration of Single-Cell, Spatial, and Bulk RNA-Seq Deciphers Endothelial Heterogeneity and WWTR1-Mediated Vascular Reprogramming in Keloids Nana Sun, Zetong Zheng, Zhiming Yang, Luqi Peng, Yang Wen, Junyi Xu, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9319391/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Keloids are complex fibroproliferative disorders characterized by persistent inflammation and vascular dysfunction. However, the high-resolution cellular landscape of their endothelial niche remains poorly defined due to the limited availability of specialized transcriptomic datasets. In this study, we addressed this critical knowledge gap by integrating single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics with our in-house bulk RNA-seq data from clinical keloid and normal skin samples, thereby significantly expanding the available transcriptomic resources for keloid vascular research. Our multi-omic analysis identified ten distinct endothelial cell (EC) subpopulations and revealed a marked expansion of a pro-pathological “Stress-endo” cluster within the keloid microenvironment. By leveraging our self-sequenced clinical data for cross-validation, we characterized the Stress-endo subpopulation as a pivotal mechanical-inflammatory nexus and identified WWTR1 (TAZ) as the master transcription factor driving its pathological transition. Developmental trajectory and cell–cell communication analyses further demonstrated that this activated EC niche actively orchestrates immune cell recruitment and tissue remodeling through IL-17 and Visfatin signaling pathways. The upregulation of WWTR1 in keloid tissues was further validated at both the mRNA and protein levels by quantitative PCR and western blotting, respectively. Collectively, our study provides an enriched and refined transcriptomic framework for keloid pathogenesis, positioning WWTR1-mediated endothelial stress as a central orchestrator of chronic inflammation and fibrosis, thereby offering a more robust foundation for future vascular-targeted therapeutic strategies. Keloid Single-cell RNA sequencing Spatial transcriptomics Endothelial cell heterogeneity WWTR1 Machine learning Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1. Introduction Excessive extracellular matrix (ECM) deposition that extends beyond the initial wound boundaries and infrequently regresses spontaneously are the hallmarks of keloid, a fibroproliferative disorder[ 1 , 2 ]. According to estimates, the incidence among people of African descent ranges from 4.5% to 16% and are more commonly found in Africans and Asians than in Caucasians[ 3 , 4 ]. Excessive scarring, which includes keloids and hypertrophic scars, was self-reported by 2.4% of Black participants, 1.1% of Asians, and 0.4% of White participants in a population-based UK cohort[ 5 ]. A positive family history increases the risk for the development of keloids although no specific gene has been identified. Even though keloids are benign, they can result in pain, pruritus, and contractures that severely lower patients' quality of life and cause financial and psychological hardships[ 6 ] Keloids exhibit aberrant fibroblast proliferation, dense collagen bundles, and persistent inflammation [ 7 ]. Fibroblasts, immune cells, and vascular components have been shown in earlier research to play a crucial role in extracellular matrix overproduction, inflammation, and aberrant angiogenesis during keloid formation[ 8 – 10 ]. These cell populations work together to create the fibrotic and chronic inflammatory microenvironment that characterizes keloid pathology. There is growing evidence that vascular abnormalities, such as aberrant angiogenesis and endothelial dysfunction, are critical in maintaining fibrosis and inflammation in keloid tissue[ 11 , 12 ]. In addition to forming the inner lining of blood vessels, endothelial cells (ECs) actively regulate fibroblast activation, extracellular matrix remodeling, and immune cell trafficking[ 13 ]. Furthermore, it has been demonstrated that extracellular vesicles derived from endothelial progenitor cells mediate pro-fibrotic signaling and wound healing processes [ 14 ]. However, only a small number of studies have thoroughly investigated the heterogeneity of endothelial cells in fibrotic skin conditions. While the variety and functional states of endothelial cells are still poorly understood, the majority of prior keloid research has concentrated on fibroblast and immune compartments [ 15 – 17 ]. While recent single-cell and spatial transcriptomic analyses have yielded previously unheard-of insights into the cellular landscape of keloid tissue, these studies have primarily focused on immune-mediated and fibroblast-driven mechanisms[ 16 , 18 , 19 ]. Endothelial heterogeneity in keloid has not yet been the subject of a thorough single-cell analysis. In order to clarify their molecular signatures and spatial organization within the fibrotic microenvironment, we sought to characterize the endothelial cell heterogeneity in keloid using integrated single-cell and spatial transcriptomic analyses. 2. Materials and Methods 2.1. Keloid and normal skin samples This study was approved by the Medical and Ethics Committees of Dermatology, Affiliated Hospital of Zunyi Medical University (KLL-2022-0763) and samples were collected from 2022 to 2023, written informed consent was obtained from all patients, and a prospective study design was performed. Keloid tissues were harvested during plastic surgery from 7 patients confirmed to have clinical evidence of keloid. The keloids used in this study were mature (non-growing and burned-out, which can be removed surgically) and the whole area of the keloid samples, including the center and edge of the samples, was used for analyses. The patients received chemotherapy, radiotherapy, or intralesional steroid treatment prior to surgery were excluded for sample collection. Normal skin tissues were obtained from 8 patients who underwent elective surgery. Keloids and normal scars were diagnosed based on their clinical appearance, history, anatomical location, and pathology. The excised skin was washed with physiological saline and then immediately frozen and stored at -80 C before being sent to Chongqing Knorigene Technologies (Chongqing, China) for RNA-Seq. Informed consent was obtained from all subjects involved in the study. Written informed consent has been obtained from the patient to publish this paper. 2.2. RNA isolation and sequencing Total RNA was extracted with TRIzol reagent (Takara, Japan). The quantity (> 1 µg) and quality (260/280 > 1.8) of the RNA were determined with a NanoDrop 2000 (Thermo Fisher, USA). Total RNA was reverse transcribed with Oligo dT primers to produce cDNA. The generated first-strand cDNA was co-reacted by the RNase H enzyme, DNA polymerase and T4 ligase to generate double-stranded cDNA; the double-stranded cDNA was fragmented by the Tn5 enzyme and added the remedial design (RD) sequence required for sequencing at both ends. The sequencing primers at both ends of P5 and P7 were connected by RD sequences at both ends and PCR enrichment was performed. Successful library construction was sequenced. All RNA-Seq procedures and initial bioinformatics were performed by Chongqing Knorigene Technologies (Chongqing, China). 2.3. Data acquisition and processing We got single-cell RNA sequencing (scRNA-seq) data for keloids from the GEO database ( http://www.ncbi.nlm.nih.gov/geo/ ) with the accession number GSE163973[ 8 ]. From this dataset, we chose three samples of keloids and three samples of normal controls. A strict quality control (QC) protocol was put in place to make sure that the single-cell data was accurate. Cells were filtered based on the following criteria: gene detection (nFeature_RNA) between 200 and 5,000, total UMI counts (nCount_RNA) between 200 and 30,000, mitochondrial gene content (pMT) below 10%, and hemoglobin gene content (pHB) below 5%. After filtering, 40,685 high-quality cells were kept for further analysis. We used the "Log-normalization" method with linear regression to normalize the data. Then we used the "FindVariableFeatures" function to find the 2,000 genes that changed the most. After that, Principal Component Analysis (PCA) was used to reduce the number of dimensions. We used Harmony, a strong and scalable R package, to combine multiple datasets and get rid of variations caused by batch processing[ 20 ]. The "FindClusters" function was used to cluster cells with a resolution of 0.5 and the first 15 principal components (pc.num = 1:15). 2.4. Cell type identification We used the FindAllMarkers function in Seurat to perform a differential expression analysis on all of the cell clusters to find out what types of cells they were. The following criteria were used to choose marker genes: an adjusted p-value of less than 0.05, expression in more than 25% of the cells in the cluster (min.pct = 0.25), and an absolute log2 fold change of more than 0.25. The top-ranked differentially expressed genes for each cluster were identified as cluster-specific highly expressed genes. Cell type annotation was achieved by synthesizing the functional roles of these marker genes with findings from prior studies [ 21 , 22 ] utilizing the same database. 2.5. MILO Differential abundance testing Milo is a scalable statistical framework that tests for differential abundance by placing cells into partially overlapping neighborhoods on a k-nearest neighbor graph [ 23 ]. We performed the analysis by following the official miloR work-flow ( https://github.com/MarioniLab/milo).W e converted the Seurat object into a Single-CellExperiment format and made a k-nearest neighbor graph. We used calcNhoodDistance to figure out the distances between cells, testNhoods to test for differences in abundance, and then we constructed a neighborhood graph to show the results. 2.6. Differential analysis and multiple enrichment analysis We applied the FindMarkers function for the differential analysis, setting min.pct to 0.1 and logfc.threshold to 0.1. Genes exhibiting a logFC greater than 0.5 and a p-value less than 0.05 were categorized as up-regulated, whereas those with a logFC less than − 0.5 and a p-value less than 0.05 were deemed down-regulated. After that, these differentially expressed genes underwent several functional enrichment analyses. We used the Metascape [ 24 ] online platform, which combines functional enrichment, interactome analysis, and gene annotation, to carry out Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. These analyses enabled us systematically describe the biological pathways and protein functions associated to the gene sets we found. 2.7. Gene set variation analysis,Gene Set Enrichment Analysis and cell scoring. To evaluate the biological activity across different endothelial subpopulations, we first calculated the average gene expression for each subpopulation using the AvergaeExpression function. Subsequently Gene Set Variation Analysis (GSVA) [ 25 ] was performed on these average expression profiles using the "h.all.v2025.1.Hs.symbols.gmt" gene set from the MSigDB Hallmark collection.We observed differentially expressed genes (DEGs) in keloid endothelial cells when we compared them to other endothelial cells. We used a threshold of |logFC| > 0.1 and expression in more than 10% of cells (min.pct = 0.1). We then used Gene Set Enrichment Analysis (GSEA) with the HALLMARK gene set collection (h.all.v2025.1.Hs.entrez.gmt) from the MSigDB database to conduct a pathway enrichment analysis on these DEGs. We got gene sets for "Hypoxia," "Inflammatory Response," "MYC Targets V1," and "TNFA Signaling via NFKB" from the MSigDB database. Finally, we used AUCell, a R package made to measure gene set activity, to find the biological activity scores for each endothelial cell [ 26 ].We used the AddModuleScore algorithm to look at the activity of the gene signatures "KEGG_ECM_RECEPTOR_INTERACTION", "KEGG_TGF_BETA_SIGNALING_PATHWAY" and "HALLMARK_HYPOXIA" in the spatial transcriptomics data. The goal of this analysis was to confirm that these pathways are active in space and that they are located near the expression of our target gene. 2.8. Pseudo-time analysis and CytoTRACE2 We utilized Monocle pseudo-temporal ordering analysis to systematically sort cells into two distinct states based on their gene expression profiles for trajectory reconstruction. Then, we used the scopR package ( https://github.com/mengxu98/scop ) to show the results.To identify temporally regulated genes during the differentiation of endothelial cells, we performed a thorough differential expression analysis along the pseudotime continuum (differentialGeneTest). We utilized the “plot_genes_in_pseudotime” function to illustrate the dynamic expression patterns of the targeted genes. To evaluate the differentiation potential of endothelial cell populations, we employed CytoTRACE2 for quantitative assessment of developmental progression across various subsets of endothelial cells. 2.9. Cell communication To investigate cell communication differences between endothelial cell subsets, we employed the CellChat R package [ 27 ]. This tool infers intercellular communication by integrating known ligand-receptor interactions with gene expression data. Our analysis revealed significant disparities in communication networks between subsets, identifying key signaling pathways involved in disease progression. 2.10. Biomarker discovery using machine learning approaches To systematically identify the endothelial cells’ biomarker, we implemented a multi-platform validation strategy. First, four machine learning methods were employed for effective feature selection: LASSO regression, Support Vector Machine (SVM), Boruta algorithm, and Random Forest (RF) with 2000 trees and Gini importance scoring. Before the LASSO regression analysis, the randomcolo R package in R was used to generate 40 different colors. The random seed was set at set.seed(1). After that, the LASSO model was run with set.seed(11) and an alpha value of 1. Using 5-fold cross-validation (nfold = 5), the SVM model was set up. The Boruta algorithm was run with set.seed(1), doTrace = 2, and maxRuns = 500 [ 28 ]. For the Random Forest model, we set the random seed to set.seed(3). We employed an online Venn diagram tool to visualize the genes that the different machine learning algorithms discovered to be related to each other [ 29 ]. Additionally, their expression patterns were evaluated using box plots in the comprehensive transcriptomic dataset. 2.11. Spatial Data Integration and Microenvironment Modeling We processed the public keloid ST dataset (GSM9225694) using Seurat (v5.0). Normalization and technical artifact removal were performed via SCTransform, followed by PCA-based dimensionality reduction (top 15 PCs) and unsupervised clustering to define spatial domains. To resolve spot-level cellular composition, RCTD was executed using a scRNA-seq reference to estimate cell-type proportions [ 30 ]. Functional states, including ECM pathway, hopoxia and TGF-β, were quantified using the AddModuleScore function. Finally, the MISTyR framework was utilized to model the tissue microenvironment. By integrating multi-view spatial data, MISTyR identified proximity-dependent intercellular communication networks and signaling hubs driving the spatial heterogeneity of keloid lesions [ 31 ]. 2.12. Western blotting Proteins were extracted from keloid tissues for analysis using a BCA protein assay kit (Beyotime Biotechnology, Cat# P0010). After mixing with loading buffer, samples were heat‑denatured at 95°C for 5 min and resolved on 10% SDS‑polyacrylamide gels. The separated proteins were then electrotransferred onto PVDF membranes. Membranes were blocked with 5% BSA and subsequently probed overnight at 4°C with primary antibodies: anti‑WWTR1 (1:1,000, Abcam, Cat# ab242313, mouse monoclonal) and Anti-beta Actin (1:1000, Abcam, Cat# ab8226, mouse monoclonal). Following primary antibody incubation, membranes were incubated with a combination of fluorescent secondary antibodies: AffiniPure Goat Anti-Mouse IgG H&L (1:20,000, Bioss, Cat# bs-40296G-IRDye800CW). Protein bands were detected and quantified using an Odyssey infrared imaging system. 2.13. q-PCR Total RNA was isolated from keloid tissue and subjected to reverse transcription with the Evo M-MLV RT Premix Kit (Accurate Biology, Cat# AG11728). Quantitative PCR was then performed using the SYBR Green Pro Tag HS Premixed qPCR Kit (Accurate Biology, Cat# AG11701). The following primers were used: WWTR1 forward 5’-GGTCCATGGCAGTATCCCAG-3’ and reverse 5’-GGATTCTCTGAAGCCGCAGT-3’, along with β-actin forward 5’-ACCCTTAAGAGGGATGCTGC-3’ and reverse 5’-CCCAATACGGCCAAATCCGT-3’ as the reference. 3. Results 3.1. Single-cell RNA sequencing landscape of keloid Quality control was essential for making sure that the data was reliable for analysis later on. After a thorough evaluation (Fig. 1 A), 6 high-quality samples (3 from the keloid group and 3 from the normal control group, comprising 40,658 cells) were chosen for additional analysis. To reduce technical variability, batch effect correction was implemented, leading to a harmonized data distribution and the eradication of batch-related discrepancies (Supplementary Fig. 1A-B). A later clustering analysis put all the cells into 21 different groups (Supplementary Fig. 1C). We used known cell markers to find 10 main types of cells, such as endothelial cells, fibroblasts, and myeloid cells (Fig. 1 B). The cycling cell population was validated by significantly elevated G2/M scores, confirming their active proliferation (Fig. 1 C). Additionally, cells were depicted in a three-dimensional reduced dimensionality space and colored by their pre-defined group assignments(Fig. 1 D-E). We made a heatmap with the "ClusterGVis" package[ 32 ] to show cell type-specific marker genes. The input gene set consisted of the top 20 markers for each cell type, as determined by the “FindAllMarkers” function. Following that, the "org.Hs.eg.db" database was employed to do a Gene Ontology Biological Process (GOBP) enrichment analysis on these marker genes. This analysis of a keloid sample using single-cell RNA sequencing shows that there are different groups of cells, each with its own gene expression profiles and biological pathways that are more active. For instance, the Endothelial cell cluster (C1) is characterized by elevated expression of genes such as SELE and AQP1. Pathway analysis shows that these cells are more involved in processes that control vascular permeability, endothelial cell differentiation, and the growth of the endothelium as a whole. The top 20 marker genes in the Endothelial cell cluster are linked to these vascular-related functions, which means they may play a role in angiogenesis or vascular remodeling in the keloid microenvironment. Other clusters also have unique marker genes and pathways that are more common, which gives us more information about how different types of cells work in the keloid tissue (Fig. 1 F). A comparison of the density of keloid and normal scar tissues (Fig. 1 G) demonstrates that the cells in keloids are very different from those in normal scars. In particular, the density plots show that keloid tissues have more endothelial cells and smooth muscle cells than normal scars. On the other hand, the density of fibroblasts and keratinocytes is lower in keloids, which may mean that there is an imbalance in the types of cells that make up keloids. This changed cellular environment, which has more endothelial and smooth muscle cells and fewer fibroblasts and keratinocytes, shows how the cellular processes that lead to keloid scars are not working properly. To further confirm the correctness of our cell annotation results, we used well-known cellular markers as a standard. These markers validated the accurate identification of principal cell types within our dataset (Fig. 1 H). After that, we made cell proportion plots that showed the differences between keloid and normal scar tissues. These plots showed that the number of endothelial cells and smooth muscle cells in keloid samples went up a lot. In contrast, we noted a relative reduction in the proportion of fibroblasts and keratinocytes in keloids, thereby supporting the hypothesis that imbalances in cell populations play a role in keloid pathology (Fig. 1 I). 3.2. Alterations in Cellular Composition and Functional State We methodically delineated the transcriptional profiles among various cell types in keloid and normal scar tissues. To start, the FindAllMarkers function was used to find cell type-specific marker genes, which included both the most upregulated and downregulated transcripts. We then used the jjVolcano function from the scRNAtoolVis [ 33 ] R package to show these markers (Fig. 2 A). A notable elevation in endothelial cell density was observed in keloid tissues, as determined by differential abundance analysis utilizing MiloR, in contrast to normal scar samples (Fig. 2 B-C).Together, these findings about the number and types of cells made us want to focus our next studies on the endothelial cells in keloid tissue. We subsequently conducted a differential gene expression analysis on endothelial cells comparing keloid and normal scar tissues. This analysis found 42 genes that were turned off (e.g., IL6, G0S2, HLA-DRB5) and 29 genes that were turned on (e.g., DCD, MMP1, COL1A1) in keloids (Fgi.2D-E).Gene Ontology analysis identified biologically coherent themes that connect the transcriptomic changes to keloid pathology. The 29 upregulated genes were significantly enriched in pathways that regulate "extracellular matrix organization" and "external encapsulating structure organization." These are processes that are important for the excessive fibrosis and collagen deposition that are typical of keloid lesions (Fig. 2 F). On the other hand, the 42 down-regulated genes were more common in immune regulatory pathways, such as "response to bacterial molecules" and "regulation of leukocyte adhesion." This suggests that the normal immune surveillance and resolution processes in the keloid microenvironment may be disrupted (Fig. 2 G). 3.3. Endothelial Cell Subclustering and Functional Profiling A total of 10 discrete endothelial subsets were identified through graph-based clustering (resolution 0.3, 15 PCs) post-normalization. To ensure objective annotation, we employed the GPTCelltype framework to interpret cluster-specific markers and define the functional roles of each subpopulation[ 34 ]. The 10 endothelial subpopulations and their defining characteristics are as follows: inflammatory and antigen-presenting ECs (C0: HLA-DRB1/5 , NFKBIA ; C5: SERPINE1 , VCAM1/SELE ); remodeling and fibrosis-associated ECs (C2: COL1A1/2 ; C3: MMP1 , HMOX1 ; C6: MMP2 , SPARC ); and metabolic or synthetic ECs (C1: RPL/RPS genes). Additionally, we identified homeostatic (C4: CXCL12 ), angiogenic (C7: PECAM1 , EGFL7 ), immunomodulatory (C8: ACKR1 , CD74 ), and migratory (C9: S100A14 , CD44 ) subpopulations. This diverse landscape underscores the functional specialization of the keloid endothelium.This detailed profiling reveals a remarkable functional specialization within the keloid endothelium(Fig. 3 A-C). Analysis of the EC subpopulation composition revealed considerable heterogeneity between keloid and normal scar tissues. Keloids exhibited an increase in pro-fibrotic (Fibro-endo), stressed (Stress-endo), and En-doMT-like (EMT-endo) populations, coupled with a relative reduction in immunomodulatory and antigen-presenting clusters (Fig. 3 B,E). Significantly, the Stress-endo subpopulation became a pivotal center. Differential expression and enrichment analyses (Wilcoxon test; logFC > 0.8) demonstrated that genes upregulated in Stress-endo were predominantly enriched in cell-cell adhesion and IL-17 signaling (Fig. 3 F). Additional GSVA profiling corroborated that In-flam-endo was associated with IL-6/JAK/STAT3 signaling, whereas Stress-endo demonstrated significant activity in angiogenesis, epithelial-mesenchymal transition (EMT), and Notch signaling (Fig. 3 G). These results identify the Stress-endo subpopulation as a pivotal mediator, connecting mechanical stress to chronic inflammation and vascular remodeling within the keloid microenvironment. 3.4. Multiple enrichment analysis in Keloid’s endothelial cells In keloid tissues, we consolidated the proportionally expanded fibroblast subpopulations—"Ribo-Endo," "Fibro-Endo," "Stress-Endo," and "EMT-Endo"—into a unified group termed "Keloid-endo," while the remaining subpopulations were categorized as "other-endo." Differential gene expression analysis between these two groups was performed using the FindMarkers algorithm, followed by Gene Set Enrichment Analysis (GSEA). The Keloid-endo cell population drives keloid progression by activating key pathways that promote fibrosis (TGF-β), proliferation (MYC), and inflammation (TNF-α), while simultaneously suppressing apoptosis and anti-growth signals (IFN-α response)(Figure 4 A,D). This coordinated molecular program confers sustained growth advantage, enhanced survival, and pathological tissue remodeling. Complementing these findings, Metascape analysis of the top upregulated genes further revealed activation of the VEGFA-VEGFR2 pathway—supporting angiogenesis—along with enhanced cell migration, inhibition of apoptosis, and a robust response to oxidative stress. Together, these mechanisms enable Keloid-endo to stimulate pathological tissue remodeling, ensure its own survival, and expand within the inflammatory keloid microenvironment༈Figure 4 B༉.Based on the gene sets associated with MYC TARGETS V1, inflammatory response, TNFA signaling, and hypoxia, we computed module scores across endothelial subpopulations. The analysis revealed uniformly elevated scores in the Stress-endo cluster, whereas the Angio-endo cluster exhibited comparatively lower activity. These results further underscore the highly active state of Stress-endo within the pathological context of keloid.༈Figure 4 C༉ 3.5. CytoTRACE2 and Trajectory analysis To delineate the differentiation trajectory of endothelial cell (EC) subpopulations in keloid, we applied CytoTRACE 2 to determine the root state for pseudotime analysis using Monocle. CytoTRACE 2 analysis indicated that Stress-endo cells resided at an early differentiation stage, while Angio-endo and Qui-endo represented more advanced stages (Fig. 5 A, B). Accordingly, we reconstructed the endothelial developmental pseudotime, which unfolded from left to right and branched at three key nodes into seven distinct states (Fig. 5 C, D, F). Consistent with the CytoTRACE 2 findings, Stress-endo was positioned at the start of the trajectory and exhibited progenitor-like properties. Moreover, the top five marker genes of Stress-endo (CXCL8, DNASE1L3, HMOX1, MMP1, and STC1) were highly expressed in this subpopulation, and their expression levels progressively declined along the pseudotime (Fig. 5 E). Finally, we employed Slingshot to visualize the trajectory, which partitioned all ECs into four distinct lineages (Fig. 5 G, H). 3.6. Cell communication analysis within keloid To further elucidate the functional roles of distinct endothelial cell (EC) subpopulations, we performed a cell-cell communication analysis across all major cell types, with a particular focus on endothelial cells. Our analysis revealed that the Stress-endo and Fibro-endo subpopulations exhibited the highest number and strength of interactions, primarily communicating with smooth muscle cells (SMCs), fibroblasts, and myeloid cells (Fig. 6 A-C). Among the four EC subpopulations with significantly expanded proportions in keloid tissue (Stress-endo, Ribo-endo, Fibro-endo, and EMT-endo), we observed a robust network of inter-subpopulation communication (Fig. 6 E). Globally, endothelial cells displayed stronger incoming than outgoing signaling patterns. Focusing on specific pathways, the Stress-endo subpopulation demonstrated markedly enhanced signaling activity in the CCL, VISFATIN, and TRAIL pathways compared to other ECs (Fig. 6 D). Further dissection of these pathways revealed distinct roles: in the TRAIL pathway, Angio-endo served as the dominant sender, whereas Stress-endo functioned primarily as a receiver, influencer, and mediator. Within the VISFATIN pathway, signals originated from myeloid cells and were received and transduced by EMT-endo and Stress-endo, which subsequently acted as key influencers and mediators, pointing to their critical role in keloid pathogenesis. 3.7. Identification of the Optimal Biomarker in Keloid Progression Initially, we identified 27 Stress-endo marker genes exhibiting a log2 fold-change exceeding 0.8. We did a differential expression analysis and made a volcano plot to show the results. The plot shows the 10 genes that were most significantly up- and down-regulated in keloid tissue (Fig. 7 A). Following that, we used the Gene Ontology Biological Process (GOBP) database to do Gene Set Variation Analysis (GSVA) on these 27 genes to figure out what they do (Fig. 7 B-C). The GSVA indicated substantial enrichment of these genes in various biological pathways that may be essential to keloid formation. Upregulated genes were significantly implicated in the processing and presentation of exogenous and peptide antigens, indicating that endothelial cells may play an essential part in immune activation and the amplification of inflammation within the keloid microenvironment. On the other hand, pathways related to aorta/artery development and morphogenesis, amide metabolism, apoptotic signaling, and aggrephagy had more downregulated genes. The inhibition of vascular development pathways may indicate compromised angiogenic regulation in keloids. Changes in amide metabolism may indicate fundamental cellular metabolic disruptions. Blocking apoptotic signaling may help cells live longer, which could make tissue hyperplasia worse. Moreover, the downregulation of aggrephathy pathways suggests potential autophagy dysfunction, which may lead to the accumulation of damaged proteins and organelles, consequently advancing fibrosis and keloid development. Finally, a correlation analysis of the 27 genes found a number of strong positive correlations, like between WWTR1 and MMP1 and between SYNJ2 and SOX4. This suggests that these genes may have functional links or co-regulatory relationships that need to be looked into further. After that, we used several machine learning algorithms to find the best biomarkers for keloid progression. The Least Absolute Shrinkage and Selection Operator (LASSO), Support Vector Machine (SVM), Random Forest, and Boruta methods identified 7, 3, 6, and 6 candidate genes, respectively (Fig. 7 D-I). We identified WWTR1 as a high-confidence biomarker by combining the results from these different methods (Fig. 7 J). In line with this observation, WWTR1 expression was markedly elevated in keloid tissues (Fig. 7 K). 3.8. Validation of WWTR1 Expression and Its Functional Implication In our scRNA-seq atlas, WWTR1 was identified as a significantly upregulated driver, primarily localized within endothelial cells and fibroblasts (Fig. 8 A–C). To further resolve its spatial architecture, we performed ST analysis on keloid sections (GSM9225694). Quality control maps demonstrated consistent spatial distributions of nFeature and nCount, and unsupervised clustering categorized the tissue into 12 transcriptionally distinct domains (Fig. 8 D-F). By integrating scRNA-seq and ST data via RCTD deconvolution, we mapped 11 major cell lineages across the tissue. Notably, WWTR1 + ECs exhibited a distinct spatial organization, appearing as scattered puncta or small focal clusters (Fig. 8 G). Further spatial expression profiling revealed that WWTR1 was predominantly concentrated in the pathological core of the tissue. Intringuingly, its spatial distribution highly coincided with the activity gradients of gene signatures associated with ECM organization, hypoxia, and TGF-β signaling (Fig. 8 H-K), suggesting that WWTR1 expression defines a hyper-fibrotic and hypoxic niche within the keloid lesion. To decipher the intercellular communication within this niche, we employed the MISTyR framework. Our analysis demonstrated that WWTR1 + ECs possessed significantly stronger spatial interaction scores with neighboring fibroblasts, keratinocytes, and smooth muscle cells compared to their WWTR1 $ - counterparts (Fig. 8 L-N). This proximity-aware signaling network underscores the pivotal role of WWTR1 in coordinating the multicellular pathological response. Finally, the significant upregulation of WWTR1 in human keloid samples was validated at both the mRNA and protein levels via qPCR and western blot (Fig. 8 M-P), reinforcing its potential as a core therapeutic target(The full membrane has been provided in Supplementary Material). 3.9. Cell communication of WWRT1 labeled endothelial cells To further investigate the functional role of WWTR1 in endothelial cells, we stratified ECs into WWTR1-expressing (WWTR1⁺) and non-expressing (WWTR1⁻) subpopulations. Overall, WWTR1⁺ ECs exhibited greater communication activity, particularly in incoming signaling patterns, and demonstrated the strongest interactions with fibroblasts (Fig. 9 A–C). Specifically, WWTR1⁺ ECs showed significantly enhanced communication in pathways including VISFATIN, VEGF, ANGPTL, and ANGPT, compared to WWTR1⁻ ECs (Fig. 9 D). Notably, in interactions with fibroblasts, the NAMPT–ACKR1 ligand–receptor pair was uniquely active in WWTR1⁺ ECs but absent in WWTR1⁻ cells (Fig. 9 E). Within the VISFATIN signaling network, WWTR1⁺ ECs strongly communicated with fibroblasts and myeloid cells, primarily functioning as key receivers, mediators, and influencers. These cells appeared to receive signals from myeloid cells, potentially promoting keloid progression (Fig. 9 F–G). Similarly, in the VEGF pathway, WWTR1⁺ ECs also played a central role, suggesting a mechanistic contribution to keloid pathogenesis(Figure.9H-I) . 4. Discussion The single-cell and spatial transcriptomic landscape of keloid-associated endothelial cells (ECs) was methodically described in this study. We discovered ten transcriptionally distinct endothelial subpopulations with varying functional states by integrating scRNA-seq, spatial transcriptomics, and bulk transcriptomic data. This revealed a hitherto unidentified heterogeneity within the keloid vasculature[ 16 , 18 ]. Among these, a subset of endothelial cells known as Stress-endo showed strong activation of inflammatory, angiogenic, and profibrotic signaling pathways[ 9 ] and was significantly enlarged in keloid lesions. We discovered WWTR1 (TAZ), a fundamental part of the Hippo pathway, as a crucial transcriptional regulator controlling endothelial reprogramming in fibrotic skin using a variety of computational and machine learning techniques. These results show that keloid development and persistence are actively influenced by a vascular-centered mechanism[ 12 ]. 4.1. Endothelial heterogeneity and functional reprogramming in keloids Endothelial cells have traditionally been thought of as passive structural components in fibrotic skin conditions, mainly reacting to inflammatory or fibroblast-derived stimulation[ 8 ]. However, recent transcriptomic atlases have shown significant variation in dermal endothelial populations during wound healing, fibrosis, and development [ 35 , 36 ]. By providing a high-resolution taxonomy of endothelial subpopulations in keloid tissue, our study expands on these discoveries. Significant changes in endothelial composition were found; immunomodulatory and antigen-presenting subsets were reduced, whereas Stress-endo, Fibro-endo, Ribo-endo, and EMT-like subtypes were enriched, suggesting active endothelial reprogramming under prolonged fibrotic stress [ 9 , 16 ]. Stress-endo and EMT-like ECs were further identified at fibrovascular interfaces by spatial transcriptomic analysis, which is consistent with their functions in encouraging stromal activation and aberrant neovascularization. These phenotypes indicate that endothelial plasticity directly contributes to matrix deposition and tissue remodeling by bridging the vascular and mesenchymal compartments [ 37 , 38 ]. In keloid scars, this endothelial transition most likely reflects an adaptive but pathological reaction to persistent mechanical and inflammatory stimuli. 4.2. Stress-endo as a signaling hub in keloid progression Previous studies in pulmonary, hepatic, and renal fibrosis have described stress-activated endothelial phenotypes characterized by Notch, TGF-β, and hypoxia pathway activation, implicating endothelial dysfunction as a driver of fibrotic remodeling[ 39 , 40 ]. Our results extend this paradigm to cutaneous fibrosis, demonstrating that Stress-endo cells are uniquely enriched in keloid lesions and exhibit activation of Notch, IL-17, oxidative stress, and ECM-remodeling pathways—key mediators of fibrotic signaling[ 41 ]. Intercellular communication analysis suggested that Stress-endo cells may serve as a central signaling hub connecting fibroblasts, macrophages, and smooth muscle cells through CCL, VISFATIN (NAMPT), VEGF, and TRAIL pathways[ 12 , 42 ]. These interactions indicate that endothelial cells do not merely respond to fibrosis but orchestrate it by releasing angiogenic and inflammatory cues that activate fibroblasts and myeloid cells. The reciprocal paracrine feedback maintains endothelial activation and stress, forming a self-reinforcing fibrotic circuit. 4.3. WWTR1-Driven Endothelial Reprogramming as a Central Mechanism in Keloid Fibrosis The Hippo–YAP/TAZ pathway plays essential roles in organ growth, mechanotransduction, and fibrosis across multiple tissues[ 43 ], yet its function in endothelial cells during skin fibrosis has remained poorly defined. Here, we identify WWTR1 (TAZ) as a central transcriptional regulator driving pathogenic endothelial reprogramming in keloids. Spatial transcriptomic analysis revealed that WWTR1⁺ endothelial cells were enriched within fibrotic niches characterized by elevated TGF-β signaling, hypoxia, and extracellular matrix remodeling, and exhibited enhanced intercellular communication through VEGF, ANGPTL, and NAMPT–ACKR1 signaling axes. Our results support a model in which aberrant WWTR1 activation acts as a mechanosensitive switch integrating biophysical stress with biochemical cues to sustain a pathogenic endothelial state, given the established role of YAP/TAZ signaling in promoting endothelial-to-mesenchymal transition (EndMT) and matrix production[ 44 ]. Specifically, by increasing vascular dysfunction, encouraging fibroblast recruitment, and sustaining a chronic inflammatory microenvironment, the Stress-endo subpopulation seems to act as an active driver of fibrosis [ 44 , 45 ]. All of these findings point to a conceptual change in keloid pathogenesis, viewing endothelial cells as active participants in the development of fibrosis rather than passive responders. They also imply that targeting WWTR1 or its downstream pathways may provide a novel therapeutic approach to interfere with the self-reinforcing vascular–fibrotic circuit. 4.4. An endothelial-centered model of keloid fibrosis Integrating these findings, we propose an endothelial-centered model of keloid pathogenesis. In this model, Stress-endo cells—characterized by high WWTR1 activity—act as initiating nodes that secrete angiogenic and profibrotic mediators (VEGF, CCL, VISFATIN/NAMPT, ANGPTL) to stimulate fibroblast proliferation and collagen synthesis, while receiving feedback signals that perpetuate endothelial stress. This bidirectional EC–fibroblast interaction creates a self-sustaining fibrotic loop that may underlie the chronic and recurrent nature of keloid scars[ 46 ]. Additionally, Stress-endo cells may recruit immune cells through CXCL and ICAM1 signaling, integrating vascular and immune inflammation into the fibrotic milieu[ 47 ]. These findings highlight vascular signaling as an upstream orchestrator of tissue remodeling and suggest that targeting WWTR1 or the NAMPT–ACKR1 axis could offer novel therapeutic strategies for keloid intervention. 4.5. Limitations This study has limitations. The sample size was limited and sourced from public datasets, potentially failing to represent the full spectrum of patient heterogeneity. While multi-omic integration yielded significant correlations between WWTR1 and fibrotic programming, the establishment of causality necessitates validation via endothelial-specific perturbation in vivo. Furthermore, transcriptomic data cannot elucidate protein-level dynamics or post-transcriptional modifications. Subsequent investigations ought to integrate proteomic, epigenomic, and live-imaging data to clarify the temporal and spatial alterations of endothelial subtypes throughout scar development [ 48 ]. Even with these problems, our results show that endothelial cells in keloid lesions are very different from each other and can change how they work. The identification of Stress-endo cells as a potentially active participant in fibrosis, along with the connection between WWTR1 (TAZ) and endothelial activation, establishes a framework for comprehending vascular contributions to keloid pathogenesis. These findings enhance our comprehension of endothelial plasticity in fibrotic skin and may inform subsequent investigations into underlying mechanisms and prospective therapies. 5. Conclusions In summary, this study offers the first integrative single-cell and spatial transcriptomic map of endothelial heterogeneity in keloid tissue. We identify Stress-endo as a critical signaling node and WWTR1 as a transcriptional driver of endothelial reprogramming. These insights highlight the active role of endothelial cells in scar persistence and open novel avenues for anti-keloid therapies targeting vascular–fibrotic crosstalk. Abbreviations The following abbreviations are used in this manuscript: APC Antigen-presenting cell ANGPTL Angiopoietin-like CCL Chemokine ligand DEG Differentially expressed gene DESeq2 Differential Expression Sequencing 2 EC Endothelial cell ECM Extracellular matrix EndMT Endothelial-to-mesenchymal transition FDR False discovery rate GEO Gene Expression Omnibus GO Gene Ontology GOBP Gene Ontology Biological Process GSEA Gene Set Enrichment Analysis GSVA Gene Set Variation Analysis IPA Ingenuity Pathway Analysis KEGG Kyoto Encyclopedia of Genes and Genomes k-NN k-nearest neighbor LASSO Least Absolute Shrinkage and Selection Operator log2FC Log2 fold-change MSigDB Molecular Signatures Database NAMPT Nicotinamide phosphoribosyltransferase (Visfatin) PCA Principal Component Analysis pHB Hemoglobin gene content pMT Mitochondrial gene content QC Quality control RF Random Forest scRNA-seq Single-cell RNA sequencing SMC Smooth muscle cell SVM Support Vector Machine TAZ Transcriptional coactivator with PDZ-binding motif (WWTR1) TGF-β Transforming growth factor beta TNF-α Tumor necrosis factor alpha TRAIL TNF-related apoptosis-inducing ligand UMI Unique molecular identifier VEGF Vascular endothelial growth factor WWTR1 WW domain containing transcription regulator 1 YAP Yes-associated protein Declarations Author Contributions: Conceptualization, N.S., Z.Z. and Z.L. ; methodology, N.S. and Z.Z. ; software, Z.Z. ; validation, N.S., Z.Z., Z.Y., L.P. and Y.W. ; formal analysis, Z.Z. and Z.L. ; investigation, N.S., Z.Z., Z.Y., L.P. and Y.W. ; resources, N.Z., B.L. and Z.L. ; data curation, Z.Z. ; writing—original draft preparation, N.S. and Z.Z. ; writing—review and editing, N.Z., B.L. and Z.L. ; visualization, Z.Z. and Z.L. ; supervision, N.Z., B.L. and Z.L. ; project administration, N.Z., B.L. and Z.L. ; funding acquisition, N.Z. and B.L.. All authors have read and agreed to the published version of the manuscript. Funding: The project is supported by the Zunyi Municipal Bureau of Science, Technology, and Big Data(Grant HZ 2021 No.034)，(Grant HZ 2022 No.354) Data Availability Statement: The single-cell RNA sequencing and spatial transcriptomics datasets analyzed in this study are publicly available in the Gene Expression Omnibus database, as specified in the "Materials and Methods" section. All other data supporting the findings of this study are available from the corresponding author upon reasonable request. Acknowledgments: During the preparation of this manuscript/study, the author(s) used [Chat-GPT 4] for the purposes of [the identification of the proper name of endothelial cells’ subsets. The authors have reviewed and edited the output and take full responsibility for the content of this publication. Conflicts of Interest: The authors declare no conflicts of interest. Clinical trial number: not applicable. 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Violin plots showing the distribution of the number of detected genes (nFeature_RNA), total RNA counts (nCount_RNA), and the percentage of mitochondrial (pMT) and hemoglobin (pHB) genes across all samples.(B) UMAP plot illustrating the identification of ten major cell lineages across the integrated dataset.(C) UMAP plot displaying the G2M phase scores across the ten cell types, reflecting the proliferative potential of different cell populations.(D–E) 3D Principal Component Analysis plots demonstrating the spatial distribution and clustering of cell types (D) and the distinct separation between the Keloid and Normal Scar groups (E).(F) Integrated heatmap of cluster-specific markers and pathways. A complex heatmap showing the top differentially expressed genes for each cell type and their corresponding significantly enriched functional pathways.(G) Cell density distribution. Density-based UMAP plots highlighting the shifts in cellular concentration and distribution across different clinical states.(H) Validation of cell type identity. UMAP plots showing the expression of canonical marker genes used to verify the accuracy of cell type annotation.(I)Stacked bar plots illustrating the relative proportions and composition shifts of the ten cell types between the Keloid and Normal Scar groups.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9319391/v1/a1e0a43b55c3264e1b45437d.jpg"},{"id":106305548,"identity":"26915d81-bbb9-4b8e-a776-c74692cea2aa","added_by":"auto","created_at":"2026-04-07 09:52:21","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":6656498,"visible":true,"origin":"","legend":"\u003cp\u003eAlterations in Cellular Composition and Functional State: (A) The plot highlights the top two significantly up-regulated and down-regulated differentially expressed genes (DEGs) across all identified cell lineages.; (B–C) UMAP visualization (B) and beeswarm plot (C) showing the significant enrichment or depletion of specific cell states between the Keloid and Normal Scar groups, identifying dysregulated cellular niches.(D) The network illustrates the functional connectivity and hub genes among the significantly up-regulated transcripts in endothelial cells from keloid tissues.(E) Identification of significant DEGs in endothelial cells, comparing Keloid vs. Normal Scar groups (|log_2FC| \u0026gt; 0.5, \u0026nbsp;P \u0026lt; 0.05).(F–G) Gene Ontology (GO) terms (F) and KEGG pathways (G) enriched in the up-regulated gene set of keloid-derived endothelial cells\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9319391/v1/747b8c2267b98edba513983d.jpg"},{"id":106403992,"identity":"1f863789-761c-473a-8853-a39fe143ee19","added_by":"auto","created_at":"2026-04-08 09:15:20","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":4145826,"visible":true,"origin":"","legend":"\u003cp\u003eEndothelial Cell Subclustering and Functional Profiling (a)UMAP visualization revealing 9 distinct EC subclusters, categorized by their unique transcriptomic signatures.l; (B)UMAP plots showing the spatial and numerical distribution of EC subclusters in Keloid vs. Normal Scar samples.(C)A dot plot illustrating the canonical and specific marker genes used to define each EC subcluster.(D)A complex heatmap showing the top differentially expressed genes for each clusters.（E）Density umap in Keloid vs Normal Scar.（F）Functional enrichment analysis of up-regulated genes in stress-endo. Gene Ontology (GO) terms and KEGG pathways enriched in the up-regulated gene set.（G）A heatmap displaying the Gene Set Variation Analysis (GSVA) scores, highlighting the differential activity of key signaling pathways and biological processes across different EC subclusters.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9319391/v1/897d231ee1315567a3d227ea.jpg"},{"id":106404696,"identity":"bb7c19c1-a230-4f9b-ae3a-55fbca6b9ebe","added_by":"auto","created_at":"2026-04-08 09:16:36","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":3956651,"visible":true,"origin":"","legend":"\u003cp\u003eMultiple enrichment analysis in Keloid’s endothelial cells: (A) Ridge plots illustrating the Gene Set Enrichment Analysis (GSEA) results, highlighting significantly up-regulated biological processes in keloid-derived endothelial cells compared to non-significant gene sets.;(B) Metascape enrichment analysis of the significantly up-regulated genes in keloid ECs, identifying the top-ranked biological themes and signaling networks involved in keloid pathogenesis. (C) Violin plots showing the distribution of cellular scores for key pathological pathways based on the AddModuleScore method. (D) Representative GSEA enrichment plots for individual hallmark pathways, confirming the robust activation of these processes in the keloid EC niche.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9319391/v1/33b65ef7c74f868b18c003d7.jpg"},{"id":106403244,"identity":"3f4b09ee-01d2-4823-b989-b7b2f7dd7b23","added_by":"auto","created_at":"2026-04-08 09:13:57","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2986927,"visible":true,"origin":"","legend":"\u003cp\u003eCytoTRACE2 and Trajectory analysis: (A–B) UMAP visualization (A) and box plot (B) of CytoTRACE2 scores across EC subclusters, indicating the relative differentiation plasticity and stemness of each population.; (C–D, F) Computational modeling of EC differentiation trajectories, illustrating a progressive transition from a progenitor-like state to specialized pathological phenotypes, moving from left to right along the pseudotime axis.(E) Line plots showcasing the dynamic expression changes of the top five marker genes specifically enriched in the Stress-endo subpopulation as they progress through the developmental trajectory.(G–H)UMAP-based trajectory plots partitioning the endothelial development into four distinct lineages, highlighting the divergent fate decisions of ECs within the keloid microenvironment.\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9319391/v1/2a56c8c315ddeedbf63224e8.jpg"},{"id":106305549,"identity":"1fbfb7de-72e0-4e1c-b218-70b5930b4e3f","added_by":"auto","created_at":"2026-04-07 09:52:21","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":4853701,"visible":true,"origin":"","legend":"\u003cp\u003eCell communication analysis within keloid: (a) (A-B) Circle plot and heatmap illustrating the number and strength of cellular interactions between various endothelial cell (EC) subsets and other cell types in keloid tissues.; (C-D) Comparison of the total incoming and outgoing communication intensity across all identified cell types.(E) Chord diagram showing the specific signaling patterns and interaction strength of characteristic EC subpopulations in keloid samples.(F-I) Hierarchy plots and heatmaps showing the enrichment of stress-associated endothelial cell (Stress-endo) specific signaling pathways, highlighting the TRAIL and VISFATIN signaling networks.\u003c/p\u003e","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9319391/v1/cf3f80fed8b1c876d0689ec4.jpg"},{"id":106305551,"identity":"cca207cc-1594-41f8-91f6-9953fe31b7d7","added_by":"auto","created_at":"2026-04-07 09:52:21","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":6178033,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of the Optimal Biomarker in Keloid Progression: Volcano plot showing DEGs in the bulk dataset, with significantly up-regulated and down-regulated genes highlighted.(\u003cstrong\u003eb\u003c/strong\u003e) GSVA enrichment analysis of signature genes derived from the Stress-endo subpopulation within the bulk cohort.\u003cstrong\u003e(C)\u003c/strong\u003e Correlation heatmap illustrating the expression patterns of 27 highly expressed candidate genes.\u003cstrong\u003e(D-I)\u003c/strong\u003e Feature selection and biomarker screening using four machine learning algorithms: LASSO regression, Support Vector Machine (SVM), Random Forest (RF), and Boruta.\u003cstrong\u003e(J)\u003c/strong\u003e Venn diagram showing the intersection of key genes identified by the four machine learning models.\u003cstrong\u003e(K)\u003c/strong\u003e Box plots demonstrating the expression levels of the core biomarker \u003cem\u003eWWTR1\u003c/em\u003e across different clinical\u003c/p\u003e","description":"","filename":"Figure7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9319391/v1/aa4dca0c896775114d6bece5.jpg"},{"id":106403924,"identity":"9ebc2c79-3d4c-444a-aaae-ad55b5c6e7e9","added_by":"auto","created_at":"2026-04-08 09:15:13","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":13069121,"visible":true,"origin":"","legend":"\u003cp\u003eValidation of \u003cem\u003eWWTR1\u003c/em\u003e expression and its functional implications: \u003cstrong\u003e(A)\u003c/strong\u003e Violin plots depicting WWTR1 expression levels across different clinical groups in the single-cell dataset (***p* \u0026lt; 0.001).\u003cstrong\u003e(B–C)\u003c/strong\u003e Density plot and UMAP visualization showing the specific distribution of WWTR1 within cell clusters.\u003cstrong\u003e(D–F)\u003c/strong\u003e Spatial distribution of nCount, nFeature, and 12 transcriptionally distinct clusters in keloid tissue sections.\u003cstrong\u003e(G)\u003c/strong\u003e Spatial cell type mapping inferred by RCTD deconvolution, showing the localization of major cell lineages.\u003cstrong\u003e(H–K)\u003c/strong\u003e Spatial feature plots illustrating WWTR1 expression and the enrichment scores of extracellular matrix organization, hypoxia, and TGF-β signaling pathways, revealing their spatial co-localization with WWTR1.\u003cstrong\u003e(L–N)\u003c/strong\u003e MISTyR analysis identifying spatial interactions between WWTR1-labeled endothelial cells and other cell types at intra-, juxta-5, and para-15 distance scales.\u003cstrong\u003e(O–Q)\u003c/strong\u003e Validation of WWTR1 upregulation in keloid tissues at the mRNA level by qPCR (O) and at the protein level by western blotting(P-Q)\u003c/p\u003e","description":"","filename":"Figure8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9319391/v1/d579b03967f50bd5e785b088.jpg"},{"id":106305552,"identity":"1a041154-c472-4c55-a683-a17b43740720","added_by":"auto","created_at":"2026-04-07 09:52:21","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":7273607,"visible":true,"origin":"","legend":"\u003cp\u003eCell communication of WWRT1 labeled endothelial cells: (\u003cstrong\u003eA-B\u003c/strong\u003e) Circle plot and heatmap illustrating the number and strength of cellular interactions between WWTR1elabeled endothelial cel lsubsets and other cell types in keloid tissues(C-D) Comparison of the total incoming and outgoing communication intensity across all identified cell types.(E):The chord plot shows the cross-talk between fibroblasts and WWTR1-labeled endothelial cells sunsets.(F-I)Hierarchy plots and heatmaps showing the enrichment of WWTR1+ endothelial cells specific signaling pathways, highlighting the VEGFand VISFATIN signaling networks.\u003c/p\u003e","description":"","filename":"Figure9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9319391/v1/707e939871569123aeab5037.jpg"},{"id":106959437,"identity":"a4f2eba1-d18d-44ff-aa0b-f32902d60112","added_by":"auto","created_at":"2026-04-15 09:09:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":19963980,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9319391/v1/7926cc49-5d62-4daa-9079-588070c80a90.pdf"},{"id":106305545,"identity":"824ce99f-b1fc-4f98-8701-522b155f5b28","added_by":"auto","created_at":"2026-04-07 09:52:21","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":234183,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfile.docx","url":"https://assets-eu.researchsquare.com/files/rs-9319391/v1/d0f5de679f94d2eb23b93d8f.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Multi-Omic Integration of Single-Cell, Spatial, and Bulk RNA-Seq Deciphers Endothelial Heterogeneity and WWTR1-Mediated Vascular Reprogramming in Keloids","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eExcessive extracellular matrix (ECM) deposition that extends beyond the initial wound boundaries and infrequently regresses spontaneously are the hallmarks of keloid, a fibroproliferative disorder[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. According to estimates, the incidence among people of African descent ranges from 4.5% to 16% and are more commonly found in Africans and Asians than in Caucasians[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Excessive scarring, which includes keloids and hypertrophic scars, was self-reported by 2.4% of Black participants, 1.1% of Asians, and 0.4% of White participants in a population-based UK cohort[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. A positive family history increases the risk for the development of keloids although no specific gene has been identified. Even though keloids are benign, they can result in pain, pruritus, and contractures that severely lower patients' quality of life and cause financial and psychological hardships[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eKeloids exhibit aberrant fibroblast proliferation, dense collagen bundles, and persistent inflammation [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Fibroblasts, immune cells, and vascular components have been shown in earlier research to play a crucial role in extracellular matrix overproduction, inflammation, and aberrant angiogenesis during keloid formation[\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. These cell populations work together to create the fibrotic and chronic inflammatory microenvironment that characterizes keloid pathology.\u003c/p\u003e \u003cp\u003eThere is growing evidence that vascular abnormalities, such as aberrant angiogenesis and endothelial dysfunction, are critical in maintaining fibrosis and inflammation in keloid tissue[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. In addition to forming the inner lining of blood vessels, endothelial cells (ECs) actively regulate fibroblast activation, extracellular matrix remodeling, and immune cell trafficking[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Furthermore, it has been demonstrated that extracellular vesicles derived from endothelial progenitor cells mediate pro-fibrotic signaling and wound healing processes [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHowever, only a small number of studies have thoroughly investigated the heterogeneity of endothelial cells in fibrotic skin conditions. While the variety and functional states of endothelial cells are still poorly understood, the majority of prior keloid research has concentrated on fibroblast and immune compartments [\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. While recent single-cell and spatial transcriptomic analyses have yielded previously unheard-of insights into the cellular landscape of keloid tissue, these studies have primarily focused on immune-mediated and fibroblast-driven mechanisms[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Endothelial heterogeneity in keloid has not yet been the subject of a thorough single-cell analysis.\u003c/p\u003e \u003cp\u003eIn order to clarify their molecular signatures and spatial organization within the fibrotic microenvironment, we sought to characterize the endothelial cell heterogeneity in keloid using integrated single-cell and spatial transcriptomic analyses.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Keloid and normal skin samples\u003c/h2\u003e \u003cp\u003eThis study was approved by the Medical and Ethics Committees of Dermatology, Affiliated Hospital of Zunyi Medical University (KLL-2022-0763) and samples were collected from 2022 to 2023, written informed consent was obtained from all patients, and a prospective study design was performed. Keloid tissues were harvested during plastic surgery from 7 patients confirmed to have clinical evidence of keloid. The keloids used in this study were mature (non-growing and burned-out, which can be removed surgically) and the whole area of the keloid samples, including the center and edge of the samples, was used for analyses. The patients received chemotherapy, radiotherapy, or intralesional steroid treatment prior to surgery were excluded for sample collection. Normal skin tissues were obtained from 8 patients who underwent elective surgery. Keloids and normal scars were diagnosed based on their clinical appearance, history, anatomical location, and pathology. The excised skin was washed with physiological saline and then immediately frozen and stored at -80 C before being sent to Chongqing Knorigene Technologies (Chongqing, China) for RNA-Seq.\u0026nbsp;Informed consent was obtained from all subjects involved in the study. Written informed consent has been obtained from the patient to publish this paper.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. RNA isolation and sequencing\u003c/h2\u003e \u003cp\u003eTotal RNA was extracted with TRIzol reagent (Takara, Japan). The quantity (\u0026gt;\u0026thinsp;1 \u0026micro;g) and quality (260/280\u0026thinsp;\u0026gt;\u0026thinsp;1.8) of the RNA were determined with a NanoDrop 2000 (Thermo Fisher, USA). Total RNA was reverse transcribed with Oligo dT primers to produce cDNA. The generated first-strand cDNA was co-reacted by the RNase H enzyme, DNA polymerase and T4 ligase to generate double-stranded cDNA; the double-stranded cDNA was fragmented by the Tn5 enzyme and added the remedial design (RD) sequence required for sequencing at both ends. The sequencing primers at both ends of P5 and P7 were connected by RD sequences at both ends and PCR enrichment was performed. Successful library construction was sequenced. All RNA-Seq procedures and initial bioinformatics were performed by Chongqing Knorigene Technologies (Chongqing, China).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Data acquisition and processing\u003c/h2\u003e \u003cp\u003eWe got single-cell RNA sequencing (scRNA-seq) data for keloids from the GEO database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"http://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) with the accession number GSE163973[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. From this dataset, we chose three samples of keloids and three samples of normal controls. A strict quality control (QC) protocol was put in place to make sure that the single-cell data was accurate. Cells were filtered based on the following criteria: gene detection (nFeature_RNA) between 200 and 5,000, total UMI counts (nCount_RNA) between 200 and 30,000, mitochondrial gene content (pMT) below 10%, and hemoglobin gene content (pHB) below 5%. After filtering, 40,685 high-quality cells were kept for further analysis. We used the \"Log-normalization\" method with linear regression to normalize the data. Then we used the \"FindVariableFeatures\" function to find the 2,000 genes that changed the most. After that, Principal Component Analysis (PCA) was used to reduce the number of dimensions. We used Harmony, a strong and scalable R package, to combine multiple datasets and get rid of variations caused by batch processing[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The \"FindClusters\" function was used to cluster cells with a resolution of 0.5 and the first 15 principal components (pc.num\u0026thinsp;=\u0026thinsp;1:15).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Cell type identification\u003c/h2\u003e \u003cp\u003eWe used the FindAllMarkers function in Seurat to perform a differential expression analysis on all of the cell clusters to find out what types of cells they were. The following criteria were used to choose marker genes: an adjusted p-value of less than 0.05, expression in more than 25% of the cells in the cluster (min.pct\u0026thinsp;=\u0026thinsp;0.25), and an absolute log2 fold change of more than 0.25. The top-ranked differentially expressed genes for each cluster were identified as cluster-specific highly expressed genes.\u003c/p\u003e \u003cp\u003eCell type annotation was achieved by synthesizing the functional roles of these marker genes with findings from prior studies [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] utilizing the same database.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. MILO Differential abundance testing\u003c/h2\u003e \u003cp\u003eMilo is a scalable statistical framework that tests for differential abundance by placing cells into partially overlapping neighborhoods on a k-nearest neighbor graph [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. We performed the analysis by following the official miloR work-flow (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/MarioniLab/milo).W\u003c/span\u003e\u003cspan address=\"https://github.com/MarioniLab/milo).W\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003ee converted the Seurat object into a Single-CellExperiment format and made a k-nearest neighbor graph. We used calcNhoodDistance to figure out the distances between cells, testNhoods to test for differences in abundance, and then we constructed a neighborhood graph to show the results.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Differential analysis and multiple enrichment analysis\u003c/h2\u003e \u003cp\u003eWe applied the FindMarkers function for the differential analysis, setting min.pct to 0.1 and logfc.threshold to 0.1. Genes exhibiting a logFC greater than 0.5 and a p-value less than 0.05 were categorized as up-regulated, whereas those with a logFC less than \u0026minus;\u0026thinsp;0.5 and a p-value less than 0.05 were deemed down-regulated. After that, these differentially expressed genes underwent several functional enrichment analyses.\u003c/p\u003e \u003cp\u003eWe used the Metascape [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] online platform, which combines functional enrichment, interactome analysis, and gene annotation, to carry out Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. These analyses enabled us systematically describe the biological pathways and protein functions associated to the gene sets we found.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7. Gene set variation analysis,Gene Set Enrichment Analysis and cell scoring.\u003c/h2\u003e \u003cp\u003eTo evaluate the biological activity across different endothelial subpopulations, we first calculated the average gene expression for each subpopulation using the AvergaeExpression function. Subsequently Gene Set Variation Analysis (GSVA) [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] was performed on these average expression profiles using the \"h.all.v2025.1.Hs.symbols.gmt\" gene set from the MSigDB Hallmark collection.We observed differentially expressed genes (DEGs) in keloid endothelial cells when we compared them to other endothelial cells. We used a threshold of |logFC| \u0026gt; 0.1 and expression in more than 10% of cells (min.pct\u0026thinsp;=\u0026thinsp;0.1). We then used Gene Set Enrichment Analysis (GSEA) with the HALLMARK gene set collection (h.all.v2025.1.Hs.entrez.gmt) from the MSigDB database to conduct a pathway enrichment analysis on these DEGs. We got gene sets for \"Hypoxia,\" \"Inflammatory Response,\" \"MYC Targets V1,\" and \"TNFA Signaling via NFKB\" from the MSigDB database. Finally, we used AUCell, a R package made to measure gene set activity, to find the biological activity scores for each endothelial cell [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].We used the AddModuleScore algorithm to look at the activity of the gene signatures \"KEGG_ECM_RECEPTOR_INTERACTION\", \"KEGG_TGF_BETA_SIGNALING_PATHWAY\" and \"HALLMARK_HYPOXIA\" in the spatial transcriptomics data. The goal of this analysis was to confirm that these pathways are active in space and that they are located near the expression of our target gene.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8. Pseudo-time analysis and CytoTRACE2\u003c/h2\u003e \u003cp\u003eWe utilized Monocle pseudo-temporal ordering analysis to systematically sort cells into two distinct states based on their gene expression profiles for trajectory reconstruction. Then, we used the scopR package (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/mengxu98/scop\u003c/span\u003e\u003cspan address=\"https://github.com/mengxu98/scop\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to show the results.To identify temporally regulated genes during the differentiation of endothelial cells, we performed a thorough differential expression analysis along the pseudotime continuum (differentialGeneTest). We utilized the \u0026ldquo;plot_genes_in_pseudotime\u0026rdquo; function to illustrate the dynamic expression patterns of the targeted genes. To evaluate the differentiation potential of endothelial cell populations, we employed CytoTRACE2 for quantitative assessment of developmental progression across various subsets of endothelial cells.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9. Cell communication\u003c/h2\u003e \u003cp\u003eTo investigate cell communication differences between endothelial cell subsets, we employed the CellChat R package [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. This tool infers intercellular communication by integrating known ligand-receptor interactions with gene expression data. Our analysis revealed significant disparities in communication networks between subsets, identifying key signaling pathways involved in disease progression.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.10. Biomarker discovery using machine learning approaches\u003c/h2\u003e \u003cp\u003eTo systematically identify the endothelial cells\u0026rsquo; biomarker, we implemented a multi-platform validation strategy. First, four machine learning methods were employed for effective feature selection: LASSO regression, Support Vector Machine (SVM), Boruta algorithm, and Random Forest (RF) with 2000 trees and Gini importance scoring.\u003c/p\u003e \u003cp\u003eBefore the LASSO regression analysis, the randomcolo R package in R was used to generate 40 different colors. The random seed was set at set.seed(1). After that, the LASSO model was run with set.seed(11) and an alpha value of 1. Using 5-fold cross-validation (nfold\u0026thinsp;=\u0026thinsp;5), the SVM model was set up. The Boruta algorithm was run with set.seed(1), doTrace\u0026thinsp;=\u0026thinsp;2, and maxRuns\u0026thinsp;=\u0026thinsp;500 [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. For the Random Forest model, we set the random seed to set.seed(3). We employed an online Venn diagram tool to visualize the genes that the different machine learning algorithms discovered to be related to each other [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Additionally, their expression patterns were evaluated using box plots in the comprehensive transcriptomic dataset.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.11. Spatial Data Integration and Microenvironment Modeling\u003c/h2\u003e \u003cp\u003eWe processed the public keloid ST dataset (GSM9225694) using Seurat (v5.0). Normalization and technical artifact removal were performed via SCTransform, followed by PCA-based dimensionality reduction (top 15 PCs) and unsupervised clustering to define spatial domains.\u003c/p\u003e \u003cp\u003eTo resolve spot-level cellular composition, RCTD was executed using a scRNA-seq reference to estimate cell-type proportions [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Functional states, including ECM pathway, hopoxia and TGF-β, were quantified using the AddModuleScore function. Finally, the MISTyR framework was utilized to model the tissue microenvironment. By integrating multi-view spatial data, MISTyR identified proximity-dependent intercellular communication networks and signaling hubs driving the spatial heterogeneity of keloid lesions [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e2.12. Western blotting\u003c/h2\u003e \u003cp\u003eProteins were extracted from keloid tissues for analysis using a BCA protein assay kit (Beyotime Biotechnology, Cat# P0010). After mixing with loading buffer, samples were heat‑denatured at 95\u0026deg;C for 5 min and resolved on 10% SDS‑polyacrylamide gels. The separated proteins were then electrotransferred onto PVDF membranes.\u003c/p\u003e \u003cp\u003eMembranes were blocked with 5% BSA and subsequently probed overnight at 4\u0026deg;C with primary antibodies: anti‑WWTR1 (1:1,000, Abcam, Cat# ab242313, mouse monoclonal) and Anti-beta Actin (1:1000, Abcam, Cat# ab8226, mouse monoclonal). Following primary antibody incubation, membranes were incubated with a combination of fluorescent secondary antibodies: AffiniPure Goat Anti-Mouse IgG H\u0026amp;L (1:20,000, Bioss, Cat# bs-40296G-IRDye800CW). Protein bands were detected and quantified using an Odyssey infrared imaging system.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e2.13. q-PCR\u003c/h2\u003e \u003cp\u003eTotal RNA was isolated from keloid tissue and subjected to reverse transcription with the Evo M-MLV RT Premix Kit (Accurate Biology, Cat# AG11728). Quantitative PCR was then performed using the SYBR Green Pro Tag HS Premixed qPCR Kit (Accurate Biology, Cat# AG11701).\u003c/p\u003e \u003cp\u003eThe following primers were used: WWTR1 forward 5\u0026rsquo;-GGTCCATGGCAGTATCCCAG-3\u0026rsquo; and reverse 5\u0026rsquo;-GGATTCTCTGAAGCCGCAGT-3\u0026rsquo;, along with β-actin forward 5\u0026rsquo;-ACCCTTAAGAGGGATGCTGC-3\u0026rsquo; and reverse 5\u0026rsquo;-CCCAATACGGCCAAATCCGT-3\u0026rsquo; as the reference.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Single-cell RNA sequencing landscape of keloid\u003c/h2\u003e \u003cp\u003eQuality control was essential for making sure that the data was reliable for analysis later on. After a thorough evaluation (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA), 6 high-quality samples (3 from the keloid group and 3 from the normal control group, comprising 40,658 cells) were chosen for additional analysis. To reduce technical variability, batch effect correction was implemented, leading to a harmonized data distribution and the eradication of batch-related discrepancies (Supplementary Fig.\u0026nbsp;1A-B). A later clustering analysis put all the cells into 21 different groups (Supplementary Fig.\u0026nbsp;1C). We used known cell markers to find 10 main types of cells, such as endothelial cells, fibroblasts, and myeloid cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). The cycling cell population was validated by significantly elevated G2/M scores, confirming their active proliferation (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Additionally, cells were depicted in a three-dimensional reduced dimensionality space and colored by their pre-defined group assignments(Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD-E).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe made a heatmap with the \"ClusterGVis\" package[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] to show cell type-specific marker genes. The input gene set consisted of the top 20 markers for each cell type, as determined by the \u0026ldquo;FindAllMarkers\u0026rdquo; function. Following that, the \"org.Hs.eg.db\" database was employed to do a Gene Ontology Biological Process (GOBP) enrichment analysis on these marker genes. This analysis of a keloid sample using single-cell RNA sequencing shows that there are different groups of cells, each with its own gene expression profiles and biological pathways that are more active. For instance, the Endothelial cell cluster (C1) is characterized by elevated expression of genes such as SELE and AQP1. Pathway analysis shows that these cells are more involved in processes that control vascular permeability, endothelial cell differentiation, and the growth of the endothelium as a whole. The top 20 marker genes in the Endothelial cell cluster are linked to these vascular-related functions, which means they may play a role in angiogenesis or vascular remodeling in the keloid microenvironment. Other clusters also have unique marker genes and pathways that are more common, which gives us more information about how different types of cells work in the keloid tissue (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF).\u003c/p\u003e \u003cp\u003eA comparison of the density of keloid and normal scar tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eG) demonstrates that the cells in keloids are very different from those in normal scars. In particular, the density plots show that keloid tissues have more endothelial cells and smooth muscle cells than normal scars. On the other hand, the density of fibroblasts and keratinocytes is lower in keloids, which may mean that there is an imbalance in the types of cells that make up keloids. This changed cellular environment, which has more endothelial and smooth muscle cells and fewer fibroblasts and keratinocytes, shows how the cellular processes that lead to keloid scars are not working properly. To further confirm the correctness of our cell annotation results, we used well-known cellular markers as a standard. These markers validated the accurate identification of principal cell types within our dataset (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eH). After that, we made cell proportion plots that showed the differences between keloid and normal scar tissues. These plots showed that the number of endothelial cells and smooth muscle cells in keloid samples went up a lot. In contrast, we noted a relative reduction in the proportion of fibroblasts and keratinocytes in keloids, thereby supporting the hypothesis that imbalances in cell populations play a role in keloid pathology (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eI).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Alterations in Cellular Composition and Functional State\u003c/h2\u003e \u003cp\u003eWe methodically delineated the transcriptional profiles among various cell types in keloid and normal scar tissues. To start, the FindAllMarkers function was used to find cell type-specific marker genes, which included both the most upregulated and downregulated transcripts. We then used the jjVolcano function from the scRNAtoolVis [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] R package to show these markers (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). A notable elevation in endothelial cell density was observed in keloid tissues, as determined by differential abundance analysis utilizing MiloR, in contrast to normal scar samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB-C).Together, these findings about the number and types of cells made us want to focus our next studies on the endothelial cells in keloid tissue.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe subsequently conducted a differential gene expression analysis on endothelial cells comparing keloid and normal scar tissues. This analysis found 42 genes that were turned off (e.g., IL6, G0S2, HLA-DRB5) and 29 genes that were turned on (e.g., DCD, MMP1, COL1A1) in keloids (Fgi.2D-E).Gene Ontology analysis identified biologically coherent themes that connect the transcriptomic changes to keloid pathology. The 29 upregulated genes were significantly enriched in pathways that regulate \"extracellular matrix organization\" and \"external encapsulating structure organization.\" These are processes that are important for the excessive fibrosis and collagen deposition that are typical of keloid lesions (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF). On the other hand, the 42 down-regulated genes were more common in immune regulatory pathways, such as \"response to bacterial molecules\" and \"regulation of leukocyte adhesion.\" This suggests that the normal immune surveillance and resolution processes in the keloid microenvironment may be disrupted (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Endothelial Cell Subclustering and Functional Profiling\u003c/h2\u003e \u003cp\u003eA total of 10 discrete endothelial subsets were identified through graph-based clustering (resolution 0.3, 15 PCs) post-normalization. To ensure objective annotation, we employed the GPTCelltype framework to interpret cluster-specific markers and define the functional roles of each subpopulation[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. The 10 endothelial subpopulations and their defining characteristics are as follows: inflammatory and antigen-presenting ECs (C0: \u003cem\u003eHLA-DRB1/5\u003c/em\u003e, \u003cem\u003eNFKBIA\u003c/em\u003e; C5: \u003cem\u003eSERPINE1\u003c/em\u003e, \u003cem\u003eVCAM1/SELE\u003c/em\u003e); remodeling and fibrosis-associated ECs (C2: \u003cem\u003eCOL1A1/2\u003c/em\u003e; C3: \u003cem\u003eMMP1\u003c/em\u003e, \u003cem\u003eHMOX1\u003c/em\u003e; C6: \u003cem\u003eMMP2\u003c/em\u003e, \u003cem\u003eSPARC\u003c/em\u003e); and metabolic or synthetic ECs (C1: \u003cem\u003eRPL/RPS\u003c/em\u003e genes). Additionally, we identified homeostatic (C4: \u003cem\u003eCXCL12\u003c/em\u003e), angiogenic (C7: \u003cem\u003ePECAM1\u003c/em\u003e, \u003cem\u003eEGFL7\u003c/em\u003e), immunomodulatory (C8: \u003cem\u003eACKR1\u003c/em\u003e, \u003cem\u003eCD74\u003c/em\u003e), and migratory (C9: \u003cem\u003eS100A14\u003c/em\u003e, \u003cem\u003eCD44\u003c/em\u003e) subpopulations. This diverse landscape underscores the functional specialization of the keloid endothelium.This detailed profiling reveals a remarkable functional specialization within the keloid endothelium(Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA-C).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAnalysis of the EC subpopulation composition revealed considerable heterogeneity between keloid and normal scar tissues. Keloids exhibited an increase in pro-fibrotic (Fibro-endo), stressed (Stress-endo), and En-doMT-like (EMT-endo) populations, coupled with a relative reduction in immunomodulatory and antigen-presenting clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB,E). Significantly, the Stress-endo subpopulation became a pivotal center. Differential expression and enrichment analyses (Wilcoxon test; logFC\u0026thinsp;\u0026gt;\u0026thinsp;0.8) demonstrated that genes upregulated in Stress-endo were predominantly enriched in cell-cell adhesion and IL-17 signaling (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF). Additional GSVA profiling corroborated that In-flam-endo was associated with IL-6/JAK/STAT3 signaling, whereas Stress-endo demonstrated significant activity in angiogenesis, epithelial-mesenchymal transition (EMT), and Notch signaling (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG). These results identify the Stress-endo subpopulation as a pivotal mediator, connecting mechanical stress to chronic inflammation and vascular remodeling within the keloid microenvironment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Multiple enrichment analysis in Keloid\u0026rsquo;s endothelial cells\u003c/h2\u003e \u003cp\u003eIn keloid tissues, we consolidated the proportionally expanded fibroblast subpopulations\u0026mdash;\"Ribo-Endo,\" \"Fibro-Endo,\" \"Stress-Endo,\" and \"EMT-Endo\"\u0026mdash;into a unified group termed \"Keloid-endo,\" while the remaining subpopulations were categorized as \"other-endo.\" Differential gene expression analysis between these two groups was performed using the FindMarkers algorithm, followed by Gene Set Enrichment Analysis (GSEA). The Keloid-endo cell population drives keloid progression by activating key pathways that promote fibrosis (TGF-β), proliferation (MYC), and inflammation (TNF-α), while simultaneously suppressing apoptosis and anti-growth signals (IFN-α response)(Figure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA,D). This coordinated molecular program confers sustained growth advantage, enhanced survival, and pathological tissue remodeling. Complementing these findings, Metascape analysis of the top upregulated genes further revealed activation of the VEGFA-VEGFR2 pathway\u0026mdash;supporting angiogenesis\u0026mdash;along with enhanced cell migration, inhibition of apoptosis, and a robust response to oxidative stress. Together, these mechanisms enable Keloid-endo to stimulate pathological tissue remodeling, ensure its own survival, and expand within the inflammatory keloid microenvironment༈Figure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB༉.Based on the gene sets associated with MYC TARGETS V1, inflammatory response, TNFA signaling, and hypoxia, we computed module scores across endothelial subpopulations. The analysis revealed uniformly elevated scores in the Stress-endo cluster, whereas the Angio-endo cluster exhibited comparatively lower activity. These results further underscore the highly active state of Stress-endo within the pathological context of keloid.༈Figure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC༉\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.5. CytoTRACE2 and Trajectory analysis\u003c/h2\u003e \u003cp\u003eTo delineate the differentiation trajectory of endothelial cell (EC) subpopulations in keloid, we applied CytoTRACE 2 to determine the root state for pseudotime analysis using Monocle. CytoTRACE 2 analysis indicated that Stress-endo cells resided at an early differentiation stage, while Angio-endo and Qui-endo represented more advanced stages (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA, B). Accordingly, we reconstructed the endothelial developmental pseudotime, which unfolded from left to right and branched at three key nodes into seven distinct states (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC, D, F). Consistent with the CytoTRACE 2 findings, Stress-endo was positioned at the start of the trajectory and exhibited progenitor-like properties. Moreover, the top five marker genes of Stress-endo (CXCL8, DNASE1L3, HMOX1, MMP1, and STC1) were highly expressed in this subpopulation, and their expression levels progressively declined along the pseudotime (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE). Finally, we employed Slingshot to visualize the trajectory, which partitioned all ECs into four distinct lineages (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eG, H).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e3.6. Cell communication analysis within keloid\u003c/h2\u003e \u003cp\u003eTo further elucidate the functional roles of distinct endothelial cell (EC) subpopulations, we performed a cell-cell communication analysis across all major cell types, with a particular focus on endothelial cells. Our analysis revealed that the Stress-endo and Fibro-endo subpopulations exhibited the highest number and strength of interactions, primarily communicating with smooth muscle cells (SMCs), fibroblasts, and myeloid cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA-C).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAmong the four EC subpopulations with significantly expanded proportions in keloid tissue (Stress-endo, Ribo-endo, Fibro-endo, and EMT-endo), we observed a robust network of inter-subpopulation communication (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE). Globally, endothelial cells displayed stronger incoming than outgoing signaling patterns. Focusing on specific pathways, the Stress-endo subpopulation demonstrated markedly enhanced signaling activity in the CCL, VISFATIN, and TRAIL pathways compared to other ECs (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003eFurther dissection of these pathways revealed distinct roles: in the TRAIL pathway, Angio-endo served as the dominant sender, whereas Stress-endo functioned primarily as a receiver, influencer, and mediator. Within the VISFATIN pathway, signals originated from myeloid cells and were received and transduced by EMT-endo and Stress-endo, which subsequently acted as key influencers and mediators, pointing to their critical role in keloid pathogenesis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e3.7. Identification of the Optimal Biomarker in Keloid Progression\u003c/h2\u003e \u003cp\u003eInitially, we identified 27 Stress-endo marker genes exhibiting a log2 fold-change exceeding 0.8. We did a differential expression analysis and made a volcano plot to show the results. The plot shows the 10 genes that were most significantly up- and down-regulated in keloid tissue (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFollowing that, we used the Gene Ontology Biological Process (GOBP) database to do Gene Set Variation Analysis (GSVA) on these 27 genes to figure out what they do (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB-C).\u003c/p\u003e \u003cp\u003eThe GSVA indicated substantial enrichment of these genes in various biological pathways that may be essential to keloid formation. Upregulated genes were significantly implicated in the processing and presentation of exogenous and peptide antigens, indicating that endothelial cells may play an essential part in immune activation and the amplification of inflammation within the keloid microenvironment. On the other hand, pathways related to aorta/artery development and morphogenesis, amide metabolism, apoptotic signaling, and aggrephagy had more downregulated genes. The inhibition of vascular development pathways may indicate compromised angiogenic regulation in keloids. Changes in amide metabolism may indicate fundamental cellular metabolic disruptions. Blocking apoptotic signaling may help cells live longer, which could make tissue hyperplasia worse. Moreover, the downregulation of aggrephathy pathways suggests potential autophagy dysfunction, which may lead to the accumulation of damaged proteins and organelles, consequently advancing fibrosis and keloid development.\u003c/p\u003e \u003cp\u003eFinally, a correlation analysis of the 27 genes found a number of strong positive correlations, like between WWTR1 and MMP1 and between SYNJ2 and SOX4. This suggests that these genes may have functional links or co-regulatory relationships that need to be looked into further.\u003c/p\u003e \u003cp\u003eAfter that, we used several machine learning algorithms to find the best biomarkers for keloid progression. The Least Absolute Shrinkage and Selection Operator (LASSO), Support Vector Machine (SVM), Random Forest, and Boruta methods identified 7, 3, 6, and 6 candidate genes, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD-I). We identified WWTR1 as a high-confidence biomarker by combining the results from these different methods (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eJ). In line with this observation, WWTR1 expression was markedly elevated in keloid tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eK).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e3.8. Validation of WWTR1 Expression and Its Functional Implication\u003c/h2\u003e \u003cp\u003eIn our scRNA-seq atlas, WWTR1 was identified as a significantly upregulated driver, primarily localized within endothelial cells and fibroblasts (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA\u0026ndash;C). To further resolve its spatial architecture, we performed ST analysis on keloid sections (GSM9225694). Quality control maps demonstrated consistent spatial distributions of nFeature and nCount, and unsupervised clustering categorized the tissue into 12 transcriptionally distinct domains (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eD-F).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBy integrating scRNA-seq and ST data via RCTD deconvolution, we mapped 11 major cell lineages across the tissue. Notably, WWTR1\u0026thinsp;+\u0026thinsp;ECs exhibited a distinct spatial organization, appearing as scattered puncta or small focal clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eG). Further spatial expression profiling revealed that WWTR1 was predominantly concentrated in the pathological core of the tissue. Intringuingly, its spatial distribution highly coincided with the activity gradients of gene signatures associated with ECM organization, hypoxia, and TGF-β signaling (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eH-K), suggesting that WWTR1 expression defines a hyper-fibrotic and hypoxic niche within the keloid lesion.\u003c/p\u003e \u003cp\u003eTo decipher the intercellular communication within this niche, we employed the MISTyR framework. Our analysis demonstrated that WWTR1\u0026thinsp;+\u0026thinsp;ECs possessed significantly stronger spatial interaction scores with neighboring fibroblasts, keratinocytes, and smooth muscle cells compared to their WWTR1\u003cspan\u003e$\u003c/span\u003e- counterparts (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eL-N). This proximity-aware signaling network underscores the pivotal role of WWTR1 in coordinating the multicellular pathological response. Finally, the significant upregulation of WWTR1 in human keloid samples was validated at both the mRNA and protein levels via qPCR and western blot (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eM-P), reinforcing its potential as a core therapeutic target(The full membrane has been provided in Supplementary Material).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e3.9. Cell communication of WWRT1 labeled endothelial cells\u003c/h2\u003e \u003cp\u003eTo further investigate the functional role of WWTR1 in endothelial cells, we stratified ECs into WWTR1-expressing (WWTR1⁺) and non-expressing (WWTR1⁻) subpopulations. Overall, WWTR1⁺ ECs exhibited greater communication activity, particularly in incoming signaling patterns, and demonstrated the strongest interactions with fibroblasts (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA\u0026ndash;C).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSpecifically, WWTR1⁺ ECs showed significantly enhanced communication in pathways including VISFATIN, VEGF, ANGPTL, and ANGPT, compared to WWTR1⁻ ECs (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eD). Notably, in interactions with fibroblasts, the NAMPT\u0026ndash;ACKR1 ligand\u0026ndash;receptor pair was uniquely active in WWTR1⁺ ECs but absent in WWTR1⁻ cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003eWithin the VISFATIN signaling network, WWTR1⁺ ECs strongly communicated with fibroblasts and myeloid cells, primarily functioning as key receivers, mediators, and influencers. These cells appeared to receive signals from myeloid cells, potentially promoting keloid progression (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eF\u0026ndash;G). Similarly, in the VEGF pathway, WWTR1⁺ ECs also played a central role, suggesting a mechanistic contribution to keloid pathogenesis(Figure.9H-I) .\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe single-cell and spatial transcriptomic landscape of keloid-associated endothelial cells (ECs) was methodically described in this study. We discovered ten transcriptionally distinct endothelial subpopulations with varying functional states by integrating scRNA-seq, spatial transcriptomics, and bulk transcriptomic data. This revealed a hitherto unidentified heterogeneity within the keloid vasculature[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Among these, a subset of endothelial cells known as Stress-endo showed strong activation of inflammatory, angiogenic, and profibrotic signaling pathways[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] and was significantly enlarged in keloid lesions. We discovered WWTR1 (TAZ), a fundamental part of the Hippo pathway, as a crucial transcriptional regulator controlling endothelial reprogramming in fibrotic skin using a variety of computational and machine learning techniques. These results show that keloid development and persistence are actively influenced by a vascular-centered mechanism[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Endothelial heterogeneity and functional reprogramming in keloids\u003c/h2\u003e \u003cp\u003eEndothelial cells have traditionally been thought of as passive structural components in fibrotic skin conditions, mainly reacting to inflammatory or fibroblast-derived stimulation[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. However, recent transcriptomic atlases have shown significant variation in dermal endothelial populations during wound healing, fibrosis, and development [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. By providing a high-resolution taxonomy of endothelial subpopulations in keloid tissue, our study expands on these discoveries. Significant changes in endothelial composition were found; immunomodulatory and antigen-presenting subsets were reduced, whereas Stress-endo, Fibro-endo, Ribo-endo, and EMT-like subtypes were enriched, suggesting active endothelial reprogramming under prolonged fibrotic stress [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eStress-endo and EMT-like ECs were further identified at fibrovascular interfaces by spatial transcriptomic analysis, which is consistent with their functions in encouraging stromal activation and aberrant neovascularization. These phenotypes indicate that endothelial plasticity directly contributes to matrix deposition and tissue remodeling by bridging the vascular and mesenchymal compartments [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. In keloid scars, this endothelial transition most likely reflects an adaptive but pathological reaction to persistent mechanical and inflammatory stimuli.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Stress-endo as a signaling hub in keloid progression\u003c/h2\u003e \u003cp\u003ePrevious studies in pulmonary, hepatic, and renal fibrosis have described stress-activated endothelial phenotypes characterized by Notch, TGF-β, and hypoxia pathway activation, implicating endothelial dysfunction as a driver of fibrotic remodeling[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Our results extend this paradigm to cutaneous fibrosis, demonstrating that Stress-endo cells are uniquely enriched in keloid lesions and exhibit activation of Notch, IL-17, oxidative stress, and ECM-remodeling pathways\u0026mdash;key mediators of fibrotic signaling[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIntercellular communication analysis suggested that Stress-endo cells may serve as a central signaling hub connecting fibroblasts, macrophages, and smooth muscle cells through CCL, VISFATIN (NAMPT), VEGF, and TRAIL pathways[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. These interactions indicate that endothelial cells do not merely respond to fibrosis but orchestrate it by releasing angiogenic and inflammatory cues that activate fibroblasts and myeloid cells. The reciprocal paracrine feedback maintains endothelial activation and stress, forming a self-reinforcing fibrotic circuit.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003e4.3. WWTR1-Driven Endothelial Reprogramming as a Central Mechanism in Keloid Fibrosis\u003c/h2\u003e \u003cp\u003eThe Hippo\u0026ndash;YAP/TAZ pathway plays essential roles in organ growth, mechanotransduction, and fibrosis across multiple tissues[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], yet its function in endothelial cells during skin fibrosis has remained poorly defined. Here, we identify WWTR1 (TAZ) as a central transcriptional regulator driving pathogenic endothelial reprogramming in keloids. Spatial transcriptomic analysis revealed that WWTR1⁺ endothelial cells were enriched within fibrotic niches characterized by elevated TGF-β signaling, hypoxia, and extracellular matrix remodeling, and exhibited enhanced intercellular communication through VEGF, ANGPTL, and NAMPT\u0026ndash;ACKR1 signaling axes.\u003c/p\u003e \u003cp\u003eOur results support a model in which aberrant WWTR1 activation acts as a mechanosensitive switch integrating biophysical stress with biochemical cues to sustain a pathogenic endothelial state, given the established role of YAP/TAZ signaling in promoting endothelial-to-mesenchymal transition (EndMT) and matrix production[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Specifically, by increasing vascular dysfunction, encouraging fibroblast recruitment, and sustaining a chronic inflammatory microenvironment, the Stress-endo subpopulation seems to act as an active driver of fibrosis [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. All of these findings point to a conceptual change in keloid pathogenesis, viewing endothelial cells as active participants in the development of fibrosis rather than passive responders. They also imply that targeting WWTR1 or its downstream pathways may provide a novel therapeutic approach to interfere with the self-reinforcing vascular\u0026ndash;fibrotic circuit.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec30\" class=\"Section2\"\u003e \u003ch2\u003e4.4. An endothelial-centered model of keloid fibrosis\u003c/h2\u003e \u003cp\u003eIntegrating these findings, we propose an endothelial-centered model of keloid pathogenesis. In this model, Stress-endo cells\u0026mdash;characterized by high WWTR1 activity\u0026mdash;act as initiating nodes that secrete angiogenic and profibrotic mediators (VEGF, CCL, VISFATIN/NAMPT, ANGPTL) to stimulate fibroblast proliferation and collagen synthesis, while receiving feedback signals that perpetuate endothelial stress. This bidirectional EC\u0026ndash;fibroblast interaction creates a self-sustaining fibrotic loop that may underlie the chronic and recurrent nature of keloid scars[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAdditionally, Stress-endo cells may recruit immune cells through CXCL and ICAM1 signaling, integrating vascular and immune inflammation into the fibrotic milieu[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. These findings highlight vascular signaling as an upstream orchestrator of tissue remodeling and suggest that targeting WWTR1 or the NAMPT\u0026ndash;ACKR1 axis could offer novel therapeutic strategies for keloid intervention.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec31\" class=\"Section2\"\u003e \u003ch2\u003e4.5. Limitations\u003c/h2\u003e \u003cp\u003eThis study has limitations. The sample size was limited and sourced from public datasets, potentially failing to represent the full spectrum of patient heterogeneity. While multi-omic integration yielded significant correlations between WWTR1 and fibrotic programming, the establishment of causality necessitates validation via endothelial-specific perturbation in vivo. Furthermore, transcriptomic data cannot elucidate protein-level dynamics or post-transcriptional modifications. Subsequent investigations ought to integrate proteomic, epigenomic, and live-imaging data to clarify the temporal and spatial alterations of endothelial subtypes throughout scar development [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEven with these problems, our results show that endothelial cells in keloid lesions are very different from each other and can change how they work. The identification of Stress-endo cells as a potentially active participant in fibrosis, along with the connection between WWTR1 (TAZ) and endothelial activation, establishes a framework for comprehending vascular contributions to keloid pathogenesis. These findings enhance our comprehension of endothelial plasticity in fibrotic skin and may inform subsequent investigations into underlying mechanisms and prospective therapies.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eIn summary, this study offers the first integrative single-cell and spatial transcriptomic map of endothelial heterogeneity in keloid tissue. We identify Stress-endo as a critical signaling node and WWTR1 as a transcriptional driver of endothelial reprogramming. These insights highlight the active role of endothelial cells in scar persistence and open novel avenues for anti-keloid therapies targeting vascular\u0026ndash;fibrotic crosstalk.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eThe following abbreviations are used in this manuscript:\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"491\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eAPC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 416px;\"\u003e\n \u003cp\u003eAntigen-presenting cell\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eANGPTL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 416px;\"\u003e\n \u003cp\u003eAngiopoietin-like\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eCCL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 416px;\"\u003e\n \u003cp\u003eChemokine ligand\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eDEG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 416px;\"\u003e\n \u003cp\u003eDifferentially expressed gene\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eDESeq2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 416px;\"\u003e\n \u003cp\u003eDifferential Expression Sequencing 2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eEC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 416px;\"\u003e\n \u003cp\u003eEndothelial cell\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eECM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 416px;\"\u003e\n \u003cp\u003eExtracellular matrix\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eEndMT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 416px;\"\u003e\n \u003cp\u003eEndothelial-to-mesenchymal transition\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eFDR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 416px;\"\u003e\n \u003cp\u003eFalse discovery rate\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eGEO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 416px;\"\u003e\n \u003cp\u003eGene Expression Omnibus\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eGO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 416px;\"\u003e\n \u003cp\u003eGene Ontology\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eGOBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 416px;\"\u003e\n \u003cp\u003eGene Ontology Biological Process\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eGSEA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 416px;\"\u003e\n \u003cp\u003eGene Set Enrichment Analysis\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eGSVA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 416px;\"\u003e\n \u003cp\u003eGene Set Variation Analysis\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eIPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 416px;\"\u003e\n \u003cp\u003eIngenuity Pathway Analysis\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eKEGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 416px;\"\u003e\n \u003cp\u003eKyoto Encyclopedia of Genes and Genomes\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003ek-NN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 416px;\"\u003e\n \u003cp\u003ek-nearest neighbor\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eLASSO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 416px;\"\u003e\n \u003cp\u003eLeast Absolute Shrinkage and Selection Operator\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003elog2FC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 416px;\"\u003e\n \u003cp\u003eLog2 fold-change\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eMSigDB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 416px;\"\u003e\n \u003cp\u003eMolecular Signatures Database\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eNAMPT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 416px;\"\u003e\n \u003cp\u003eNicotinamide phosphoribosyltransferase (Visfatin)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003ePCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 416px;\"\u003e\n \u003cp\u003ePrincipal Component Analysis\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003epHB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 416px;\"\u003e\n \u003cp\u003eHemoglobin gene content\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003epMT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 416px;\"\u003e\n \u003cp\u003eMitochondrial gene content\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eQC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 416px;\"\u003e\n \u003cp\u003eQuality control\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eRF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 416px;\"\u003e\n \u003cp\u003eRandom Forest\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003escRNA-seq\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 416px;\"\u003e\n \u003cp\u003eSingle-cell RNA sequencing\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eSMC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 416px;\"\u003e\n \u003cp\u003eSmooth muscle cell\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eSVM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 416px;\"\u003e\n \u003cp\u003eSupport Vector Machine\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eTAZ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 416px;\"\u003e\n \u003cp\u003eTranscriptional coactivator with PDZ-binding motif (WWTR1)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eTGF-\u0026beta;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 416px;\"\u003e\n \u003cp\u003eTransforming growth factor beta\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eTNF-\u0026alpha;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 416px;\"\u003e\n \u003cp\u003eTumor necrosis factor alpha\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eTRAIL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 416px;\"\u003e\n \u003cp\u003eTNF-related apoptosis-inducing ligand\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eUMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 416px;\"\u003e\n \u003cp\u003eUnique molecular identifier\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eVEGF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 416px;\"\u003e\n \u003cp\u003eVascular endothelial growth factor\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eWWTR1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 416px;\"\u003e\n \u003cp\u003eWW domain containing transcription regulator 1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eYAP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 416px;\"\u003e\n \u003cp\u003eYes-associated protein\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u0026nbsp;\u003c/strong\u003eConceptualization, N.S., Z.Z. and Z.L. ; methodology, N.S. and Z.Z. ; software, Z.Z. ; validation, N.S., Z.Z., Z.Y., L.P. and Y.W. ; formal analysis, Z.Z. and Z.L. ; investigation, N.S., Z.Z., Z.Y., L.P. and Y.W. ; resources, N.Z., B.L. and Z.L. ; data curation, Z.Z. ; writing\u0026mdash;original draft preparation, N.S. and Z.Z. ; writing\u0026mdash;review and editing, N.Z., B.L. and Z.L. ; visualization, Z.Z. and Z.L. ; supervision, N.Z., B.L. and Z.L. ; project administration, N.Z., B.L. and Z.L. ; funding acquisition, N.Z. and B.L.. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e The project is supported by the Zunyi Municipal Bureau of Science, Technology, and Big Data(Grant HZ 2021 No.034)，(Grant HZ 2022 No.354)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement:\u003c/strong\u003e The single-cell RNA sequencing and spatial transcriptomics datasets analyzed in this study are publicly available in the Gene Expression Omnibus database, as specified in the \u0026quot;Materials and Methods\u0026quot; section. All other data supporting the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u003c/strong\u003e During the preparation of this manuscript/study, the author(s) used [Chat-GPT 4] for the purposes of [the identification of the proper name of endothelial cells\u0026rsquo; subsets. The authors have reviewed and edited the output and take full responsibility for the content of this publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest:\u003c/strong\u003e The authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number:\u003c/strong\u003e not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003eThis study was approved by the Medical and Ethics Committees of Dermatology, Affiliated Hospital of Zunyi Medical University (KLL-2022-0763).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e\u003cem\u003eInformed consent was obtained from all individual participants included in the study\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eR. 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Liang, Decoding the molecular landscape of keloids: new insights from single-cell tran-scriptomics, Burns Trauma 11:tkad017 (2023). https://doi.org/10.1093/burnst/tkad017.\u003c/li\u003e\n\u003cli\u003eM.M. Mia, M.K. Singh, New Insights into Hippo/YAP Signaling in Fibrotic Diseases, Cells 11 (2022). https://doi.org/10.3390/cells11132065.\u003c/li\u003e\n\u003cli\u003eF. Ma, P.S. Tsou, M. Gharaee-Kermani, Systems-based identification of the Hippo pathway for promoting fibrotic mes-enchymal differentiation in systemic sclerosis, Nat Commun 2024;15(1):210 (2024). https://doi.org/10.1038/s41467-023-44645-6.\u003c/li\u003e\n\u003cli\u003eS. KR, M. F, T. PS, Single-cell sequencing reveals Hippo signaling as a driver of fibrosis in hidradenitis suppurativa, J Clin Invest 134 (2024). https://doi.org/10.1172/JCI169225.\u003c/li\u003e\n\u003cli\u003eX. Yang, R. Zou, X. Dai, X. Wu, F. Yuan, Y. Feng, YAP is critical to inflammation, endothelial-mesenchymal transition and sub-retinal fibrosis in experimental choroidal neovascularization, Exp Cell Res 417 (2022). https://doi.org/10.1016/j.yexcr.2022.113221.\u003c/li\u003e\n\u003cli\u003eC. Feng, M. Shan, Y. Xia, Single-cell RNA sequencing reveals distinct immunology profiles in human keloid, Front Im-Munol 13 (2022). https://doi.org/10.3389/fimmu.2022.940645.\u003c/li\u003e\n\u003cli\u003eM.D. Luecken, F.J. Theis, Current best practices in single-cell RNA-seq analysis: a tutorial, Mol Syst Biol 2019;15(6):e8746 (2019). https://doi.org/10.15252/msb.20188746.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Keloid, Single-cell RNA sequencing, Spatial transcriptomics, Endothelial cell heterogeneity, WWTR1, Machine learning","lastPublishedDoi":"10.21203/rs.3.rs-9319391/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9319391/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eKeloids are complex fibroproliferative disorders characterized by persistent inflammation and vascular dysfunction. However, the high-resolution cellular landscape of their endothelial niche remains poorly defined due to the limited availability of specialized transcriptomic datasets. In this study, we addressed this critical knowledge gap by integrating single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics with our in-house bulk RNA-seq data from clinical keloid and normal skin samples, thereby significantly expanding the available transcriptomic resources for keloid vascular research. Our multi-omic analysis identified ten distinct endothelial cell (EC) subpopulations and revealed a marked expansion of a pro-pathological \u0026ldquo;Stress-endo\u0026rdquo; cluster within the keloid microenvironment. By leveraging our self-sequenced clinical data for cross-validation, we characterized the Stress-endo subpopulation as a pivotal mechanical-inflammatory nexus and identified WWTR1 (TAZ) as the master transcription factor driving its pathological transition. Developmental trajectory and cell\u0026ndash;cell communication analyses further demonstrated that this activated EC niche actively orchestrates immune cell recruitment and tissue remodeling through IL-17 and Visfatin signaling pathways. The upregulation of WWTR1 in keloid tissues was further validated at both the mRNA and protein levels by quantitative PCR and western blotting, respectively. Collectively, our study provides an enriched and refined transcriptomic framework for keloid pathogenesis, positioning WWTR1-mediated endothelial stress as a central orchestrator of chronic inflammation and fibrosis, thereby offering a more robust foundation for future vascular-targeted therapeutic strategies.\u003c/p\u003e","manuscriptTitle":"Multi-Omic Integration of Single-Cell, Spatial, and Bulk RNA-Seq Deciphers Endothelial Heterogeneity and WWTR1-Mediated Vascular Reprogramming in Keloids","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-07 09:52:13","doi":"10.21203/rs.3.rs-9319391/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"25c4b8c2-9d5e-49c8-a29c-2d4cc0d6f908","owner":[],"postedDate":"April 7th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-09T21:25:13+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-07 09:52:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9319391","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9319391","identity":"rs-9319391","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}