Meta-Analysis of Single-Cell RNA-Seq Data Reveals the Mechanism of Formation and Heterogeneity of Tertiary Lymphoid Organ in Vascular Disease

HomeArteriosclerosis, Thrombosis, and Vascular BiologyVol. 43, No. 10Meta-Analysis of Single-Cell RNA-Seq Data Reveals the Mechanism of Formation and Heterogeneity of Tertiary Lymphoid Organ in Vascular Disease Open AccessResearch ArticlePDF/EPUBAboutView PDFView EPUBSections ToolsAdd to favoritesDownload citationsTrack citationsPermissionsDownload Articles + Supplements ShareShare onFacebookTwitterLinked InMendeleyReddit Jump toSupplemental MaterialOpen AccessResearch ArticlePDF/EPUBMeta-Analysis of Single-Cell RNA-Seq Data Reveals the Mechanism of Formation and Heterogeneity of Tertiary Lymphoid Organ in Vascular Disease Xuejing Sun, Yao Lu, Junru Wu, Qing Wen, Zhengxin Li, Yan Tang, Yunmin Shi, Tian He, Lun Liu, Wei Huang, Chunyan Weng, Qing Wu, Qingzhong Xiao, Hong Yuan, Qingbo Xu and Jingjing Cai Xuejing SunXuejing Sun Department of Cardiology (X.S., J.W., Q. Wen, Z.L., Y.T., Y.S., T.H., L.L., W.H., C.W., J.C.), Central South University, Changsha, China. , Yao LuYao Lu https://orcid.org/0000-0001-6743-7870 The Center of Clinical Pharmacology (Y.L., H.Y.), Central South University, Changsha, China. , Junru WuJunru Wu https://orcid.org/0000-0002-8593-0703 Department of Cardiology (X.S., J.W., Q. Wen, Z.L., Y.T., Y.S., T.H., L.L., W.H., C.W., J.C.), Central South University, Changsha, China. , Qing WenQing Wen https://orcid.org/0009-0001-0482-9583 Department of Cardiology (X.S., J.W., Q. Wen, Z.L., Y.T., Y.S., T.H., L.L., W.H., C.W., J.C.), Central South University, Changsha, China. , Zhengxin LiZhengxin Li Department of Cardiology (X.S., J.W., Q. Wen, Z.L., Y.T., Y.S., T.H., L.L., W.H., C.W., J.C.), Central South University, Changsha, China. , Yan TangYan Tang Department of Cardiology (X.S., J.W., Q. Wen, Z.L., Y.T., Y.S., T.H., L.L., W.H., C.W., J.C.), Central South University, Changsha, China. , Yunmin ShiYunmin Shi Department of Cardiology (X.S., J.W., Q. Wen, Z.L., Y.T., Y.S., T.H., L.L., W.H., C.W., J.C.), Central South University, Changsha, China. , Tian HeTian He Department of Cardiology (X.S., J.W., Q. Wen, Z.L., Y.T., Y.S., T.H., L.L., W.H., C.W., J.C.), Central South University, Changsha, China. , Lun LiuLun Liu Department of Cardiology (X.S., J.W., Q. Wen, Z.L., Y.T., Y.S., T.H., L.L., W.H., C.W., J.C.), Central South University, Changsha, China. , Wei HuangWei Huang Department of Cardiology (X.S., J.W., Q. Wen, Z.L., Y.T., Y.S., T.H., L.L., W.H., C.W., J.C.), Central South University, Changsha, China. , Chunyan WengChunyan Weng Department of Cardiology (X.S., J.W., Q. Wen, Z.L., Y.T., Y.S., T.H., L.L., W.H., C.W., J.C.), Central South University, Changsha, China. , Qing WuQing Wu https://orcid.org/0009-0008-6100-9910 The Third Xiangya Hospital and High-Performance Computing Center (Q. Wu), Central South University, Changsha, China. , Qingzhong XiaoQingzhong Xiao https://orcid.org/0000-0001-9101-0498 Centre for Clinical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, United Kingdom (Q. Xiao, Q. Xu). , Hong YuanHong Yuan The Center of Clinical Pharmacology (Y.L., H.Y.), Central South University, Changsha, China. , Qingbo XuQingbo Xu Correspondence to: Qingbo Xu, MD, Department of Cardiology, The First Affiliated Hospital, Zhejiang University, 79 Qingchun Rd, Hangzhou 310003, Zhejiang, China, Email E-mail Address: [email protected] https://orcid.org/0000-0003-2474-8155 Centre for Clinical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, United Kingdom (Q. Xiao, Q. Xu). Department of Cardiology, The First Affiliated Hospital, School of Medicine, Zhejiang University, China (Q. Xu). and Jingjing CaiJingjing Cai Jingjing Cai, MD, Department of Cardiology, The Third Xiangya Hospital of Central South University, 138 Tong-Zi-Po Rd, Changsha, Hunan, 410013, China, Email E-mail Address: [email protected], E-mail Address: [email protected] https://orcid.org/0000-0002-7820-536X Department of Cardiology (X.S., J.W., Q. Wen, Z.L., Y.T., Y.S., T.H., L.L., W.H., C.W., J.C.), Central South University, Changsha, China. Originally published17 Aug 2023https://doi.org/10.1161/ATVBAHA.123.318762Arteriosclerosis, Thrombosis, and Vascular Biology. 2023;43:1867–1886Other version(s) of this articleYou are viewing the most recent version of this article. Previous versions: August 17, 2023: Ahead of Print AbstractBACKGROUND:Tertiary lymphoid organs (TLOs) are ectopic lymphoid organs developed in nonlymphoid tissues with chronic inflammation, but little is known about their existence in different types of vascular diseases and the mechanism that mediated their development.METHODS:To take advantage of single-cell RNA sequencing techniques, we integrated 28 single-cell RNA sequencing data sets containing 5 vascular disease models (atherosclerosis, abdominal aortic aneurysm, intimal hyperplasia, isograft, and allograft) to explore TLOs existence and environment supporting its growth systematically. We also searched Medline, Embase, PubMed, and Web of Science from inception to January 2022 for published histological images of vascular remodeling for histological evidence to support TLO genesis.RESULTS:Accumulation and infiltration of innate and adaptive immune cells have been observed in various remodeling vessels. Interestingly, the proportion of such immune cells incrementally increases from atherosclerosis to intimal hyperplasia, abdominal aortic aneurysm, isograft, and allograft. Importantly, we uncovered that TLO structure cells, such as follicular helper T cells and germinal center B cells, present in all remodeled vessels. Among myeloid cells and lymphocytes, inflammatory macrophages, and T helper 17 cells are the major lymphoid tissue inducer cells which were found to be positively associated with the numbers of TLO structural cells in remodeled vessels. Vascular stromal cells also actively participate in vascular TLO genesis by communicating with myeloid cells and lymphocytes via CCLs (C-C motif chemokine ligands), CXCL (C-X-C motif ligand), lymphotoxin, BMP (bone morphogenetic protein) chemotactic, FGF-2 (fibroblast growth factor-2), and IGF (insulin growth factor) proliferation mechanisms, particularly for lymphoid tissue inducer cell aggregation. Additionally, the interaction between stromal cells and immune cells modulates extracellular matrix remodeling. Among TLO structure cells, follicular helper T, and germinal center B cells have strong interactions via TCR (T-cell receptor), CD40 (cluster of differentiation 40), and CXCL signaling, to promote the development and maturation of the germinal center in TLO. Consistently, by reviewing the histological images from the literature, TLO genesis was found in those vascular remodeling models.CONCLUSIONS:Our analysis showed the existence of TLOs across 5 models of vascular diseases. The mechanisms that support TLOs formation in different models are heterogeneous. This study could be a valuable resource for understanding and discovering new therapeutic targets for various forms of vascular disease.Download figureDownload PowerPointHighlightsVascular remodeling involves inflammatory response and complex cellular networks that change the geometry of the blood vessel.Both innate and adaptive immune responses are involved in various vascular diseases.Tertiary lymphoid organs (TLOs) are ectopic lymphoid organs that develop in nonlymphoid tissues at sites of chronic inflammation.TLOs are present in remodeled vessels and the mechanisms mediating TLO formation vary by vascular pathologies.Inflammatory macrophages and T helper 17 cells are the major lymphoid tissue inducer cells in remodeled vessels. The lymphoid tissue inducer cells are attracted by stromal cells to the site of inflammation via a chemotactic mechanism.The levels of inflammatory responses in lymphoid tissue inducer cells are tightly associated with the numbers of TLO structural cells (ie, follicular helper T cells and germinal center B cells) at the late stage of vascular remodeling.Vascular stromal cells, including endothelial cells, smooth muscle cells, fibroblast, and mesenchymal stem cells, also actively participate in vascular TLO genesis by communicating with myeloid cells and lymphocytes. In addition to strong chemotactic effects, vascular tissues are shaped by such communication through extracellular matrix remodeling.TLO structure cells, including the follicular helper T cells and germinal center B cells, interact tightly via TCR (T-cell receptor), CD40 (cluster of differentiation 40), and CXCL (C-X-C motif ligand) signaling, to promote the development and maturation in the germinal center.Our results can serve as a resource for a deeper understanding of vascular TLO formation and may provide valuable insights for new therapeutic strategies targeting various forms of vascular injury.Vascular remodeling is a broad term that includes cardiovascular diseases like atherosclerosis, arterial aneurysm, and intimal hyperplasia (IH) after vascular intervention and bypass surgery.1 Such active processes involve a complex cellular network that changes the geometry of the blood vessel.2 Accumulating evidence has shown that both innate and adaptive immune cells infiltrate into vascular and perivascular tissue to play important roles in modulating local inflammation and vascular remodeling.3–10 In addition to diffuse perivascular infiltrates, recent discoveries have shown that heterogeneous immune cells form organized local tertiary lymphoid organs (TLOs) in response to various inflammatory signals from the vascular microenvironment.11 However, the precise mechanism and function of TLO in vascular remodeling have not been fully established.TLOs are ectopic lymphoid organs that develop in nonlymphoid tissues at sites of chronic inflammation, including tumors, autoimmune diseases, and transplanted organs.12 Typical TLOs are composed of a T-cell–rich zone with mature dendritic cells (DCs) juxtaposing a B-cell follicle with germinal center (GC) characteristics and are surrounded by plasma cells.13 Specialized vessels in this structure termed high endothelial venules allow entry of lymphocytes into TLOs. TLO is a highly active site for recruited naive T-cell and B-cell differentiation and activation into effector T cells and B cells under the exposure of neighboring antigens and cofactors, including cytokine and chemokine.14 In different diseases, their presences are associated with different outcomes and prognoses. For example, in tumors, TLO can induce a long-lasting antitumor response and is associated with a favorable prognosis in solid tumors.15 In patients with lupus nephritis, the presence of TLO has been increasingly recognized to be associated with poorer renal outcomes.16 Importantly, emerging evidence shows the existence of TLOs surrounding atherosclerotic lesions, which are tightly associated with the progression of lesions.17–20 Recently, our group also found the local formation of TLOs in transplant arteriosclerosis, and their presence is detrimental to the long-term survival of solid organs.21 Thus, understanding the pathological mechanisms of TLOs may yield novel therapeutic targets for controlling vascular remodeling.In this study, integrated published and newly generated single-cell RNA sequencing (scRNA-seq) data, we mapped the structure cells of TLOs across 5 vascular remodeling models, including atherosclerosis, abdominal aortic aneurysm (AAA), wire injury–induced IH, isograft, and allograft artery remodeling. The mechanisms that support TLO formation and their heterogeneities in different models were also characterized. We also systemically reviewed vascular remodeling studies and obtained direct evidence of histological images that supports TLO genesis. This study could be valuable resource for understanding and discovering new therapeutic targets of various forms of vascular disease.METHODSData AvailabilityThe data that support the findings of this study are available from the corresponding author on reasonable request. scRNA-seq data of our study are available in Gene Expression Omnibus (GSE234651 for scRNA-seq of carotid artery wire injury IH model). Publicly available data obtained from mouse and human subjects were analyzed, and the data sets involved in this study and details of the scRNA-seq data are available in the public domain as outlined in Table S10.Meta-Analysis and Data AcquisitionA comprehensive meta-analysis of the atlas for scRNA sequencing studies involving 5 data sets of healthy mouse vessels and 28 data sets of remodeling vessels was performed. Each raw data set was derived from the Gene Expression Omnibus (Figure 1A). The workflow for the collection of data sets of interest is summarized in Figure 1A and 1B. Briefly, the data sets of remodeling vessels included (1) 14 atherosclerotic arterial data sets containing gene-induced (eg, apoE knockout [ApoE−/−] and Ldlr−/− mice) and high-fat diet–induced mouse atherosclerosis; (2) 2 data sets of elastase-induced aneurysm remodeling vessels; (3) 2 data sets of surgical model–induced vascular remodeling after mechanical injury; and (4) 5 data sets of surgical model–induced isograft and allograft remodeling vessels. Details of these data sets and cells harvested for single-cell sequencing are presented in Table S10.Download figureDownload PowerPointFigure 1. Design of the meta-analysis study. A, Overview of the study. This analysis is based on 28 data sets (5 healthy artery and 23 remodeled vessel data sets, including atherosclerosis, arterial aneurysm, intimal hyperplasia after vascular intervention, and bypass surgery [including isograft and allograft data sets] from published studies in mice). Atherosclerosis was induced in mouse models using genetic knockout (low-density lipoprotein receptor knockout [Ldlr−/−] and apoE knockout [ApoE−/−]) and a high-fat diet model. The arterial aneurysm model was built by applying elastase to aorta in C57BL6J mice. The intimal hyperplasia model was established by introducing a flexible wire into carotid arteries and achieving an effect in endothelial denudation in C57BL6J mice. In the isograft model, C57BL6J mice received vessels from mice with same gene background. In the allograft model, C57BL6J mice received vessels from Balb/c strain. Single cells were phenotyped by RNA sequencing (RNA-seq). Dimensionality reduction and clustering were used to identify cell types and gene signatures. Based on the gene signatures, genetic labeling was used to visualize and sort cell types to gain functional insights and in-depth transcriptome information. Pathological analysis of the histological images collected from published articles was performed and vascular–tertiary lymphoid organ (TLO) immunofluorescent staining for each model was provided. The study focused on cellular diversity and TLO formation in vascular remodeling. B, Flowchart showing the procedure for the meta-analysis study. Only single-cell RNA sequencing (scRNA-seq) data were collected, and the bulk RNA sequencing and sn-RNA sequencing data were excluded. The included studies were limited by mice. Harmony was used to integrate multiple data sets and eliminate the batch effect. Then the cell clustering and annotation were performed after data quality control. The data sets of the sorted vessel cells were not included in the analysis when calculating and comparing cell type ratios. However, those sorted data sets were included when performing cell function, cell-cell interaction analyses, etc. Ctrl indicates control.Inclusion and Exclusion for Mice Data SetsWe conducted a systematic search of the uploaded data sets in the Gene Expression Omnibus database in January 2022.Inclusion CriteriaDatabase: Gene Expression Omnibus database.MeSH terms: atherosclerosis, aneurysm, hyperplasia, isograft, and allograft.Species: Mus musculus.Dates: from inception to January 2022.The search terms included the following: atherosclerosis, aneurysm, hyperplasia, isograft, and allograft. Atherosclerotic models contained arteries of ApoE−/− or low-density lipoprotein knockout (LDL−/−) mice fed a high-fat diet. The grafting arteries were isolated from Balb/c mice in allograft models. The healthy arteries, aneurysm, wire injury–induced hyperplasia, and isograft vasculopathies models were established on mice with C57BL6J background. Anatomic location of vessels in each disease model and cell sorting strategies are listed in Table S10.Excluded Studies That Met the Following CriteriaVascular data other than the abovementioned models (atherosclerosis, aneurysm, hyperplasia, isograft, and allograft) were not included. We only collected scRNA sequencing data, and the bulk RNA sequencing and sn-RNA sequencing data were excluded. Among them, the scRNA sequencing data are the data sets obtained by digesting the healthy or remodeling vessel (Figure 1B).For the data sets in which cells were sorted (eg, sorted CD45+ cells), none of them were included in the analysis when calculating the proportion. However, those sorted data sets integrated with unsorted data sets when performing functional analysis or cell-cell interaction analysis for certain types of cells. No language restrictions were applied. The included data sets and studies are listed in Table S10.Meta-Analysis StrategyAfter collecting data sets that met the inclusion criteria, we first used Harmony R package to integrate multiple data sets and mitigate batch effects, as it has been proven effective in achieving harmonized data integration and reducing unwanted variation caused by batch effects.22 Then the cell annotation was performed after data quality control by R package Seurat (version 4.3.0). Briefly, gene features expressed in at least 5 cells, and cells with at least 500 detected genes, <10% mitochondrial counts were kept. Red blood cells (expressing Alas2) were also filtered and removed. The data sets of the sorted vessel cells were not included in the analysis when calculating and comparing cell type ratios. However, those sorted data sets were included when performing other analyses. We compared the expression levels of different vital functional gene sets for TLO formation in stromal cells and immune cells in remodeling vessels with those in healthy aortas. For different cell types, more detailed analysis (such as the expression of characteristic functional gene sets, cell-cell communication analysis, etc) was further performed.Data Analysis and VisualizationSingle-cell expression count matrix barcodes and gene IDs from each study were downloaded from the Gene Expression Omnibus. Doublet detection for those scRNA-seq data using artificial nearest neighbors was conducted via R package DoubletFinder (version 2.0.3).23 To normalize the data, Unique Molecular Identifiers, the unique tags attached to each molecule, were used to facilitate the identification and removal of polymerase chain reaction duplicates.21,23 Unique molecular identifier counts were scaled by library size and natural log transformation; gene counts for each cell were divided by the total unique molecular identifier count of that cell, scaled by a factor of 10 000, and then transformed via a natural log plus 1 function (NormalizeData). For downstream analysis, normalized data were additionally scaled so that the mean expression across cells was 0 and the variance was 1 (function ScaleData). To reduce the dimensionality of the data for clustering functions, principal component analysis was utilized, and we identified the first 30 principal components that explained sufficient observed variance (function RunPCA). The determination of sufficiency is based on the examination of the elbow plot (function ElbowPlot), which displays the eigenvalues of the principal components in descending order and the eigenvalues began to level off after the first 30 PCs in elbow plot. Next, to identify clusters within the reduced dimensional space, cells were embedded in a k-nearest neighborhood-based graph structure (function FindNeighbors) and were then partitioned into clusters (function FindClusters). Finally, for visualization, Uniform Manifold Approximation and Projection was run over the reduced dimensional space (function RunUMAP), and identified clusters were projected onto the Uniform Manifold Approximation and Projection plot. To address noise and batch differences between the studies, a strategy involving the use of reference cells from each data set was used. To facilitate integration, pairs of reference cells, known as anchors, were identified and scored based on their proximity using a k-nearest neighbor approach (function FindIntegrationAnchors). These anchors were then used to measure the expression difference between studies (function IntegrateData), which was then removed from the corresponding normalized data. Integration was run between the normalization and scaling steps.24 All the functions used are based on Seurat.Clustering was performed in Seurat. To establish the reliability of this method, the clustering algorithm was compared between Seurat and an alternative approach using the Harmony R package (https://github.com/immunogenomics/harmony)22 This package scales the data to make nearby cells more similar when clustering is performed. Harmony is also easily incorporated into the Seurat pipeline. The results using Harmony-based clustering were essentially identical to those from the Seurat clustering. In this meta-analysis, batch effect was only corrected once in the initial data integration, and removal for batch effects was not repeatedly performed in downstream analysis.22,25Cellular Similarity DendrogramsFor an unsupervised comparison of the cellular subpopulations identified from multiple vascular remodeling types, the following steps were performed.Here, we used the batch-corrected expression value via a method named Scanorama.26Identify a set of highly variable genes across different cellular subpopulations.Calculate the mean expression of genes in each cluster.For hierarchical clustering, the distance defined as (1-Pearson correlation coefficient)/2 was used.For major myeloid lineages comparison across multiple vascular remodeling types, we used the top 1000 highly variable genes. For other subpopulations, comparison, the top 800 highly variable genes were used.Correlation AnalysisExpression levels were averaged over cells and plotted among cell types in Figures 2H, 3G, 4H, and 5E. Scatter plots were fit with linear and quadratic regression models to show potential relationships. The correlation was assessed via scatter plots and Spearman correlation coefficient.26Download figureDownload PowerPointFigure 2. Chronic inflammation in the pathogenesis of vascular remodelings. A, Uniform Manifold Approximation and Projection (UMAP) plot showing the major cell types in healthy and remodeled arteries. B, Bar graphs displaying the proportion of each cell type in total artery cells in the indicated groups as determined by single-cell RNA sequencing (scRNA-seq). C, Bar graphs displaying the proportions of lymphocytes (green), myeloid cells (orange), and stromal cells (blue) in total artery cells in the indicated groups as determined by scRNA-seq. D, Line graphs comparing the proportions of stromal cells and immune cells in total artery cells in the healthy and remodeled arteries as determined by scRNA-seq. E, UMAP plots showing cell types in normal control (NC) arteries and the atherosclerosis (AS), abdominal aortic aneurysm (AAA), intimal hyperplasia (IH), isograft (IG), and allograft (AG) models. F, Hierarchical clustering of major cell lineages. G, Heatmap (column scaled) showing the average gene expression levels in the indicated cells. H, Scatterplot showing the Pearson correlation coefficients between the proportions of lymphocytes in the late stage (divided by the total cells) and the expression scores of proinflammatory (left), antigen presentating–related (middle), and lymphatic-inducing (right) genes for innate immune cells in the indicated groups. I, Heatmap and forest plot comparing the cell proportions and expression levels of lymphocyte recruitment-, chemokine-, and cell proliferation–related genes in the indicated cells of remodeled vessels with those of healthy arteries. Cell types or previously published biomarkers are shown as rows, and individual scRNA-seq data sets are shown as columns. The heatmap indicates the proportion of a specific cell type or the effect size of a given gene marker in each data set (measured as the log2 odds ratio [OR] for the cell proportion and as the average gene expression in the indicated data sets vs the cell proportion or the average gene expression in the control group (NC) derived from logistic regression). The right-hand forest plot shows the overall effect size and significance of each marker in the meta-analysis across all studies based on effect sizes and SEs. The P values shown are derived from meta-analysis (random effects analysis because of the different vascular remodeling types). J, Heatmap and forest plot comparing the expression of angiogenesis-related, adhesion molecule–related, and lymphocyte recruitment–related genes in all stromal cells between remodeled vessels and healthy arteries. Adj P indicates adjusted P value; DC, dendritic cell; DZB, dark zone B cell; EC, endothelial cell; FC, fold change; LZB, light zone B cell; MSC, mesenchymal stem cell; NK, natural killer cell; pDC, plasmacytoid dendritic cell; SMC, smooth muscle cell; and VR, vascular remodeling.Download figureDownload PowerPointFigure 3. Myeloid cell population and transcriptional signaling for developing tertiary lymphoid organs (TLOs). A, Uniform Manifold Approximation and Projection (UMAP) plot showing the myeloid cell subtypes in healthy and remodeled arteries. B, Bar charts displaying the proportion of each myeloid cell subtype in overall artery cells of the indicated groups by single-cell RNA sequencing (scRNA-seq). C, UMAP plots showing the myeloid cell subtypes in normal control (NC) artery, atherosclerosis (AS), abdominal aortic aneurysm (AAA), intimal hyperplasia (IH), isograft (IG), and allograft (AG). D, Heatmap showing the average gene expression of TLO-inducing genes (up) and proinflammatory genes (bottom) in the indicated cell subtypes (row) and groups (column). E, Gene expression signature of cytokine, chemokine, angiogenesis, phagocytosis, antigen-presenting, and adhesion molecule for specific myeloid cell subtypes of healthy and remodeled arteries; average gene expression for the reference is normalized between 0 and 1. F, Gene expression signature of cytokine, chemokine, angiogenesis, phagocytosis, antigen-presenting, and adhesion molecule of the specific myeloid models in different myeloid cell subtypes; average gene expression for the reference is normalized between 0 and 1. G, Scatterplot showing the Pearson correlation coefficients between the proportions of lymphocytes in the late stage (divided by the total cells) and the expression scores of TLO-inducing genes for myeloid cells in the indicated groups. H, Cellular interaction pairs between myeloid cells and stromal cells in the indicated models. The direction of the arrow represents the direction of cellular interaction, starting from the ligand cell and ending in the recipient cell. The numbers represent the number of ligand-receptor pairs. I, Violin plot showing the expression level for the related genes of ligand-receptor pairs between myeloid cells and stromal cells in the indicated models. J, Forest plot showing the overall effect size and significance of each marker in meta-analysis across all studies, based on effect sizes and SEs. P values are shown from meta-analysis (random effects, on account of the different vascular remodeling types). K, Schematic diagram showing the role of myeloid cells in the formation of TLO. Adj P indicates adjusted P value; CCL, chemokine (C-C motif) ligand; CXCL, chemokine (C-X-C motif) ligand; DC, dendritic cell; DZB, dark zone B cell; HEV, high endothelial venule; Lamp3, lysosomal-associated membrane protein 3; LZB, light zone B cell; Res-like, resident-like; Scl1, scarecrow-like protein 1; Tfh, follicular helper T-cell; Th, T helper; TREM2, triggering receptor expressed on myeloid cells 2; and VR, vascular remodeling.Download figureDownload PowerPointFigure 4. T lymphocytes and transcriptional signaling for developing tertiary lymphoid organs (TLOs). A, Uniform Manifold Approximation and Projection (UMAP) plot showing the T-cell subtypes in healthy and remodeled arteries. B, Bar charts displaying the proportion of each T-cell subtype in overall T cells of the indicated groups by single-cell RNA sequencing (scRNA-seq). C, Bar charts displaying the ratio of proinflammatory to anti-inflammatory T cells of the indicated groups by scRNA-seq. D, Dot plots showing the expression of cytokine-related genes in each subset at early or late stage of vascular remodeling. E, UMAP plots showing follicular helper T cells (Tfh; blue) and T helper 17 (Th17; purple) in normal control (NC) artery, atherosclerosis (AS), abdominal aortic aneurysm (AAA), intimal hyperplasia (IH), isograft (IG), and allograft (AG). F, Bar charts displaying the proportion of Tfh (blue) and Th17 (purple) in total T cells of the indicated groups. G, Heatmap showing the expression of lymphatic tissue–inducing genes (column) in the indicated T-cell subtypes (row). H, Scatterplot showing the Pearson correlation of the gene expression of proinflammatory genes in T cells with lymphatic-inducing gene expression in Th17 (left) and the percentage of Tfh cell (right) by the indicated groups. I, Heatmap and forest plot comparing the expression of T-cell proliferation, TCR (T-cell receptor)-signaling activation, T-cell activation, an