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. 2025 Feb 17;25:104. doi: 10.1186/s12872-025-04571-5

Uncovering endothelial to mesenchymal transition drivers in atherosclerosis via multi-omics analysis

Qingyan Huang 1,2, Yuhong Gan 3, Xiaoqi Zheng 1,2, Zhikang Yu 2, Qionghui Huang 1,2, Mingfeng Huang 1,2,4,✉
PMCID: PMC11831781  PMID: 39956907

Abstract

Purpose

This study aimed to identify novel candidates that regulate Endothelial to mesenchymal transition(EndMT) in atherosclerosis by integrating multi-omics data.

Methods

The single-cell RNA sequencing (scRNA-seq) dataset GSE159677, bulk RNA-seq dataset GSE118446 and microarray dataset GSE56309 were obtained from the Gene Expression Omnibus (GEO) database. The uniform manifold approximation and projection (UMAP) were used for downscaling and cluster identification. Differentially expressed genes (DEGs) from GSE118446 and GSE56309 were analyzed using limma package. Functional enrichment analysis was applied by DAVID functional annotation tool. Quantitative real-time polymerase chain reaction (qPCR) and western blotting were used for further validation.

Results

Nine endothelial cell (EC) clusters were identified in human plaques, with EC cluster 5 exhibiting an EndMT phenotype. The intersection of genes from EC cluster 5 and common DEGs in vitro EndMT models revealed seven mesenchymal candidates: PTGS2, TPM1, SERPINE1, FN1, RASD1, SEMA3C, and ESM1. Validation of these findings was carried out through qPCR analysis.

Conclusion

Through the integration of multi-omics data using bioinformatics methods, our study identified seven novel EndMT candidates: PTGS2, TPM1, SERPINE1, FN1, RASD1, SEMA3C, and ESM1.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12872-025-04571-5.

Keywords: EndMT, Atherosclerosis, Mesenchymal candidates, Bioinformation

Introduction

EndMT is the process that endothelial cells loss the endothelial characteristics and transform into mesenchymal-like cells [1]. During this dynamic process, endothelial cells gradually express vascular smooth muscle cell or fibroblast markers such as α-smooth muscle actin (α-SMA), TAGLN and fibroblast-specific protein-1 (FSP-1), accompanied by morphological and functional changes [2]. EndMT has been described during heart development [3], neovascularization and tissue repair [4]. In recent years, robust studies showed that EndMT contributes to a variety diseases such as pulmonary arterial hypertension (PAH) [5], cerebral cavernous malformation (CCM) [6], and atherosclerosis [7].

Atherosclerosis is characterized by lipid deposition in the blood vessels, forming plaques that can lead to myocardial infarction and stroke upon rupture. The initiation of atherosclerosis involves abnormal changes in a various cell types, including endothelial cells, smooth muscle cells, fibroblasts, and immune cells [8]. Endothelial cells constitute the first barrier of blood vessel, responsible for sensing and transduction biological signals [9] and endothelial dysfunction was considered as the early pathological event of atherosclerosis. EndMT was demonstrated to aggravate the progress of atherosclerosis. Previous studies using the Cre-lox mouse models to study EndMT in atherosclerosis has reported the provocative and prominent role of EndMT in atherosclerosis. For instance, Evrard et al. [10] found that fibroblast-like cells derived from EndMT were predominant in atherosclerotic lesions and aggravated the instability of plaque through collagen production. Endothelial-specific deletion of FGF receptor substrate 2α (Frs2a) resulted in extensive EndMT, which increased coronary atherosclerosis by enhancing plaque burden, deposition of fibronectin, and neointima formation [7]. In addition, EndMT can be induced by the factors associated with atherosclerosis, such as inflammation [11], shear stress [12] and ox-LDL [13]. Mechanistically, the canonical TGF-β signaling pathway has been demonstrated as key a driver of EndMT. Further, non-coding RNAs and epigenetic mechanisms have been implicated in regulating EndMT in atherosclerosis [14]. However, the precise mechanisms through which EndMT influences atherosclerosis require further elucidation.

Single-cell RNA sequencing has revealed the plasticity and heterogeneity of endothelial cells in human atherosclerotic plaques. It has identified an endothelial cell cluster expressing smooth muscle cell markers, suggesting the presence of EndMT within plaques [15]. The advent of scRNA-seq has provided insights into gene expression profiles at the individual cell level. Here, we aim to further deepen the understanding towards the role of ECs during atherosclerotic progression. We utilized scRNA-seq data from human atherosclerotic plaques and their patient-matched proximal adjacent (PA) segments of the carotid artery, along with bulk RNA sequencing and microarray data from in vitro models of EndMT obtained from the GEO database. This approach was employed to elucidate the transcriptional alterations associated with EndMT. We might uncover novel molecular targets and further elucidate the role of EndMT in atherosclerosis.

Materials and methods

ScRNA sequencing data processing

The scRNA-seq data of GSE159677 was retrieved from the Gene Expression Omnibus (GEO) database. GSE159677 was performed on GPL18573, Illumina NextSeq 500 (Homo sapiens), containing three human calcified atherosclerotic core (AC) plaques and the matched proximal adjacent (PA) portions of carotid artery. The Seurat R package (Version 4.2.2) was used Uniform Manifold Approximation and Projection (UMAP) analysis. Cells with < 500 genes, > 3,000 genes, or > 10% mitochondrial genes were filtered out. A total of 40,049 filtered cells were selected for analysis. The “FindClusters” function was used to classify the cells into different clusters with a resolution of 0.5.

Microarray and bulk sequencing data processing

The original data of GSE56309 and GSE118446 was downloaded from the GEO database (https://www.ncbi.nlm.nih.gov/geo/). GSE56309 was performed on GPL10558, Illumina HumanHT-12 V4.0 expression beadchip. GSE56309 contained 22 samples, including three HUVECs samples and four HUVECs samples treated with TGFβ2 and H2O2. GSE118446 was performed on GPL16791, Illumina HiSeq 2500 (Homo sapiens), which contained 18 samples, including three HUVECs samples, three HUVECs samples treated with TGFβ2 and three HUVECs samples treated IL1β + TGFβ2. The DEGs were analyzed using limma package (Version 3.5.1). The genes with an adjusted P value < 0.05, and an absolute logFC > 1 were selected as significantly dysregulated genes. The main features of the datasets in this study were presented in Supplementary Table 1.

GO and KEGG enrichment analysis

The genes of the cluster 5 were subjected to Gene Ontology (GO) enrichment analysis (molecular function (MF), biological process (BP) and cellular component) and signaling pathway Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis on the DAVID web tool (https://david.ncifcrf.gov/).

Cell culture

Human umbilical vein endothelial cells (HUVECs) were purchased from CHI SCIENTIFIC (71,074, Jiangsu, China) and were cultured in ECM ( Sciencell, 1001) supplemented with 5% FBS, 1% endothelial cell growth addition (ECGS), and 1% penicillin–streptomycin. To induce EndMT, the cells were treated with 10 ng/mL of transforming growth factor-β2 (PeproTech, 100-35B-10) for 48 h.

Quantitative real-time polymerase chain reaction

Total RNA was extracted from cells using the RNA kit (HaiGene, B0132) and cDNA was synthesized using PrimeScript™ RT Master Mix (TaKaRa, RR036A) Quantitative PCR was performed in 9600 PCR system, using TB Green® Premix Ex Taq™ II (TaKaRa, RR820A). The cycling conditions were 95 °C for 30 s, followed by 40 cycles of 95 °C for 5 s, 60 °C for 30 s. Gene expression was normalized to the constitutively expressed housekeeping gene GAPDH. The relative expression level of gene was calculated using the 2−ΔΔCT method. Primer sequences are listed in Supplementary Table 2.

Western blotting

Cells were lysed in RIPA buffer (Beyotime, P0013B) for protein extraction. An equivalent amount of protein was submitted to 10% SDS-PAGE and transferred onto a PVDF membrane (Merck Millipore, 0.45 μm). Membranes were blocked in Tris-buffered saline containing 0.5% milk for 1 h at room temperature. The blocked membranes were incubated overnight at 4 °C with the following primary antibodies: SM22 (proteintech, 10,493–1-AP, 1:1000), α-SMA (proteintech, 23,660–1-AP, 1:1000), Vimentin (CST, 5741 s, 1:1000), GAPDH (abcam, ab181602, 1:1000). Then, membranes were washed and incubated with secondary anti-rabbit antibody (Invitrogen, 31,460) for 1 h at room temperature. The chemiluminescence signal was visualized with ChemiDoc MP (BIO-RAD) using ECL (biosharp, BL520B-2).

Statistical analysis

ScRNA-seq and transcriptional data analysis was performed in R software (version 4.2.2). Unpaired t test and graphs for qPCR data were performed by GraphPad Prism software (version 9.0). A protein–protein interaction (PPI) network was constructed with the STRING tool (https://cn.string-db.org/). P value < 0.05 was considered statistically significant.

Results

EC heterogeneity exists in human aorta and plaque

To further explore the heterogeneity of ECs in the human aorta and atherosclerotic plaques, we visualized the data using a uniform manifold approximation and projection (UMAP) plot. Based on an unsupervised clustering method, single cells from human aorta and atherosclerotic plaque were annotated to 10 clusters (Fig. 1A) and the cell type distribution was shown in Fig. 1E. Each cluster was labeled with the known cell type makers (Fig. 1D). In order to analyze the difference of ECs between human aorta and atherosclerotic plaque, ECs were isolated and then divided into nine distinct clusters (Fig. 1B and C). The UMAP visualizations of ECs with different resolution parameters was presented in Figure S2.

Fig. 1.

Fig. 1

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Identification of EC clusters in human aorta and plaque. A Cluster annotation and cell types identified by UMAP in GSE159677, colored for 10 clusters. B UMAP plot of ECs clusters in human aorta and plaque. C UMAP plot of ECs clusters among plaques and adjacent tissues. D Expression of typical marker of cell types. E Cell counts of each cell types

EC cluster 5 exhibits EndMT trait

We compared the expression of mesenchymal markers (TAGLN, MYL9, ITGAV, FN1 and FBLN5) among the EC clusters. Those mesenchymal markers were widely expressed in the EC cluster 5 (Fig. 2A). Furthermore, we also examined the recognized EC and mesenchymal markers in those nine EC clusters (Fig. 2B and supplementary Table 3). Cells in EC cluster 5 gained mesenchymal characteristic by specifically expressing TAGLN, MYL9, ITGAV, FN1 and FBLN5, indicating the EC cluster 5 might switch to a mesenchymal phenotype. To identify the cellular functions in EC cluster 5 subpopulations, we extracted the genes for GO and KEGG enrichment analysis (Fig. 2C and D). Those terms suggested that the genes were related to extracellular matrix and mesenchymal transition. Our findings implied that EC cluster 5 was more “mesenchymal-like”.

Fig. 2.

Fig. 2

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Characterization of EC cluster 5. A Expression of mesenchymal markers in ECs. B Violin plot of endothelial and mesenchymal markers in the identified ECs clusters. C and D Main terms of GO/KEGG of EC cluster 5

Identification of mesenchymal candidates in EC cluster 5

In order to find novel regulating mesenchymal candidates, we analyzed bulk sequencing and microarray data from three EndMT in vitro models. 147 upregulated genes were common in the results of EndMT in vitro models induced by different stimulus. Given that cluster 5 exhibited a mesenchymal phenotype, we further investigated the overlapping upregulated genes between 147 genes from EndMT models and 120 genes in cluster 5. Venn diagram showed overlapping of seven mesenchymal related genes (PTGS2, TPM1, SERPINE1, FN1, RASD1, SEMA3C, ESM1) (Fig. 3A). The expression of seven mesenchymal genes in each EC cluster was shown in the violin plot (Fig. 3B and supplementary Table 3). Compared with other EC clusters, those genes were highly expressed in cluster 5. Further, the expression of seven mesenchymal genes in three EndMT in vitro models was presented in heatmap (Fig. 3C-E).

Fig. 3.

Fig. 3

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Identification of mesenchymal candidates in EC cluster 5. A Venn diagram of uP-regulated DEGs of three EndMT invitro models and EC cluster 5, Benjamini–Hochberg test, adjusted P-value < 0.05. B Violin plot of mesenchymal candidates in the identified ECs clusters. C-E Heatmap of mesenchymal candidates in GSE56309 and GSE118446

Validation by EndMT in vitro model

To verify the applicability of the multi-omics results, we established an EndMT model by inducing HUVECs with TGFβ2 (Figure S1). QPCR was used to detect the expression level of seven mesenchymal genes. Similar to the results of the omics analysis, the expression of these genes was uP-regulated in the model (Fig. 4).

Fig. 4.

Fig. 4

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Expression levels of mesenchymal candidates in invitro EndMT model. The data are presented as means ± SD, n = 3. *p < 0.05, **p < 0.01, ***p < 0.001

Discussion

The pathogenesis of atherosclerosis is multifactorial, involving multiple cell types, cellular processes, and regulators. Atherosclerotic plaques contain complex components, among which are endothelial-derived mesenchymal cells [16, 17]. Given the heterogeneity and plasticity of endothelial cells, we sought to uncover new molecules regulating EndMT utilizing multi-omics data. This is the first study to use scRNA-seq, bulk sequencing and micoarray data to discover the common regulated genes in EndMT for AS.

ScRNA-seq enables to reflect the intricate cellular composition within tissues, as well as the molecular changes of individual cells. In current study, we performed scRNA-seq analysis across human atherosclerotic plaques and patient-matched proximal adjacent (PA) portions of carotid artery to characterize EC heterogeneity and molecular changes. The UMAP plot revealed 10 distinct cell types within human plaque, and further analysis identified nine sub-clusters among the ECs. By analyzing the expression of EndMT relative markers, EC cluster 5 was more mesenchymal accompanied with expressing VSMC markers. The results of the enrichment analysis suggested that EC cluster 5 exhibited EndMT characteristics, including enrichment in the classical TGF-β signaling pathway. Interestingly, Zhao et al. [18] also identified eight EC clusters in the diabetic atherosclerosis mice aorta, three of which expressed mesenchymal markers such as Tgfbr2, Fn1, Eln, Vim, Dcn, and Mgp. Likewise, those EndMT+ cells were associated with many proatherogenic biological processes including extracellular matrix organization, apoptosis, and adhesion molecule binding. To ditermine the impact of disturbed flow on ECs, Andueza et al. [19] conducted scRNA-seq and scATAC-seq analyses on mouse carotid arteries subsequent to partial carotid ligation. Their findings revealed eight distinct EC clusters and demonstrated that disturbed flow induced a transition of ECs into EndMT cells. In another advanced atherosclerotic plaque single cell analysis, six different cell types and 12 cell sub-populations were identified. Similarly, one of the EC subpopulation expressed the typical VSMC markers, such as ACTA2, NOTCH3, and MYH11, indicating the sign of EndMT [15]. Regardless of human or murine, these findings illustrated that ECs exhibit heterogeneity and plasticity, EndMT contributes to the pathogenesis of atherosclerosis.

The in vitro EndMT models are useful tools to study molecular mechanisms. To establish models, endothelial cells are typically induced to simulate EndMT by stimuli including, transforming growth factor-beta (TGF-β), interleukin (IL)-1β or hydrogen peroxide (H2O2) [10, 20]. To investigate promising candidates involved in EndMT, datasets from three in vitro EndMT models induced by TGF-β2, IL1β, and H2O2 were analyzed. We obtained 142 commonly upregulated genes based on analyzing DEGs. After intersection 142 upregulating genes with those genes in EC cluster 5, seven mesenchymal genes (PTGS2, TPM1, SERPINE1, FN1, RASD1, SEMA3C, and ESM1) were identified.

PTGS2, also known as COX2, is the key enzyme that catalyzes the conversion of arachidonic acid into prostacyclin [21]. COX2 did not express in normal arteries, but was presented in atherosclerotic lesions [22, 23]. The expression of COX2 was related to inflammatory reactions in AS. However, the impact of inhibiting COX2 on AS progression remains controversial, partly due to its function in pathophysiological processes [24, 25]. Qi et al. showed that overexpression of COX2 in endothelial cells lead to apoptosis [26]. Furthermore, Cox2 has been extensively implicated in the process of inflammation induced epithelial-mesenchymal transition (EMT) in cancers [27, 28]. In our study, we observed an upregulation of COX2 in endothelial cells, suggesting that it may serve as an indicator of inflammation-related endothelial-to-mesenchymal transition in AS.

As a member of the tropomyosin family, TPM1 plays a vital role in the contractile systems of both striated and smooth muscles, as well as in the cytoskeletal organization of non-muscle cells. Simoneau et al. [29] discovered that TPM1 regulated endothelial dysfunction under oxidative stress injury. Furthermore, while upregulation of TPM1 was observed in the carotid and middle cerebral arteries of atherosclerotic rabbit models, its role in AS requires further elucidation [30]. Importantly, TPM1 serves as an EMT biomarker inducible by TGFβ2 and is involved in TGFβ-mediated actin fiber formation and matrix adhesion, [31, 32] implicating its potential role in regulating EndMT via the TGFβ pathway.

SERPINE1, an inhibitor of plasminogen activator, is required for blood blots degradation [33]. Many studies has reported that SERPINE1 contributed to ischemic stroke by regulating peripheral neutrophil recruitment [34] and astrocyte apoptotic [35]. ScRNA-seq analysis suggested that SERPINE1 might be a hub gene for heart failure. Studies also found that SERPINE1 promoted metastasis through EndMT in cervical squamous cell carcinoma [36] and triple-negative breast cancer [37]. Our results supported that SERPINE1 was a potential gene regulating ECs transform to mesenchymal cells during AS.

FN1 is a typical marker for EndMT, playing a critical role in cell adhesion and migration during processes such as embryogenesis, wound healing, blood coagulation, host defense, and metastasis. Our study observed elevated FN1 expression in both EC cluster 5 and EndMT in vitro models.

RASD1 is a member of the RAS superfamily of small G-proteins and could be induced by dexamethasone [38]. RASD1 was implicated in cardiovascular diseases such as hypertrophic cardiomyopathy [39] and ischemic stroke [40]. Knocking down RASD1 in the cardiovascular system significantly increases atrial natriuretic factor (ANF) secretion and disrupts cardiovascular homeostasis [41]. Decreased the expression of RASD1 in zebrafishes led to alterations in heartbeat and ventricle volume, ultimately causing bradycardia and a reduction in ventricle size [42]. These changes may be attributed to the interaction between RASD1 and renin, as RASD1 is involved in the transcriptional regulation of renin [43]. What’s more, RASD1 regulated tumor migration and hepatic lipogenesis by PI3K/AKT/mTOR pathway. Considering the close relationship between the AKT/mTOR pathway and EndMT [44], we speculated that RASD1 affect AS through AKT-mediated EndMT.

The SEMA3C gene encodes a secreted glycoprotein belonging to the semaphorin class 3 family. During cardiac outflow tract (OFT) septation, SEMA3C plays a crucial role by activating its receptor NRP1 in the endothelium of the OFT. This signaling pathway promotes EndMT, which is essential for forming the endocardial cushions and repositioning cardiac neural crest cells within the OFT [45]. Yang et al. investigated the impact of Semaphorin-3C (Sema3C) on pathological retinal angiogenesis associated with retinopathy of prematurity, and discovered its potent anti-angiogenic effects mediated through Neuropilin-1 and PlexinD1 receptors [46]. Additionally, SEMA3C regulated cell metastasis through the Wnt/β-catenin pathway [47].

ESM1, also known as endocan, is a soluble proteoglycan that is secreted by vascular endothelial cells [48]. Encocan mediated many biological processes including cell adhesion, migration, proliferation and neovascularization [49]. Many studies have highlighted the role of endocan or ESM-1 as biomarker in various cardiovascular conditions, including atherosclerosis and ischemic heart disease, and their potential for early detection and risk assessment [50–52].

Those seven mesenchymal genes was abundantly expressed in EC cluster 5 compared with other EC clusters. Among the 10 cell types identified in GSE159677 dataset, those genes mainly expressed in VSMC, fibroblast and EC. We further induced in vitro EndMT models for qPCR to verify the bioinformation findings. Our results aligned with the expectations by our bioinformatics analysis. Although the roles and functions of these genes in AS have not been fully elucidated, but their involvement in regulating multiple signaling pathways associated with EndMT suggests that they may be potentially important targets for AS and deserve further investigation. And the PPI network (Figure S3) of these genes indicated the possible interaction underlying EndMT.

Overall, this study provides insights into the diversity of ECs in AS plaque and highlights seven novel candidates for EndMT in AS.

Limitations

Our study combined scRNA-seq, bulk RNA-seq, and microarray data to identify new EndMT candidates, suggesting a potential correlation between hub genes expression and EndMT. However, we only validated the findings by qPCR in vitro. It lacks comprehensive functional assays to establish a direct link between the identified genes and both EndMT and the progression of atherosclerosis.

Conclusion

By utilizing bioinformation methods to integrate multi-omics data, our study identified seven hub genes (PTGS2, TPM1, SERPINE1, FN1, RASD1, SEMA3C, and ESM1) associated with EndMT in AS. But the underlying mechanisms should be further validated through experimental studies.

Supplementary Information

Supplementary Material 1. (24.4KB, docx)
12872_2025_4571_MOESM2_ESM.pdf (269KB, pdf)

Supplementary Material 2: Figure S1 Validation of the invtro EndMT model by WB. Figure S2 UMAP plot of EC population testing with different resolution. Figure S3 PPI network of the seven newly mesenchymal genes.

Acknowledgements

We acknowledge support by Scientific Research and Cultivation Project of Meizhou People’s Hospital (PY-C2020030).

Disclosure

The author reports no conflicts of interest in this work.

Authors’ contributions

QYH and MFH designed the study. ZKY collected data. YHG and XQZ completed qPCR and WB validation. QYH, QHH and MFH analyzed the data. QYH prepared the manuscript. MFH and QYH reviewed the manuscript. All authors were responsible for critical revisions, and all authors read and approved the final version of this work.

Funding

This work was supported by Scientific Research and Cultivation Project of Meizhou People’s Hospital (PY-C2020030).

Data availability

The datasets analyzed during the current study are publicly availability.

Declarations

Ethics approval and consent to participate

Ethical and IRB approval of this analysis were not required as no human or animal subjects were involved.

Consent for publication

All authors approved the final manuscript and gave consent for publication.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Material 1. (24.4KB, docx)
12872_2025_4571_MOESM2_ESM.pdf (269KB, pdf)

Supplementary Material 2: Figure S1 Validation of the invtro EndMT model by WB. Figure S2 UMAP plot of EC population testing with different resolution. Figure S3 PPI network of the seven newly mesenchymal genes.

Data Availability Statement

The datasets analyzed during the current study are publicly availability.

Articles from BMC Cardiovascular Disorders are provided here courtesy of BMC

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