Single-Cell Analysis of the Normal Mouse Aorta Reveals Functionally Distinct Endothelial Cell Populations
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA729675 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR14521580_1.fastq.gz | Fastqsanger.gz | |||
| SRR14521580_2.fastq.gz | Fastqsanger.gz | |||
| SRR14521581_1.fastq.gz | Fastqsanger.gz | |||
| SRR14521581_2.fastq.gz | Fastqsanger.gz | |||
| SRR14521582_1.fastq.gz | Fastqsanger.gz |
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Project description:The cells that form the arterial wall contribute to multiple vascular diseases. The extent of cellular heterogeneity within these populations has not been fully characterized. Recent advances in single-cell RNA-sequencing make it possible to identify and characterize cellular subpopulations. Clustering analysis of gene expression from aortic cells identified 10 populations of cells representing each of the main arterial cell types: fibroblasts, vascular smooth muscle cells, endothelial cells (ECs), and immune cells, including monocytes, macrophages, and lymphocytes. The most significant cellular heterogeneity was seen in the 3 distinct EC populations. Gene set enrichment analysis of these EC subpopulations identified a lymphatic EC cluster and 2 other populations more specialized in lipoprotein handling, angiogenesis, and extracellular matrix production. These subpopulations persist and exhibit similar changes in gene expression in response to a Western diet. Immunofluorescence for Vcam1 and Cd36 demonstrates regional heterogeneity in EC populations throughout the aorta. We present a comprehensive single-cell atlas of all cells in the aorta. By integrating expression from >1900 genes per cell, we are better able to characterize cellular heterogeneity compared with conventional approaches. Gene expression signatures identify cell subpopulations with vascular disease-relevant functions.
2021-05-14 | GSE174384 | GEO
Project description:BackgroundThe cells that form the arterial wall contribute to multiple vascular diseases. The extent of cellular heterogeneity within these populations has not been fully characterized. Recent advances in single-cell RNA-sequencing make it possible to identify and characterize cellular subpopulations.MethodsWe validate a method for generating a droplet-based single-cell atlas of gene expression in a normal blood vessel. Enzymatic dissociation of 4 whole mouse aortas was followed by single-cell sequencing of >10 000 cells.ResultsClustering analysis of gene expression from aortic cells identified 10 populations of cells representing each of the main arterial cell types: fibroblasts, vascular smooth muscle cells, endothelial cells (ECs), and immune cells, including monocytes, macrophages, and lymphocytes. The most significant cellular heterogeneity was seen in the 3 distinct EC populations. Gene set enrichment analysis of these EC subpopulations identified a lymphatic EC cluster and 2 other populations more specialized in lipoprotein handling, angiogenesis, and extracellular matrix production. These subpopulations persist and exhibit similar changes in gene expression in response to a Western diet. Immunofluorescence for Vcam1 and Cd36 demonstrates regional heterogeneity in EC populations throughout the aorta.ConclusionsWe present a comprehensive single-cell atlas of all cells in the aorta. By integrating expression from >1900 genes per cell, we are better able to characterize cellular heterogeneity compared with conventional approaches. Gene expression signatures identify cell subpopulations with vascular disease-relevant functions.
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Project description:Background: Acute thoracic aortic dissection (ATAD) is a fatal condition characterized by tear of intima and formation of false lumen whereby leading to the rupture of aorta. To better understand the potential mechanism of ATAD, we analyzed characterization of different cell types between ATAD and control group and predicted interactions among different cell types which might promote the progression of ATAD. Methods: Single-cell RNA sequencing (scRNA-seq) was performed including 5 patients with ATAD and 4 healthy controls. Cells were digested from ascending aortic tissues and subpopulations of most cell types were identified on the basis of scRNA-seq data. The gene expression profiles of each cell type and related changes between ATAD and control group were analyzed via bioinformatic analysis. We also predicted potential interaction among different cell types and verified the results by immunohistochemistry (IHC) and immunofluorescence (IF). Results: We got 8 cell types from human ascending aorta and 50 subpopulations were identified after re-clustering including vascular smooth muscle cells (VSMCs), endothelial cells, fibroblasts, neutrophils, monocytes and macrophages. Higher proportion of synthetic VSMCs, dysfunctional neutrophils and monocytes were seen in ATAD group compared with control group. Next, we predicted cell differentiation trajectory of VSMCs, fibroblasts and neutrophils via CytoTRACE, then identified subpopulations with higher differentiation potential. Moreover, the hub genes of VSMCs, fibroblasts and neutrophils were examined by weighted gene co-expression network analysis. Further analysis revealed that six transmembrane epithelial antigen of prostate 4 metalloreductase (STEAP4) might be a new marker for isolation of synthetic VSMCs by verification with IHC and IF. We also found synthetic VSMCs-derived C-X-C motif chemokine ligand 12 (CXCL12) might interact with neutrophils and fibroblasts via combination with C-X-C motif chemokine receptor 4 (CXCR4) and atypical chemokine receptor 3 (ACKR3), respectively, which might recruit neutrophils and induce transdifferentitation of FBs into synthetic VSMCs. Conclusion: In this study, we demonstrated integrative composition of different cell types in normal and dissected human ascending aorta and identified a new marker favorable to isolation of synthetic VSMCs. Furthermore, we also proposed a potential mechanism that synthetic VSMCs could interact with neutrophils and fibroblasts via CXCL12-CXCR4/ACKR3 axis whereby deteriorating the progression of ATAD. In total, these findings in our study might provide new insights to better understand the development and progression of ATAD.
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