bioRxivBioinformaticsPreprint
Single-cell RNA sequencing reveals aortic cellular heterogeneity in MSC_ITGB3-treated ApoE-/- mice on a high-fat diet
Haijuan HuiD, Demin LiuiD, Xue Hui GengiD, Mei HaniD, Wei CuiiD
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Abstract
The aorta contains various cell types that are involved in the development of vascular inflammation and atherosclerosis. However, the cellular atlas of heterogeneous aorta cells, cellular responses, and intercellular communication has not been investigated in the background of a high-fat diet (HFD) and treatment with integrin beta 3-modified mesenchymal stem cells (MSC_ITGB3). In this study, 33,782 individual cells from mouse aortas under HFD with or without MSC_ITGB3 treatment were subjected to single-cell RNA sequencing as an unbiased analysis strategy. We generated a compendium of 30 different clusters, mainly smooth muscle cells (SMCs), endothelial cells, and immune cells. The proportion of the different cell types was considerably influenced by HFD and MSC_ITGB3. In the HFD group, genes associated with proliferation, migration, and collagen were highly expressed in the major SMC subpopulations. However, the expression of contraction-related genes in SMC subpopulations was significantly higher in the MSC_ITGB3 group than in the HFD group. After HFD consumption, subpopulations of ECs with active PI3K-Akt signaling pathway, ECM-receptor interaction, and contraction-related genes were significantly enriched. In the MSC_ITGB3 group, the number of dendritic cells (DCs), which are positively correlated with atherosclerotic lesion progression and contribute to lipid accumulation, and levels of inflammatory factors notably decreased. Our findings provide data on the composition, signaling pathways, and cellular communication of the aorta cells following stem cell treatment as well as on the evolution and progression of atherosclerotic disease. The findings may help in improving the treatment of atherosclerosis.
- organism: mouse
- assay: scRNA-seq
- research focus: cell type annotation
- research focus: differential expression
- research focus: cell-cell communication
- approach: statistical model
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