Single-cell RNA sequencing reveals hub genes of myocardial infarction-associated endothelial cells

Abstract

Background Myocardial infarction (MI) is a cardiovascular disease that seriously threatens human health. Dysangiogenesis of endothelial cells (ECs) primarily inhibits recovery from MI, but the specific mechanism remains to be further elucidated. Methods In this study, the single-cell RNA-sequencing data from both MI and Sham mice were analyzed by the Seurat Package (3.2.2). The number of ECs in MI and Sham groups were compared by PCA and tSNE algorithm. FindMarkers function of Seurat was used to analyze the DEGs between the MI and Sham groups. Then, the ECs was further clustered into 8 sub-clusters for trajectory analysis. The BEAM was used to analyze the branch point 3 and cluster the results. In addition, the DEGs in the microarray data set of MI and Sham mice were cross-linked, and the cross-linked genes were used to construct PPI networks. The key genes with the highest degree were identified and analyzed for functional enrichment. Finally, this study cultured human umbilical vein endothelial cells (HUVECs), established hypoxia models, and interfered with hub gene expression in cells. The impact of hub genes on the migration and tube formation of hypoxic-induced HUVECs were verified by Wound healing assays and tubule formation experiments. Results The number and proportion of ECs in the MI group were significantly lower than those in the Sham group. Meantime, 225 DEGs were found in ECs between the MI and Sham groups. Through trajectory analysis, EC4 was found to play an important role in MI. Then, by using BEAM to analyze the branch point 3, and clustering the results, a total of 495 genes were found to be highly expressed in cell Fate2 (mainly EC4). In addition, a total of 194 DEGs were identified in Micro array dataset containing both MI and Sham mice. The hub genes (Timp1 and Fn1) with the highest degree were identified. Inhibiting Timp1 and Fn1 expression promoted the migration and tube formation of HUVECs. Conclusions Our data highlighted the non-linear dynamics of ECs in MI, and provided a foothold for analyzing cardiac homeostasis and pro-angiogenesis in MI.

Genomic characteristics of adipose-derived stromal cells induced into neurons based on single-cell RNA sequencing

“…Once the overall "trajectory" of gene expression changes is known, Monocle can place each cell in the appropriate position in the trajectory [ 53 ]. After determining the differentiation starting point of ADSCs, Monocle can simulate the differentiation time of each cell, find the heat map and distribution map of gene expression that gradually increases or decreases with the differentiation time, namely Beam analysis, and then obtain the determinant genes related to differentiation [ 54 ]. Beam analysis of neuronal marker genes and stem cell marker genes in this study showed the trend of gene expression during the differentiation of ADSCs into neurons.…”