Comprehensive scRNA-seq Model Reveals Artery Endothelial Cell Heterogeneity and Metabolic Preference in Human Vascular Disease.

Abstract

Vascular disease is one of the major causes of death worldwide. Endothelial cells are important components of the vascular structure. A better understanding of the endothelial cell changes in the development of vascular disease may provide new targets for clinical treatment strategies. Single-cell RNA sequencing can serve as a powerful tool to explore transcription patterns, as well as cell type identity. Our current study is based on comprehensive scRNA-seq data of several types of human vascular disease datasets with deep-learning-based algorithm. A gene set scoring system, created based on cell clustering, may help to identify the relative stage of the development of vascular disease. Metabolic preference patterns were estimated using a graphic neural network model. Overall, our study may provide potential treatment targets for retaining normal endothelial function under pathological situations.