Integrating scRNA-seq and GWAS data reveals potentially critical endothelial cells in large artery atherosclerotic stroke.

Introduction: Stroke is a devastating cerebrovascular disease with limited treatment options. Structural and functional abnormalities in the cerebral vasculature contribute significantly to the development of stroke. Specifically, the blood-brain barrier formed by a combination of endothelial cells, smooth muscle cells, pericytes, and glial cells is proposed to play a critical role in stroke pathogenesis. However, the primary causative cell types in stroke are not well-defined. Methods: Here we integrated single-cell RNA-sequencing data from cerebrovascular zonation structures with genome-wide association study (GWAS) data of various types of strokes to analyze stroke risk SNP loci enrichment in various cell types through heritability partitioning. Fourteen vascular cell clusters were identified and profiled using cell-type expression-specific integration for complex traits (CELLEX) to assess gene expression specificity. Heritability enrichment was analyzed across three GWAS datasets: general stroke, large-artery atherosclerotic stroke (Stroke_Large_AS), and small-vessel ischemic stroke. Gene set enrichment analysis (GSEA) and pseudotime trajectory modeling (Monocle2) were performed to characterize the functional and developmental dynamics of critical endothelial subtypes. Transcriptional regulatory networks were further interrogated using SCENIC. Results: We found two specific endothelial cell subtypes play a potentially critical role in large artery atherosclerotic stroke. These subtypes demonstrated distinct expression profiles and pathway enrichments, with aEC_cluster associated with atherosclerosis and inflammatory signaling, and EC2_cluster with epithelial cell regulation and muscle tissue development. Pseudotime analysis revealed that EC2_cluster cells represent an earlier endothelial state that differentiates into the aEC_cluster, the terminal pathogenic state. Gene regulatory network analysis identified transcription factors Gata2, Irf7, and Jund as central regulators in aEC_cluster differentiation, orchestrating the upregulation of key molecules such as Fos and Nr4a1. Conclusion: Our study provides insights into the molecular mechanisms in stroke and suggests new therapeutic strategies for this debilitating disease.