Single-cell sequencing combined with Bulk RNA-Seq to explore the molecular mechanism of cell communication in atrial fibrillation

Abstract

Abstract Atrial fibrillation (AF) represents a rapid atrial arrhythmia and is associated with the potential for adverse cardiovascular outcomes, the precise pathophysiological mechanisms underpinning AF remain incompletely elucidated. In the present study, the single-cell dataset GSE224995 was retrieved from the Gene Expression Omnibus (GEO) database, and its utilization facilitated the identification of cell subtypes involved in AF. Weighted Gene Co-expression Network Analysis (WGCNA) was constructed to systematically identify crucial gene models. We also conduct comprehensive immune infiltration analysis, perform functional enrichment analysis, and elucidate the intricate associations between these pivotal genes and regulatory genes governing AF. Five cell subtypes were identified using single-cell sequencing, the most active cell subtype, tissue stem cell, was identified by cell communication analysis, and 20 gene modules were identified by WGCNA algorithm. We intersect the marker gene of tissue stem cell with the WGCNA module mostly associated AF and eventually ascertained with three key biomarkers, including are ABTB2, NAV2 and RBFOX1. These novel biomarkers for AF hold substantial promise in offering novel insights for the prevention and therapeutic intervention of this condition.