Integration of single-cell and bulk RNA sequencing data identified an aortic dissection-enriched ANGPTL4+ macrophage subpopulation.

BACKGROUND: Macrophages play an indispensable role in the pathogenesis of aortic dissection (AD). However, the underlying molecular and cellular mechanisms remain incompletely understood. METHODS: We integrated six publicly available single-cell RNA sequencing (scRNA-seq) datasets and constructed an atlas of AD, identifying an AD-enriched ANGPTL4+ macrophage subpopulation. Subsequent comprehensive bioinformatic analyses explored its potential mechanisms in AD. Using machine learning (ML) algorithms on integrated bulk RNA sequencing (bulk RNA-seq) data, we screened the signature genes of ANGPTL4+ macrophage and constructed a clinical prediction model. The correlation between the proportion of ANGPTL4+ macrophage and clinical features was investigated after CIBERSORTx deconvolution. Finally, we performed in vitro experiments to validate our findings. RESULTS: Bioinformatic analyses revealed that ANGPTL4+ macrophage was predominantly present in AD, exhibited a distinct glycolytic phenotype, represented an early monocyte-derived macrophage state in the dissected aorta, predicted to be regulated by PPARG, and showed inferred ligand-receptor interactions with structural cells through ANGPTL and SPP1 signaling pathways. ML algorithms identified ANGPTL4, PHLDA1, and SERPINE1 as signatures of ANGPTL4+ macrophage. The proportion of ANGPTL4+ macrophage was positively associated with clinical metrics like monocyte count. In vitro experiments validated the remarkable enrichment of ANGPTL4+ macrophage and considerable up-regulation and co-localization of the signatures. CONCLUSIONS: In-depth investigation into the potential mechanisms of ANGPTL4+ macrophage, an AD-enriched macrophage subpopulation, and its signatures ANGPTL4, PHLDA1, and SERPINE1, provides novel insights into the pathogenesis and prospective therapeutic targets of AD.