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.