Single-cell and bulk transcriptome analyses revealed the role of macrophage cholesterol metabolism in atherosclerosis
Brief intro:
- Author: Jiaxing Ke, Shuling Chen, Lingjia Li, Chenxin Liao, Feng Peng, Dajun Chai & Jinxiu Lin
- Journal: Lipids in Health and Disease
- Doi: https://www.doi.org/10.1186/s12944-025-02773-6
- Publication Date: 2025/11/26
Abstract
Background
Atherosclerosis (AS) is a complex cardiovascular disease characterized by dysregulated macrophage cholesterol metabolism (CM), a central driver of foam cell formation and plaque progression. However, how macrophage CM becomes dysregulated is still not fully understood. Single-cell RNA sequencing (scRNA-seq) was combined with bulk RNA-seq data to identify CM-related genes with diagnostic and therapeutic potential.
Methods
Data for this study were sourced from Gene Expression Omnibus (GEO), comprising one scRNA-seq dataset and several bulk mRNA transcriptomic datasets. ScRNA-seq was utilized to investigate the heterogeneity of CM in different cells in AS-affected tissues and identify genes associated with macrophage CM. For the bulk RNA-seq dataset, machine learning was applied to identify key genes tied to macrophage CM. A risk scoring model was derived with logistic regression and validated externally. Furthermore, in vitro experiments were conducted to validate the expression levels of key genes, and FILIP1L was overexpressed to investigate its effects on macrophage CM.
Results
Analysis of a scRNA-seq dataset employing diverse scoring algorithms revealed a significant increase in CM activity during the lipid plaque stage, particularly in macrophages. By employing machine learning algorithms to analyse bulk RNA-seq data, three feature genes, FABP4, RNASET2, and FILIP1L, were identified as potential hallmark genes for AS. A risk score model constructed with three feature genes demonstrated high accuracy across multiple external datasets. Additionally, these genes were found to be correlated with immune cell infiltration, suggesting their involvement in the immune response to AS. Consensus clustering analysis revealed distinct CM patterns in patients, with Cluster 1 showing increased immune and inflammatory activity. The three feature genes were closely associated with the progression of AS and were implicated in the SPP1 pathway. Cellular experiments confirmed the differential expression of these genes in macrophages before and after intervention with oxidized low-density lipoprotein (oxLDL). FILIP1L overexpression reduces the accumulation of oxLDL in macrophages.
Conclusion
This study provides a comprehensive understanding of macrophage CM in AS and highlights the potential of FABP4, RNASET2, and FILIP1L as diagnostic hallmark genes and therapeutic targets.
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