Unveiling Atherosclerotic Plaque Heterogeneity and SPP1⁺/VCAN⁺ Macrophage Subtype Prognostic Significance Through Integrative Single-Cell and Bulk-Seq Analysis

Abstract

Background

Dysregulated macrophages are important causes of Atherosclerosis (AS) formation and increased plaque instability, but the heterogeneity of these plaques and the role of macrophage subtypes in plaque instability have yet to be clarified.

Methods

This study integrates single-cell and bulk-seq data to analyze atherosclerotic plaques. Unsupervised clustering was used to reveal distinct plaque subtypes, while survival analysis and gene set variation analysis (GSVA) methods helped in understanding their clinical outcomes. Enrichment of differential expression of macrophage genes (DEMGs) score and pseudo-trajectory analysis were utilized to explore the biological functions and differentiation stages of macrophage subtypes in AS progression. Additionally, CellChat and the BayesPrism deconvolution method were used to elucidate macrophage subtype interaction and their prognostic significance at single-cell resolution. Finally, the expression of biomarkers was validated in mouse experiments.

Results

Three distinct AS plaque subtypes were identified, with cluster 3 plaque subtype being particularly associated with higher immune infiltration and poorer prognosis. The DEMGs score exhibited a significant elevation in three macrophage subtypes (SPP1⁺/VCAN⁺ macrophages, IL1B⁺ macrophages, and FLT3LG⁺ macrophages), associated with cluster 3 plaque subtype and highlighted the prognostic significance of these subtypes. Activation trajectory of the macrophage subtypes is divided into three states (Pre-branch, Cell fate 1, and Cell fate 2), and Cell fate 2 (SPP1⁺/VCAN⁺ macrophages, IL1B⁺ macrophages, and FLT3LG⁺ macrophages dominant) exhibiting the highest DEMGs score, distinct interactions with other cell components, and relating to poorer prognosis of ischemic events. This study also uncovered a unique SPP1⁺/VCAN⁺ macrophage subtype, rare in quantity but significant in influencing AS progression. Machine learning algorithms identified 10 biomarkers crucial for AS diagnosis. The validation of these biomarkers was performed using Mendelian Randomization analysis and in vitro methods, supporting their relevance in AS pathology.

Conclusion

Our study provides a comprehensive view of AS plaque heterogeneity and the prognostic significance of macrophage subtypes in plaque instability.

Graphical Abstract

Keywords:

• atherosclerosis
• macrophage subtypes
• single-cell analysis
• plaque heterogeneity
• biomarkers
• pseudo-trajectory analysis

Data Sharing Statement

Publicly available datasets were analyzed in this study. This data can be found here: The processed scRNA-seq dataset was deposited at the Gene expression omnibus (GEO) data repository under the accession code GSE159677 and GSE155512. The bulk RNA-seq dataset of the AS and AAS cohort could be downloaded from GEO under the accession numbers GSE21545, GSE41571, GSE43292, GSE28829, and GSE163154. The remaining data are available from the authors upon reasonable request.

Ethics Statement

The study was approved by The Second Affiliated Hospital of Kunming Medical University (No. PJ-2022-14) and was carried out in accordance with the Declaration of Helsinki. The animal experimental protocols were reviewed and approved by the Laboratory Animal Welfare Ethics Committee of Yunnan University (approval no. YNU20220411). This animal study complies with The Guide for the Care and Use of Laboratory Animals.

Acknowledgments

We would like to acknowledge reviewers and editors for their helpful comments on this paper.

Author Contributions

All authors contributed to data analysis, drafting or revising the article, have agreed on the journal to which the article will be submitted, gave final approval of the version to be published, and agree to be accountable for all aspects of the work.

Disclosure

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Additional information

Funding

This study was supported by grants from the Yunnan Province high-level personnel Training support program (No. YNWR-MY-2020-024), Major Project of Yunnan Science and Technology Program (202002AA100007, 202102AA100007-3), Scientific Research Project of The Second Affiliated Hospital of Kunming Medical University (No. 2021yk13), and Medical Reserve Personnel Training Program of Yunnan Provincial Health and Family Planning Commission (No. H-2017019).