Integrative Single-Cell Transcriptomics, Multi-Omics Analyses, and Computational Pharmacology Reveal Macrophage...

Coronary atherosclerosis (CA) is characterized by profound macrophage heterogeneity that drives plaque progression and vulnerability, yet the precise subpopulations and their niche-specific functions remain incompletely defined. Here, we constructed a single-cell transcriptomic atlas of human CA using dataset GSE131778 and identified six distinct macrophage subpopulations. Among these, the THBS1+ macrophage subset emerged as a terminally differentiated, hypoxia-adaptive population with elevated TGF-β signaling and FOSB-driven transcriptional regulation. High-dimensional weighted gene co-expression network analysis revealed 12 transcriptional modules, with module NEW10 serving as a specific molecular signature of THBS1+ macrophages. Cell-cell communication analysis predicted a THBS1-CD36 ligand-receptor interaction potentially contributing to crosstalk between THBS1+ macrophages and lymphatic endothelial cells. Molecular docking and 100-ns molecular dynamics simulations predicted a thermodynamically favorable and conformationally stable interaction between SMS121 and CD36, providing an initial computational rationale for further experimental evaluation. Finally, leveraging the NEW10 module genes, we trained 13 machine learning classifiers to distinguish ACS from sCAD and evaluated their performance in differentiating ruptured from stable plaques; Support Vector Machine with linear kernel and Naive Bayes achieved the highest accuracy in an independent testing cohort, both attaining an AUC of 0.9333. These findings illuminate macrophage heterogeneity and intercellular communication in CA, identify the predicted THBS1-CD36 interaction as a candidate for further mechanistic and pharmacological investigation, and establish a macrophage-derived transcriptional signature for precise risk stratification.