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TRUVACE RECORD VERSION
record: TRV-2026-1245
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-10-02T06:55:46.398619Z
status: published
lens: g_space
sector: health
headline: Integrative Single-Cell Transcriptomics, Multi-Omics Analyses, and Computational Pharmacology Reveal Macrophage Heterogeneity and the Putative THBS1-CD36 Axis in Coronary Atherosclerosis
dek: 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 a…
gain_title: Training 13 machine learning classifiers on the NEW10 macrophage module enabled accurate distinction of acute coronary syndrome from stable coronary artery disease and ruptured from stable plaques, with SVM linear and Naive Bayes reaching AUC 0.9333 in independent testing.
problem_title: (none)
trace_subject: (none)
gain_reading: Training 13 machine learning classifiers on the NEW10 macrophage module enabled accurate distinction of acute coronary syndrome from stable coronary artery disease and ruptured from stable plaques, with SVM linear and Naive Bayes reaching AUC 0.9333 in independent testing.
gain_evidence: 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 | we trained 13 machine learning classifiers to distinguish ACS from sCAD and evaluated their performance in differentiating ruptured from stable plaques
problem_reading: (none)
problem_evidence: (none)
quick_read: Researchers constructed a single-cell atlas of human coronary atherosclerosis and identified six macrophage subpopulations, focusing on a THBS1+ subset linked to hypoxia adaptation and TGF-β signaling. They derived a 12-module co-expression network with NEW10 as a THBS1+ signature, predicted THBS1-CD36 crosstalk with lymphatic endothelial cells, and computationally screened SMS121 against CD36.

By October 2026, the team had trained 13 machine learning classifiers on NEW10 genes to distinguish acute coronary syndrome from stable disease and ruptured from stable plaques, reporting AUC 0.9333 for SVM linear and Naive Bayes in an independent cohort. The work remains in silico, positioning the signature and predicted THBS1-CD36 axis as candidates for future mechanistic and pharmacological validation rather than deployed clinical tools.
limitation: Findings remain computational predictions requiring further experimental validation, with THBS1-CD36 interaction and SMS121-CD36 binding presented as candidates for future investigation.
tag: Evidence-backed gain
key_points: Single-cell atlas from dataset GSE131778 identified six distinct macrophage subpopulations in human coronary atherosclerosis. | THBS1+ macrophages were described as terminally differentiated, hypoxia-adaptive with elevated TGF-β signaling and FOSB-driven regulation. | High-dimensional WGCNA identified 12 modules, with module NEW10 as specific signature of THBS1+ macrophages. | Cell-cell communication predicted THBS1-CD36 ligand-receptor crosstalk between THBS1+ macrophages and lymphatic endothelial cells. | Molecular docking and 100-ns molecular dynamics predicted favorable stable interaction between SMS121 and CD36.
rundown: The study built a single-cell transcriptomic atlas of human coronary atherosclerosis from GSE131778, defining six macrophage subsets and highlighting THBS1+ macrophages as hypoxia-adaptive with TGF-β signaling. WGCNA produced 12 transcriptional modules, with NEW10 marking THBS1+ cells.

Communication analysis predicted THBS1-CD36 interaction with lymphatic endothelial cells, while docking and 100-ns MD simulations suggested SMS121 binds CD36 stably. The NEW10 signature was then used to train classifiers for ACS versus sCAD and ruptured versus stable plaque discrimination.
sources:
- peer_reviewed | Chemical Biology & Drug Design | https://doi.org/10.1111/cbdd.70399 | 2026-10-01
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