Evidence map›Paper›PMID 42811239›Full record

ArticleChemical biology & drug design2026

Integrative Single-Cell Transcriptomics, Multi-Omics Analyses, and Computational Pharmacology Reveal Macrophage Heterogeneity and the Putative THBS1-CD36 Axis in Coronary Atherosclerosis.

Jianpeng Li, Xinrui Liu, Run Shi, Lin Yang, Rong Gu

Abstract read
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Article in Chemical biology & drug design, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Jianpeng LiDepartment of Cardiology, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, China.ORCID https://orcid.org/0009-0007-1203-4960
Xinrui LiuQueen Mary College, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, China.
Run ShiDepartment of Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.ORCID https://orcid.org/0000-0003-4667-4812
Lin YangDepartment of Cardiology, Taizhou Second People's Hospital Affiliated to Yangzhou University, Taizhou, China.
Rong GuDepartment of Cardiology, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, China.ORCID https://orcid.org/0000-0002-5203-6668

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

CD36 AntigensCoronary Artery DiseaseMacrophagesThrombospondin 1HumansMachine LearningMolecular Docking SimulationMolecular Dynamics SimulationSingle-Cell AnalysisTranscriptomeCD36 AntigensCD36 protein, humanThrombospondin 1thrombospondin-1, humancoronary atherosclerosisligand‐receptor axismachine learningmacrophage heterogeneitysingle‐cell RNA sequencing

Identifiers

PMID42811239
PMCPMC13624054

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.