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Immunothrombotic Cell-Cell Communication Networks in Coronary Atherosclerosis: Critical Insights from Single-Cell and
Beata Krasińska1, Antoni Staniewski2, Oliwia Kalus2
1Department of Hypertensiology, Angiology, and Internal Medicine, Poznan University of Medical Sciences, 61-848 Poznan, Poland.
None:
Coronary artery disease (CAD) is increasingly recognized as a thromboinflammatory disorder in which innate immune activation and coagulation are tightly coupled within the plaque microenvironment. Emerging single-cell and spatial technologies have refined this paradigm by demonstrating that these processes are not diffusely distributed but instead concentrated within discrete cellular niches. This narrative review critically evaluates mechanistic and translational studies integrating single-cell RNA sequencing, spatial transcriptomics, and ligand-receptor modeling to characterize cell-cell communication networks driving immunothrombosis in CAD. Converging evidence from single-cell and spatial studies indicates substantial heterogeneity among macrophages, neutrophils, and smooth muscle cells, with functionally distinct subpopulations contributing differentially to inflammation, matrix remodeling, and thrombogenicity. Spatial analyses further demonstrate that procoagulant and inflammatory programs converge in anatomically defined high-risk regions, particularly at the plaque shoulder and sites of endothelial dysfunction. However, whether these transcriptional states represent causal drivers or epiphenomena remains unresolved. Many insights are derived from murine models or dissociated tissues, raising concerns regarding translational relevance and loss of spatial context. Additionally, computational inference of intercellular communication remains indirect and requires functional validation. In conclusion, immunothrombosis in CAD should be interpreted as an emergent property of spatially organized cellular networks rather than a uniform inflammatory state. While these approaches identify candidate therapeutic nodes, their clinical translation and the central challenge is to distinguish causal regulatory nodes from transcriptional correlates generated by high-dimensional profiling.
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