Nature CommunicationsCell BiologyPeer-reviewed journal article
Integrated single-cell atlas of human atherosclerotic plaques
Korbinian TraeubleiD, Matthias MunziD, Jessica PauliiD, Nadja SachsiD, Eshan Vafadarnejad, Tania Carrillo‐RoaiD, Lars MäegdefesseliD, Peter KastneriD, Matthias HeinigiD
Abstract
Atherosclerosis, a major cause of cardiovascular diseases, is characterized by the buildup of lipids and chronic inflammation in the arteries, leading to plaque formation and potential rupture. The underlying causal immune mechanisms and alterations in structural cell composition and plasticity driving plaque progression remain incompletely defined. Recent advances in single-cell transcriptomics (scRNA-seq) have provided deeper insights into the roles of immune and non-immune cells in atherosclerosis. However, existing public scRNA-seq datasets often lack comprehensive cell type coverage and consistent annotations, posing challenges for downstream analyses. In this study, we present an integrated single-cell atlas of human atherosclerotic plaques, encompassing 261,747 high-quality annotated cells from carotid, coronary, and femoral arteries. By benchmarking and applying the best-performing data integration method, scPoli, we achieved robust cell type annotations validated by expert consensus and surface protein measurements. This comprehensive atlas enables accurate automatic cell type annotation of new datasets, optimal experimental design, and deconvolution of existing as well as novel bulk RNA-seq data to comprehensively determine cell type proportions in human atherosclerotic lesions. It facilitates future studies by providing an interactive WebUI for easy data annotation and experimental design, while supporting various downstream applications, including integration of genetic association studies and experimental planning.
- organism: human
- assay: scRNA-seq
- research focus: cell type annotation
- research focus: deconvolution
- research focus: database / resource
- approach: deep learning
- approach: statistical model
- Citations
- 72
- FWCI
- 17.88
- Field percentile
- top 1%
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