ArticlemedRxiv : the preprint server for health sciences2023
High-Dimensional Single-Cell Multimodal Landscape of Human Carotid Atherosclerosis.
Article in medRxiv : the preprint server for health sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed, 11 citations in OpenAlex.
- Foam Cells: The Origin of Formation and Target for Plant-derived Bioactive Compounds.Current medicinal chemistry · 2026Review
Corrections and comments
- Updated by
Authors and funding
16 authors at 4 institutions in 3 countries.
Funding
Abstract
Background: Atherosclerotic plaques are complex tissues composed of a heterogeneous mixture of cells. However, we have limited understanding of the comprehensive transcriptional and phenotypical landscape of the cells within these lesions. Methods: To characterize the landscape of human carotid atherosclerosis in greater detail, we combined cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) and single-cell RNA sequencing (scRNA-seq) to classify all cell types within lesions (n=21; 13 symptomatic) to achieve a comprehensive multimodal understanding of the cellular identities of atherosclerosis and their association with clinical pathophysiology. Results: We identified 25 distinct cell populations each having a unique multi-omic signature, including macrophages, T cells, NK cells, mast cells, B cells, plasma cells, neutrophils, dendritic cells, endothelial cells, fibroblasts, and smooth muscle cells (SMCs). Within the macrophage populations, we identified 2 proinflammatory subsets that were enriched in IL1B or C1Q expression, 2 distinct TREM2 positive foam cell subsets, one of which also expressed inflammatory genes, as well as subpopulations displaying a proliferative gene expression signature and one expressing SMC-specific genes and upregulation of fibrotic pathways. An in-depth characterization uncovered several subsets of SMCs and fibroblasts, including a SMC-derived foam cell. We localized this foamy SMC to the deep intima of coronary atherosclerotic lesions. Using CITE-seq data, we also developed the first flow cytometry panel, using cell surface proteins CD29, CD142, and CD90, to isolate SMC-derived cells from lesions. Last, we found that the proportion of efferocytotic macrophages, classically activated endothelial cells, contractile and modulated SMC-derived cell types were reduced, and inflammatory SMCs were enriched in plaques of clinically symptomatic vs. asymptomatic patients. Conclusions: Our multimodal atlas of cell populations within atherosclerosis provides novel insights into the diversity, phenotype, location, isolation, and clinical relevance of the unique cellular composition of human carotid atherosclerosis. This facilitates both the mapping of cardiovascular disease susceptibility loci to specific cell types as well as the identification of novel molecular and cellular therapeutic targets for treatment of the disease.
Identifiers
What OpenQuestion holds
Registered trials
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.