ReviewJournal of lipid and atherosclerosis2019
Single Cell RNA-Sequencing for the Study of Atherosclerosis.
Review in Journal of lipid and atherosclerosis, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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
10 citing papers in PubMed.
- Boosting Sensory Nerve-to-Bone Interactions Enhances Hedgehog Mediated Calvarial Bone Repair.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Single-Cell Analysis, Spatial Transcriptomics and Molecular Docking Unveil Potential Therapeutic Targets for Carotid Atherosclerosis.Balkan medical journal · 2026Article
- Single-cell RNA sequencing in human atherosclerotic plaques reveals a novel smooth muscle cell subtype that possesses multi differentiation potential and shapes the microenvironment.Clinical and experimental medicine · 2025Article
- Unveiling Atherosclerotic Plaque Heterogeneity and SPP1Journal of inflammation research · 2024Article
- Roles of non-coding RNAs in intercellular crosstalk in cardiovascular diseases.The Korean journal of physiology & pharmacology : official journal of the Korean Physiological Society and the Korean Society of Pharmacology · 2023Review
- Vulnerable Atherosclerotic Plaque: Is There a Molecular Signature?International journal of molecular sciences · 2022Review
- Identification of diagnostic biomarkers and therapeutic targets in peripheral immune landscape from coronary artery disease.Journal of translational medicine · 2022Article
- Survey of Approaches for Investigation of Atherosclerosis In Vivo.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Heterogeneity of immune cells in human atherosclerosis revealed by scRNA-Seq.Cardiovascular research · 2021Review
- Harnessing Single-Cell RNA Sequencing to Better Understand How Diseased Cells Behave the Way They Do in Cardiovascular Disease.Arteriosclerosis, thrombosis, and vascular biology · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Atherosclerosis is a major cause of coronary artery disease and stroke. A massive and new type of data has finally arrived in the field of atherosclerosis: single cell RNA sequencing (scRNAseq). Recently, scRNAseq has been successfully applied to the study of atherosclerosis to identify previously uncharacterized cell populations. scRNAseq is an effective approach to evaluate heterogeneous cell populations by measuring the transcriptomic profiles at the single cell level. Besides the studies of atherosclerosis, scRNAseq is being employed in various areas of biology, including cancer research and organ development. In order to analyze these new massive datasets, various analytic approaches have been developed. This review aims to enhance the understanding of this new technology by exploring how the single cell transcriptome has been applied to the study of atherosclerosis and further discuss potential analysis of using scRNAseq.
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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.