ReviewNature reviews. Cardiology2023
Single-cell transcriptomics for the assessment of cardiac disease.
Review in Nature reviews. Cardiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 81 papers, 1 of them a synthesis that pooled 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.
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
81 citing papers in PubMed, 1 synthesis or guideline pooled it, 111 citations in OpenAlex.
- Pooled it
- Genetics-Informed Mapping Identifies a CRIM1-Associated Endocardial Inflammatory Remodeling State in Acute Myocardial Infarction.Medical science monitor : international medical journal of experimental and clinical research · 2026Article
- Review
- Preemptive cardioprotection with a small molecule in rodents that suppresses genes predictive of heart failure.Science advances · 2026Article
- The pleuroparenchymal fibroelastosis atlas reveals aberrant cell states and their zonation as an alternate roadmap to lung fibrosis.Science advances · 2026Article
- Advanced in vitro cardiac models for drug evaluation: integration of organoids, engineered tissues, and microphysiological systems.Microsystems & nanoengineering · 2026Review
- Decoding the Cardiac Immune Microenvironment and Fibroblast Crosstalk in Radiotherapy Combined with Immunotherapy-Induced Cardiac Fibrosis Based on Single-Cell Transcriptomic Analysis.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- An integrative single-nucleus multiomic atlas of the human left ventricle identifies gene regulatory network dynamics across cardiac development, aging, and disease.Genome biology · 2026Article
- Cardiac Macrophages and Fibroblasts Modulate Atrial Fibrillation Maintenance.Circulation research · 2026Article
- stSCI: A multi-task learning framework for integrative analysis of single-cell and spatial transcriptomics data.Innovation (Cambridge (Mass.)) · 2026Article
- Single-Cell Multimodal Profiling Highlights Persistent Aortic Smooth Muscle Cell Changes in Diabetic Mice Despite Glycemic Control.Arteriosclerosis, thrombosis, and vascular biology · 2026Article
- Single-Cell Multi-Omics Profiling of Human Septal Myectomy Tissue: Toward Precision Medicine in Obstructive Hypertrophic Cardiomyopathy.Journal of personalized medicine · 2026Review
- Single cell transcriptional perturbome in pluripotent stem cell models.Molecular systems biology · 2026Article
- Cross-dataset transcriptomic analyses identify a conserved ENPP2+ macrophage-fibroblast activation axis in hypertrophic cardiomyopathy.Briefings in bioinformatics · 2026Article
- Decoding Dementia Mechanisms: Identification of Key Oligodendrocyte- Associated Genes through Integrative Bioinformatics and Machine Learning.Current topics in medicinal chemistry · 2026Article
- Micro Zhēngjiǎ as a reversible microstructural lesion network in diabetic myocardial fibrosis: mechanistic insights and therapeutic implications of Huoxue-Tongluo therapy.American journal of translational research · 2026Review
- CellPredX, a computational framework for cross-data type, cross-sample, and cross-protocol cell type annotation through domain adaptation and deep metric learning.PLoS computational biology · 2026Article
- Unraveling the cellular characteristics of cardiomyopathy with rare variant-driven gene signatures using multi-omics analysis.Scientific reports · 2025Article
- A novel paradigm for single-cell annotation in stem cell research.Stem cell reports · 2025Review
- Glia of the heart's nervous system.Nature reviews. Neuroscience · 2025Review
21 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 6 institutions in 4 countries.
Funding
Abstract
Cardiovascular disease is the leading cause of death globally. An advanced understanding of cardiovascular disease mechanisms is required to improve therapeutic strategies and patient risk stratification. State-of-the-art, large-scale, single-cell and single-nucleus transcriptomics facilitate the exploration of the cardiac cellular landscape at an unprecedented level, beyond its descriptive features, and can further our understanding of the mechanisms of disease and guide functional studies. In this Review, we provide an overview of the technical challenges in the experimental design of single-cell and single-nucleus transcriptomics studies, as well as a discussion of the type of inferences that can be made from the data derived from these studies. Furthermore, we describe novel findings derived from transcriptomics studies for each major cardiac cell type in both health and disease, and from development to adulthood. This Review also provides a guide to interpreting the exhaustive list of newly identified cardiac cell types and states, and highlights the consensus and discordances in annotation, indicating an urgent need for standardization. We describe advanced applications such as integration of single-cell data with spatial transcriptomics to map genes and cells on tissue and define cellular microenvironments that regulate homeostasis and disease progression. Finally, we discuss current and future translational and clinical implications of novel transcriptomics approaches, and provide an outlook of how these technologies will change the way we diagnose and treat heart disease.
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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.