ReviewFrontiers in cardiovascular medicine2018
Single Cell Gene Expression to Understand the Dynamic Architecture of the Heart.
Review in Frontiers in cardiovascular medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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
8 citing papers in PubMed, 18 citations in OpenAlex.
- Metformin inhibits proliferation of residing fibroadipogenic progenitor cells from failing human hearts.ESC heart failure · 2026Article
- Article
- Recent Advances in Single-Cell Profiling and Multispecific Therapeutics: Paving the Way for a New Era of Precision Medicine Targeting Cardiac Fibroblasts.Current cardiology reports · 2021Review
- Single Cell RNA Sequencing in Autoimmune Inflammatory Rheumatic Diseases: Current Applications, Challenges and a Step Toward Precision Medicine.Frontiers in medicine · 2021Review
- Understanding the Adult Mammalian Heart at Single-Cell RNA-Seq Resolution.Frontiers in cell and developmental biology · 2021Review
- Single-cell RNA Sequencing in Immunology.Current genomics · 2020Review
- Integrative Cluster Analysis of Whole Hearts Reveals Proliferative Cardiomyocytes in Adult Mice.Cells · 2020Article
- Cardiac Remodeling in Chronic Kidney Disease.Toxins · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 3 institutions in 1 country.
Funding
Abstract
The recent development of single cell gene expression technologies, and especially single cell transcriptomics, have revolutionized the way biologists and clinicians investigate organs and organisms, allowing an unprecedented level of resolution to the description of cell demographics in both healthy and diseased states. Single cell transcriptomics provide information on prevalence, heterogeneity, and gene co-expression at the individual cell level. This enables a cell-centric outlook to define intracellular gene regulatory networks and to bridge toward the definition of intercellular pathways otherwise masked in bulk analysis. The technologies have developed at a fast pace producing a multitude of different approaches, with several alternatives to choose from at any step, including single cell isolation and capturing, lysis, RNA reverse transcription and cDNA amplification, library preparation, sequencing, and computational analyses. Here, we provide guidelines for the experimental design of single cell RNA sequencing experiments, exploring the current options for the crucial steps. Furthermore, we provide a complete overview of the typical data analysis workflow, from handling the raw sequencing data to making biological inferences. Significantly, advancements in single cell transcriptomics have already contributed to outstanding exploratory and functional studies of cardiac development and disease models, as summarized in this review. In conclusion, we discuss achievable outcomes of single cell transcriptomics' applications in addressing unanswered questions and influencing future cardiac clinical applications.
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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.