Evidence map›Paper›PMID 34629873›Full record

ReviewVascular health and risk management2021

Single-Cell RNA Sequencing (scRNA-seq) in Cardiac Tissue: Applications and Limitations.

Mingqiang Wang, Mingxia Gu, Ling Liu, Yu Liu, Lei Tian

Abstract readReview
In one paragraph

Review in Vascular health and risk management, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

15 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Mingqiang WangStanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA, 94305, USA.ORCID 0000-0003-2389-5591
Mingxia GuPerinatal Institute, Division of Pulmonary Biology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, 45229, USA.
Ling LiuDepartment of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, 94305, USA.
Yu LiuStanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA, 94305, USA.ORCID 0000-0001-7110-127X
Lei TianStanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA, 94305, USA.ORCID 0000-0002-7485-9366

Funding

Uncovering compensatory mechanisms in family members with disease causing mutations of pulmonary hypertensionR00HL135258 · NHLBI · CINCINNATI CHILDRENS HOSP MED CTR · PI GU, MINGXIA · 2020 to 2022
$747k
NHLBI NIH HHS R00 HL135258
6 · The paper itself

Abstract

Cardiovascular diseases (CVDs) are a group of disorders of the blood vessels and heart, which are considered as the leading causes of death worldwide. The pathology of CVDs could be related to the functional abnormalities of multiple cell types in the heart. Single-cell RNA sequencing (scRNA-seq) technology is a powerful method for characterizing individual cells and elucidating the molecular mechanisms by providing a high resolution of transcriptomic changes at the single-cell level. Specifically, scRNA-seq has provided novel insights into CVDs by identifying rare cardiac cell types, inferring the trajectory tree, estimating RNA velocity, elucidating the cell-cell communication, and comparing healthy and pathological heart samples. In this review, we summarize the different scRNA-seq platforms and published single-cell datasets in the cardiovascular field, and describe the utilities and limitations of this technology. Lastly, we discuss the future perspective of the application of scRNA-seq technology into cardiovascular research.

Indexed as

Sequence Analysis, RNASingle-Cell AnalysisCardiovascular DiseasesGene Expression ProfilingGenomicsHumanscardiovascular diseasescell–cell communicationclusteringRNA velocityspatial genomicstrajectory inference

Identifiers

PMID34629873
PMCPMC8495612

What OpenQuestion holds

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Registered trials

None linked

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.