Evidence map›Paper›PMID 35646075›Full record

ReviewFrontiers in genetics2022

Heterogeneous Skeletal Muscle Cell and Nucleus Populations Identified by Single-Cell and Single-Nucleus Resolution Transcriptome Assays.

Katherine Williams, Kyoko Yokomori, Ali Mortazavi

Open access · goldAbstract readReview
In one paragraph

Review in Frontiers in genetics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.

0numbers the graph read from it
0cells of the map it votes in
31citing papers in PubMed
4.2field-weighted citation impact, top 4% of its field
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

31 citing papers in PubMed, 40 citations in OpenAlex.

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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

3 authors at 1 institution in 1 country.

Katherine WilliamsDepartment of Developmental and Cell Biology, University of California, Irvine, Irvine, CA, United States.
Kyoko YokomoriDepartment of Biological Chemistry, School of Medicine, University of California, Irvine, Irvine, CA, United States.
Ali MortazaviDepartment of Developmental and Cell Biology, University of California, Irvine, Irvine, CA, United States.
University of California, Irvine · US

Funding

Genetic and epigenetic mechanisms of FSHD pathogenesisR01AR071287 · NIAMS · UNIVERSITY OF CALIFORNIA-IRVINE · PI Seyed Ali Mortazavi, Kyoko Yokomori · 2017 to 2026
$4.6M
NIAMS NIH HHS R01 AR071287
6 · The paper itself

Abstract

Single-cell RNA-seq (scRNA-seq) has revolutionized modern genomics, but the large size of myotubes and myofibers has restricted use of scRNA-seq in skeletal muscle. For the study of muscle, single-nucleus RNA-seq (snRNA-seq) has emerged not only as an alternative to scRNA-seq, but as a novel method providing valuable insights into multinucleated cells such as myofibers. Nuclei within myofibers specialize at junctions with other cell types such as motor neurons. Nuclear heterogeneity plays important roles in certain diseases such as muscular dystrophies. We survey current methods of high-throughput single cell and subcellular resolution transcriptomics, including single-cell and single-nucleus RNA-seq and spatial transcriptomics, applied to satellite cells, myoblasts, myotubes and myofibers. We summarize the major myonuclei subtypes identified in homeostatic and regenerating tissue including those specific to fiber type or at junctions with other cell types. Disease-specific nucleus populations were found in two muscular dystrophies, FSHD and Duchenne muscular dystrophy, demonstrating the importance of performing transcriptome studies at the single nucleus level in muscle.

Indexed as

myonuclei heterogeneitysingle-cell RNA-seqsingle-nucleus RNA-seqskeletal musclespatial transcriptomics

Identifiers

PMID35646075
PMCPMC9136090
OpenAlexW4280580547

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.