Evidence map›Paper›PMID 28247360›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2017

Systematic Identification of Genes Regulating Muscle Stem Cell Self-Renewal and Differentiation.

Krishnamoorthy Sreenivasan, Thomas Braun, Johnny Kim

Open access · hybridAbstract read
PubMed Publisher
In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
2.3field-weighted citation impact, top 13% 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

3 citing papers in PubMed, 5 citations in OpenAlex.

  1. Article
  2. Article
  3. Review
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.

Krishnamoorthy SreenivasanDepartment of Cardiac Development and Remodelling, Max Planck Institute for Heart and Lung Research, Ludwigstraße 43, 61231, Bad Nauheim, Germany.
Thomas BraunDepartment of Cardiac Development and Remodelling, Max Planck Institute for Heart and Lung Research, Ludwigstraße 43, 61231, Bad Nauheim, Germany.
Johnny KimDepartment of Cardiac Development and Remodelling, Max Planck Institute for Heart and Lung Research, Ludwigstraße 43, 61231, Bad Nauheim, Germany.
Max Planck Institute for Heart and Lung Research · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The hallmark of stem cells is their capability to either self-renew or to differentiate into a different cell type. Adult skeletal muscle contains a resident muscle stem cell population (MuSCs) known as satellite cells, which enables regeneration of damaged muscle tissue throughout most of adult life. During skeletal muscle regeneration, few MuSCs self-renew to maintain the muscle stem cell pool while others expand rapidly and subsequently undergo myogenic differentiation to form new myofibers. However, like for other stem cell types, the molecular networks that govern self-renewal and/or differentiation of MuSCs remain largely elusive. We recently reported a method to isolate sufficient amounts of purified MuSCs from skeletal muscle which enables us to study their cell autonomous properties. Here, we describe a lentiviral, image-based loss-of function screening pipeline on primary MuSCs that enables systematic identification of genes that regulate muscle stem cell function.

Indexed as

BiomarkersCell DifferentiationCell Self RenewalFlow CytometryGene Expression ProfilingGene LibraryGenetic VectorsHEK293 CellsHumansImmunophenotypingLentivirusMuscle, SkeletalRNA InterferenceRNA, Small InterferingStem CellsTransduction, GeneticBiomarkersRNA, Small InterferingFAC sortingLentiviralMuscle stem cells (MuSCs)RNAi screen

Identifiers

PMID28247360
OpenAlexW2592996972

What OpenQuestion holds

Textmetadata
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.