Evidence map›Paper›PMID 27042394›Full record

ArticlePeerJ2016

A molecular classification of human mesenchymal stromal cells.

Florian Rohart, Elizabeth A Mason, Nicholas Matigian, Rowland Mosbergen, Othmar Korn, Tyrone Chen, Suzanne Butcher, Jatin Patel, Kerry Atkinson, Kiarash Khosrotehrani and 3 more

Open access · goldAbstract read
In one paragraph

Article in PeerJ, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed
4.4field-weighted citation impact, top 5% 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

25 citing papers in PubMed, 51 citations in OpenAlex.

  1. Integrated workflow for univariate and multivariate evaluation of batch correction reliability.Metabolomics : Official journal of the Metabolomic Society · 2026
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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

13 authors at 4 institutions in 1 country.

Florian RohartAustralian Institute for Bioengineering and Nanotechnology, University of Queensland, Brisbane, Queensland, Australia; The University of Queensland Diamantina Institute, Translational Research Institute, University of Queensland, Brisbane, Queensland, Australia.
Elizabeth A MasonAustralian Institute for Bioengineering and Nanotechnology, University of Queensland , Brisbane, Queensland , Australia.
Nicholas MatigianAustralian Institute for Bioengineering and Nanotechnology, University of Queensland, Brisbane, Queensland, Australia; The University of Queensland Diamantina Institute, Translational Research Institute, University of Queensland, Brisbane, Queensland, Australia.
Rowland MosbergenAustralian Institute for Bioengineering and Nanotechnology, University of Queensland, Brisbane, Queensland, Australia; Department of Anatomy and Neuroscience, Faculty of Medicine, University of Melbourne, Melbourne, Victoria, Australia.
Othmar KornAustralian Institute for Bioengineering and Nanotechnology, University of Queensland , Brisbane, Queensland , Australia.
Tyrone ChenAustralian Institute for Bioengineering and Nanotechnology, University of Queensland, Brisbane, Queensland, Australia; Department of Anatomy and Neuroscience, Faculty of Medicine, University of Melbourne, Melbourne, Victoria, Australia.
Suzanne ButcherAustralian Institute for Bioengineering and Nanotechnology, University of Queensland, Brisbane, Queensland, Australia; Department of Anatomy and Neuroscience, Faculty of Medicine, University of Melbourne, Melbourne, Victoria, Australia.
Jatin PatelThe University of Queensland Centre for Clinical Research, University of Queensland , Brisbane, Queensland , Australia.
Kerry AtkinsonThe University of Queensland Centre for Clinical Research, University of Queensland , Brisbane, Queensland , Australia.
Kiarash KhosrotehraniThe University of Queensland Centre for Clinical Research, University of Queensland, Brisbane, Queensland, Australia; Centre for Advanced Prenatal Care, Royal Brisbane & Women's Hospital, Brisbane, Queensland, Australia.
Nicholas M FiskThe University of Queensland Centre for Clinical Research, University of Queensland, Brisbane, Queensland, Australia; Centre for Advanced Prenatal Care, Royal Brisbane & Women's Hospital, Brisbane, Queensland, Australia.
Kim-Anh Lê CaoThe University of Queensland Diamantina Institute, Translational Research Institute, University of Queensland , Brisbane, Queensland , Australia.
Christine A WellsAustralian Institute for Bioengineering and Nanotechnology, University of Queensland, Brisbane, Queensland, Australia; Department of Anatomy and Neuroscience, Faculty of Medicine, University of Melbourne, Melbourne, Victoria, Australia.
University of Queensland · AUUniversity of Melbourne · AUTranslational Research Institute · AURoyal Brisbane and Women's Hospital · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Mesenchymal stromal cells (MSC) are widely used for the study of mesenchymal tissue repair, and increasingly adopted for cell therapy, despite the lack of consensus on the identity of these cells. In part this is due to the lack of specificity of MSC markers. Distinguishing MSC from other stromal cells such as fibroblasts is particularly difficult using standard analysis of surface proteins, and there is an urgent need for improved classification approaches. Transcriptome profiling is commonly used to describe and compare different cell types; however, efforts to identify specific markers of rare cellular subsets may be confounded by the small sample sizes of most studies. Consequently, it is difficult to derive reproducible, and therefore useful markers. We addressed the question of MSC classification with a large integrative analysis of many public MSC datasets. We derived a sparse classifier (The Rohart MSC test) that accurately distinguished MSC from non-MSC samples with >97% accuracy on an internal training set of 635 samples from 41 studies derived on 10 different microarray platforms. The classifier was validated on an external test set of 1,291 samples from 65 studies derived on 15 different platforms, with >95% accuracy. The genes that contribute to the MSC classifier formed a protein-interaction network that included known MSC markers. Further evidence of the relevance of this new MSC panel came from the high number of Mendelian disorders associated with mutations in more than 65% of the network. These result in mesenchymal defects, particularly impacting on skeletal growth and function. The Rohart MSC test is a simple in silico test that accurately discriminates MSC from fibroblasts, other adult stem/progenitor cell types or differentiated stromal cells. It has been implemented in the www.stemformatics.org resource, to assist researchers wishing to benchmark their own MSC datasets or data from the public domain. The code is available from the CRAN repository and all data used to generate the MSC test is available to download via the Gene Expression Omnibus or the Stemformatics resource.

Indexed as

Data integrationMesenchymal stromal cellsMeta-analysisSparse classificationStem cell classificationTranscriptome

Identifiers

PMID27042394
PMCPMC4811172
OpenAlexW2311949915

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.