A molecular classification of human mesenchymal stromal cells

Rohart, Florian, Mason, Elizabeth A., Matigian, Nicholas, Mosbergen, Rowland, Korn, Othmar, Chen, Tyrone, Butcher, Suzanne, Patel, Jatin, Atkinson, Kerry, Khosrotehrani, Kiarash, Fisk, Nicholas M., Cao, Kim-Anh Le and Wells, Christine A. (2016) A molecular classification of human mesenchymal stromal cells. PeerJ, 2016 . doi:10.7717/peerj.1845


Author Rohart, Florian
Mason, Elizabeth A.
Matigian, Nicholas
Mosbergen, Rowland
Korn, Othmar
Chen, Tyrone
Butcher, Suzanne
Patel, Jatin
Atkinson, Kerry
Khosrotehrani, Kiarash
Fisk, Nicholas M.
Cao, Kim-Anh Le
Wells, Christine A.
Title A molecular classification of human mesenchymal stromal cells
Journal name PeerJ   Check publisher's open access policy
ISSN 2167-8359
Publication date 2016-03-24
Year available 2016
Sub-type Article (original research)
DOI 10.7717/peerj.1845
Open Access Status DOI
Volume 2016
Total pages 23
Place of publication London, United Kingdom
Publisher PeerJ
Collection year 2017
Language eng
Formatted 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 distinguishedMSCfrom 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 RohartMSCtest is a simple in silico test that accurately discriminatesMSC 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.
Keyword Data integration
Mesenchymal stromal cells
Meta-analysis
Sparse classification
Stem cell classification
Transcriptome
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

 
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