A molecular classification of human mesenchymal stromal cells

Author:

Rohart Florian12,Mason Elizabeth A.1,Matigian Nicholas12,Mosbergen Rowland13,Korn Othmar1,Chen Tyrone13,Butcher Suzanne13,Patel Jatin4,Atkinson Kerry4,Khosrotehrani Kiarash45,Fisk Nicholas M.45,Lê Cao Kim-Anh2,Wells Christine A.13

Affiliation:

1. Australian Institute for Bioengineering and Nanotechnology, University of Queensland, Brisbane, Queensland, Australia

2. The University of Queensland Diamantina Institute, Translational Research Institute, University of Queensland, Brisbane, Queensland, Australia

3. Department of Anatomy and Neuroscience, Faculty of Medicine, University of Melbourne, Melbourne, Victoria, Australia

4. The University of Queensland Centre for Clinical Research, University of Queensland, Brisbane, Queensland, Australia

5. Centre for Advanced Prenatal Care, Royal Brisbane & Women’s Hospital, Brisbane, Queensland, Australia

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 simplein silicotest that accurately discriminates MSC from fibroblasts, other adult stem/progenitor cell types or differentiated stromal cells. It has been implemented in thewww.stemformatics.orgresource, 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.

Funder

Australian Research Council

ARC discovery project

JEM Research Foundation

NHMRC

NHMRC career development fellowship

National Heart Foundation Australia

Australian Cancer Research Foundation (ACRF)

Publisher

PeerJ

Subject

General Agricultural and Biological Sciences,General Biochemistry, Genetics and Molecular Biology,General Medicine,General Neuroscience

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