A molecular classification of human mesenchymal stromal cells

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 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.

mixOmics: an R package for ‘omics feature selection and multiple data integration

“…Our framework proposes graphical visualisation tools to understand the identified molecular signature across all independent studies. Our own applications of the method to full data sets have showed strong potential of the method to identify reliable and robust biomarkers across independent transcriptomics studies Rohart et al (2016Rohart et al ( , 2017.…”

“…For all supervised methods, the tuning function outputs the optimal number of components that achieve the best performance based on the overall error rate or BER. The assessment is data-driven and similar to the process detailed in Rohart et al (2016), where one-sided t-tests assess whether there is a gain in performance when adding components to the model. In practice (see some of our examples in the Results Section), we found that K − 1 components, where K is the number of classes, was sufficient to achieve the best classification performance Lê Cao et al (2011); Shah et al (2016).…”

Human hepatocellular carcinomas with a periportal phenotype have the lowest potential for early recurrence after curative resection

“…S2), as described. (13) After further marker validation in the inhouse cohort by real-time PCR, we established a score to predict CTNNB1 mutations. Independently, robust HCC subclasses were identified using hierarchical cluster analysis defined by a stepwise algorithm (see Supporting Information).…”

MINT: A multivariate integrative method to identify reproducible molecular signatures across independent experiments and platforms

“…For instance, the breast cancer subtype classification relied on the PAM50 intrinsic classifier proposed by Parker et al (2009), which we admit is still controversial in the literature (Curtis et al, 2012). Similarly, the biological definition of hiPSC differs across research groups (Bilic and Belmonte, 2012;Newman and Cooper, 2010), which results in poor reproducibility among experiments and makes the integration of stem cell studies challenging (Rohart et al 2016). The expertise and exhaustive screening required to homogeneously annotate samples hinders data integration, and because it is a process upstream to the statistical analysis, data integration approaches, including MINT, can not address it.…”