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.

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Stemformatics is an established gene expression data portal containing over 420 public gene expression datasets derived from microarray, RNA sequencing and single cell profiling technologies. Developed for the stem cell community, it has a major focus on pluripotency, tissue stem cells, and staged differentiation. Stemformatics includes curated ‘collections’ of data relevant to cell reprogramming, as well as hematopoiesis and leukaemia. Rather than simply rehosting datasets as they appear in public repositories, Stemformatics uses a stringent set of quality control metrics and its own pipelines to process handpicked datasets from raw files. This means that about 30% of datasets processed by Stemformatics fail the quality control metrics and never make it to the portal, ensuring that Stemformatics data are of high quality and have been processed in a consistent manner. Stemformatics provides easy-to-use and intuitive tools for biologists to visually explore the data, including interactive gene expression profiles, principal component analysis plots and hierarchical clusters, among others. The addition of tools that facilitate cross-dataset comparisons provides users with snapshots of gene expression in multiple cell and tissues, assisting the identification of cell-type restricted genes, or potential housekeeping genes. Stemformatics is freely available at stemformatics.org.

Stromal support is critical for lung homeostasis and the maintenance of an effective epithelial barrier. Despite this, previous studies have found a positive association between the number of mesenchymal stromal cells (MSCs) isolated from the alveolar compartment and human lung diseases associated with epithelial dysfunction. We hypothesised that bronchoalveolar lavage derived MSCs (BAL-MSCs) are dysfunctional and distinct from resident lung tissue MSCs (LT-MSCs). In this study, we comprehensively interrogated the phenotype and transcriptome of human BAL-MSCs and LT-MSCs. We found that MSCs were rarely recoverable from the alveolar space in healthy humans, but could be readily isolated from lung transplant recipients by bronchoalveolar lavage. BAL-MSCs exhibited a CD90 , CD73 , CD45 , CD105 immunophenotype and were bipotent, lacking adipogenic potential. In contrast, MSCs were readily recoverable from healthy human lung tissue and were CD90 , CD73 , CD45 , CD105 and had full tri-lineage potential. Transcriptional profiling of the two populations confirmed their status as bona fide MSCs and revealed a high degree of similarity between each other and the archetypal bone-marrow MSC. 105 genes were differentially expressed; 76 of which were increased in BAL-MSCs including genes involved in fibroblast activation, extracellular matrix deposition and tissue remodelling. Finally, we found the fibroblast markers collagen 1A1 and α-smooth muscle actin were increased in BAL-MSCs. Our data suggests that in healthy humans, lung MSCs reside within the tissue, but in disease can differentiate to acquire a profibrotic phenotype and migrate from their in-tissue niche into the alveolar space. Stem Cells 2016;34:2548-2558.