Our findings support an entirely novel endothelial hierarchy, from EVP to TA to D, as defined by self-renewal, differentiation, and molecular profiling of an endothelial progenitor. This paradigm shift in our understanding of vascular-resident endothelial progenitors in tissue regeneration opens new avenues for better understanding of cardiovascular biology.

Highlights d scRNA sequencing reveals two distinct aortic endothelial populations d Sequencing analysis confirms transitionary cells rather than discrete populations d Endovascular progenitors are slow cycling and have distinct mitochondrial content

The term placenta is a highly vascularized tissue and is usually discarded upon birth. Our objective was to isolate clinically relevant quantities of fetal endothelial colony-forming cells (ECFCs) from human term placenta and to compare them to the well-established donor-matched umbilical cord blood (UCB)-derived ECFCs. A sorting strategy was devised to enrich for CD45-CD34+CD31Lo cells prior to primary plating to obtain pure placental ECFCs (PL-ECFCs) upon culture. UCB-ECFCs were derived using a well-described assay. PL-ECFCs were fetal in origin and expressed the same cell surface markers as UCB-ECFCs. Most importantly, a single term placenta could yield as many ECFCs as 27 UCB donors. PL-ECFCs and UCB-ECFCs had similar in vitro and in vivo vessel forming capacities and restored mouse hind limb ischemia in similar proportions. Gene expression profiles were only minimally divergent between PL-ECFCs and UCB-ECFCs, probably reflecting a vascular source versus a circulating source. Finally, PL-ECFCs and UCB-ECFCs displayed similar hierarchies between high and low proliferative colonies. We report a robust strategy to isolate ECFCs from human term placentas based on their cell surface expression. This yielded much larger quantities of ECFCs than UCB, but the cells were comparable in immunophenotype, gene expression, and in vivo functional ability. We conclude that PL-ECFCs have significant bio-banking and clinical translatability potential.

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.