Single-Cell Transcriptome Atlas of Human Mesenchymal Stem Cells

The heterogeneity of mesenchymal stem cells (MSCs) remains incompletely inventoried, which often hampers reproducibility in clinical applications and basic research. Advanced single-cell RNA sequencing (scRNAseq) is a robust tool for dissecting cellular heterogeneity, while the comprehensive single-cell atlas still has not been achieved for human MSCs.Using massively parallel multiplexing scRNAseq, we constructed this atlas of >130,000 single-MSC transcriptomes across multiple tissues and donors to decipher their heterogeneity. The most widely clinical-utilized tissue-resources for MSCs were collected, including normal bone marrow (n=3), adipose (n=3), umbilical cord (n=2), and dermis (n=3).Based on this high-quality data, we identified the 7 tissue-specific and 5 conserved MSC subpopulations with distinct gene-expression signatures from multiple tissue origins, which has not been achieved previously. We noticed that extracellular matrix hugely contributes to MSC heterogeneity. Notably, tissue-specific MSC subpopulations exhibited hugely heterogeneous on ECM-associated immune regulation, antigen processing/presentation, and senescence, which also contributed to inter-donor and intra-tissue heterogeneity. The variable dynamics of ECM-associated genes depicted the discrete trajectory patterns across multiple tissues. Additionally, the conserved and tissue-specific transcriptomic-regulons and protein-protein interactions were identified, representing common or tissue-specific MSC potentially functional roles. Furthermore, we also discovered that the umbilical-cord-specific subpopulation possessed advantages in immunosuppressive properties.In summary, our work provides timely and exciting insights into MSC heterogeneity on multiple levels. In addition to resource value, this MSC atlas taxonomy provides a comprehensive understanding of cellular heterogeneity, revealing the potential improvements in MSC-based therapeutic efficacy. Examination of 11 MSC samples from 4 tissues using single-cell RNAseq