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  • PDGF, TGF-beta, and FGF signaling is important for differentiation and growth of mesenchymal stem cells (MSCs): transcriptional profiling can identify markers and signaling pathways important in differentiation of MSCs into adipogenic, chondrogenic, and osteogenic lineages.

PDGF, TGF-beta, and FGF signaling is important for differentiation and growth of mesenchymal stem cells (MSCs): transcriptional profiling can identify markers and signaling pathways important in differentiation of MSCs into adipogenic, chondrogenic, and osteogenic lineages.

Blood (2008-03-12)
Felicia Ng, Shayne Boucher, Susie Koh, Konduru S R Sastry, Lucas Chase, Uma Lakshmipathy, Cleo Choong, Zheng Yang, Mohan C Vemuri, Mahendra S Rao, Vivek Tanavde
ABSTRACT

We compared the transcriptomes of marrow-derived mesenchymal stem cells (MSCs) with differentiated adipocytes, osteocytes, and chondrocytes derived from these MSCs. Using global gene-expression profiling arrays to detect RNA transcripts, we have identified markers that are specific for MSCs and their differentiated progeny. Further, we have also identified pathways that MSCs use to differentiate into adipogenic, chondrogenic, and osteogenic lineages. We identified activin-mediated transforming growth factor (TGF)-beta signaling, platelet-derived growth factor (PDGF) signaling and fibroblast growth factor (FGF) signaling as the key pathways involved in MSC differentiation. The differentiation of MSCs into these lineages is affected when these pathways are perturbed by inhibitors of cell surface receptor function. Since growth and differentiation are tightly linked processes, we also examined the importance of these 3 pathways in MSC growth. These 3 pathways were necessary and sufficient for MSC growth. Inhibiting any of these pathways slowed MSC growth, whereas a combination of TGF-beta, PDGF, and beta-FGF was sufficient to grow MSCs in a serum-free medium up to 5 passages. Thus, this study illustrates it is possible to predict signaling pathways active in cellular differentiation and growth using microarray data and experimentally verify these predictions.