Please use this identifier to cite or link to this item: https://doi.org/10.1186/s13287-023-03363-y
Title: Insight into muscle stem cell regeneration and mechanobiology
Authors: Pang, Kuin Tian 
Loo, Larry Sai Weng
Chia, Sean
Ong, Francesca Yi Teng
Yu, Hanry 
Walsh, Ian
Keywords: Skeletal muscle stem cells
Mechanobiology
Stiffness
Viscoelasticity
Strain
Topography
Issue Date: 12-May-2023
Publisher: BMC
Citation: Pang, Kuin Tian, Loo, Larry Sai Weng, Chia, Sean, Ong, Francesca Yi Teng, Yu, Hanry, Walsh, Ian (2023-05-12). Insight into muscle stem cell regeneration and mechanobiology. Stem Cell Research & Therapy 14 (1). ScholarBank@NUS Repository. https://doi.org/10.1186/s13287-023-03363-y
Abstract: Stem cells possess the unique ability to differentiate into specialized cell types. These specialized cell types can be used for regenerative medicine purposes such as cell therapy. Myosatellite cells, also known as skeletal muscle stem cells (MuSCs), play important roles in the growth, repair, and regeneration of skeletal muscle tissues. However, despite its therapeutic potential, the successful differentiation, proliferation, and expansion processes of MuSCs remain a significant challenge due to a variety of factors. For example, the growth and differentiation of MuSCs can be greatly influenced by actively replicating the MuSCs microenvironment (known as the niche) using mechanical forces. However, the molecular role of mechanobiology in MuSC growth, proliferation, and differentiation for regenerative medicine is still poorly understood. In this present review, we comprehensively summarize, compare, and critically analyze how different mechanical cues shape stem cell growth, proliferation, differentiation, and their potential role in disease development (Fig. 1). The insights developed from the mechanobiology of stem cells will also contribute to how these applications can be used for regenerative purposes using MuSCs.
Source Title: Stem Cell Research & Therapy
URI: https://scholarbank.nus.edu.sg/handle/10635/241518
ISSN: 1757-6512
DOI: 10.1186/s13287-023-03363-y
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