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Selective Development of Myogenic Mesenchymal Cells from Human Embryonic and Induced Pluripotent Stem Cells

Overview of attention for article published in PLOS ONE, December 2012
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Title
Selective Development of Myogenic Mesenchymal Cells from Human Embryonic and Induced Pluripotent Stem Cells
Published in
PLOS ONE, December 2012
DOI 10.1371/journal.pone.0051638
Pubmed ID
Authors

Tomonari Awaya, Takeo Kato, Yuta Mizuno, Hsi Chang, Akira Niwa, Katsutsugu Umeda, Tatsutoshi Nakahata, Toshio Heike

Abstract

Human embryonic stem (ES) cells and induced pluripotent stem (iPS) cells are promising sources for the cell therapy of muscle diseases and can serve as powerful experimental tools for skeletal muscle research, provided an effective method to induce skeletal muscle cells is established. However, the current methods for myogenic differentiation from human ES cells are still inefficient for clinical use, while myogenic differentiation from human iPS cells remains to be accomplished. Here, we aimed to establish a practical differentiation method to induce skeletal myogenesis from both human ES and iPS cells. To accomplish this goal, we developed a novel stepwise culture method for the selective expansion of mesenchymal cells from cell aggregations called embryoid bodies. These mesenchymal cells, which were obtained by dissociation and re-cultivation of embryoid bodies, uniformly expressed CD56 and the mesenchymal markers CD73, CD105, CD166, and CD29, and finally differentiated into mature myotubes in vitro. Furthermore, these myogenic mesenchymal cells exhibited stable long-term engraftment in injured muscles of immunodeficient mice in vivo and were reactivated upon subsequent muscle damage, increasing in number to reconstruct damaged muscles. Our simple differentiation system facilitates further utilization of ES and iPS cells in both developmental and pathological muscle research and in serving as a practical donor source for cell therapy of muscle diseases.

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Geographical breakdown

Country Count As %
France 1 <1%
Belgium 1 <1%
South Africa 1 <1%
Unknown 105 97%

Demographic breakdown

Readers by professional status Count As %
Researcher 25 23%
Student > Ph. D. Student 19 18%
Student > Master 13 12%
Student > Bachelor 12 11%
Student > Doctoral Student 10 9%
Other 13 12%
Unknown 16 15%
Readers by discipline Count As %
Agricultural and Biological Sciences 44 41%
Biochemistry, Genetics and Molecular Biology 22 20%
Medicine and Dentistry 15 14%
Neuroscience 3 3%
Engineering 3 3%
Other 3 3%
Unknown 18 17%