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Efficient Generation of iPS Cells from Skeletal Muscle Stem Cells

Overview of attention for article published in PLOS ONE, October 2011
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Title
Efficient Generation of iPS Cells from Skeletal Muscle Stem Cells
Published in
PLOS ONE, October 2011
DOI 10.1371/journal.pone.0026406
Pubmed ID
Authors

Kah Yong Tan, Sarah Eminli, Simone Hettmer, Konrad Hochedlinger, Amy J. Wagers

Abstract

Reprogramming of somatic cells into inducible pluripotent stem cells generally occurs at low efficiency, although what limits reprogramming of particular cell types is poorly understood. Recent data suggest that the differentiation status of the cell targeted for reprogramming may influence its susceptibility to reprogramming as well as the differentiation potential of the induced pluripotent stem (iPS) cells that are derived from it. To assess directly the influence of lineage commitment on iPS cell derivation and differentiation, we evaluated reprogramming in adult stem cell and mature cell populations residing in skeletal muscle. Our data using clonal assays and a second-generation inducible reprogramming system indicate that stem cells found in mouse muscle, including resident satellite cells and mesenchymal progenitors, reprogram with significantly greater efficiency than their more differentiated daughters (myoblasts and fibroblasts). However, in contrast to previous reports, we find no evidence of biased differentiation potential among iPS cells derived from myogenically committed cells. These data support the notion that adult stem cells reprogram more efficiently than terminally differentiated cells, and argue against the suggestion that "epigenetic memory" significantly influences the differentiation potential of iPS cells derived from distinct somatic cell lineages in skeletal muscle.

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The data shown below were compiled from readership statistics for 102 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 2%
Spain 1 <1%
Singapore 1 <1%
Unknown 98 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 28 27%
Researcher 24 24%
Student > Master 14 14%
Student > Bachelor 6 6%
Professor > Associate Professor 5 5%
Other 14 14%
Unknown 11 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 60 59%
Biochemistry, Genetics and Molecular Biology 11 11%
Medicine and Dentistry 7 7%
Engineering 5 5%
Chemical Engineering 2 2%
Other 5 5%
Unknown 12 12%