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Establishment of LIF-Dependent Human iPS Cells Closely Related to Basic FGF-Dependent Authentic iPS Cells

Overview of attention for article published in PLOS ONE, June 2012
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Title
Establishment of LIF-Dependent Human iPS Cells Closely Related to Basic FGF-Dependent Authentic iPS Cells
Published in
PLOS ONE, June 2012
DOI 10.1371/journal.pone.0039022
Pubmed ID
Authors

Hiroyuki Hirai, Meri Firpo, Nobuaki Kikyo

Abstract

Human induced pluripotent stem cells (iPSCs) can be divided into a leukemia inhibitory factor (LIF)-dependent naïve type and a basic fibroblast growth factor (bFGF)-dependent primed type. Although the former are more undifferentiated than the latter, they require signal transduction inhibitors and sustained expression of the transgenes used for iPSC production. We used a transcriptionally enhanced version of OCT4 to establish LIF-dependent human iPSCs without the use of inhibitors and sustained transgene expression. These cells belong to the primed type of pluripotent stem cell, similar to bFGF-dependent iPSCs. Thus, the particular cytokine required for iPSC production does not necessarily define stem cell phenotypes as previously thought. It is likely that the bFGF and LIF signaling pathways converge on unidentified OCT4 target genes. These findings suggest that our LIF-dependent human iPSCs could provide a novel model to investigate the role of cytokine signaling in cellular reprogramming.

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Mendeley readers

The data shown below were compiled from readership statistics for 69 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 4%
Indonesia 1 1%
Portugal 1 1%
France 1 1%
Unknown 63 91%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 21 30%
Researcher 17 25%
Student > Bachelor 6 9%
Other 6 9%
Student > Master 6 9%
Other 8 12%
Unknown 5 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 40 58%
Medicine and Dentistry 8 12%
Biochemistry, Genetics and Molecular Biology 7 10%
Computer Science 2 3%
Engineering 2 3%
Other 5 7%
Unknown 5 7%