Title |
Proteome adaptation in cell reprogramming proceeds via distinct transcriptional networks
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Published in |
Nature Communications, December 2014
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DOI | 10.1038/ncomms6613 |
Pubmed ID | |
Authors |
Marco Benevento, Peter D. Tonge, Mira C. Puri, Samer M. I. Hussein, Nicole Cloonan, David L. Wood, Sean M. Grimmond, Andras Nagy, Javier Munoz, Albert J. R. Heck |
Abstract |
The ectopic expression of Oct4, Klf4, c-Myc and Sox2 (OKMS) transcription factors allows reprogramming of somatic cells into induced pluripotent stem cells (iPSCs). The reprogramming process, which involves a complex network of molecular events, is not yet fully characterized. Here we perform a quantitative mass spectrometry-based analysis to probe in-depth dynamic proteome changes during somatic cell reprogramming. Our data reveal defined waves of proteome resetting, with the first wave occurring 48 h after the activation of the reprogramming transgenes and involving specific biological processes linked to the c-Myc transcriptional network. A second wave of proteome reorganization occurs in a later stage of reprogramming, where we characterize the proteome of two distinct pluripotent cellular populations. In addition, the overlay of our proteome resource with parallel generated -omics data is explored to identify post-transcriptionally regulated proteins involved in key steps during reprogramming. |
X Demographics
Geographical breakdown
Country | Count | As % |
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United States | 2 | 22% |
Japan | 1 | 11% |
Australia | 1 | 11% |
Spain | 1 | 11% |
India | 1 | 11% |
France | 1 | 11% |
Unknown | 2 | 22% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 6 | 67% |
Scientists | 3 | 33% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 3 | 2% |
Netherlands | 1 | <1% |
France | 1 | <1% |
Brazil | 1 | <1% |
United Kingdom | 1 | <1% |
Taiwan | 1 | <1% |
Australia | 1 | <1% |
China | 1 | <1% |
Belgium | 1 | <1% |
Other | 2 | 1% |
Unknown | 146 | 92% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 44 | 28% |
Student > Ph. D. Student | 35 | 22% |
Student > Master | 17 | 11% |
Professor | 10 | 6% |
Professor > Associate Professor | 9 | 6% |
Other | 25 | 16% |
Unknown | 19 | 12% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 60 | 38% |
Biochemistry, Genetics and Molecular Biology | 47 | 30% |
Chemistry | 8 | 5% |
Medicine and Dentistry | 8 | 5% |
Immunology and Microbiology | 4 | 3% |
Other | 15 | 9% |
Unknown | 17 | 11% |