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Transcriptional Signature and Memory Retention of Human-Induced Pluripotent Stem Cells

Overview of attention for article published in PLOS ONE, September 2009
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Title
Transcriptional Signature and Memory Retention of Human-Induced Pluripotent Stem Cells
Published in
PLOS ONE, September 2009
DOI 10.1371/journal.pone.0007076
Pubmed ID
Authors

Maria C. N. Marchetto, Gene W. Yeo, Osamu Kainohana, Martin Marsala, Fred H. Gage, Alysson R. Muotri

Abstract

Genetic reprogramming of somatic cells to a pluripotent state (induced pluripotent stem cells or iPSCs) by over-expression of specific genes has been accomplished using mouse and human cells. However, it is still unclear how similar human iPSCs are to human Embryonic Stem Cells (hESCs). Here, we describe the transcriptional profile of human iPSCs generated without viral vectors or genomic insertions, revealing that these cells are in general similar to hESCs but with significant differences. For the generation of human iPSCs without viral vectors or genomic insertions, pluripotent factors Oct4 and Nanog were cloned in episomal vectors and transfected into human fetal neural progenitor cells. The transient expression of these two factors, or from Oct4 alone, resulted in efficient generation of human iPSCs. The reprogramming strategy described here revealed a potential transcriptional signature for human iPSCs yet retaining the gene expression of donor cells in human reprogrammed cells free of viral and transgene interference. Moreover, the episomal reprogramming strategy represents a safe way to generate human iPSCs for clinical purposes and basic research.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 317 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
France 4 1%
Brazil 2 <1%
Spain 2 <1%
Italy 1 <1%
Australia 1 <1%
India 1 <1%
Ireland 1 <1%
Belgium 1 <1%
Japan 1 <1%
Other 0 0%
Unknown 303 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 84 26%
Researcher 70 22%
Student > Master 48 15%
Student > Bachelor 22 7%
Professor > Associate Professor 16 5%
Other 41 13%
Unknown 36 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 144 45%
Biochemistry, Genetics and Molecular Biology 53 17%
Medicine and Dentistry 32 10%
Neuroscience 18 6%
Engineering 7 2%
Other 19 6%
Unknown 44 14%