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Integration-Free iPS Cells Engineered Using Human Artificial Chromosome Vectors

Overview of attention for article published in PLOS ONE, October 2011
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Title
Integration-Free iPS Cells Engineered Using Human Artificial Chromosome Vectors
Published in
PLOS ONE, October 2011
DOI 10.1371/journal.pone.0025961
Pubmed ID
Authors

Masaharu Hiratsuka, Narumi Uno, Kana Ueda, Hajime Kurosaki, Natsuko Imaoka, Kanako Kazuki, Etsuya Ueno, Yutaro Akakura, Motonobu Katoh, Mitsuhiko Osaki, Yasuhiro Kazuki, Masato Nakagawa, Shinya Yamanaka, Mitsuo Oshimura

Abstract

Human artificial chromosomes (HACs) have unique characteristics as gene-delivery vectors, including episomal transmission and transfer of multiple, large transgenes. Here, we demonstrate the advantages of HAC vectors for reprogramming mouse embryonic fibroblasts (MEFs) into induced pluripotent stem (iPS) cells. Two HAC vectors (iHAC1 and iHAC2) were constructed. Both carried four reprogramming factors, and iHAC2 also encoded a p53-knockdown cassette. iHAC1 partially reprogrammed MEFs, and iHAC2 efficiently reprogrammed MEFs. Global gene expression patterns showed that the iHACs, unlike other vectors, generated relatively uniform iPS cells. Under non-selecting conditions, we established iHAC-free iPS cells by isolating cells that spontaneously lost iHAC2. Analyses of pluripotent markers, teratomas and chimeras confirmed that these iHAC-free iPS cells were pluripotent. Moreover, iHAC-free iPS cells with a re-introduced HAC encoding Herpes Simplex virus thymidine kinase were eliminated by ganciclovir treatment, indicating that the HAC safeguard system functioned in iPS cells. Thus, the HAC vector could generate uniform, integration-free iPS cells with a built-in safeguard system.

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Geographical breakdown

Country Count As %
United States 4 3%
Portugal 1 <1%
Canada 1 <1%
Brazil 1 <1%
Unknown 121 95%

Demographic breakdown

Readers by professional status Count As %
Researcher 33 26%
Student > Ph. D. Student 29 23%
Student > Bachelor 16 13%
Student > Master 10 8%
Professor 8 6%
Other 18 14%
Unknown 14 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 60 47%
Biochemistry, Genetics and Molecular Biology 29 23%
Medicine and Dentistry 11 9%
Neuroscience 4 3%
Chemistry 2 2%
Other 6 5%
Unknown 16 13%