Systematic search for recipes to generate induced pluripotent stem cells.
Systematic search for recipes to generate induced pluripotent stem cells.
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DOI：
10.1371/journal.pcbi.1002300
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发表时间：
2011-12
影响因子：
4.3
通讯作者：
Wang W
中科院分区：
文献类型：
--
作者：
Chang R;Shoemaker R;Wang W
Generation of induced pluripotent stem cells (iPSCs) opens a new avenue in regenerative medicine. One of the major hurdles for therapeutic applications is to improve the efficiency of generating iPSCs and also to avoid the tumorigenicity, which requires searching for new reprogramming recipes. We present a systems biology approach to efficiently evaluate a large number of possible recipes and find those that are most effective at generating iPSCs. We not only recovered several experimentally confirmed recipes but we also suggested new ones that may improve reprogramming efficiency and quality. In addition, our approach allows one to estimate the cell-state landscape, monitor the progress of reprogramming, identify important regulatory transition states, and ultimately understand the mechanisms of iPSC generation. Converting somatic cells back to the stem cell state (called induced pluripotent stem cells or iPSCs) exemplifies the recent advancement of cellular reprogramming that holds great promise for developing regenerative medicine. Generation of iPSCs is often achieved by overexpressing three to four genes in somatic cells that are critical for regulating pluripotency. Developing optimal reprogramming recipe is a non-trivial task that requires significant effort. We present here a computational method that can facilitate discovery of effective recipes to generate iPSCs with high efficiency and better quality. In addition, our approach provides a new way to estimate the landscape in the cell-state space and monitor the trajectory of cellular reprogramming from a differentiated cell to an iPS cell. This work provides not only practical recipes for iPSC generation but also theoretical understanding of the reprogramming process.
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DOI：
10.1109/tcbb.2011.18
发表时间：
2011-09
期刊：
IEEE/ACM transactions on computational biology and bioinformatics
影响因子：
--
作者：
Chang R;Shoemaker R;Wang W
通讯作者：
Wang W
影响因子：
12.3
作者：
Kim WK;Krumpelman C;Marcotte EM
通讯作者：
Marcotte EM
影响因子：
5.2
作者：
Hyslop, L;Stojkovic, M;Lako, M
通讯作者：
Lako, M
影响因子：
30.8
作者：
Kunarso, Galih;Chia, Na-Yu;Bourque, Guillaume
通讯作者：
Bourque, Guillaume
影响因子：
64.5
作者：
Bonneau, Richard;Facciotti, Marc T.;Baliga, Nitin S.
通讯作者：
Baliga, Nitin S.