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A Quantitative System for Discriminating Induced Pluripotent Stem Cells, Embryonic Stem Cells and Somatic Cells

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  • Anyou Wang
  • Ying Du
  • Qianchuan He
  • Chunxiao Zhou

Abstract

Induced pluripotent stem cells (iPSCs) derived from somatic cells (SCs) and embryonic stem cells (ESCs) provide promising resources for regenerative medicine and medical research, leading to a daily identification of new cell lines. However, an efficient system to discriminate the different types of cell lines is lacking. Here, we develop a quantitative system to discriminate the three cell types, iPSCs, ESCs, and SCs. The system consists of DNA-methylation biomarkers and mathematical models, including an artificial neural network and support vector machines. All biomarkers were unbiasedly selected by calculating an eigengene score derived from analysis of genome-wide DNA methylations. With 30 biomarkers, or even with as few as 3 top biomarkers, this system can discriminate SCs from pluripotent cells (PCs, including ESCs and iPSCs) with almost 100% accuracy. With approximately 100 biomarkers, the system can distinguish ESCs from iPSCs with an accuracy of 95%. This robust system performs precisely with raw data without normalization as well as with converted data in which the continuous methylation levels are accounted. Strikingly, this system can even accurately predict new samples generated from different microarray platforms and the next-generation sequencing. The subtypes of cells, such as female and male iPSCs and fetal and adult SCs, can also be discriminated with this method. Thus, this novel quantitative system works as an accurate framework for discriminating the three cell types, iPSCs, ESCs, and SCs. This strategy also supports the notion that DNA-methylation generally varies among the three cell types.

Suggested Citation

  • Anyou Wang & Ying Du & Qianchuan He & Chunxiao Zhou, 2013. "A Quantitative System for Discriminating Induced Pluripotent Stem Cells, Embryonic Stem Cells and Somatic Cells," PLOS ONE, Public Library of Science, vol. 8(2), pages 1-10, February.
    • Handle: RePEc:plo:pone00:0056095
    DOI: 10.1371/journal.pone.0056095
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    1. Ryan Lister & Mattia Pelizzola & Yasuyuki S. Kida & R. David Hawkins & Joseph R. Nery & Gary Hon & Jessica Antosiewicz-Bourget & Ronan O’Malley & Rosa Castanon & Sarit Klugman & Michael Downes & Ruth , 2011. "Hotspots of aberrant epigenomic reprogramming in human induced pluripotent stem cells," Nature, Nature, vol. 471(7336), pages 68-73, March.
    2. Shinya Yamanaka, 2009. "Elite and stochastic models for induced pluripotent stem cell generation," Nature, Nature, vol. 460(7251), pages 49-52, July.
    3. Charles L. Sawyers, 2008. "The cancer biomarker problem," Nature, Nature, vol. 452(7187), pages 548-552, April.
    4. Ryan Lister & Mattia Pelizzola & Robert H. Dowen & R. David Hawkins & Gary Hon & Julian Tonti-Filippini & Joseph R. Nery & Leonard Lee & Zhen Ye & Que-Minh Ngo & Lee Edsall & Jessica Antosiewicz-Bourg, 2009. "Human DNA methylomes at base resolution show widespread epigenomic differences," Nature, Nature, vol. 462(7271), pages 315-322, November.
    5. Franz-Josef Müller & Louise C. Laurent & Dennis Kostka & Igor Ulitsky & Roy Williams & Christina Lu & In-Hyun Park & Mahendra S. Rao & Ron Shamir & Philip H. Schwartz & Nils O. Schmidt & Jeanne F. Lor, 2008. "Regulatory networks define phenotypic classes of human stem cell lines," Nature, Nature, vol. 455(7211), pages 401-405, September.
    6. Rachel Jones, 2010. "Biomarkers: casting the net wide," Nature, Nature, vol. 466(7310), pages 11-12, August.
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