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Profiling of MicroRNA in Human and Mouse ES and iPS Cells Reveals Overlapping but Distinct MicroRNA Expression Patterns

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Listed:
  • Siti Razila Abdul Razak
  • Kazuko Ueno
  • Naoya Takayama
  • Naoki Nariai
  • Masao Nagasaki
  • Rika Saito
  • Hideto Koso
  • Chen-Yi Lai
  • Miyako Murakami
  • Koichiro Tsuji
  • Tatsuo Michiue
  • Hiromitsu Nakauchi
  • Makoto Otsu
  • Sumiko Watanabe

Abstract

Using quantitative PCR-based miRNA arrays, we comprehensively analyzed the expression profiles of miRNAs in human and mouse embryonic stem (ES), induced pluripotent stem (iPS), and somatic cells. Immature pluripotent cells were purified using SSEA-1 or SSEA-4 and were used for miRNA profiling. Hierarchical clustering and consensus clustering by nonnegative matrix factorization showed two major clusters, human ES/iPS cells and other cell groups, as previously reported. Principal components analysis (PCA) to identify miRNAs that segregate in these two groups identified miR-187, 299-3p, 499-5p, 628-5p, and 888 as new miRNAs that specifically characterize human ES/iPS cells. Detailed direct comparisons of miRNA expression levels in human ES and iPS cells showed that several miRNAs included in the chromosome 19 miRNA cluster were more strongly expressed in iPS cells than in ES cells. Similar analysis was conducted with mouse ES/iPS cells and somatic cells, and several miRNAs that had not been reported to be expressed in mouse ES/iPS cells were suggested to be ES/iPS cell-specific miRNAs by PCA. Comparison of the average expression levels of miRNAs in ES/iPS cells in humans and mice showed quite similar expression patterns of human/mouse miRNAs. However, several mouse- or human-specific miRNAs are ranked as high expressers. Time course tracing of miRNA levels during embryoid body formation revealed drastic and different patterns of changes in their levels. In summary, our miRNA expression profiling encompassing human and mouse ES and iPS cells gave various perspectives in understanding the miRNA core regulatory networks regulating pluripotent cells characteristics.

Suggested Citation

  • Siti Razila Abdul Razak & Kazuko Ueno & Naoya Takayama & Naoki Nariai & Masao Nagasaki & Rika Saito & Hideto Koso & Chen-Yi Lai & Miyako Murakami & Koichiro Tsuji & Tatsuo Michiue & Hiromitsu Nakauchi, 2013. "Profiling of MicroRNA in Human and Mouse ES and iPS Cells Reveals Overlapping but Distinct MicroRNA Expression Patterns," PLOS ONE, Public Library of Science, vol. 8(9), pages 1-16, September.
    • Handle: RePEc:plo:pone00:0073532
    DOI: 10.1371/journal.pone.0073532
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    1. Teruhisa Kawamura & Jotaro Suzuki & Yunyuan V. Wang & Sergio Menendez & Laura Batlle Morera & Angel Raya & Geoffrey M. Wahl & Juan Carlos Izpisúa Belmonte, 2009. "Linking the p53 tumour suppressor pathway to somatic cell reprogramming," Nature, Nature, vol. 460(7259), pages 1140-1144, August.
    2. Daniel D. Lee & H. Sebastian Seung, 1999. "Learning the parts of objects by non-negative matrix factorization," Nature, Nature, vol. 401(6755), pages 788-791, October.
    3. Jochen Utikal & Jose M. Polo & Matthias Stadtfeld & Nimet Maherali & Warakorn Kulalert & Ryan M. Walsh & Adam Khalil & James G. Rheinwald & Konrad Hochedlinger, 2009. "Immortalization eliminates a roadblock during cellular reprogramming into iPS cells," Nature, Nature, vol. 460(7259), pages 1145-1148, August.
    4. Paul J. Tesar & Josh G. Chenoweth & Frances A. Brook & Timothy J. Davies & Edward P. Evans & David L. Mack & Richard L. Gardner & Ronald D. G. McKay, 2007. "New cell lines from mouse epiblast share defining features with human embryonic stem cells," Nature, Nature, vol. 448(7150), pages 196-199, July.
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