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Integrity of Induced Pluripotent Stem Cell (iPSC) Derived Megakaryocytes as Assessed by Genetic and Transcriptomic Analysis

Author

Listed:
  • Kai Kammers
  • Margaret A Taub
  • Ingo Ruczinski
  • Joshua Martin
  • Lisa R Yanek
  • Alyssa Frazee
  • Yongxing Gao
  • Dixie Hoyle
  • Nauder Faraday
  • Diane M Becker
  • Linzhao Cheng
  • Zack Z Wang
  • Jeff T Leek
  • Lewis C Becker
  • Rasika A Mathias

Abstract

Previously, we have described our feeder-free, xeno-free approach to generate megakaryocytes (MKs) in culture from human induced pluripotent stem cells (iPSCs). Here, we focus specifically on the integrity of these MKs using: (1) genotype discordance between parent cell DNA to iPSC cell DNA and onward to the differentiated MK DNA; (2) genomic structural integrity using copy number variation (CNV); and (3) transcriptomic signatures of the derived MK lines compared to the iPSC lines. We detected a very low rate of genotype discordance; estimates were 0.0001%-0.01%, well below the genotyping error rate for our assay (0.37%). No CNVs were generated in the iPSCs that were subsequently passed on to the MKs. Finally, we observed highly biologically relevant gene sets as being upregulated in MKs relative to the iPSCs: platelet activation, blood coagulation, megakaryocyte development, platelet formation, platelet degranulation, and platelet aggregation. These data strongly support the integrity of the derived MK lines.

Suggested Citation

  • Kai Kammers & Margaret A Taub & Ingo Ruczinski & Joshua Martin & Lisa R Yanek & Alyssa Frazee & Yongxing Gao & Dixie Hoyle & Nauder Faraday & Diane M Becker & Linzhao Cheng & Zack Z Wang & Jeff T Leek, 2017. "Integrity of Induced Pluripotent Stem Cell (iPSC) Derived Megakaryocytes as Assessed by Genetic and Transcriptomic Analysis," PLOS ONE, Public Library of Science, vol. 12(1), pages 1-16, January.
    • Handle: RePEc:plo:pone00:0167794
    DOI: 10.1371/journal.pone.0167794
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    1. Samer M. Hussein & Nizar N. Batada & Sanna Vuoristo & Reagan W. Ching & Reija Autio & Elisa Närvä & Siemon Ng & Michel Sourour & Riikka Hämäläinen & Cia Olsson & Karolina Lundin & Milla Mikkola & Ras , 2011. "Copy number variation and selection during reprogramming to pluripotency," Nature, Nature, vol. 471(7336), pages 58-62, March.
    2. Thomas Moreau & Amanda L. Evans & Louella Vasquez & Marloes R. Tijssen & Ying Yan & Matthew W. Trotter & Daniel Howard & Maria Colzani & Meera Arumugam & Wing Han Wu & Amanda Dalby & Riina Lampela & G, 2016. "Large-scale production of megakaryocytes from human pluripotent stem cells by chemically defined forward programming," Nature Communications, Nature, vol. 7(1), pages 1-16, September.
    3. Smyth Gordon K, 2004. "Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 3(1), pages 1-28, February.
    4. Martin F. Pera, 2011. "The dark side of induced pluripotency," Nature, Nature, vol. 471(7336), pages 46-47, March.
    5. Scharpf, Robert B. & Irizarry, Rafael A. & Ritchie, Matthew E. & Carvalho, Benilton & Ruczinski, Ingo, 2011. "Using the R Package crlmm for Genotyping and Copy Number Estimation," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 40(i12).
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