4.7 Article

A Continuous Molecular Roadmap to iPSC Reprogramming through Progression Analysis of Single-Cell Mass Cytometry

Journal

CELL STEM CELL
Volume 16, Issue 3, Pages 323-337

Publisher

CELL PRESS
DOI: 10.1016/j.stem.2015.01.015

Keywords

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Funding

  1. NIH NRSA [F32 GM093508-01]
  2. California Institute for Regenerative Medicine (CIRM) [TG2-01159]
  3. National Science Foundation (NSF) Graduate Research Fellowship [DGE-114747]
  4. NIH [U19 AI057229, U54CA149145, N01-HV-00242, 1U19AI100627, 5R01AI07372405, R01CA184968, 1 R33 CA183654, R33 CA183692]
  5. NIH-Baylor Research Institute [41000411217]
  6. NIH-Northrop Grumman [7500108142]
  7. CIRM [DR1-01477, RB2-01592]
  8. Department of Defense [OC110674, 11491122]
  9. European Commission [Health.2010.1.2-1]
  10. Food and Drug Administration [HHSF223201210194C]
  11. Bill and Melinda Gates Foundation [OPP 1017093]
  12. Alliance for Lupus Research
  13. Entertainment Industry Foundation (NWCRA)
  14. Rachford and Carlota A. Harris Endowed Professorship

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To analyze cellular reprogramming at the single-cell level, mass cytometry was used to simultaneously measure markers of pluripotency, differentiation, cell-cycle status, and cellular signaling throughout the reprogramming process. Time-resolved progression analysis of the resulting data sets was used to construct a continuous molecular roadmap for three independent reprogramming systems. Although these systems varied substantially in Oct4, Sox2, Klf4, and c-Myc stoichiometry, they presented a common set of reprogramming landmarks. Early in the reprogramming process, Oct4(high) Klf4(high) cells transitioned to a CD73(high) CD104(high) CD54(low) partially reprogrammed state. Ki67(low) cells from this intermediate population reverted to a MEF-like phenotype, but Ki67(high) cells advanced through the M-E-T and then bifurcated into two distinct populations: an ESC-like Nanog(high) Sox2(high) CD54(high) population and a mesendoderm-like Nanog low Sox2 low Lin28 high CD24(high) PDGFR-alpha(high) population. The methods developed here for time-resolved, single-cell progression analysis may be used for the study of additional complex and dynamic systems, such as cancer progression and embryonic development.

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