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High-resolution analysis with novel cell-surface markers identifies routes to iPS cells

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Abstract

The generation of induced pluripotent stem (iPS) cells presents a challenge to normal developmental processes. The low efficiency and heterogeneity of most methods have hindered understanding of the precise molecular mechanisms promoting, and roadblocks preventing, efficient reprogramming. Although several intermediate populations have been described1,2,3,4,5,6,7, it has proved difficult to characterize the rare, asynchronous transition from these intermediate stages to iPS cells. The rapid expansion of minor reprogrammed cells in the heterogeneous population can also obscure investigation of relevant transition processes. Understanding the biological mechanisms essential for successful iPS cell generation requires both accurate capture of cells undergoing the reprogramming process and identification of the associated global gene expression changes. Here we demonstrate that in mouse embryonic fibroblasts, reprogramming follows an orderly sequence of stage transitions, marked by changes in the cell-surface markers CD44 and ICAM1, and a Nanog–enhanced green fluorescent protein (Nanog–eGFP) reporter. RNA-sequencing analysis of these populations demonstrates two waves of pluripotency gene upregulation, and unexpectedly, transient upregulation of several epidermis-related genes, demonstrating that reprogramming is not simply the reversal of the normal developmental processes. This novel high-resolution analysis enables the construction of a detailed reprogramming route map, and the improved understanding of the reprogramming process will lead to new reprogramming strategies.

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Figure 1: FACS analysis during secondary reprogramming of MEFs with CD44/ICAM1 double staining.
Figure 2: CD44/ICAM1 subpopulations represent distinct stages of reprogramming.
Figure 3: Global gene expression changes during the stage transition.

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RNA-sequencing data are deposited in the ArrayExpress under accession number E-MTAB-1654.

References

  1. Sridharan, R. et al. Role of the murine reprogramming factors in the induction of pluripotency. Cell 136, 364–377 (2009)

    Article  CAS  Google Scholar 

  2. Golipour, A. et al. A late transition in somatic cell reprogramming requires regulators distinct from the pluripotency network. Cell Stem Cell 11, 769–782 (2012)

    Article  CAS  Google Scholar 

  3. Samavarchi-Tehrani, P. et al. Functional genomics reveals a BMP-driven mesenchymal-to-epithelial transition in the initiation of somatic cell reprogramming. Cell Stem Cell 7, 64–77 (2010)

    Article  CAS  Google Scholar 

  4. Mikkelsen, T. S. et al. Dissecting direct reprogramming through integrative genomic analysis. Nature 454, 49–55 (2008)

    Article  ADS  CAS  Google Scholar 

  5. Buganim, Y. et al. Single-cell expression analyses during cellular reprogramming reveal an early stochastic and a late hierarchic phase. Cell 150, 1209–1222 (2012)

    Article  CAS  Google Scholar 

  6. Polo, J. M. et al. A molecular roadmap of reprogramming somatic cells into iPS cells. Cell 151, 1617–1632 (2012)

    Article  CAS  Google Scholar 

  7. Chen, J. et al. H3K9 methylation is a barrier during somatic cell reprogramming into iPSCs. Nature Genet. 45, 34–42 (2013)

    Article  CAS  Google Scholar 

  8. Stadtfeld, M., Maherali, N., Breault, D. T. & Hochedlinger, K. Defining molecular cornerstones during fibroblast to iPS cell reprogramming in mouse. Cell Stem Cell 2, 230–240 (2008)

    Article  CAS  Google Scholar 

  9. Brambrink, T. et al. Sequential expression of pluripotency markers during direct reprogramming of mouse somatic cells. Cell Stem Cell 2, 151–159 (2008)

    Article  CAS  Google Scholar 

  10. Li, R. et al. A mesenchymal-to-epithelial transition initiates and is required for the nuclear reprogramming of mouse fibroblasts. Cell Stem Cell 7, 51–63 (2010)

    Article  CAS  Google Scholar 

  11. Woltjen, K. et al. piggyBac transposition reprograms fibroblasts to induced pluripotent stem cells. Nature 458, 766–770 (2009)

    Article  ADS  CAS  Google Scholar 

  12. Kaji, K. et al. Virus-free induction of pluripotency and subsequent excision of reprogramming factors. Nature 458, 771–775 (2009)

    Article  ADS  CAS  Google Scholar 

  13. Chambers, I. et al. Nanog safeguards pluripotency and mediates germline development. Nature 450, 1230–1234 (2007)

    Article  ADS  CAS  Google Scholar 

  14. Esteban, M. A. et al. Vitamin C enhances the generation of mouse and human induced pluripotent stem cells. Cell Stem Cell 6, 71–79 (2010)

    Article  CAS  Google Scholar 

  15. Maherali, N. & Hochedlinger, K. Tgfβ signal inhibition cooperates in the induction of iPSCs and replaces Sox2 and cMyc. Curr. Biol. 19, 1718–1723 (2009)

    Article  CAS  Google Scholar 

  16. Chan, E. M. et al. Live cell imaging distinguishes bona fide human iPS cells from partially reprogrammed cells. Nature Biotechnol. 27, 1033–1037 (2009)

    Article  CAS  Google Scholar 

  17. Carey, B. W. et al. Reprogramming of murine and human somatic cells using a single polycistronic vector. Proc. Natl Acad. Sci. USA 106, 157–162 (2009)

    Article  ADS  CAS  Google Scholar 

  18. Hanna, J. et al. Direct cell reprogramming is a stochastic process amenable to acceleration. Nature 462, 595–601 (2009)

    Article  ADS  CAS  Google Scholar 

  19. Silva, J. et al. Nanog is the gateway to the pluripotent ground state. Cell 138, 722–737 (2009)

    Article  CAS  Google Scholar 

  20. Islam, S. et al. Characterization of the single-cell transcriptional landscape by highly multiplex RNA-seq. Genome Res. 21, 1160–1167 (2011)

    Article  CAS  Google Scholar 

  21. Islam, S. et al. Highly multiplexed and strand-specific single-cell RNA 5′ end sequencing. Nature Protocols 7, 813–828 (2012)

    Article  CAS  Google Scholar 

  22. Halbritter, F., Vaidya, H. J. & Tomlinson, S. R. GeneProf: analysis of high-throughput sequencing experiments. Nature Methods 9, 7–8 (2012)

    Article  CAS  Google Scholar 

  23. Anders, S. & Huber, W. Differential expression analysis for sequence count data. Genome Biol. 11, R106 (2010)

    Article  CAS  Google Scholar 

  24. Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010)

    Article  CAS  Google Scholar 

  25. Gentleman, R. C. et al. Bioconductor: open software development for computational biology and bioinformatics. Genome Biol. 5, R80 (2004)

    Article  Google Scholar 

  26. Huang, d. W., Sherman, B. T. & Lempicki, R. A. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nature Protocols 4, 44–57 (2009)

    Article  CAS  Google Scholar 

  27. Kim, J., Chu, J., Shen, X., Wang, J. & Orkin, S. H. An extended transcriptional network for pluripotency of embryonic stem cells. Cell 132, 1049–1061 (2008)

    Article  CAS  Google Scholar 

  28. Xu, H., Lemischka, I. R. & Ma’ayan, A. SVM classifier to predict genes important for self-renewal and pluripotency of mouse embryonic stem cells. BMC Syst. Biol. 4, 173 (2010)

    Article  Google Scholar 

  29. Nemajerova, A., Kim, S. Y., Petrenko, O. & Moll, U. M. Two-factor reprogramming of somatic cells to pluripotent stem cells reveals partial functional redundancy of Sox2 and Klf4. Cell Death Differ. 19, 1268–1276 (2012)

    Article  CAS  Google Scholar 

  30. Soufi, A., Donahue, G. & Zaret, K. S. Facilitators and impediments of the pluripotency reprogramming factors’ initial engagement with the genome. Cell 151, 994–1004 (2012)

    Article  CAS  Google Scholar 

  31. Gaszner, M. & Felsenfeld, G. Insulators: exploiting transcriptional and epigenetic mechanisms. Nature Rev. Genet. 7, 703–713 (2006)

    Article  CAS  Google Scholar 

  32. Fu, H. et al. Preventing gene silencing with human replicators. Nature Biotechnol. 24, 572–576 (2006)

    Article  CAS  Google Scholar 

  33. Wang, W. et al. Chromosomal transposition of PiggyBac in mouse embryonic stem cells. Proc. Natl Acad. Sci. USA 105, 9290–9295 (2008)

    Article  ADS  CAS  Google Scholar 

  34. Saldanha, A. J. Java Treeview–extensible visualization of microarray data. Bioinformatics 20, 3246–3248 (2004)

    Article  CAS  Google Scholar 

  35. de Hoon, M. J., Imoto, S., Nolan, J. & Miyano, S. Open source clustering software. Bioinformatics 20, 1453–1454 (2004)

    Article  CAS  Google Scholar 

  36. Ligges, U. & Maechler, M. scatterplot3d — an R package for visualizing multivariate data. J. Stat. Softw. 8, 1–20 (2003)

    Article  Google Scholar 

  37. Irizarry, R. A. et al. Summaries of Affymetrix GeneChip probe level data. Nucleic Acids Res. 31, e15 (2003)

    Article  Google Scholar 

  38. Gautier, L., Cope, L., Bolstad, B. M. & Irizarry, R. A. affy–analysis of Affymetrix GeneChip data at the probe level. Bioinformatics 20, 307–315 (2004)

    Article  CAS  Google Scholar 

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Acknowledgements

We thank A. Nagy and K. Woltjen for providing the 6c iPS cell line, I. Chambers for providing TNG mice, S. Monard and O. Rodrigues for assistance with flow cytometry, and T. Kunath, T. Burdon, S. Lowell and N. Festuccia for discussions and comments on the manuscript. We also thank L. Robertson for technical assistance, and Biomed unit staff for mouse husbandry. This work was supported by ERC grants ROADTOIPS (no. 261075) and BRAINCELL (no. 261063), and the Anne Rowling Regenerative Neurology Clinic. J.O.’M. and T.R. are funded by an MRC PhD Studentship and a Darwin Trust of Edinburgh Scholarship, respectively.

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Authors and Affiliations

Authors

Contributions

J.O.’M. designed and performed flow cytometry analysis and sorting experiments, prepared RNA for sequencing, carried out immunofluorescence imaging, and collected, analysed and interpreted data, and wrote the manuscript. S.S. analysed RNA-sequencing and published microarray data sets. K.A.I. carried out single-cell PCR analysis. E.C. performed primary reprogramming and FACS analysis. T.R. carried out immunofluorescence and confocal imaging. S.R.T. performed microarray analysis to identify cell-surface marker candidates. A.J. and S.L. performed multiplexed RNA-sequencing and collected data. K.K. conceived the study, identified the surface markers, generated the D6s4B5 iPS cell line, analysed RNA-sequencing data, supervised experiment design and data interpretation, and wrote the manuscript.

Corresponding author

Correspondence to Keisuke Kaji.

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The authors declare no competing financial interests.

Supplementary information

Supplementary Figures

This file contains Supplementary Figures 1-18. (PDF 3715 kb)

Supplementary Data

This zipped file contains Supplementary Tables 1-9. Supplementary Table 1 shows differentially expressed genes (DEGs) between samples, Supplementary Table 2 shows genes in A-E category from O'Malley subpopulation data, Supplementary Table 3 displays gene ontology from O'Malley subpopulation data, Supplementary Table 4 contains signal values of pluripotency and epidermis genes, Supplementary Table 5 shows epidermis genes EST profile, Supplementary Table 6 shows genes in pA-pD’ category from piPSC data, Supplementary Table 7 contains genes in tA’-tD category from time course data, Supplementary Table 8 shows genes in TSO A1-TSO E category from Thy1/SSEA1/Oct4-GFP subpopulation data and Supplementary Table 9 shows flow cytometry conditions and TaqMan Gene Expression Assay ID. (ZIP 3076 kb)

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O’Malley, J., Skylaki, S., Iwabuchi, K. et al. High-resolution analysis with novel cell-surface markers identifies routes to iPS cells. Nature 499, 88–91 (2013). https://doi.org/10.1038/nature12243

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