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A predictive computational framework for direct reprogramming between human cell types

Nature Genetics volume 48, pages 331–335 (2016)Cite this article

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Abstract

Transdifferentiation, the process of converting from one cell type to another without going through a pluripotent state, has great promise for regenerative medicine. The identification of key transcription factors for reprogramming is currently limited by the cost of exhaustive experimental testing of plausible sets of factors, an approach that is inefficient and unscalable. Here we present a predictive system (Mogrify) that combines gene expression data with regulatory network information to predict the reprogramming factors necessary to induce cell conversion. We have applied Mogrify to 173 human cell types and 134 tissues, defining an atlas of cellular reprogramming. Mogrify correctly predicts the transcription factors used in known transdifferentiations. Furthermore, we validated two new transdifferentiations predicted by Mogrify. We provide a practical and efficient mechanism for systematically implementing novel cell conversions, facilitating the generalization of reprogramming of human cells. Predictions are made available to help rapidly further the field of cell conversion.

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Figure 1: The Mogrify algorithm for predicting transcription factors for cell conversion.
Figure 2: Mogrify predictions for some of the known transdifferentiations that are published in the literature.
Figure 3: Induction of keratinocytes from dermal fibroblasts.
Figure 4: Induction of microvascular endothelial cells from keratinocytes.

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Gene Expression Omnibus

References

  1. Firas, J., Liu, X., Lim, S.M. & Polo, J.M. Transcription factor–mediated reprogramming: epigenetics and therapeutic potential. Immunol. Cell Biol. 93, 284–289 (2015).

    Article  CAS  PubMed  Google Scholar 

  2. Takahashi, K. & Yamanaka, S. Induction of pluripotent stem cells from mouse embryonic and adult fibroblast cultures by defined factors. Cell 126, 663–676 (2006).

    Article  CAS  PubMed  Google Scholar 

  3. Takahashi, K. et al. Induction of pluripotent stem cells from adult human fibroblasts by defined factors. Cell 131, 861–872 (2007).

    Article  CAS  PubMed  Google Scholar 

  4. Vierbuchen, T. et al. Direct conversion of fibroblasts to functional neurons by defined factors. Nature 463, 1035–1041 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. Ieda, M. et al. Direct reprogramming of fibroblasts into functional cardiomyocytes by defined factors. Cell 142, 375–386 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Du, Y. et al. Human hepatocytes with drug metabolic function induced from fibroblasts by lineage reprogramming. Cell Stem Cell 14, 394–403 (2014).

    Article  CAS  PubMed  Google Scholar 

  7. Sekiya, S. & Suzuki, A. Direct conversion of mouse fibroblasts to hepatocyte-like cells by defined factors. Nature 475, 390–393 (2011).

    Article  CAS  PubMed  Google Scholar 

  8. Pfisterer, U. et al. Direct conversion of human fibroblasts to dopaminergic neurons. Proc. Natl. Acad. Sci. USA 108, 10343–10348 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Addis, R.C. et al. Optimization of direct fibroblast reprogramming to cardiomyocytes using calcium activity as a functional measure of success. J. Mol. Cell. Cardiol. 60, 97–106 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Wilson, D., Charoensawan, V., Kummerfeld, S.K. & Teichmann, S.A. DBD—taxonomically broad transcription factor predictions: new content and functionality. Nucleic Acids Res. 36, D88–D92 (2008).

    Article  CAS  PubMed  Google Scholar 

  11. Vaquerizas, J.M., Kummerfeld, S.K., Teichmann, S.A. & Luscombe, N.M. A census of human transcription factors: function, expression and evolution. Nat. Rev. Genet. 10, 252–263 (2009).

    Article  CAS  PubMed  Google Scholar 

  12. Fulton, D.L. et al. TFCat: the curated catalog of mouse and human transcription factors. Genome Biol. 10, R29 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  13. Vickaryous, M.K. & Hall, B.K. Human cell type diversity, evolution, development, and classification with special reference to cells derived from the neural crest. Biol. Rev. Camb. Philos. Soc. 81, 425–455 (2006).

    Article  PubMed  Google Scholar 

  14. Heinäniemi, M. et al. Gene-pair expression signatures reveal lineage control. Nat. Methods 10, 577–583 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  15. Lang, A.H., Li, H., Collins, J.J. & Mehta, P. Epigenetic landscapes explain partially reprogrammed cells and identify key reprogramming genes. PLoS Comput. Biol. 10, e1003734 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  16. Crespo, I. & Del Sol, A. A general strategy for cellular reprogramming: the importance of transcription factor cross-repression. Stem Cells 31, 2127–2135 (2013).

    Article  PubMed  Google Scholar 

  17. Davis, F.P. & Eddy, S.R. Transcription factors that convert adult cell identity are differentially polycomb repressed. PLoS One 8, e63407 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Morris, S.A. et al. Dissecting engineered cell types and enhancing cell fate conversion via CellNet. Cell 158, 889–902 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Cahan, P. et al. CellNet: network biology applied to stem cell engineering. Cell 158, 903–915 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. D'Alessio, A.C. et al. A systematic approach to identify candidate transcription factors that control cell identity. Stem Cell Reports doi:10.1016/j.stemcr.2015.09.016 (23 October 2015).

  21. FANTOM Consortium and the RIKEN PMI and CLST (DGT). A promoter-level mammalian expression atlas. Nature 507, 462–470 (2014).

  22. Franceschini, A. et al. STRING v9.1: protein-protein interaction networks, with increased coverage and integration. Nucleic Acids Res 41, D808–D815 (2013).

    Article  CAS  PubMed  Google Scholar 

  23. FANTOM Consortium. The transcriptional network that controls growth arrest and differentiation in a human myeloid leukemia cell line. Nat. Genet. 41, 553–562 (2009).

  24. Yu, J. et al. Induced pluripotent stem cell lines derived from human somatic cells. Science 318, 1917–1920 (2007).

    Article  CAS  PubMed  Google Scholar 

  25. Huangfu, D. et al. Induction of pluripotent stem cells from primary human fibroblasts with only Oct4 and Sox2. Nat. Biotechnol. 26, 1269–1275 (2008).

    Article  CAS  PubMed  Google Scholar 

  26. Xie, H., Ye, M., Feng, R. & Graf, T. Stepwise reprogramming of B cells into macrophages. Cell 117, 663–676 (2004).

    Article  CAS  PubMed  Google Scholar 

  27. Rapino, F. et al. C/EBPα induces highly efficient macrophage transdifferentiation of B lymphoma and leukemia cell lines and impairs their tumorigenicity. Cell Reports 3, 1153–1163 (2013).

    Article  CAS  PubMed  Google Scholar 

  28. Fu, J.-D. et al. Direct reprogramming of human fibroblasts toward a cardiomyocyte-like state. Stem Cell Reports 1, 235–247 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Zou, Q. et al. Direct conversion of human fibroblasts into neuronal restricted progenitors. J. Biol. Chem. 289, 5250–5260 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Huang, P. et al. Induction of functional hepatocyte-like cells from mouse fibroblasts by defined factors. Nature 475, 386–389 (2011).

    Article  CAS  PubMed  Google Scholar 

  31. Kogiso, T., Nagahara, H., Otsuka, M., Shiratori, K. & Dowdy, S.F. Transdifferentiation of human fibroblasts into hepatocyte-like cells by defined transcriptional factors. Hepatol. Int. 7, 937–944 (2013).

    Article  PubMed  Google Scholar 

  32. Bar-Nur, O. et al. Lineage conversion induced by pluripotency factors involves transient passage through an iPSC stage. Nat. Biotechnol. 33, 761–768 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Maza, I. et al. Transient acquisition of pluripotency during somatic cell transdifferentiation with iPSC reprogramming factors. Nat. Biotechnol. 33, 769–774 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Qiu, X., Ding, S. & Shi, T. From understanding the development landscape of the canonical fate-switch pair to constructing a dynamic landscape for two-step neural differentiation. PLoS One 7, e49271 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Bhattacharya, S., Zhang, Q. & Andersen, M.E. A deterministic map of Waddington's epigenetic landscape for cell fate specification. BMC Syst. Biol. 5, 85 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  36. Flöttmann, M., Scharp, T. & Klipp, E. A stochastic model of epigenetic dynamics in somatic cell reprogramming. Front. Physiol. 3, 216 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Lattanzi, L. et al. High efficiency myogenic conversion of human fibroblasts by adenoviral vector–mediated MyoD gene transfer. An alternative strategy for ex vivo gene therapy of primary myopathies. J. Clin. Invest. 101, 2119–2128 (1998).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We would like to thank all members of the FANTOM5 Consortium for contributing to the generation of samples and analysis of the data set and thank GeNAS for data production. J.G. and O.J.L.R. were supported by grants from the Biotechnology and Biological Sciences research council and the Japanese Society for the Promotion of Science. J.M.P. was supported by a Silvia and Charles Senior Medical Viertel Fellowship, the Metcalf award from the National Stem Cell Foundation of Australia, National Health and Medical Research Council of Australia (NHMRC) project grant APP1085302 and the Australia Research Council's special initiative Stem Cells Australia. FANTOM5 was made possible by a Research Grant for the RIKEN Omics Science Center from MEXT to Y.H. and a grant of Innovative Cell Biology by Innovative Technology (Cell Innovation Program) from MEXT, Japan, to Y.H.

Author information

Author notes

  1. Owen J L Rackham, Jaber Firas and Yoshihide Hayashizaki: These authors contributed equally to this work.

Authors and Affiliations

  1. Department of Computer Science, University of Bristol, Bristol, UK

    Owen J L Rackham, Hai Fang, Matt E Oates & Julian Gough

  2. Program in Cardiovascular and Metabolic Disorders, Duke–National University of Singapore Medical School, Singapore

    Owen J L Rackham & Enrico Petretto

  3. Department of Anatomy and Developmental Biology, Monash University, Clayton, Victoria, Australia

    Jaber Firas, Melissa L Holmes, Anja S Knaupp, Christian M Nefzger & Jose M Polo

  4. Australian Regenerative Medicine Institute, Monash University, Clayton, Victoria, Australia

    Jaber Firas, Melissa L Holmes, Christian M Nefzger & Jose M Polo

  5. Development and Stem Cells Program, Monash Biomedicine Discovery Institute, Monash University, Clayton, Victoria, Australia,

    Jaber Firas, Melissa L Holmes, Anja S Knaupp, Christian M Nefzger & Jose M Polo

  6. RIKEN Omics Science Center, Yokohama, Japan (ceased to exist as of 1 April 2013 owing to reorganization),

    Harukazu Suzuki, Carsten O Daub, Jay W Shin & Alistair R R Forrest

  7. Division of Genomic Technologies, RIKEN Center for Life Science Technologies, Yokohama, Japan

    Harukazu Suzuki, Carsten O Daub, Jay W Shin, Alistair R R Forrest & Yoshihide Hayashizaki

  8. Department of Biosciences and Nutrition, Karolinska Institutet, Stockholm, Sweden

    Carsten O Daub

  9. Harry Perkins Institute of Medical Research, Queen Elizabeth II Medical Centre and Centre for Medical Research, University of Western Australia, Nedlands, Western Australia, Australia

    Alistair R R Forrest

  10. RIKEN Preventive Medicine and Diagnosis Innovation Program, Wako, Japan

    Yoshihide Hayashizaki

Authors
  1. Owen J L Rackham
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  2. Jaber Firas
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  9. Carsten O Daub
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  10. Jay W Shin
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  14. Jose M Polo
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  15. Julian Gough
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Consortia

The FANTOM Consortium

Contributions

O.J.L.R. and J.G. initiated the project on the basis of discussions with Y.H. about FANTOM5. J.M.P. led the experimental contribution and helped further develop the Mogrify algorithm. J.F. performed all the experimental validations with contributions from M.L.H., A.S.K. and C.M.N. O.J.L.R. performed the data analysis and interpretation, with significant input from J.G. in the early stages of the work. O.J.L.R., J.M.P. and J.G. prepared the manuscript with input from all named authors at various stages. M.E.O., E.P. and H.F. provided help and advice for technical aspects of the implementation. H.S. and J.W.S. were involved in early discussion on cell conversion concepts. A.R.R.F. and C.O.D. were involved in FANTOM5 management. A.R.R.F. coordinated the collection of the primary cells and tissues profiled in FANTOM5.

Corresponding authors

Correspondence to Owen J L Rackham, Jose M Polo or Julian Gough.

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Competing interests

A provisional specification for a patent application directed to this work has been filed with the Australian patent office.

Additional information

A list of members and affiliations appears in the Supplementary Note.

Integrated supplementary information

Supplementary Figure 1 Benchmarking against existing cell conversion TF techniques.

In order to show how the performance of Mogrify compares with that of other published methods for retrieving sets of TFs for cell conversions, two statistics are reported. First (top), the recovery rate of each of the techniques. A recovery rate of 100% means that the technique also found all of the sets of TFs that were used in the published conversion. As a result, if that technique had been used to design the experiment, then the known conversion set would have been discovered in the first iteration. For Mogrify, this is the case for six of ten of the published conversions; for CellNet and D’Allessio et al., this is only true for one of ten of the published conversions. Second (bottom), the average rank of the recovered TFs is plotted. Ignoring TFs that were missed by each of the techniques, this test shows how well each technique managed to prioritize the required TFs. With the exception of the conversion between fibroblasts and heart (cardiomyocytes), Mogrify performed the best in every case. In the case where none of the correct TFs were predicted, no average rank is shown. This is the case for four conversions in CellNet and one conversion for D’Alessio et al.

Supplementary Figure 2 The reprogramming landscape of human cell types.

Samples are grouped using the cell ontology terms provided by Forrest et al.21. The expression profiles of the ontology terms that contain replicates are arranged in the xy plane using multidimensional scaling, resulting in cell types with similar expression profiles being close together. The height on the landscape is then calculated according to the normalized cumulative coverage of the top eight TFs according to Mogrify; for such a conversion where the top ranked TF regulates all of the required genes, the height would be one and the opposite would result in a height of zero.

Supplementary Figure 3 Comparison to published conversions.

The added coverage value for each conversion as an additional transcription factor is added to the list, showing that the coverage has always reached close to 100% within eight transcription factors.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–3, Supplementary Tables 1–4 and 7, and Supplementary Note. (PDF 7316 kb)

Supplementary Table 5

Benchmarking results comparing the performance of Mogrify, CellNet and D'Alessio et al. For each of the conversions in Figure 2, the prediction for each of the techniques is shown. The ranked lists from CellNet and D'Alessio et al. are cut off at the size of the sets from Mogrify. In order to compare these sets, the average rank and overall recovery efficiency from the published sets are extracted. These statistics are a guide to show the performance that each technique would have achieved on these conversions. Failure to identify the published transcription factors does not necessarily mean that the predicted transcription factors from each technique would not be capable of converting the cells; this benchmark is designed to evaluate performance based on the available data only. For the predictions for Myoblast for CellNet, the skeletal muscle GRN was used. (XLSX 59 kb)

Supplementary Table 6

Benchmarking results comparing the performance of Mogrify and that of its individual components (MARA, STRING and Differential Expression). For each of the conversions in Figure 2, the predictions for Mogrify and each individual component of Mogrify are shown. The ranked lists from the MARA, STRING and Differential Expression components are cut off at the size of the set predicted by Mogrify. In order to compare these sets, the average rank and overall recovery efficiency from the published sets are extracted. These statistics are a guide to show the performance that each technique would have achieved on these conversions. Failure to identify the published transcription factors does not necessarily mean that the predicted transcription factors from each technique would not be capable of converting the cells; this benchmark is designed to evaluate performance based on the available data only. (XLSX 53 kb)

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Rackham, O., Firas, J., Fang, H. et al. A predictive computational framework for direct reprogramming between human cell types. Nat Genet 48, 331–335 (2016). https://doi.org/10.1038/ng.3487

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