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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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

    • Owen J L Rackham
    •  & Jaber Firas

    These authors contributed equally to this work.

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

Consortia

  1. The FANTOM Consortium

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

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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.

Competing interests

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

Corresponding authors

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

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Supplementary information

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  1. 1.

    Supplementary Text and Figures

    Supplementary Figures 1–3, Supplementary Tables 1–4 and 7, and Supplementary Note.

Excel files

  1. 1.

    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.

  2. 2.

    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.

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DOI

https://doi.org/10.1038/ng.3487

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