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A predictive computational framework for direct reprogramming between human cell types

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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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.

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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.

Corresponding authors

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

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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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