A predictive computational framework for direct reprogramming between human cell types

• Published in: Nature genetics Vol. 48; no. 3; pp. 331 - 335
• Main Authors: Rackham, Owen J L, Firas, Jaber, Fang, Hai, Oates, Matt E, Holmes, Melissa L, Knaupp, Anja S, Suzuki, Harukazu, Nefzger, Christian M, Daub, Carsten O, Shin, Jay W, Petretto, Enrico, Forrest, Alistair R R, Hayashizaki, Yoshihide, Polo, Jose M, Gough, Julian
• Format: Journal Article
• Language: English
• Published: New York Nature Publishing Group US 01.03.2016; Nature Publishing Group
• Subjects: 14/1; 38/39; 42/109; 631/208/200; 631/553/1833; 631/61/2035; Agriculture; Animal Genetics and Genomics; Biomedicine; Cancer Research; Cell Differentiation - genetics; Cell Transdifferentiation - genetics; Cellular control mechanisms; Cellular Reprogramming - genetics; Computational biology; Councils; Epigenetics; Fibroblasts; Gene expression; Gene Function; Gene Regulatory Networks; Genes; Genetic aspects; Genotype; Grants; Human Genetics; Humans; Induced Pluripotent Stem Cells; Innovations; letter; Medicin och hälsovetenskap; Methods; Observations; Properties; Regenerative Medicine; Transcription factors; Transcription Factors - biosynthesis; Transcription Factors - genetics
• ISSN: 1061-4036; 1546-1718; 1546-1718
• DOI: 10.1038/ng.3487

Owen Rackham, Jose Polo, Julian Gough and colleagues present a method, Mogrify, for predicting sets of transcription factors that can induce transdifferentiation between cell types. They show that Mogrify is able to predict known factors for published cell conversions and experimentally validate factors for two new conversions. 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.