A predictive computational framework for direct reprogramming between human cell types

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dc.contributor.authorRackham, O.J.L.
dc.contributor.authorFiras, J.
dc.contributor.authorFang, H.
dc.contributor.authorOates, M.E.
dc.contributor.authorHolmes, M.L.
dc.contributor.authorKnaupp, A.S.
dc.contributor.authorSuzuki, H.
dc.contributor.authorNefzger, C.M.
dc.contributor.authorDaub, C.O.
dc.contributor.authorShin, J.W.
dc.contributor.authorPetretto, E.
dc.contributor.authorForrest, A.R.R.
dc.contributor.authorHayashizaki, Y.
dc.contributor.authorPolo, J.M.
dc.contributor.authorGough, J.
dc.date.issued2016
dc.description.abstractTransdifferentiation, 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.
dc.identifier.citationNature Genetics, 2016; 48(3):331-335
dc.identifier.doi10.1038/ng.3487
dc.identifier.issn1061-4036
dc.identifier.issn1546-1718
dc.identifier.orcidPolo, J.M. [0000-0002-2531-778X]
dc.identifier.urihttps://hdl.handle.net/2440/133480
dc.language.isoen
dc.publisherNATURE PUBLISHING GROUP
dc.source.urihttps://doi.org/10.1038/ng.3487
dc.subjectFANTOM Consortium
dc.subjectFibroblasts
dc.subjectHumans
dc.subjectTranscription Factors
dc.subjectRegenerative Medicine
dc.subjectCell Differentiation
dc.subjectGene Regulatory Networks
dc.subjectCell Transdifferentiation
dc.subjectInduced Pluripotent Stem Cells
dc.subjectCellular Reprogramming
dc.subject.meshFibroblasts
dc.subject.meshHumans
dc.subject.meshTranscription Factors
dc.subject.meshRegenerative Medicine
dc.subject.meshCell Differentiation
dc.subject.meshGene Regulatory Networks
dc.subject.meshCell Transdifferentiation
dc.subject.meshInduced Pluripotent Stem Cells
dc.subject.meshCellular Reprogramming
dc.titleA predictive computational framework for direct reprogramming between human cell types
dc.typeJournal article
pubs.publication-statusPublished

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