Abstract
Controlling cell fate has great potential for regenerative medicine, drug discovery, and basic research. Although transcription factors are able to promote cell reprogramming and transdifferentiation, methods based on their upregulation often show low efficiency. Small molecules that can facilitate conversion between cell types can ameliorate this problem working through safe, rapid, and reversible mechanisms. Here, we present DECCODE, an unbiased computational method for identification of such molecules based on transcriptional data. DECCODE matches a large collection of drug-induced profiles for drug treatments against a large dataset of primary cell transcriptional profiles to identify drugs that either alone or in combination enhance cell reprogramming and cell conversion. Extensive validation in the context of human induced pluripotent stem cells shows that DECCODE is able to prioritize drugs and drug combinations enhancing cell reprogramming. We also provide predictions for cell conversion with single drugs and drug combinations for 145 different cell types.
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DECCODE method for identification of drugs that promote cell conversion
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Predicts drugs that alone or in combination increase reprogramming efficiency
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Validated for reprogramming of human fibroblast to hiPSCs
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Treatment suggestions provided for 145 target cell types
In this article, Napolitano, Rapakoulia, and colleagues present DECCODE, a method for automatic identification of drugs that alone or in combination promote cell conversion and reprogramming. They validate the method for reprogramming of human fibroblasts to induced pluripotent stem cells (hiPSCs), and provide predicted treatments for conversion to 145 target cell types.