Automatic identification of small molecules that promote cell conversion and reprogramming
Francesco Napolitano, Trisevgeni Rapakoulia, Patrizia Annunziata, Akira Hasegawa, Mélissa Cardon, Sara Napolitano, Lorenzo Vaccaro, Antonella Iuliano, Luca G. Wanderlingh, Takeya Kasukawa, Diego L. Medina, Davide Cacchiarelli, Xin Gao, Diego di Bernardo, Erik Arner
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.