Automatic identification of small molecules that promote cell conversion and reprogramming
Wanderlingh, Luca Giorgio
Medina, Diego L.
di Bernardo, Diego
KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Computer Science Program
Computational Bioscience Research Center (CBRC)
Permanent link to this recordhttp://hdl.handle.net/10754/662486
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AbstractControlling 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.
CitationNapolitano, F., Rapakoulia, T., Annunziata, P., Hasegawa, A., Cardon, M., Napolitano, S., … Arner, E. (2021). Automatic identification of small molecules that promote cell conversion and reprogramming. Stem Cell Reports. doi:10.1016/j.stemcr.2021.03.028
SponsorsE.A. was supported by a Research Grant from MEXT to the RIKEN Center for Integrative Medical Sciences. X.G. was supported by funding from King Abdullah University of Science and Technology (KAUST), under award number FCC/1/1976-18-01, FCC/1/1976-23-01, FCC/1/1976-25-01, FCC/1/1976-26-01, FCS/1/4102-02-01, REI/1/4216-01-01, and REI/1/4437-01-01. D.L.M. was supported by the Italian Telethon Foundation under project number TMDMCBX16TT. D.C. was supported by Fondazione Telethon Core Grant, Armenise-Harvard Foundation Career Development Award, European Research Council (grant agreement 759154, CellKarma), and the Rita-Levi Montalcini program from MIUR.
JournalStem Cell Reports
Except where otherwise noted, this item's license is described as This is an open access article under the CC BY-NC-ND license.
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