DECCODE
Napolitano F, Rapakoulia T, Annunziata P, Hasegawa A, Cardon M, Napolitano S, Vaccaro L, Iuliano A, Wanderlingh LG, Kasukawa T, Medina DL, Cacchiarelli D, Gao X, di Bernardo D, Arner E
2021 · Stem cell reports
Controlling cell fate has great potential for regenerative medicine, drug discovery, and basic research.
Abstract
From the original paper, Stem cell reports · PubMed
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.
Summary
Editorial summary pending review by the maintainer. The paper's own abstract appears above; the Atlas summary in the maintainer's voice will explain how DECCODE relates to the cross-modality inverse-design framework of the review.
Why this level
Level 2 because the method instantiates the inverse-design objective through an empirical or learned proxy response map rather than a mechanistic intervention-dependent model. Representation family is signature / state-matching. Cited in §3.3 of the review. Editorial rationale pending review by the maintainer.