Automatic identification of small molecules that promote cell conversion and reprogramming

Abstract

Summary 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.

Microfluidics for Neuronal Cell and Circuit Engineering

“…Besides TFs, small molecules are also able to manipulate cell fate choices. − Such molecules typically act by modulating cell signaling cascades, epigenetic mechanisms, and metabolic pathways. ,, In combination with TFs, certain small molecules can also improve reprogramming and forward programming efficiencies. − Overexpression of the Neurogenin-2 TF together with small molecules, for instance retinoic acid, enhances the yield of multiple subtypes of stem cell-derived motor neurons . Additionally, combinations of small molecules can also induce reprogramming independent of TFs and thereby overcome the clinical and translational concerns associated with exogenous gene delivery. ,, Moreover, small molecules can easily cross the cell membrane, are generally inexpensive to synthetize and preserve, and their dosing can be tightly controlled in a straightforward manner. ,,− These properties make small molecules attractive to be used in patterned and gradient-generating microfluidic platforms. In general, an optimal multimodal neuronal cell niche engineering platform should be able to incorporate the use of both TFs and small molecules for high yield and robust forward programming, while also supporting the utilization of neurotrophic and axonal attractant-repellent gradients to control neuronal cell polarity.…”

“…272 Additionally, combinations of small molecules can also induce reprogramming independent of TFs and thereby overcome the clinical and translational concerns associated with exogenous gene delivery. 263,267,273 Moreover, small molecules can easily cross the cell membrane, are generally inexpensive to synthetize and preserve, and their dosing can be tightly controlled in a straightforward manner. 263,267,274−276 These properties make small molecules attractive to be used in patterned and gradient-generating microfluidic platforms.…”

Computational approaches for direct cell reprogramming: from the bulk omics era to the single cell era

“…For example, the continuously expanding LINCS database [ 49 ] catalogs the transcriptional responses of multiple cell lines to a wide variety of drugs. Napolitano and colleagues used this to develop DECCODE [ 50 ], a computational method to identify drugs that may increase the efficacy of direct cell reprogramming experiments by incorporating their transcriptional responses. They validate this on reprogramming fibroblasts to human-induced pluripotent stem cells, and curate a list of predicted drugs that may facilitate a range of cell conversions.…”

ChemPert: mapping between chemical perturbation and transcriptional response for non-cancer cells

“…In addition, they offer computational tools for drug prediction based on GSEA of query genes. A similar approach has been proposed for identifying chemical compounds for enhancing cellular reprogramming ( 5 ). However, the majority of the gene expression profiles in these compendia consist of cancer cell lines, which are known to exhibit signal transduction pathways and gene regulatory networks that are significantly different from those of non-cancer cells ( 6 ).…”