TransSynW: A single-cell RNA-sequencing based web application to guide cell conversion experiments

The goal of regenerative medicine is to create functional and faithful cell types that can replace damaged tissues. Although substantial progress has been achieved with novel reprogramming techniques, generating the cells of interest still poses problems.
The advent of single cell RNA sequencing (scRNA-seq) opened doors to characterize the heterogeneity within a cell population and allows us to characterize genes that drive conversion between subpopulations. However, to successfully convert between cell types, the identity TFs for the cell type of interest must be able to regulate genes that are silenced and inaccessible for expression in the original cell. Indeed, it has been described that a subset of TFs, denominated as pioneer factors (PFs), possesses the remarkable ability of overcoming such constraints and access genes embedded in closed chromatin, playing a key role in initiating cell conversion mechanisms and improving the efficiency of conversion protocols.
Based on this concept, we developed TransSynW, the only computational web application to date that can unbiasedly identify cell conversion TFs for any cell population identified by scRNA-seq data. Based on the information theoretic measure of transcriptional synergy, TransSynW predicts transcription regulatory cores that consist of population-specific TFs and non-specifically expressed PFs. Furthermore, it predicts new markers for each analyzed population, enabling researchers to assess the performance of their cell conversion experiments. We applied TransSynW to different cell systems and our results well-recapitulated known cell conversion TFs, markers, and made novel predictions in the various systems.
In particular, we applied TransSynW and identified novel TFs critical for subtype specification of human ventral midbrain dopaminergic neurons (hDA). Using a flexible CRISPR-dCas9 system, we intend to target the predicted TFs and develop a novel protocol that allows control over hDA subtype generation, with increased efficiency and fidelity. Accomplishing efficient cell conversion and providing control over cell subtype generation will be an unparalleled breakthrough for translational applications, such as cell transplantation and disease modelling.

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