Generation of desired cell types by cell conversion remains a challenge. In particular, derivation of novel cell subtypes identified by single‐cell technologies will open up new strategies for cell therapies. The recent increase in the generation of single‐cell RNA‐sequencing (scRNA‐seq) data and the concomitant increase in the interest expressed by researchers in generating a wide range of functional cells prompted us to develop a computational tool for tackling this challenge.
University of Luxembourg researchers have developed a web application, TransSynW, which uses scRNA‐seq data for predicting cell conversion transcription factors (TFs) for user‐specified cell populations. TransSynW prioritizes pioneer factors among predicted conversion TFs to facilitate chromatin opening often required for cell conversion. In addition, it predicts marker genes for assessing the performance of cell conversion experiments. Furthermore, TransSynW does not require users’ knowledge of computer programming and computational resources. The researchers applied TransSynW to different levels of cell conversion specificity, which recapitulated known conversion TFs at each level. They foresee that TransSynW will be a valuable tool for guiding experimentalists to design novel protocols for cell conversion in stem cell research and regenerative medicine.
A, Application of TransSynW to stem cell research and regenerative medicine. B, Schematic overview of TransSynW algorithm (see also Methods). First, transcription factors (TFs) most specifically expressed in the selected target cell population (specific TFs) and nonspecifically expressed pioneer factors (PFs) are computed. The most synergistic combination of specific TFs and nonspecific PFs is then identified. The predicted set of TFs are ranked by expression fold change between target and starting cell populations. In parallel, top 10 candidate marker genes for target cell population are computed by JSD
Availability – TransSynW web application is available at https://transsynw.lcsb.uni.lu/. The code repository is available at https://git-r3lab.uni.lu/mariana.ribeiro/transsynw.