SinCMat : A single-cell based method for predicting maturation transcription factors#

Authors#

Sybille Barvaux, Satoshi Okawa, Antonio Del Sol Mesa

Abstract#

One of the major goals of regenerative medicine is to generate tissue-specific mature and functional cell types for clinical applications. However, current direct cell differentiation or reprogramming protocols are still unable to systematically produce fully mature and functional cells, especially in in vitro systems. Existing computational approaches aim at predicting transcription factors (TFs) for direct cell conversion, but there is not method that specifically targets cell maturation. To address this important challenge in the field, we have developed SinCMat, a single-cell based computational method for predicting cell maturation TFs for in vitro use. Based on a concept that integrates identity and environment factors, SinCMat predicted known and novel TFs candidates for further experimental validations. We expect SinCMat to be an important resource, complementary to preexisting computational methods, for studies aiming at producing functionally mature cells for clinical applications.