 The study proposes a new method called single-cell multi-task network inference, SCM-TNI. This method uses both single-cell RNA sequencing, SCRNA-SEC, and single-cell accessible transpose and accessible chromatin using sequencing, SCATACSEC, to identify gene expression patterns associated with cell types. The method then uses these patterns to infer the underlying gene regulatory networks, GI-ONs, for each cell type along a lineage. The authors demonstrate that SCM-TNI is able to accurately infer GI-ON dynamics and identify key regulators of fate transitions for diverse processes such as cellular reprogramming and differentiation. This article was authored by Shilu Zhong, Saptashipayin, Stefan Piechak, and others.