Characterization of cell-fate decision landscapes by estimating transcription factor dynamics.

Transcriptional regulation is a fundamental process during cell subtype specification. By modulating the rate of gene expression dynamically, transcription factors promote cell diversity and functional specialization. Despite their crucial role in cell fate decisions, no experimental assays allow the estimation of transcription factors' regulatory activity in a high-throughput manner and at the single- cell resolution. Here, we present FateCompass, a computational method for identifying lineage- specific transcription factors across differentiation. Our pipeline uses single-cell RNA sequencing data to infer differentiation trajectories and transcription factor activities. We combined a probabilistic framework with RNA velocities or a differentiation potential to estimate transition probabilities and perform stochastic simulations. Also, we implemented a linear model of gene regulation to learn transcription factor activities. Taking into account dynamic changes and correlations, we identified lineage-specific regulators. We applied FateCompass to an islet cell formation dataset from the mouse embryo, and we found known and novel potential cell-type dependent drivers. Also, when applied to a differentiation protocol dataset of human embryonic stem cells towards beta-like cells, our approach pinpointed undescribed regulators of an off-target population of intestinal-like cells. Thus, as a framework for identifying lineage-specific transcription factors, FateCompass could have broader implications on hypothesis generation to increase the understanding of the gene regulatory networks driving cell fate choices during differentiation. ### Competing Interest Statement The authors have declared no competing interest.