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bioRxiv · 10.1101/2022.04.01.486696

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

Jimenez, S.·Schreiber, V.·Gradwohl, G.·Molina, N.
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Abstract

Modulation of gene expression during differentiation by transcription factors promotes cell diversity. Despite their role in cell fate decisions, no experimental assays estimate their regulatory activity in a high-throughput manner and at the single-cell resolution. We present FateCompass for identifying lineage-specific transcription factors across differentiation. It uses single-cell transcriptomics 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 learned transcription factor activities using a linear model of gene regulation. Considering 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 drivers. Also, when applied to a differentiation protocol dataset towards beta-like cells, we pinpointed undescribed regulators of an off-target population, which were experimentally validated. Thus, as a framework for identifying lineage-specific transcription factors, FateCompass could have implications on hypothesis generation to increase the understanding of the gene regulatory networks driving cell fate choices. HighlightsO_LIWe developed FateCompass, a flexible pipeline to estimate transcription factor activities during cell-fate decision using single-cell RNA seq data. C_LIO_LIFateCompass outlines gene expression stochastic trajectories by infusing the direction of differentiation using RNA velocity or a differentiation potential when RNA velocity fails. C_LIO_LITranscription factor dynamics allow the identification of time-specific regulatory interactions. C_LIO_LIFateCompass predictions revealed known and novel cell-subtype-specific regulators of mouse pancreatic islet cell development. C_LIO_LIDifferential motif analysis predicts lineage-specific regulators of stem cell-derived human {beta}- cells and sheds light on the cellular heterogeneity of {beta}-cell differentiation protocols. C_LIO_LIExperimental validation supports the proposed GRN controlling SC-EC differentiation predicted by FateCompass. C_LI

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Jimenez, S., Schreiber, V., Gradwohl, G., Molina, N.. 2022-04-01. Characterization of cell-fate decision landscapes by estimating transcription factor dynamics.. https://doi.org/10.1101/2022.04.01.486696

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