ENTRAIN: Integrating Trajectory Inference and Gene Regulatory Networks with Spatial Data to Co-localize the Receptor-ligand Interactions That Specify Cell Fate

Motivation: Cell fate is commonly studied by profiling the gene expression of single cells to infer developmental trajectories based on expression similarity, RNA velocity, or statistical mechanical properties. However, current approaches do not recover microenvironmental signals from the cellular niche that drive a differentiation trajectory.

Results: We resolve this with environment-aware trajectory inference (ENTRAIN), a computational method that integrates trajectory inference methods with ligand-receptor pair gene regulatory networks to identify extracellular signals and evaluate their relative contribution towards a differentiation trajectory. The output from ENTRAIN can be superimposed on spatial data to co-localize cells and molecules in space and time to map cell fate potentials to cell-cell interactions. We validate and benchmark our approach on single-cell bone marrow and spatially resolved embryonic neurogenesis datasets to identify known and novel environmental drivers of cellular differentiation.

Availability And Implementation: ENTRAIN is available as a public package at https://github.com/theimagelab/entrain and can be used on both single-cell and spatially resolved datasets.