ENTRAIN: integrating trajectory inference and gene regulatory networks with spatial data to co-localize the receptor–ligand interactions that specify cell fate

Author:

Kyaw Wunna12,Chai Ryan C23,Khoo Weng Hua23,Goldstein Leonard D24,Croucher Peter I23,Murray John M5,Phan Tri Giang12ORCID

Affiliation:

1. Precision Immunology Program, Garvan Institute of Medical Research , Darlinghurst, NSW 2010, Australia

2. St Vincent’s Healthcare Clinical Campus, Faculty of Medicine and Health, UNSW Sydney , Darlinghurst, NSW 2010, Australia

3. Cancer Plasticity and Dormancy Program, Garvan Institute of Medical Research , Darlinghurst, NSW 2010 Australia

4. Data Science Platform, Garvan Institute of Medical Research , Darlinghurst, NSW 2010, Australia

5. School of Mathematics and Statistics, Faculty of Science, UNSW Sydney , Kensington, NSW 2033, Australia

Abstract

Abstract 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.

Funder

Australian National Health and Medical Research Council

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability

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