Approximated gene expression trajectories for gene regulatory network inference on cell tracks

Area of Science:

• Developmental Biology
• Systems Biology
• Computational Biology

Background:

• Gene regulatory networks (GRNs) control pattern formation during development.
• Traditional GRN inference methods often assume pattern formation and morphogenesis are separable.
• Most animal development involves tightly linked pattern formation and morphogenesis, complicating GRN analysis.

Purpose of the Study:

• To develop a novel framework for inferring GRNs that explicitly incorporates cell movement.
• To enable the study of GRNs in systems where pattern formation and morphogenesis are co-occurring.
• To provide a more accurate understanding of developmental mechanisms in complex biological systems.

Main Methods:

• Integration of quantitative data from live and fixed embryos.
• Approximation of gene expression trajectories (AGETs) in single cells.
• Reverse-engineering of GRNs using AGETs and incorporating cell movement.

Main Results:

• Generated candidate GRNs that successfully recapitulate tissue-level patterns.
• Captured gene expression dynamics at the single-cell level.
• Recovered known genetic interactions and recapitulated experimental perturbations.
• Successfully incorporated cell movements into GRN inference for the first time.

Conclusions:

• The developed framework is effective for reverse-engineering GRNs in systems with coupled pattern formation and morphogenesis.
• Explicitly including cell movement is essential for accurate GRN inference in many animal development scenarios.
• This methodology advances our ability to understand complex developmental processes and genetic interactions.