Mechanistic models of cell-fate transitions from single-cell data

TL;DR

This paper reviews how mechanistic mathematical models integrate single-cell data and imaging to understand cell-fate decisions and self-organization in development, highlighting recent advances and examples.

Contribution

It synthesizes recent literature on mechanistic models that connect single-cell data with spatial organization in development, emphasizing their role in hypothesis testing.

Findings

Models help test hypotheses about cell-fate mechanisms.

Single-cell data combined with modeling elucidates developmental processes.

Imaging and modeling together reveal cell self-assembly dynamics.

Abstract

Our knowledge of how individual cells self-organize to form complex multicellular systems is being revolutionized by a data outburst, coming from high-throughput experimental breakthroughs such as single-cell RNA sequencing and spatially resolved single-molecule FISH. This information is starting to be leveraged by machine learning approaches that are helping us establish a census and timeline of cell types in developing organisms, shedding light on how biochemistry regulates cell-fate decisions. In parallel, imaging tools such as light-sheet microscopy are revealing how cells self-assemble in space and time as the organism forms, thereby elucidating the role of cell mechanics in development. Here we argue that mathematical modeling can bring together these two perspectives, by enabling us to test hypotheses about specific mechanisms, which can be further validated experimentally. We review the recent literature on this subject, focusing on representative examples that use modeling to better understand how single-cell behavior shapes multicellular organisms.