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

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...

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Bibliographic Details
Published inCurrent opinion in systems biology Vol. 26; pp. 79 - 86
Main Authors Torregrosa, Gabriel, Garcia-Ojalvo, Jordi
Format Journal Article
LanguageEnglish
Published Elsevier Ltd 01.06.2021
Subjects
Biochemical models
Cell-fate decisions
Cellular mechanics
Embryonic development
Embryonic development
Cellular mechanics
Biochemical models
Cell-fate decisions
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Summary: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.
ISSN:2452-3100
2452-3100
DOI:10.1016/j.coisb.2021.04.004