Statistically derived geometrical landscapes capture principles of decision-making dynamics during cell fate transitions
Year: 2021
Type: article
Abstract: Fate decisions in developing tissues involve cells transitioning between a set of discrete cell states, each defined by a distinct gene expression profile. Geometric models, often referred to as Waddington landscapes, in which developmental paths are given by the gradient and cell states by the minima of the model, are an appealing way to describe differentiation dynamics and developmental decisions. To construct and validate accurate dynamical landscapes, quantitative methods based on experimental data are necessary. To this end we took advantage of the differentiation of neural and mesodermal cells from pluripotent mouse embryonic stem cells exposed to different combinations and durations of signalling factors. We developed a principled statistical approach using flow cytometry data to quantify differentiating cell states. Then, using a framework based on Catastrophe Theory and approximate Bayesian computation, we constructed the corresponding dynamical landscape. The result was a quantitative model that accurately predicted the proportions of neural and mesodermal cells differentiating in response to specific signalling regimes. Analysis of the geometry of the landscape revealed two distinct ways in which cells make a binary choice between one of two fates. We discuss the biological relevance of these mechanisms and suggest that they represent general archetypal designs for developmental decisions. Taken together, the approach we describe is broadly applicable for the quantitative analysis of differentiation dynamics and for determining the logic of developmental cell fate decisions.
Source: Cell Systems
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: hybrid
Estimated APC paid: $8,900
Funders: National Science Foundation, Gordon and Betty Moore Foundation, National Sleep Foundation, Wellcome Trust, Francis Crick Institute, Cancer Research UK, University of Warwick, European Commission, National Institutes of Health, Medical Research Council, Engineering and Physical Sciences Research Council, European Research Council, Horizon 2020, National Institute of General Medical Sciences, Kavli Institute for Theoretical Physics, University of California, Santa Barbara, H2020 European Research Council
Awards: Geometry, Genetics and Development, G1871477277, G2012630070, G2022142116, G2500807902, Using catastrophes, dynamics & data analysis to uncover how differentiating cells make decisions, G3009324719, Mathematical Foundations of Information and Decisions in Dynamic Cell Signalling, G3446018, G348518476, G3773749580, G4116657650, G434337789, G439613525, G4999696324, The molecular and cellular logic of vertebrate neural development, G5261598906, G5432664585, G5603896232, G6339342330, G8386048894, Kavli Institute for Theoretical Physics, KITP Interdisciplinary Biology Initiative, G881252239, G985207216
OpenAlex ID: W3200674343