Cross-talk between Rho and Rac GTPases drives deterministic exploration of cellular shape space and morphological heterogeneity

Abstract

One goal of cell biology is to understand how cells adopt different shapes in response to varying environmental and cellular conditions. Achieving a comprehensive understanding of the relationship between cell shape and environment requires a systems-level understanding of the signalling networks that respond to external cues and regulate the cytoskeleton. Classical biochemical and genetic approaches have identified thousands of individual components that contribute to cell shape, but it remains difficult to predict how cell shape is generated by the activity of these components using bottom-up approaches because of the complex nature of their interactions in space and time. Here, we describe the regulation of cellular shape by signalling systems using a top-down approach. We first exploit the shape diversity generated by systematic RNAi screening and comprehensively define the shape space a migratory cell explores. We suggest a simple Boolean model involving the activation of Rac and Rho GTPases in two compartments to explain the basis for all cell shapes in the dataset. Critically, we also generate a probabilistic graphical model to show how cells explore this space in a deterministic, rather than a stochastic, fashion. We validate the predictions made by our model using live-cell imaging. Our work explains how cross-talk between Rho and Rac can generate different cell shapes, and thus morphological heterogeneity, in genetically identical populations.

Mechanisms of cell polarization

“…Examples of the outcomes of cells treated with RNAi include [146,147]. Some theoretical analysis of the cell shapes was provided in [62].…”

Identification of clinically predictive metagenes that encode components of a network coupling cell shape to transcription by image-omics

“…In the case of cells, quantifiable phenotypes such as cell shape can be used to infer the activation state of different signaling networks that regulate aspects of cell physiology, such as proliferation, survival, migration, and differentiation, even if the signaling activity of all these networks cannot be directly measured (Bakal et al 2007;Sailem et al 2014). Thus, visual phenotypes such as shape can be used to infer signaling states (Fig.…”

Building cell models and simulations from microscope images

“…For example, statistical models of cell shape have been created using descriptive features and used to discover gene perturbations that affect cell shape [28–30]. Work has also been done using eigenshape methods that can be generative [31, 32].…”