Screening by changes in stereotypical behavior during cell motility

Luke Tweedy,Robert H Insall,Robert G Endres,Patrick Witzel,Doris Heinrich

doi:10.1038/s41598-019-45305-w

Abstract

Stereotyped behaviors are series of postures that show very little variability between repeats. They have been used to classify the dynamics of individuals, groups and species without reference to the lower-level mechanisms that drive them. Stereotypes are easily identified in animals due to strong constraints on the number, shape, and relative positions of anatomical features, such as limbs, that may be used as landmarks for posture identification. In contrast, the identification of stereotypes in single cells poses a significant challenge as the cell lacks these landmark features, and finding constraints on cell shape is a non-trivial task. Here, we use the maximum caliber variational method to build a minimal model of cell behavior during migration. Without reference to biochemical details, we are able to make behavioral predictions over timescales of minutes using only changes in cell shape over timescales of seconds. We use drug treatment and genetics to demonstrate that maximum caliber descriptors can discriminate between healthy and aberrant migration, thereby showing potential applications for maximum caliber methods in automated disease screening, for example in the identification of behaviors associated with cancer metastasis.

Highlights

Moving in the right way at the right time can be a matter of life and death
Known as morphological profiling, shows great promise in biomedicine[10]. We explore this question using Dictyostelium discoideum, a model for chemotaxis noted for its reproducible directional migration towards cyclic adenosine monophosphate[11,12], which it senses using typical G-protein coupled receptors
To capture cell shape at any given point in time, we employ Fourier shape descriptors, a rotationally invariant method of quantifying shapes used previously to show that cell shape and environment are intrinsically linked[13] (Fig. 1A). These shape data are naturally of high dimensionality, making further analysis difficult. We reduce their dimensionality using principal component analysis (PCA), a method used previously to obtain the key directions of variability from the shapes of cells[13,14,15] and animals

Summary

Introduction

Moving in the right way at the right time can be a matter of life and death. Whether avoiding a predator or searching for food, choosing the correct movements in response to specific stimuli is a crucial part of how an organism interacts with its environment. To capture cell shape (or posture) at any given point in time, we employ Fourier shape descriptors, a rotationally invariant method of quantifying shapes used previously to show that cell shape and environment are intrinsically linked[13] (Fig. 1A) These shape data are naturally of high dimensionality, making further analysis difficult. There are many potential ways to do so[18,19], we have adapted the variational maximum caliber (MaxCal) approach[20,21] to this end These methods have several advantages over conventional alternatives: Firstly, Fourier descriptors capture all available information on shape, and the subsequent PCA provides a natural quantitative means of discarding its less informative elements. We demonstrate the usefulness of these methods by showing that they successfully discriminate between the behavior of drug-treated or genetically altered cells and their parental strains

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Results

Conclusion