Spontaneous transitions between amoeboid and keratocyte-like modes of migration

A dissipative mass conserved reaction-diffusion system reveals switching between coexisting polar and oscillatory cell motility states

Motile eukaryotic cells display distinct modes of migration that often occur within the same cell type. It remains unclear, however, whether transitions between the migratory modes require changes in external conditions, or whether the different modes are coexisting states that emerge from the underlying signaling network. Using a simplified mass-conserved reaction-diffusion model of small GTPase signaling with F-actin mediated feedback, we uncover a distinct bistable mechanism (involving gradient-like phase-separation and traveling waves) and a regime where a polarized mode of migration coexists with spatiotemporal oscillations; the latter, in larger domains, including in three-dimensional surface geometry, result in disordered patterns even in the absence of noise or shape deformations. Indeed, experimental observations of Dictyostelium discoideum show that, upon collision with a rigid boundary, cells may switch from polarized to disordered motion.

Effects of drag coefficients on substrate-based cell motility

Substrate-based cell motility is crucial for biological processes, and heterogeneity in the physical properties of the substrate can influence the outcomes of these processes. We demonstrate numerically the impact of different adhesion strengths on one substrate, achieved by adjusting the drag coefficients of different regions on the substrate, on cellular dynamics. We observed that, given the same initial cell location relative to the interface between two regions with different adhesion strengths, the behavior of a cell differs depending on whether it is initially a static cell or a stationary moving cell. Furthermore, we also introduced external stimulation to the cell. The cellular motility behavior around the interface can also be affected by adjusting the magnitude, range, and duration of the external stimulation.

Fibronectin induces a transition from amoeboid to a fan morphology and modifies migration in Entamoeba histolytica

Cell migration modes can vary, depending on a number of environmental and intracellular factors. The high motility of the pathogenic amoeba Entamoeba histolytica is a decisive factor in its ability to cross the human colonic barrier. We used quantitative live imaging techniques to study the migration of this parasite on fibronectin, a key tissue component. Entamoeba histolytica amoebae on fibronectin contain abundant podosome-like structures. By using a laminar flow chamber, we determined that the adhesion forces generated on fibronectin were twice those on non-coated glass. When migrating on fibronectin, elongated amoeboid cells converted into fan-shaped cells characterized by the presence of a dorsal column of F-actin and a broad cytoplasmic extension at the front. The fan shape depended on the Arp2/3 complex, and the amoebae moved laterally and more slowly. Intracellular measurements of physical variables related to fluid dynamics revealed that cytoplasmic pressure gradients were weaker within fan-shaped cells; hence, actomyosin motors might be less involved in driving the cell body forward. We also found that the Rho-associated coiled-coil containing protein kinase regulated podosome dynamics. We conclude that E. histolytica spontaneously changes its migration mode as a function of the substrate composition. This adaptive ability might favour E. histolytica's invasion of human colonic tissue. By combining microfluidic experiments, mechanical modelling, and image analysis, our work also introduces a computational pipeline for the study of cell migration.

Three-component contour dynamics model to simulate and analyze amoeboid cell motility in two dimensions

Amoeboid cell motility is relevant in a wide variety of biomedical processes such as wound healing, cancer metastasis, and embryonic morphogenesis. It is characterized by pronounced changes of the cell shape associated with expansions and retractions of the cell membrane, which result in a crawling kind of locomotion. Despite existing computational models of amoeboid motion, the inference of expansion and retraction components of individual cells, the corresponding classification of cells, and the a priori specification of the parameter regime to achieve a specific motility behavior remain challenging open problems. We propose a novel model of the spatio-temporal evolution of two-dimensional cell contours comprising three biophysiologically motivated components: a stochastic term accounting for membrane protrusions and two deterministic terms accounting for membrane retractions by regularizing the shape and area of the contour. Mathematically, these correspond to the intensity of a self-exciting Poisson point process, the area-preserving curve-shortening flow, and an area adjustment flow. The model is used to generate contour data for a variety of qualitatively different, e.g., polarized and non-polarized, cell tracks that visually resemble experimental data very closely. In application to experimental cell tracks, we inferred the protrusion component and examined its correlation to common biomarkers: the F-actin density close to the membrane and its local motion. Due to the low model complexity, parameter estimation is fast, straightforward, and offers a simple way to classify contour dynamics based on two locomotion types: the amoeboid and a so-called fan-shaped type. For both types, we use cell tracks segmented from fluorescence imaging data of the model organism Dictyostelium discoideum. An implementation of the model is provided within the open-source software package AmoePy, a Python-based toolbox for analyzing and simulating amoeboid cell motility.

A Hands-on Guide to AmoePy - a Python-Based Software Package to Analyze Cell Migration Data

Amoeboid cell motility is fundamental for a multitude of biological processes such as embryogenesis, immune responses, wound healing, and cancer metastasis. It is characterized by specific cell shape changes: the extension and retraction of membrane protrusions, known as pseudopodia. A common approach to investigate the mechanisms underlying this type of cell motility is to study phenotypic differences in the locomotion of mutant cell lines. To characterize such differences, methods are required to quantify the contour dynamics of migrating cells. AmoePy is a Python-based software package that provides tools for cell segmentation, contour detection as well as analyzing and simulating contour dynamics. First, a digital representation of the cell contour as a chain of nodes is extracted from each frame of a time-lapse microscopy recording of a moving cell. Then, the dynamics of these nodes-referred to as virtual markers-are tracked as the cell contour evolves over time. From these data, various quantities can be calculated that characterize the contour dynamics, such as the displacement of the virtual markers or the local stretching rate of the marker chain. Their dynamics is typically visualized in space-time plots, the so-called kymographs, where the temporal evolution is displayed for the different locations along the cell contour. Using AmoePy, you can straightforwardly create kymograph plots and videos from stacks of experimental bright-field or fluorescent images of motile cells. A hands-on guide on how to install and use AmoePy is provided in this chapter.

From actin waves to mechanism and back: How theory aids biological understanding

Actin dynamics in cell motility, division, and phagocytosis is regulated by complex factors with multiple feedback loops, often leading to emergent dynamic patterns in the form of propagating waves of actin polymerization activity that are poorly understood. Many in the actin wave community have attempted to discern the underlying mechanisms using experiments and/or mathematical models and theory. Here, we survey methods and hypotheses for actin waves based on signaling networks, mechano-chemical effects, and transport characteristics, with examples drawn from Dictyostelium discoideum, human neutrophils, Caenorhabditis elegans, and Xenopus laevis oocytes. While experimentalists focus on the details of molecular components, theorists pose a central question of universality: Are there generic, model-independent, underlying principles, or just boundless cell-specific details? We argue that mathematical methods are equally important for understanding the emergence, evolution, and persistence of actin waves and conclude with a few challenges for future studies.