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Analysis of protrusion dynamics in amoeboid cell motility by means of regularized contour flows
(2021)
Amoeboid cell motility is essential for a wide range of biological processes including wound healing, embryonic morphogenesis, and cancer metastasis. It relies on complex dynamical patterns of cell shape changes that pose long-standing challenges to mathematical modeling and raise a need for automated and reproducible approaches to extract quantitative morphological features from image sequences. Here, we introduce a theoretical framework and a computational method for obtaining smooth representations of the spatiotemporal contour dynamics from stacks of segmented microscopy images. Based on a Gaussian process regression we propose a one-parameter family of regularized contour flows that allows us to continuously track reference points (virtual markers) between successive cell contours. We use this approach to define a coordinate system on the moving cell boundary and to represent different local geometric quantities in this frame of reference. In particular, we introduce the local marker dispersion as a measure to identify localized membrane expansions and provide a fully automated way to extract the properties of such expansions, including their area and growth time. The methods are available as an open-source software package called AmoePy, a Python-based toolbox for analyzing amoeboid cell motility (based on time-lapse microscopy data), including a graphical user interface and detailed documentation. Due to the mathematical rigor of our framework, we envision it to be of use for the development of novel cell motility models. We mainly use experimental data of the social amoeba Dictyostelium discoideum to illustrate and validate our approach. <br /> Author summary Amoeboid motion is a crawling-like cell migration that plays an important key role in multiple biological processes such as wound healing and cancer metastasis. This type of cell motility results from expanding and simultaneously contracting parts of the cell membrane. From fluorescence images, we obtain a sequence of points, representing the cell membrane, for each time step. By using regression analysis on these sequences, we derive smooth representations, so-called contours, of the membrane. Since the number of measurements is discrete and often limited, the question is raised of how to link consecutive contours with each other. In this work, we present a novel mathematical framework in which these links are described by regularized flows allowing a certain degree of concentration or stretching of neighboring reference points on the same contour. This stretching rate, the so-called local dispersion, is used to identify expansions and contractions of the cell membrane providing a fully automated way of extracting properties of these cell shape changes. We applied our methods to time-lapse microscopy data of the social amoeba Dictyostelium discoideum.
As society paves its way towards device miniaturization and precision medicine, micro-scale actuation and guided transport become increasingly prominent research fields, with high potential impact in both technological and clinical contexts. In order to accomplish directed motion of micron-sized objects, as biosensors and drug-releasing microparticles, towards specific target sites, a promising strategy is the use of living cells as smart biochemically-powered carriers, building the so-called bio-hybrid systems. Inspired by leukocytes, native cells of living organisms efficiently migrating to critical targets as tumor tissue, an emerging concept is to exploit the amoeboid crawling motility of such cells as mean of transport for drug delivery applications.
In the research work described in this thesis, I synergistically applied experimental, computational and theoretical modeling approaches to investigate the behaviour and transport mechanism of a novel kind of bio-hybrid system for active transport at the micro-scale, referred to as cellular truck. This system consists of an amoeboid crawling cell, the carrier, attached to a microparticle, the cargo, which may ideally be drug-loaded for specific therapeutic treatments.
For the purposes of experimental investigation, I employed the amoeba Dictyostelium discoideum as crawling cellular carrier, being a renowned model organism for leukocyte migration and, in general, for eukaryotic cell motility. The performed experiments revealed a complex recurrent cell-cargo relative motion, together with an intermittent motility of the cellular truck as a whole. The evidence suggests the presence of cargoes on amoeboid cells to act as mechanical stimulus leading cell polarization, thus promoting cell motility and giving rise to the observed intermittent dynamics of the truck. Particularly, bursts in cytoskeletal polarity along the cell-cargo axis have been
found to occur in time with a rate dependent on cargo geometrical features, as particle diameter. Overall, the collected experimental evidence pointed out a pivotal role of cell-cargo interactions in the emergent cellular truck motion dynamics. Especially, they can determine the transport capabilities of amoeboid cells, as the cargo size significantly impacts the cytoskeletal activity and repolarization dynamics along the cell-cargo axis, the latter responsible for truck displacement and reorientation.
Furthermore, I developed a modeling framework, built upon the experimental evidence on cellular truck behaviour, that connects the relative dynamics and interactions arising at the truck scale with the actual particle transport dynamics. In fact, numerical simulations of the proposed model successfully reproduced the phenomenology of the cell-cargo system, while enabling the prediction of the transport properties of cellular trucks over larger spatial and temporal scales. The theoretical analysis provided a deeper understanding of the role of cell-cargo interaction on mass transport, unveiling in particular how the long-time transport efficiency is governed by the interplay between the persistence time of cell polarity and time scales of the relative dynamics stemming from cell-cargo interaction. Interestingly, the model predicts the existence of an optimal cargo size, enhancing the diffusivity of cellular trucks; this is in line with previous independent experimental data, which appeared rather counterintuitive and had no explanation prior to this study.
In conclusion, my research work shed light on the importance of cargo-carrier interactions in the context of crawling cell-mediated particle transport, and provides a prototypical, multifaceted framework for the analysis and modelling of such complex bio-hybrid systems and their perspective optimization.