Collective motion of mammalian cell cohorts in 3D

Disentangling intrinsic motion from neighborhood effects in heterogeneous collective motion

Most real-world collectives, including animal groups, pedestrian crowds, active particles, and living cells, are heterogeneous. The differences among individuals in their intrinsic properties have emergent effects at the group level. It is often of interest to infer how the intrinsic properties differ among the individuals based on their observed movement patterns. However, the true individual properties may be masked by the nonlinear interactions in the collective. We investigate the inference problem in the context of a bidisperse collective with two types of agents, where the goal is to observe the motion of the collective and classify the agents according to their types. Since collective effects, such as jamming and clustering, affect individual motion, the information in an agent's own movement is insufficient for accurate classification. A simple observer algorithm, based only on individual velocities, cannot accurately estimate the level of heterogeneity of the system and often misclassifies agents. We propose a novel approach to the classification problem, where collective effects on an agent's motion are explicitly accounted for. We use insights about the phenomenology of collective motion to quantify the effect of the neighborhood on an agent's motion using a neighborhood parameter. Such an approach can distinguish between agents of two types, even when their observed motion is identical. This approach estimates the level of heterogeneity much more accurately and achieves significant improvements in classification. Our results demonstrate that explicitly accounting for neighborhood effects is often necessary to correctly infer intrinsic properties of individuals.

Intracellular signaling dynamics and their role in coordinating tissue repair

Tissue repair is a complex process that requires effective communication and coordination between cells across multiple tissues and organ systems. Two of the initial intracellular signals that encode injury signals and initiate tissue repair responses are calcium and extracellular signal-regulated kinase (ERK). However, calcium and ERK signaling control a variety of cellular behaviors important for injury repair including cellular motility, contractility, and proliferation, as well as the activity of several different transcription factors, making it challenging to relate specific injury signals to their respective repair programs. This knowledge gap ultimately hinders the development of new wound healing therapies that could take advantage of native cellular signaling programs to more effectively repair tissue damage. The objective of this review is to highlight the roles of calcium and ERK signaling dynamics as mechanisms that link specific injury signals to specific cellular repair programs during epithelial and stromal injury repair. We detail how the signaling networks controlling calcium and ERK can now also be dissected using classical signal processing techniques with the advent of new biosensors and optogenetic signal controllers. Finally, we advocate the importance of recognizing calcium and ERK dynamics as key links between injury detection and injury repair programs that both organize and execute a coordinated tissue repair response between cells across different tissues and organs. This article is categorized under: Models of Systems Properties and Processes > Mechanistic Models Biological Mechanisms > Cell Signaling Laboratory Methods and Technologies > Imaging Models of Systems Properties and Processes > Organ, Tissue, and Physiological Models.

When Seeing Isn't Believing: How Math Can Guide Our Interpretation of Measurements and Experiments

Mathematical thought experiments probe the meaning and pitfalls of experimental measurements and demonstrate that caution is in order when measuring heterogeneity.

The migration and fusion events related to ROCK activity strongly influence the morphology of chicken embryo intestinal organoids

The method of organoid culture has become a tool widely used in gastrointestinal research, but so far, the migration of organoids derived from gut epithelium and formed in 3D Matrigel matrix has not been reported and studied. The intestinal epithelial tissue derived from 19-day-old chicken embryo was cultured in Matrigel and the dynamic properties of the forming organoids were analyzed by time-lapse image analysis. It was observed that about one in ten organoids actively moved through the matrix, at a speed of 10-20 μm/h. Moreover, rotation was observed in the majority of organoids that did not migrate long distances. The fusion events took place between organoids, which collided during the movement or growth. In our previous paper, we showed that the presence of Toll-like receptor 4 ligand, Escherichia coli lipopolysaccharide (LPS, 1 μg/ml), increased the mean organoid diameter. Here, we confirm this result and demonstrate that the Rho-associated protein kinase (ROCK) inhibitor Y-27632 (10 μM) did not completely abolish organoid migration, but prevented the fusion events, in both LPS-treated and untreated cultures. In consequence, in the presence of Y-27632, the differences between cultures incubated with and without LPS were not visible. We conclude that migration and fusion of organoids may influence their morphology and suggest that these phenomena should be taken into account during the design of experimental settings.

Multidisciplinary approaches to understanding collective cell migration in developmental biology

Mathematical models are becoming increasingly integrated with experimental efforts in the study of biological systems. Collective cell migration in developmental biology is a particularly fruitful application area for the development of theoretical models to predict the behaviour of complex multicellular systems with many interacting parts. In this context, mathematical models provide a tool to assess the consistency of experimental observations with testable mechanistic hypotheses. In this review, we showcase examples from recent years of multidisciplinary investigations of neural crest cell migration. The neural crest model system has been used to study how collective migration of cell populations is shaped by cell–cell interactions, cell–environmental interactions and heterogeneity between cells. The wide range of emergent behaviours exhibited by neural crest cells in different embryonal locations and in different organisms helps us chart out the spectrum of collective cell migration. At the same time, this diversity in migratory characteristics highlights the need to reconcile or unify the array of currently hypothesized mechanisms through the next generation of experimental data and generalized theoretical descriptions.

Semblance of Heterogeneity in Collective Cell Migration

Cell population heterogeneity is increasingly a focus of inquiry in biological research. For example, cell migration studies have investigated the heterogeneity of invasiveness and taxis in development, wound healing, and cancer. However, relatively little effort has been devoted to exploring when heterogeneity is mechanistically relevant and how to reliably measure it. Statistical methods from the animal movement literature offer the potential to analyze heterogeneity in collections of cell tracking data. A popular measure of heterogeneity, which we use here as an example, is the distribution of delays in directional cross-correlation. Employing a suitably generic, yet minimal, model of collective cell movement in three dimensions, we show how using such measures to quantify heterogeneity in tracking data can result in the inference of heterogeneity where there is none. Our study highlights a potential pitfall in the statistical analysis of cell population heterogeneity, and we argue that this can be mitigated by the appropriate choice of null models.

Modeling, signaling and cytoskeleton dynamics: integrated modeling-experimental frameworks in cell migration

Cell migration is a complex and multistep process involved in homeostasis maintenance, morphogenesis, and disease development, such as cancer metastasis. Modeling cell migration and the relevant cytoskeleton dynamics have profound implications for studying fundamental development and disease diagnosis. This review focuses on some recent models of both cell migration and migration-related cytoskeleton dynamics, addressing issues such as the difference between amoeboid and mesenchymal migration modes, and between single-cell migration and collective cell migration. The review also highlights the computational integration among variable external cues, especially the biochemical and mechanical signaling that affects cell migration. Finally, we aim to identify the gaps in our current knowledge and potential strategies to develop integrated modeling-experimental frameworks for multiscale behavior integrating gene expression, cell signaling, mechanics, and multicellular dynamics. WIREs Syst Biol Med 2017, 9:e1365. doi: 10.1002/wsbm.1365 For further resources related to this article, please visit the WIREs website.

Impact of the physical microenvironment on tumor progression and metastasis

The tumor microenvironment is increasingly understood to contribute to cancer development and progression by affecting the complex interplay of genetic and epigenetic changes within the cells themselves. Moreover, recent research has highlighted that, besides biochemical cues from the microenvironment, physical cues can also greatly alter cellular behavior such as proliferation, cancer stem cell properties, and metastatic potential. Whereas initial assays have focused on basic ECM physical properties, such as stiffness, novel in vitro systems are becoming increasingly sophisticated in differentiating between distinct physical cues-ECM pore size, fiber alignment, and molecular composition-and elucidating the different roles these properties play in driving tumor progression and metastasis. Combined with advances in our understanding of the mechanisms responsible for how cells sense these properties, a new appreciation for the role of mechanics in cancer is emerging.