Mean-cluster approach indicates cell sorting time scales are determined by collective dynamics

Segregation in Binary Mixture with Differential Contraction among Active Rings

Cell cortex contraction is vital for shaping cells, enabling movement, division, and responding to mechanical signals-processes crucial for multicellular organisms. Differential membrane contractions between cells significantly influence segregation. We present a model where active particle rings interact through differential contraction, showing that segregation arises from this mechanism, with ring activity functioning as an effective temperature. The interface decay exponent is close to λ∼1/3, differing from previous cluster fusion and diffusion model predictions.

Cell segregation via differential collision modes between heterotypic cell populations

In tissue development and regeneration, the establishment of sharp boundaries between heterotypic cells is essential for the differentiation of tissue functions. During the dynamic rearrangements of constituent cells that result from cell division and collective migration, the segregation boundary encounters various challenges. Several studies have suggested that cortical actomyosin structures play a crucial role in the maintenance of the boundary interface of segregated cell populations, implicating actin-mediated stresses. Examining physical cellular properties such as motility, traction, and intercellular stress, we investigated the formation and maintenance of the stable segregation between epithelial and mesenchymal cell populations devoid of heterotypic adhesions. At the contact boundary, the homotypic adhesion-mediated epithelial aggregates exerted collision-mediated compression against the surrounding mesenchymal cells. Our results demonstrated that heterotypic cell populations established a robust interfacial boundary by accumulating stress from active collisions and repulsions between two dissimilar cell types. Furthermore, the moment of the heterotypic collisions was identified by the existence of a sharp rise in maximum shear stress within the cell cluster.

Is cell segregation like oil and water: Asymptotic versus transitory regime

Understanding the segregation of cells is crucial to answer questions about tissue formation in embryos or tumor progression. Steinberg proposed that separation of cells can be compared to the separation of two liquids. Such a separation is well described by the Cahn-Hilliard (CH) equations and the segregation indices exhibit an algebraic decay with exponent 1/3 with respect to time. Similar exponents are also observed in cell-based models. However, the scaling behavior in these numerical models is usually only examined in the asymptotic regime and these models have not been directly applied to actual cell segregation data. In contrast, experimental data also reveals other scaling exponents and even slow logarithmic scaling laws. These discrepancies are commonly attributed to the effects of collective motion or velocity-dependent interactions. By calibrating a 2D cellular automaton (CA) model which efficiently implements a dynamic variant of the differential adhesion hypothesis to 2D experimental data from Méhes et al., we reproduce the biological cell segregation experiments with just adhesive forces. The segregation in the cellular automaton model follows a logarithmic scaling initially, which is in contrast to the proposed algebraic scaling with exponent 1/3. However, within the less than two orders of magnitudes in time which are observable in the experiments, a logarithmic scaling may appear as a pseudo-algebraic scaling. In particular, we demonstrate that the cellular automaton model can exhibit a range of exponents ≤1/3 for such a pseudo-algebraic scaling. Moreover, the time span of the experiment falls into the transitory regime of the cellular automaton rather than the asymptotic one. We additionally develop a method for the calibration of the 2D Cahn-Hilliard model and find a match with experimental data within the transitory regime of the Cahn-Hilliard model with exponent 1/4. On the one hand this demonstrates that the transitory behavior is relevant for the experiment rather than the asymptotic one. On the other hand this corroborates the ambiguity of the scaling behavior, when segregation processes can be only observed on short time spans.

Activity-driven phase separation and ordering kinetics of passive particles

The steady state and phase ordering kinetics in a pure active Brownian particle system are studied in recent years. In binary mixture of active and passive Brownian particles passive particles are used as probe to understand the properties of active medium. In our present study, we study the mixture of passive and active Brownian particles. Here, we aim to understand the steady state and kinetics of small passive particles in the mixture. In our system, the passive particles are small in size and large in number, whereas ABPs are large in size and small in number. The system is studied on a two-dimensional substrate using overdamped Langevin dynamic simulation. The steady state and kinetics of passive particles are studied for various size and activity of active particles. Passive particles are purely athermal in nature and have dynamics only due to bigger ABPs. For small size ratio and activity, the passive particles remain homogeneous in the system, whereas on increasing size ratio and activity they form periodic hexagonal close pack (HCP) spanning clusters in the system. We have also studied the kinetics of growing passive particle clusters. The mass of the largest cluster shows a much slower growth kinetics in contrast to conserved growth kinetics in ABP system. Our study provides an understanding of steady state and kinetics of passive particles in the presence of bigger active particles. The mixture can be thought of as effect of big microorganism moving in passive medium.

Large-scale simulations of biological cell sorting driven by differential adhesion follow diffusion-limited domain coalescence regime

Cell sorting, whereby a heterogeneous cell mixture segregates and forms distinct homogeneous tissues, is one of the main collective cell behaviors at work during development. Although differences in interfacial energies are recognized to be a possible driving source for cell sorting, no clear consensus has emerged on the kinetic law of cell sorting driven by differential adhesion. Using a modified Cellular Potts Model algorithm that allows for efficient simulations while preserving the connectivity of cells, we numerically explore cell-sorting dynamics over very large scales in space and time. For a binary mixture of cells surrounded by a medium, increase of domain size follows a power-law with exponent n = 1/4 independently of the mixture ratio, revealing that the kinetics is dominated by the diffusion and coalescence of rounded domains. We compare these results with recent numerical studies on cell sorting, and discuss the importance of algorithmic differences as well as boundary conditions on the observed scaling.

A single active ring model with velocity self-alignment

Cellular tissue behavior is a multiscale problem. At the cell level, out of equilibrium, biochemical reactions drive physical cell-cell interactions in a typical active matter process. Cell modeling computer simulations are a robust tool to explore countless possibilities and test hypotheses. Here, we introduce a two-dimensional, extended active matter model for biological cells. A ring of interconnected self-propelled particles represents the cell. Neighboring particles are subject to harmonic and bending potentials. Within a characteristic time, each particle's self-velocity tends to align with its scattering velocity after an interaction. Translational modes, rotational modes, and mixtures of these appear as collective states. Using analytical results derived from active Brownian particles, we identify effective characteristic time scales for ballistic and diffusive movements. Finite-size scale investigation shows that the ring diffusion increases linearly with its size when in collective movement. A study on the ring shape reveals that all collective states are present even when bending forces are weak. In that case, when in a translational mode, the collective velocity aligns with the largest ring's direction in a spontaneous polarization emergence.

Differences in mechanical properties lead to anomalous phase separation in a model cell co-culture

During the morphogenesis of tissues and tumors, cells often interact with neighbors with different mechanical properties, but the understanding of its role is lacking. We use active Brownian dynamics simulations to study a model co-culture consisting of two types of cells with the same size and self-propulsion speed, but different mechanical stiffness and cell-cell adhesion. As time evolves, the system phase separates out into clusters with distinct morphologies and transport properties for the two cell types. The density structure factors and the growth of cell clusters deviate from behavior characteristic of the phase separation in binary fluids. Our results capture emergent structure and motility previously observed in co-culture experiments and provide mechanistic insights into intercellular phase separation during development and disease.

Coarsening dynamics in the Vicsek model of active matter

We study the flocking model introduced by Vicsek et al. (Phys. Rev. Lett. 75, 1226 (1995)) in the "coarsening" regime. At standard self-propulsion speeds, we find two distinct growth laws for the coupled density and velocity fields. The characteristic length scale of the density domains grows as [Formula: see text] (with [Formula: see text] , while the velocity length scale grows much faster, viz., [Formula: see text] (with [Formula: see text] . The spatial fluctuations in the density and velocity fields are studied by calculating the two-point correlation function and the structure factor, which show deviations from the well-known Porod's law. This is a natural consequence of scattering from irregular morphologies that dynamically arise in the system. At large values of the scaled wave vector, the scaled structure factors for the density and velocity fields decay with powers -2.6 and -1.52 , respectively.

Trapping and sorting active particles: Motility-induced condensation and smectic defects

We present an experimental realization of the collective trapping phase transition [Kaiser et al., Phys. Rev. Lett. 108, 268307 (2012)PRLTAO0031-900710.1103/PhysRevLett.108.268307], using motile polar granular rods in the presence of a V-shaped obstacle. We offer a theory of this transition based on the interplay of motility-induced condensation and liquid-crystalline ordering and show that trapping occurs when persistent influx overcomes the collective expulsion of smectic defect structures. In agreement with the theory, our experiments find that a trap fills to the brim when the trap angle θ is below a threshold θ_{c}, while all particles escape for θ>θ_{c}. Our simulations support a further prediction, that θ_{c} goes down with increasing rotational noise. We exploit the sensitivity of trapping to the persistence of directed motion to sort particles based on the statistical properties of their activity.

Enhancing Cancer Cell Collective Motion and Speeding up Confluent Endothelial Dynamics through Cancer Cell Invasion and Aggregation

We report the experimental observation of speeded-up collective motion of the monolayer endothelia-cancer mixture on a collagen-coated substrate, after the invasion of a small fraction of motile cancer cells into the confluent endothelial monolayer, through disrupting cell-cell junctions. It is found that, with an increasing waiting time, the cancer-free confluent endothelial monolayer exhibits a dynamical slowing-down of liquidlike micromotion with a gradually decreasing degree of superdiffusion. After invasion, cancer cells aggregate and exhibit turbulentlike cooperative motion, which is enhanced with the increasing size of gradually aggregated cancer clusters, confined by the fluctuating boundaries of surrounding endothelial cells. It, in turn, enhances the surrounding endothelial cell motion and speeds up the originally slowed-down motion.