Leader cells in collective chemotaxis: Optimality and trade-offs

Photoactivatable substrates show diverse phenotypes of leader cells in collective migration when moving along different extracellular matrix proteins

In cancer metastasis, collectively migrating clusters are discriminated into leader and follower cells that move through extracellular matrices (ECMs) with different characteristics. The impact of changes in ECM protein types on leader cells and migrating clusters is unknown. To address this, we investigated the response of leader cells and migrating clusters upon moving from one ECM protein to another using a photoactivatable substrate bearing photocleavable PEG (PCP), whose surface changes from protein-repellent to protein-adhesive in response to light. We chose laminin and collagen I for our study since they are abundant in two distinct regions in living tissues, namely basement membrane and connective tissue. Using the photoactivatable substrates, the precise deposition of the first ECM protein in the irradiated areas was achieved, followed by creating well-defined cellular confinements. Secondary irradiation enabled the deposition of the second ECM protein in the new irradiated regions, resulting in region-selective heterogeneous and homogenous ECM protein-coated surfaces. Different tendencies in leader cell formation from laminin into laminin compared to those migrating from laminin into collagen were observed. The formation of focal adhesion and actin structures for cells within the same cluster in the ECM proteins responded according to the underlying ECM protein type. Finally, integrin β1 was crucial for the appearance of leader cells for clusters migrating from laminin into collagen. However, when it came to laminin into laminin, integrin β1 was not responsible. This highlights the correlation between leader cells in collective migration and the biochemical signals that arise from underlying extracellular matrix proteins.

Picking winners in cell-cell collisions: Wetting, speed, and contact

Groups of eukaryotic cells can coordinate their crawling motion to follow cues more effectively, stay together, or invade new areas. This collective cell migration depends on cell-cell interactions, which are often studied by colliding pairs of cells together. Can the outcome of these collisions be predicted? Recent experiments on trains of colliding epithelial cells suggest that cells with a smaller contact angle to the surface or larger speeds are more likely to maintain their direction ("win") upon collision. When should we expect shape or speed to correlate with the outcome of a collision? To investigate this question, we build a model for two-cell collisions within the phase field framework, which allows for cell shape changes. We can reproduce the observation that cells with high speed and small contact angles are more likely to win with two different assumptions for how cells interact: (1) velocity aligning, in which we hypothesize that cells sense their own velocity and align to it over a finite timescale, and (2) front-front contact repolarization, where cells polarize away from cell-cell contact, akin to contact inhibition of locomotion. Surprisingly, though we simulate collisions between cells with widely varying properties, in each case, the probability of a cell winning is completely captured by a single summary variable: its relative speed (in the velocity-aligning model) or its relative contact angle (in the contact repolarization model). Both models are currently consistent with reported experimental results, but they can be distinguished by varying cell contact angle and speed through orthogonal perturbations.

Collective behavior and nongenetic inheritance allow bacterial populations to adapt to changing environments

Collective behaviors require coordination among a group of individuals. As a result, individuals that are too phenotypically different from the rest of the group can be left out, reducing heterogeneity, but increasing coordination. If individuals also reproduce, the offspring can have different phenotypes from their parent(s). This raises the question of how these two opposing processes-loss of diversity by collective behaviors and generation of it through growth and inheritance-dynamically shape the phenotypic composition of an isogenic population. We examine this question theoretically using collective migration of chemotactic bacteria as a model system, where cells of different swimming phenotypes are better suited to navigate in different environments. We find that the differential loss of phenotypes caused by collective migration is environment-dependent. With cell growth, this differential loss enables migrating populations to dynamically adapt their phenotype compositions to the environment, enhancing migration through multiple environments. Which phenotypes are produced upon cell division depends on the level of nongenetic inheritance, and higher inheritance leads to larger composition adaptation and faster migration at steady state. However, this comes at the cost of slower responses to new environments. Due to this trade-off, there is an optimal level of inheritance that maximizes migration speed through changing environments, which enables a diverse population to outperform a nondiverse one. Growing populations might generally leverage the selection-like effects provided by collective behaviors to dynamically shape their own phenotype compositions, without mutations.

Collective gradient sensing with limited positional information

Eukaryotic cells sense chemical gradients to decide where and when to move. Clusters of cells can sense gradients more accurately than individual cells by integrating measurements of the concentration made across the cluster. Is this gradient-sensing accuracy impeded when cells have limited knowledge of their position within the cluster, i.e., limited positional information? We apply maximum likelihood estimation to study gradient-sensing accuracy of a cluster of cells with finite positional information. If cells must estimate their location within the cluster, this lowers the accuracy of collective gradient sensing. We compare our results with a tug-of-war model where cells respond to the gradient by polarizing away from their neighbors without relying on their positional information. As the cell positional uncertainty increases, there is a trade-off where the tug-of-war model responds more accurately to the chemical gradient. However, for sufficiently large cell clusters or sufficiently shallow chemical gradients, the tug-of-war model will always be suboptimal to one that integrates information from all cells, even if positional uncertainty is high.