Colec12 and Trail signaling confine cranial neural crest cell trajectories and promote collective cell migration

Variations in non-local interaction range lead to emergent chase-and-run in heterogeneous populations

In a chase-and-run dynamic, the interaction between two individuals is such that one moves towards the other (the chaser), while the other moves away (the runner). Examples can be found in both interacting cells and animals. Here, we investigate the behaviours that can emerge at a population level, for a heterogeneous group that contains subpopulations of chasers and runners. We show that a wide variety of patterns can form, from stationary patterns to oscillatory and population-level chase-and-run, where the latter describes a synchronized collective movement of the two populations. We investigate the conditions under which different behaviours arise, specifically focusing on the interaction ranges: the distances over which cells or organisms can sense one another's presence. We find that when the interaction range of the chaser is sufficiently larger than that of the runner-or when the interaction range of the chase is sufficiently larger than that of the run-population-level chase-and-run emerges in a robust manner. We discuss the results in the context of phenomena observed in cellular and ecological systems, with particular attention to the dynamics observed experimentally within populations of neural crest and placode cells.

Modeling the extracellular matrix in cell migration and morphogenesis: a guide for the curious biologist

The extracellular matrix (ECM) is a highly complex structure through which biochemical and mechanical signals are transmitted. In processes of cell migration, the ECM also acts as a scaffold, providing structural support to cells as well as points of potential attachment. Although the ECM is a well-studied structure, its role in many biological processes remains difficult to investigate comprehensively due to its complexity and structural variation within an organism. In tandem with experiments, mathematical models are helpful in refining and testing hypotheses, generating predictions, and exploring conditions outside the scope of experiments. Such models can be combined and calibrated with in vivo and in vitro data to identify critical cell-ECM interactions that drive developmental and homeostatic processes, or the progression of diseases. In this review, we focus on mathematical and computational models of the ECM in processes such as cell migration including cancer metastasis, and in tissue structure and morphogenesis. By highlighting the predictive power of these models, we aim to help bridge the gap between experimental and computational approaches to studying the ECM and to provide guidance on selecting an appropriate model framework to complement corresponding experimental studies.

Collectins and ficolins in neonatal health and disease

The immune system starts to develop early in embryogenesis. However, at birth it is still immature and associated with high susceptibility to infection. Adaptation to extrauterine conditions requires a balance between colonization with normal flora and protection from pathogens. Infections, oxidative stress and invasive therapeutic procedures may lead to transient organ dysfunction or permanent damage and perhaps even death. Newborns are primarily protected by innate immune mechanisms. Collectins (mannose-binding lectin, collectin-10, collectin-11, collectin-12, surfactant protein A, surfactant protein D) and ficolins (ficolin-1, ficolin-2, ficolin-3) are oligomeric, collagen-related defence lectins, involved in innate immune response. In this review, we discuss the structure, specificity, genetics and role of collectins and ficolins in neonatal health and disease. Their clinical associations (protective or pathogenic influence) depend on a variety of variables, including genetic polymorphisms, gestational age, method of delivery, and maternal/environmental microflora.