A mathematical analysis of focal adhesion lifetimes and their effect on cell motility

Focal adhesions, reticular adhesions, flat clathrin lattices: what divides them, what unites them?

The majority of cells within multicellular organisms requires anchorage to their surroundings in the form of cell-cell or cell-matrix adhesions. In regards to cell-matrix adhesions, the transmembrane receptors of the integrin family have long been recognized as the central scaffold around which these adhesion complexes are built. Via their extracellular domains integrins bind extracellular matrix ligands while their intracellular tails interact with a plethora of proteins that link integrin-based adhesions to the cytoskeleton and turn them also into important signaling platforms. Depending on the specific intracellular interactome of the integrins, different types of integrin adhesion complexes have been classified. The best-studied ones are the focal adhesions, in which integrins become firmly linked to contractile actomyosin fibers, allowing force transduction. But integrins also form an integral part of adhesion structures that lack the strong actomyosin link and are enriched in endocytic proteins. These have been named reticular adhesions, flat clathrin lattices, or clathrin plaques. Initially, the different types of integrin adhesion complexes have been viewed as discrete entities with their own separate life cycles. However, in the past years it has become more and more apparent how closely intertwined they are. In fact, it was shown that they can trigger each other's biogenesis or can even directly convert into each other. Here, we describe similarities as well as differences between integrin adhesion complexes, focusing on the versatile αvβ5 integrins, and discuss the recently discovered close links and interconversion modes between the different αvβ5 integrin adhesion types.

YAP phosphorylation within integrin adhesions: Insights from a computational model

Mechanical and biochemical cues intricately activate Yes-associated protein (YAP), which is pivotal for the cellular responses to these stimuli. Recent findings reveal an unexplored role of YAP in influencing the apoptotic process. It has been shown that, on soft matrices, YAP is recruited to small adhesions, phosphorylated at Y357, and translocated into the nucleus triggering apoptosis. Interestingly, YAP Y357 phosphorylation is significantly reduced in larger mature focal adhesions on stiff matrices. Building upon these novel insights, we have developed a stochastic model to delve deeper into the complex dynamics of YAP phosphorylation within integrin adhesions. Our findings emphasize several key points: firstly, increasing the cytosolic diffusion rate of YAP correlates with higher levels of phosphorylated YAP (pYAP); secondly, increasing the number of binding sites and distributing them across the membrane surface, mimicking smaller adhesions, leads to higher pYAP levels, particularly at lower diffusion rates. Moreover, we show that the binding and release rate of YAP to adhesions as well as adhesion lifetimes significantly influence the size effect of adhesion-induced YAP phosphorylation. The results highlight the complex and dynamic interplay between adhesion lifetime, the rate of pYAP unbinding from adhesions, and dephosphorylation rates, collectively shaping overall pYAP levels. In summary, our work advances the understanding of YAP mechanotransduction and opens avenues for experimental validation.

Substrate stress relaxation regulates neural stem cell fate commitment

Adult neural stem cells (NSCs) reside in the dentate gyrus of the hippocampus, and their capacity to generate neurons and glia plays a role in learning and memory. In addition, neurodegenerative diseases are known to be caused by a loss of neurons and glial cells, resulting in a need to better understand stem cell fate commitment processes. We previously showed that NSC fate commitment toward a neuronal or glial lineage is strongly influenced by extracellular matrix stiffness, a property of elastic materials. However, tissues in vivo are not purely elastic and have varying degrees of viscous character. Relatively little is known about how the viscoelastic properties of the substrate impact NSC fate commitment. Here, we introduce a polyacrylamide-based cell culture platform that incorporates mismatched DNA oligonucleotide-based cross-links as well as covalent cross-links. This platform allows for tunable viscous stress relaxation properties via variation in the number of mismatched base pairs. We find that NSCs exhibit increased astrocytic differentiation as the degree of stress relaxation is increased. Furthermore, culturing NSCs on increasingly stress-relaxing substrates impacts cytoskeletal dynamics by decreasing intracellular actin flow rates and stimulating cyclic activation of the mechanosensitive protein RhoA. Additionally, inhibition of motor-clutch model components such as myosin II and focal adhesion kinase partially or completely reverts cells to lineage distributions observed on elastic substrates. Collectively, our results introduce a unique system for controlling matrix stress relaxation properties and offer insight into how NSCs integrate viscoelastic cues to direct fate commitment.

Learning dynamical models of single and collective cell migration: a review

Single and collective cell migration are fundamental processes critical for physiological phenomena ranging from embryonic development and immune response to wound healing and cancer metastasis. To understand cell migration from a physical perspective, a broad variety of models for the underlying physical mechanisms that govern cell motility have been developed. A key challenge in the development of such models is how to connect them to experimental observations, which often exhibit complex stochastic behaviours. In this review, we discuss recent advances in data-driven theoretical approaches that directly connect with experimental data to infer dynamical models of stochastic cell migration. Leveraging advances in nanofabrication, image analysis, and tracking technology, experimental studies now provide unprecedented large datasets on cellular dynamics. In parallel, theoretical efforts have been directed towards integrating such datasets into physical models from the single cell to the tissue scale with the aim of conceptualising the emergent behaviour of cells. We first review how this inference problem has been addressed in both freely migrating and confined cells. Next, we discuss why these dynamics typically take the form of underdamped stochastic equations of motion, and how such equations can be inferred from data. We then review applications of data-driven inference and machine learning approaches to heterogeneity in cell behaviour, subcellular degrees of freedom, and to the collective dynamics of multicellular systems. Across these applications, we emphasise how data-driven methods can be integrated with physical active matter models of migrating cells, and help reveal how underlying molecular mechanisms control cell behaviour. Together, these data-driven approaches are a promising avenue for building physical models of cell migration directly from experimental data, and for providing conceptual links between different length-scales of description.