arXiv subjects

Quirine J. S. Braat

Publications and source records attributed to Quirine J. S. Braat.

4 recordsLinked to original sources

arXiv2026

Nuclear mechanics controls the temporal dynamics of cell unjamming

Cell unjamming in dense tissues is a complex but essential process in embryogenesis and cancer metastasis. Increasing evidence suggests that nuclear mechanics and density effects play a vital role in collective cell unjamming. However, state-of-the-art cell-shape-based theories fail to include nuclear and density effects, while computer models featuring rigid nuclei disagree with experimental observations of elongated nuclei promoting unjamming. Here, we introduce a computational model of confluent cells with explicitly deformable nuclei to study the dynamics of cell unjamming. Our simulations show an unjamming transition controlled by nuclear size and shape, reconciling conflicting theories of density-driven versus shape-driven mechanisms. We predict general relations connecting cellular and nuclear shape to collective cell motion, verified experimentally in distinct monolayers of MCF-10A and MDA-MB-436 breast cells, with striking accuracy. Our work establishes a rational connection between nuclear mechanics and tissue-scale rigidity transitions, highlighting the nucleus's key role in collective cell unjamming.

physics.bio-ph↗

arXiv2024

Formation of motile cell clusters in heterogeneous model tumors: the role of cell-cell alignment

Circulating tumor cell clusters play an important role in the metastatic cascade. These clusters can acquire a migratory and more invasive phenotype, and coordinate their motion to migrate as a collective. Before such clusters can form by collectively detaching from a primary tumor, however, the cluster must first aggregate in the tumor interior. The mechanism of this cluster formation process is still poorly understood. One of the possible ways for cells to cluster is by aligning their direction of motion with their neighboring cells. This work aims to investigate the role of this cell-cell alignment interaction on the formation of motile cell clusters inside the bulk of a tumor using computer simulations. We employ a Cellular Potts model in which we model a two-dimensional heterogeneous confluent layer containing both motile and non-motile cells. Our results indicate that the degree of clustering is governed by two distinct processes: the formation of clusters due to the presence of cell-cell alignment interactions among motile cells, and the suppression of clustering due to the presence of the dynamic cellular environment (comprised of the non-motile cells). We find that the largest motile clusters are formed for intermediate alignment strengths, contrary to what is observed for motile cells in free space (that is, unimpeded by a dense cellular environment), in which case stronger cell-cell alignment always leads to larger clustering. Our findings suggest that the presence of a densely-packed cellular environment and strong cell-cell alignment inhibits the formation of large migratory clusters within the primary tumor, providing physical insight into potential factors at play during the early stages of metastasis.

physics.bio-ph↗

arXiv2024

Shape matters: Inferring the motility of confluent cells from static images

Cell motility in dense cell collectives is pivotal in various diseases like cancer metastasis and asthma. A central aspect in these phenomena is the heterogeneity in cell motility, but identifying the motility of individual cells is challenging. Previous work has established the importance of the average cell shape in predicting cell dynamics. Here, we aim to identify the importance of individual cell shape features, rather than collective features, to distinguish between high-motility (active) and low-motility (passive) cells in heterogeneous cell layers. Employing the Cellular Potts Model, we generate simulation snapshots and extract static features as inputs for a simple machine-learning model. Our results show that when the passive cells are non-motile, this machine-learning model can accurately predict whether a cell is passive or active using only single-cell shape features. Furthermore, we explore scenarios where passive cells also exhibit some degree of motility, albeit less than active cells. In such cases, our findings indicate that a neural network trained on shape features can accurately classify cell motility, particularly when the number of active cells is low, and the motility of active cells is significantly higher compared to passive cells. This work offers potential for physics-inspired predictions of single-cell properties with implications for inferring cell dynamics from static histological images.

physics.bio-ph↗

arXiv2024

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.

q-bio.TO↗