Biology subjects

Bovyn, M. J.

Publications and source records attributed to Bovyn, M. J..

3 recordsLinked to original sources

bioRxiv2023

Tight junctions regulate lumen morphology via hydrostatic pressure and junctional tension

Formation of fluid filled lumen by epithelial tissues is a fundamental process for organ development. How epithelial cells regulate the hydraulic and cortical forces to control lumen morphology is not completely understood. Here, we quantified the mechanical role of tight junctions in lumen formation using genetically modified MDCKII cysts. We found that the paracellular ion barrier formed by claudin receptors is not required for hydraulic inflation of lumen. However, depletion of the zonula occludens scaffold resulted in lumen collapse and folding of apical membranes. Combining quantitative measurements and perturbations of hydrostatic lumen pressure and junctional tension with modelling, we were able to predict lumen morphologies from the pressure-tension force balance. We found that in MDCK tissue the tight junction promotes formation of spherical lumen by decreasing cortical tension via inhibition of myosin. In addition, we found that the apical surface area of cells is largely uncoupled from lumen volume changes, suggesting that excess apical area contributes to lumen opening in the low-pressure regime. Overall, our findings provide a mechanical understanding of how epithelial cells use tight junctions to modulate tissue and lumen shape.

cell biology↗

bioRxiv2021

Deep Learning Assisted Mechanotyping of Individual Cells Through Repeated Deformations and Relaxations in Undulating Channels

Mechanical properties of cells are important features that are tightly regulated, and are dictated by various pathologies. Deformability cytometry allows for the characterization of mechanical properties of hundreds of cells per second, opening the way to differentiating cells via mechanotyping. A remaining challenge for detecting and classifying rare sub-populations is the creation of a combined experimental and analysis protocol that would assure classification accuracy approaching 100%. In order to maximize the accuracy, we designed a microfluidic channel that subjects each cell to repeated deformations and relaxations. We also track the shape dynamics of individual cells with high time resolution, and apply sequence-based deep learning models for feature extraction. HL60 cells with and without treatment with cytochalasin D (cytoD), a reagent previously shown to perturb the actin network, were used as a model system to understand the classification potential of our approach. Multiple recurrent and convolutional neural network architectures were trained using time sequences of cell shapes, and shown to achieve high classification accuracy based on cytoskeletal properties alone. The best model classified the two sub-populations of HL60 cells with an accuracy of 95%. This work establishes the application of sequence-based deep learning models to dynamic deformability cytometry.

biophysics↗

bioRxiv2020

Roles of motor on-rate and cargo mobility in intracellular transport

Molecular motors like kinesin are critical for cellular organization and biological function including in neurons. There is detailed understanding of how they move and how factors such as applied force and the presence of microtubule-associated proteins can alter this single-motor travel. In order to walk, the cargo-motor complex must first attach to a microtubule. This attachment process is less studied. Here, we use a combination of single-molecule bead experiments, modeling, and simulation to examine how cargos with kinesin-1 bind to microtubules. In experiment, we find that increasing cargo size and environment viscosity both signficantly slow cargo binding time. We use modeling and simulation to examine how the single motor on rate translates to the on rate of the cargo. Combining experiment and modeling allows us to estimate the single motor on rate as 100 s-1. This is a much higher value than previous estimates. We attribute the difference between our measurements and previous estimates to two factors: first, we are directly measuring initial motor attachment (as opposed to re-binding of a second motor) and second, the theoretical framework allows us to account for missed events (i.e. binding events not detected by the experiments due to their short duration). This indicates that the mobility of the cargo itself, determined by its size and interaction with the cytoplasmic environment, play a previously underestimated role in determining intracellular transport kinetics.

cell biology↗