Biology subjects

Lavi, Y.

Publications and source records attributed to Lavi, Y..

4 recordsLinked to original sources

bioRxiv2026

Local mechanical gradients underlie coordinated cascades of epithelial cell-cycle advance

Cell division mechanically perturbs the local environment in epithelial tissues, yet whether and how these perturbations propagate to coordinate cell-cycle progression across neighboring cells remains unclear. Here, we combine live cell-cycle tracking with mechanical analysis to examine how division events organize in space and time. We find that coordinated changes and spatial gradients in forces, morphology, and dynamics precede synchronized G1[->]S transitions in nearby cells. These transitions emerge within localized high-tension zones and give rise to spatiotemporal clusters of cell divisions, indicating that division events are mechanically coupled and propagate across neighboring cells. Supporting this biophysical picture, similar mechanical patterns arising from cell extrusion are sufficient to induce cell-cycle re-entry in neighboring cells. Together, these findings suggest a mechanically mediated framework for coordinated proliferation, possibly driven by positive mechanical feedback.

biophysics↗

bioRxiv2025

3D-printed toolkit for regulating epithelial unjamming

During wound healing and cancer invasion, epithelial monolayers transition from a solid-like, jammed state to a fluid-like unjammed state. However, affordable and accessible tools for modeling and precise tuning of this response remain limited. Here, we present a simple, lithography-free 3D-printed toolkit of customizable inserts and scratchers for fabricating micro-gaps with defined geometries and widths down to [~]50 {micro}m, without requiring cleanroom facilities or specialized expertise. Using epithelial monolayers, we demonstrate that gap width fundamentally regulates unjamming dynamics. Narrow gaps induced rapid, coordinated migration with high initial velocities, whereas wide gaps suppressed early unjamming and produced slower, spatially heterogeneous closure. Cell shape dynamics confirmed conserved elongation, cell rounding, and closure behaviors across conditions. The presented toolkit provides new opportunities for investigating the mechanical and molecular mechanisms that regulate epithelial plasticity, enabling fine control of unjamming transitions and allowing the modeling of both healthy and pathological wound-response regimes.

biophysics↗

bioRxiv2023

ConfluentFUCCI for fully-automated analysis of cell-cycle progression in a highly dense collective of migrating cells

Understanding mechanisms underlying various physiological and pathological processes requires accurate and fully automated analysis of dense cell populations that collectively migrate, and specifically, relations between biophysical features and cell cycle progression aspects. A seminal tool that led to a leap in real-time study of cell cycle is the fluorescent ubiquitination-based cell cycle indicator (FUCCI). Here, we introduce ConfluentFUCCI, an open-source graphical user interface-based framework designed for fully automated analysis of cell cycle progression, cellular dynamics, and cellular morphology, in highly dense migrating cell collectives. Leveraging state-of-the-art tools, some incorporate deep learning, ConfluentFUCCI offers accurate nuclear segmentation and tracking using FUCCI tags, enabling comprehensive investigation of cell cycle progression at both the tissue and single-cell levels. We compare ConfluentFUCCI to the most recent relevant tool, showcasing its accuracy and efficiency in handling large datasets. Furthermore, we demonstrate the ability of ConfluentFUCCI to monitor cell cycle transitions, dynamics, and morphology within densely packed epithelial cell populations, enabling insights into mechanotransductive regulation of cell cycle progression. The presented tool provides a robust approach for investigating cell cycle-related phenomena in complex biological systems, offering potential applications in cancer research and other fields.

cell biology↗

bioRxiv2018

Idiosyncratic choice bias in decision tasks naturally emerges from neuronal network dynamics

Idiosyncratic tendency to choose one alternative over others in the absence of an identified reason is a common observation in two-alternative forced-choice experiments. It is tempting to account for it as resulting from the (unknown) participant-specific history and thus treat it as a measurement noise. Here we quantify idiosyncratic choice biases in a perceptual discrimination task and a motor task. We report substantial and significant biases in both cases that cannot be accounted for by the experimental context. Then, we present theoretical evidence that even in idealized experiments, in which the settings are symmetric, idiosyncratic choice bias is expected to emerge from the dynamics of competing neuronal networks. We thus argue that idiosyncratic choice bias reflects the microscopic dynamics of choice and therefore is virtually inevitable in any comparison or decision task.

neuroscience↗