Biology subjects

Amit, Y.

Publications and source records attributed to Amit, Y..

3 recordsLinked to original sources

bioRxiv2025

Investigating active dynamics of contractile actomyosin gels with Micro Particle Image Velocimetry (Micro-PIV) analysis

Micro Particle Image Velocimetry (Micro-PIV), an advanced imaging technique, enables high-resolution velocity field measurements by tracking fluorescent tracers in microscopic environments. Here, we adapt conventional micro-PIV to study the rapidly contractile dynamics of active poroelastic gels. We demonstrate how frame-to-frame correlation improves signal-to-noise ratios and how the elastic nature of the solid phase of the gel can be included in the analysis. To do this, we average the gel displacement data under an axisymmetric assumption to extract radial strain profiles that reliably reveal local deformations of the gel. By analyzing gels of varying shapes, we further show that our method extends robustly to gels that are not completely circular or that do not displace symmetrically towards their geometric center. The analysis reveals common underlying features in the radial profiles of gel deformation. These strain profiles will allow the inference of the spatial and orientational distribution of motor-generated active stresses with appropriate constitutive models for the gel mechanics. Our findings emphasize the importance of tailored micro-PIV methodologies for analyzing complex fluids, particularly autonomously contracting poroelastic materials. This approach significantly enhances understanding of cytoskeletal dynamics and self-organization processes, with broad implications for cell motility, morphogenesis, and active matter physics.

biophysics↗

bioRxiv2024

Actin turnover and myosin contractility determine emergent Thickness Robustness of Cell-Mimicking Cortex

Cell shape deformation in eukaryotes, is primarily determined by the actin cortex, a thin network of actin filaments and myosin motors. This network is attached to the plasma membrane endowing cells with their mechanical stability and structural integrity. Since inside the cells hundreds of proteins are associated with the actin cortex, identifying the roles of the individual components and the mechanisms maintaining a functional cortex is highly challenging. Here, using a minimal set of components, we succeeded in recreating long-lasting dynamic treadmilling in an artificial actomyosin cortex on a bilayer membrane with cell-mimicking characteristics, both with and without myosin-II motors, replicating features observed in cells, such as myosin-induced increased cortical thickness and stress-dependent dynamics. We mechanistically reveal how treadmilling is regulated by actin network disassembly factors and myosin contractility, and the emergence of robustness of the cortical thickness to concentration fluctuations in cytoskeletal components. The robustness and responsiveness of the actin cortex which we found has a fundamental significance for the ability of Eukaryotic cells to maintain the mechanical integrity of their membrane, despite concentration fluctuation of the cytoskeleton components. Unlike complex biological cells, this system enables high-resolution and systematic studies of cortical dynamics under controlled conditions. It thus provides a cell-mimicking artificial system for designing interventions to modify the cellular cortex to improve our understanding of the basic mechanisms driving fundamental biological processes as well as for potential medical applications. SignificanceWe report a breakthrough in understanding the actin cortex, a key structure governing cell shape, stability, and motility. By reconstructing in-vitro a treadmilling actomyosin cortex on a lipid bilayer using minimal components, we replicate essential cellular features. This system reveals how cortical thickness is maintained despite concentration fluctuations, highlighting an actin turnover mechanism that buffers variability. The inclusion of myosin motors is found to modify the cortical dynamics, mimicking cellular properties such as the increased thickness and stress-dependent dynamics. Unlike complex living cells, this artificial system enables systematic studies of cortical behavior, with fine control over the components and high-resolution imaging. Our artificial system provides a platform for designing interventions to modify cortical dynamics, with broad implications for biology and medicine. One-Sentence SummaryIn-vitro recreation of a cell-like, membrane-bound cortical actin skeleton composed of a minimal set of building-blocks with continuous turnover and emergent robustness.

biophysics↗

bioRxiv2024

Visual image familiarity learning at multiple timescales in the primate inferotemporal cortex

Humans and other primates can rapidly detect familiar objects and distinguish them from never-before-seen novel objects. We have an astonishing capacity to remember the details of visual scenes even after a single, fleeting experience. This ability is thought to rely in part on experience-dependent changes in the primate inferotemporal cortex (IT). Single neurons in IT encode visual familiarity by discriminating between novel and familiar stimuli, with stronger neural activity on average for novel images. However, key open questions are to understand how neural encoding in IT changes as images progress from novel to highly familiar, and what learning rules and computations can account for learning-dependent changes in IT activity. Here, we investigate the timescales over which novel stimuli become familiar by recording in IT as initially novel images become increasingly familiar both within and across days. We identified salient and persistent memory-related signals in IT that spanned multiple timescales of minutes, hours, and days. Average neural activity progressively decreased with familiarity as firing rates were strongest for novel, weaker for intermediately familiar, and weakest for highly familiar images. Neural signatures of familiarity learning were slow to develop as response reductions to initially-novel images emerged gradually over multiple days (or hundreds of views) of visual experience. In addition to slow changes that emerged across sessions, neural responses to novel images showed rapid decreases with familiarity within single sessions. To gain insight into the mechanisms underlying changes of visual responses with familiarity, we use computational modeling to investigate which plasticity rules are consistent with these changes. Learning rules inferred from the neural data revealed a strong diversity with many neurons following a negative plasticity rule as they exhibited synaptic depression over the course of learning across multiple days. A recurrent network model with two plasticity time constants - a slow time constant for long timescales and a fast time constant for short timescales - captured key dynamic features accompanying the transition from novel to familiar, including a gradual decrease in firing rates over multiple sessions, and a rapid decrease in firing rates within single sessions. Our findings suggest that distinct and complementary plasticity rules operating at different timescales may underlie the inferotemporal code for visual familiarity.

neuroscience↗