The European Physical Journal E

Epithelial sheets must maintain robust barrier function while enduring severe mechanical deformations across various physiological environments. While baseline actomyosin contractility is understood to stabilize intercellular junctions and hence cell-cell contact integrity, how cell-generated active forces interact with external physical strain to dictate contact integrity remains poorly understood. In this study, we investigated the biophysical trade-offs between actomyosin contractility and barrier resilience when Madin-Darby Canine Kidney (MDCK) cell islands are subject to large stretch. In contrast to a high concentration (50 M) of the non-muscle myosin II inhibitor blebbistatin that disrupted cell-cell contacts, we first identified a lower concentration (10 M) that maintained cell-cell contact integrity in the absence of any stretch. Such moderate inhibition of non-muscle myosin II reduced, but preserved some level of actin bundle organization. Remarkably, when challenged with a pathological 38% linear stretch using a custom-built biaxial stretching device, 10 M blebbistatin treated epithelial islands exhibited significantly fewer cell-cell contact ruptures than untreated controls, demonstrating a potent protective effect against mechanical strain. Traction force microscopy revealed diminished cell-generated strain energy by over 60% indicating a partial but significant reduction in contractility upon 10 M blebbistatin treatment. Nanoindentation measurements revealed that moderate contractility inhibition decreased the cellular Young's modulus by more than 40%. Consequently, moderate contractility inhibition safeguards epithelial junctions through a dual mechanical effect: it simultaneously reduces baseline active tensile stresses due to cell contractility and lowers the passive elastic forces generated within the softened cell island during external stretch. Our findings indicate that this systemic reduction in forces dominates over any loss of biochemical adhesion strength at cell-cell contacts. We propose that shifting the epithelium from a rigid, highly stressed continuum to a more compliant, relaxed state by moderate contractility inhibition can serve as a general biophysical mechanism to preserve barrier integrity under severe mechanical challenge.

Many adherent eukaryotic cells exhibit amoeboid locomotion, where traction stress exerted on the substrate is essential for movement. In this study, we investigated the spatiotemporal development of these forces in Amoeba proteus to clarify the mechanical dynamics underlying bleb-driven migration. By performing a multipole analysis of the stress distribution, we characterized the spatiotemporal patterns exhibited by motile cells. Furthermore, we tracked the behavior of individual localized peak structures within these profiles, which are thought to correspond to focal contact sites. These analyses revealed that the front-back asymmetry in the traction distribution correlates with the direction of migration. We also found that A. proteus exhibits a periodic pattern in which inward-directed stresses are alternately strengthened and weakened at the cell poles. Crucially, we identified a distinctive feature not observed in other cell types: the generation of large lateral traction forces at the cell center. Together, these results highlight both the universality and diversity of the biophysical mechanisms driving amoeboid locomotion.

The cytoskeleton is a dynamic biopolymer network whose shear rheological properties are crucial for cellular physiology and pathology. However, its mechanical behavior spans multiple spatiotemporal scales, and the coupling of dynamic remodeling and viscoelastic dissipation mechanisms poses a challenge for traditional models to comprehensively capture complex cellular responses. This study aims to establish a multiscale cytoskeletal network model that integrates the bio-chemo-mechanical properties of local linked proteins, the viscoelasticity of actin filaments, and their deformation states. Developing a boundary-modified finite element method with an incremental iterative algorithm, we demonstrated the dynamic remodeling of network and the resultant rheological properties of cytoskeleton by extending the predictive time scale to one thousand seconds. The results not only reproduced the short- and intermediate-term power-law creep behavior and long-term strain plateau response of the cytoskeletal network observed in shear rheological experiments, but also indicate that the synergy among the chemo-mechanical coupling of cross-linked proteins and the bending-to-tension transition of actin filaments govern both the network remodeling and its power-law response evolutionary, whereas the steady-state properties of actin filaments determine the long-term network behavior. Simulations of cancerous and drug effects show that cancer-induced softening and reduced filament viscosity lead to accelerated cytoskeletal responses and decreased apparent shear modulus, respectively; and drug-enhanced filament prestress, along with promoting association or inhibiting dissociation of cross-linked proteins, can effectively increase the steady-state shear modulus. These findings advance the understanding of the spatiotemporal evolution and pathological mechanisms of cellular mechanical responses and provide insights for regulating polymer network performance.

Buzz pollination is an important behavior in which bees use vibrations to extract pollen from poricidal anthers. However, the extent to which vibration frequency influences pollen release remains unclear. Here, we quantified pollen expulsion from Solanum sisymbriifolium anthers subjected to harmonic excitation over a broad frequency range encompassing the anthers first natural frequency. We excited anthers to expel pollen and measured anther kinematics and pollen release using high-speed videography. Particle tracking enabled continuous estimation of pollen release throughout each buzzing event, allowing both initial pollen flux and total pollen released to be quantified. Pollen release depended strongly on excitation frequency. Initial pollen flux, total pollen release, and anther kinematics peaked when excitation frequency approached the anthers natural frequency. Anther tip velocity amplitude exhibited the strongest correlation with total pollen release (r = 0.755) and initial pollen flux (r = 0.898). Experimental observations were compared with nonlinear and linear statistical models of pollen release. While both models captured trends in normalized pollen flux, they overpredicted total pollen release, suggesting that adhesive interactions play important roles during extended buzzing events. These findings demonstrate that anther structural dynamics influence pollen release and suggest that vibration amplification may improve the efficiency of buzz pollination.

Circuit theory has been successfully applied to ecological connectivity modelling, notably via the Circuitscape software, which is typically run locally on a laptop or via a server. For downstream geospatial web applications relying on connectivity analysis, backend infrastructure is required, which can be costly and require advanced data governance. Recent developments in WebAssembly now allow fast C++ or Rust code to be run directly in a sandboxed browser environment for edge computing. We present a WebAssembly/Rust toolset with a geospatial data pipeline and efficient edge-computing implementation of connectivity analysis. This approach may be useful for geospatial modelling software where rasters and memory footprint are small enough for the browser context. Our results show that as expected, Circuitscape solves 1000x1000 raster networks 1-2x faster, but requires further file writes. Accounting for total program runtime, our web implementation can be faster for the given context.

Poly-L-Lysine (PLL) mediates the non-specific adhesion of cells and is commonly used in Atomic Force Microscopy (AFM) measurements, to ensure that cells remain attached to the substrate. However, it is acknowledged that adhesion affects the measured mechanical properties, in particular in the case Red Blood Cells (RBCs). This results in a wide range of Youngs modulus E reported in the literature. The present study aims at providing a systematic approach to the impact of non-specific adhesion on the rheology of RBCs. It provides a correlation between the topography profile of adherent RBCs and their rheology, from weak (cPLL = 10-3 mg/mL) to strong-adhesion (cPLL = 100 mg/mL) regimes. Using RICM and AFM, we find that there is a continuum of RBC shapes promoted by adhesion, from concave to dome-shaped, as predicted by the theory of vesicle adhesion. Their elastic properties discriminate them into two populations depending on adhesion strength, where stiffer RBCs (E {gtrsim} 100 Pa) correlate with dome-shaped cells. These findings are supported by rheology measurements of the dynamic complex shear modulus G*(f): while the storage modulus increases with cell-substrate adhesion, reflective of an increased membrane shear modulus, the loss modulus remains unchanged. Finally, further analysis inspired by membrane theory shows that different deformation modes may be triggered during indentation of either weakly or strongly adhering RBCs, illustrating the limits of the Hertz model.

Integrins are adhesion proteins that diffuse along the cell membrane, bind to ligands, and cluster with each other in the early stage of cell adhesion. Integrin clustering and its specific spatial distribution play important roles in subsequent biological processes; however, the mechanisms that give rise to their characteristic spatial distribution remain poorly understood. To address this issue, we developed a cell adhesion model that incorporates cell membrane deformation and integrin dynamics. A hybrid continuous/discrete model was applied to represent membrane deformation, whereas Brownian dynamics combined with a transition state model was used to describe integrin dynamics and binding kinetics. Comparison of numerical simulations of cell adhesion to a substrate with experimental observations at the early stage of adhesion successfully reproduced the characteristic spatial distribution of integrin clusters, in which high-density clusters formed at the periphery of the region adhering to the substrate. These results suggest that the cellular-scale distribution of integrin clusters can be reproduced using only minimal elements, such as adhesion-driven membrane deformation and integrin-ligand binding. In addition, we found that the strength of integrin-ligand binding regulates the degree of clustering by changing the size of the part of the membrane that is deformed, thereby mechanically supporting the mechanical involvement of the actin cytoskeleton in integrin clustering. Furthermore, the formation and spatial distribution of integrin clusters were shown to be determined not only by the static mechanical equilibrium of membrane deformation and physical adsorption, but also by membrane spreading/deformation and the dynamic behavior of integrins. This suggests that the size and spatial distribution of integrin clusters may be controllable by modulating the speed of membrane spreading.

Precise, non-invasive manipulation of individual living cells remains a central challenge in biomedical science, with far-reaching implications for single-cell analysis, tissue engineering, and the study of cell-cell interactions. Here, we report the first demonstration of single-cell control using bulk acoustic standing-wave acoustofluidics with closed-loop feedback. We introduce VeLO (Vector-based Local Optimization), a model-free, reinforcement learning-inspired algorithm that enables programmable two-dimensional manipulation of individual cells using a single piezoelectric transducer. Without prior calibration or physical modeling, VeLO learns system dynamics online from acoustically induced cell displacements and automatically adapts to nonlinear, time-varying conditions. We achieve robust control across multiple cell types (DU-145, Jurkat, K-562) and independent manipulation of multiple cells, including controlled cell-cell contact. By combining simplicity of hardware with autonomous, adaptive control, this approach establishes multimodal acoustofluidics as a versatile tool for label-free, high-precision single-cell manipulation.

Cell shape and mechanics are intricately connected and tightly regulated by mechanochemical events including biochemical signaling, cytoskeletal remodeling, and plasma membrane mechanics. While experimental advances in microscopy have shed light on the intricate coordination involved in cell shape change in response to different cues, the ability to conduct three-dimensional simulations in realistic geometries remains an open computational challenge. In this work, we develop a finite-element framework that incorporates advection-diffusion-reaction equations coupled with equations governing the kinematics of a deformable interface representing the cell membrane. We applied this framework to three distinct coupled mechanochemical systems, each governed by geometric partial differential equations, resulting in large deformations of the interface. In all three examples, our simulations revealed the emergence of feedback between cellular signaling, cytoskeletal organization, and cell shape. In our first two sets of simulations, we observed that cell migration and neutrophil protrusion were regulated by membrane tension-mediated feedback. In our final application, we predicted shape changes of a dendritic spine starting from a realistic geometry, and found that the complex shape of the spine gives rise to localized regimes of actin cytoskeleton remodeling not previously observed with idealized geometries. Thus, our finite-element framework allows us to generate new mechanistic insights for biophysical problems.

Tumor spheroid mechanics arise from both the mechanical properties of individual cells and the adhesive interactions that organize them into tissues. The relative contribution of these two factors to the bulk mechanical behavior, however, remains difficult to disentangle experimentally. Here, we develop a computational model of micropipette aspiration to compare the mechanical response of isolated cells and multicellular spheroids within a common computational framework. By independently varying single-cell stiffness and cell-cell adhesion, we quantify their effects on aspiration dynamics, effective elastic modulus, and viscoelastic relaxation. Our results show that increasing single-cell stiffness substantially alters the mechanics of isolated cells but has limited influence on the effective elastic modulus of multicellular spheroids. In contrast, changes in cell-cell adhesion produce pronounced effects on spheroid effective elastic modulus. Nevertheless, both parameters increase the retardation time governing the transition from the initial elastic response to long-time viscous deformation. These findings suggest that multicellular elasticity is governed primarily by intercellular mechanical coupling, whereas the dynamical response to applied stress depends jointly on cell-scale mechanics and cell-cell adhesion.

Wound repair is an important mechanism to preserve tissue integrity in organisms after injury. However, why different tissues exhibit different mechanisms to repair wounds is a long-standing question that remains unanswered. In this work, we theoretically explore the role of the purse string, an actomyosin contractile cable used by tissues to close small wounds. Does the tissue 3D geometry influence the efficiency of the purse string in driving wound closure? Using a 3D biophysical model, we study in silico tissues with the same cell volumes but different aspect ratios, ranging from squamous to thick and tall tissues. The model predicts that taller cells are easily deformed by the purse string. In contrast, very squamous cells require a very strong purse string that might demand additional cellular mechanisms to close the gap. These findings establish a theoretical framework to predict the optimal biophysical mechanisms of wound healing in different tissues. Graphical abstractCells of different aspect ratios can be observed in a range of organisms with different function and mechanics. The wound healing efficiency of the purse string increases with the cell aspect ratio in our theoretical exploration. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=135 SRC="FIGDIR/small/743165v1_ufig1.gif" ALT="Figure 1"> View larger version (23K): org.highwire.dtl.DTLVardef@d44ab0org.highwire.dtl.DTLVardef@1737cbaorg.highwire.dtl.DTLVardef@101b5d4org.highwire.dtl.DTLVardef@1487f26_HPS_FORMAT_FIGEXP M_FIG C_FIG

Interactions between nearby cells are a key driver of cell movement in many biological systems, including collective cell migration and the immune response to cancer. However, inferring the interaction rules in a given system in a manner that is both accurate and biologically interpretable remains a challenge. A valuable experimental method for analysing cell-cell interaction dynamics is the tracking of individual cell locations over a series of time-lapse images, and in this work we present a model, based on the theory of deep attention networks, that learns how cell-cell interactions affect cell movement directly from cell trajectory data. Our approach requires no a priori assumptions about the mechanisms governing cell behaviour, enabling its application to cell trajectory data originating from a diverse range of biological systems. In addition to the model, we develop a suite of tools that exploit the models attention-based structure to present the learned interaction dynamics in an interpretable manner. Our model extends previous applications of deep attention networks to cell movement by providing deeper insights into cell-cell interaction dynamics, moving beyond inferring whether cells interact to inferring how these interactions affect cell movement, and providing the ability to infer type dependent cell-cell interaction dynamics in multi-type cell movement systems. By combining data-driven learning and structural interpretability, our approach represents a highly general methodology for linking cell trajectory data to mechanistic hypotheses, showing that deep attention networks constitute a powerful exploratory tool for characterising the effect of cell-cell interactions on cell movement in complex cellular systems.

Since the earliest microscopic observations, the geometric organisation of cells has captured biologists interest. Recent work by Gorgi et al. showed that bacterial colony organisation, including biofilms, can be explained across diverse species by radial expansion from fixed initial seeding sites and contact-inhibited growth, with little need for species-specific mechanisms. Here, we extend this geometric framework by incorporating seeding time as an additional driver of colony organisation. Using simulations and analytical models for expected colony size, we show that staggered seeding yields order of magnitude increases in the expected size of early seeded founder colonies. At realistic biofilm growth rates, a 2-day lag between founder and subsequent colony seeding produces an approximately 10-fold increase in expected founder size, while a 1-week lag produces a 25-fold increase. These findings provide a simple geometric basis for biological priority effects, illustrating temporal advantage alone can generate substantial spatial dominance, with implications for cardiovascular devices where host and bacterial cells compete in a race for the surface.

Electrostatic actuation is an emerging technology for generating tactile sensations on capacitive touchscreens through voltage-induced attractive forces between a fingertip and the surface. However, accurate control of electrostatic attraction during natural touchscreen interactions remains challenging because the applied normal force and sliding speed continuously vary, and their effects on the fingertip-screen contact and resulting actuation strength are not fully characterized. Here, we show how normal force and sliding speed systematically alter fingertip- screen contact area and electrical impedance, and use these measured changes to estimate electrostatic attraction during sliding. Contact area, interaction forces, and electrical impedance were measured simultaneously as participants slid their fingertips across an electrostatic surface under systematically varied normal forces and sliding speeds. These measurements revealed condition-dependent changes in fingertip contact, electrical interaction impedance, effective capacitance, derived effective gap thickness, and electrostatic attraction. We then incorporated these measured contact quantities into a physics-informed, data-driven model based on parallel-plate capacitor theory, in which effective capacitance, apparent contact area, and effective voltage determine the estimated electrostatic attraction. The resulting model links force- and speed-dependent changes in these quantities to electrostatic attraction while accounting for inter-participant variability through a participant-specific scaling factor. These findings provide experimentally grounded guidance for designing electrostatic surface-haptic feedback and future adaptive control strategies under realistic touch conditions.

We introduce a 3D mechanically adaptive viscoelastic cell-network model that links single-cell interactions to emergent tissue rheology. Unlike existing continuum or cell-based models, viscoelasticity is embedded within discrete, mechanically adaptive intercellular connections, allowing tissue-scale rheology and phenomena such as swirling and jamming to arise from single-cell behaviors and connection remodeling. The framework is motivated by recent advances in three-dimensional imaging and structural analysis that resolve single-cell behaviors within aggregates. It is validated against two gold-standard bulk assays performed on spherical aggregates: micropipette aspiration and Hertzian plate compression. Under aspiration, the model demonstrates a transition from elastic deformation to viscous creep governed by localized packing and emergent jamming at the aspirated neck, accompanied by increased mechanically adaptive remodeling. Under compression, core rheology determines deformation mode: liquid-like aggregates exhibit enhanced swirling, consistent with experimental observations, whereas solid-like aggregates exhibit affine, Poisson-like deformation. These results bridge cell-scale dynamics and quantifiable tissue rheology including elastic modulus and vicosity, providing a framework to interpret emerging 3D measurements of multicellular mechanics.

Neutrophils move through narrow pores, convoluted channels, and tight spaces in tissue to find infection sites. Their ability to sense weak chemical gradients, undergo directed motion, and solve such path-finding problems rests on internal GTPase signaling circuits that control the front protrusion and rear retraction of the cell. Here we explore several variants of known core polarity circuits, with local and long-ranged negative feedback, including inhibitor downstream of Rac, Rac-Rho antagonism, and effects of membrane tension. The resulting reaction-diffusion (RD) equations for Rac and Rho are then used to simulate protrusion-retractions along the edge of a simulated motile cell. We visualize how cells navigate through narrow tracks with sharp corners and weak chemical gradients in 2D. Our metrics for cell performance include polarity initiation, wall-collision intensity, and track completion. In this way, we expose how Rac and Rho, together with their immediate down and upstream components can fine-tune neutrophil motility through complex environments. Author SummaryWhite blood cells, attracted to sites of infection, migrate through complex tissues to find their target. Such movement requires a balance between robust polarity in one direction versus flexibility in response to spatial cues such as obstacles and sharp turns. Here we use mathematical modeling to explore known intracellular circuits that regulate front protrusion and rear retraction in directed cell migration. We test several such circuits in simulations of cells moving along zigzag tracks with sharp turns. We demonstrate that a basic cell polarity circuit, on its own, has limited success, since cells tend to get trapped in sharp corners. Known modulators of this core, which add local negative feedback, mutual front-back antagonism, and long-range feedback from membrane tension, improve cell performance. A cell with the full front-back-membrane tension regulatory circuit avoids delays due to traps and obstacle collisions, and moves swiftly through a convoluted passage to its target site.

The complex interplay between extracellular matrix stiffness and actomyosin contractility regulates long-term integrin-based adhesion and signaling in mammalian cells. However, how cells sense and respond to the stiffness of the environment during the first minutes of initiating adhesion remains elusive. Here, we show that fibroblasts upon initiating adhesion to fibronectin switch between two distinct mechanosensitive adhesion modes. The first mode of "slow" adhesion strengthening, shared between 5{beta}1 and v{beta}3 integrins and depends modestly on fibronectin stiffness. Fibroblasts adapt this slow adhesion strengthening mode, which is independent of actomyosin contractility and intracellular signaling, on soft fibronectin substrates (<5 kPa). On stiff fibronectin substrates (>5 kPa), however, 5{beta}1 integrins but not v{beta}3 integrins switch to a "fast" adhesion strengthening mode that considerably strengthens adhesion within seconds. The switch to the fast mode depends on myosin II-mediated contractility and a mechanosensitive signaling hub that includes the 5{beta}1 integrin-FN catch bond, paxillin, and focal adhesion kinase. The mechanistic findings highlight the similarities and differences of integrin-type specific adhesion strengthening and intracellular regulation, which depend on mechanotransduction in fibroblasts during adhesion initiation.

ObjectiveRobotic-assisted needle insertions lack haptic feedback, a key sensory cue for detecting tissue transitions and regulating puncture force. Modeling this feedback requires an understanding of soft-tissue biomechanics during insertion. Vibroacoustic signals generated by needle-tissue interactions may provide an additional sensing modality, but their interpretation requires validation against anatomical ground truth. MethodsA multimodal framework was developed to correlate vibroacoustic signals with high-resolution post-puncture microCT ({micro}CT) imaging in Manduca sexta, an insect model containing interconnected soft-tissue layers. A custom clip-on prototype recorded vibroacoustic signals during manual needle insertions. Three trajectory-marking strategies were evaluated to determine 3D coordinates of soft-tissue layer crossings and to assess correlations between acoustic events and anatomical transitions. Distances between layer crossings and needle displacement were used for spatiotemporal alignment of vibroacoustic and {micro}CT data. ResultsA {micro}CT-compatible nylon string preserved puncture trajectories without artifacts and enabled high-resolution 3D reconstruction of anatomy and needle paths. Fusion of vibroacoustic and imaging data allowed identification of acoustic events associated with tissue entry, exit, and transitions. ConclusionBy integrating high-resolution {micro}CT imaging with vibroacoustic sensing, this study establishes a biologically grounded framework for validating the relationship between vibroacoustic signals and anatomical tissue transitions during needle insertion, providing a basis for future quantitative analyses. SignificanceThis work provides initial evidence for correlating vibroacoustic signals recorded during needle insertion with corresponding {micro}CT-identified tissue barriers. Because vibroacoustics offers substantially higher temporal and spatial resolution than most imaging modalities, it has the potential to improve tissue sensing and procedural accuracy in future needle-based interventions.

Keratin proteins are fundamental structural components of hair fibers, contributing to their mechanical resilience, elasticity, and fracture resistance. However, systematic molecular-scale characterization of keratin unfolding mechanics across protein types remains limited, restricting the connection between protein-level deformation mechanisms and hierarchical hair fiber mechanics. Here, we establish a comparative molecular-dynamics-based framework for characterizing the unfolding behavior and nanomechanical response of a curated dataset of 51 keratin proteins. We conduct implicit atomistic molecular dynamics (MD) simulations, including equilibration and steered molecular dynamics (SMD) under four accelerated pulling velocities, to quantify unfolding forces, energy absorption, and structure-property relationships. These accelerated pulling conditions are interpreted as computational probes of relative molecular-scale trends, rather than direct reproductions of experimental hair-fiber strain-rate regimes. Across these accelerated SMD conditions, the simulations show rate-sensitive increases in unfolding force and energy absorption, consistent with constrained molecular relaxation during faster molecular pulling. Stronger correlations between nanomechanical properties and molecular descriptors emerge at higher pulling rates, and the nanomechanical responses of different keratin types (Type I and II) are also compared. The findings provide molecular-level insights into protein unfolding mechanisms that may contribute to the mechanical behavior of hierarchical keratin structures. This study establishes a quantitative framework for comparative keratin unfolding mechanics, providing molecular-level descriptors for future multiscale modeling of hair fiber behavior. These results support applications in biomaterial design, hair fiber durability analysis, and bioinspired material engineering. Future work will integrate these nanomechanical descriptors with fiber-level mechanics and machine learning-based keratin design.

Single-stranded DNA-binding (SSB) proteins play a crucial role in DNA replication by binding to single-stranded DNA (ssDNA) in multiple binding modes, depending on conditions such as salt and protein concentrations. The coverage-dependent effects on the kinetics of these binding modes remain incompletely understood. In particular, the bimodal binding kinetics and the further SSB-ssDNA shortening observed when SSB is removed from the media. Here, we develop a kinetic model extending the Tonks-McGhee-von Hippel framework to incorporate ligand crowding and mode transformations, capturing the inhibition of SSB binding and transitions to higher binding modes as coverage increases. This model quantitatively reproduces experimental binding kinetics and coverage-dependent behaviors observed for human mitochondrial SSB (HmtSSB) and E. coli SSB (EcoSSB). Our findings elucidate the impact of ligand crowding on SSB-ssDNA interactions and provide a generalizable framework for studying multimode ligand binding to polymers, with implications for understanding genome maintenance mechanisms.