Biology subjects

Matsunaga, D.

Publications and source records attributed to Matsunaga, D..

10 recordsLinked to original sources

bioRxiv2026

Chlamylipo, a Chlamydomonas-in-liposome microswimmer: self-propelled swimming and associated lipid membrane flow

Developing active transport systems for microcargo delivery is challenging and requires overcoming the low Reynolds number constraints. We developed a bio-hybrid micro-swimmer, "chlamylipo" consisting of the green alga Chlamydomonas reinhardtii, encapsulated within a giant liposome. Although internal encapsulation offers cargo protection, it requires a mechanism to transmit the propulsion force across a closed membrane. We demonstrated that chlamylipo exhibited forward swimming and phototactic directional control. High-speed imaging of membrane shape and fluid flow revealed that the driving force originated from periodic membrane deformations and was accompanied by characteristic fluid dynamics. Flow analysis showed rapid oscillations at tens of hertz corresponding to flagellar beating, superimposed on slower axial migration at approximately 4 Hz associated with cell rotation. Corresponding flow signatures were also detected in the external fluid, indicating mechanical coupling across the lipid bilayer. Membrane domain tracking further showed that fluid motions inside and outside the membrane were coupled through viscous friction and membrane deformation, generating a characteristic four-vortex flow field consistent with a two-point force model. Together, these results suggest that membrane flow mainly reflects force transmission across the bilayer, whereas forward propulsion is primarily driven by periodic membrane deformation. This study elucidates the physical mechanism of force transmission in encapsulated swimmers, demonstrating that internal hydrodynamic power can effectively drive the motion of macroscopic containers. SignificanceThe development of autonomous micro-swimmers for targeted drug delivery is a major challenge in biophysics. We present "chlamylipo," a hybrid system in which a swimming alga is encapsulated inside a lipid vesicle. This study is significant because it demonstrates that an enclosed swimmer can propel a macroscopic container solely via hydrodynamic coupling across a closed membrane without direct external mechanical links. Furthermore, we achieved external directional control using phototaxis. This study provides physical insights into fluid-membrane interactions and proposes a novel strategy for designing light-guided active transport carriers.

biophysics↗

bioRxiv2025

Mapping molecular diffusion across the whole cell with spatial statistics-based FRAP

Understanding molecular diffusion within cells is crucial for gaining insights into cellular biophysical mechanisms. Fluorescence recovery after photobleaching (FRAP) is a powerful technique for assessing molecular diffusion, yet its localized measurement approach hinders whole-cell analysis. To overcome this limitation, we developed Probabilistic FRAP (Pro-FRAP), a novel approach that integrates FRAP with sequential Gaussian simulation (SGS), an advanced spatial statistical method incorporating probabilistic modeling to estimate diffusion in unmeasured regions. Pro-FRAP applies SGS to standardize measured FRAP data, perform conditional simulations based on spatial correlations, and generate statistically robust estimates. In separate analyses, numerical simulations were conducted to optimize the spatial arrangement of measurement points, enhancing data accuracy and coverage. Unlike deterministic interpolation methods, Pro-FRAP captures spatial variability and quantifies uncertainty in intracellular diffusion, providing a more detailed representation of molecular transport. Thus, this framework extends FRAP beyond localized measurements, offering a refined approach for mapping intracellular diffusion with improved spatial coverage and statistical reliability. Significance statementUnderstanding molecular diffusion is essential for deciphering cellular functions and regulation. However, conventional fluorescence recovery after photobleaching (FRAP) techniques are constrained to localized measurements, limiting their ability to capture diffusion dynamics across entire cells. Here, we describe Probabilistic FRAP (Pro-FRAP), a novel framework that integrates FRAP with sequential Gaussian simulation (SGS), a spatial statistical method that enables the estimation of molecular diffusion in unmeasured regions. By incorporating probabilistic modeling, Pro-FRAP generates high-resolution diffusion maps with enhanced statistical reliability. This approach provides a more comprehensive perspective on intracellular transport, allowing for deeper insights into the spatial organization of cellular processes.

biophysics↗

bioRxiv2023

Mutual interplay of actin meshwork and stress fibers in cellular adaptive response: Insights from percolation dynamics

Cells dynamically remodel their internal structures by modulating the arrangement of actin filaments (AFs). In this process, individual AFs exhibit stochastic behavior without knowing macroscopic higher-order structures they are meant to create or disintegrate. Cellular adaptation to environmental cues is accompanied with this type of self-assembly and disassembly, but the mechanism allowing for the stochastic process-driven remodeling of the cell structure remains incompletely understood. Here we employ percolation theory to explore how AFs interacting only with neighboring ones without recognizing the overall configuration can nonetheless construct stress fibers (SFs) at particular locations. To achieve this, we determine the binding and unbinding probabilities of AFs undergoing cellular tensional homeostasis, a fundamental property maintaining intracellular tension. We showed that the duration required for the assembly of SFs is shortened by the amount of preexisting actin meshwork, while the disassembly occurs independently of the presence of actin meshwork. This asymmetry between the assembly and disassembly, consistently observed in actual cells, is explained by considering the nature of intracellular tension transmission. Thus, our percolation analysis provides insights into the role of coexisting higher-order actin structures in their flexible responses during cellular adaptation.

biophysics↗

bioRxiv2023

Knowledge "installed" diffusion model predicts the geometry of actin cytoskeleton from cell morphology

Cells exhibit various morphological characteristics due to their physiological activities, and changes in cell morphology are inherently accompanied by the assembly and disassembly of the actin cytoskeleton. Stress fibers are a prominent component of the actin-based intracellular structure and are highly involved in numerous physiological processes, e.g., mechanotransduction and maintenance of cell morphology. Although it is widely accepted that variations in cell geometry interact with the distribution and localization of stress fibers, it remains unclear if there are underlying geometric principles between the cell morphology and actin cytoskeleton. Here we present a machine learning system, which uses the diffusion model, that can convert the cell shape to the distribution of stress fibers. By training with corresponding datasets of cell shape and stress fibers, our system learns the conversion to generate the stress fiber images from its corresponding cell shape. The predicted stress fiber distribution has good agreement with the experimental data, and the overlap region of predicted and experimentally observed stress fibers reaches 79.3 {+/-}12.4%. We then found some unknown natures such as a linear relation relationship between the stress fiber length and cell area. With this "installed" conversion relation between cellular morphology and corresponding stress fibers localization, our system could perform virtual experiments that provide a visual map showing the probability of stress fiber distribution from the virtual cell shape. Our system provides a powerful approach to seek further hidden geometric principles between the cell morphologies and actin cytoskeletons.

bioinformatics↗

bioRxiv2022

A model explaining environmental stiffness-dependent migration of fibroblasts with a focus on maturation of intracellular structures

Cell migration is fundamental to many biological processes, while it remains elusive how cells modulate their migration upon different environmental stiffness. In this work, we focus on the structural maturity of actin stress fibers to explain the substrate stiffness-dependent emergence of different cell migration velocity. We demonstrate that fibroblasts migrate longer distances on softer elastic substrates, and the distance is increased by lowering the myosin-driven contractile force. Stress fibers, the major intracellular structure to generate and sustain contractile forces, were found to be less mature in structure on soft substrate than on stiff substrate. Based on these experimental results, we present a minimal mathematical model to capture the salient features of how the substrate stiffness alters the migration velocity. Specifically, the ability of cells to generate large contractile forces is limited on soft substrate according to the Hookes law. The inverse relationship between the cellular force and migration velocity is described by the Hills muscle equation. These mathematical descriptions suggest that the migration velocity is raised on softer substrate where cells exert a lower magnitude of contractile forces. Cells undergoing faster movement make stress fibers less mature in structure as mathematically described by the maturation model, thereby limiting the ability to sustain the force and in turn allowing for consistent increase in cell migration velocity on soft substrate again according to the Hookes law and Hills muscle equation, respectively. Thus, our model, reproducing the basic trend of the experimental results, provides insights into the mechanisms of environmental cue-dependent migratory behavior of cells.

biophysics↗

bioRxiv2022

Analysis of chemical and mechanical behaviors in living cells by continuum mechanics-based FRAP

Fluorescence recovery after photobleaching (FRAP) is a common technique to analyze the turnover of molecules in living cells. Numerous physicochemical models have been developed to quantitatively evaluate the rate of turnover driven by chemical reaction and diffusion that occurs in a few seconds to minutes. On the other hand, they have limitations in interpreting long-term FRAP responses where intracellular active movement inevitably provides target molecular architectures with additional effects other than chemical reaction and diffusion, namely directed transport and structural deformation. To overcome the limitations, we develop a continuum mechanics-based model that allows for decoupling FRAP response into the intrinsic turnover rate and subcellular mechanical characteristics such as displacement vector and strain tensor. Our approach was validated using fluorescently-labeled beta-actin in an actomyosin-mediated contractile apparatus called stress fibers, revealing spatially distinct patterns of the multi-physicochemical events, in which the turnover rate of beta-actin was significantly higher at the center of the cell. We also found that the turnover rate is negatively correlated with the strain rate along stress fibers but, interestingly, not with the absolute strain magnitude. Moreover, stress fibers are subjected to centripetal flow as well as both contractile and tensile strains along them. Taken together, this novel framework for long-term FRAP analysis allows for unveiling the contribution of overlooked microscopic mechanics to molecular turnover in living cells.

biophysics↗

bioRxiv2022

A model for determining the length distribution of actin filaments in cells

Tensional homeostasis is a cellular process whereby nonmuscle cells such as fibroblasts keep a constant level of intracellular tension and signaling activities. Cells are allowed thanks to tensional homeostasis to adapt to mechanical stress, but the detailed mechanism remains unclear. Here we address from a theoretical point of view what is required for maintaining cellular tensional homeostasis. A constrained optimization problem is formulated to analytically determine the probability function of the length of individual actin filaments (AFs) responsible for sustaining cellular tension. An objective function composed of two entropic quantities measuring the extent of formation and dispersion of AFs within cells is optimized under two constraint functions dictating a constant amount of actin molecules and tension that are arguably the two most salient features of tensional homeostasis. We then derive a specific probability function of AFs that is qualitatively consistent with previous experimental observations, in which short AF populations preferably appear. Regarding the underlying mechanism, our analyses suggest that the constraint for keeping the constant tension level makes long AF populations smaller in number because long AFs have a higher chance to be involved in bearing larger forces. The specific length distribution of AFs is thus required for achieving the constrained objectives, by which individual cells are endowed with the ability to stably maintain a homeostatic tension throughout the cell, thereby potentially allowing cells to locally detect deviation in the tension, keep resulting biological functions, and hence enable subsequent adaptation to mechanical stress. Although minimal essential factors are included given the actual complexity of cells, our approach would provide a theoretical basis for understanding complicated homeostatic and adaptive behavior of the cell.

biophysics↗

bioRxiv2021

Long-term molecular turnover of actin stress fibers revealed by advection-reaction analysis in fluorescence recovery after photobleaching

Fluorescence recovery after photobleaching (FRAP) is a versatile technique to evaluate the intracellular molecular exchange called turnover. Physicochemical models of FRAP typically consider the molecular diffusion and chemical reaction that simultaneously occur on a time scale of seconds to minutes. Particularly for long-term measurements, however, an advection effect can no longer be ignored, which transports the proteins in specific directions within the cells and accordingly shifts the spatial distribution of the local chemical equilibrium. Nevertheless, existing FRAP models have not considered the spatial shift, and as such, the turnover rate is often analyzed without considering the spatiotemporally updated chemical equilibrium. Here we develop a new FRAP model aimed at long-term measurements to quantitatively determine the two distinct effects of the advection and chemical reaction, i.e., the different major sources of the change in fluorescence intensity. To validate this approach, we carried out FRAP experiments on actin in stress fibers over a time period of more than 900 s, and the advection rate was shown to be comparable in magnitude to the chemical dissociation rate. We further found that the actin-myosin interaction and actin polymerization differently affect the advection and chemical dissociation. Our results thus suggest that the distinction between the two effects is indispensable to extract the intrinsic chemical properties of the actin cytoskeleton from the observations of complicated turnover in cells.

biophysics↗

bioRxiv2021

Machine learning based cellular contractile force detection: a new approach to predict cell mechanics by images

Combining experiments with artificial intelligence algorithms, we propose a new machine learning based approach to extract the cellular force distributions from the microscope images. The full process can be divided into three steps. First, we culture the cells on a special substrate allowing to measure both the cellular traction force on the substrate and the corresponding substrate wrinkles simultaneously. The cellular forces are obtained using the traction force microscopy (TFM), at the same time that cell-generated contractile forces wrinkle their underlying substrate. Second, the wrinkle positions are extracted from the microscope images. Third, we train the machine learning system with GAN (generative adversarial network) by using sets of corresponding two images, the traction field and the input images (raw microscope images or extracted wrinkle images), as the training data. The network understands the way to convert the input images of the substrate wrinkles to the traction distribution from the training. After sufficient training, the network is utilized to predict the cellular forces just from the input images. Our system provides a powerful tool to evaluate the cellular forces efficiently because the forces can be predicted just by observing the cells under the microscope, which is a way simpler method compared to the TFM experiment. Additionally, the machine learning based approach presented here has the profound potential for being applied to diverse cellular assays for studying mechanobiology of cells. Significance StatementCell-generated forces are indispensable determinants of fundamental cell functions such as motility and cell division. As such, quantifying how the forces change upon perturbations to the cells such as gene mutations and drug administration is of profound importance. Here we present a novel machine learning based system that allows for efficient estimations of the forces that are determined only by "observing" microscope images. Given that the cellular traction forces are regulated downstream of diverse signaling pathways, our system - that helps significantly improve the throughput of the measurements - presents a new, high throughput platform for real time analysis of the effects of a massive number of genetic and molecular perturbations on the forces and resulting cell mechanics.

biophysics↗

bioRxiv2020

Determining the domain-level reaction-diffusion properties of cellular proteins by incorporating genetic engineering to FRAP-based framework

Proteins in cells undergo repeated association to other molecules, thereby reducing the apparent extent of their intracellular diffusion. While much effort has been made to analytically decouple these combined effects of pure diffusion and chemical reaction, it is difficult to attribute the measured quantities to the nature of specific domains of the probed proteins particularly if, as is often the case, the protein has multiple domains to independently interact with the same types but different molecules. Motivated by the common goal in cell signaling research aimed at identifying the protein domains responsible for particular intermolecular interactions, here we describe a new approach to determining the domain-level reaction and pure diffusion properties. To validate this methodology, we apply it to transgelin-2, an actin-binding protein whose intracellular dynamics remains elusive. We develop a fluorescence recovery after photobleaching (FRAP)-based framework, in which comprehensive combinations of domain-deletion mutants are created with genetic engineering, and the difference among the mutants in FRAP response is analyzed. We demonstrate that transgelin-2 in cells interacts with F-actin via two separate domains, and the chemical equilibrium constant of the interaction is determined at the individual domain levels. Its pure diffusion properties independent of the association to F-actin is also obtained. This approach requires some effort to construct the mutants, but instead enables in situ domain-level determination of the physicochemical properties, which will be useful, as specifically shown here for transgelin-2, in addressing the signaling mechanism of cellular proteins.

bioengineering↗