Biology subjects

Deguchi, S.

Publications and source records attributed to Deguchi, S..

5 recordsLinked to original sources

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

Proteome of actin stress fibers

Stress fibers (SFs), which are actomyosin structures, reorganize in response to various cues to maintain cellular homeostasis. Currently, the protein components of SFs are only partially identified, limiting our understanding of their responses. Here we isolate SFs from huma fibroblasts HFF-1 to determine with proteomic analysis the whole protein components and how they change with replicative senescence (RS), a state where cells decline in ability to replicate after repeated divisions. We found that at least 263 proteins are associated with SFs, and 101 of them are upregulated with RS, by which SFs become larger in size. Among them, we focused on eEF2 (eukaryotic translation elongation factor 2) as it exhibited upon RS the most significant increase in abundance. We show that eEF2 is critical to the reorganization and stabilization of SFs in senescent fibroblasts. Our findings provide a novel molecular basis for SFs to be reinforced to resist cellular senescence.

cell biology↗

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

Statistical profiling reveals correlations between the cell response to and structural sequence of Rho-GAPs

Rho-GTPase-activating proteins (Rho-GAPs) are essential upstream regulators of the Rho family of GTPases. Currently, it remains unclear if the phenotypic change caused by perturbations to a Rho-GAP is predictable from its structural sequence. Here we analyze the relationship between the morphological response of cells to the silencing of Rho-GAPs and their primary structure. For all possible pairs of 57 different Rho-GAPs expressed in MCF10A epithelial cells, the similarity in the Rho-GAP silencing-induced morphological change was quantified and compared to the similarity in the primary structure of the corresponding pairs. We found a distinct correlation between the morphological and structural similarities in a specific group of RhoA-targeting Rho-GAPs. Thus, the family-wide analysis revealed a common feature shared by the specific Rho-GAPs.

cell biology↗

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↗