Satoshi Kozawa

Shootin1–cortactin interaction mediates signal–force transduction for axon outgrowth

The shootin1–cortactin interaction participates in netrin-1–induced F-actin–adhesion coupling and in the promotion of traction forces for axon outgrowth.

Real-time prediction of cell division timing in developing zebrafish embryo

Combination of live-imaging and live-manipulation of developing embryos in vivo provides a useful tool to study developmental processes. Identification and selection of target cells for an in vivo live-manipulation are generally performed by experience- and knowledge-based decision-making of the observer. Computer-assisted live-prediction method would be an additional approach to facilitate the identification and selection of the appropriate target cells. Herein we report such a method using developing zebrafish embryos. We choose V2 neural progenitor cells in developing zebrafish embryo as their successive shape changes can be visualized in real-time in vivo. We developed a relatively simple mathematical method of describing cellular geometry of V2 cells to predict cell division-timing based on their successively changing shapes in vivo. Using quantitatively measured 4D live-imaging data, features of V2 cell-shape at each time point prior to division were extracted and a statistical model capturing the successive changes of the V2 cell-shape was developed. By applying sequential Bayesian inference method to the model, we successfully predicted division-timing of randomly selected individual V2 cells while the cell behavior was being live-imaged. This system could assist pre-selecting target cells desirable for real-time manipulation–thus, presenting a new opportunity for in vivo experimental systems.

Subsurface imaging for anti-personal mine detection by Bayesian super-resolution with a smooth-gap prior

Ground-penetrating radars (GPRs) have been studied in order to reconstruct subsurface images. The signal observed by GPR typically includes very strong noise, and reconstruction of the image is a difficult task. We propose a new subsurface imaging method based on the framework of Bayesian super-resolution. In the framework, we can incorporate additional information into the reconstructed image by considering a smooth-gap prior, which can represent the smoothness of the subsurface image and gaps between materials, and which improves the quality of the reconstructed image. We investigated the performance of the proposed method with a synthetic GPR dataset, and confirmed the validity of the proposed method.

The Body-wide Transcriptome Landscape of Disease Models

Summary Virtually all diseases affect multiple organs. However, our knowledge of the body-wide effects remains limited. Here, we report the body-wide transcriptome landscape across 13–23 organs of mouse models of myocardial infarction, diabetes, kidney diseases, cancer, and pre-mature aging. Using such datasets, we find (1) differential gene expression in diverse organs across all models; (2) skin as a disease-sensor organ represented by disease-specific activities of putative gene-expression network; (3) a bone-skin cross talk mediated by a bone-derived hormone, FGF23, in response to dysregulated phosphate homeostasis, a known risk-factor for kidney diseases; (4) candidates for the signature activities of many more putative inter-organ cross talk for diseases; and (5) a cross-species map illustrating organ-to-organ and model-to-disease relationships between human and mouse. These findings demonstrate the usefulness and the potential of such body-wide datasets encompassing mouse models of diverse disease types as a resource in biological and medical sciences. Furthermore, the findings described herein could be exploited for designing disease diagnosis and treatment.

Diverse yet highly selective interorgan crosstalk mechanisms shape the bodywide transcriptome landscape

The network of functionally diverse organs is vital for organismal development and function. Despite such importance, the knowledge of the bodywide interorgan communication network is severely limited. Hence, we generate comprehensive bodywide transcriptome datasets following the systematic organ-ablations and organ-specific gene mutations in zebrafish and the data are integrated into a mechanistic interorgan network model. The experimental validation of the model unveils unexpectedly more diverse and selective interorgan crosstalk mechanisms than conventionally assumed to orchestrate the expression of 73 genes implicated for differential organ development and function across 8 organs. The findings provide an important insight into how differential organ development and function may be regulated at the organismal level by the cardiovascular system, which is a primary mediator of the interogan crosstalk in all vertebrates. Furthermore, the panoramic bodywide landscape shown herein and available at i-organs.atr.jp serves as a platform resource for studying organ-to-organ interactions at the organismal level.The cardiovascular system facilitates body-wide distribution of oxygen, a vital process for development and survival of virtually all vertebrates. However, zebrafish, a vertebrate model organism, appears to form organs and survive mid-larval periods without the functional cardiovascular system. Despite such dispensability, it is the first organ to develop. Such enigma prompted us to hypothesize yet other cardiovascular functions that are important for developmental and/or physiological processes. Hence, systematic cellular ablations and functional perturbations are performed on zebrafish cardiovascular system to gain comprehensive and body-wide understanding of such functions and to elucidate underlying mechanisms. This approach identifies a set of organ-specific genes, each implicated for important functions. The study also unveils distinct cardiovascular mechanisms, each differentially regulating their expressions in organ-specific and oxygen-independent manners. Such mechanisms are mediated by organ-vessel interactions, circulation-dependent signals, and circulation-independent beating-heart-derived signals. Hence, a comprehensive and body-wide functional landscape of the cardiovascular system reported herein may provide a clue as to why it is the first organ to develop. Furthermore, the dataset herein could serve as a resource for the study of organ development and function.

Bayesian Cell Force Estimation Considering Force Directions

Abstract Traction force microscopy is a useful technique for measuring mechanical forces generated by cells. In this method, fluorescent nano beads are embedded in the elastic substrate of cell culture, on which cells are cultured. Then, cellular forces are estimated from bead displacements, which represent the force-induced deformation of the substrate under the cell. Estimating the forces from the bead displacements is not easy when the bead density is low or the locations of cellular attachments are unknown. In this study, we propose a Bayesian algorithm by introducing a prior force direction that is based on cellular morphology. We apply the Bayesian framework to synthetic datasets in conditions under which the bead density is low and cellular attachment points are unknown. We demonstrate that the Bayesian algorithm improves accuracy in force estimation compared with the previous algorithms.

An estimation of cell forces with hierarchical bayes approach considering cell morphology

Traction Force Microscopy (TFM) is one of popular techniques for measuring mechanical forces in a cell. TFM estimates the traction forces a cell produces from the displacements of the fluorescent nano beads in the culture gel substrate of the cell. However, the estimation is difficult due to the limitation in the distirubtion of beads. To improve the estimation accuracy, we proposed a Bayesian estimation method that introduces priors to the cell morphology and the force directions and simultaneously estimates the forces and the parameters with the expectation-maximization algorithm. Our method significantly improved the accuracy in our numerical experiments using synthetic and experimental datasets compared with the conventional ridge regression method.