Biology subjects

Kawase, T.

Publications and source records attributed to Kawase, T..

12 recordsLinked to original sources

bioRxiv2026

Machine learning-guided spatial omics for tissue-scale discovery of cell-type-specific architectures

Multiplexed protein imaging enables spatially resolved analysis of molecular organization in tissues, but existing spatial proteomics platforms remain constrained in scalability, throughput, and integration with RNA measurements and interpretable computational analysis. Here, we present an integrated spatial omics framework that combines highly multiplexed protein and RNA imaging with explainable machine learning to map cell-type-specific molecular and structural architectures at tissue scale. Using this platform, we simultaneously profiled up to 46 proteins and 79 RNA species across [~]370,000 cells in intact mouse brain tissue at diffraction-limited subcellular resolution ([~]260 nm). We developed a scalable, open-source computational pipeline for large-scale image processing and analysis, and show that nuclear protein and chromatin features alone are sufficient to accurately classify brain cell types and their spatial organization. Incorporation of explainable deep learning further enabled identification of human-interpretable, cell-type-specific subnuclear structural features directly from imaging data, with independent quantitative validation. Together, this integrated experimental and computational framework enables tissue-scale spatial proteomics-based cell-type classification and structural feature discovery, providing a broadly applicable platform for mechanistic studies, high-content screening, and translational applications.

systems biology↗

bioRxiv2025

Transcriptomic insights into the tripartite plant-pathogen-mycoparasite interaction reveal novel mechanisms involving fungal secretomes and plant amino acid metabolism

O_LIThe tomato-Cladosporium fulvum (syn. Fulvia fulva) pathosystem has served as a model for the gene-for-gene concept of effectors and resistance proteins, but this binary framework does not include the potential influence of other microbial participants. Here we describe the dramatic changes in gene expression of all members of the tritrophic interaction among tomato, C. fulvum, and mycoparasitic fungus Hansfordia pulvinata. C_LIO_LITranscriptomic analyses of the mycoparasite H. pulvinata during parasitism of C. fulvum on tomato on planta and in vitro revealed a dramatic upregulation of genes encoding small secreted proteins during mycoparasitism, notably, a Nep1-like protein (HpNlp1) lacked typical necrosis-inducing activity but induced the accumulation of antifungal compounds inhibiting spore germination of C. fulvum. C_LIO_LISimilarly, in C. fulvum parasitized by H. pulvinata, effector genes were highly expressed. Strikingly, effector protein Ecp2 was found to share structural similarity with pathogen killer toxin 4 proteins and had broad-spectrum antifungal activity, indicating a dual function in fungal competition and Cf-ECP2-mediated plant resistance. C_LIO_LIIn tomato plants infected by C. fulvum parasitized by H. pulvinata, primary metabolism and defense-related genes were exclusively activated. These results suggest that in the tritrophic interaction, the mycoparasite simultaneously suppressed the pathogen and induced plant resistance. This study uncovers a multilayered molecular network in which the mycoparasite coordinates pathogen suppression and plant defense within the tritrophic interaction. C_LI

microbiology↗

bioRxiv2025

HortaCloud: An Open and Collaborative Platform for Whole Brain Neuronal Reconstructions

HortaCloud is a cloud-based, open-source platform designed to facilitate the collaborative reconstruction of long-range projection neurons from whole-brain light microscopy data. By providing virtual environments directly within the cloud, it eliminates the need for costly and time-consuming data downloads, allowing researchers to work efficiently with terabyte-scale volumetric datasets. Standardization of computational resources in the cloud make deployment easier and more predictable. The pay-as-you-go cloud model reduces adoption barriers by eliminating upfront investments in expensive hardware. Finally, HortaClouds decentralized architecture enables global collaboration between researchers and between institutions.

neuroscience↗

bioRxiv2024

Mutant p53 regulates cancer cell invasion in complex three-dimensional environments through mevalonate pathway-dependent Rho/ROCK signaling.

Certain mutations can confer neomorphic gain of function (GOF) activities to the p53 protein that affect cancer progression. Yet the concept of mutant p53 GOF has been challenged. Here, using various strategies to alter the status of mutant versions of p53 in different cell lines, we demonstrate that mutant p53 stimulates cancer cell invasion in three-dimensional environments. Mechanistically, mutant p53 enhances RhoA/ROCK-dependent cell contractility and cell-mediated extracellular matrix (ECM) re-organization via increasing mevalonate pathway-dependent RhoA localization to the membrane. In line with this, RhoA-dependent pro-invasive activity is also mediated by IDI-1, a mevalonate pathway product. Further, the invasion-enhancing effect of mutant p53 is dictated by the biomechanical properties of the surrounding ECM, thereby adding a cell-independent layer of regulation to mutant p53 GOF activity that is mediated by dynamic reciprocal cell-ECM interactions. Together our findings link mutant p53 metabolic GOF activity with an invasive cellular phenotype in physiologically relevant and context-dependent settings. SignificanceThis study addresses the contribution of mutant p53 to the process of cancer cell dissemination in physiologically relevant three-dimensional environments - a key characteristic of metastatic disease. Several mutant p53 proteins display pro-oncogenic activity with respect to cancer cell invasion in 3D environments via mevalonate pathway-dependent Rho/ROCK signaling axis.

cancer biology↗

bioRxiv2024

Deep-Tissue Spatial Omics: Imaging Whole-Embryo Transcriptomics and Subcellular Structures at High Spatial Resolution

The inherent limitations of fluorescence microscopy, notably the restricted number of color channels, have long constrained comprehensive spatial analysis in biological specimens. Here, we introduce cycleHCR technology that leverages multicycle DNA barcoding and Hybridization Chain Reaction (HCR) to surpass the conventional color barrier. cycleHCR facilitates high-specificity, single-shot imaging per target for RNA and protein species within thick specimens, mitigating the molecular crowding issues encountered with other imaging-based spatial omics techniques. We demonstrate whole-mount transcriptomics imaging of 254 genes within an E6.5[~]7.0 mouse embryo, achieving precise three-dimensional gene expression and cell fate mapping across a specimen depth of [~] 310 {micro}m. Utilizing expansion microscopy alongside protein cycleHCR, we unveil the complex network of 10 subcellular structures in primary mouse embryonic fibroblasts. Furthermore, in mouse hippocampal slice, we image 8 protein targets and profile the transcriptome of 120 genes, uncovering complex gene expression gradients and cell-type specific nuclear structural variances. cycleHCR provides a unifying framework for multiplex RNA and protein imaging, offering a quantitative solution for elucidating spatial regulations in deep tissue contexts for research and potentially diagnostic applications.

developmental biology↗

bioRxiv2024

Song Torrent: A modular, open-source 96-chamber audio and video recording apparatus with optogenetic activation and inactivation capabilities for Drosophila

BackgroundO_LIMany Drosophila species use acoustic communication during courtship and studies of these communication systems have provided insight into neurobiology, behavioral ecology, ethology, and evolution. C_LIO_LIRecording Drosophila courtship sounds and associated behavior is challenging, especially at high throughput, and previously designed devices are relatively expensive and complex to assemble. C_LI ResultsO_LIWe present construction plans for a modular system utilizing mostly off-the-shelf, relatively inexpensive components that provides simultaneous high-resolution audio and video recording of 96 isolated or paired Drosophila individuals. C_LIO_LIWe provide open-source control software to record audio and video. C_LIO_LIWe designed high intensity LED arrays that can be used to perform optogenetic activation and inactivation of labelled neurons. C_LIO_LIThe basic design can be modified to facilitate novel study designs or to record insects larger than Drosophila. C_LIO_LIFewer than 96 microphones can be used in the system if the full array is not required or to reduce costs. C_LI ImplicationsO_LIOur hardware design and software provide an improved platform for reliable and comparatively inexpensive high-throughput recording of Drosophila courtship acoustic and visual behavior and perhaps for recording acoustic signals of other small animals. C_LI

animal behavior and cognition↗

bioRxiv2022

NeuronBridge: an intuitive web application for neuronal morphology search across large data sets

Neuroscience research in Drosophila is benefiting from large-scale connectomics efforts using electron microscopy (EM) to reveal all the neurons in a brain and their connections. In order to exploit this knowledge base, researchers target individual neurons and study their function. Therefore, vast libraries of fly driver lines expressing fluorescent reporter genes in sets of neurons have been created and imaged using confocal light microscopy (LM). However, creating a fly line for driving gene expression within a single neuron found in the EM connectome remains a challenge, as it typically requires identifying a pair of fly lines where only the neuron of interest is expressed in both. This task and other emerging scientific workflows require finding similar neurons across large data sets imaged using different modalities. Here, we present NeuronBridge, a web application for easily and rapidly finding putative morphological matches between large datasets of neurons imaged using different modalities. We describe the functionality and construction of the NeuronBridge service, including its user-friendly GUI, data model, serverless cloud architecture, and massively parallel image search engine. NeuronBridge is openly accessible at http://neuronbridge.janelia.org/.

neuroscience↗

bioRxiv2021

Rapid reconstruction of neural circuits using tissue expansion and lattice light sheet microscopy

Electron microscopy (EM) allows for the reconstruction of dense neuronal connectomes but suffers from low throughput, limiting its application to small numbers of reference specimens. We developed a protocol and analysis pipeline using tissue expansion and lattice light-sheet microscopy (ExLLSM) to rapidly reconstruct selected circuits across many samples with single synapse resolution and molecular contrast. We validate this approach in Drosophila, demonstrating that it yields synaptic counts similar to those obtained by EM, can be used to compare counts across sex and experience, and to correlate structural connectivity with functional connectivity. This approach fills a critical methodological gap in studying variability in the structure and function of neural circuits across individuals within and between species.

neuroscience↗

bioRxiv2020

A Connectome and Analysis of the Adult Drosophila Central Brain

The neural circuits responsible for animal behavior remain largely unknown. We summarize new methods and present the circuitry of a large fraction of the brain of the fruit fly Drosophila melanogaster. Improved methods include new procedures to prepare, image, align, segment, find synapses in, and proofread such large data sets. We define cell types, refine computational compartments, and provide an exhaustive atlas of cell examples and types, many of them novel. We provide detailed circuits consisting of neurons and their chemical synapses for most of the central brain. We make the data public and simplify access, reducing the effort needed to answer circuit questions, and provide procedures linking the neurons defined by our analysis with genetic reagents. Biologically, we examine distributions of connection strengths, neural motifs on different scales, electrical consequences of compartmentalization, and evidence that maximizing packing density is an important criterion in the evolution of the flys brain.

bioinformatics↗

bioRxiv2020

A Connectome of the Adult Drosophila Central Brain

The neural circuits responsible for behavior remain largely unknown. Previous efforts have reconstructed the complete circuits of small animals, with hundreds of neurons, and selected circuits for larger animals. Here we (the FlyEM project at Janelia and collaborators at Google) summarize new methods and present the complete circuitry of a large fraction of the brain of a much more complex animal, the fruit fly Drosophila melanogaster. Improved methods include new procedures to prepare, image, align, segment, find synapses, and proofread such large data sets; new methods that define cell types based on connectivity in addition to morphology; and new methods to simplify access to a large and evolving data set. From the resulting data we derive a better definition of computational compartments and their connections; an exhaustive atlas of cell examples and types, many of them novel; detailed circuits for most of the central brain; and exploration of the statistics and structure of different brain compartments, and the brain as a whole. We make the data public, with a web site and resources specifically designed to make it easy to explore, for all levels of expertise from the expert to the merely curious. The public availability of these data, and the simplified means to access it, dramatically reduces the effort needed to answer typical circuit questions, such as the identity of upstream and downstream neural partners, the circuitry of brain regions, and to link the neurons defined by our analysis with genetic reagents that can be used to study their functions. Note: In the next few weeks, we will release a series of papers with more involved discussions. One paper will detail the hemibrain reconstruction with more extensive analysis and interpretation made possible by this dense connectome. Another paper will explore the central complex, a brain region involved in navigation, motor control, and sleep. A final paper will present insights from the mushroom body, a center of multimodal associative learning in the fly brain.

neuroscience↗

bioRxiv2019

When 90% of the variance is not enough: residual EMG from muscle synergy extraction influences task performance

Muscle synergies are usually identified via dimensionality reduction techniques, such that the identified synergies reconstruct the muscle activity to a level of accuracy defined heuristically, such as 90% of the variance explained. Here, we question the assumption that the residual muscle activity not explained by the synergies is due to noise. We hypothesize instead that the residual activity is structured and can therefore influence the execution of a motor task. Young healthy subjects performed an isometric reaching task in which surface electromyography of 10 arm muscles was mapped onto estimated two-dimensional forces used to control a cursor. Three to five synergies were extracted to account for 90% of the variance explained. We then altered the muscle-force mapping via \"hard\" and \"easy\" virtual surgeries. Whereas in both surgeries the forces associated with synergies spanned the same single dimension of the virtual environment, the muscle-force mapping was as close as possible to the initial mapping in the easy surgery and as far as possible in the hard surgery. This design therefore maximized potential differences in reaching errors attributable to the residual muscle activity. Results show that the easy surgery produced much smaller directional errors than the hard task. In addition, systematic estimations of the errors for easy and hard surgeries constructed with 1 to 10 synergies show that the errors differ significantly for up to 8 synergies, which account for 98% of the variance on average. Our study therefore indicates the need for cautious interpretations of results derived from synergy extraction techniques based on heuristics with lenient levels of accuracy.\n\nAuthor summaryThe muscle synergy hypothesis states that the central nervous system simplifies motor control by grouping muscles that share common functions into modules called muscle synergies. Current techniques use unsupervised dimensionality reduction algorithms to identify these synergies. However, these techniques rely on arbitrary criteria to determine the number of synergies, which is actually unknown. An example of such criteria is that the identified synergies must be able to reconstruct the measured muscle activity to at least a 90% level of accuracy. Thus, the residual muscle activity, the remaining 10% of the muscle activity, is often disregarded as noise. We show that residual muscle activity following muscle synergy identification has a large systematic effect on movements even when the number of synergies approaches the number of muscles. This suggests that current synergy extraction techniques may discard a component of muscle activity that is important for motor control. Therefore, current synergy extraction techniques must be updated to identify true physiological synergies.

neuroscience↗

bioRxiv2018

Color depth MIP mask search: a new tool to expedite Split-GAL4 creation

The GAL4-UAS system has proven its versatility in studying the function and expression patterns of neurons the Drosophila central nervous system. Although the GAL4 system has been used for 25 years, recent genetic intersectional tools have enabled genetic targeting of very small numbers of neurons aiding in the understanding of their function. This split-GAL4 system is extremely powerful for studying neuronal morphology and the neural basis of animal behavior. However, choosing lines to intersect that have overlapping patterns restricted to one to a few neurons has been cumbersome. This challenge is now growing as the collections of GAL4 driver lines has increased. Here we present a new method and software plug-in for Fiji to dramatically improve the speed of querying large databases of potential lines to intersect and aid in the split-GAL4 creation. We also provide pre-computed datasets for the Janelia GAL4 (5,738 lines) and VT GAL4 (7,429 lines) of the Drosophila central nervous system (CNS). The tool reduced our split-GAL4 creation effort dramatically.

bioinformatics↗