Biology subjects

Tomizawa, Y.

Publications and source records attributed to Tomizawa, Y..

5 recordsLinked to original sources

bioRxiv2026

Emergent Tissue Rheology in a 3D Mechanically Adaptive Viscoelastic Cell Network Model

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.

biophysics↗

bioRxiv2024

Lightsheet microscopy integrates single-cell optical visco-elastography and fluorescence cytometry of 3D live tissues

Most common cytometry methods, including flow cytometry, observe suspended or fixed cells and cannot evaluate their structural roles in 3D tissues. However, cellular physical interactions are critical in physiological, developmental, and pathological processes. Here, we present a novel optical visco-elastography that characterizes single-cellular physical interactions by applying in-situ micro-mechanical perturbation to live microtissues under 3D lightsheet microscopy. The 4D digital image correlation (DIC) analysis of [~]20,000 nodes tracked the compressive deformation of 3D tissues containing [~]500 cells. The computational 3D image segmentation allowed cell-by-cell qualitative observation and statistical analysis, directly correlating multi-channel fluorescence and viscoelasticity. To represent epithelia-stroma interactions, we used a 3D organoid model of maternal-fetal interface and visualized solid-like, well-aligned displacement and liquid-like random motion between individual cells. The statistical analysis through our unique cytometry confirmed that endometrial stromal fibroblasts stiffen in response to decidualization. Moreover, we demonstrated in the 3D model that interaction with placental extravillous trophoblasts partially reverses the attained stiffness, which was supported by the gene expression analysis. Placentation shares critical cellular and molecular significance with various fundamental biological events such as cancer metastasis, wound healing, and gastrulation. Our analysis confirmed existing beliefs and discovered new insights, proving the broad applicability of our method.

bioengineering↗

bioRxiv2023

Harnessing deep learning to analyze cryptic morphological variability of Marchantia polymorpha

Characterizing phenotypes is a fundamental aspect of biological sciences, although it can be challenging due to various factors. For instance, the liverwort (Marchantia polymorpha), a model system for plant biology, exhibits morphological variability, making it difficult to identify and quantify distinct phenotypic features using objective measures. To address this issue, we utilized a deep learning-based image classifier that can handle plant images directly without manual extraction of phenotypic features, and analyzed bright-field images of M. polymorpha. This dioicous plant species exhibits morphological differences between male and female wild accessions at an early stage of gemmaling growth, although it remains elusive whether the differences are attributable to sexual dimorphism or autosomal genetic variation. To dissect the genomic factors, we established a male and female set of recombinant inbred lines (RILs) from a set of male and female wild accessions. We then trained deep-learning models to classify the sexes of the RILs and the wild accessions. Our results showed that the trained classifiers accurately classified male and female gemmalings of wild accessions in the first week of growth, confirming the intuition of plant researchers in a reproducible and objective manner. In contrast, the RILs were less distinguishable, indicating that the differences between the parental wild accessions arose from autosomal variations instead of sexual dimorphism. Furthermore, we validated our trained models by an "explainable AI" technique that highlights image regions relevant to the classification. Our findings demonstrate that the classifier-based approach provides a powerful tool for analyzing plant species that lack standardized phenotyping metrics.

plant biology↗

bioRxiv2020

A genetically encoded red fluorescence dopamine biosensor enables dual imaging of dopamine and norepinephrine

Dopamine (DA) and norepinephrine (NE) are pivotal neuromodulators that regulate a broad range of brain functions, often in concert. Despite their physiological importance, untangling the relationship between DA and NE in finely controlling output functions is currently challenging, primarily due to a lack of techniques to visualize spatiotemporal dynamics with sufficiently high selectivity. Although genetically encoded fluorescent biosensors have been developed to detect DA, their poor selectivity prevents distinguishing DA from NE. Here, we report the development of a red fluorescent genetically encoded GPCR (G protein-coupled receptor)-activation reporter for DA termed R-GenGAR-DA. More specifically, a circular permutated red fluorescent protein (cpmApple) was inserted into the third intracellular loop of human DA receptor D1 (DRD1) followed by the screening of mutants within the linkers between DRD1 and cpmApple. We developed two variants: R-GenGAR-DA1.1, which brightened following DA stimulation, and R-GenGAR-DA1.2, which dimmed. R-GenGAR-DA1.2 demonstrated reasonable dynamic range ({Delta}F/F0 = -50%) and DA affinity (EC50 = 0.7 {micro}M) as well as the highest selectivity for DA over NE (143-fold) amongst available DA biosensors. Due to its high selectivity, R-GenGAR-DA1.2 allowed dual-color fluorescence live imaging for monitoring DA and NE, combined with the existing green-NE biosensor GRABNE1m, which has high selectivity for NE over DA (>350-fold) in HeLa cells and hippocampal neurons grown from primary culture. By enabling precise measurement of DA, as well as simultaneous visualization of DA and NE, the red-DA biosensor R-GenGAR-DA1.2 is promising in advancing our understanding of the interplay between DA and NE in organizing key brain functions. Significance StatementThe neuromodulators dopamine and norepinephrine modulate a broad range of brain functions, often in concert. One current challenge is to measure dopamine and norepinephrine dynamics simultaneously with high spatial and temporal resolution. We therefore developed a red-dopamine biosensor that has 143-fold higher selectivity for dopamine over norepinephrine. Taking advantage of its high selectivity for dopamine over norepinephrine, this red-dopamine biosensor allowed dual-color fluorescence live imaging for monitoring dopamine and norepinephrine in both HeLa cells and hippocampal neurons in vitro combined with the existing green-norepinephrine biosensor that has 350-fold selectivity for norepinephrine over dopamine. Thus, this approach can provide new opportunities to advance our understanding of high spatial and temporal dynamics of dopamine and norepinephrine in normal and abnormal brain functions.

cell biology↗

bioRxiv2017

Trans-omic analysis reveals fed and fasting insulin signal across phosphoproteome, transcriptome, and metabolome

The concentration and temporal pattern of insulin selectively regulate multiple cellular functions. To understand how insulin dynamics are interpreted by cells, we constructed a trans-omic network of insulin action in FAO hepatoma cells from three networks--a phosphorylation-dependent cellular functions regulatory network using phosphoproteomic data, a transcriptional regulatory network using phosphoproteomic and transcriptomic data, and a metabolism regulatory network using phosphoproteomic and metabolomic data. With the trans-omic regulatory network, we identified selective regulatory networks that mediate differential responses to insulin. Akt and Erk, hub molecules of insulin signaling, encode information of a wide dynamic range of dose and time of insulin. Down-regulated genes and metabolites in glycolysis had high sensitivity to insulin (fasting insulin signal); up-regulated genes and dicarboxylic acids in the TCA cycle had low sensitivity (fed insulin signal). This integrated analysis enables molecular insight into how cells interpret physiologically fed and fasting insulin signals.\n\nHighlightsO_LIWe constructed a trans-omic network of insulin action using multi-omic data.\nC_LIO_LIThe trans-omic network integrates phosphorylation, transcription, and metabolism.\nC_LIO_LIWe classified signaling, transcriptome, and metabolome by sensitivity to insulin.\nC_LIO_LIWe identified fed and fasting insulin signal flow across the trans-omic network.\nC_LI

systems biology↗