Biology subjects

Yamauchi, K. A.

Publications and source records attributed to Yamauchi, K. A..

9 recordsLinked to original sources

bioRxiv2026

OptiCell3D: Precise inference of mechanical cell properties from microscopy imaging

We introduce OptiCell3D, an open-source image-based framework for inferring cellular mechanical properties directly from 3D microscopy data. By integrating short physical simulations with gradient-based optimization via backpropagation, our method substantially improves accuracy compared to existing approaches. Moreover, OptiCell3D enables mechanical inference in tissues that are too large to be fully imaged, extending its applicability to more complex biological systems. We demonstrate the power of our approach by applying OptiCell3D to five morphologically diverse mouse epithelial tissues. By combining inferred parameters with simulations and morphometric analysis, we find that the ratio of apical to lateral surface tension predicts cell aspect ratio across epithelial subtypes, linking a single mechanical parameter to the broad morphological diversity of epithelia. Finally, we apply our framework to stratified tissues, finding greater variability in pressure and surface tension between cells and tension gradients along the apico-basal axis.

biophysics↗

bioRxiv2025

The emergence of the fractal bronchial tree

The fractal design of the bronchial tree, as described by the Hess-Murray law, facilitates energy-efficient lung ventilation, yet its developmental origins remain unclear. Here, we quantify the rearrangement of the embryonic bronchial tree into its fractal architecture, using SkelePlex, a new neural network-based image processing pipeline, and elucidate the biophysical principles governing its formation. We find that the branch shapes are such that pressure drop, shear, axial and hoop stress are equal across all branches. The seemingly random diameter of sister branches reflects the different number of tips they connect to in the asymmetric lung tree. By analysing lungs after pneumonectomy and those affected by chronic obstructive pulmonary disease (COPD), we show the same principles to persist in adulthood and disease, potentially providing quantitative biomarkers for disease progression and therapeutic guidance.

developmental biology↗

bioRxiv2024

Surforama: interactive exploration of volumetric data by leveraging 3D surfaces

MotivationVisualization and annotation of segmented surfaces is of paramount importance for studying membrane proteins in their native cellular environment by cryogenic electron tomography (cryo-ET). Yet, analyzing membrane proteins and their organization is challenging due to their small sizes and the need to consider local context constrained to the membrane surface. ResultsTo interactively visualize, annotate, and analyze proteins in cellular context from cryo-ET data, we have developed Surforama, a Python package and napari plugin. For interactive visualization of membrane proteins in tomograms, Surforama renders the local densities projected on the surface of the segmentations. Suforama additionally provides tools to annotate and analyze particles on the membrane surfaces. Finally, for compatibility with other tools in the cryo-ET analysis ecosystem, results can be exported as RELION-formatted STAR files. As a demonstration, we performed subtomogram averaging and neighborhood analysis of photosystem II proteins in thylakoid membranes from the green alga Chlamydomonas reinhardtii. Availability and implementationPython package, code and examples are available at: https://github.com/cellcanvas/surforama

bioinformatics↗

bioRxiv2024

MemBrain v2: an end-to-end tool for the analysis of membranes in cryo-electron tomography

Cryo-electron tomography (cryo-ET) provides unique insights into macromolecular complexes in their native environments, yet membrane analysis remains a major bottleneck due to low signal-to-noise ratios, missing wedge artifacts, and the complexity of membrane-associated proteins. Existing tools often require extensive manual annotation, struggle with generalization across datasets, and lack integrated solutions for segmentation, protein localization, and quantitative analysis. We introduce MemBrain v2, a deep learning-enabled framework that unifies these tasks into a streamlined pipeline. MemBrain-seg leverages a diverse, collaboratively generated training dataset and specialized model training strategies to achieve generalizable membrane segmentation across variable tomographic conditions. MemBrain-pick enables data-efficient localization of membrane-bound proteins by integrating geometric constraints with deep learning, reducing the need for extensive manual annotation. MemBrain-stats provides quantitative insights into protein distributions, computing spatial metrics to analyze intra-membrane particle organization. MemBrain v2 integrates seamlessly into cryo-ET workflows, providing an accessible and structured approach to membrane analysis. The full package is available at https://github.com/CellArchLab/MemBrain-v2.

bioinformatics↗

bioRxiv2023

Morphometry and mechanical instability at the onset of epithelial bladder cancer

Malignancies of epithelial tissues, called carcinomas, account for the majority of cancer cases. Much cancer research has focused on genetic alterations and their relation to different carcinoma phenotypes. Besides a rewiring in the signalling networks, carcinoma progression is accompanied by mechanical changes in the epithelial cells and the extracellular matrix. Here, we reveal intricate morphologies in the basement membrane at the onset of bladder cancer, and propose that they emerge from a mechanical buckling instability upon epithelial overgrowth. Using a combination of microscopy imaging of the mouse and human bladder tissue, elasticity theory, and numerical simulations of differential growth in the bladder mucosa, we find that aberrant tissue morphologies can emerge through stiffness changes in the different mucosa layers. The resulting thickening, wrinkles and folds exhibit qualitative and quantitative similarity with imaged early papillary tumors and carcinomas in situ. Atomic force microscopy indeed reveals local stiffness changes in the pathological basement membrane. Our findings suggest a mechanical origin of the different carcinoma subtypes in the bladder, which have vastly different clinical prognosis. They might provide the basis for a new line of attack in medical carcinoma treatment and prophylaxis.

cancer biology↗

bioRxiv2023

napari-threedee: a toolkit for human-in-the-loop 3D image analysis in napari

With recent advances in three-dimensional bioimaging acquisition and analysis methods, imaging data are an important tool for unveiling the complex architecture of biological organisms. However, extracting insights from these complex datasets requires interactive exploration as well as annotation for training supervised analytical methods. Here we present napari-threedee, a library of composable components and napari plugins for interactively exploring and annotating image data in 3D. We demonstrate how napari-threedee can be used for human-in-the-loop 3D image analysis in diverse image data including fluorescence microscopy and cryoelectron tomography.

bioengineering↗

bioRxiv2023

SpatialData: an open and universal data framework for spatial omics

Spatially resolved omics technologies are transforming our understanding of biological tissues. However, handling uni- and multi-modal spatial omics datasets remains a challenge owing to large volumes of data, heterogeneous data types and the lack of unified spatially-aware data structures. Here, we introduce SpatialData, a framework that establishes a unified and extensible multi-platform file-format, lazy representation of larger-than-memory data, transformations, and alignment to common coordinate systems. SpatialData facilitates spatial annotations and cross-modal aggregation and analysis, the utility of which is illustrated via multiple vignettes, including integrative analysis on a multi-modal Xenium and Visium breast cancer study.

bioinformatics↗

bioRxiv2022

FollicleFinder: automated three-dimensional segmentation of human ovarian follicles

In vitro fertilization (IVF) treatment protocols require frequent monitoring of the ovarian follicle growth process. We report FollicleFinder, an open source pipeline for the automated, 3D segmentation of ovarian follicles. FollicleFinder also accurately measures clinically-relevant morphological properties such as diameter, surface area, and volume. AvailabilityThe FollicleFinder pipeline is available at https://git.bsse.ethz.ch/iber/ovary-analysis and the graphical user interface is available at https://git.bsse.ethz.ch/iber/follicle-tracker.

bioinformatics↗

bioRxiv2019

Heterogeneity and targeted therapy-induced adaptations in lung cancer revealed by longitudinal single-cell RNA sequencing

Lung cancer, the leading cause of cancer mortality, exhibits heterogeneity that enables adaptability, limits therapeutic success, and remains incompletely understood. Single-cell RNA sequencing (scRNAseq) of metastatic lung cancer was performed using 44 tumor biopsies obtained longitudinally from 27 patients before and during targeted therapy. Over 20,000 cancer and tumor microenvironment (TME) single-cell profiles exposed a rich and dynamic tumor ecosystem. scRNAseq of cancer cells illuminated targetable oncogenes beyond those detected clinically. Cancer cells surviving therapy as residual disease (RD) expressed an alveolar-regenerative cell signature suggesting a therapy-induced primitive cell state transition, whereas those present at on-therapy progressive disease (PD) upregulated kynurenine, plasminogen, and gap junction pathways. Active T-lymphocytes and decreased macrophages were present at RD and immunosuppressive cell states characterized PD. Biological features revealed by scRNAseq were biomarkers of clinical outcomes in independent cohorts. This study highlights how therapy-induced adaptation of the multi-cellular ecosystem of metastatic cancer shapes clinical outcomes.

cancer biology↗