Biology subjects

Funahashi, A.

Publications and source records attributed to Funahashi, A..

15 recordsLinked to original sources

bioRxiv2026

Profiling cell proliferation after whole-genome duplication in human cells

Though whole-genome duplication (WGD) contributes to cancer progression, the mechanism of post-WGD cell proliferation remains unclear. Here, using 6-day live-imaging, we analyzed the proliferation dynamics of more than 150 post-WGD HCT116 cell lineages. A quantitative comparison of mitotic patterns and cell fates between proliferative and non-proliferative lineages revealed that multipolar chromosome segregation in early mitosis is a key factor limiting the proliferative capacity of post-WGD progenies. Multipolar chromosome segregation suppressed post-WGD cell viability, particularly when accompanied by drastic chromosome loss or when it repeatedly occurred. Tracing proliferative lineages elucidated that they proliferated mainly by imposing the risk of multipolar chromosome segregation on one of two sub-lineages that formed after the first bipolar division. Meanwhile, a considerable proportion of proliferative lineages consisted entirely of progeny of early multipolar chromosome segregation events. Our results highlight key cellular events that determine the proliferation dynamics and diversity of post-WGD progenies, providing a fundamental reference for understanding WGD-associated bioprocesses. Summary statementLive image tracing of >150 cell lineages reveals the cross-generation dynamics of multipolar chromosome segregation that determine the fates of post-whole-genome duplication progeny cells.

cell biology↗

bioRxiv2025

An interplay between cytoplasmic flow and cell morphology during cytokinesis simulated by the moving-particle semi-implicit method

Cells are composite mechanical systems: a viscoelastic cytoplasm enclosed by a deformable cell cortex. These two components exchange forces continuously--fluid motion deforms the cortex, and cortical deformation in turn redirects intracellular flow. Yet most cellular mechanics simulations treat either the fluid or the cortex in isolation, leaving their mechanical interplay largely unexplored. To address this gap, we developed a fluid-structure interaction framework based on the moving-particle semi-implicit (MPS) method that solves the coupled dynamics of cytoplasmic flow, internal pressure, and cortical elasticity. We applied this framework to cytokinesis, focusing on how the cleavage plane is positioned. While the mitotic spindle is known to specify the furrow site, prior work has emphasized biochemical cues emanating from the spindle, whereas a direct mechanical cue has been difficult to interrogate. Our simulations show that spindle elongation generates cytoplasmic flows that create a pressure minimum near the spindle midzone; when the cortex is modeled as an elastic shell, this pressure landscape drives a localized cortical invagination at that site. We therefore propose that spindle-driven cytoplasmic flow provides a mechanical positional cue for cleavage-furrow initiation. Author SummaryDividing cells exhibit two contrasting properties: dynamic state changes and the maintenance of homeostasis. During cell division, a dividing cell breaks its identity while successful daughter cells inherit a certain set of its components for survival. Heterogeneous physical properties such as cell shape, cytoplasmic flow, and internal pressure interact to maintain an appropriate range of cellular conditions. The interaction of such physical components is highly dynamic; therefore, simulation studies play an important role in understanding the mechanisms. We constructed a model based on the moving-particle semi-implicit method that recapitulates the behavior of highly dynamic physical objects such as cytoplasmic fluid with practical computational time. The simulation showed that the change in cell shape is caused by internal cell pressure, which is evoked by cytoplasmic flow during elongation of spindle microtubules. The developed simulation platform provided a tool to integrate physical measurements to understand the mechanisms that balance physical dynamics during cell division. Such quantitative simulation combined with the data from molecular or genetic experiments may predict how internal dynamics maintain homeostasis during drastic changes in the cell state.

systems biology↗

bioRxiv2025

Mixing features of transcription factors and genes enables accurate prediction of gene regulation relationships for unknown transcription factors

Identifying regulatory relationships between transcription factors (TFs) and genes is essential to understand diverse biological phenomena related to gene expression. Recently, deep learning-based models to predict TFs that bind to genes from nucleotide sequences of the target genes have been developed, yet these models are trained to predict known TFs only. In this study we developed a deep learning model, GReNIMJA (Gene Regulatory Network Inference by Mixing and Jointing features of Amino acid and nucleotide sequences), to predict gene regulation even by unknown TFs. Our model is designed to mix the features of the TF amino acid sequences and nucleotide sequences of the target genes using a 2D LSTM architecture and to perform binary classification with the aim of determining the presence or absence of a regulatory relationship. The accuracy of our model in predicting regulatory relationships was 84.4% for known TFs (higher than those of conventional models) and 68.5% for unknown TFs; the latter is an unsolved task for conventional deep learning-based models. We expect our model to advance identification of unknown gene regulatory networks and contribute to the understanding of diverse biological phenomena.

systems biology↗

bioRxiv2024

Label-free multiplex microscopic imaging by image-to-image translation overcoming the trade-off between pixel- and image-level similarity

Establishment of multiplex microscopic imaging without labeling is indispensable for understanding complex interactions of subcellular components. Toward the establishment of label-free multiplex microscopic imaging, image-to-image translation models that extract images of multiple subcellular components from bright-field images via nonlinear processing have attracted attention. However, the performance of conventional models is limited by a trade-off relationship between pixel- and image-level similarity, which degrades imaging performance. Here, we developed an image-to-image Wasserstein Schrodinger Bridge model to achieve high-performance image-to-image translation at the pixel level using Schrodinger Bridge while minimizing Wasserstein distance at the image level. Our model dramatically outperformed the conventional models at both levels simultaneously, reducing the mean squared error by 410-fold and improving the structural similarity index measure by 17.1-fold. Label-free multiplex microscopic imaging based on our model paves a way for the analysis of the interactions of subcellular components.

systems biology↗

bioRxiv2024

Four-dimensional label-free live cell image segmentation for predicting live birth potential of mouse embryos

1Selection of high-quality embryos is critical in assisted reproductive technology (ART), but it relies on visual assessment by experts, and the birth rate remains low. We previously developed a deep learning method to predict the birth of mouse embryos by quantifying the morphological features of cell nuclei. This method involves cell nuclear segmentation on fluorescence microscopy images, but fluorescence labeling of nuclei is not feasible in medical applications. Here, we developed FL2-Net, a nuclear segmentation method for time-series three-dimensional bright-field microscopy images of mouse embryos without fluorescence labeling. FL2-Net outperformed existing state-of-the-art segmentation methods. We predicted the birth potential of mouse embryos from the nuclear features quantified by bright-field microscopy image segmentation. Birth prediction accuracy of FL2-Net (81.63%) exceeded those of other methods and experts (55.32%). We expect that FL2-Net, which can quantify nuclear features of embryos non-invasively and with high accuracy, might be useful in ART.

systems biology↗

bioRxiv2024

Deep learning-based automated prediction of mouse seminiferous tubule stage by using bright-field microscopy

Infertility is a global issue, with approximately 50% of cases attributed to defective spermatogenesis. For studies into spermatogenesis and spermatogenic dysfunction, evaluating the seminiferous tubule stage is essential. However, the current method of evaluation involves labor-intensive and time-consuming manual tasks such as staining, observation, and image analysis. Lack of reproducibility is also a problem owing to the subjective nature of visual evaluation by experts. In this study, we propose a deep learning-based method for automatically and objectively evaluating the seminiferous tubule stage. Our approach automatically predicts which of 12 seminiferous tubule stages is represented in bright-field microscopic images of mouse seminiferous tubules stained by hematoxylin-PAS. For training and validation of our model, we created a dataset of 1229 tissue images, each labeled with one of 12 distinct seminiferous tubule stages. The maximum prediction accuracy was 79.58% which rose to 98.33% with allowance for a prediction error of {+/-}1 stage. Remarkably, although the model was not explicitly trained on the patterns of transition between stages, it inferred characteristic structural patterns involved in the process of spermatogenesis. This method not only advances our understanding of spermatogenesis but also holds promise for improving the automated diagnosis of infertility.

systems biology↗

bioRxiv2024

Inference of gene regulatory networks for overcoming low performance in real-world data

The identification of gene regulatory networks is important for understanding the mechanisms of various biological phenomena. Many methods have been proposed to infer networks from time-series gene expression data obtained by high-throughput next-generation sequencings. Such methods can effectively infer gene regulatory networks for in silico data, but inferring the networks accurately from in vivo data remiains a challenge because of the large noise and low time sampling rate. Here, we proposed a novel unsupervised learning method, Multi-view attention Long-short term memory for Network inference (MaLoN). It can infer gene regulatory networks with temporal changes in gene regulation using the multi-view attention Long Short-term memory model. Using in vivo benchmark datasets in Saccharomyces cerevisiae and Escherichia coli, we showed that MaLoN can infer gene regulatory networks more accurately than existing methods. The ablated models indicated that the multi-view attention mechanism suppressed false positives. The order of activation of gene regulations inferred by MaLoN was consistent with existing knowledge.

systems biology↗

bioRxiv2024

Cell segmentation without annotation by unsupervised domain adaptation based on cooperative self-learning

Semantic cell segmentation from microscopic images is essential for the quantitative evaluation of cell morphology. Although supervised deep-learning-based models offer accurate segmentation, their performance degrades for unknown cell types. To address this problem, unsupervised domain adaptation methods based on adversarial training, self-training, or a combination of these approaches have been developed in recent years. These methods train the model using inference labels from the unknown domain as pseudo labels with reliability to resolve the discrepancy between the features of the unknown and known domains. However, conventional methods require a predefined threshold to calculate pseudo-labels reliability, leading to costly hyperparameter tuning. Here, we developed an unsupervised domain adaptation for semantic cell segmentation with cooperative self-learning (CULPICO: Cooperative Unsupervised Learning for PIxel-wise COloring) that does not require predefined threshold of the pseudo-labels reliability. The proposed method consists of two independent segmentation models and a mutual exchange mechanism of inference data. The models infer a label probability at each pixel and generate a pseudo-label as unsupervised learning. The pseudo-labels created by each model are mutually used as ground truth in the other model. Loss function is corrected by considering pixel-level discrepancies between the label probabilities inferred by the two models. The proposed method, despite being an unsupervised learning method, can segment efficiently the unknown cell types without labels with an accuracy comparable to supervised learning models. Our method, which could solve the performance degradation problem without constructing new datasets, is expected to accelerate life science by reducing the cost of extracting quantitative biological knowledge.

systems biology↗

bioRxiv2022

A versatile and interoperable computational framework for the analysis and modeling of COVID-19 disease mechanisms

The COVID-19 Disease Map project is a large-scale community effort uniting 277 scientists from 130 Institutions around the globe. We use high-quality, mechanistic content describing SARS-CoV-2-host interactions and develop interoperable bioinformatic pipelines for novel target identification and drug repurposing. Community-driven and highly interdisciplinary, the project is collaborative and supports community standards, open access, and the FAIR data principles. The coordination of community work allowed for an impressive step forward in building interfaces between Systems Biology tools and platforms. Our framework links key molecules highlighted from broad omics data analysis and computational modeling to dysregulated pathways in a cell-, tissue- or patient-specific manner. We also employ text mining and AI-assisted analysis to identify potential drugs and drug targets and use topological analysis to reveal interesting structural features of the map. The proposed framework is versatile and expandable, offering a significant upgrade in the arsenal used to understand virus-host interactions and other complex pathologies.

systems biology↗

bioRxiv2021

Deep learning-based algorithm for predicting the live birth potential of mouse embryos

In assisted reproductive technology (ART), embryos produced by in vitro fertilization (IVF) are graded according to their live birth potential, and high-grade embryos are preferentially transplanted. However, the rate of live birth following clinical ART remains low worldwide, suggesting that grading is inaccurate. One explanation is that grading is classically based on the characteristic shape of embryos at a limited number of developmental stages and does not consider the shape of embryos and intracellular structures, e.g., nuclei, at various stages important for normal embryogenesis. Therefore, here we developed a Normalized Multi-View Attention Network (NVAN) that directly predicts live birth potential from nuclear structural features in live-cell fluorescence images taken of mouse embryos across a wide range of stages. The classification accuracy of our method was 83.87%, which greatly exceeded that of existing machine-learning methods and that of visual inspection by embryo culture specialists. By visualizing the features that contributed most to the prediction of live birth potential, we found that the size and shape of the cell nucleus at the morula stage and at the time of cell division were important for live birth prediction. We anticipate that our method will help ART and developmental engineering as a new basic technology for IVF embryo selection.

developmental biology↗

bioRxiv2020

COVID-19 Disease Map, a computational knowledge repository of SARS-CoV-2 virus-host interaction mechanisms

We describe a large-scale community effort to build an open-access, interoperable, and computable repository of COVID-19 molecular mechanisms - the COVID-19 Disease Map. We discuss the tools, platforms, and guidelines necessary for the distributed development of its contents by a multi-faceted community of biocurators, domain experts, bioinformaticians, and computational biologists. We highlight the role of relevant databases and text mining approaches in enrichment and validation of the curated mechanisms. We describe the contents of the Map and their relevance to the molecular pathophysiology of COVID-19 and the analytical and computational modelling approaches that can be applied for mechanistic data interpretation and predictions. We conclude by demonstrating concrete applications of our work through several use cases and highlight new testable hypotheses.

systems biology↗

bioRxiv2020

Cas9-mediated Genome Editing Reveals a Significant Contribution of Calcium Signaling Pathways to Anhydrobiosis in Pv11

Pv11 is an insect cell line established from the midge Polypedilum vanderplanki that exhibits an extreme desiccation tolerance known as anhydrobiosis. Pv11 has also an anhydrobiotic ability which is induced by trehalose treatment. Here we report the successful construction of the genome editing system for Pv11 cells and its application for identifying the signaling pathways in the anhydrobiosis. Using the Cas9-mediated gene knock-in system, we established GCaMP3-stably expressing Pv11 cells to monitor intracellular Ca2+ mobilization. Intriguingly, trehalose treatment evoked a transient increase of cytosolic Ca2+ concentration, and further experiments indicated the contribution of the calmodulin - calcineurin - NFAT pathway to the tolerance for trehalose treatment as well as the desiccation tolerance, while the calmodulin - calmodulin Kinase - CREB pathway conferred only the desiccation tolerance on Pv11 cells. Thus, our results show the critical contribution of the trehalose-induced Ca2+ surge to the anhydrobiosis and the temporal different roles of each signaling pathway.

molecular biology↗

bioRxiv2018

Predicting the future direction of cell movement with convolutional neural networks

Image-based deep learning systems, such as convolutional neural networks (CNNs), have recently been applied to cell classification, producing impressive results; however, application of CNNs has been confined to classification of the current cell state from the image. Here, we focused on cell movement where current and/or past cell shape can influence the future cell fate. We demonstrate that CNNs prospectively predicted the future direction of cell movement with high accuracy from a single image patch of a cell at a certain time. Furthermore, by visualizing the image features that were learned by the CNNs, we could identify morphological features, e.g., the protrusions and trailing edge that have been experimentally reported to determine the direction of cell movement. Our results indicate that CNNs have the potential to predict the future cell fate from current cell shape, and can be used to automatically identify those morphological features that influence future cell fate.

systems biology↗

bioRxiv2018

Transcriptome analysis of Pv11 cells infers the mechanism of desiccation tolerance and recovery

The larvae of the African midge, Polypedilum vanderplanki, can enter an ametabolic state called anhydrobiosis to conquer fatal desiccation stress. The Pv11 cell line, derived from embryos of the midge, shows desiccation tolerance by pretreatment with trehalose before desiccation; they can resume proliferation after rehydration. To address the underlying molecular mechanisms, we desiccated Pv11 cells after pretreatment with the medium containing trehalose and induced proliferation by rehydration. We collected the cells at each before and after desiccation and rehydration step and performed CAGE-seq of mRNA of those cells. By analysing differentially expressed genes (DEGs) among the results of CAGE-seq, we detected 384 DEGs after trehalose treatment and 14 DEGs after rehydration. Hierarchical clustering of the identified DEGs indicated that rehydration returns their expression pattern to that in the control culture state. DEGs involved in various stress responses, detoxification of harmful chemicals, and regulation of oxidoreduction were upregulated by trehalose treatment. DEGs for rehydration supported that DNA repair is one of the potential mechanisms involves recovery. This study provided initial insight into the molecular mechanisms underlying the extreme desiccation tolerance of Pv11 cells with a potential for proliferation following rehydration.

genomics↗

bioRxiv2018

Development of Convolutional Neural Network Based Instance Segmentation Algorithm to Acquire Quantitative Criteria of Mouse Development

In embryology, analytical approaches such as segmentation are applied to acquiring quantitative criteria from time-series three-dimensional microscopic images. When used to segment cells or intracellular organelles, several current machine learning techniques outperform traditional image processing algorithms. However segmentation algorithms still have unsolved problems, especially in bioimage processing. The most critical issue is that the existing algorithms can perform only semantic segmentation, which distin-guishes whether a pixel is within an object (for example, nucleus) or not. In this study, we implemented a novel segmentation algorithm, based on deep learning, which segments each nucleus and adds different labels to the detected objects. This segmentation algorithm is called instance segmentation. Our instance segmentation algorithm, implemented as a neural network, which we named QCA Net, substantially out-performed 3D U-Net, which is the best semantic segmentation algorithm that uses deep learning. Using QCA Net, we quantified the nuclear number, volume, surface area, and center of gravity coordinates during the development of mouse embryos. In particular, QCA Net distinguished nuclei of embryonic cells from those of polar bodies formed in meiosis. We consider that QCA Net can greatly contribute to bioimage segmentation in embryology by generating quantitative criteria from segmented images.

systems biology↗