Biology subjects

Okochi, Y.

Publications and source records attributed to Okochi, Y..

6 recordsLinked to original sources

bioRxiv2026

Asymmetric turnover dynamics of Nav1.6 voltage-gated sodium channels at the axon initial segment

Nav1.6 voltage-gated sodium channels are critical for shaping action potentials, and their precise localization at the axon initial segment (AIS) is essential for neuronal excitability. However, the turnover mechanisms that maintain this spatial pattern remain unclear. Here, we present a genetically engineered mouse in which endogenous Nav1.6 carries a Cre-switchable fluorescent tag, enabling us to simultaneously trace the turnover of pre-existing and newly synthesized Nav1.6 without perturbing AIS structure. Using this system, we determine the physiological lifetimes of Nav1.6 at the AIS in vivo and in vitro, and further uncover the spatially asymmetric turnover dynamics, with the proximal and distal AIS organizing a source-to-sink gradient. Based on the quantified parameters, computational modeling clarifies that this asymmetry promotes efficient clearance of older molecules, thereby supporting AIS quality control. Collectively, these findings provide a technical and conceptual foundation for understanding how molecular turnover contributes to AIS homeostasis.

neuroscience↗

bioRxiv2026

The mental conflict in risk-taking behavior: Decoding bias between optimism and pessimism

Humans and animals often face risky situations that require decision-making. Such decisions can be high-risk, high-return at some times, and low-risk, low-return at other times, depending on the balance between optimism and pessimism. However, how this optimism-pessimism bias is regulated across contexts remains unclear. Here, we introduced a computational model of decision-making in a risk-taking task based on the free-energy principle, together with a machine-learning framework that inversely estimates cognitive updating and optimism-pessimism bias from behavioral data. Applying this framework to monkey behavioral data, we found that a monkey quickly and accurately recognized the degree of risk, while frequently switching between optimism and pessimism during the task. In addition, we identified a characteristic control rule for optimism-pessimism bias that is distinct from reward-dependent regulation. Our framework provided a principled tool for understanding the latent cognitive processes underlying risky decision-making in animals and humans.

neuroscience↗

bioRxiv2026

Inferring division-associated stochasticity from time-series single-cell transcriptomes

Cell division is fundamental to multicellular organisms and stochastic partitioning of cellular components can strongly affect genome-wide gene expression states. However, how cell division-associated partitioning noise shapes the dynamics of proliferating cells is poorly understood. Here, we propose scDIVIDE, a neural stochastic differential equation framework to infer continuous cellular dynamics and division rates while accounting for partitioning noise. We combined birth-death-mutation processes from population genetics with dynamical optimal transport and revealed that the birth rate is embedded in the diffusion coefficient, enabling its inference from time-series scRNA-seq data. scDIVIDE accurately inferred birth rates in synthetic data and the inferred birth rates recapitulated turnover-related programs in mouse hematopoiesis data. By exploiting the birth-diffusion coupling, scDIVIDE provides a biologically-informed constraint on growth rate estimation, outperforming existing methods in predicting future cell distributions. scDIVIDE provides a conceptual avenue for quantitatively dissecting how partitioning noise shapes fate decisions in multicellular systems.

bioinformatics↗

bioRxiv2022

Zero-shot reconstruction of mutant spatial transcriptomes

Mutant analysis is the core of biological/pathological research, and measuring spatial gene expression can facilitate the understanding of the disorganised tissue phenotype1-5. The large numbers of mutants are worth investigating; however, the high cost and technically challenging nature of experiments to measure spatial transcriptomes may act as bottlenecks6. Spatial transcriptomes have been computationally predicted from single-cell RNA sequencing data based on teaching data of spatial gene expression of certain genes7; nonetheless, this process remains challenging because teaching data for most mutants are unavailable. In various machine-learning tasks, zero-shot learning offers the potential to tackle general prediction problems without using teaching data8. Here, we provide the first zero-shot framework for predicting mutant spatial transcriptomes from mutant single-cell RNA sequencing data without using teaching data, such as a mutant spatial reference atlas. We validated the zero-shot framework by accurately predicting the spatial transcriptomes of Alzheimers model mice3 and mutant zebrafish embryos with lost Nodal signaling9. We propose a spatially informed screening approach based on zero-shot framework prediction that identified novel Nodal-downregulated genes in zebrafish. We expect that the zero-shot framework will provide novel phenotypic insights by leveraging the enormous mutant/disease single-cell RNA sequencing data collected.

bioinformatics↗

bioRxiv2021

Single-cell transcriptome atlas of Drosophila gastrula 2.0

During development, positional information directs cells to specific fates, leading them to differentiate with their own transcriptomes and express specific behaviors and functions. However, the mechanisms underlying these processes in a genome-wide view remain ambiguous, partly because the single-cell transcriptomic data of early developing embryos containing both accurate spatial and lineage information is still lacking. Here, we report a new single-cell transcriptome atlas of Drosophila gastrulae, divided into 65 transcriptomically distinct clusters. We found that the expression profiles of plasma-membrane-related genes, but not those of transcription factor genes, represented each germ layer, supporting the nonequivalent contribution of each transcription factor mRNA level to effector gene expression profiles at the transcriptome level. We also reconstructed the spatial expression patterns of all genes at the single-cell stripe level as the smallest unit. This atlas is an important resource for the genome-wide understanding of the mechanisms by which genes cooperatively orchestrate Drosophila gastrulation.

developmental biology↗

bioRxiv2020

Model-based prediction of spatial gene expression via generative linear mapping

Decoding spatial transcriptomes from single-cell RNA sequencing (scRNA-seq) data has become a fundamental technique for understanding multicellular systems; however, existing computational methods lack both accuracy and biological interpretability due to their model-free frameworks. Here, we introduced Perler, a model-based method to integrate scRNA-seq data with reference in situ hybridization (ISH) data. To calibrate differences between these datasets, we developed a biologically interpretable model that uses generative linear mapping based on a Gaussian-mixture model using the Expectation-Maximization algorithm. Perler accurately predicted the spatial gene expression of Drosophila embryos, zebrafish embryos, mammalian liver, and mouse visual cortex from scRNA-seq data. Furthermore, the reconstructed transcriptomes did not over-fit the ISH data and preserved the timing information of the scRNA-seq data. These results demonstrated the generalizability of Perler for dataset integration, thereby providing a biologically interpretable framework for accurate reconstruction of spatial transcriptomes in any multicellular system.

genomics↗