Biology subjects

Ozaki, H.

Publications and source records attributed to Ozaki, H..

16 recordsLinked to original sources

bioRxiv2026

Spatial engineering of posterior organizers in cerebral organoids via controlled morphogen exposure within hydrogels

Cerebral cortex organoids are powerful in vitro models that recapitulate key features of human development. However, conventional methods produce cortical organoids with spontaneous, spatially disorganized cortical regions due to limited control over morphogen distribution within local environments. Here, we present a spatially engineered hydrogel platform that drives localized posterior organizer formation in cortical organoids through controlled, localized exposure to morphogens. Using a combination of bulk photopolymerization, thermal crosslinking, and digital light processing (DLP) approaches, we fabricated hydrogels with stiffness-controlled layers that preferentially deliver morphogens to one side of the organoid, selectively inducing posterior organizer formation on the exposed face. We further validated this platform by delivering fluorescently tagged dextran, used as molecular weight-matched model morphogens, to visualize spatiotemporal delivery dynamics at the organoid interface. As a proof of principle, we also demonstrated that DLP fabrication enables the printing of dual morphogen hubs, serving as a model for establishing two opposing gradients within a single organoid. Together, this hydrogel platform enables systematic spatial patterning of cell populations in organoids, more faithfully recapitulating the spatial organization and cellular diversity of native tissues and advancing higher-fidelity models for studying human development and disease.

bioengineering↗

bioRxiv2026

Ancient Ryukyu Jomon contributed to past and current genetic structure of Japanese populations

Characterized by the earliest use of pottery, the Jomon culture was a unique Neolithic culture that spread throughout the Japanese Archipelago. Previous archaeological evidence suggests that Jomon hunter-gatherers colonized the southernmost islands, the Ryukyu Archipelago, by approximately 7,000 years before present (YBP). However, genetic characteristics of the Ryukyu Jomon population and its contribution to the modern population have not been elucidated yet. In this study, we newly sequenced 273 modern and 25 ancient (6,700-900 YBP) whole genomes collected across the Ryukyu Archipelago. Our analysis demonstrated a genetic differentiation between the Hondo (Japanese mainland) and Ryukyu Jomon, dating back to [~]6,900 YBP. After the divergence from the Hondo Jomon, the Ryukyu Jomon experienced severe bottlenecks, with an effective population size of [~]2,000. Admixture between the Ryukyu Jomon and migrants from the historic Hondo population occurred [~]1,000 YBP, which corresponds to the widespread adoption of iron tools and agriculture in the Central Ryukyus. Different demographic histories between modern Hondo and Ryukyu populations resulted in different rates of Jomon ancestry in these populations. By providing a new perspective on the peopling of the Ryukyu Archipelago, this study significantly enhances our understanding of cultural transitions in the region.

evolutionary biology↗

bioRxiv2025

Morphogen-guided neocortical organoids recapitulate regional areal identity and model neurodevelopmental disorder pathology

The human neocortex exhibits characteristic regional patterning (arealization) critical for higher-order cognitive function. Disrupted arealization is strongly implicated in neurodevelopmental disorders (NDDs), but current neocortical organoid models largely fail to recapitulate this patterning, limiting mechanistic understanding. Here, we establish a straightforward method for generating arealized organoids through short-term early exposure to anterior (FGF8) or posterior (BMP4/CHIR-99021) morphogens. These treatments created distinct anterior and posterior signaling centers, supporting long-lasting polarization, which we validated with single-cell RNA sequencing that revealed area-specific molecular signatures matching prenatal human cortex. To demonstrate the utility of this platform, we modeled Fragile X Syndrome (FXS) in organoids with distinct anterior and posterior regional identities. FXS organoids showed highly disrupted SOX4/SOX11 expression gradients along the anterior-posterior axis, consistent with alterations found in autism spectrum disorder (ASD) and demonstrate how regional patterning defects may contribute to NDD pathology. Together, our study provides a robust platform for generating neocortical organoids with anterior-posterior molecular signatures and highlights the importance of modeling NDDs using experimental platforms with neuroanatomic specificity.

developmental biology↗

bioRxiv2025

Global loss of responsiveness in key regulator metabolites and elevated enzyme proteins as metabolic dysregulation in skeletal muscle and liver of obese mice during starvation

Starvation induces complex metabolic adaptations in skeletal muscle, a key tissue for maintaining energy homeostasis; however, these adaptations are largely impaired in obesity. How obesity alters global metabolic adaptations to starvation in skeletal muscle remains unclear. Here, we analyzed the metabolic adaptations on a trans-omics scale during starvation in skeletal muscle from wild-type (WT) and leptin-deficient obese (ob/ob) mice. We measured multi-omics data during starvation and constructed global trans-omics networks in WT and ob/ob mice. We found that starvation induces "responsiveness" in WT mice, characterized by increases or decreases in key regulator metabolites, including ATP and AMP, as well as enzyme proteins, leading to global regulation of metabolic pathways, which was lost in ob/ob mice. In contrast, during starvation, ob/ob mice exhibit "difference" in comparison to WT mice, manifested by the persistently elevated expression of metabolic enzymes. These features were similarly found in liver, another key metabolic organ. Thus, global loss of responsiveness and elevated enzyme proteins are systemic features of metabolic dysregulation in ob/ob mice.

systems biology↗

bioRxiv2024

A negative correlation between the rate coefficient of repair after photoinhibition of cold acclimated plants and the mean annual temperature of the habitats of Arabidopsis thaliana ecotypes

Both the activity of photosynthesis and the repair of damaged photosystems decline in cold environments, which may increase the extent of the damage of photosynthetic machinery by light, namely photoinhibition. We hypothesized that plants in colder habitats may possess greater tolerance to photoinhibition, especially in low temperature conditions. We measured the rate of photoinhibition, rate of photoinhibition repair and other thylakoid activities in cold environments using 298 Arabidopsis thaliana ecotypes and studied the relationships among the indicators of photoinhibition tolerance and climatic data of the habitat of each ecotype. The plants acclimated to cold conditions (12{degrees}C) for three days showed a negative correlation between the rate of photoinhibition repair at 5{degrees}C and the mean annual temperature of habitats, although we could not see this correlation with the control plants grown in 22{degrees}C. This result would indicate that the acclimation capacity of photoinhibition tolerance in cold conditions can affect the distribution of plants especially in colder regions.

plant biology↗

bioRxiv2024

Structural robustness and temporal vulnerability of the starvation-responsive metabolic network in liver of healthy and obese mice

Adaptation to starvation is a multi-molecular and temporally ordered process, that could be impaired in obesity. To elucidate how the healthy liver regulates various molecules in a temporally ordered manner during starvation and how obesity disrupts this process, we measured time course multiomic data in the liver of wild-type (WT) and leptin-deficient obese (ob/ob) mice during starvation. Using the measured data, we constructed a starvation-responsive metabolic network, that is a transomic network including responsive molecules and their regulatory relationships during starvation, and analyzed the structure of the network. In WT mice, ATP and AMP, the energy indicators, regulated various metabolic reactions in the network as the hub molecules, both of which were not responsive in ob/ob mice. However, the structural properties of the network were maintained in ob/ob mice. In WT mice, the molecules in the network were temporally ordered through metabolic process coordinated by the hub molecules including ATP and AMP and were positively or negatively co-regulated. By contrast, both temporal order and co-regulation were disrupted in ob/ob mice. Taken together, the starvation-responsive metabolic network is structurally robust, but temporally vulnerable by the loss of responsiveness of the hub molecules in obesity. In addition, we proposed a potential therapeutic target to treat the negative effects of obesity on intermittent fasting to extend lifespan. One Sentence SummaryHub molecules activate or inhibit various molecules in a temporally ordered manner in healthy liver, and the regulatory network is structurally robust but temporally vulnerable to obesity.

systems biology↗

bioRxiv2023

Brain organoid-on-a-chip to create multiple domains in forebrain organoids

Brain organoids are three-dimensionally reconstructed brain tissue derived from pluripotent stem cells in vitro. 3D tissue cultures have opened new avenues for exploring development and disease modeling. However, some physiological conditions, including signaling gradients in 3D cultures, have not yet been easily achieved. Here, we introduce Brain Organoid-on-a-Chip platforms that generate signaling gradients that in turn enable the induction of topographic forebrain organoids. This creates a more continuous spectrum of brain regions and provides a more complete mimic of the human brain for evaluating neurodevelopment and disease in unprecedented detail.

bioengineering↗

bioRxiv2022

Single-cell transcriptome analysis illuminating the characteristics of species-specific innate immune responses against viral infections

Bats harbor various viruses without severe symptoms and act as their natural reservoirs. The tolerance of bats against viral infections is assumed to originate from the uniqueness of their immune system. However, how immune responses vary between primates and bats remains unclear. Here, we characterized differences in the immune responses by peripheral blood mononuclear cells to various pathogenic stimuli between primates (humans, chimpanzees, and macaques) and bats (Egyptian fruit bats) using single-cell RNA sequencing. We show that the induction patterns of key cytosolic DNA/RNA sensors and antiviral genes differed between primates and bats. A novel subset of monocytes induced by pathogenic stimuli specifically in bats was identified. Furthermore, bats robustly respond to DNA virus infection even though major DNA sensors are dampened in bats. Overall, our data suggest that immune responses are substantially different between primates and bats, presumably underlying the difference in viral pathogenicity among the mammalian species tested.

microbiology↗

bioRxiv2022

Automation of yeast spot assays using an affordable liquid handling robot

The spot assay of the budding yeast Saccharomyces cerevisiae is an experimental method that is used to evaluate the effect of genotypes, medium conditions, and environmental stresses on cell growth and survival. Automation of the spot assay experiments from preparing a dilution series to spotting to observing spots continuously has been implemented based on large laboratory automation devices and robots, especially for high-throughput functional screening assays. However, there has yet to be an affordable solution for the automated spot assays suited to researchers in average laboratories and with high customizability for end-users. To make reproducible spot assay experiments widely available, we have automated the plate-based yeast spot assay of budding yeast using Opentrons OT-2 (OT-2), an affordable liquid-handling robot, and a flatbed scanner. We prepared a 3D-printed mount for the Petri dish to allow for precise placement of the Petri dish inside the OT-2. To account for the uneven height of the agar plates, which were made by human hands, we devised a method to adjust the z-position of the pipette tips which is based on the weight of each agar plate. During the incubation of the agar plates, a flatbed scanner was used to automatically take images of the agar plates over time, allowing researchers to quantify and compare the cell density within the spots at optimal time points a posteriori. Furthermore, the accuracy of the newly developed automated spot assay was verified by performing spot assays with human experimenters and the OT-2 and quantifying the yeast-grown area of the spots. This study will contribute to the introduction of automated spot assays and the automated acquisition of growth processes in conventional laboratories that are not adapted for high-throughput laboratory automation.

bioengineering↗

bioRxiv2022

MOCCS profile analysis clarifies the cell type dependency of transcription factor-binding sequences and cis-regulatory SNPs in humans

Transcription factors (TFs) show heterogeneous DNA-binding specificities in individual cells and whole organisms in natural conditions): de novo motif discovery usually provides multiple motifs even from a single ChIP-seq sample. Despite the accumulation of ChIP-seq data and ChIP-seq-derived motifs, the diversity of DNA-binding specificities across different TFs and cell types remains largely unexplored. Here, we propose MOCCS profiles, the new representation of DNA-binding specificity of TFs, which describes a ChIP-seq sample as a profile of TF-binding specificity scores (MOCCS2scores) for every k-mer sequence. Using our k-mer-based motif discovery method MOCCS2, we systematically computed MOCCS profiles for >10,000 human TF ChIP-seq samples across diverse TFs and cell types. Comparison of MOCCS profiles revealed the global distributions of DNA-binding specificities, and found that one-third of the analyzed TFs showed differences in DNA-binding specificities across cell types. Moreover, we showed that the differences in MOCCS2scores ({Delta}MOCCS2scores) predicted the effect of variants on TF binding, validated by in vitro and in vivo assay datasets. We also demonstrate {Delta}MOCCS2scores can be used to interpret non-coding GWAS-SNPs as TF-affecting SNPs and provide their candidate responsible TFs and cell types. Our study provides the basis for investigating gene expression regulation and non-coding disease-associated variants in humans.

bioinformatics↗

bioRxiv2022

In vivo transomic analyses of glucose-responsive metabolism in skeletal muscle reveal core differences between the healthy and obese states

Metabolic regulation in skeletal muscle is essential for blood glucose homeostasis. Obesity causes insulin resistance in skeletal muscle, leading to hyperglycemia and type 2 diabetes. In this study, we performed multiomic analysis of the skeletal muscle of wild-type (WT) and genetically obese (ob/ob) mice, and constructed regulatory transomic networks for metabolism after oral glucose administration. Our network revealed that metabolic regulation by glucose-responsive metabolites had a major effect on WT mice, especially carbohydrate metabolic pathways. By contrast, in ob/ob mice, much of the metabolic regulation by glucose-responsive metabolites was lost and metabolic regulation by glucose-responsive genes was largely increased, especially in carbohydrate and lipid metabolic pathways. We present some characteristic metabolic regulatory pathways found in central carbon, branched amino acids, and ketone body metabolism. Our transomic analysis will provide insights into how skeletal muscle responds to changes in blood glucose and how it fails to respond in obesity.

systems biology↗

bioRxiv2022

CCPLS reveals cell-type-specific spatial dependence of transcriptomes in single cells

MotivationCell-cell communications regulate internal cellular states, e.g., gene expression and cell functions, and play pivotal roles in normal development and disease states. Furthermore, single-cell RNA sequencing methods have revealed cell-to-cell expression variability of highly variable genes (HVGs), which is also crucial. Nevertheless, the regulation on cell-to-cell expression variability of HVGs via cell-cell communications is still largely unexplored. The recent advent of spatial transcriptome methods has linked gene expression profiles to the spatial context of single cells, which has provided opportunities to reveal those regulations. The existing computational methods extract genes with expression levels influenced by neighboring cell types. However, limitations remain in the quantitativeness and interpretability: they neither focus on HVGs nor consider the effects of multiple neighboring cell types. ResultsHere, we propose CCPLS (Cell-Cell communications analysis by Partial Least Square regression modeling), which is a statistical framework for identifying cell-cell communications as the effects of multiple neighboring cell types on cell-to-cell expression variability of HVGs, based on the spatial transcriptome data. For each cell type, CCPLS performs PLS regression modeling and reports coefficients as the quantitative index of the cell-cell communications. Evaluation using simulated data showed our method accurately estimated the effects of multiple neighboring cell types on HVGs. Furthermore, applications to the two real datasets demonstrate that CCPLS can extract biologically interpretable insights from the inferred cell-cell communications. AvailabilityThe R package is available at https://github.com/bioinfo-tsukuba/CCPLS. The data are available at https://github.com/bioinfo-tsukuba/CCPLS_paper. Contactharuka.ozaki@md.tsukuba.ac.jp Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2020

MotiMul: A significant discriminative sequence motif discovery algorithm with multiple testing correction

Sequence motifs play essential roles in intermolecular interactions such as DNA-protein interactions. The discovery of novel sequence motifs is therefore crucial for revealing gene functions. Various bioinformatics tools have been developed for finding sequence motifs, but until now there has been no software based on statistical hypothesis testing with statistically sound multiple testing correction. Existing software therefore could not control for the type-1 error rates. This is because, in the sequence motif discovery problem, conventional multiple testing correction methods produce very low statistical power due to overly-strict correction. We developed MotiMul, which comprehensively finds significant sequence motifs using statistically sound multiple testing correction. Our key idea is the application of Tarones correction, which improves the statistical power of the hypothesis test by ignoring hypotheses that never become statistically significant. For the efficient enumeration of the significant sequence motifs, we integrated a variant of the PrefixSpan algorithm with Tarones correction. Simulation and empirical dataset analysis showed that MotiMul is a powerful method for finding biologically meaningful sequence motifs. The source code of MotiMul is freely available at https://github.com/ko-ichimo-ri/MotiMul.

bioinformatics↗

bioRxiv2020

Integrated analysis of glycan and RNA in single cells

Single-cell sequencing has emerged as an indispensable technology to dissect cellular heterogeneity but never been applied to the simultaneous analysis of glycan and RNA. Using oligonucleotide-labeled lectins, we first established lectin-based glycan profiling of single cells by sequencing (scGlycan-seq). We then combined the scGlycan-seq with single-cell transcriptome profiling (scRNA-seq) for joint analysis of glycan and RNA in single cells (scGR-seq). Using scGR-seq, we analyzed the two modalities in human induced pluripotent stem cells (hiPSCs) before and after differentiation into neural progenitor cells at the single cell resolution. The combination of RNA and glycan separated the two cell types clearer than either one of them. Furthermore, integrative analysis of glycan and RNA modalities in single cells found known and novel lectins that were specific to hiPSCs and coordinated with neural differentiation. Taken together, we demonstrate that scGR-seq can reveal the cellular heterogeneity and biological roles of glycans across multicellular systems.

genomics↗

bioRxiv2019

A NMF-based approach to discover overlooked differentially expressed gene regions from single-cell RNA-seq data

Single-cell RNA sequencing has enabled researchers to quantify the transcriptomes of individual cells, infer cell types, and investigate differential expression among cell types, which will lead to a better understanding of the regulatory mechanisms of cell states. Transcript diversity caused by phenomena such as aberrant splicing events have been revealed, and differential expression of previously unannotated transcripts might be overlooked by annotation-based analyses. Accordingly, we have developed an approach to discover overlooked differentially expressed (DE) gene regions that complements annotation-based methods. We applied our algorithm to two datasets and discovered several intriguing DE transcripts, including a transcript related to the modulation of neural stem/progenitor cell differentiation.

bioinformatics↗

bioRxiv2019

Millefy: visualizing cell-to-cell heterogeneity in read coverage of single-cell RNA sequencing datasets

BackgroundRead coverage of RNA sequencing data reflects gene expression and RNA processing events. Single-cell RNA sequencing (scRNA-seq) methods, particularly "full-length" ones, provide read coverage of many individual cells and have the potential to reveal cellular heterogeneity in RNA transcription and processing. However, visualization tools suited to highlighting cell-to-cell heterogeneity in read coverage are still lacking. ResultsHere, we have developed Millefy, a tool for visualizing read coverage of scRNA-seq data in genomic contexts. Millefy is designed to show read coverage of all individual cells at once in genomic contexts and to highlight cell-to-cell heterogeneity in read coverage. By visualizing read coverage of all cells as a heat map and dynamically reordering cells based on diffusion maps, Millefy facilitates discovery of "local" region-specific, cell-to-cell heterogeneity in read coverage, including variability of transcribed regions. ConclusionsMillefy simplifies the examination of cellular heterogeneity in RNA transcription and processing events using scRNA-seq data. Millefy is available as an R package (https://github.com/yuifu/millefy) and a Docker image to help use Millefy on the Jupyter notebook (https://hub.docker.com/r/yuifu/datascience-notebook-millefy).

bioinformatics↗