Biology subjects

Nakai, K.

Publications and source records attributed to Nakai, K..

18 recordsLinked to original sources

bioRxiv2026

VINTER: a generative vision-language model for annotating and interrogating spatial tissues

Spatial transcriptomics links gene expression to tissue structure, yet current models learn embeddings whose meaning is assigned afterwards or read histology without measured expression. Tissue decisions are therefore rarely made from morphology, genes and neighbourhood together, or examined through them. Here we introduce VINTER, a generative vision-language model aligned on 1.45 million histology-caption pairs that reads histology, measured gene expression and spatial neighbourhood as one sequence. It returns the probability of every tissue class, and because genes enter as text, its molecular evidence can be edited with the image held fixed. Reading all three sources together, VINTER annotated tissue more accurately than spatial-domain methods and a spatial foundation model, and transferred across cohorts without target labels. Editing only the neighbourhood resolved the follicular B-cell and plasma-cell programmes behind its reading of tertiary lymphoid structure (TLS) maturation in lung and kidney cancer. Its class probabilities exposed what single labels conceal in triple-negative breast cancer: immune heterogeneity within stroma and cancer hidden beneath benign labels. Without retraining, the same model transferred to Visium HD and single-cell Xenium data and resolved follicular architecture within colorectal cancer TLS. VINTER thus turns spatial annotation into an interrogable reading of tissue across platforms and resolutions.

bioinformatics↗

bioRxiv2026

Novel Drosophila cis-regulatory elements can be uncovered by footprinting transcription factor binding sites in ATAC-seq data

Developmental processes depend on carefully coordinated gene expression. Expression is modulated by the binding of transcription factors (TFs) to cis-regulatory elements (CREs), like enhancers and promoters. Many computational and experimental approaches have been developed to find CREs, particularly enhancers, in the genome, each with strengths and caveats. Given the increasing availability of ATAC-seq data and methods to find TF binding therein, we hypothesized that we could use TF footprinting tools to find clusters of TF binding events within accessible chromatin that may act as CREs. Using Drosophila anterior-posterior patterning network as a test bed, we used a digital genomic footprinting tool (DGT), TOBIAS, on previously published early embryo ATAC-seq data to characterize the TF footprint landscape of 16 TFs essential for embryonic patterning. Even in this system, with its extensive enhancer annotation, most footprinted TF binding sites lie outside of known enhancers, with intergenic and intronic regions hosting the highest TF footprint count, albeit at low density. To find potential novel enhancers, we identified high-density TF footprint clusters that are highly conserved and overlap with active enhancer histone mark signals. Five high confidence candidates were selected for reporter assay validation and all five were found to drive spatially patterned expression in the embryo. This study shows that even in a highly characterized system, the analysis of footprinted TF binding sites in ATAC-seq data can uncover new regulatory regions and suggests this approach may be helpful in using existing ATAC-seq data to find novel CREs. ARTICLE SUMMARYGiven the increasing availability of ATAC-seq datasets, workflows to exploit the data to uncover new cis-regulatory elements (CREs), including enhancers, are valuable. Using early anterior-posterior patterning in the Drosophila embryo as a test case, we find that previously published transcription factor footprinting tools and ATAC-seq data can be analyzed to yield new candidate CREs. Experimental validation confirms the activity of selected candidate CREs, suggesting that existing data can be analyzed to find novel regulatory elements.

genomics↗

bioRxiv2026

Spatial transcriptomic landscape of the Ciona adult brain: functional zonalisation and cellular composition in a sessile chordate brain and a novel insight into neural gland function

The ascidian Ciona provides a key model for understanding the evolutionary origin of the vertebrate brain. While the larval nervous system has been extensively characterized, the molecular and cellular organization of the adult neural complex remains poorly defined. Here, we generated spatial transcriptomic maps of the adult Ciona neural complex from three individuals, with four serial sections per donor, using the 10x Visium platform. Clustering-based analysis identified five major tissue domains, including the cerebral ganglion, neural gland, ciliated funnel, neural gland duct/dorsal strand, and body wall muscle. To further refine spatial resolution, we computationally reconstructed approximately 980 super-resolution gene expression maps by integrating transcriptomic measurements with histological image features. The super-resolution maps enabled precise delineation of molecular territories within the neural complex. In the cerebral ganglion, high-resolution reconstruction revealed clear molecular zonation, distinguishing the cortex and medulla. Within the cortex, the central region facing the neural gland and anteroposterior distal regions showed distinct molecular properties. In the neural gland, we identified coordinated enrichment of cell-cell interaction- and extracellular matrix-related genes, suggesting specialized structural and physiological properties. We propose that the neural gland play a pivotal role for the cerebral ganglion in maintaining homeostasis, supporting development, and providing a signaling interface, which is reminiscent of a primitive form of the choroid plexus and meninges found in vertebrates. Together, this study provides the first spatially resolved transcriptomic atlas of the adult Ciona neural complex and establishes a molecular framework for investigating functional regionalization and brain evolution in chordates.

genomics↗

bioRxiv2025

SCOIGET: Predicting Spatial Tumor Evolution Pattern by Inferring Spatial Copy Number Variation Distributions

Constructing a comprehensive spatiotemporal map of tumor heterogeneity is essential for understanding tumor evolution, with copy number variation (CNV) as a significant feature. Existing studies often rely on tools originally developed for single-cell data, which fail to utilize spatial information. Our study aims to develop a model that fully leverages spatial omics data to elucidate spatio-temporal changes in tumor evolution. Here, we introduce SCOIGET (Spatial COpy number Inference by Graph on Evolution of Tumor), a novel framework using graph neural networks with graph attention layers to learn spatial neighborhood features of gene expression and infer copy number variations. This approach integrates spatial multi-omics features to create a comprehensive spatial map of tumor heterogeneity. Notably, SCOIGET achieves a substantial reduction in error metrics (e.g., mean squared error, cosine similarity, and distance measures) and produces superior clustering performance as indicated by higher Silhouette Scores compared to existing methods. Our model significantly enhances the efficiency and accuracy of tumor evolution depiction, capturing detailed spatial and temporal changes within the tumor microenvironment. It is versatile and applicable to various downstream tasks, demonstrating strong generalizability across different spatial omics technologies and cancer types. This robust performance improves research efficiency and provides valuable insights into tumor progression. In conclusion, SCOIGET offers an innovative solution by integrating multiple features and advanced algorithms, providing a detailed and accurate representation of tumor heterogeneity and evolution, aiding in the development of personalized cancer treatment strategies. Key PointsO_ST_ABS1. Innovative Framework for Spatially Contextualized CNV InferenceC_ST_ABSIntroduces SCOIGET, a graph-based model that integrates spatial omics data with graph neural networks and attention mechanisms to accurately infer spatial copy number features. 2. Detailed Tumor Heterogeneity MappingConstructs representations of tumor evolution, capturing heterogeneity and clonal dynamics at both cellular and subcellular resolutions. 3. Versatile and Robust PerformanceOutperforms existing methods across multiple datasets, demonstrating reliability and adaptability to diverse spatial omics platforms and cancer types. 4. Broad Downstream ApplicationsEnables tasks such as tumor subclone identification, clustering, and evolutionary trajectory analysis, facilitating precision oncology strategies.

bioinformatics↗

bioRxiv2025

SpliceSelectNet: A Hierarchical Transformer-Based Deep Learning Model for Splice Site Prediction

Accurate RNA splicing is essential for gene expression and protein function, yet the mechanisms governing splice site recognition remain incompletely understood. Aberrant splicing caused by mutations can lead to severe diseases, including cancer and genetic disorders, underscoring the need for accurate computational tools to predict splice sites and detect disruptions. Existing methods have made significant advances in splice site prediction but are often limited in handling long-range dependencies due to high computational costs, a factor critical to splicing regulation. Moreover, many models lack interpretability, hindering efforts to elucidate the underlying biological mechanisms. Here, we present SpliceSelectNet (SSNet), a hierarchical Transformer-based deep learning model that predicts splice sites from DNA sequences spanning up to 100 kb. By integrating local and global attention mechanisms, SSNet efficiently captures both proximal and distal regulatory signals while maintaining single-nucleotide resolution. Across multiple benchmark datasets, SSNet achieves state-of-the-art performance in splice site prediction and aberrant splicing detection. Systematic in-silico mutagenesis demonstrates that attention scores reflect functional sequence importance, supporting their biological relevance. Long-range sequence perturbation experiments further show that SSNet captures distal regulatory effects beyond conventional receptive fields. Together, these results establish SSNet as a biologically interpretable framework for modeling long-range splicing regulation from genomic sequence.

bioinformatics↗

bioRxiv2024

Gene2role: a role-based gene embedding method for comparative analysis of signed gene regulatory networks

MotivationUnderstanding the dynamics of gene regulatory networks (GRNs) across various cellular states is crucial for deciphering the underlying mechanisms governing cell behavior and functionality. However, current comparative analytical methods, which often focus on simple topological information such as the degree of genes, are limited in their ability to fully capture the similarities and differences among the complex GRNs. ResultsWe present Gene2role, a gene embedding approach that leverages multi-hop topological information from genes within signed GRNs. Initially, we demonstrated the effectiveness of Gene2role in capturing the intricate topological nuances of genes using GRNs inferred from four distinct data sources. Then, applying Gene2role to integrated GRNs allowed us to identify genes with significant topological changes across cell types or states, offering a fresh perspective beyond traditional differential gene expression analysis. Additionally, we quantified the stability of gene modules between two cellular states by measuring the changes in the gene embeddings within these modules. In conclusion, our method augments the existing toolkit for probing the dynamic regulatory landscape, thereby opening new avenues for understanding gene behavior and interaction patterns across cellular transitions.

bioinformatics↗

bioRxiv2024

DenoiseST: A dual-channel unsupervised deep learning-based denoising method to identify spatial domains and functionally variable genes in spatial transcriptomics

Spatial transcriptomics provides a unique opportunity for understanding cellular organization and function in a spatial context. However, spatial transcriptome exists the problem of dropout noise, exposing a major challenge for accurate downstream data analysis. Here, we proposed DenoiseST, a dual-channel unsupervised adaptive deep learning-based denoising method for data imputing, clustering, and identifying functionally variable genes in spatial transcriptomics. To leverage spatial information and gene expression profiles, we proposed a dual-channel joint learning strategy with graph convolutional networks to sufficiently explore both linear and nonlinear representation embeddings in an unsupervised manner, enhancing the discriminative information learning ability from the global perspectives of data distributions. In particular, DenoiseST enables the adaptively fitting of different gene distributions to the clustered domains and employs tissue-level spatial information to accurately identify functionally variable genes with different spatial resolutions, revealing their enrichment in corresponding gene pathways. Extensive validations on a total of 18 real spatial transcriptome datasets show that DenoiseST obtains excellent performance and results on brain tissue datasets indicate it outperforms the state-of-the-art methods when handling artificial dropout noise with a remarkable margin of [~]15%, demonstrating its effectiveness and robustness. Case study results demonstrate that when applied to identify biological structural regions on human breast cancer spatial transcriptomic datasets, DenoiseST successfully detected biologically significant immune-related structural regions, which are subsequently validated through Gene Ontology (GO), cell-cell communication, and survival analysis. In conclusion, we expect that DenoiseST is a novel and efficient method for spatial transcriptome analysis, offering unique insights into spatial organization and function.

bioinformatics↗

bioRxiv2023

STAIG: Spatial Transcriptomics Analysis via Image-Aided Graph Contrastive Learning for Domain Exploration and Alignment-Free Integration

Spatial transcriptomics is an essential application for investigating cellular structures and interactions and requires multimodal information to precisely study spatial domains. Here, we propose STAIG, a novel deep-learning model that integrates gene expression, spatial coordinates, and histological images using graph-contrastive learning coupled with high-performance feature extraction. STAIG can integrate tissue slices without prealignment and remove batch effects. Moreover, it was designed to accept data acquired from various platforms, with or without histological images. By performing extensive benchmarks, we demonstrated the capability of STAIG to recognize spatial regions with high precision and uncover new insights into tumor microenvironments, highlighting its promising potential in deciphering spatial biological intricates.

bioinformatics↗

bioRxiv2023

Comparative single-cell transcriptomic analysis reveals key differentiation drivers and the potential origin of the vertebrate retina

Despite known single-cell expression profiles in vertebrate retinas, understanding of their developmental and evolutionary expression patterns among homologous cell classes remains limited. We examined and compared approximately 240, 000 retinal cells from four species and found significant similarities among homologous cell classes, indicating inherent regulatory patterns. To understand these shared patterns, we constructed gene regulatory networks for each developmental stage for three of these species. We identified 690 regulons governed by 530 regulators across three species, along with 10 common cell class-specific regulators and 16 preserved regulons. RNA velocity analysis pinpointed conserved putative driver genes and regulators to retinal cell differentiation in both mouse and zebrafish. Investigation of the origins of retinal cells by examining conserved expression patterns between vertebrate retinal cells and invertebrate Ciona intestinalis photoreceptor-related cells implied functional similarities in light transduction mechanisms. Our findings offer insights into the evolutionarily conserved regulatory frameworks and differentiation drivers of vertebrate retinal cells.

bioinformatics↗

bioRxiv2023

HyGAnno: Hybrid graph neural network-based cell type annotation for single-cell ATAC sequencing data

Reliable cell type annotations are crucial for investigating cellular heterogeneity in single-cell omics data. Although various computational approaches have been proposed for single-cell RNA sequencing (scRNA-seq) annotation, high-quality cell labels are still lacking in single-cell ATAC sequencing (scATAC-seq) data, because of extreme sparsity and inconsistent chromatin accessibility between datasets. This calls for novel cell type annotation methods in scATAC-seq, to better explore cell type-specific gene regulatory mechanisms and provide a complementary epigenomic layer to scRNA-seq data. Here, we present a novel automated cell annotation method that transfers cell type information from a well-labeled scRNA-seq reference to an unlabeled scATAC-seq target, via a parallel graph neural network, in a semi-supervised manner. Unlike existing methods that utilize only gene expression or gene activity features, HyGAnno integrates genomewide accessibility peak features to facilitate the training process. In addition, HyGAnno reconstructs a reference-target cell graph that can be used to detect cells with low prediction reliability, according to their specific graph connectivity patterns. HyGAnno was tested using large datasets and demonstrated the advantages of accurate cell annotation, interpretable cell embedding, robustness to noisy reference data, and adaptability to tumor tissues.

bioinformatics↗

bioRxiv2023

A computational approach for deciphering the interactions between proximal and distal regulators in B cell differentiation

Delineating the intricate interplay between promoter-proximal and -distal regulators is crucial for understanding the function of transcriptional mediator complexes implicated in the regulation of gene expression. The aim of the present study was to develop a computational method for accurately modeling the spatial proximal and distal regulatory interactions. Our method combined regression-based models to identify key regulators through gene expression prediction and a graph- embedding approach to detect coregulated genes. This approach enabled a detailed investigation of the gene regulatory mechanisms underlying peripheral B cell differentiation, accompanied by dramatic rearrangements of the genome structure. We found that while the promoter-proximal elements were the principal regulators of gene expression, the distal regulators fine-tuned transcription. Moreover, our approach unveiled the presence of modular regulators, such as structural cofactors and proximal/distal transcriptional factors, which were co-expressed with their target genes. These findings imply that the dysregulation of interactions between transcriptional and structural factors is associated with chromatin reorganization failure and ultimately an increased risk of malignancy. We envisage that our computational approach will help crack the transcriptional cis-regulatory code of the three-dimensional network regulating gene expression.

bioinformatics↗

bioRxiv2023

Multi-omics computational analysis unveils the in-volvement of AP-1 and CTCF in hysteresis of chromatin states during macrophage polarization

Macrophages display extreme plasticity, and the mechanisms and applications of polarization and de/repolarization of macrophages have been extensively investigated. However, the regulation of macrophage hysteresis de/repolarization remains unclear. In this study, by using a large-scale computational analysis of macrophage omics data, we report a list of hysteresis genes that maintain their expression patterns after polarization after ulti- and de/repolarization. While the polarization in M1 macrophages leads to a higher level of hysteresis in genes associated with cell cycle progression, cell migration, and enhancement of the immune response, we found weak levels of hysteresis after M2 polarization. During the polarization process from M0 to M1 and back to M0, the factors IRFs/STAT, AP-1, and CTCF regulate hysteresis by altering their binding sites to the chromatin. Overall, our results show that a history of polarization can lead to hysteresis in gene expression and chromatin changes over a given period. This study contributes to the understanding of de-/repolarization memory in macrophages.

bioinformatics↗

bioRxiv2023

Epigenetic characterization of housekeeping core promoters and their importance in tumor suppression

There has been extensive research on describing cell type-specific (CTS) regulatory interactions, especially between enhancers and promoters. However, constitutively active interactions between CREs have been less studied. In this research, we elucidate the presence of around 11,000 housekeeping CREs (HK-CREs) and describe their main characteristics. Most of the HK-CREs are located in promoter regions, but contrary to expectations, they are not only the promoters of housekeeping genes and are involved in a broader role beyond housekeeping gene regulation. HK-CREs are conserved regions rich in unmethylated CpG sites. Their distribution across chromosomes highly correlates with that of protein-coding genes, and they interact with a large number of target genes in long-distance interactions. In the context of cancer, we observed a reduction in the activity of a subset of HK-CREs, particularly those located at the end of chromosome 19 and associated with zinc finger genes. We investigated the effect of these genes on samples from diverse cancer subtypes, observing a significant reduction in their expression due to aberrant methylation of their core promoters. Finally, an analysis of more than 5,000 patients from 17 cancer subtypes showed an increase in the survival probability of patients with higher expression of these genes, suggesting them as housekeeping tumor suppressor genes. Overall, our work unravels the presence of ubiquitously active CREs indispensable for the maintenance and stability of cells.

genomics↗

bioRxiv2022

DeepBIO is an automated and interpretable deep-learning platform for biological sequence prediction, functional annotation, and visualization analysis

Here, we present DeepBIO, the first-of-its-kind automated and interpretable deep-learning platform for high-throughput biological sequence functional analysis. DeepBIO is a one-stop-shop web service that enables researchers to develop new deep-learning architectures to answer any biological question. Specifically, given any biological sequence data, DeepBIO supports a total of 42 state-of-the-art deep-learning algorithms for model training, comparison, optimization, and evaluation in a fully automated pipeline. DeepBIO provides a comprehensive result visualization analysis for predictive models covering several aspects, such as model interpretability, feature analysis, functional sequential region discovery, etc. Additionally, DeepBIO supports 9 base-level functional annotation tasks using deep-learning architectures, with comprehensive interpretations and graphical visualizations to validate the reliability of annotated sites. Empowered by high-performance computers, DeepBIO allows ultra-fast prediction with up to million-scale sequence data in a few hours, demonstrating its usability in real application scenarios. Case study results show that DeepBIO provides an accurate, robust, and interpretable prediction, demonstrating the power of deep learning in biological sequence functional analysis. Overall, we expect DeepBIO to ensure the reproducibility of deep-learning biological sequence analysis, lessen the programming and hardware burden for biologists, and provide meaningful functional insights at both sequence-level and base-level from biological sequences alone. DeepBIO is publicly available at http://inner.wei-group.net/DeepBIO.

bioinformatics↗

bioRxiv2021

Electrophysiological maturation and increased excitability of human iPSC-derived neurons in HTR2A variant-related sleep bruxism

Sleep bruxism (SB) is a sleep-related movement disorder characterized by grinding and clenching of the teeth during sleep. We previously found a significant association between SB and a single nucleotide polymorphism (SNP), rs6313, in the neuronal serotonin 2A receptor gene (HTR2A), and established human induced pluripotent stem cell (hiPSC)-derived neurons from SB patients with a genetic variant. To elucidate the electrophysiological characteristics of SB iPSC-derived neural cells bearing a SB-related genetic variant, we generated ventral hindbrain neurons from two SB patients and two unaffected controls and explored the intrinsic membrane properties of these neurons by patch-clamp technique. We found that the electrophysiological properties of the iPSC-derived neurons from the control line mature in a time-dependent manner in long-term cultures. In the early stage of neurogenesis, neurons from two SB lines tended to display shorter action potential (AP) half durations, which led to an increased cell capability of evoked firing. This is the first in vitro modelling of SB using disease-specific hiPSCs. The revealed electrophysiological characteristics may serve as a benchmark for further investigation of pathogenic mechanisms underlying SB. Summary StatementSleep bruxism patient-specific iPSC-derived neurons with the HTR2A variant show altered electrophysiological characteristics, providing the foremost narration of sleep bruxism neurological phenotypes in vitro from any species.

neuroscience↗

bioRxiv2020

Extreme value theory as a general framework for understanding mutation frequency distribution in cancer genomes

Currently, there is no recognized population genetics framework describing the population dynamics of cancer cells that is applicable to real cancer genome data. By focusing on cancer as a Darwinian evolutionary system, we formulated and analyzed the observed mutation frequency among tumors (MFaT) as a proxy for the hypothesized sequence read frequency and beneficial fitness effect of a cancer driver mutation. Analogous to intestinal crypts, we assumed that sample donor patients are separate culture tanks where proliferating cells follow certain population dynamics described by extreme value theory (EVT). To validate this, we analyzed three large-scale cancer genome datasets, each harboring > 10 000 tumor samples and in total involving > 177 898 observed mutation sites. We clarified the necessary premises for the application of EVT in the strong selection and weak mutation (SSWM) regime in relation to cancer genome sequences at scale. We also confirmed that the stochastic distribution of MFaT is likely of the Frechet type, which challenges the well-known Gumbel hypothesis of beneficial fitness effects. Based on statistical data analysis, we demonstrated the potential of EVT as a population genetics framework to understand and explain the stochastic behavior of driver-mutation frequency in cancer genomes as well as its applicability in real cancer genome sequence data.

genomics↗

bioRxiv2020

Helicobacter small RNA regulates host adaptation and carcinogenesis

Type-1 carcinogenic Helicobacter pylori that is known to evolve during long-term infection, enters the stomach orally and causes gastric cancer using the carcinogenic protein CagA1. However, little is known about the adaptation mechanisms of H. pylori when the environment changes from the outside to the inside of the living body. Here we show that small non-coding RNA HPnc4160 is a crucial novel RNA molecule of H. pylori that negatively regulates bacterial-host adaptation and gastric cancer. H. pylori isolated from gerbils stomachs eight weeks post-infection acquired mutations in the increased number of T-repeats within the upstream region of the HPnc4160 coding region, which leads to reduced HPnc4160 expression levels that also seen in cancer patients-derived H. pylori. By comparing RNA-seq and iTRAQ analysis between wild-type and hpnc4160 deficient mutant strains, we identified eight targets of HPnc4160 including cagA and unknown factors. Mice infection experiment revealed that the hpnc4160 deficient mutant had a higher number of colonized bacteria in the mice stomach than the wild-type strain, indicating that reduced expression levels of HPnc4160 was important for bacterial host adaptation. The expression level of HPnc4160 was lower in the clinical isolates derived from gastric cancer patients compared with non-cancer-derived strains, while the mRNA expression levels of target factors were higher. Our findings highlight the first discovery that HPnc4160 is an important small RNA for bacteria to adapt to the host environment leading to gastric carcinogenesis.

microbiology↗

bioRxiv2019

A systematic NGS-based approach for contaminant detection and functional inference

BackgroundMicrobial contamination impedes successful biological and biomedical research. Computational approaches utilizing next-generation sequencing (NGS) data offer promising diagnostics to assess the presence of contaminants. However, as host cells are often contaminated by multiple microorganisms, these approaches require careful attention to intra- and interspecies sequence similarities, which have not yet been fully addressed.\n\nResultsWe present a computational approach that rigorously investigates the genomic origins of sequenced reads, including those mapped to multiple species that have been discarded in previous studies. Through the analysis of large-scale synthetic and public NGS samples, we approximated that 1,000-100,000 microbial reads prevail when one million host reads are sequenced by RNA-seq. The microbe catalog we established included Cutibacterium as a prevalent contaminant, suggesting that contamination mostly originates from the laboratory environment. Importantly, by applying a systematic method to infer the functional impact of contamination, we revealed that host-contaminant interactions cause profound changes in the host molecular landscapes, as exemplified by changes in inflammatory and apoptotic pathways during Mycoplasma infection.\n\nConclusionsThese findings reinforce the concept that precise determination of the origins and functional impacts of contamination is imperative for quality research and illustrate the usefulness of the proposed approach to comprehensively characterize contamination landscapes.

bioinformatics↗