Biology subjects

Nikaido, I.

Publications and source records attributed to Nikaido, I..

At least 19 recordsLinked to original sources

bioRxiv2026

GenPerturb: sequence-grounded interpretation of perturbation transcriptomes using pretrained genomic models

BackgroundPerturb-seq captures transcriptional responses to thousands of genetic and chemical perturbations, but does not directly resolve the cis-regulatory elements or transcription factor motifs underlying those responses. Existing approaches rely on indirect post hoc analyses or external epigenomic annotations, making it difficult to connect gene-level responses to specific regulatory element ResultsWe present GenPerturb, a framework that leverages pretrained sequence-to-expression models to link perturbation-induced expression changes to candidate cis-regulatory elements. By contrasting perturbation and control states, GenPerturb prioritizes regulatory regions and transcription factor motifs associated with each perturbation. The model recapitulates perturbation-dependent gene expression patterns and enables sequence-level interpretation without requiring matched chromatin data. Across multiple perturbation types, GenPerturb identifies biologically meaningful regulatory programs, including lineage-specific and signaling-associated motif activities, even when corresponding transcription factor expression changes are limited. ConclusionsGenPerturb converts gene-level expression responses from Perturb-seq into perturbation-specific, sequence-grounded cis-regulatory hypotheses. By prioritizing candidate regulatory elements and transcription factor motifs responsive to each perturbation without requiring matched chromatin data, GenPerturb enables mechanistic interpretation of transcriptional regulation and guides downstream experimental validation.

bioinformatics↗

bioRxiv2026

Cycling persister clones with elevated NR2F1-mediated cholesterol biosynthesis cause chemotherapy resistance

While a subpopulation termed cycling persisters (CPs) that is characterized by sustained proliferation, even under chemotherapy drug exposure, contributes more directly to tumor relapse, the molecular basis for the emergence of CPs has been unclear. Here, we used the human tongue cancer organoid (TCO) library to continuously track the in vitro fate of individual cancer cell clones during and after chemotherapy exposure using time-lapse imaging. Among the heterogeneous clones, we identified CPs that formed larger clusters than the others, which barely grew and remained small (non-CPs). Using differences in cell cluster size as an indicator, thousands of CP and non-CP clones were directly sampled from 3D matrix organoid cultures and were analyzed. Notably, tumor-intrinsic interferon (IFN) signaling and hypoxic pathways were inactivated, whereas the NR2F1-mediated cholesterol biosynthesis pathway was distinctly activated in CPs compared to non-CPs. Indeed, inhibiting cholesterol biosynthesis with simvastatin significantly suppressed the appearance of CP clones, showing that elevated cholesterol biosynthesis is essential for the emergence of CPs. These findings suggest that clonal-level variations in the intensity of these signaling pathways determine the fate of individual tumor cells exposed to chemotherapeutic agents, which may provide insights into cancer relapse mechanisms and identify potential molecular targets of CPs.

cancer biology↗

bioRxiv2025

Elucidation of the mechanism of byproduct DNA synthesis in whole-transcript amplification by poly-A tagging and development of its specific suppression

Poly-A tagging is a whole-transcript amplification method that uses terminal transferase to add poly-A to single-stranded cDNA in a non-template-dependent manner, and is used in highly sensitive single-cell RNA-seq. However, this method has long been reported to amplify byproduct DNA derived from reverse-transcription primers, particularly under conditions of low RNA abundance, leading to experimental complexity and reduced accuracy in quantifying gene expression. In this study, we elucidated the mechanism by which byproduct DNA is amplified at the molecular level and developed a new strategy of suppressing it named Suppression Tagging (SupTag). SupTag makes use of TdT, which has extremely low extension efficiency at the 3' recessive end, to stabilize dA/dT base pairs and inhibit poly-A extension of oligo-dT primers, thereby preventing the formation of precursor DNA for byproduct DNA. We demonstrated that SupTag can specifically suppress byproduct DNA using chemicals such as tetramethylammonium chloride (TMAC), modified nucleotides (e.g., 2F-dATP), and Super T-modified reverse-transcription primers. SupTag improves usability by streamlining purification processes and prevents reductions in sequence read output and quantitative sensitivity caused by byproduct DNA. Additionally, SupTag demonstrated the ability to suppress byproduct DNA amplification without using suppression PCR and is compatible with cDNA amplification introducing different adapter sequences at both ends. This study presents a novel suppression method based on the mechanism of amplification of byproduct DNA in the poly-A tagging method, providing a practical technology enabling high-precision transcriptomic analysis from low-abundance RNA.

molecular biology↗

bioRxiv2025

Genomic characterization of sub-populations in human pluripotent stem cell-derived retinal progenitor cells that drive retinal layer structure

The mechanism underlying retinal spheroid layer formation was investigated using Rax::GFP-positive retinal progenitor cells from human embryonic stem cell-derived retinal organoids. Single-cell RNA sequencing revealed that well-layered spheroids transiently activated canonical WNT2B-FZD7 signaling followed by temporary expression of non-canonical WNT5A. Despite structural differences in vitro, non- layered and well-layered retinal spheroids on differentiation Day 60 successfully developed into cone and rod subtypes of photoreceptors and established synaptic connections with host bipolar cells after transplantation in a retinal degeneration rat model, resulting in light-evoked functional recovery. Additionally, part of the presumable Rax::GFP-positive retinal progenitor cells differentiated into non- retinal lineages, including ciliary marginal zone-like, retinal pigment epithelium, and spinal cord-like tissues in vitro, reflecting the developmental plasticity of RAX-positive cells. These findings suggest that canonical and non-canonical WNT signaling pathways sequentially orchestrate early retinal morphogenesis, whereas environmental factors within the host retina strongly drive the alignment and functional integration of graft photoreceptors.

cell biology↗

bioRxiv2025

MitoDelta: identifying mitochondrial DNA deletions at cell-type resolution from single-cell RNA sequencing data

BackgroundDeletion variants in mitochondrial DNA (mtDNA) are associated with various diseases, such as mitochondrial disorders and neurodegenerative diseases. Traditionally, mtDNA deletions have been studied using bulk DNA sequencing, but bulk methods average signals across cells, thereby masking the cell-type-specific mutational landscapes. Resolving mtDNA deletions at single-cell resolution is beneficial for understanding how these mutations affect distinct cell populations. To date, no specialized method exists for detecting cell-type-specific mtDNA deletions from single-cell RNA sequencing data. Notably, mtDNA possesses unique molecular features: a high copy number, stable transcription, and compact structure of the mitochondrial genome. This results in a relatively high abundance of mtDNA-derived reads even in single-cell RNA sequencing data, suggesting the possibility of detecting mtDNA deletion variants directly from transcriptomic data. ResultsHere, we present MitoDelta, a computational pipeline that enables the detection of mtDNA deletions at cell-type resolution solely from single-cell RNA sequencing data. MitoDelta combines a sensitive alignment strategy with robust statistical filtering based on a beta-binomial model, allowing accurate identification of deletion events even from noisy single-cell transcriptomes. To capture cell-type-specific deletion patterns, MitoDelta analyzes reads pooled by annotated cell types, enabling quantification of deletion burden across distinct cellular populations. We benchmarked MitoDelta against existing mtDNA deletion detection tools and demonstrated superior overall performance. As a practical application, we applied MitoDelta to a published single-nucleus RNA sequencing dataset for Parkinsons disease and revealed distinct mtDNA deletion burdens across neuronal subtypes. ConclusionsMitoDelta enables the transcriptome-integrated, cell-type-specific detection of mtDNA deletions from single-cell RNA sequencing data alone, offering a valuable framework for reanalyzing public datasets and studying mitochondrial genome alterations at cell-type resolution. This integrated approach enables insights into how mtDNA deletions are distributed across specific cell types and cellular states, providing new opportunities to investigate the role of mtDNA deletions in cell-type-specific disease mechanisms. The tool is available at https://github.com/NikaidoLaboratory/mitodelta.

bioinformatics↗

bioRxiv2024

Transcriptomic Signatures of WNT-Driven Pathways and Granulosa Cell-Oocyte Interactions during Primordial Follicle Activation

Primordial follicle activation (PFA) is a pivotal event in female reproductive biology, coordinating the transition from quiescent to growing follicles. This study employed comprehensive single-cell RNA sequencing to gain insights into the detailed regulatory mechanisms governing the synchronized dormancy and activation between granulosa cells (GCs) and oocytes with the progression of the PFA process. Wntless (Wls) conditional knockout (cKO) mice served as a unique model, suppressing the transition from pre-GCs to GCs, and disrupting somatic cell-derived WNT signaling in the ovary. Our data revealed immediate transcriptomic changes in GCs post-PFA in Wls cKO mice, leading to a divergent trajectory, while oocytes exhibited modest transcriptomic alterations. Subpopulation analysis identified the molecular pathways affected by WNT signaling on GC maturation, along with specific gene signatures linked to dormant and activated oocytes. Despite minimal evidence of continuous up-regulation of dormancy-related genes in oocytes, the loss of WNT signaling in (pre-)GCs impacted gene expression in oocytes even before PFA, subsequently influencing them globally. The infertility observed in Wls cKO mice was attributed to compromised GC-oocyte molecular crosstalk and the microenvironment for oocytes. Our study highlights the pivotal role of the WNT-signaling pathway and its molecular signature, emphasizing the importance of intercellular crosstalk between (pre-)GCs and oocytes in orchestrating folliculogenesis. Author summaryIn mammalian ovaries, primordial follicles protect dormant oocytes from wasteful consumption. We explored the complex molecular dynamics of the critical process of primordial follicle activation (PFA) that underlies female reproductive health, with a focus on the role of WNT signaling. By utilizing a unique mouse model with disrupted WNT signaling, our study reveals novel insights into the regulatory dynamics of granulosa cells (GCs) and oocytes during PFA. Single-cell transcriptome profiling uncovered distinct cell populations and highlighted the impact of WNT signaling on ovarian cell types. This study reveals distinct transcriptomic changes in GCs post-PFA, which affects their differentiation trajectories and highlights the critical role of WNT signaling in establishing GC function. We further examined the consequences of disrupted microenvironments on oocytes and GC-oocyte interactions, which elucidated altered oocyte subclusters and potential links to infertility. These findings provide a foundation for understanding the connections between WNT signaling and ovarian function. Our comprehensive analysis contributes valuable knowledge to reproductive biology, emphasizing the clinical relevance of WNT signaling in female fertility.

bioinformatics↗

bioRxiv2022

Distinctiveness and continuity in transcriptome and connectivity in the anterior-posterior axis of the paraventricular nucleus of thalamus

The paraventricular nucleus of the thalamus (PVT) projects axons to multiple areas and mediates a wide range of behaviors. Heterogeneity of functions and axonal projections in PVT have been reported, but what cell types exist in PVT and how different they are have not been addressed. We applied single-cell RNA sequencing to depict transcriptomic characteristics of mouse PVT neurons. The transcriptome of PVT neurons had a continuous distribution with the largest variance corresponding to the anterior-posterior axis. Although the single-cell transcriptome classified PVT neurons into four types, transcriptomic and histological analyses showed their continuity. Similarly, anterior and posterior subpopulations had nearly non-overlapping axon projection patterns, while another population showed intermediate patterns. In addition, they responded differently to appetite-related neuropeptides, and their chemogenetic activation showed opposing effects in food consumption. Our studies showed contrasts and continuity of PVT neurons underlying their function as a behavior-modulating hub.

neuroscience↗

bioRxiv2021

Imaging and single cell sequencing analyses of super-enhancer activation mediated by NF-κB in B cells

The transcription factor NF-{kappa}B, which plays an important role in cell fate determination, is involved in the activation of super-enhancers (SEs). However, the biological functions of the NF-{kappa}B SEs in gene control are not fully elucidated. We investigated the characteristics of NF-{kappa}B-mediated SE activity using fluorescence live-cell imaging of RelA, single-cell transcriptome, and chromatin accessibility analyses in anti-IgM-stimulated B cells. Cell stimulation induced nuclear foci formation of RelA and gene expression in a switch-like manner. The gained SEs induced a higher fold-change expression and enhanced cell-to-cell variability in transcriptional response. These properties were correlated with the number of gained cis-regulatory interactions, while switch-like gene induction was associated with the number of NF-{kappa}B binding sites in SE. Our study suggests that NF-{kappa}B SEs have unique roles for quantitative control of gene expression through direct binding to accessible DNA and enhanced DNA contacts.

molecular biology↗

bioRxiv2019

The local state of chromatin compaction at transcription start sites controls transcription levels

A defined amount of transcript is produced from transcription start sites (TSSs) of each gene, suggesting that the binding frequency of RNA polymerase varies among genes. However, what structure in chromatin controls this frequency remains elusive. We established a method to fractionate chromatin according to its degree of three-dimensional compaction. Nucleosomes were evenly detected through all of the fractions, but histone H1 was more highly enriched in the more compact chromatin fractions. Similarly, HP1 and MBD2b were more abundant in more compact chromatin, while the levels of tri-methylated H3 (Lys9) and 5-methyl cytosine subtly increased. Via genome-wide analyses, nearly the entire genome was found to exist in compact chromatin without variations between repeat and non-repeat sequences; however, active TSSs were rarely found in compact chromatin. Based on a correlation between weak compaction and RNA polymerase binding at TSSs, it appears that local states of chromatin compaction determine transcription levels.

molecular biology↗

bioRxiv2019

Genome-wide analysis of transcriptional bursting-induced noise in mammalian cells

Transcriptional bursting is stochastic activation and inactivation of promoters, leading to discontinuous production of mRNA, and is considered to be a contributing factor to cell-to-cell heterogeneity in gene expression. However, it remains elusive how the kinetic properties of transcriptional bursting (e.g., burst size, burst frequency, and noise induced by transcriptional bursting) are regulated in mammalian cells. In this study, we performed a genome-wide analysis of transcriptional bursting in mouse embryonic stem cells (mESCs) using single-cell RNA-sequencing. We found that the kinetics of transcriptional bursting was determined by a combination of promoter and gene body binding proteins, including polycomb repressive complex 2 and transcription elongation-related factors. Furthermore, large-scale CRISPR-Cas9-based screening and functional analysis revealed that the Akt/MAPK signaling pathway regulated bursting kinetics by modulating transcription elongation efficiency. These results uncover key molecular mechanisms underlying transcriptional bursting and cell-to-cell gene expression noise in mammalian cells.

systems biology↗

bioRxiv2019

Benchmarking principal component analysis for large-scale single-cell RNA-sequencing

Principal component analysis (PCA) is an essential method for analyzing single-cell RNA-seq (scRNA-seq) datasets, but large-scale scRNA-seq datasets require long computational times and a large memory capacity.\n\nIn this work, we review 21 fast and memory-efficient PCA implementations (10 algorithms) and evaluate their application using 4 real and 18 synthetic datasets. Our benchmarking showed that some PCA algorithms are faster, more memory efficient, and more accurate than others. In consideration of the differences in the computational environments of users and developers, we have also developed guidelines to assist with selection of appropriate PCA implementations.

bioinformatics↗

bioRxiv2019

Benchmarking Single-Cell RNA Sequencing Protocols for Cell Atlas Projects

Single-cell RNA sequencing (scRNA-seq) is the leading technique for charting the molecular properties of individual cells. The latest methods are scalable to thousands of cells, enabling in-depth characterization of sample composition without prior knowledge. However, there are important differences between scRNA-seq techniques, and it remains unclear which are the most suitable protocols for drawing cell atlases of tissues, organs and organisms. We have generated benchmark datasets to systematically evaluate techniques in terms of their power to comprehensively describe cell types and states. We performed a multi-center study comparing 13 commonly used single-cell and single-nucleus RNA-seq protocols using a highly heterogeneous reference sample resource. Comparative and integrative analysis at cell type and state level revealed marked differences in protocol performance, highlighting a series of key features for cell atlas projects. These should be considered when defining guidelines and standards for international consortia, such as the Human Cell Atlas project.

genomics↗

bioRxiv2019

Uncovering hypergraphs of cell-cell interaction from single cell RNA-sequencing data

Complex biological systems can be described as a multitude of cell-cell interactions (CCIs). Recent single-cell RNA-sequencing technologies have enabled the detection of CCIs and related ligand-receptor (L-R) gene expression simultaneously. However, previous data analysis methods have focused on only one-to-one CCIs between two cell types. To also detect many-to-many CCIs, we propose scTensor, a novel method for extracting representative triadic relationships (hypergraphs), which include (i) ligand-expression, (ii) receptor-expression, and (iii) L-R pairs. When applied to simulated and empirical datasets, scTensor was able to detect some hypergraphs including paracrine/autocrine CCI patterns, which cannot be detected by previous methods.

bioinformatics↗

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↗

bioRxiv2018

CellFishing.jl: an ultrafast and scalable cell search method for single-cell RNA-sequencing

Recent technical improvements in single-cell RNA sequencing (scRNA-seq) have enabled massively parallel profiling of transcriptomes, thereby promoting large-scale studies encompassing a wide range of cell types of multicellular organisms. With this background, we propose CellFishing.jl, a new method for searching atlas-scale datasets for similar cells and detecting noteworthy genes of query cells with high accuracy and throughput. Using multiple scRNA-seq datasets, we validate that our method demonstrates comparable accuracy to and is markedly faster than the state-of-the-art software. Moreover, CellFishing.jl is scalable to more than one million cells, and the throughput of the search is approximately 1,600 cells per second.

bioinformatics↗

bioRxiv2017

An efficient gene regulatory network inference algorithm for early Drosophila melanogaster embryogenesis

The spatial patterns of gene expression in early Drosophila melanogaster embryogenesis have been studied experimentally and theoretically to reveal the molecular basis of morphogenesis. In particular, the gene regulatory network (GRN) of gap genes has been investigated through mathematical modeling and simulation. Although these simulation-based approaches are useful for describing complex dynamics and have revealed several important regulations in spatial patterning, they are computationally intensive because they optimize GRN with iterative simulation. Recently, the advance of experimental technologies is enabling the acquisition of comprehensive spatial expression data, and an efficient algorithm will be necessary to analyze such large-scale data. In this research, we developed an efficient algorithm to infer the GRN based on a linear reaction-diffusion model. First, we qualitatively analyzed the GRNs of gap genes and pair-rule genes based on our algorithm and showed that two mutual repressions are fundamental regulations. Then, we inferred the GRN from gap gene data, and identified asymmetric regulations in addition to the two mutual repressions. We analyzed the effect of these asymmetric regulations on spatial patterns, and showed that they have the potential to adjust peak position. Our algorithm runs in sub-second time, which is significantly smaller than the runtime of simulation-based approaches (between 8 and 160 h, for exmaple). Neverthe-less, our inferred GRN was highly correlated with the simulation-based GRNs. We also analyzed the gap gene network of Clogmia albipunctata and showed that different mutual repression regulations might be important in comparison with those of Drosophila melanogaster. As our algorithm can infer GRNs efficiently and can be applied to several different network analysis, it will be a valuable approach for analyzing large-scale data.

bioinformatics↗

bioRxiv2017

Quartz-Seq2: a high-throughput single-cell RNA-sequencing method that effectively uses limited sequence reads

High-throughput single-cell RNA-seq methods assign limited unique molecular identifier (UMI) counts as gene expression values to single cells from shallow sequence reads and detect limited gene counts. We thus developed a high-throughput single-cell RNA-seq method, Quartz-Seq2, to overcome these issues. Our improvements in the reaction steps make it possible to effectively convert initial reads to UMI counts (at a rate of 30%-50%) and detect more genes. To demonstrate the power of Quartz-Seq2, we analyzed approximately 10,000 transcriptomes in total from in vitro embryonic stem cells and an in vivo stromal vascular fraction with a limited number of reads.

genomics↗