Biology subjects

Tsuyuzaki, K.

Publications and source records attributed to Tsuyuzaki, K..

7 recordsLinked to original sources

bioRxiv2025

Uncertainty-aware transcription factor activity and perturbation inference without additional training

Understanding how transcription factors (TFs) control gene expression is essential for deciphering cellular regulatory programs. However, estimating TF activity remains challenging. Current methods rely on either broad TF-target databases, which increase false positives, or on curated sets with limited coverage. Most methods lack uncertainty quantification. Here, we introduce TFActProfiler, an integrated resource and computational framework that learns signed TF-mRNA effects by pretraining on prior TF-mRNA interaction knowledge and large RNA-seq atlases. Given an expression profile as input, TFActProfiler infers per-sample TF activity together with a confidence score that quantifies agreement between predicted and observed mRNA levels. Across TF knockdown datasets spanning multiple human cell types, TFActProfiler consistently improves TF activity inference over approaches based on unfiltered priors or narrowly curated target sets while retaining broad TF and mRNA coverage. The learned effect map further enables training-free prediction of transcriptome responses to TF perturbations. By coupling TF activity inference with training-free perturbation modeling and confidence estimates, TFActProfiler offers a tool for dissecting regulatory programs across diverse cellular states.

bioinformatics↗

bioRxiv2025

Neuropeptide signalling and perineurial barrier generate a persistent stress-induced internal state in Drosophila

Although fear conditioning has elucidated cue-evoked acute fear responses, the mechanisms by which stress experiences induce generalized internal states linked to anxiety are poorly understood. Here, we report that robust stress induces a persistent behavioral change characterized by avoidance of a confined space, claustrophobia-like behavior in Drosophila. Unlike aversive memory formation, the development of claustrophobia-like behavior does not require dopamine receptors. Our neuronal screening determined that neuropeptide signalling via Allatostatin-A inactivates the downstream neurons via its receptor AstA-R1, causally inducing claustrophobia-like behavior. Moreover, gene expression profiling of individual fly heads revealed that immune response activation in perineurial barrier is involved in claustrophobia-like behavior. Our data demonstrate that stress-induced persistent behavioral change would not be related to a canonical mechanism of aversive memory formation, rather involves neuropeptidergic signalling and perineurial barrier, providing the mechanism determining internal states which persistently change behavioral modes.

neuroscience↗

bioRxiv2022

scTensor detects many-to-many cell-cell interactions from single cell RNA-sequencing data

Complex biological systems are described as a multitude of cell-cell interactions (CCIs). Recent single-cell RNA-sequencing studies focus on CCIs based on ligand-receptor (L-R) gene co-expression but the analytical methods are not appropriate to detect many-to-many CCIs. In this work, we propose scTensor, a novel method for extracting representative triadic relationships (or hypergraphs), which include ligand-expression, receptor-expression, and related L-R pairs. Through extensive studies with simulated and empirical datasets, we have shown that scTensor can detect some hypergraphs that cannot be detected using conventional CCI detection methods, especially when they include many-to-many relationships. scTensor is implemented as a freely available R/Bioconductor package.

bioinformatics↗

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

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↗

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↗