Biology subjects

Kageyama, S.-I.

Publications and source records attributed to Kageyama, S.-I..

3 recordsLinked to original sources

bioRxiv2026

SpatialCompassV (SCOMV): De novo cell and gene spatial pattern classification and spatially differential gene identification

Spatial omics technologies enable the detection of gene expression together with spatial information in tissues. However, many existing analytical methods rely on prior biological knowledge or predefined annotations, while being limited in their ability to systematically characterize spatial distribution patterns. Here, we developed SpatialCompassV (SCOMV), a computational tool that clusters genes and cell types based on vectorial relationships between transcript locations and regions of interest, such as tumors. This tool quantifies the spatial positioning of genes and cells relative to a defined reference region by encoding their distance and direction into structured feature representations. SCOMV captured tumor-associated spatial patterns and enabled the unsupervised classification of genes into internal, peripheral, partially peripheral, and ubiquitous distribution types in breast and lung cancer spatial transcriptomic datasets of Xenium. Notably, SCOMV detected immune cell-related signatures that were preferentially localized in CAF-low regions. Extending the analysis to multiple regions of interest further enabled malignant state discrimination. Moreover, SCOMV identifies genes that differ not only in gene expression levels, but also in spatial distribution patterns, which we termed spatially differential genes (spatially DEGs).

bioinformatics↗

bioRxiv2024

Giant extrachromosomal element "Inocle" potentially expands the adaptive capacity of the human oral microbiome

Survival strategy of bacteria is expanded by extrachromosomal elements (ECEs). However, their genetic diversity and functional roles for adaptability are largely unknown. Here, we discovered a novel family of intracellular ECEs using 56 saliva samples by developing an efficient microbial DNA extraction method coupled with long-read metagenomics assembly. Even though this ECE family was not hitherto unidentified, our global prevalence analysis using 476 salivary metagenomic datasets elucidated that these ECEs reside in 74% of the population. These ECEs, which we named, "Inocles", are giant plasmid-like circular genomic elements of 395 kb in length, having Streptococcus as a host bacterium. Inocles encode a series of genes that contribute to intracellular stress tolerance, such as oxidative stress and DNA damage, and cell wall biosynthesis and modification involved in the interactions with oral epithelial cells. Moreover, Inocles exhibited significant positive correlations with immune cells and proteins responding to microbial infection in peripheral blood. Intriguingly, we examined and found their marked reductions among 68 patients of head and neck cancers and colorectal cancers, suggesting its potential usage for a novel biomarker of gastrointestinal cancers. Our results suggest that Inocles potentially boost the adaptive capacity of host bacteria against various stressors in the oral environment.

genomics↗

bioRxiv2024

Spatial domain analysis to estimate spatiotemporal pathological mechanisms in microenvironment with single-cell spatial omics data

Single-cell spatial omics analysis requires consideration of biological functions and mechanisms in a microenvironment. However, microenvironment analysis using bioinformatic methods is limited by the need to detect histological morphology. In this study, we developed SpatialKNife (SKNY), an image-processing-based toolkit that detects spatial domains that potentially reflect histology and extends these domains to the microenvironment. The SKNY algorithm identified tumour spatial domains from spatial transcriptomic data of breast cancer, followed by clustering of these domains, trajectory estimation, and spatial extension to the tumour microenvironment (TME). The results of the trajectory estimation were consistent with the known mechanisms of cancer progression. We observed endothelial cell and macrophage infiltration into the TME at mid-stage progression. Our results suggest that analysis using the spatial domain as a unit reflects pathological mechanisms in the TME. This approach may be applicable to the biological estimation of diverse microenvironments.

bioinformatics↗