Tadahaya Mizuno

This paper introduces a biosensor-based method for single-molecule enzyme activity profiling without enzyme-specific substrates, enabling absolute quantification of active enzyme molecules in cellular and circulating samples.

This study develops a blood-based liquid biopsy platform using single-molecule enzyme activity profiling to detect pancreatic ductal adenocarcinoma with high specificity and moderate sensitivity across multiple stages.

This study develops water-soluble fluorogenic probes for single-molecule glycosidase activity profiling in blood, revealing -mannosidase as a potential biomarker for liver injury.

The paper develops a bioorthogonal fluorescence probe and engineered enzyme for low-background cancer imaging in mouse models.

This paper introduces a programmable chemical strategy called SCCR for automated synthesis of protease-activatable molecules, demonstrating its application in generating fluorogenic probes and ADC linkers.

This paper develops a single-molecule enzyme activity assay for serine hydrolases using activity-based probes and demonstrates its application in detecting active granzyme B as a biomarker of liver damage.

The paper identifies CXCR2+ MDSC-like cells in the olfactory bulb that suppress antiviral immunity, leading to worse outcomes after VSV infection.

This paper introduces a liquid biopsy platform that detects early-stage pancreatic adenocarcinoma by analyzing single-molecule protease/peptidase activity in blood samples.

GLDADec deconvolves bulk gene expression data using marker gene names to estimate cell type proportions, outperforming existing methods and enabling clinical applications.

This study evaluates normalization procedures for transcriptome profiles of compounds, finding that batch-corrected baseline distributions work well for large datasets and offering guidance for small datasets.

This paper develops a rat-specific deconvolution method to retrieve immune cell trafficking information from toxicogenomics databases, finding species-specific differences and identifying compound clusters based on immune cell profiles.

This study evaluates using CNN-extracted latent representations from toxicopathological images to estimate compound properties in vivo, finding that layer selection significantly impacts performance.

The paper evaluates a liver-specific deconvolution method on legacy data, showing improved extrapolation when using optimized reference cell sets for liver transcriptome analysis.

This paper introduces OLSA, a method to decompose drug effects into basic components using transcriptome data, showing its ability to distinguish drug effects and predict novel drug activities.