Ayako Suzuki

SPATNIC is a variational autoencoder that identifies cancer cells from spatial and single-cell transcriptomic data in colorectal cancer, outperforming existing tools and identifying a rare cancer stem cell population.

The paper identifies POSTN-expressing cancer-associated fibroblasts (CAFs) linked to chemoradiotherapy resistance in rectal cancer using spatial multi-omics data.

This study uses molecular pixelation to profile CAR protein organization dynamics in CAR-T therapy, revealing co-localization network remodeling linked to clinical outcomes.

The paper introduces SpatialKNife, a toolkit for analyzing single-cell spatial omics data to estimate spatiotemporal pathological mechanisms in microenvironments.

The paper analyzes single-cell immune data from healthy individuals to reveal cell diversity and daily fluctuations, showing how immune responses vary between individuals and over time.

The paper identifies a novel class of structural aberrations in lung cancers using long-read sequencing and shows their occurrence in vivo and in cancer-related genes.