Berend Snijder

The paper identifies an RFX-driven ciliogenic cell state in colorectal cancer that is sensitive to FGFR inhibitors and associated with poor prognosis.

This paper introduces scClone2DR, a framework predicting tumour drug response at the clone level using single-cell DNA/RNA sequencing and drug-screening data, showing improved accuracy in identifying clonal sensitivities and resistance.

The paper introduces IRIS, a platform integrating high-resolution imaging and single-cell RNA sequencing to link morphology with transcriptomes, revealing new nuclear-ER architectures in CD8+ T cells.

The paper identifies an epigenetic neural signature in glioblastoma that correlates with poor prognosis and therapeutic vulnerability.

This paper shows self-supervised vision transformers can accurately decode cellular state heterogeneity from microscopy data without prior training.

The paper uses multiplexed imaging and deep learning to analyze immune cell phenotypes across human cohorts, revealing associations with age, gender, and health markers.

The paper profiles the metabolome of 180 cancer cell lines and identifies two major metabolic types linked to epithelial-mesenchymal transition (EMT).

scROSHI identifies cell types without training data by using pre-existing gene sets and hierarchical relationships, outperforming methods in low-data and high-diversity scenarios.

This paper introduces LUX-MS, a light-triggered optoproteomic technology to map surfaceome receptor interactions and nanoscale organization, revealing insights into intercellular signaling and drug action.

SCIM matches single-cell measurements across technologies by learning a technology-invariant latent space and applying bipartite matching, achieving high accuracy in real-world datasets.