Dan J. Woodcock

This paper introduces a 3D microdissection method combining CNC milling and light-sheet microscopy to study tumor evolution in complex 3D structures.

This study evaluates the impact of a 3D pathology workflow on DNA, RNA, and protein integrity, showing minimal effect on amplifiability and transcriptomic profiles.

This study uses spatial transcriptomic analysis and machine learning to redefine the topology of human bone marrow in health and myeloproliferative neoplasms, revealing new insights into cellular heterogeneity and fibrosis.

The paper evaluates the accuracy of transcript-based inferred tumor phylogenies compared to DNA-based phylogenies in prostate cancer using single-cell, spatial transcriptomic, and bulk data.

The paper uses spatial transcriptomic data to show that bulk genomic signature scoring in prostate cancer biopsies is confounded by tumour heterogeneity, leading to inaccurate risk assessments.

The paper uses spatial transcriptomics to analyze copy number variations in benign and malignant tissues, revealing clonal patterns and suggesting early cancer evolution mechanisms.

This study shows genomic copy number changes can predict oesophageal cancer up to 10 years before diagnosis, enabling earlier treatment and reduced unnecessary monitoring.

This paper defines and mathematically characterizes multiplexing in signaling systems, provides experimental evidence for NF-κB's potential to multiplex information, and introduces new methods for analyzing dynamic models.

The paper identifies Paupar, a lncRNA that interacts with KAP1 to regulate chromatin structure and neurogenesis in the subventricular zone.