Alexander Swarbrick

SIMPlex generates matched spatial and single-nucleus transcriptomic profiles from the same tissue section to resolve cell states with spatial signatures in mouse and human cancer tissues.

This paper introduces wSIR, a scalable supervised dimensionality reduction method for spatial transcriptomics data, which retains predictive power of spatial coordinates and is demonstrated on breast cancer and embryonic development datasets.

The paper identifies stromal cell subsets in breast cancer that influence T-cell infiltration and patient outcomes through spatial and clinical analysis.

This study uses single-cell RNA-sequencing and spatial transcriptomics to analyze epithelial and fibroblast states in hormone therapy-naive prostate cancer, identifying novel phenotypes and molecular perturbations.

This study uses single-cell multiomics to show that AZA response in MDS involves hematopoietic regeneration from mutated HSPCs and that AZA-refractive sub-populations contribute to disease progression.

This paper shows that ROCK2 inhibition reduces ECM remodelling and cell growth while impairing migration and invasion in breast cancer models.

ProCanFDL uses federated deep learning to subtype cancers using proteomic data while preserving privacy, achieving performance gains over local models.

This study introduces a method to generate high-quality single-nucleus transcriptomic data from FFPE samples using recent single-cell genomic technology.

This study identifies ID4 as a repressor of myoepithelial differentiation in mammary stem cells through its interaction with HEB, using integrated transcriptomic, proteomic, and ChIP-sequencing data.

This paper introduces RAGE-seq, a method combining long-read and short-read sequencing to analyze lymphocyte clonal and transcriptional features in breast cancer tissues.

This study identifies ID4's role in DNA damage response in BLBC and HGSOV by showing its interaction with MDC1 and DNA repair proteins, linking it to BRCA1-mutant tumors.

This study uses single-cell transcriptomics to reveal molecular subtype and functional heterogeneity in breast cancer models, identifying subpopulations with potential clinical relevance.