William F. Flynn

This study uses high-resolution spatial transcriptomics to map fibroblast and neuroimmune microenvironments in endometriosis lesions, identifying spatial cellular interactions linked to pain.

The paper maps the spatial distribution of voltage-gated ion channels in mouse mammillary bodies using FISH and links it to molecularly defined neuronal populations.

This study identifies immunosuppressive mechanisms in osteosarcoma using single-cell and spatial transcriptomics, revealing interactions between MDSCs, T-cells, and tumor cells that promote an immunosuppressive microenvironment.

STAMP enables low-cost, high-throughput single-cell analysis by integrating transcriptomics and proteomics imaging, supporting both single and multimodal profiling of diverse cell types.

This study develops a high-throughput assay to assess drug sensitivity of Acanthamoeba castellanii cysts and identifies five active agents.

The paper identifies host genetic factors influencing anti-PD-1 response in mice and humans by mapping QTLs and finding a core gene network with prognostic value in human tumors.

This study uses single-cell transcriptomics to analyze endometriosis, identifying novel cell types and mechanisms in both eutopic and ectopic tissues.

This paper defines the cellular composition of the mouse urinary bladder using single-cell profiling and spatial analysis, identifying novel cell types and fibroblast populations associated with aging.

The paper uses scRNA-seq to classify cell types in the mouse ventral posterior hypothalamus, revealing distinct neuronal and non-neuronal populations and their spatial organization in the mammillary bodies.

The paper identifies a rare subpopulation of slow-cycling germline stem cells regulated by PLZF using RNAseq and single-cell RNA sequencing.

This paper develops machine learning models to predict the primary site of origin and molecular subtype of metastatic cancers using gene expression data from TCGA, achieving high accuracy across multiple cancer types.

The paper uses a Potts Hamiltonian model to infer epistatic effects in HIV-1 protease mutations, showing how these interactions influence drug resistance and fitness.