Benjamin D. Cosgrove

SEngEAR is a web platform for annotating senescent cells, visualizing communication networks, and comparing senescence signatures across datasets and contexts.

The paper investigates the effects of combined PR agonism and antagonism on TNBC cell viability and PI3K inhibitor cytotoxicity, finding enhanced cytotoxicity when targeting both canonical and non-canonical PRs.

The paper compares single-cell transcriptomes of mouse and human tubal epithelium to show injury-driven regeneration in humans may contribute to ovarian cancer initiation.

This study uses micropatterned cardioids to investigate mesoderm-endoderm interactions in heart development, finding that pattern size affects cavity formation and signaling.

This study identifies 44 genes, including novel markers like OAS2 and OASL, as potential early diagnostic and prognostic markers for serous endometrial carcinoma using a mouse model and multi-omics approaches.

This study identifies PDGFRβ signaling as a regulator of myocyte fusion and muscle regeneration, showing that its suppression enhances muscle repair and myofiber size.

The paper identifies pre-ciliated tubal epithelial cells as a cancer-prone state in high-grade serous ovarian carcinoma by analyzing mouse uterine tube cell states and lineage dynamics.

This study examines the chemoresistance of triple-negative breast cancer cell lines to PARP/PI3K dual inhibition and identifies treatment schedules that enhance its efficacy.

This study identifies DHH as a key ligand in the Hedgehog signaling pathway that regulates fibro/adipogenic progenitor fate during skeletal muscle regeneration, preventing adipogenesis and promoting fibrosis.

This paper analyzes single-cell transcriptomes from mouse skeletal muscle across lifespan to identify age-related changes in stem cell states and senescence associated with impaired regeneration.

This paper introduces a method to spatially map the total transcriptome by enabling detection of non-polyadenylated RNAs through in situ polyadenylation.

The paper integrates large-scale single-cell and spatial transcriptomic data to identify rare, transient muscle progenitor cell states involved in muscle regeneration.