Biology subjects

Barnfather, I.

Publications and source records attributed to Barnfather, I..

3 recordsLinked to original sources

bioRxiv2025

Transmembrane Serine Protease TMPRSS11B promotes an acidified tumor microenvironment and immune suppression in lung squamous cell carcinoma

Lung cancer is the leading cause of cancer-related deaths worldwide. Existing therapeutic options have limited efficacy, particularly for lung squamous cell carcinoma (LUSC), underscoring the critical need for the identification of new therapeutic targets. We previously demonstrated that the Transmembrane Serine Protease TMPRSS11B promotes transformation of human bronchial epithelial cells and enhances lactate export from LUSC cells. To determine the impact of TMPRSS11B activity on the host immune system and the tumor microenvironment (TME), we evaluated the effect of Tmprss11b depletion in a syngeneic mouse model. Tmprss11b depletion significantly reduced tumor burden in immunocompetent mice and triggered an infiltration of immune cells. RNA FISH analysis and spatial transcriptomics in the autochthonous Rosa26-Sox2-Ires-GfpLSL/LSL; Nkx2-1fl/fl; Lkb1fl/fl (SNL) model revealed an enrichment of Tmprss11b expression in LUSC tumors, specifically in Krt13+ hillock-like cells. Ultra-pH sensitive nanoparticle imaging and metabolite analysis identified regions of acidification, elevated lactate, and enrichment of M2-like macrophages in LUSC tumors. These results demonstrate that TMPRSS11B promotes an acidified and immunosuppressive TME and nominate this enzyme as a therapeutic target in LUSC.

cancer biology↗

bioRxiv2025

Scaling up spatial transcriptomics for large-sized tissues: uncovering cellular-level tissue architecture beyond conventional platforms with iSCALE

Recent advances in spatial transcriptomics (ST) technologies have transformed our ability to profile gene expression while retaining the crucial spatial context within tissues. However, existing ST platforms suffer from high costs, long turnaround times, low resolution, limited gene coverage, and small tissue capture areas, which hinder their broad applications. Here we present iSCALE, a method that predicts super-resolution gene expression and automatically annotates cellular-level tissue architecture for large-sized tissues that exceed the capture areas of standard ST platforms. The accuracy of iSCALE were validated by comprehensive evaluations, involving benchmarking experiments, immunohistochemistry staining, and manual annotation by pathologists. When applied to multiple sclerosis human brain samples, iSCALE uncovered lesion associated cellular characteristics that were undetectable by conventional ST experiments. Our results demonstrate iSCALEs utility in analyzing large-sized tissues with automatic and unbiased tissue annotation, inferring cell type composition, and pinpointing regions of interest for features not discernible through human visual assessment.

genomics↗

bioRxiv2024

Deep Gaussian Process with Uncertainty Estimation for Microsatellite Instability and Immunotherapy Response Prediction Based on Histology

Determining tumor microsatellite status has significant clinical value because tumors that are microsatellite instability-high (MSI-H) or mismatch repair deficient (dMMR) respond well to immune check-point inhibitors (ICIs) and oftentimes not to chemotherapeutics. We propose MSI-SEER, a deep Gaussian process-based Bayesian model that analyzes H&E whole-slide images in weakly-supervised-learning to predict microsatellite status in gastric and colorectal cancers. We performed extensive validation using multiple large datasets comprised of patients from diverse racial backgrounds. MSI-SEER achieved state-of-the-art performance with MSI prediction, which was by integrating uncertainty prediction. We achieved high accuracy for predicting ICI responsiveness by combining tumor MSI status with stroma-to-tumor ratio. Finally, MSI-SEERs tile-level predictions revealed novel insights into the role of spatial distribution of MSI-H regions in the tumor microenvironment and ICI response.

bioinformatics↗