Biology subjects

Tate, B.

Publications and source records attributed to Tate, B..

3 recordsLinked to original sources

bioRxiv2026

SCORPy: Lowering the computational barrier to reproducible multiplexed imaging spatial single cell proteomics analysis

Spatially resolved single-cell proteomic imaging technologies, including cyclic immunofluorescence (CycIF), generate high-dimensional data, critical for tissue-scale biological analysis. However, single-cell analysis remains computationally demanding, lacks standardization across platforms and is often inaccessible to experimental biologists without programming expertise. Here we present SCORPy (Single-Cell proteOmics Research Platform), a standalone, cross-platform desktop application that provides an end-to-end, code-free workflow for the analysis of single-cell proteomic data extracted from imaging experiments. SCORPy introduces methodological advances for preprocessing multiplexed imaging data: an exposure-aware, cycle-matched background correction strategy, and a normalization framework that harmonizes signal distributions across markers while enabling batch correction across experiments. These approaches are integrated with quality control, interactive thresholding and cell phenotyping using a hierarchical cell reference library, and downstream compositional and spatial analyses within a unified interface. Sample-level metadata can be incorporated throughout the workflow to support integrative analyses and facilitate generation of publication-ready visualizations. By combining robust preprocessing methods with an accessible implementation, SCORPy reduces computational barriers and promotes broader adoption of spatial single-cell proteomics analysis.

cell biology↗

bioRxiv2026

Synergistic Interaction Between Nitrofurantoin and Dequalinium Provides a Promising Approach to Treating UTIs caused by Antibiotic-Resistant Klebsiella pneumoniae

Nitrofurantoin is commonly prescribed as a first-line treatment for urinary tract infections (UTIs) and is effective against most uropathogens; however, resistance among Klebsiella spp. remains high. In this study, we investigated synergistic drug combinations--defined as pairs of drugs whose combined effect exceeds that of each agent alone--to enhance treatment efficacy against drug-resistant Klebsiella pneumoniae. A high-throughput drug screen identified six and two small molecules that exhibited >50% synergistic activity in tested strains in combination with nitrofurantoin and ciprofloxacin, respectively. We further validated the top three candidates using nitrofurantoin-susceptible and resistant clinical isolates. Notably, the combination of nitrofurantoin and dequalinium demonstrated strong synergistic activity, observed in 68% of susceptible Escherichia coli strains and 94% of resistant Klebsiella pneumoniae strains tested. Combined inhibition of the citric acid cycle by nitrofurantoin and F1-ATPase by dequalinium resulted in a significant reduction in ATP levels compared to either treatment alone. Importantly, decreased ATP levels did not increase persister cell formation, and dequalinium alone reduced persister cell populations more effectively than nitrofurantoin. However, nitrofurantoin is processed into a poorly understood reactive intermediate whose specific metabolic targets have not been fully elucidated. Using metabolomic analyses, we identified aconitase and isocitrate dehydrogenase--key enzymes in the citric acid cycle--as altered in response to nitrofurantoin. Together, these findings demonstrate that dual targeting of bacterial metabolism by nitrofurantoin and dequalinium represents a promising therapeutic strategy for treating drug-resistant Klebsiella spp. in UTIs.

microbiology↗

bioRxiv2023

Machine learning links T cell function and spatial localization to neoadjuvant immunotherapy and clinical outcome in pancreatic cancer

Tumor molecular datasets are becoming increasingly complex, making it nearly impossible for humans alone to effectively analyze them. Here, we demonstrate the power of using machine learning to analyze a single-cell, spatial, and highly multiplexed proteomic dataset from human pancreatic cancer and reveal underlying biological mechanisms that may contribute to clinical outcome. A novel multiplex immunohistochemistry antibody panel was used to audit T cell functionality and spatial localization in resected tumors from treatment-naive patients with localized pancreatic ductal adenocarcinoma (PDAC) compared to a second cohort of patients treated with neoadjuvant agonistic CD40 (CD40) monoclonal antibody therapy. In total, nearly 2.5 million cells from 306 tissue regions collected from 29 patients across both treatment cohorts were assayed, and more than 1,000 tumor microenvironment (TME) features were quantified. We then trained machine learning models to accurately predict CD40 treatment status and disease-free survival (DFS) following CD40 therapy based upon TME features. Through downstream interpretation of the machine learning models predictions, we found CD40 therapy to reduce canonical aspects of T cell exhaustion within the TME, as compared to treatment-naive TMEs. Using automated clustering approaches, we found improved DFS following CD40 therapy to correlate with the increased presence of CD44+ CD4+ Th1 cells located specifically within cellular spatial neighborhoods characterized by increased T cell proliferation, antigen-experience, and cytotoxicity in immune aggregates. Overall, our results demonstrate the utility of machine learning in molecular cancer immunology applications, highlight the impact of CD40 therapy on T cells within the TME, and identify potential candidate biomarkers of DFS for CD40-treated patients with PDAC.

cancer biology↗