Ben-uri, R. | bioRxiv Science

bioRxiv2026

Single-section multiplexed imaging enables comprehensive lung cancer diagnosis

Accurate and timely diagnosis is essential for effective lung cancer treatment. However, contemporary workflows rely on sequential immunohistochemistry of small biopsy specimens, which can exhaust tissue, limit biomarker assessment, and delay treatment decisions. Here, we demonstrate that multiplexed imaging addresses these limitations by enabling comprehensive lung cancer diagnosis from a single tissue section. We developed and validated a clinically informed multiplexed antibody panel that integrates tumor classification, predictive biomarker assessment, and immune profiling. In diagnostic biopsies, multiplexed imaging achieved 96% concordance with standard pathology, while enabling accurate automated PD-L1 scoring and rapid detection of clinically approved and emerging actionable targets. Simultaneously measuring dozens of proteins improves standard pathology by incorporating complex multi-protein biomarkers, supporting quantitative computational analysis to streamline diagnosis, and generating spatial data for translational research. By reducing turnaround time and preserving scarce tissue, this workflow has the potential to accelerate treatment decisions, improve patient outcomes and bridge clinical care with translational discovery.

cancer biology↗

bioRxiv2025

CellTune: An integrative software for accurate cell classification in spatial proteomics

Spatial proteomics measures multiple proteins in situ, capturing tissue complexity. However, cell classification in densely packed tissues remains challenging due to the lack of efficient classification algorithms, annotation tools, and high-quality labeled datasets to benchmark computational methods. We introduce CellTune, an integrated software for analysis of large spatial proteomics datasets, which streamlines precise cell classification through an optimized human-in-the-loop active learning workflow. It advances core capabilities across within a unified, intuitive, and code-free interface. To evaluate CellTune, we created CellTuneDepot, a resource of 40k manually-annotated cells and 3.5 million high-quality labeled cells across 60 cell types. CellTune outperforms alternative methods, achieving accuracy comparable to human performance while enabling increased classification resolution and discovery of novel cell types. Together, CellTune and CellTuneDepot provide researchers with a tool for state-of-the-art classification accuracy and resolution at scale to drive biological insights.

bioinformatics↗