A semi-automated workflow to quantify γ-H2AX DNA damage in pulmonary cell models and lung tissue sections

The quantification of DNA double-strand breaks via γ-H2AX immunolabeling is a cornerstone of genotoxicity assessment, yet classical nucleus-based counting is frequently constrained by cell morphology, high confluence, and complex tissue architectures. To overcome these limitations, we developed and validated a semi-automated image analysis workflow that combines manual scoring with an optimized tool using two key metrics: the number of positive nuclei and the surface area of γ-H2AX foci. Validated across diverse biological systems, hAELVi, HPMEC-ST1.6 R cells and rat lung tissue sections exposed to varying genotoxic stressors, γ-irradiation (1 Gy), etoposide (10 µg·mL⁻¹), or bleomycin (2 U·kg⁻¹), our approach demonstrates excellent concordance with manual counting where segmentation is feasible. Specifically, the workflow was optimized to allow precise nucleus-based segmentation for the hAELVi model and tissue lung sections. However, for models where segmentation is not feasible, such as HPMEC-ST1.6 R, the surface-based metric was exclusively applied. Crucially, this surface-based metric successfully captured DNA damage induction in complex samples where per-nucleus segmentation was previously impossible. By providing a scalable, versatile alternative that bridges the gap between traditional cell culture assays and lung tissue sections, this methodology represents a standardized workflow for evaluating genotoxic stress in experimental contexts that were previously inaccessible to conventional quantification.