Quantitative immunofluorescence is widely used to assess molecular expression and cellular distribution across biological tissues, yet the analysis of large image datasets remains time-consuming and prone to user-dependent variability. To address these limitations, we herein developed a semi-automated workflow that integrates ImageJ/Fiji for image processing, StarDist for nuclear segmentation, and spreadsheet- or Python-based routines for data curation. The pipeline standardizes critical analytical steps, including scale calibration, region-of-interest (ROI) definition, channel selection, and z-stack handling, while preserving essential metadata through a structured file-naming system. Optical density and cell-number metrics are exported automatically in a consistent format, enabling efficient consolidation into a unified dataset. Subsequent curation can be performed either manually in a spreadsheet software or fully automatically through custom Python scripts, allowing extraction of sample identifiers, regions, and markers, as well as calculation of normalized intensity values. Comparison with existing protocols proved that this workflow adheres to widely accepted quantification principles while markedly improving reproducibility, consistency, and analytical throughput. This method offers a straightforward, transparent, and scalable solution for fluorescence-based quantification suitable for laboratories with varying levels of computational expertise.
Marques et al. (Fri,) studied this question.
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