Development of a Novel Automated Workflow in Fiji ImageJ for Batch Analysis of Confocal Imaging Data to Quantify...

Confocal microscopy is integral to molecular and cellular biology, enabling high-resolution imaging and colocalization studies to elucidate biomolecular interactions in cells. Despite its utility, challenges in handling large datasets, particularly in preprocessing Z-stacks and calculating colocalization metrics like the Manders coefficient, limit efficiency and reproducibility. Manually processing large numbers of imaging data for colocalization analysis is prone to observer bias and inefficiencies. This study presents an automated workflow integrating Python-based preprocessing with Fiji ImageJ's BIOP-JACoP plugin to streamline Z-stack refinement and colocalization analysis. We generated an executable Windows application and made it publicly available on GitHub (https://github.com/weiyue99/Yue-Colocalization), allowing even those without Python experience to directly run the Python code required in the current protocol. The workflow systematically removes signal-free Z-slices that sometimes exist at the beginning and/or end of the Z-stacks using auto-thresholding, creates refined substacks, and performs batch analysis to calculate the Manders coefficient. It is designed for high-throughput applications, significantly reducing human error and hands-on time. By ensuring reproducibility and adaptability, this protocol addresses critical gaps in confocal image analysis workflows, facilitating efficient handling of large datasets and offering broad applicability in protein colocalization studies. Key features • Automated image analysis: use of Python-based code for substack creation based on auto-thresholding to eliminate observer bias. • Manders coefficient calculation: quantification of colocalization using BIOP-JACoP in Fiji ImageJ. • High-throughput compatibility: efficient for large datasets using macro codes to run in batch mode; minimal manual intervention.