ICOBA: A highly customizable iterative imagej macro for optimization of image co-localization batch analysis

Abstract

Co-localization analysis is pivotal for understanding protein interactions in biomedical research, yet existing ImageJ and FIJI plug-ins often lack automated multi-channel capabilities, impeding throughput and introducing potential user bias. We introduce ICOBA (Iterative Channel Overlay Batch Analysis), a freely available ImageJ macro designed to streamline and standardize co-localization workflows across large image datasets. As a demonstration of the workflow and to validate its performance, cardiac fibroblasts were immunostained and imaged on a Leica DMi8 microscope, with .tiff files exported for processing. Compared to traditional manual approaches, ICOBA demonstrated significantly faster single-channel and two-channel processing times without sacrificing quantitative accuracy. By leveraging ImageJ's built-in “record” functionality and a customizable macro script, ICOBA accommodates variable staining conditions and threshold parameters, ensuring both reproducibility and flexibility. These attributes make ICOBA a versatile solution for high-throughput, multi-channel co-localization analyses across diverse research fields, from routine lab applications to advanced tissue-imaging studies.