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

Area of Science:

• Biomedical Research
• Microscopy
• Image Analysis

Background:

• Co-localization analysis is crucial for studying protein interactions.
• Existing ImageJ/FIJI plugins often lack automated multi-channel capabilities, limiting throughput and introducing bias.

Purpose of the Study:

• Introduce ICOBA (Iterative Channel Overlay Batch Analysis), a novel ImageJ macro.
• Streamline and standardize multi-channel co-localization workflows for large datasets.

Main Methods:

• Developed ICOBA as a freely available ImageJ macro.
• Utilized ImageJ's recording functionality and customizable scripting.
• Validated with immunostained cardiac fibroblasts imaged on a Leica DMi8 microscope.

Main Results:

• ICOBA significantly reduces processing times for single- and two-channel analyses compared to manual methods.
• Quantitative accuracy is maintained without sacrificing speed.
• The macro offers flexibility for variable staining and threshold parameters.

Conclusions:

• ICOBA provides a versatile, reproducible, and flexible solution for high-throughput co-localization.
• It is suitable for diverse research fields, from basic lab work to advanced tissue imaging.