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

Area of Science:

• Molecular and Cellular Biology
• Biophysics
• Bioimaging

Background:

• Confocal microscopy is crucial for high-resolution imaging and studying biomolecular interactions.
• Analyzing large confocal datasets, especially Z-stacks, presents challenges in preprocessing and colocalization quantification.
• Manual analysis of imaging data for colocalization is inefficient and susceptible to observer bias.

Purpose of the Study:

• To develop an automated workflow for efficient and reproducible Z-stack refinement and colocalization analysis.
• To address limitations in handling large datasets and reduce human error in confocal image analysis.
• To provide a publicly available tool for researchers without extensive programming experience.

Main Methods:

• Integration of Python-based preprocessing with Fiji ImageJ's BIOP-JACoP plugin.
• Automated removal of signal-free Z-slices using auto-thresholding for Z-stack refinement.
• Batch processing for high-throughput Manders coefficient calculation.

Main Results:

• Significant reduction in hands-on time and human error for colocalization analysis.
• Streamlined workflow for processing large confocal Z-stack datasets.
• Development of an executable Windows application and GitHub repository for accessibility.

Conclusions:

• The automated workflow enhances efficiency, reproducibility, and adaptability in confocal image analysis.
• The tool facilitates high-throughput protein colocalization studies by simplifying complex data processing.
• This protocol addresses critical gaps in current confocal image analysis, offering broad applicability.