TissueProf: An ImageJ/Fiji Plugin for Tissue Profiling Based on Fluorescent Signals

Area of Science:

• Biomedical imaging
• Cell biology
• Neuroscience

Background:

• Fluorescence immunohistochemistry is crucial for analyzing cell populations in tissues.
• High cell density and extracellular background signals pose challenges for accurate cell identification and marker coexpression analysis.
• Existing image analysis software often lacks the automation and flexibility required by researchers.

Purpose of the Study:

• To develop a user-friendly ImageJ/Fiji plugin for semiautomated analysis of multichannel microscopy images.
• To address the challenges of cell identification and marker coexpression analysis in dense tissue samples.
• To reduce the experimental workload and time investment for researchers.

Main Methods:

• Developed a semiautomated image analysis pipeline implemented as an ImageJ/Fiji plugin.
• The pipeline includes cell segmentation, optional manual correction, and molecule coexpression analysis.
• Utilizes deep learning networks for fluorescent signal localization and spatial analysis for ROI-based coexpression quantification.

Main Results:

• The plugin automates the identification of regions-of-interest (ROIs) based on fluorescent signals.
• It quantifies cells expressing individual molecules and their combinations, along with signal intensities.
• Outputs are provided in Excel files detailing cell counts and coexpression patterns within user-defined zones.

Conclusions:

• The developed plugin offers a flexible and semiautomated solution for analyzing complex multichannel microscopy images.
• It significantly mitigates the workload and time required for analyzing tissue samples, particularly in neuroscience.
• This tool enhances the efficiency and accuracy of cell identification and marker coexpression studies.