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

Emre Düşünceli1, Seiya Yamada1,2, Takashi Namba1,3,4

  • 1Neuroscience Center, HiLIFE - Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland.

The European Journal of Neuroscience
|April 3, 2025
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Summary

Researchers developed a user-friendly ImageJ/Fiji plugin for semiautomated analysis of multichannel microscopy images. This tool simplifies cell identification and marker coexpression analysis, reducing workload in fields like neuroscience.

Keywords:
cell countingcoexpressionimage analysis

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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.