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Updated: Nov 23, 2025

Open Source High Content Analysis Utilizing Automated Fluorescence Lifetime Imaging Microscopy
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FLIMJ: An open-source ImageJ toolkit for fluorescence lifetime image data analysis.

Dasong Gao1, Paul R Barber2, Jenu V Chacko1

  • 1Laboratory for Optical and Computational Instrumentation, Center for Quantitative Cell Imaging, University of Wisconsin, Madison, WI, United States of America.

Plos One
|December 30, 2020
PubMed
Summary

FLIMJ is a new ImageJ plugin that simplifies Fluorescence Lifetime Imaging Microscopy (FLIM) data analysis. It offers extensible workflows for FLIM analysis, improving computational tools for this powerful cellular imaging technique.

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Area of Science:

  • * Fluorescence microscopy
  • * Cellular imaging
  • * Computational biology

Background:

  • * Fluorescence Lifetime Imaging Microscopy (FLIM) provides microenvironment information beyond standard fluorescence imaging.
  • * FLIM measurements are sensitive to fluorophore proximity, pH, and hydrophobicity.
  • * Growing FLIM use necessitates advanced computational tools for data integration and analysis.

Purpose of the Study:

  • * Introduce FLIMJ, an ImageJ plugin for streamlined FLIM data analysis.
  • * Develop an extensible toolkit for creating complex FLIM analysis workflows.
  • * Integrate FLIM analysis with ImageJ, Jupyter notebooks, and scientific programming languages.

Main Methods:

  • * Developed FLIMJ based on the FLIMLib decay curve fitting library and ImageJ Ops framework.
  • * Implemented FLIM fitting routines with seamless integration into ImageJ.
  • * Demonstrated extensibility through lifetime-based image segmentation and colocalization analyses.

Main Results:

  • * FLIMJ provides integrated FLIM fitting routines within the ImageJ environment.
  • * The plugin supports the creation of complex, extensible FLIM analysis workflows.
  • * FLIMJ routines are compatible with Jupyter notebooks and scientific programming languages like Python.
  • * Validation confirmed the accuracy of FLIMJ fitting routines against industry standards.

Conclusions:

  • * FLIMJ enhances the accessibility and utility of FLIM data analysis.
  • * The toolkit facilitates advanced FLIM applications in cellular imaging.
  • * FLIMJ promotes integration of FLIM analysis into broader computational biology workflows.