FLIMJ: An open-source ImageJ toolkit for fluorescence lifetime image data analysis

Journal Article · Wed Dec 30 00:00:00 EST 2020 · PLoS ONE

DOI:https://doi.org/10.1371/journal.pone.0238327· OSTI ID:1852751

Gao, Dasong ^[1]; Barber, Paul R. ^[2]; Chacko, Jenu V. ^[3]; Kader Sagar, Md. Abdul ^[4]; Rueden, Curtis T. ^[3]; Grislis, Aivar R. ^[3]; Hiner, Mark C. ^[3]; Eliceiri, Kevin W. ^[5]

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1. Univ. of Wisconsin, Madison, WI (United States). Center for Quantitative Cell Imaging. Lab. for Optical and Computational Instrumentation; Univ. of Wisconsin, Madison, WI (United States)
2. Univ. College London (United Kingdom). UCL Cancern Inst.
3. Univ. of Wisconsin, Madison, WI (United States). Center for Quantitative Cell Imaging. Lab. for Optical and Computational Instrumentation
4. Univ. of Wisconsin, Madison, WI (United States). Center for Quantitative Cell Imaging. Lab. for Optical and Computational Instrumentation; Univ. of Wisconsin, Madison, WI (United States). Dept. of Biomedical Engineering
5. Univ. of Wisconsin, Madison, WI (United States). Center for Quantitative Cell Imaging. Lab. for Optical and Computational Instrumentation; Univ. of Wisconsin, Madison, WI (United States). Dept. of Biomedical Engineering; Univ. of Wisconsin, Madison, WI (United States). Dept. of Physics; Univ. of Wisconsin, Madison, WI (United States). Morgridge Inst. for Research

In the field of fluorescence microscopy, there is continued demand for dynamic technologies that can exploit the complete information from every pixel of an image. One imaging technique with proven ability for yielding additional information from fluorescence imaging is Fluorescence Lifetime Imaging Microscopy (FLIM). FLIM allows for the measurement of how long a fluorophore stays in an excited energy state, and this measurement is affected by changes in its chemical microenvironment, such as proximity to other fluorophores, pH, and hydrophobic regions. This ability to provide information about the microenvironment has made FLIM a powerful tool for cellular imaging studies ranging from metabolic measurement to measuring distances between proteins. The increased use of FLIM has necessitated the development of computational tools for integrating FLIM analysis with image and data processing. To address this need, we have created FLIMJ, an ImageJ plugin and toolkit that allows for easy use and development of extensible image analysis workflows with FLIM data. Built on the FLIMLib decay curve fitting library and the ImageJ Ops framework, FLIMJ offers FLIM fitting routines with seamless integration with many other ImageJ components, and the ability to be extended to create complex FLIM analysis workflows. Building on ImageJ Ops also enables FLIMJ’s routines to be used with Jupyter notebooks and integrate naturally with science-friendly programming in, e.g., Python and Groovy. We show the extensibility of FLIMJ in two analysis scenarios: lifetime-based image segmentation and image colocalization. We also validate the fitting routines by comparing them against industry FLIM analysis standards.

• Research Organization: Univ. of Wisconsin, Madison, WI (United States)
• Sponsoring Organization: Cancer Research UK; National Institutes of Health (NIH); Semiconductor Research Corporation; USDOE Office of Science (SC)
• Grant/Contract Number: SC0019013
• OSTI ID: 1852751
• Journal Information: PLoS ONE, Journal Name: PLoS ONE Journal Issue: 12 Vol. 15; ISSN 1932-6203
• Publisher: Public Library of Science
• Country of Publication: United States
• Language: English

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