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

• Published in: PloS one Vol. 15; no. 12; p. e0238327
• Main Authors: Gao, Dasong, Barber, Paul R., Chacko, Jenu V., Kader Sagar, Md. Abdul, Rueden, Curtis T., Grislis, Aivar R., Hiner, Mark C., Eliceiri, Kevin W.
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 30.12.2020; Public Library of Science (PLoS)
• Subjects: Algorithms; BASIC BIOLOGICAL SCIENCES; Biologists; Biology; Biomedical engineering; Cancer; Cell division; Chemical compounds; Computer and Information Sciences; Computer applications; Curve fitting; Data analysis; Data processing; Energy transfer; Engineering and Technology; Extensibility; Fluorescence; Fluorescence microscopy; Fluorophores; Head & neck cancer; Hydrophobicity; Image analysis; Image Interpretation, Computer-Assisted - methods; Image processing; Image Processing, Computer-Assisted - methods; Image segmentation; Imaging techniques; Informatics; Laboratories; Libraries; Lifetime; Medicine and Health Sciences; Methods; Microscopy; Microscopy, Fluorescence - methods; Open source software; OTHER INSTRUMENTATION; Physical Sciences; Programming languages; Public software; Research and Analysis Methods; Routines; Science & Technology - Other Topics; Software; Toolkits; United States; United Kingdom > UK; United States > US; Fiji
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0238327

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.