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Related Experiment Video

Updated: Jul 9, 2026

Visualizing Intracellular SNARE Trafficking by Fluorescence Lifetime Imaging Microscopy
08:55

Visualizing Intracellular SNARE Trafficking by Fluorescence Lifetime Imaging Microscopy

Published on: December 29, 2017

FLIMExplorer: interactive GUI for object-based visualization and analysis of fluorescence lifetime images.

Blanche Ter Hofstede1, Samantha Morganti1, Daniela De Hoyos Canales1

  • 1Biomedical Engineering, Texas A&M University, College Station, TX, United States of America.

Jphys Photonics
|July 8, 2026
PubMed
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This summary is machine-generated.

FLIMExplorer simplifies cellular metabolism analysis by integrating single-cell fluorescence lifetime imaging microscopy (FLIM) data. This tool connects quantitative metabolic measurements with their image context for improved quality control and analysis.

Area of Science:

  • Biophysics
  • Cellular Metabolism
  • Microscopy

Background:

  • Fluorescence lifetime imaging microscopy (FLIM) offers non-invasive, high-resolution cellular metabolism analysis.
  • Current FLIM workflows are complex, fragmented, and lack integration between quantitative data and image context, hindering quality control.

Purpose of the Study:

  • To introduce FLIMExplorer, an interactive Python-based tool for streamlined single-cell FLIM data analysis and visualization.
  • To bridge the gap between quantitative FLIM measurements and their corresponding image data, enhancing transparency and quality control.

Main Methods:

  • FLIMExplorer processes pixel-level FLIM outputs or pre-processed cell-average datasets.
  • It integrates quantitative FLIM endpoints (e.g., NAD(P)H and FAD lifetimes and amplitudes) with image visualization.
Keywords:
cellular heterogeneityfluorescence lifetime imaging microscopyimage processingmultiphoton microscopysingle cell image analysisstatistical analysis

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Last Updated: Jul 9, 2026

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  • The tool provides a graphical user interface for exploring single-cell features and performing statistical comparisons.

    • Main Results:

    • FLIMExplorer links quantitative FLIM data points to their specific image objects.
    • Users can visualize and analyze key FLIM features at the single-cell level within a unified platform.
    • The tool facilitates statistical comparisons across experimental groups, improving data interpretation.

    Conclusions:

    • FLIMExplorer enhances downstream FLIM analysis by integrating visualization, quality control, and statistical analysis.
    • It provides a more transparent and context-aware approach to single-cell metabolic profiling using FLIM.
    • This tool improves the interpretability and reproducibility of FLIM-based cellular metabolism studies.