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Related Experiment Video

Updated: Apr 13, 2026

Visualizing Intracellular SNARE Trafficking by Fluorescence Lifetime Imaging Microscopy

Visualizing Intracellular SNARE Trafficking by Fluorescence Lifetime Imaging Microscopy

Published on: December 29, 2017

Resolving fluorescently labeled species using highly multiplexed spectral FLIM.

Mohamadreza Fazel^1,2,3, Reza Hoseini^1,2, Ayush Saurabh^1,2

¹Center for Biological Physics, Arizona State University, Tempe, AZ, 85287, USA.

Scientific Reports

|April 11, 2026

Summary

This summary is machine-generated.

Bayes-S-FLIM enhances spectral fluorescence lifetime imaging (S-FLIM) by learning spectra and lifetimes simultaneously, even with low photon counts. This method efficiently distinguishes multiple fluorophore species, overcoming limitations of current S-FLIM analyses.

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Last Updated: Apr 13, 2026

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Published on: December 9, 2013

Area of Science:

Biophysics
Optical Imaging
Computational Biology

Background:

Spectral fluorescence lifetime imaging (S-FLIM) combines spectral and lifetime data for multi-fluorophore analysis.
Current S-FLIM methods face challenges with pre-calibration, spectral overlap, and high photon requirements.
Existing techniques struggle to accurately decode complex cellular fluorescence data.

Purpose of the Study:

To develop a novel framework, Bayes-S-FLIM, for efficient S-FLIM data analysis.
To enable simultaneous learning of spectra and lifetimes from photon-by-photon data.
To reduce photon budget requirements and improve discrimination of co-localized fluorophores.

Main Methods:

Developed a Bayesian framework (Bayes-S-FLIM) for photon-by-photon spectral and lifetime analysis.
Integrated simultaneous spectral and lifetime learning within the S-FLIM analysis pipeline.
Validated the framework using both synthetic and experimental S-FLIM data.

Main Results:

Bayes-S-FLIM effectively distinguishes 3 species with as few as 5000 photons, demonstrating high data efficiency.
The framework operates effectively in low photon count regimes, achieving sub-nanosecond lifetime discrimination.
Synthetic data analysis indicates potential to deconvolve up to 9 species with significant spectral overlap.

Conclusions:

Bayes-S-FLIM offers a significant advancement in S-FLIM analysis, overcoming key limitations of existing methods.
The framework provides a more robust and efficient approach for analyzing complex multi-fluorophore environments.
This method has the potential to enhance biological imaging by enabling detailed analysis of spectral and lifetime properties.