Fluorescence lifetime: Beating the IRF and interpulse window.

Area of Science:

• Biophysics
• Chemical Physics
• Microscopy

Background:

• Fluorescence lifetime imaging (FLIM) is crucial for mapping chemical species in cells.
• Quantitative analysis of FLIM data is challenging due to complex biological environments and overlapping lifetimes.
• Existing methods struggle with mixtures of species exhibiting very similar or extreme lifetimes (short/long).

Purpose of the Study:

• To develop a novel method for analyzing fluorescence lifetime data from complex biological samples.
• To broaden the applicability of fluorescence lifetime analysis beyond intermediate, well-separated lifetimes.
• To enable accurate lifetime determination for unknown mixtures of arbitrary lifetimes.

Main Methods:

• Application of Bayesian nonparametrics (BNP) to analyze fluorescence lifetime data from single confocal spots.
• Development of a new algorithm, termed BNP lifetime analysis, for simultaneous treatment of unknown lifetime mixtures.
• Benchmarking the algorithm using synthetic and experimental fluorescence lifetime imaging data.

Main Results:

• The BNP lifetime analysis method successfully handles unknown mixtures of arbitrary lifetimes, including those outside the typical 'Goldilocks zone'.
• The algorithm demonstrates robustness, accurately distinguishing and deducing lifetimes even with as few as 500 photon counts.
• Successful validation across a range of synthetic and experimental datasets.

Conclusions:

• Bayesian nonparametrics provides a powerful framework for advancing quantitative fluorescence lifetime imaging analysis.
• The developed BNP lifetime analysis method significantly expands the capabilities for interpreting complex FLIM data in cellular environments.
• This approach offers a more versatile and sensitive tool for researchers studying cellular chemistry and dynamics.