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Related Experiment Videos

Global analysis of fluorescence lifetime imaging microscopy data.

P J Verveer1, A Squire, P I Bastiaens

  • 1Cell Biophysics Laboratory, Imperial Cancer Research Fund, London WC2A 3PX, England.

Biophysical Journal
|March 29, 2000
PubMed
Summary

New global analysis techniques improve frequency domain fluorescence lifetime imaging microscopy (FLIM). These methods accurately separate fluorescent protein distributions in living cells by analyzing spatially invariant lifetimes.

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Area of Science:

  • Biophysics
  • Microscopy
  • Spectroscopy

Background:

  • Frequency domain fluorescence lifetime imaging microscopy (FLIM) provides insights into molecular environments.
  • Analyzing complex biological samples with FLIM often requires advanced data processing techniques.
  • Prior knowledge of limited, spatially invariant fluorescent species can simplify analysis.

Purpose of the Study:

  • To develop and present global analysis techniques for frequency domain FLIM data.
  • To implement algorithms that exploit the constraint of spatially invariant lifetimes.
  • To enhance the capability of FLIM for resolving multiple fluorescent species in biological samples.

Main Methods:

  • Described two approaches: lifetime invariant fit and a superior global analysis technique.

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  • The global analysis method simultaneously fits fractional contributions and spatially invariant lifetimes across all pixels.
  • Tested algorithms on simulated data before application to real biological samples.

    • Main Results:

    • The global analysis method allows fitting twice the number of lifetimes compared to conventional approaches.
    • It is possible to discern two lifetimes using a single-frequency FLIM setup.
    • Successfully separated cellular distributions of coexpressed green fluorescent proteins in living cells.

    Conclusions:

    • Global analysis techniques offer a powerful approach for analyzing frequency domain FLIM data.
    • These methods improve the resolution and accuracy of lifetime and fractional contribution estimations.
    • The developed algorithms are effective for resolving complex biological structures and processes.