Global and pixel kinetic data analysis for FRET detection by multi-photon time-domain FLIM

  • Barber P
  • Ameer-Beg S
  • Gilbey J
 et al. 
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FLIM/FRET is an extremely powerful technique that can microscopically locate nanometre-scale protein-protein interactions within live or fixed cells, both in vitro and in vivo. The key to performing sensitive FRET, via FLIM, besides the use of appropriate fluorophores, is the analysis of the time-resolved data present at each image pixel. The fluorescent transient will, in general, exhibit multi-exponential kinetics: at least two exponential components arise from both the interacting and non-interacting protein. We shall describe a novel method and computer program for the global analysis of time resolved data, either at the single level or through global analysis of grouped pixel data. Kinetic models are fitted using the Marquardt algorithm and iterative convolution of the excitation signal, in a computationally-efficient manner. The fitting accuracy and sensitivity of the algorithm has been tested using modelled data, including the addition of simulated Poisson noise and repetitive excitation pulses which are typical of a TCSPC system. We found that the increased signal to noise ratio offered by both global and invariance fitting is highly desirable. When fitting mono-exponential data, the effects of a ca.12.5 ns (ca. 80 MHz) repetitive excitation do not preclude the accurate extraction of populations with lifetimes in the range 0.1 to 10 ns, even when these effects are not represented in the fitting algorithm. Indeed, with global or invariance fitting of a 32x32 pixel area, the error in extracted lifetime can be lower than 0.4 % for signals with a peak of 500 photon counts or more. In FRET simulations, modelling GFP with a non-interacting lifetime of 2.15 ns, it was possible to accurately detect a 10 % interacting population with a lifetime of 0.8 ns.

Author-supplied keywords

  • flim
  • fret
  • global analysis
  • kinetic fitting
  • tcspc
  • time-resolved microscopy

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  • Paul R Barber

  • Simon M Ameer-Beg

  • Julian D. Gilbey

  • Richard J Edens

  • Ifeome Ezike

  • Borivoj Vojnovic

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