Upgrading time domain FLIM using an adaptive Monte Carlo data inflation algorithm.

ABSTRACT

Fluorescence Lifetime Imaging Microscopy (FLIM) is a powerful technique to investigate the local environment of fluorophores in living cells. To correctly estimate all lifetime parameters, time domain FLIM imaging requires a high number of photons and consequently long laser exposure times. This is an issue because long exposure times are incompatible with the observation of dynamic molecular events and induce cellular stress. To minimize exposure time, we have developed an original approach that statistically inflates the number of collected photons. Our approach, called Adaptive Monte Carlo Data Inflation (AMDI), combines the well-known bootstrap technique with an adaptive Parzen kernel. We here demonstrate using both Monte Carlo simulations and live cells that our robust method accurately estimate fluorescence lifetimes with exposure time reduced up to 50 times for monoexponential decays (corresponding to a minimum of 20 photons/pixel), and 10 times for biexponential decays (corresponding to a minimum of 5,000 photons/pixel), compared to standard fitting method. Thanks to AMDI, in Förster resonance energy transfer experiments, it is possible to estimate all fitting parameters accurately without constraining any parameters. By reducing the commonly used spatial binning factor, our technique also improves the spatial resolution of FLIM images.