Improving time domain fluorescence lifetime imaging with an adaptive Monte Carlo data inflation (AMDI) algorithm

Abstract

Fluorescence Lifetime Imaging Microscopy (FLIM) is a powerful technique which gives access to the local environment of fluorophores in living cells. However, to correctly estimate all lifetime parameters, time domain FLIM imaging requires a high number of photons and consequently a long laser exposure time which is not compatible with the observation of dynamic molecular events and which induces cellular stress phenomena. For reducing this exposure time, we have developed an original approach to statistically inflate the number of collected photon. This approach called Adaptive Monte Carlo Data Inflation (AMDI) combines the well-known bootstrap technique with an adaptive Parzen kernel. We have evaluated its potential on experimental FLIM data in vivo. We have demonstrated that our robust method allows estimating precisely fluorescence lifetime with exposure time reduced up to 50 times for mono-exponential (corresponding to a minimum of 20 photons/pixel) and 10 times for bi-exponential decays (corresponding to a minimum of 5000 photons/pixel) in comparison with the standard fitting method. Furthermore, thanks to AMDI, we demonstrate that it becomes possible to estimate accurately all fitting parameters in FRET experiments without constraining any parameter. An additional benefit of our technique is that it improves the spatial resolution of the FLIM images by reducing the commonly used spatial binning factor.