bioRxiv2022
Fluorescence lifetime imaging (FLIM) has been essential in capturing spatial distributions of chemical species across cellular environments employing pulsed illumination confocal setups. However, quantitative interpretation of lifetime data continues to face critical challenges. For instance, fluorescent species with known in vitro excited state lifetimes may split into multiple species with unique lifetimes when introduced into complex living environments. What is more, mixtures of species, that may be both endogenous and introduced into the sample, may exhibit; 1) very similar lifetimes; as well as 2) wide ranges of lifetimes including lifetimes shorter than the instrumental response function (IRF) or whose duration may be long enough to be comparable to the interpulse window. By contrast, existing methods of analysis are optimized for well-separated and intermediate lifetimes. Here we broaden the applicability of fluorescence lifetime analysis by simultaneously treating unknown mixtures of arbitrary lifetimes- outside the intermediate, goldilocks, zone-for data drawn from a single confocal spot leveraging the tools of Bayesian nonparametrics (BNP). We benchmark our algorithm, termed BNP-lifetime analysis of BNP-LA, using a range of synthetic and experimental data. Moreover, we show that the BNP-LA method can distinguish and deduce lifetimes using photon counts as small as 500.