Independent component analysis disentangles fluorescence signals from diffusing single molecules
Multiparameter measurements offer new possibilities of single-molecule fluorescence experiments for detecting and quantifying heterogeneity in freely diffusing molecules. However, data analysis remains challenging due to limited photon rates from individual molecules. Here, we present independent fluorescence component analysis (IFCA), a universal analytical framework that applies independent component analysis (ICA) to unmix multiparameter fluorescence signals. In this work, we developed an efficient algorithm of ICA for multiparameter photon data based on third-order cumulant tensor decomposition. To validate its performance, we applied IFCA to a static mixture of fluorescent dyes and found that IFCA enables robust separation of signals from five species in a nanomolar mixture solution with a 5 {micro}s binning time. We further analyzed a photon dataset from a FRET-labeled DNA construct using IFCA, demonstrating its capability to quantitatively characterize signals from subpopulations exchanging on a sub-millisecond time scale in a model-independent manner. These findings highlight the high ability of IFCA for quantitative analysis of complex molecular systems exploiting these advantages.