Studying and describing the dynamic nature of cellular processes is challenging. Fluorescence image spectroscopy is explicitly well suited to answer questions on localization, function, interaction, and structure in living cells. Molecular questions are most precise and accurately answered by models adapted to the underlying questions and data of high quality. We introduce a workflow for time-resolved image spectroscopy fluorescence that maximizes the data qualityfrom FLIM and Multiparameter Image Spectroscopy (MFIS) data through noise reduction by pixel selective averaging and pixel classification steps enabling data qualities in cells similar to in vitro experiments. Moreover, we introduce a set of models that optimize the analysis of time-resolved fluorescent protein FRET data. We validate the models in silico and experimentally by eGFP-mCherry fusion proteins fused via
variable linker sequences in different cellular compartments. Our workflow applied to protein complexes, such as a receptor-like-kinase membrane complex in planta , as well as the immune-responsive Guanylate-Binding Proteins and the receptor CD95 in mammals, unveils interaction affinities, distances, complex stoichiometry, and domain orientations.