Identifying multiple kinetic populations of DNA binding proteins in live cells using single-molecule fluorescence imaging

Understanding how multi-protein complexes function in cells requires detailed quantitative understanding of their association and dissociation kinetics. Analysis of the heterogeneity of binding lifetimes enables interrogation of the various intermediate states formed during the reaction. Single-molecule fluorescence imaging permits the measurement of reaction kinetics inside living organisms with minimal perturbation. However, poor photo-physical properties of fluorescent probes limit the dynamic range and accuracy of measurements of off rates in live cells. Time-lapse single-molecule fluorescence imaging can partially overcome the limits of photobleaching, however, limitations of this technique remain uncharacterized. Here, we present a structured analysis of which timescales are most accessible using the time-lapse imaging approach and explore uncertainties in determining kinetic sub-populations. We demonstrate the effect of shot noise on the precision of the measurements, as well as the resolution and dynamic range limits that are inherent to the method. Our work provides a convenient implementation to determine theoretical errors from measurements and to support interpretation of experimental data.\n\nSTATEMENT OF SIGNIFICANCEMeasuring lifetimes of interactions between DNA-binding proteins and their substrates is important for understanding how they function in cells. In principle, time-lapse imaging of fluorescently-tagged proteins using single-molecule methods can be used to identify multiple sub-populations of DNA-binding proteins and determine binding lifetimes lasting for several tens of minutes. Despite this potential, currently available guidelines for the selection of binding models are unreliable, and the practical implementation of this approach is limited. Here, using experimental and simulated data we identify the minimum size of the dataset required to resolve multiple populations reliably and measure binding lifetimes with desired accuracy. This work serves to provide a guide to data collection, and measurement of DNA-binding lifetimes from single-molecule time-lapse imaging data.