Application of Superresolution Radial Fluctuation (SRRF) Imaging to Measurement of Single-Molecule DNA Hybridization Kinetics

Abstract

Superresolution radial fluctuations (SRRF) is a novel algorithm based superresolution imaging technique that incorporates radial intensity gradients and temporal intensity fluctuations to calculate and project SRRF images with resolution up to 2x below the diffraction limit in real time. SRRF, has been used for imaging sub-diffraction intracellular structures and for performing time-resolved superresolution measurements of live cell dynamics. However, no application to single-molecule localization and tracking techniques has been shown. Here, we characterize the capabilities of the SRRF algorithm on measuring DNA hybridization kinetics from a DNA capture surface via single-molecule localization and tracking. We find that SRRF data has some obvious advantages such as a substantial increase in the signal-to-background ratio of single-molecule data. This is a side effect of the image radiality transform which yields low radiality values for regions which contain randomized intensity values on the distance scales of the radiality calculation. This is particularly usefully when measuring hybridization kinetics with slow on rates which necessitate higher solution phase concentrations and resulting in higher background levels. We also find that with sparse data such as these, molecule localization can be performed by calculation of the 1st moment centroids of the SRRF point spread functions (PSF) which can deliver spatial resolution similar to non-linear fitting of the optical PSF but with much faster computational times. Finally, we explore the ability for SRRF to resolve individual molecules in high DNA surface density conditions which result in many overlapping optical PSFs. We find that measuring single-molecule data following the SRRF radiality transform allows for resolving much higher densities of molecules than diffraction limited images thus allowing for faster collection of statistically significant population data.