Dynamic superresolution by Bayesian nonparametric image processing.

Assessing dynamic processes at single molecule scales is key toward capturing life at the level of its molecular actors. Widefield superresolution methods, such as STORM, PALM, and PAINT, provide nanoscale localization accuracy, even when distances between fluorescently labeled single molecules ("emitters") fall below light's diffraction limit. However, as these superresolution methods rely on rare photophysical events to distinguish emitters from both each other and background, these methods are largely limited to static samples. In contrast, here we leverage spatiotemporal correlations of dynamic widefield imaging data to extend superresolution to simultaneous multiple emitter tracking without relying on photodynamics even as emitter distances fall from one another below their diffraction limit. That is, we simultaneously determine emitter numbers and their tracks (localization and linking) with the same localization resolution per frame as static emitters using widefield superresolution microscopy (≈50 nm). We demonstrate our results for both in vivo data and, for benchmarking purposes, on synthetic data as well. To achieve this, we avoid the existing tracking paradigm relying on completely or partially separating the tasks of emitter number determination, localization of emitters and linking emitter positions across frames. Instead, we employ Bayesian nonparametrics and develop a fully joint posterior distribution over the quantities of interest, including emitter tracks and their total, otherwise unknown, number. Our posterior allows us to estimate emitter numbers alongside their associated tracks, with uncertainty propagated from multiple origins including shot and detector noise, pixelation, and out-of-focus motion.