We consider the problem of single-molecule identification in super-resolution microscopy. Super-resolution microscopy overcomes the diffraction limit by localizing individual fluorescing molecules in a field of view. This is particularly difficult since each individual molecule appears and disappears randomly across time and because the total number of molecules in the field of view is unknown. Additionally, data sets acquired with super-resolution microscopes can contain a large number of spurious fluorescent fluctuations caused by background noise.
To address these problems, we present a Bayesian nonparametric framework capable of identifying individual emitting molecules in super-resolved time series. We tackle the localization problem in the case in which each individual molecule is already localized in space. First, we collapse observations in time and develop a fast algorithm that builds upon the Dirichlet process. Next, we augment the model to account for the temporal aspect of fluorophore photophysics. Finally, we assess the performance of our methods with ground-truth data sets having known biological structure.
The third author was supported by ONR N00014-17-1-2843, NSF NeuroNex Award DBI-1707398 and The Gatsby Charitable Foundation.
We also wish to acknowledge support from the Army Research Office under contract/grant number W911NF-16-1-0368.
We thank Melike Lakadamyali, Francesca Cella, Jonas Reis and Yiming Li for providing published data sets to validate our methods.
"A Bayesian nonparametric approach to super-resolution single-molecule localization." Ann. Appl. Stat. 15 (4) 1742 - 1766, December 2021. https://doi.org/10.1214/21-AOAS1441