A BAYESIAN NONPARAMETRIC APPROACH TO SUPER-RESOLUTION SINGLE-MOLECULE LOCALIZATION

• 成果类型: Article
• 署名作者: Gabitto, Mariano, I; Marie-Nelly, Herve; Pakman, Ari; Pataki, Andras; Darzacq, Xavier; Jordan, Michael, I
• 署名单位: Simons Foundation; Flatiron Institute; Stanford University; Li Ka Shing Center; University of California System; University of California Berkeley; Columbia University; Columbia University; University of California System; University of California Berkeley
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/21-AOAS1441
• 发表日期: 2021
• 页码: 1742-1766
• 关键词: imagej plug-in; variational inference; protein heterogeneity; cluster-analysis; microscopy; palm; identification; platform

摘要

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.