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High-precision estimation of emitter positions using Bayesian grouping of localizations
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High-precision estimation of emitter positions using Bayesian grouping of localizations
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Journal Article
High-precision estimation of emitter positions using Bayesian grouping of localizations
2022
Overview
Single-molecule localization microscopy super-resolution methods rely on stochastic blinking/binding events, which often occur multiple times from each emitter over the course of data acquisition. Typically, the blinking/binding events from each emitter are treated as independent events, without an attempt to assign them to a particular emitter. Here, we describe a Bayesian method of inferring the positions of the tagged molecules by exploring the possible grouping and combination of localizations from multiple blinking/binding events. The results are position estimates of the tagged molecules that have improved localization precision and facilitate nanoscale structural insights. The Bayesian framework uses the localization precisions to learn the statistical distribution of the number of blinking/binding events per emitter and infer the number and position of emitters. We demonstrate the method on a range of synthetic data with various emitter densities, DNA origami constructs and biological structures using DNA-PAINT and dSTORM data. We show that under some experimental conditions it is possible to achieve sub-nanometer precision.
Single-molecule localization microscopy relies on stochastic blinking events, treated as independent events without assignment to a particular emitter. Here, BaGoL takes low precision localizations generated from multiple emitter blinkings during DNAPAINT and dSTORM and finds the underlying emitter positions with high precision.
