Robust, Fiducial-Free Drift Correction for Super-resolution Imaging

Wester, Michael J.; Schodt, David W.; Mazloom-Farsibaf, Hanieh; Fazel, Mohamadreza; Pallikkuth, Sandeep; Lidke, Keith A.

doi:10.1101/2021.03.26.437196

Cited by 2 publications

(2 citation statements)

References 34 publications

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“…The frame-connection problem deals with combining these repeated localizations into a single localization with higher precision. To the best of our knowledge, only two solutions to the frame-connection problem are in use: 1) combining any localizations within N frames and d pixels of one another, as is done in the popular ThunderSTORM package ( Ovesný et al, 2014 ) (we’ll refer to this method as the “classical” approach); or 2) by a hypothesis test assuming Gaussian localization noise (“hypothesis test”) ( Wester et al, 2021 ). A modification to the classical approach involves setting the separation threshold d to be some multiple of the localization error, as is done in the PYMEVisualize package ( Marin et al, 2021 ) (referred to as “chaining” in that work and as “revised classical” here).…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Clustering of Repeated Super-Resolution Localizations via Linear Assignment Problem

Schodt

Lidke

2021

Front. Bioinform.

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Many fluorescence super-resolution techniques, such as (d)STORM, PALM, and DNA-PAINT, generate datasets wherein multiple localizations across many camera frames may arise from a single blinking event of an emitter. These repeated localizations not only hinder interpretation and analysis of such datasets, but also represent an incomplete use of the fluorescence photons. Such localizations are typically combined into a single localization either by clustering with hard distance and time thresholds, or by classical hypothesis testing assuming Gaussian localization errors. In this work, we describe a method for clustering that accounts for localization precision, local emitter density estimates, and a kinetic model for blinking which is used to optimize connections within a group of spatiotemporally colocated localizations.

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Section: Introductionmentioning

confidence: 99%

Spatiotemporal Clustering of Repeated Super-Resolution Localizations via Linear Assignment Problem

Schodt

Lidke

2021

Front. Bioinform.

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“…For the examples shown, we find that it is most important to characterize resolution lost on time-scales shorter than the time-scale of drift-correction. Fortunately, numerous methods exist to correct for rigid drift on time-scales relevant to the temporal correlations of many SMLM probes (34)(35)(36)(37)(38)(39)(40)(41), suggesting that the method presented in this report is broadly applicable for a range of experimental conditions.…”

Section: Discussionmentioning

confidence: 99%

A method to estimate the effective point spread function of static single molecule localization microscopy images

Fazekas

Kim

Flanagan-Natoli

et al. 2022

Preprint

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Single molecule localization microscopy (SMLM) permits the visualization of cellular structures an order of magnitude smaller than the diffraction limit of visible light, and an accurate, objective evaluation of the resolution of an SMLM dataset is an essential aspect of the image processing and analysis pipeline. Here we present a simple method that uses the pair autocorrelation function evaluated both in space and time to measure the time-interval dependent effective point spread function of SMLM images of static samples. Using this approach, we demonstrate that experimentally obtained images typically have effective point spread functions that are broader than expected from the localization precision alone, due to additional uncertainty arising from factors such as drift and drift correction algorithms. The method is demonstrated on simulated localizations, DNA origami rulers, and cellular structures labelled by dye-conjugated antibodies, DNA-PAINT, or fluorescent fusion proteins.STATEMENT OF SIGNIFICANCESingle molecule localization microscopy (SMLM) is a class of imaging methods that resolve fluorescently labeled structures beyond the optical resolution limit of visible light. SMLM detects stochastically blinking labels over time, and localizes each blink with precision of order 10 nm. The effective resolution depends on factors such as signal-to-noise ratio, localization algorithm, and several post-processing steps such as stage drift correction. We present a method to evaluate this effective resolution by taking advantage of temporal correlations of fluorophore blinking to separate the distribution of pairs of localizations from the same molecule from those from different molecules. The method is robust on useful timescales for a range of SMLM probes.

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Robust, Fiducial-Free Drift Correction for Super-resolution Imaging

Cited by 2 publications

References 34 publications

Spatiotemporal Clustering of Repeated Super-Resolution Localizations via Linear Assignment Problem

Spatiotemporal Clustering of Repeated Super-Resolution Localizations via Linear Assignment Problem

A method to estimate the effective point spread function of static single molecule localization microscopy images

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