Correction of multiple-blinking artifacts in photoactivated localization microscopy

Jensen, L.G.; Hoh, Tjun Yee; Williamson, David J.; Griffié, Juliette; Sage, Daniel; Rubin-Delanchy, Patrick; Owen, Dylan M.

doi:10.1038/s41592-022-01463-w

Cited by 23 publications

(16 citation statements)

References 37 publications

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“…To do so, we use a range of biological data from microtubules, CD82, a scaffolding protein found at the plasma membrane, and labeled EGF bound to EGFR (Supplementary Figs. [13][14]. Furthermore, we demonstrate BaGoL's performance using synthetic dSTORM data generated using different emitter separations ranging from 5 to 15 nm (Supplementary Fig.…”

Section: Resultsmentioning

confidence: 89%

“…Next, we show that BaGoL assists examination of biological structures and inspection of the spatial distribution of molecules using dSTORM data, although it does not exhibit much improvement in precisions due to small number of localizations per emitter (Supplementary Figs. [13][14][15]. To do so, we use a range of biological data from microtubules, CD82, a scaffolding protein found at the plasma membrane, and labeled EGF bound to EGFR (Supplementary Figs.…”

Section: Resultsmentioning

confidence: 99%

“…16 ). Although grouping of localizations in PALM data could also yield benefits 14 , and the BaGoL algorithm could be applied to SMLM data from PALM, we did not explore BaGoL performance with real or synthetic PALM data in this manuscript.…”

Section: Discussionmentioning

confidence: 99%

“…The grouping of localizations into emitters makes it possible for downstream analysis such as cluster analysis of the resulting emitter positions (Supplementary Figs. [13][14]. BaGoL is particularly suited for the quantitative analysis of small oligomers, such as dimers, separated by several nanometers (Supplementary Fig.…”

Section: Discussionmentioning

confidence: 99%

“…There have been a few attempts to correct for multiple localizations per emitter, however, these methods are specialized to only dSTORM and PALM 13 , 14 as they rely on photobleaching to enumerate emitters. These methods use maximum likelihood estimation which does not propagate uncertainty from all the existing sources, e.g., uncertainty in the number of emitters and localization precisions, throughout the problem.…”

Section: Introductionmentioning

confidence: 99%

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High-precision estimation of emitter positions using Bayesian grouping of localizations

et al. 2022

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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.

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Section: Resultsmentioning

confidence: 89%

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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High-precision estimation of emitter positions using Bayesian grouping of localizations

et al. 2022

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SuperResNET: Model‐Free Single‐Molecule Network Analysis Software Achieves Molecular Resolution of Nup96

Li,

Khater,

Hallgrimson

et al. 2024

Advanced Intelligent Systems

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SuperResNET is an integrated machine learning‐based analysis software for visualizing and quantifying 3D point cloud data acquired by single‐molecule localization microscopy (SMLM). SuperResNET computational modules include correction for multiple blinking of single fluorophores, denoising, segmentation (clustering), feature extraction used for cluster group identification, modularity analysis, blob retrieval, and visualization in 2D and 3D. Here, a graphical user interface version of SuperResNET was applied to publicly available direct stochastic optical reconstruction microscopy (dSTORM) data of nucleoporin Nup96 and Nup107 labeled nuclear pores that present a highly organized octagon structure of eight corners. SuperResNET effectively segments nuclear pores and Nup96 corners based on differential proximity threshold analysis from 2D and 3D SMLM datasets. SuperResNET quantitatively analyzes features from segmented nuclear pores, including complete structures with eightfold symmetry, and from segmented corners. SuperResNET modularity analysis of segmented corners from 2D SMLM distinguishes two modules at 10.7 ± 0.1 nm distance, corresponding to two individual Nup96 molecules. SuperResNET is therefore a model‐free tool that can reconstruct network architecture and molecular distribution of subcellular structures without the bias of a specified prior model, attaining molecular resolution from dSTORM data. SuperResNET provides flexibility to report on structural diversity in situ within the cell, providing opportunities for biological discovery.

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Quantitative Single‐Molecule Electrochemiluminescence Bioassay

et al. 2023

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A single-molecule electrochemiluminescence bioassay is developed here which allows imaging and direct quantification of single biomolecules. Imaging single biomolecules is realized by localizing the electrochemiluminescence events of the labeled molecules. Such an imaging system allows mapping the spatial distribution of biomolecules with electrochemiluminescence and contains quantitative single-molecule insights. We further quantify biomolecules by spatiotemporally merging the repeated reactions at one molecule site and then counting the clustered molecules. The proposed single-molecule electrochemiluminescence bioassay is used to detect carcinoembryonic antigen, showing a limit of detection of 67 attomole concentration which is 10 000 times better than conventional electrochemiluminescence bioassays. This spatial resolution and sensitivity enable single-molecule electrochemiluminescence bioassay a new toolbox for both specific bioimaging and ultrasensitive quantitative analysis.

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Correction of multiple-blinking artifacts in photoactivated localization microscopy

Cited by 23 publications

References 37 publications

High-precision estimation of emitter positions using Bayesian grouping of localizations

High-precision estimation of emitter positions using Bayesian grouping of localizations

SuperResNET: Model‐Free Single‐Molecule Network Analysis Software Achieves Molecular Resolution of Nup96

Quantitative Single‐Molecule Electrochemiluminescence Bioassay

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