scholarly journals Bayesian cluster identification in single-molecule localization microscopy data

2015 ◽  
Vol 12 (11) ◽  
pp. 1072-1076 ◽  
Author(s):  
Patrick Rubin-Delanchy ◽  
Garth L Burn ◽  
Juliette Griffié ◽  
David J Williamson ◽  
Nicholas A Heard ◽  
...  
Keyword(s):  
Single Molecule ◽  
Localization Microscopy ◽  
Cluster Identification ◽  
Microscopy Data ◽  
Bayesian Cluster ◽  
Single Molecule Localization Microscopy

scholarly journals A Bayesian cluster analysis method for single-molecule localization microscopy data

2016 ◽  
Vol 11 (12) ◽  
pp. 2499-2514 ◽  
Author(s):  
Juliette Griffié ◽  
Michael Shannon ◽  
Claire L Bromley ◽  
Lies Boelen ◽  
Garth L Burn ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Single Molecule ◽  
Analysis Method ◽  
Localization Microscopy ◽  
Bayesian Cluster Analysis ◽  
Cluster Analysis Method ◽  
Microscopy Data ◽  
Bayesian Cluster ◽  
Single Molecule Localization Microscopy

scholarly journals SMoLR: visualization and analysis of single-molecule localization microscopy data in R

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Maarten W. Paul ◽  
H. Martijn de Gruiter ◽  
Zhanmin Lin ◽  
Willy M. Baarends ◽  
Wiggert A. van Cappellen ◽  
...  
Keyword(s):  
Single Molecule ◽  
Localization Microscopy ◽  
Microscopy Data ◽  
Single Molecule Localization Microscopy

scholarly journals A workflow for sizing oligomeric biomolecules based on cryo single molecule localization microscopy

2020 ◽  
Author(s):  
Magdalena C. Schneider ◽  
Roger Telschow ◽  
Gwenael Mercier ◽  
Montserrat López-Martinez ◽  
Otmar Scherzer ◽  
...  
Keyword(s):  
Single Molecule ◽  
High Reliability ◽  
Limiting Factor ◽  
Dye Molecules ◽  
New Approach ◽  
Localization Microscopy ◽  
Additional Information ◽  
Localization Precision ◽  
Microscopy Data ◽  
Single Molecule Localization Microscopy

ABSTRACTSingle molecule localization microscopy (SMLM) has enormous potential for resolving subcellular structures below the diffraction limit of light microscopy: Localization precision in the low digit nanometer regime has been shown to be achievable. In order to record localization microscopy data, however, sample fixation is inevitable to prevent molecular motion during the rather long recording times of minutes up to hours. Eventually, it turns out that preservation of the sample’s ultrastructure during fixation becomes the limiting factor. We propose here a workflow for data analysis, which is based on SMLM performed at cryogenic temperatures. Since molecular dipoles of the fluorophores are fixed at low temperatures, such an approach offers the possibility to use the orientation of the dipole as an additional information for image analysis. In particular, assignment of localizations to individual dye molecules becomes possible with high reliability. We quantitatively characterized the new approach based on the analysis of simulated oligomeric structures. Side lengths can be determined with a relative error of less than 1% for tetramers with a nominal side length of 5 nm, even if the assumed localization precision for single molecules is more than 2 nm.

scholarly journals Quantitative fibre analysis of single-molecule localization microscopy data

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Ruby Peters ◽  
Juliette Griffié ◽  
Garth L. Burn ◽  
David J. Williamson ◽  
Dylan M. Owen
Keyword(s):  
Single Molecule ◽  
Localization Microscopy ◽  
Microscopy Data ◽  
Fibre Analysis ◽  
Single Molecule Localization Microscopy

Coordinate-based colocalization analysis of single-molecule localization microscopy data

2011 ◽  
Vol 137 (1) ◽  
pp. 1-10 ◽  
Author(s):  
Sebastian Malkusch ◽  
Ulrike Endesfelder ◽  
Justine Mondry ◽  
Márton Gelléri ◽  
Peter J. Verveer ◽  
...  
Keyword(s):  
Single Molecule ◽  
Localization Microscopy ◽  
Microscopy Data ◽  
Single Molecule Localization Microscopy

scholarly journals ShareLoc – an open platform for sharing localization microscopy data

2021 ◽  
Author(s):  
Jiachuan Bai ◽  
Wei Ouyang ◽  
Manish Kumar Singh ◽  
Christophe Leterrier ◽  
Paul Barthelemy ◽  
...  
Keyword(s):  
Single Molecule ◽  
Super Resolution ◽  
Data Sets ◽  
Localization Microscopy ◽  
Open Platform ◽  
Machine Learning Methods ◽  
Microscopy Data ◽  
Analytical Tools ◽  
Existing Data ◽  
Single Molecule Localization Microscopy

Novel insights and more powerful analytical tools can emerge from the reanalysis of existing data sets, especially via machine learning methods. Despite the widespread use of single molecule localization microscopy (SMLM) for super-resolution bioimaging, the underlying data are often not publicly accessible. We developed ShareLoc (https://shareloc.xyz), an open platform designed to enable sharing, easy visualization and reanalysis of SMLM data. We discuss its features and show how data sharing can improve the performance and robustness of SMLM image reconstruction by deep learning.

scholarly journals Pixel Quantum Efficiency Differences and Variance Stabilization for sCMOS Single Molecule Localization Microscopy Data Analysis

2018 ◽  
Author(s):  
Hazen P. Babcock ◽  
Fang Huang
Keyword(s):  
Quantum Efficiency ◽  
Single Molecule ◽  
Distributed Data ◽  
Localization Microscopy ◽  
Variance Stabilization ◽  
Relative Quantum ◽  
Microscopy Data ◽  
Cramer Rao Bound ◽  
Single Pixel ◽  
Single Molecule Localization Microscopy

ABSTRACTOptimal analysis of single molecule localization microscopy (SMLM) data acquired with a CMOS camera requires compensation for single pixel differences in gain, offset and readout noise. For some CMOS cameras we found that it is also necessary to compensate for pixel differences in sensitivity or relative quantum efficiency (RQE). We present the modifications to the original sCMOS analysis algorithm necessary to correct for these RQE differences. We also discuss the use of the Anscombe transform (AT) for variance stabilization. Removing the variance dependence on the mean allows simpler least squares fitting approaches to achieve the Cramer-Rao bound on the mixed Poisson and Gaussian distributed data typically acquired with an sCMOS camera.

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