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 Analyzing Single Molecule Localization Microscopy Data Using Cubic Splines

2016 ◽  
Author(s):  
Hazen P. Babcock ◽  
Xiaowei Zhuang
Keyword(s):  
Single Molecule ◽  
Super Resolution ◽  
Computation Time ◽  
Cubic Splines ◽  
Localization Algorithm ◽  
Localization Microscopy ◽  
Analytical Functions ◽  
Open Source Software Package ◽  
Microscopy Data ◽  
Single Molecule Localization Microscopy

AbstractThe resolution of super-resolution microscopy based on single molecule localization is in part determined by the accuracy of the localization algorithm. In most published approaches to date this localization is done by fitting an analytical function that approximates the point spread function (PSF) of the microscope. However, particularly for localization in 3D, analytical functions such as a Gaussian, which are computationally inexpensive, may not accurately capture the PSF shape leading to reduced fitting accuracy. On the other hand, analytical functions that can accurately capture the PSF shape, such as those based on pupil functions, can be computationally expensive. Here we investigate the use of cubic splines as an alternative fitting approach. We demonstrate that cubic splines can capture the shape of any PSF with high accuracy and that they can be used for fitting the PSF with only a 2-3x increase in computation time as compared to Gaussian fitting. We provide an open-source software package that measures the PSF of any microscope and uses the measured PSF to perform 3D single molecule localization microscopy analysis with reasonable accuracy and speed.

scholarly journals Parameter-free rendering of single-molecule localization microscopy data for parameter-free resolution estimation

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Adrien C. Descloux ◽  
Kristin S. Grußmayer ◽  
Aleksandra Radenovic
Keyword(s):  
Single Molecule ◽  
Super Resolution ◽  
Free Resolution ◽  
Temporal Separation ◽  
Localization Microscopy ◽  
Resolution Imaging ◽  
Localization Precision ◽  
Microscopy Data ◽  
Resolution Estimation ◽  
Single Molecule Localization Microscopy

AbstractLocalization microscopy is a super-resolution imaging technique that relies on the spatial and temporal separation of blinking fluorescent emitters. These blinking events can be individually localized with a precision significantly smaller than the classical diffraction limit. This sub-diffraction localization precision is theoretically bounded by the number of photons emitted per molecule and by the sensor noise. These parameters can be estimated from the raw images. Alternatively, the resolution can be estimated from a rendered image of the localizations. Here, we show how the rendering of localization datasets can influence the resolution estimation based on decorrelation analysis. We demonstrate that a modified histogram rendering, termed bilinear histogram, circumvents the biases introduced by Gaussian or standard histogram rendering. We propose a parameter-free processing pipeline and show that the resolution estimation becomes a function of the localization density and the localization precision, on both simulated and state-of-the-art experimental datasets.

scholarly journals vLUME: 3D Virtual Reality for Single-molecule Localization Microscopy

2020 ◽  
Author(s):  
Alexander Spark ◽  
Alexandre Kitching ◽  
Daniel Esteban-Ferrer ◽  
Anoushka Handa ◽  
Alexander R. Carr ◽  
...  
Keyword(s):  
Virtual Reality ◽  
Single Molecule ◽  
3D Visualization ◽  
Super Resolution ◽  
Rapid Identification ◽  
Data Sets ◽  
Localization Microscopy ◽  
Cloud Data ◽  
Global Quality ◽  
Single Molecule Localization Microscopy

AbstractSuper-Resolution (SR) Microscopy based on 3D Single-Molecule Localization Microscopy (SMLM) is now well established1,2 and its wide-spread adoption has led to the development of more than 36 software packages, dedicated to quantitative evaluation of the spatial and temporal detection of fluorophore photoswitching3. While the initial emphasis in the 3D SMLM field has clearly been on improving resolution and data quality, there is now a marked absence of 3D visualization approaches that enable the straightforward, high-fidelity exploration of this type of data. Inspired by the horological phosphorescence points that illuminate watch-faces in the dark, we present vLUME (Visualization of the Universe in a Micro Environment, pronounced ‘volume’) a free-for-academic-use immersive virtual reality-based (VR) visualization software package purposefully designed to render large 3D-SMLM data sets. vLUME enables robust visualization, segmentation and quantification of millions of fluorescence puncta from any 3D SMLM technique. vLUME has an intuitive user-interface and is compatible with all commercial VR hardware (Oculus Rift/Quest and HTC Vive, Supplementary Video 1). vLUME accelerates the analysis of highly complex 3D point-cloud data and the rapid identification of defects that are otherwise neglected in global quality metrics.

scholarly journals Quantitative super-resolution single molecule microscopy dataset of YFP-tagged growth factor receptors

2018 ◽  
Author(s):  
Tomáš Lukeš ◽  
Jakub Pospíšil ◽  
Karel Fliegel ◽  
Theo Lasser ◽  
Guy M. Hagen
Keyword(s):  
Data Analysis ◽  
Growth Factor ◽  
Single Molecule ◽  
Super Resolution ◽  
Growth Factor Receptors ◽  
Data Sets ◽  
Localization Microscopy ◽  
Super Resolution Microscopy ◽  
Resolution Single ◽  
Single Molecule Localization Microscopy

BackgroundSuper-resolution single molecule localization microscopy (SMLM) is a method for achieving resolution beyond the classical limit in optical microscopes (approx. 200 nm laterally). Yellow fluorescent protein (YFP) has been used for super-resolution single molecule localization microscopy, but less frequently than other fluorescent probes. Working with YFP in SMLM is a challenge because a lower number of photons are emitted per molecule compared to organic dyes which are more commonly used. Publically available experimental data can facilitate development of new data analysis algorithms.FindingsFour complete, freely available single molecule super-resolution microscopy datasets on YFP-tagged growth factor receptors expressed in a human cell line are presented including both raw and analyzed data. We report methods for sample preparation, for data acquisition, and for data analysis, as well as examples of the acquired images. We also analyzed the SMLM data sets using a different method: super-resolution optical fluctuation imaging (SOFI). The two modes of analysis offer complementary information about the sample. A fifth single molecule super-resolution microscopy dataset acquired with the dye Alexa 532 is included for comparison purposes.ConclusionThis dataset has potential for extensive reuse. Complete raw data from SMLM experiments has typically not been published. The YFP data exhibits low signal to noise ratios, making data analysis a challenge. These data sets will be useful to investigators developing their own algorithms for SMLM, SOFI, and related methods. The data will also be useful for researchers investigating growth factor receptors such as ErbB3.

scholarly journals Super-resolution fight club: assessment of 2D and 3D single-molecule localization microscopy software

2019 ◽  
Vol 16 (5) ◽  
pp. 387-395 ◽  
Author(s):  
Daniel Sage ◽  
Thanh-An Pham ◽  
Hazen Babcock ◽  
Tomas Lukes ◽  
Thomas Pengo ◽  
...  
Keyword(s):  
Single Molecule ◽  
Super Resolution ◽  
Localization Microscopy ◽  
Fight Club ◽  
2D And 3D ◽  
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 Super-resolution fight club: A broad assessment of 2D & 3D single-molecule localization microscopy software

2018 ◽  
Author(s):  
Daniel Sage ◽  
Thanh-An Pham ◽  
Hazen Babcock ◽  
Tomas Lukes ◽  
Thomas Pengo ◽  
...  
Keyword(s):  
Single Molecule ◽  
Double Helix ◽  
Super Resolution ◽  
Large Set ◽  
Holistic View ◽  
Localization Microscopy ◽  
Fight Club ◽  
Community Effort ◽  
2D And 3D ◽  
Single Molecule Localization Microscopy

ABSTRACTWith the widespread uptake of 2D and 3D single molecule localization microscopy, a large set of different data analysis packages have been developed to generate super-resolution images. To guide researchers on the optimal analytical software for their experiments, we have designed, in a large community effort, a competition to extensively characterise and rank these options. We generated realistic simulated datasets for popular imaging modalities – 2D, astigmatic 3D, biplane 3D, and double helix 3D – and evaluated 36 participant packages against these data. This provides the first broad assessment of 3D single molecule localization microscopy software, provides a holistic view of how the latest 2D and 3D single molecule localization software perform in realistic conditions, and ultimately provides insight into the current limits of the field.

scholarly journals Molecular resolution imaging by post-labeling expansion single-molecule localization microscopy (Ex-SMLM)

2020 ◽  
Author(s):  
Fabian U. Zwettler ◽  
Sebastian Reinhard ◽  
Davide Gambarotto ◽  
Toby D. M. Bell ◽  
Virginie Hamel ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Single Molecule ◽  
Super Resolution ◽  
Reference Structure ◽  
Positional Error ◽  
Localization Microscopy ◽  
Resolution Imaging ◽  
Endogenous Proteins ◽  
Super Resolution Microscopy ◽  
Single Molecule Localization Microscopy

AbstractExpansion microscopy (ExM) enables super-resolution fluorescence imaging of physically expanded biological samples with conventional microscopes. By combining expansion microscopy (ExM) with single-molecule localization microscopy (SMLM) it is potentially possible to approach the resolution of electron microscopy. However, current attempts to combine both methods remained challenging because of protein and fluorophore loss during digestion or denaturation, gelation, and the incompatibility of expanded polyelectrolyte hydrogels with photoswitching buffers. Here we show that re-embedding of expanded hydrogels enables dSTORM imaging of expanded samples and demonstrate that post-labeling ExM resolves the current limitations of super-resolution microscopy. Using microtubules as a reference structure and centrioles, we demonstrate that post-labeling Ex-SMLM preserves ultrastructural details, improves the labeling efficiency and reduces the positional error arising from linking fluorophores into the gel thus paving the way for super-resolution imaging of immunolabeled endogenous proteins with true molecular resolution.

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