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 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 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 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.

scholarly journals Efficient Cross-Correlation Filtering of One- and Two-Color Single Molecule Localization Microscopy Data

2021 ◽  
Vol 1 ◽  
Author(s):  
Angel Mancebo ◽  
Dushyant Mehra ◽  
Chiranjib Banerjee ◽  
Do-Hyung Kim ◽  
Elias M. Puchner
Keyword(s):  
Single Molecule ◽  
Cross Correlation ◽  
Pair Correlation ◽  
Diffraction Limit ◽  
Optical Diffraction ◽  
Data Sets ◽  
Oligomeric State ◽  
Localization Microscopy ◽  
Cross Correlation Analysis ◽  
Single Molecule Localization Microscopy

Single molecule localization microscopy has become a prominent technique to quantitatively study biological processes below the optical diffraction limit. By fitting the intensity profile of single sparsely activated fluorophores, which are often attached to a specific biomolecule within a cell, the locations of all imaged fluorophores are obtained with ∼20 nm resolution in the form of a coordinate table. While rendered super-resolution images reveal structural features of intracellular structures below the optical diffraction limit, the ability to further analyze the molecular coordinates presents opportunities to gain additional quantitative insights into the spatial distribution of a biomolecule of interest. For instance, pair-correlation or radial distribution functions are employed as a measure of clustering, and cross-correlation analysis reveals the colocalization of two biomolecules in two-color SMLM data. Here, we present an efficient filtering method for SMLM data sets based on pair- or cross-correlation to isolate localizations that are clustered or appear in proximity to a second set of localizations in two-color SMLM data. In this way, clustered or colocalized localizations can be separately rendered and analyzed to compare other molecular properties to the remaining localizations, such as their oligomeric state or mobility in live cell experiments. Current matrix-based cross-correlation analyses of large data sets quickly reach the limitations of computer memory due to the space complexity of constructing the distance matrices. Our approach leverages k-dimensional trees to efficiently perform range searches, which dramatically reduces memory needs and the time for the analysis. We demonstrate the versatile applications of this method with simulated data sets as well as examples of two-color SMLM data. The provided MATLAB code and its description can be integrated into existing localization analysis packages and provides a useful resource to analyze SMLM data with new detail.

scholarly journals FOCAL3D: A 3-dimensional clustering package for single-molecule localization microscopy

2020 ◽  
Vol 16 (12) ◽  
pp. e1008479
Author(s):  
Daniel F. Nino ◽  
Daniel Djayakarsana ◽  
Joshua N. Milstein
Keyword(s):  
Single Molecule ◽  
Clustering Algorithm ◽  
Spatial Clustering ◽  
Cluster Complex ◽  
Nuclear Pore ◽  
Data Sets ◽  
Localization Microscopy ◽  
Biological Dataset ◽  
Open Source Software Package ◽  
Single Molecule Localization Microscopy

Single-molecule localization microscopy (SMLM) is a powerful tool for studying intracellular structure and macromolecular organization at the nanoscale. The increasingly massive pointillistic data sets generated by SMLM require the development of new and highly efficient quantification tools. Here we present FOCAL3D, an accurate, flexible and exceedingly fast (scaling linearly with the number of localizations) density-based algorithm for quantifying spatial clustering in large 3D SMLM data sets. Unlike DBSCAN, which is perhaps the most commonly employed density-based clustering algorithm, an optimum set of parameters for FOCAL3D may be objectively determined. We initially validate the performance of FOCAL3D on simulated datasets at varying noise levels and for a range of cluster sizes. These simulated datasets are used to illustrate the parametric insensitivity of the algorithm, in contrast to DBSCAN, and clustering metrics such as the F1 and Silhouette score indicate that FOCAL3D is highly accurate, even in the presence of significant background noise and mixed populations of variable sized clusters, once optimized. We then apply FOCAL3D to 3D astigmatic dSTORM images of the nuclear pore complex (NPC) in human osteosaracoma cells, illustrating both the validity of the parameter optimization and the ability of the algorithm to accurately cluster complex, heterogeneous 3D clusters in a biological dataset. FOCAL3D is provided as an open source software package written in Python.

scholarly journals Systematic Tuning of Rhodamine Spirocyclization for Super-Resolution Microscopy

2021 ◽  
Author(s):  
Nicolas Lardon ◽  
Lu Wang ◽  
Aline Tschanz ◽  
Philipp Hoess ◽  
Mai Tran ◽  
...  
Keyword(s):  
Single Molecule ◽  
Large Range ◽  
Dynamic Equilibrium ◽  
Super Resolution ◽  
Live Cell ◽  
Stimulated Emission ◽  
Important Class ◽  
Localization Microscopy ◽  
Super Resolution Microscopy ◽  
Single Molecule Localization Microscopy

Rhodamines are the most important class of fluorophores for applications in live-cell fluorescence microscopy. This is mainly because rhodamines exist in a dynamic equilibrium between a fluorescent zwitterion and a non-fluorescent but cell-permeable spirocyclic form. Different imaging applications require different positions of this dynamic equilibrium, which poses a challenge for the design of suitable probes. We describe here how the conversion of the ortho-carboxy moiety of a given rhodamine into substituted acyl benzenesulfonamides and alkylamides permits the systematic tuning of the equilibrium of spirocyclization with unprecedented accuracy and over a large range. This allows to transform the same rhodamine into either a highly fluorogenic and cell-permeable probe for live-cell stimulated emission depletion (STED) microscopy, or into a spontaneously blinking dye for single molecule localization microscopy (SMLM). We used this approach to generate differently colored probes optimized for different labeling systems and imaging applications.

scholarly journals Multiplane and Spectrally-Resolved Single Molecule Localization Microscopy with Industrial Grade CMOS Cameras

2017 ◽  
Author(s):  
Hazen P. Babcock
Keyword(s):  
Open Source ◽  
Single Molecule ◽  
Open Source Software ◽  
Super Resolution ◽  
Localization Microscopy ◽  
Industrial Grade ◽  
Spectrally Resolved ◽  
Imaging Plane ◽  
Single Molecule Localization Microscopy

ABSTRACTIn this work we explore the use of industrial grade CMOS cameras for single molecule localization microscopy (SMLM). We show that the performance of these cameras in single imaging plane SMLM applications is comparable to much more expensive scientific CMOS (sCMOS) cameras. We show that these cameras can be used in more demanding biplane, multiplane and spectrally resolved SMLM applications. The 10-40× reduction in camera cost makes it practical to build SMLM setups with 4 or more cameras. In addition we provide open-source software for simultaneously controlling multiple CMOS cameras and for the reduction of the movies that are acquired to super-resolution images.

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