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

2019 ◽  
Author(s):  
D. Nino ◽  
D. Djayakarsana ◽  
J. N. Milstein
Keyword(s):  
Single Molecule ◽  
Software Package ◽  
Clustering Algorithm ◽  
Image Resolution ◽  
Nuclear Pore ◽  
Localization Microscopy ◽  
3 Dimensional ◽  
Highly Efficient ◽  
Human Osteosarcoma Cells ◽  
Single Molecule Localization Microscopy

Single-molecule localization microscopy (SMLM) yields an image resolution 1-2 orders of magnitude below that of conventional light microscopy, resolving fine details on intracellular structure and macromolecular organization. The massive pointillistic data sets generated by SMLM require the development of new and highly efficient quantification tools. Density based clustering algorithms, such as DBSCAN, can provide spatial statistics on protein/nucleic acid aggregation or dispersion while explicitly identifying macromolecular clusters. The performance of DBSCAN, however, is typically dependent upon an arbitrary, or at least highly subjective, parametric tuning of the algorithm. Moreover, DBSCAN can be computationally expensive, which makes it arduous to evaluate on large image stacks. This is all the more important in 3-dimensions where there exist limited alternatives for quantifying clustering in SMLM data, and where a 2-dimensional analysis of true 3-dimensional data may give rise to image artefacts. We have developed an open-source software package in Python for both identifying and quantifying spatial clustering in 3-dimensional SMLM datasets. FOCAL3D is an extension of our previously developed, 2-dimensional, grid based clustering algorithm FOCAL. FOCAL3D provides a highly efficient way to spatially cluster SMLM datasets, scaling linearly with the number of localizations, and the algorithmic parameters may be systematically optimized so that the resulting analysis is insensitive to variation over a range of parameter choices. We initially validate the performance and parametric insensitivity of FOCAL3D on simulated datasets, then apply the algorithm to 3-dimensional, astigmatic dSTORM images of the nuclear pore complex in human osteosarcoma cells.The data and software package are available at: http://www.utm.utoronto.ca/milsteinlab/software/

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 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 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 3D particle averaging and detection of macromolecular symmetry in localization microscopy

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Hamidreza Heydarian ◽  
Maarten Joosten ◽  
Adrian Przybylski ◽  
Florian Schueder ◽  
Ralf Jungmann ◽  
...  
Keyword(s):  
Single Molecule ◽  
Signal To Noise Ratio ◽  
Fluorescent Labeling ◽  
Dna Origami ◽  
Particle Analysis ◽  
Nuclear Pore ◽  
Localization Microscopy ◽  
Structural Template ◽  
Microscopy Techniques ◽  
Single Molecule Localization Microscopy

AbstractSingle molecule localization microscopy offers in principle resolution down to the molecular level, but in practice this is limited primarily by incomplete fluorescent labeling of the structure. This missing information can be completed by merging information from many structurally identical particles. In this work, we present an approach for 3D single particle analysis in localization microscopy which hugely increases signal-to-noise ratio and resolution and enables determining the symmetry groups of macromolecular complexes. Our method does not require a structural template, and handles anisotropic localization uncertainties. We demonstrate 3D reconstructions of DNA-origami tetrahedrons, Nup96 and Nup107 subcomplexes of the nuclear pore complex acquired using multiple single molecule localization microscopy techniques, with their structural symmetry deducted from the data.

scholarly journals A spectral demixing method for high-precision multi-color localization microscopy

2021 ◽  
Author(s):  
Leonid Andronov ◽  
Rachel Genthial ◽  
Didier Hentsch ◽  
Bruno P Klaholz
Keyword(s):  
Single Molecule ◽  
Nuclear Pore ◽  
Drift Correction ◽  
General Applicability ◽  
Localization Microscopy ◽  
Simultaneous Imaging ◽  
Chromatic Aberrations ◽  
Localization Precision ◽  
The Individual ◽  
Single Molecule Localization Microscopy

Single molecule localization microscopy (SMLM) with a dichroic image splitter can provide invaluable multi-color information regarding colocalization of individual molecules, but it often suffers from technical limitations. So far, demixing algorithms give suboptimal results in terms of localization precision and correction of chromatic aberrations. Here we present an image splitter based multi-color SMLM method (splitSMLM) that offers much improved localization precision & drift correction, compensation of chromatic aberrations, and optimized performance of fluorophores in a specific buffer to equalize their reactivation rates for simultaneous imaging. A novel spectral demixing algorithm, SplitViSu, fully preserves localization precision with essentially no data loss and corrects chromatic aberrations at the nanometer scale. Multi-color performance is further improved by using optimized fluorophore and filter combinations. Applied to three-color imaging of the nuclear pore complex (NPC), this method provides a refined positioning of the individual NPC proteins and reveals that Pom121 clusters act as NPC deposition loci, hence illustrating strength and general applicability of the method.

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