scholarly journals ColiCoords: A Python package for the analysis of bacterial fluorescence microscopy data

2019 ◽  
Author(s):  
Jochem H. Smit ◽  
Yichen Li ◽  
Eliza M. Warszawik ◽  
Andreas Herrmann ◽  
Thorben Cordes

AbstractSingle-molecule fluorescence microscopy studies of bacteria provide unique insights into the mechanisms of cellular processes and protein machineries in ways that are unrivalled by any other technique. With the cost of microscopes dropping and the availability of fully automated microscopes, the volume of microscopy data produced has increased tremendously. These developments have moved the bottleneck of throughput from image acquisition and sample preparation to data analysis. Furthermore, requirements for analysis procedures have become more stringent given the requirement of various journals to make data and analysis procedures available. To address this we have developed a new data analysis package for analysis of fluorescence microscopy data of rod-like cells. Our software ColiCoords structures microscopy data at the single-cell level and implements a coordinate system describing each cell. This allows for the transformation of Cartesian coordinates of both cellular images (e.g. from transmission light or fluorescence microscopy) and single-molecule localization microscopy (SMLM) data to cellular coordinates. Using this transformation, many cells can be combined to increase the statistical significance of fluorescence microscopy datasets of any kind. Coli-Coords is open source, implemented in the programming language Python, and is extensively documented. This allows for modifications for specific needs or to inspect and publish data analysis procedures. By providing a format that allows for easy sharing of code and associated data, we intend to promote open and reproducible research.The source code and documentation can be found via the project’s GitHub page.

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

2020 ◽  
Author(s):  
Magdalena C. Schneider ◽  
Roger Telschow ◽  
Gwenael Mercier ◽  
Montserrat López-Martinez ◽  
Otmar Scherzer ◽  
...  

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.


2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Ruby Peters ◽  
Juliette Griffié ◽  
Garth L. Burn ◽  
David J. Williamson ◽  
Dylan M. Owen

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

2021 ◽  
Author(s):  
Jiachuan Bai ◽  
Wei Ouyang ◽  
Manish Kumar Singh ◽  
Christophe Leterrier ◽  
Paul Barthelemy ◽  
...  

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.


2018 ◽  
Author(s):  
Hazen P. Babcock ◽  
Fang Huang

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.


2016 ◽  
Author(s):  
Hazen P. Babcock ◽  
Xiaowei Zhuang

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.


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