Signal-Pair Correlation Analysis of Single-Molecule Trajectories

2011 ◽  
Vol 50 (52) ◽  
pp. 12643-12646 ◽  
Author(s):  
Armin Hoffmann ◽  
Michael T. Woodside
Keyword(s):  
Correlation Analysis ◽  
Single Molecule ◽  
Pair Correlation

scholarly journals Pair Correlation Analysis Maps the Dynamic Two-Dimensional Organization of Natural Killer Cell Receptors at the Synapse

ACS Nano ◽  
2019 ◽  
Vol 13 (12) ◽  
pp. 14274-14282 ◽  
Author(s):  
Per Niklas Hedde ◽  
Elina Staaf ◽  
Sunitha Bagawath Singh ◽  
Sofia Johansson ◽  
Enrico Gratton
Keyword(s):  
Natural Killer Cell ◽  
Correlation Analysis ◽  
Natural Killer ◽  
Pair Correlation ◽  
Killer Cell ◽  
Two Dimensional ◽  
Cell Receptors ◽  
Natural Killer Cell Receptors

scholarly journals The Impact of Nuclear Architecture on EGFP Diffusion Revealed by Pair Correlation Analysis

2011 ◽  
Vol 100 (3) ◽  
pp. 67a ◽  
Author(s):  
Elizabeth Hinde ◽  
Francesco Cardarelli ◽  
Michelle A. Digman ◽  
Enrico Gratton
Keyword(s):  
Correlation Analysis ◽  
Pair Correlation ◽  
Nuclear Architecture ◽  
The Impact

scholarly journals Correlation analysis framework for localization-based superresolution microscopy

2018 ◽  
Vol 115 (13) ◽  
pp. 3219-3224 ◽  
Author(s):  
Joerg Schnitzbauer ◽  
Yina Wang ◽  
Shijie Zhao ◽  
Matthew Bakalar ◽  
Tulip Nuwal ◽  
...  
Keyword(s):  
Correlation Analysis ◽  
Single Molecule ◽  
Quantitative Description ◽  
Superior Performance ◽  
Cellular Structures ◽  
Analysis Framework ◽  
Biophysical Parameters ◽  
Superresolution Microscopy ◽  
Analysis Platform ◽  
Multiple Scenarios

Superresolution images reconstructed from single-molecule localizations can reveal cellular structures close to the macromolecular scale and are now being used routinely in many biomedical research applications. However, because of their coordinate-based representation, a widely applicable and unified analysis platform that can extract a quantitative description and biophysical parameters from these images is yet to be established. Here, we propose a conceptual framework for correlation analysis of coordinate-based superresolution images using distance histograms. We demonstrate the application of this concept in multiple scenarios, including image alignment, tracking of diffusing molecules, as well as for quantification of colocalization, showing its superior performance over existing approaches.

scholarly journals Chromatin Dynamics during DNA Repair Revealed by Pair Correlation Analysis of Molecular Flow in the Nucleus

2014 ◽  
Vol 107 (1) ◽  
pp. 55-65 ◽  
Author(s):  
Elizabeth Hinde ◽  
Xiangduo Kong ◽  
Kyoko Yokomori ◽  
Enrico Gratton
Keyword(s):  
Dna Repair ◽  
Correlation Analysis ◽  
Pair Correlation ◽  
Molecular Flow ◽  
Chromatin Dynamics

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.

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