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

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...

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Autores principales:	Angel Mancebo, Dushyant Mehra, Chiranjib Banerjee, Do-Hyung Kim, Elias M. Puchner
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	single-molecule localization microscopy photo-activated localization microscopy cross-correlation colocalization clustering Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/54541ca85d004bc89772b6570874b3dd
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spelling	oai:doaj.org-article:54541ca85d004bc89772b6570874b3dd2021-11-04T06:42:09ZEfficient Cross-Correlation Filtering of One- and Two-Color Single Molecule Localization Microscopy Data2673-764710.3389/fbinf.2021.739769https://doaj.org/article/54541ca85d004bc89772b6570874b3dd2021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fbinf.2021.739769/fullhttps://doaj.org/toc/2673-7647Single 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.Angel ManceboDushyant MehraDushyant MehraChiranjib BanerjeeDo-Hyung KimElias M. PuchnerFrontiers Media S.A.articlesingle-molecule localization microscopyphoto-activated localization microscopycross-correlationcolocalizationclusteringComputer applications to medicine. Medical informaticsR858-859.7ENFrontiers in Bioinformatics, Vol 1 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	single-molecule localization microscopy photo-activated localization microscopy cross-correlation colocalization clustering Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	single-molecule localization microscopy photo-activated localization microscopy cross-correlation colocalization clustering Computer applications to medicine. Medical informatics R858-859.7 Angel Mancebo Dushyant Mehra Dushyant Mehra Chiranjib Banerjee Do-Hyung Kim Elias M. Puchner Efficient Cross-Correlation Filtering of One- and Two-Color Single Molecule Localization Microscopy Data
description	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.
format	article
author	Angel Mancebo Dushyant Mehra Dushyant Mehra Chiranjib Banerjee Do-Hyung Kim Elias M. Puchner
author_facet	Angel Mancebo Dushyant Mehra Dushyant Mehra Chiranjib Banerjee Do-Hyung Kim Elias M. Puchner
author_sort	Angel Mancebo
title	Efficient Cross-Correlation Filtering of One- and Two-Color Single Molecule Localization Microscopy Data
title_short	Efficient Cross-Correlation Filtering of One- and Two-Color Single Molecule Localization Microscopy Data
title_full	Efficient Cross-Correlation Filtering of One- and Two-Color Single Molecule Localization Microscopy Data
title_fullStr	Efficient Cross-Correlation Filtering of One- and Two-Color Single Molecule Localization Microscopy Data
title_full_unstemmed	Efficient Cross-Correlation Filtering of One- and Two-Color Single Molecule Localization Microscopy Data
title_sort	efficient cross-correlation filtering of one- and two-color single molecule localization microscopy data
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/54541ca85d004bc89772b6570874b3dd
work_keys_str_mv	AT angelmancebo efficientcrosscorrelationfilteringofoneandtwocolorsinglemoleculelocalizationmicroscopydata AT dushyantmehra efficientcrosscorrelationfilteringofoneandtwocolorsinglemoleculelocalizationmicroscopydata AT dushyantmehra efficientcrosscorrelationfilteringofoneandtwocolorsinglemoleculelocalizationmicroscopydata AT chiranjibbanerjee efficientcrosscorrelationfilteringofoneandtwocolorsinglemoleculelocalizationmicroscopydata AT dohyungkim efficientcrosscorrelationfilteringofoneandtwocolorsinglemoleculelocalizationmicroscopydata AT eliasmpuchner efficientcrosscorrelationfilteringofoneandtwocolorsinglemoleculelocalizationmicroscopydata
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Efficient Cross-Correlation Filtering of One- and Two-Color Single Molecule Localization Microscopy Data

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