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An expectation–maximization approach to quantifying protein stoichiometry with single-molecule imaging

MOTIVATION: Single-molecule localization microscopy (SMLM) is a super-resolution technique capable of rendering nanometer scale images of cellular structures. Recently, much effort has gone into developing algorithms for extracting quantitative features from SMLM datasets, such as the abundance and...

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Detalles Bibliográficos
Autores principales: Boonkird, Artittaya, Nino, Daniel F, Milstein, Joshua N
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9710618/
https://www.ncbi.nlm.nih.gov/pubmed/36700088
http://dx.doi.org/10.1093/bioadv/vbab032
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author Boonkird, Artittaya
Nino, Daniel F
Milstein, Joshua N
author_facet Boonkird, Artittaya
Nino, Daniel F
Milstein, Joshua N
author_sort Boonkird, Artittaya
collection PubMed
description MOTIVATION: Single-molecule localization microscopy (SMLM) is a super-resolution technique capable of rendering nanometer scale images of cellular structures. Recently, much effort has gone into developing algorithms for extracting quantitative features from SMLM datasets, such as the abundance and stoichiometry of macromolecular complexes. These algorithms often require knowledge of the complicated photophysical properties of photoswitchable fluorophores. RESULTS: Here, we develop a calibration-free approach to quantitative SMLM built upon the observation that most photoswitchable fluorophores emit a geometrically distributed number of blinks before photobleaching. From a statistical model of a mixture of monomers, dimers and trimers, the method employs an adapted expectation–maximization algorithm to learn the protomer fractions while simultaneously determining the single-fluorophore blinking distribution. To illustrate the utility of our approach, we benchmark it on both simulated datasets and experimental datasets assembled from SMLM images of fluorescently labeled DNA nanostructures. AVAILABILITY AND IMPLEMENTATION: An implementation of our algorithm written in Python is available at: https://www.utm.utoronto.ca/milsteinlab/resources/Software/MMCode/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics Advances online.
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spelling pubmed-97106182023-01-24 An expectation–maximization approach to quantifying protein stoichiometry with single-molecule imaging Boonkird, Artittaya Nino, Daniel F Milstein, Joshua N Bioinform Adv Original Paper MOTIVATION: Single-molecule localization microscopy (SMLM) is a super-resolution technique capable of rendering nanometer scale images of cellular structures. Recently, much effort has gone into developing algorithms for extracting quantitative features from SMLM datasets, such as the abundance and stoichiometry of macromolecular complexes. These algorithms often require knowledge of the complicated photophysical properties of photoswitchable fluorophores. RESULTS: Here, we develop a calibration-free approach to quantitative SMLM built upon the observation that most photoswitchable fluorophores emit a geometrically distributed number of blinks before photobleaching. From a statistical model of a mixture of monomers, dimers and trimers, the method employs an adapted expectation–maximization algorithm to learn the protomer fractions while simultaneously determining the single-fluorophore blinking distribution. To illustrate the utility of our approach, we benchmark it on both simulated datasets and experimental datasets assembled from SMLM images of fluorescently labeled DNA nanostructures. AVAILABILITY AND IMPLEMENTATION: An implementation of our algorithm written in Python is available at: https://www.utm.utoronto.ca/milsteinlab/resources/Software/MMCode/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics Advances online. Oxford University Press 2021-11-13 /pmc/articles/PMC9710618/ /pubmed/36700088 http://dx.doi.org/10.1093/bioadv/vbab032 Text en © The Author(s) 2021. Published by Oxford University Press. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Paper
Boonkird, Artittaya
Nino, Daniel F
Milstein, Joshua N
An expectation–maximization approach to quantifying protein stoichiometry with single-molecule imaging
title An expectation–maximization approach to quantifying protein stoichiometry with single-molecule imaging
title_full An expectation–maximization approach to quantifying protein stoichiometry with single-molecule imaging
title_fullStr An expectation–maximization approach to quantifying protein stoichiometry with single-molecule imaging
title_full_unstemmed An expectation–maximization approach to quantifying protein stoichiometry with single-molecule imaging
title_short An expectation–maximization approach to quantifying protein stoichiometry with single-molecule imaging
title_sort expectation–maximization approach to quantifying protein stoichiometry with single-molecule imaging
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9710618/
https://www.ncbi.nlm.nih.gov/pubmed/36700088
http://dx.doi.org/10.1093/bioadv/vbab032
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