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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2021
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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. |
format | Online Article Text |
id | pubmed-9710618 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
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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