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Quantifying protein densities on cell membranes using super-resolution optical fluctuation imaging
Quantitative approaches for characterizing molecular organization of cell membrane molecules under physiological and pathological conditions profit from recently developed super-resolution imaging techniques. Current tools employ statistical algorithms to determine clusters of molecules based on sin...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5700985/ https://www.ncbi.nlm.nih.gov/pubmed/29170394 http://dx.doi.org/10.1038/s41467-017-01857-x |
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author | Lukeš, Tomáš Glatzová, Daniela Kvíčalová, Zuzana Levet, Florian Benda, Aleš Letschert, Sebastian Sauer, Markus Brdička, Tomáš Lasser, Theo Cebecauer, Marek |
author_facet | Lukeš, Tomáš Glatzová, Daniela Kvíčalová, Zuzana Levet, Florian Benda, Aleš Letschert, Sebastian Sauer, Markus Brdička, Tomáš Lasser, Theo Cebecauer, Marek |
author_sort | Lukeš, Tomáš |
collection | PubMed |
description | Quantitative approaches for characterizing molecular organization of cell membrane molecules under physiological and pathological conditions profit from recently developed super-resolution imaging techniques. Current tools employ statistical algorithms to determine clusters of molecules based on single-molecule localization microscopy (SMLM) data. These approaches are limited by the ability of SMLM techniques to identify and localize molecules in densely populated areas and experimental conditions of sample preparation and image acquisition. We have developed a robust, model-free, quantitative clustering analysis to determine the distribution of membrane molecules that excels in densely labeled areas and is tolerant to various experimental conditions, i.e. multiple-blinking or high blinking rates. The method is based on a TIRF microscope followed by a super-resolution optical fluctuation imaging (SOFI) analysis. The effectiveness and robustness of the method is validated using simulated and experimental data investigating nanoscale distribution of CD4 glycoprotein mutants in the plasma membrane of T cells. |
format | Online Article Text |
id | pubmed-5700985 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-57009852017-11-27 Quantifying protein densities on cell membranes using super-resolution optical fluctuation imaging Lukeš, Tomáš Glatzová, Daniela Kvíčalová, Zuzana Levet, Florian Benda, Aleš Letschert, Sebastian Sauer, Markus Brdička, Tomáš Lasser, Theo Cebecauer, Marek Nat Commun Article Quantitative approaches for characterizing molecular organization of cell membrane molecules under physiological and pathological conditions profit from recently developed super-resolution imaging techniques. Current tools employ statistical algorithms to determine clusters of molecules based on single-molecule localization microscopy (SMLM) data. These approaches are limited by the ability of SMLM techniques to identify and localize molecules in densely populated areas and experimental conditions of sample preparation and image acquisition. We have developed a robust, model-free, quantitative clustering analysis to determine the distribution of membrane molecules that excels in densely labeled areas and is tolerant to various experimental conditions, i.e. multiple-blinking or high blinking rates. The method is based on a TIRF microscope followed by a super-resolution optical fluctuation imaging (SOFI) analysis. The effectiveness and robustness of the method is validated using simulated and experimental data investigating nanoscale distribution of CD4 glycoprotein mutants in the plasma membrane of T cells. Nature Publishing Group UK 2017-11-23 /pmc/articles/PMC5700985/ /pubmed/29170394 http://dx.doi.org/10.1038/s41467-017-01857-x Text en © The Author(s) 2017 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Lukeš, Tomáš Glatzová, Daniela Kvíčalová, Zuzana Levet, Florian Benda, Aleš Letschert, Sebastian Sauer, Markus Brdička, Tomáš Lasser, Theo Cebecauer, Marek Quantifying protein densities on cell membranes using super-resolution optical fluctuation imaging |
title | Quantifying protein densities on cell membranes using super-resolution optical fluctuation imaging |
title_full | Quantifying protein densities on cell membranes using super-resolution optical fluctuation imaging |
title_fullStr | Quantifying protein densities on cell membranes using super-resolution optical fluctuation imaging |
title_full_unstemmed | Quantifying protein densities on cell membranes using super-resolution optical fluctuation imaging |
title_short | Quantifying protein densities on cell membranes using super-resolution optical fluctuation imaging |
title_sort | quantifying protein densities on cell membranes using super-resolution optical fluctuation imaging |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5700985/ https://www.ncbi.nlm.nih.gov/pubmed/29170394 http://dx.doi.org/10.1038/s41467-017-01857-x |
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