Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Lukeš, Tomáš, Glatzová, Daniela, Kvíčalová, Zuzana, Levet, Florian, Benda, Aleš, Letschert, Sebastian, Sauer, Markus, Brdička, Tomáš, Lasser, Theo, Cebecauer, Marek
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2017
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
_version_ 1783281234697256960
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
work_keys_str_mv AT lukestomas quantifyingproteindensitiesoncellmembranesusingsuperresolutionopticalfluctuationimaging
AT glatzovadaniela quantifyingproteindensitiesoncellmembranesusingsuperresolutionopticalfluctuationimaging
AT kvicalovazuzana quantifyingproteindensitiesoncellmembranesusingsuperresolutionopticalfluctuationimaging
AT levetflorian quantifyingproteindensitiesoncellmembranesusingsuperresolutionopticalfluctuationimaging
AT bendaales quantifyingproteindensitiesoncellmembranesusingsuperresolutionopticalfluctuationimaging
AT letschertsebastian quantifyingproteindensitiesoncellmembranesusingsuperresolutionopticalfluctuationimaging
AT sauermarkus quantifyingproteindensitiesoncellmembranesusingsuperresolutionopticalfluctuationimaging
AT brdickatomas quantifyingproteindensitiesoncellmembranesusingsuperresolutionopticalfluctuationimaging
AT lassertheo quantifyingproteindensitiesoncellmembranesusingsuperresolutionopticalfluctuationimaging
AT cebecauermarek quantifyingproteindensitiesoncellmembranesusingsuperresolutionopticalfluctuationimaging