Quantifying protein densities on cell membranes using super-resolution optical fluctuation imaging
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- 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. WeQuantitative 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.…
Autor(en): | Tomáš Lukeš, Daniela Glatzová, Zuzana Kvíčalová, Florian Levet, Aleš Benda, Sebastian Letschert, Markus Sauer, Tomáš Brdička, Theo Lasser, Marek Cebecauer |
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URN: | urn:nbn:de:bvb:20-opus-172993 |
Dokumentart: | Artikel / Aufsatz in einer Zeitschrift |
Institute der Universität: | Fakultät für Biologie / Theodor-Boveri-Institut für Biowissenschaften |
Sprache der Veröffentlichung: | Englisch |
Titel des übergeordneten Werkes / der Zeitschrift (Englisch): | Nature Communications |
Erscheinungsjahr: | 2017 |
Band / Jahrgang: | 8 |
Aufsatznummer: | 1731 |
Originalveröffentlichung / Quelle: | Nature Communications (2017) 8:1731. https://doi.org/10.1038/s41467-017-01857-x |
DOI: | https://doi.org/10.1038/s41467-017-01857-x |
PubMed-ID: | https://pubmed.ncbi.nlm.nih.gov/29170394 |
Allgemeine fachliche Zuordnung (DDC-Klassifikation): | 5 Naturwissenschaften und Mathematik / 57 Biowissenschaften; Biologie / 570 Biowissenschaften; Biologie |
Freie Schlagwort(e): | biology; fluorescence imaging; imaging the immune system; super-resolution microscopy |
Datum der Freischaltung: | 25.05.2021 |
EU-Projektnummer / Contract (GA) number: | 686271 |
EU-Projektnummer / Contract (GA) number: | 602812 |
OpenAIRE: | OpenAIRE |
Lizenz (Deutsch): | CC BY: Creative-Commons-Lizenz: Namensnennung 4.0 International |