SR-Tesseler: a method to segment and quantify localization-based super-resolution microscopy data

Nature Methods
Florian LevetJean-Baptiste Sibarita

Abstract

Localization-based super-resolution techniques open the door to unprecedented analysis of molecular organization. This task often involves complex image processing adapted to the specific topology and quality of the image to be analyzed. Here we present a segmentation framework based on Voronoï tessellation constructed from the coordinates of localized molecules, implemented in freely available and open-source SR-Tesseler software. This method allows precise, robust and automatic quantification of protein organization at different scales, from the cellular level down to clusters of a few fluorescent markers. We validated our method on simulated data and on various biological experimental data of proteins labeled with genetically encoded fluorescent proteins or organic fluorophores. In addition to providing insight into complex protein organization, this polygon-based method should serve as a reference for the development of new types of quantifications, as well as for the optimization of existing ones.

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Citations

May 7, 2016·Bioinformatics·Leonid AndronovBruno P Klaholz
Jun 16, 2016·Frontiers in Cellular Neuroscience·Isabelle A SpühlerSimon G Sprecher
Jun 14, 2016·Nature Methods·Florian BaumgartGerhard J Schütz
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Sep 29, 2017·Molecular Biology of the Cell·Ting-Sung HsiehJen Liou
Nov 5, 2016·Molecular Biology of the Cell·Raphael JorandTijana Jovanovic-Talisman
Nov 25, 2017·Nature Communications·Tomáš LukešMarek Cebecauer
Oct 31, 2017·Nature Methods·Anne BeghinJean-Baptiste Sibarita
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Oct 14, 2017·Bioinformatics·Blazej Ruszczycki, Tytus Bernas
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Jan 3, 2020·Physical Chemistry Chemical Physics : PCCP·Tina KošutaCarlo Manzo

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