F. Levet & J-B Sibarita dans Nature Methods
SR-Tesseler: une nouvelle méthode de segmentation et de quantification de données de microscopie de super-résolution par localisation
SR-Tesseler: a method to segment and quantify localization-based super-resolution microscopy data. Levet F, Hosy E, Kechkar A, Butler C, Beghin A, Choquet D, Sibarita JB.. Nat Methods. 2015 Sep 7. doi: 10.1038/nmeth.3579.
Due to the diffraction of light, the resolution of conventional light microscopy is limited as stated by Ernst Abbe in 1873. Localization-based super-resolution techniques are part of the techniques developed to break this diffraction limit and capture images at a higher resolution.
They revolutionized the quantification of molecular organization by making it possible to monitor fluorescent probes in living cells close to molecular spatial resolution (a few nanometers).
Despite its youth, this breakthrough was awarded with the Nobel Prize in Chemistry 2014. However, the analysis of the data acquired with these techniques often involves complex image processing adapted to the specific topology and quality of the image to be analyzed.
In a paper entitled “SR-Tesseler: a method to segment and quantify localization-based super-resolution microscopy data” published Monday 7 of September in Nature Methods, researchers from the IINS in Bordeaux developed a new method packaged as an open-source segmentation software. It allows precise, robust and automatic quantification of protein organization at different scales, from the cellular level down to clusters of a few fluorescent markers.
SR-Tesseler is insensitive to cell shape, molecular organization, background and noise, allowing comparing efficiently different biological conditions in a non-biased manner, and perform quantifications on various proteins and cell types. SR-Tesseler software comes with a very simple and intuitive graphical user interface, providing direct visual feedback of the results and is freely available under GPLv3 license.
A) Diffraction limited fluorescence image of a cultured neuron expressing GluA1-mEOS2. (B) PALM super-resolution image reconstructed from the localized molecule coordinates. (C) Voronoï diagram constructed from the localized molecule coordinates. Color of the polygons encodes for the local density computed from the Voronoï polygons. (D) Multi-scale segmentation obtained using SR-Tesseler revealing the cellular contour as well as molecular clusters. Read more…
For any question or request, feel free to send a message to: Florian Levet / /Ingénieur de Recherche Inserm / Bordeaux Imaging Center
Team: Quantitative Imaging of the Cell (JB Sibarita), IINS
Institute for Interdisciplinary Neuroscience (D.Choquet)
Bordeaux Imaging Center, University of Bordeaux, France.
Dernière mise à jour le 23.09.2015