SR-Tesseler: a method to segment and quantify localization-based super-resolution microscopy data.
Nat Methods. 2015-09-07; 12(11): 1065-1071
DOI: 10.1038/nmeth.3579
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1. Nat Methods. 2015 Nov;12(11):1065-71. doi: 10.1038/nmeth.3579. Epub 2015 Sep 7.
SR-Tesseler: a method to segment and quantify localization-based super-resolution
microscopy data.
Levet F(1)(2)(3)(4)(5), Hosy E(1)(2), Kechkar A(1)(2)(6), Butler C(1)(2)(7),
Beghin A(1)(2), Choquet D(1)(2)(3)(4)(5), Sibarita JB(1)(2).
Author information:
(1)Interdisciplinary Institute for Neuroscience, University of Bordeaux,
Bordeaux, France.
(2)Interdisciplinary Institute for Neuroscience, Centre National de la Recherche
Scientifique (CNRS) UMR 5297, Bordeaux, France.
(3)Bordeaux Imaging Center, University of Bordeaux, Bordeaux, France.
(4)Bordeaux Imaging Center, CNRS UMS 3420, Bordeaux, France.
(5)Bordeaux Imaging Center, INSERM US04, Bordeaux, France.
(6)Ecole Nationale Supérieure de Biotechnologie, Constantine, Algeria.
(7)Imagine Optic, Orsay, France.
Comment in
Nat Methods. 2015 Nov;12(11):1019-20.
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.
DOI: 10.1038/nmeth.3579
PMID: 26344046 [Indexed for MEDLINE]