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

Nat Methods. 2015 Nov;12(11):1065-71. doi: 10.1038/nmeth.3579. Epub 2015 Sep 7.


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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Animals
  • COS Cells
  • Chlorocebus aethiops
  • Cluster Analysis
  • Computational Biology
  • Computer Simulation
  • Fluorescent Dyes / chemistry
  • Humans
  • Image Processing, Computer-Assisted / methods*
  • Microscopy, Fluorescence / methods*
  • Neurons / metabolism
  • Neurons / physiology
  • Oocytes / metabolism
  • Pattern Recognition, Automated
  • Receptors, Glycine / metabolism*
  • Software
  • Xenopus laevis


  • Fluorescent Dyes
  • Receptors, Glycine