PALM datasets

In PALM/(d)STORM, molecules are stochastically imaged, in order to temporally separate molecules which would otherwise be indistinguishable. The position of each molecule is localized by fitting the measured photon distribution. The information from multiple imaging repetitions is then combined to form a composite super-resolution PALM/(d)STORM image that contains the locations of many single molecules, even those sharing a single diffraction-limited region.

The purpose of this website is to provide simulated and experimental data for the comparison and validation of PALM/(d)STORM localization, reconstruction and visualization software packages.

Sharing standard data sets will help to allow clear comparisons between different analysis approaches. We have made available one real biological dataset and one synthetic dataset. The results of localization of some open-source software are also available.

Reference

Suliana Manley, Julia Gunzenhäuser and Nicolas Olivier, A starter kit for point-localization super-resolution imaging, Current Opinion in Chemical Biology, vol. 15, no 6, December 2011, pp 813-82.

Localization Microscopy • 2013 ISBI Challenge

Benchmarking of Single-Molecule Localization Microscopy Software

Comprehensive review of the single-molecule localization microscopy software based on ground-truth synthetic image sequence simulating biological structures and image formation process at the nanoscale resolution.

• Complete web site of the challenge
• Datasets of the challenge
• Perspective

Tubuline AF647

The dataset "real biological sample" is a fixed cell, stained with mouse anti-alpha-tubulin primary antibody and Alexa647 secondary antibody. The intermittent increase in signal is due reactivation with a 405 nm laser.

Seashell

The dataset "synthethic sample" has been created articifically using a 3D structure shape. This shape represents a seashell of six branches with various fluorophore densities. The position of the fluorophores of exactly known. Some image sequences have additional noise.