Single molecule localization microscopy

Direct stochastic optical reconstruction microscopy
Photoactivated Localization Microscopy
Fluorescence Photoactivation Localization Microscopy
Ground State Depletion Individual Molecule Return
Stochastic optical reconstruction microscopy

SMLM is a mature but still growing field, which still lacks efficient and user-friendly analysis and visualization software platform adapted for both users and developers. We here introduce PoCA, a powerful open-source software platform dedicated to the visualization and analysis of 2D and 3D point-cloud data. PoCA allows manipulating large datasets, and integrates a plugin architecture, a native batch analysis engine and a Python code interpreter, facilitating both the analysis of data and the integration of new methods.

Visualization, segmentation and exploration of 3D SMLM data

Removal of heterogeneous background from image data of single-molecule localization microscopy, using extreme value-based emitter recovery (EVER).


EVER requires no manual adjustment of parameters and has been implemented as an easy-to-use ImageJ plugin that can immediately enhance the quality of reconstructed super-resolution images. This method is validated as an efficient way for robust nanoscale imaging of samples with heterogeneous background fluorescence, such as thicker tissue and cells.

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Software for computing single molecule localization accuracies and resolution measures

The FandPLimitTool is a GUI based software module that allows users to calculate the limits to the accuracy with which parameters can be estimated from single molecule imaging data. The software supports calculation of limits for the 2D/3D location estimation problem and the 2D/3D distance-estimation/resolution problem. The location estimation problem is concerned with the task of determining the position of a single molecule and the distance-estimation/resolution problem is concerned with the task of determining the distance of separation between two single molecules. The user can calculate limits for a variety of imaging scenarios.

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NanoJ-SQUIRREL (Super-resolution Quantitative Image Rating and Reporting of Error Locations) is a software package designed for assessing and mapping errors and artefacts within super-resolution images. This is achieved through quantitative comparison with a reference image of the same structure (typically a widefield, TIRF or confocal image). SQUIRREL produces quantitative maps of image quality and resolution as well as global image quality metrics.

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This script includes a rough feature detection and then fine 2D Gaussian algorithm to fit Gaussians within detected regions. This macro is unique because the ImageJ/Fiji curve fitting API only supports 1-D curve. I get around this by linearising the equation. This implementation is for isotropic (spherical) or anistropic (longer in x/y) diagonally covariant Gaussians but not fully covariant Gaussians (anisotropic and rotated).