5. Software

Software for analysis and visualization of SMLM datasets is crucial for the success of your project. Below I give examples for different software options to get you started. For an extensive comparison of different SMLM software options, benchmarking and sample datasets, please see the SMLM Challenge Website. The list below is from personal experience and thus not complete and strongly biased.

Before you start, it might be worth considering the following factors when testing/choosing a software:

1. Easy of use
2. Speed and precision (2D and 3D)
3. Batch processing capabilities
4. Special functionalities: multi-channel data, colocalization, clustering, resolution etc.

Some examples:

1. ThunderSTORM

ThunderSTORM was published in 2015 and quickly gained popularity since it's easy to use, relies on ImageJ/Fiji and well connects visual output with each processing step. The latter point is crucial when you start working with SMLM and you want to play with parameters and see the result immediately. The integration in ImageJ makes it easy to adopt also without any programming skills.

-> Paper -> GitHub

-> See Martens, 2018 for an extension of ThunderSTORM using Phasor-based localization

2. Picasso (GPU supported)

A python-based SMLM package that contains some extra functionalities to plan and perform PAINT experiments.

—> Paper —> GitHub

3. SMAP (GPU supported)

SMAP is a very extensive MATLAB-based software so that I wont list all its functionalities. A speciality of SMAP is the use of custom PSFs instead of Gaussian approximations.

—> Paper —> GitHub

4. Zola (GPU support)

Zola comes as an easy-to-use ImageJ plugin and allows 3D localization over a large axial range.

—> Paper —> GitHub

5. Decode

Deep learning-based localizations software.

-> Preprint -> GitHub

6. Proprietary software

Don’t go directly to another software if you sit in front of a commercial SMLM microscope. These instruments often come with well-developed (and expensive) software packages that are worth testing before going to alternatives. See for example Jimenez, 2020 for a comparison of the Nikon NSTORM software with open-source alternatives.

Beyond localizing

There are many special/advanced applications in SMLM starting with 3D or high-density fitting that have their own lists of suitable and tested software options. The website of the SMLM Challenge might also be a good starting point here.

1. LAMA, a multi-functional tool that does co-localization, clustering, registration and many things more (Malkusch, 2016)
2. SR-Tesseler, a clustering tool based on Voronoi tesselation (Levet, 2015)
3. VISP, a tool for the visualization of 3D SMLM data (El Beheiry, 2013)
4. NanoJ, a nice SMLM toolbox available as ImageJ plugin (Laine, 2019)
5. InferenceMap, a tool to visualize and quantify SPT data (El Beheiry, 2015)
6. ChimeraX, a software for protein structure manipulation that works great on tiff stacks for 3D rendering and volume quantification (ChimeraX Website)