Software

EVE: analysis software for event-based localization microscopy

EVE is a specialized software for analyzing eveSMLM (event-based SMLM) data. Event-based sensors (EBS), or neuromorphic vision sensors, offer a novel approach to imaging by recording light intensity changes asynchronously, unlike conventional cameras that capture light over fixed exposure times. EVE offers an integrated platform for detection, localization, and post-processing, with various algorithmic options tailored for the unique structure of eveSMLM data.
Source code: https://github.com/Endesfelder-Lab/EVE-software

Installation/usage instructions:

Follow instructions here: https://github.com/Endesfelder-Lab/EVE-software?tab=readme-ov-file#how-to-install-and-run-eve .

Please cite the following paper when using this software:

Weber*, Martens*, et al., "EVE is an open modular data analysis software for event-based localization microscopy" (2024), bioRxiv

TARDIS: Temporal analysis of relative distances

In TARDIS, an all-to-all distance analysis between localizations is performed with increasing temporal shifts. TARDIS outperforms existing tracking algorithms especially in complex conditions, and is even robust in scenarios that exceed the capabilities of current localization algorithms. Using TARDIS, we further show that measurements can be five-fold shorter without loss of information.
Source code: https://github.com/kjamartens/TARDIS-public/releases

Installation/usage instructions:

Install TARDISInstaller.exe for a stand-alone version. Read the TARDIS manual for instructions.

Please cite the following paper when using this software:

Martens, et al., "Temporal analysis of relative distances (TARDIS) is a robust, parameter-free alternative to single-particle tracking" (2023), Nature Methods

Low-dispersion spectral SMLM analysis

A method to analyse spectrally-resolved single-molecule emitters in which the 0th and 1st order from a grating are close-by. This enables spectrally-based SMLM with 5-fold higher density.
Source code: https://github.com/HohlbeinLab/sSMLMAnalyzer

Installation/usage instructions:

From ImageJ, run update, and select HohlbeinLab from the update sites. Then run Spectral Analyzer under the plugins menu.

Please cite the following paper when using this software:

Martens, et al., "Enabling Spectrally Resolved Single-Molecule Localization Microscopy at High Emitter Densities" (2022), Nano Letters

A Hands-On Introduction and Overview of Computational Analysis for Single-Molecule Localization Microscopy

This is an introduction to the required computational analysis of SMLM imaging, post-processing and typical data analysis. Importantly, user-friendly, ready-to-use and well-documented code in Python and MATLAB with exemplary data is provided as an interactive experience for the reader, as well as a starting point for further analysis. Our code is supplemented by descriptions of the computational problems and their implementation.
Source code: https://colab.research.google.com/drive/1fn837sOJSaq2xWdgcmgR4bkGv8ePW2yj

Installation/usage instructions:

Open the link above for fully on-line, Jupyter-notebook based software.

Please cite the following paper when using this software:

Martens, et al., "Raw Data to Results: A Hands-On Introduction and Overview of Computational Analysis for Single-Molecule Localization Microscopy" (2022), Frontiers in Bioinformatics

Phasor-SMLM SMALLLABS

Phasor-based single-molecule localization algorithms, with the option to fit 2D, and astigmatism, double-helix, tetra-pod, and saddle-point point-spread-functions. Integrated in the MATLAB-based SMALL-LABS.
Source code: https://github.com/HohlbeinLab/SMALL-LABS-pSMLM

Installation/usage instructions:

Download the entire folder. Change the working directory in Matlab to this folder and open GUI_main.m to open the GUI. See the Quick start guide GUI.pdf for a quick-start guide.

Please cite the following paper when using this software:

Martens, et al., "Integrating engineered point spread functions into the phasor-based single-molecule localization microscopy framework" (2021), Methods

MonteCarlo-Diffusion Distribution Analysis

Diffusion distribution analysis (DDA) is a method to analyse the distribution of diffusion particles to extract transition rates (i.e. binding times) with temporal precision better than the frame time.
Source code: https://www.nature.com/articles/s41467-019-11514-0#Sec34

Installation/usage instructions:

A set of scripts for MATLAB.

Please cite the following paper when using this software:

Martens, van Beljouw et al., "Visualisation of dCas9 target search in vivo using an open-microscopy framework" (2019), Nature Communications

Phasor-based localization

Phasor-based sub-pixel localization is a very fast, model-free, 2D and 3D single-molecule localization algorithm that can easily reach millions of localization positions calculated every second on standard CPUs. Its accuracy is in line with Gaussian-based fitting routines.
Source code: https://github.com/kjamartens/thunderstorm

Installation/usage instructions:

From ImageJ, run update, and select HohlbeinLab from the update sites. Then run ThunderSTORM and select the phasor-based sub-pixel fitting routine.

Please cite the following paper when using this software:

Martens et al., "Phasor based single-molecule localization microscopy in 3D (pSMLM-3D): An algorithm for MHz localization rates using standard CPUs" (2017), Journal of Chemical Physics