DiffusionLab

Software package for single-fluorophore trajectory analysis.

Link to documentation: https://diffusionlab.readthedocs.io/en/latest/.

This code was inspired by the @msdanalyzer MATLAB class for mean squared displacement analysis (Tarantino, N. et al. TNF and IL-1 exhibit distinct ubiquitin requirements for inducing NEMO–IKK supramolecular structures. Journal of Cell Biology 204, 231–245 (2014)).

This code was published in Maris, J. J. E., Rabouw, F. T., Weckhuysen, B. M. & Meirer, F. Classification-based motion analysis of single-molecule trajectories using DiffusionLab. Sci. Rep. 12, 9595 (2022). The raw and processed data sets used in the publication can be found in this repository.

Please note that the original location of this repository was https://github.com/ErikMaris/DiffusionLab. The original link remains active.

Requirements

Software has been written and tested in MATLAB 2019b.

(At least) the following MATLAB toolboxes must be installed: - Curve Fitting Toolbox - Image Fitting Toolbox

When running from the command line, please make sure that the following repositories have been downloaded and added to the MATLAB path: - printFig - plotProps - unitProps

Demo data

The trajectories from Maris, J. J. E., Rabouw, F. T., Weckhuysen, B. M. & Meirer, F. Classification-based motion analysis of single-molecule trajectories using DiffusionLab. Sci. Rep. 12, 9595 (2022) are available to test the software installation. The trajectories are available in the folder data/processed/DiffusionLab_manuscript/ with the name example_1-2_tracks_sig2_SNR3_FP1_BG2_PJ20.csv and a training set for the Classification Trainer (for DiffusionLab) can be found in the same folder with the name 1-training.mat . The trajectories in example_1-1_trainingset_tracks_sig2_SNR3_FP1_BG2_PJ15.csv were used to construct the training set.

Please open the DiffusionLab App and load example_1-2_tracks_sig2_SNR3_FP1_BG2_PJ20.csv in DiffusionLab via File > Import tracks > DoM: this should result in 10600 imported trajectories. First set the Pixel size (nm) to 64, Frame time (s) 0.050, and Exposure time (s) to 0. The shortest trajectories should be removed first to facilitate further analysis. Select Number of Points as first track property (below Track 1) and set a Property threshold of 5. Press Use property 1 as filter to select the threshold and Apply filter to all tracks to execute. Delete the first population containing the tracks with fewer than 5 points by pressing Delete current population. The remain population should have 2433 trajectories. Now, the data set can be visualized, for instance by plotting the tracks using Plot tracks or their mean squared displacement curves with Plot MSD all tracks.

For more information about the usage of the DiffusionLab graphical user interface please consult the documentation.

Project organization

• PG = project-generated
• HW = human-writable
• RO = read only ``` . ├── .gitignore ├── CITATION.md ├── LICENSE.md ├── README.md ├── requirements.txt ├── bin <- Compiled and external code, ignored by git (PG) │ └── external <- Any external source code, ignored by git (RO) ├── config <- Configuration files (HW) ├── data <- All project data, ignored by git │ ├── processed <- The final, canonical data sets for modeling. (PG) │ ├── raw <- The original, immutable data dump. (RO) │ └── temp <- Intermediate data that has been transformed. (PG) ├── docs <- Documentation notebook for users (HW) │ ├── manuscript <- Manuscript source, e.g., LaTeX, Markdown, etc. (HW) │   └── reports <- Other project reports and notebooks (e.g. Jupyter, .Rmd) (HW) ├── results │ ├── figures <- Figures for the manuscript or reports (PG) │   └── output <- Other output for the manuscript or reports (PG) └── src <- Source code for this project (HW)

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License

This project is licensed under the terms of the GPL-3.0 License