sl2pm 1.0.0

Two-photon microscopy particle tracking analysis

SL2PM

A package for tracking particles, red blood cells (RBCs), and blood vessel walls with super-localization from images recorded with two-photon microscopy (2PM). The package is based on the data analysis developed in the manuscript:

Kutuzov N., Lauritzen M., and Flyvbjerg H. Super-localization two-photon microscopy for in vivo tracking of particles and surfaces.

The data analysis in SL2PM is data-driven, i.e., you need to check if underlying assumptions of SL2PM analysis are satisfied in your data before you apply any functions from SL2PM. This is why we suggest using SL2PM with Jupyter notebooks, where you can explore your data step-by-step (following our examples) and tailor SL2PM analysis to your data, if needed.

The documentation can be found at https://sl2pm.readthedocs.io/en/latest/index.html

Installation

Install from the PyPi distribution

pip install sl2pm

Or from the source

git clone [e-mail]:drkutuzov/sl2pm.git
cd sl2pm
pip install -e .

Content

src/sl2pm contains the code for SL2PM.
docs/tutorial contains folders with specific examples of data analysis with SL2PM. Each folder contains Jupyter notebooks tutorials and examples of experimental data (as numpy arrays).

docs/tutorial/quantum_dots: Tracking single quantum dots (QDs) in brain parenchyma.
docs/tutorial/red_blood_cells: Tracking single RBCs in a capillary.
docs/tutorial/capillaries: Tracking diameter and center of a capillary.
docs/tutorial/pmt_calibration: Calibrating photomultiplier tubes (PMTs) for SL2PM.
docs/tutorial/bistable_bias: Estimating parameters of bistable bias – artefact present in some detectors (photomultiplier tubes, PMTs) used for 2PM.

Usage

• Select a folder in docs/tutorial based on the data analysis you need.
• Upload your data (e.g. as a numpy array) to the folder.
• Make a copy of the Jupyter notebook tutorial or make a new one.
• Use the notebook to analyse your data using the tutorial as a guide.

[!Tip] If you are interested in measuring diameter of a capillary, made visible with fluorescently-labeled plasma (e.g. with FITC-dextran), go to examples/capillaries/full_demo_plasma.ipynb notebook.

• Tutorial Jyputer notebooks only demonstrate the key parts of the data analysis described in the manuscript "Super-localization two-photon microscopy for in vivo tracking of particles and surfaces". Parts of the data analysis that can be done with the standard data analysis techniques, which have Python implementation (e.g. numpy, scipy, etc.), are not shown in the tutorials.
• The spatial coordinate in the Jupyter notebook examples is measured in pixels (not actual physical distance units).

[!WARNING] Always check if your data satisfy assumptions of SL2PM analysis before applying it (see the manuscript text for more details).