This workshop delves into the analysis of scientific images. Namely, we will cover:
- How images are acquired, computationally stored, and parsed
- Analysis of image time-series
- Image filtering methods for enhancing signal-to-noise
- Fitting of physical models to data
Through the exercises in this workshop, we will apply these concepts to analyze fluorescence microscopy data, studying cellular dynamics. The specific biophysical techniques to be analyzed are:
- Fluorescence Recovery After Photobleaching (FRAP)
- Single Particle Tracking (SPT)
These methods enable the quantification of diffusion of macromolecules within cells, and are commonly used to study phenomena such as protein/nucleic acid binding, cellular fluidity, rheology, etc. We will go over the fundamental biophysics knowledge required to perform the exercises, so no specific prior coursework is required. Self-contained example data of these techniques will be provided, but feel free to bring your own data if you have it!
The exercises included are written equivalently in both MATLAB and Python. You may choose whichever you prefer, or wish to practice.
- If using MATLAB:
- Required toolboxes: Image Processing Toolbox, Signal Processing Toolbox, Statistics and Machine Learning Toolbox
- If using Python:
- Scripts are provided in Notebook format
We will also use ImageJ/Fiji to view and perform initial analyses on the images. This software may be downloaded for free in a few minutes through the link below. Please ensure you have the Fiji distribution, which contains built-in plugins. We will namely be using the TrackMate plugin.
The data for this workshop may be found at this Google Drive link
- Please note that the data are unpublished images and should not be shared beyond this workshop without consent
Once the data are downloaded, move both data folders into the Scripts folder to get the path calls right in the scripts.
Workshop created as part of the McGill Initiative in Computational Medicine