This code performs the Fourier Transform Traction Cytometry to calculate the forces generated by an adherent cell on a flexible substrate. This analysis is all built upon previous published approaches which can be found in the following (not exhaustive) citations:
- Butler et al. Traction fields, moments, and strain energy that cells exert on their surroundings. Am J Physiol Cell Physiol. 282(3):C595-605 (2002)
- Sabass et al. High resolution traction force microscopy based on experimental and computational advances. Biophys J. 94(1):207-220 (2008)
- Huang et al. Traction force microscopy with optimized regularization and automated Bayesian parameter selection for comparing cells. Computer Physics Communications 256:107313 (2020))
- python, numpy, scikit-image, scipy, pandas, opencv, openpiv, matplotlib, jupyter
- sksparse (required for the bayesian calculation of the optimal order parameter - adapted from Huang et al. Computer Physics Communications 256:107313 (2020))
- For Mac users:
conda install -c conda-forge scikit-sparse - For Windows: It's complicated. See here for details: https://github.com/EmJay276/scikit-sparse
An example data set can be found here: https://drive.google.com/drive/folders/1rjT-NBQa5KlbHhyusQjkwwKu96_kNEzV?usp=sharing
It consists of a cell (488.tif and 561short.tif) along with the beads (640long.tif) and the reference image of the beads with the cell removed (640long_reference.tif). The gel has a shear modulus of 16 kPa. There is also a folder containing the flatfield images used for image correction.
An example Jupyter notebook is included in the repository to illustrate the workflow