Package: par-segmentation
PAR SegmentationTools for segmenting, straightening and quantifying the cortex of cells.
Works by combining spline-based segmentation with a custom quantification model, using a gradient descent optimisation procedure.
Designed primarily for membrane-bound PAR proteins in C. elegans zygotes.Advantages:Combine segmentation and membrane quantification in a single stepNo ground truth training data requiredWorks for single snapshots or timelapse moviesDisadvantages:Requires a small amount of manual annotation for every imageA little slow compared to some other segmentation methodsInstructionsAs a first step, I would recommend checking out thetutorial notebook. To run the notebook interactively you have a few options:Option 1: BinderTo run in the cloud using Binder, click here:(Please note that it may take several minutes to open the notebook)Option 2: DockerStep 1: Make sureDockeris installed and open on your machineStep 2: Download and run the Docker container:docker run --rm -p 8888:8888 tsmbland/par-segmentationOnce the Docker image has finished downloading, this will print two URLs at the bottom for you to copy and paste into your web browser to open up Jupyter (please try both)Step 3: Navigate toscripts/Tutorial.ipynbto run the notebookStep 4: When finished, delete the image:docker image rm tsmbland/par-segmentationOption 3: CondaYou can use the environment.yml file to set up aCondaenvironment on your machine from which the notebook can be runconda env create -f environment.yml
conda activate par-segmentation
jupyter notebookInstallationTo explore further and incorporate into your own analysis pipelines, you can install the package using pip:pip install par-segmentationMethodsStarting with an initial rough manual ROI of the cell edge, the cortex of the image is straightened (Step 1).
The program then attempts to mimic this straightened image by differentiable simulation (Step 2).
In doing so, it learns the position of the cortex, which enables the ROI to be adjusted (Step 3) and the cortex re-straightened.Cortex positions are modelled as a spline with a user-specified number of evenly spaced knots which are optimised by gradient descent:Cross-cortex intensity profiles at each position around the cortex are modelled as the sum of distinct cytoplasmic and membrane signal components:
an error function and Gaussian function respectively, representing the expected shape of a step and a point convolved by a Gaussian point spread function (PSF) in one dimension:The program learns the amplitude of these two components at each position around the cortex, so can serve as a quantification tool as well as a segmentation tool:Modelling the PSF as a Gaussian, and ignoring out-of-focus contributions, is a clear simplification of reality, but is a close enough approximation for many purposes (e.g. if you're interested in relative concentrations rather than absolute concentrations). Nevertheless, one can relax these assumptions (with some added caveats) if higher accuracy is required.
Seehere.PublicationsThis package has been used in the following publications:Optimized dimerization of the PAR-2 RING domain drives cooperative and selective membrane recruitment for robust feedback-driven cell polarization.BioRxiv (2023).Design principles for selective polarization of PAR proteins by cortical flows.Journal of Cell Biology (2023).Cleavage furrow-directed cortical flows bias mechanochemical pathways for PAR polarization in the C . elegans germ lineage.BioRxiv (2022).An analog sensitive allele permits rapid and reversible chemical inhibition of PKC-3 activity in C. elegans.Micropublication Biology (2022).SAIBR : A simple, platform-independent method for spectral autofluorescence correction.Development (2022).To add your paper to this list, please use the issues form, or create a pull requestLicenseThis work is licensed under aCreative Commons Attribution 4.0 International License.