Automated 3D Cell Parameterization using Spherical Harmonics Coefficients

Automated 3D Cell Parameterization using Spherical Harmonics Coefficients

In the field of cell biology, the accurate and efficient parameterization of 3D cells is a critical task. Traditional methods often involve time-consuming manual processes that are prone to errors. However, with the advancements in computational techniques, it is now possible to automate this process using spherical harmonics coefficients.

Spherical harmonics are mathematical functions that represent the shape of an object in three dimensions. By utilizing these coefficients, it is possible to create a parameterization of both the cytoplasm and nucleoplasm of 3D cells. This allows researchers to analyze and study cells in a more systematic and efficient manner.

The aicscytoparam package, developed by the Allen Institute for Cell Science, provides a comprehensive solution for automating the 3D cell parameterization process. This package utilizes numpy, matplotlib, and scikit-image libraries to facilitate the parameterization of 3D cells.

To get started with aicscytoparam, simply follow the installation process outlined in the README documentation. Once installed, you can begin using the package to parameterize 3D cells. The documentation provides a detailed example of how to create a parameterization of a 3D cell using a cell segmentation, nuclear segmentation, and a fluorescent protein image.

Throughout the example, various functions and techniques are demonstrated, including creating a cuboid cell, obtaining spherical harmonics coefficients, running cellular mapping, and morphing the fluorescent signal onto different cell shapes. The article also includes visualizations of the parameterized cells at each step, providing a clear understanding of the process.

It is worth noting that aicscytoparam allows for customization of the parameterization process by adjusting parameters such as the degree of the spherical harmonics expansion and the number of interpolation layers. This flexibility ensures that researchers can tailor the parameterization to their specific needs and requirements.

The article concludes by referencing a published paper in bioaRxiv that demonstrates the usage of aicscytoparam in analyzing a dataset of over 200k single-cell images. This reference highlights the practical application and potential impact of this technology in cell biology research.

In summary, automated 3D cell parameterization using spherical harmonics coefficients is a powerful and efficient technique for analyzing and studying cells. The aicscytoparam package provides researchers with a user-friendly tool to automate this process, saving time and reducing errors. With its customizable features and broad application potential, this technology is set to revolutionize the field of cell biology.

If you have any questions or want to learn more about the aicscytoparam package and its applications, feel free to visit the Allen Cell forum for further discussions and interactions.

References:

• Allen Institute for Cell Science
• aicscytoparam GitHub Repository
• bioaRxiv Paper: “3D Cell Parameterization”

License:

This article references the aicscytoparam package developed by the Allen Institute for Cell Science. Please refer to the repository’s licensing information for more details on the Allen Institute Software License.