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Inferring statistical properties of 3D cell geometry from 2D slices
Tristan A Sharp1, Matthias Merkel2, M Lisa Manning2,3
1Dept. of Physics and Astronomy, University of Pennsylvania, Philadelphia, PA, United States of America.
Plos One
|February 2, 2019
Summary
Quantifying 3D cell shapes in tissues is challenging. This study introduces a simple method using 2D images to accurately estimate 3D cell shape, requiring minimal data for reliable results.
Area of Science:
- Biophysics
- Cell Biology
- Materials Science
Background:
- Quantifying 3D cell shapes in tissues is complex, often needing digital reconstruction from 2D image stacks.
- Cellular morphology reflects mechanical and biochemical properties within its microenvironment.
Purpose of the Study:
- To develop a simplified technique for extracting 3D cell shape information from 2D tissue slices.
- To establish a correlation between 2D shape distributions and 3D tissue geometry.
Main Methods:
- Utilized cell vertex model geometries to simulate tissue structures.
- Analyzed the distribution of 2D cell shapes in independent 2D slices.
- Investigated error sources in estimating 3D shape indices from 2D data.
Main Results:
- Demonstrated that 2D shape distributions can accurately determine the mean 3D shape index.
- Showed that typically only a few dozen cells in 2D imagery are needed to achieve <2% uncertainty.
- Validated the method on both isotropic animal and anisotropic plant tissues.
Conclusions:
- The developed method offers a practical alternative for 3D cell shape quantification in tissues.
- The framework is robust, requiring minimal 2D data for high-accuracy 3D estimations.
- The approach is extensible for quantifying other 3D geometric features and their uncertainties.
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