Although cell shape can reflect the mechanical and biochemical properties of the cell and its environment, quantification of 3D cell shapes within 3D tissues can be difficult, typically requiring digital reconstruction from a stack of 2D images. We investigate a simple alternative technique that can provide information about the 3D shapes of cells in a tissue from non-consecutive 2D slices. Our technique uses a 3D cell vertex model to construct a mapping between the 3D properties of interest and the distribution of shapes observed in  2D slices.  We analyze the errors that arise in the estimation of a 3D shape index from 2D imagery , and find that typically only a few dozen cells in 2D imagery are required to reduce uncertainty in the mean 3D shape index below 2\\%. This framework could be naturally extended to estimate additional 3D geometric features and quantify their uncertainty in other materials.
Although cell shape can reflect the mechanical and biochemical properties of the cell and its environment, quantification of 3D cell shapes within 3D tissues remains difficult, typically requiring digital reconstruction from a stack of 2D images. We investigate a simple alternative technique to extract information about the 3D shapes of cells in a tissue ; this technique connects the ensemble of 3D shapes in the tissue with the distribution of 2D shapes observed in independent 2D slices. Using cell vertex model geometries, we find that the distribution of 2D shapes allows clear determination of the mean value of a 3D shape index. We analyze the errors that may arise in practice in the estimation of the mean 3D shape index from 2D imagery and find that typically only a few dozen cells in 2D imagery are required to reduce uncertainty below 2\\%. This framework could be naturally extended to estimate additional 3D geometric features and quantify their uncertainty in other materials.
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