Statistical properties of 3D cell geometry from 2D slices

TL;DR

This paper presents a method to infer 3D cell shape properties from 2D slices, enabling easier analysis of tissue cell geometries without full 3D reconstruction.

Contribution

It introduces a statistical approach linking 2D shape distributions to 3D cell shape indices, validated with cell vertex models.

Findings

Few dozen cells in 2D slices suffice for accurate 3D shape index estimation.

The method reduces uncertainty below 2% in typical scenarios.

Framework can be extended to other 3D geometric features.

Abstract

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.