@article{ba13e3da402b4a9da5ae4ebbc056dfba,
title = "Inferring statistical properties of 3D cell geometry from 2D slices",
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%. Even though we developed the method for isotropic animal tissues, we demonstrate it on an anisotropic plant tissue. This framework could also be naturally extended to estimate additional 3D geometric features and quantify their uncertainty in other materials.",
author = "Sharp, {Tristan A.} and Matthias Merkel and {Lisa Manning}, M. and Liu, {Andrea J.}",
note = "Funding Information: This project was supported by the National Cancer Institute of the National Institutes of Health under Physical Sciences Oncology Center (PSOC, physics.cancer.gov) award No. U54 CA193417 (TAS and AJL). We also acknowledge the Simons Collaboration on Cracking the Glass problem (454947, simonsfoundation.org) for initiating this collaboration. MM and MLM acknowledge funding from the Alfred P. Sloan Foundation (sloan.org), the Gordon and Betty Moore Foundation (moore.org), the Research Corporation for Scientific Advancement (rescorp. org), and computational support through the National Science Foundation (NSF, nsf.gov) ACI-1541396. In addition, we gratefully acknowledge support from the NSF under NSF-DMR-1506625 (TAS) and NSF-DMR-1352184 and NSF-PHY-1607416 (MLM), as well as from the Simons Foundation (445222 to MLM and 327939 to AJL). Publisher Copyright: {\textcopyright} 2019 Sharp et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.",
year = "2019",
month = feb,
doi = "10.1371/journal.pone.0209892",
language = "English (US)",
volume = "14",
journal = "PLoS One",
issn = "1932-6203",
publisher = "Public Library of Science",
number = "2",
}