Reconstructing 3D deformation dynamics for curved epithelial sheet morphogenesis from positional data of sparsely-labeled cells

Yoshihiro Morishita (), Ken-ichi Hironaka, Sang-Woo Lee, Takashi Jin and Daisuke Ohtsuka
Additional contact information
Yoshihiro Morishita: RIKEN Quantitative Biology Center
Ken-ichi Hironaka: RIKEN Quantitative Biology Center
Sang-Woo Lee: RIKEN Quantitative Biology Center
Takashi Jin: RIKEN Quantitative Biology Center
Daisuke Ohtsuka: RIKEN Quantitative Biology Center

Nature Communications, 2017, vol. 8, issue 1, 1-15

Abstract: Abstract Quantifying global tissue deformation patterns is essential for understanding how organ-specific morphology is generated during development and regeneration. However, due to imaging difficulties and complex morphology, little is known about deformation dynamics for most vertebrate organs such as the brain and heart. To better understand these dynamics, we propose a method to precisely reconstruct global deformation patterns for three-dimensional morphogenesis of curved epithelial sheets using positional data from labeled cells representing only 1–10% of the entire tissue with limited resolution. By combining differential-geometrical and Bayesian frameworks, the method is applicable to any morphology described with arbitrary coordinates, and ensures the feasibility of analyzing many vertebrate organs. Application to data from chick forebrain morphogenesis demonstrates that our method provides not only a quantitative description of tissue deformation dynamics but also predictions of the mechanisms that determine organ-specific morphology, which could form the basis for the multi-scale understanding of organ morphogenesis.

Date: 2017
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-017-00023-7 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:8:y:2017:i:1:d:10.1038_s41467-017-00023-7

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-017-00023-7

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().