Abstract

The invariant development and transparent body of the nematode Caenorhabditis elegans enables complete delineation of cell lineages throughout development. Despite extensive studies of cell division, cell migration and cell fate differentiation, cell morphology during development has not yet been systematically characterized in any metazoan, including C. elegans. This knowledge gap substantially hampers many studies in both developmental and cell biology. Here we report an automatic pipeline, CShaper, which combines automated segmentation of fluorescently labeled membranes with automated cell lineage tracing. We apply this pipeline to quantify morphological parameters of densely packed cells in 17 developing C. elegans embryos. Consequently, we generate a time-lapse 3D atlas of cell morphology for the C. elegans embryo from the 4- to 350-cell stages, including cell shape, volume, surface area, migration, nucleus position and cell-cell contact with resolved cell identities. We anticipate that CShaper and the morphological atlas will stimulate and enhance further studies in the fields of developmental biology, cell biology and biomechanics.

The systematic characterization of C. elegans morphology during development has yet to be performed. Here, the authors produce a 3D atlas of C. elegans morphology from 17 embryos and 54 developmental stages, using an automated pipeline, CShaper (combining segmentation of fluorescently labeled membranes with automated cell lineage tracing).

Details

Title
Establishment of a morphological atlas of the Caenorhabditis elegans embryo using deep-learning-based 4D segmentation
Author
Cao Jianfeng 1   VIAFID ORCID Logo  ; Guan Guoye 2   VIAFID ORCID Logo  ; Ho Vincy Wing Sze 3 ; Ming-Kin, Wong 4 ; Lu-Yan, Chan 4 ; Tang, Chao 5   VIAFID ORCID Logo  ; Zhao, Zhongying 6   VIAFID ORCID Logo  ; Hong, Yan 1 

 City University of Hong Kong, Department of Electrical Engineering, Hong Kong, China (GRID:grid.35030.35) (ISNI:0000 0004 1792 6846) 
 Peking University, Center for Quantitative Biology, Beijing, China (GRID:grid.11135.37) (ISNI:0000 0001 2256 9319) 
 Hong Kong Baptist University, Department of Biology, Hong Kong, China (GRID:grid.221309.b) (ISNI:0000 0004 1764 5980); Hong Kong University of Science and Technology, Center for Epigenomics Research, Division of Life Science, Hong Kong, China (GRID:grid.24515.37) (ISNI:0000 0004 1937 1450) 
 Hong Kong Baptist University, Department of Biology, Hong Kong, China (GRID:grid.221309.b) (ISNI:0000 0004 1764 5980) 
 Peking University, Center for Quantitative Biology, Beijing, China (GRID:grid.11135.37) (ISNI:0000 0001 2256 9319); Peking University, Peking-Tsinghua Center for Life Sciences, Beijing, China (GRID:grid.11135.37) (ISNI:0000 0001 2256 9319); Peking University, School of Physics, Beijing, China (GRID:grid.11135.37) (ISNI:0000 0001 2256 9319) 
 Hong Kong Baptist University, Department of Biology, Hong Kong, China (GRID:grid.221309.b) (ISNI:0000 0004 1764 5980); Hong Kong Baptist University, State Key Laboratory of Environmental and Biological Analysis, Hong Kong, China (GRID:grid.221309.b) (ISNI:0000 0004 1764 5980) 
Publication year
2020
Publication date
2020
Publisher
Nature Publishing Group
e-ISSN
20411723
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2473280050
Copyright
© The Author(s) 2020. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.