IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v11y2020i1d10.1038_s41467-020-19863-x.html
   My bibliography  Save this article

Establishment of a morphological atlas of the Caenorhabditis elegans embryo using deep-learning-based 4D segmentation

Author

Listed:
  • Jianfeng Cao

    (City University of Hong Kong)

  • Guoye Guan

    (Peking University)

  • Vincy Wing Sze Ho

    (Hong Kong Baptist University
    Hong Kong University of Science and Technology)

  • Ming-Kin Wong

    (Hong Kong Baptist University)

  • Lu-Yan Chan

    (Hong Kong Baptist University)

  • Chao Tang

    (Peking University
    Peking University
    Peking University)

  • Zhongying Zhao

    (Hong Kong Baptist University
    Hong Kong Baptist University)

  • Hong Yan

    (City University of Hong Kong)

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.

Suggested Citation

  • Jianfeng Cao & Guoye Guan & Vincy Wing Sze Ho & Ming-Kin Wong & Lu-Yan Chan & Chao Tang & Zhongying Zhao & Hong Yan, 2020. "Establishment of a morphological atlas of the Caenorhabditis elegans embryo using deep-learning-based 4D segmentation," Nature Communications, Nature, vol. 11(1), pages 1-14, December.
    • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-19863-x
    DOI: 10.1038/s41467-020-19863-x
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-020-19863-x
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-020-19863-x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Min Guo & Yicong Wu & Chad M. Hobson & Yijun Su & Shuhao Qian & Eric Krueger & Ryan Christensen & Grant Kroeschell & Johnny Bui & Matthew Chaw & Lixia Zhang & Jiamin Liu & Xuekai Hou & Xiaofei Han & Z, 2025. "Deep learning-based aberration compensation improves contrast and resolution in fluorescence microscopy," Nature Communications, Nature, vol. 16(1), pages 1-19, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-19863-x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.