Untwisting the Caenorhabditis elegans embryo

  1. Ryan Patrick Christensen  Is a corresponding author
  2. Alexandra Bokinsky
  3. Anthony Santella
  4. Yicong Wu
  5. Javier Marquina-Solis
  6. Min Guo
  7. Ismar Kovacevic
  8. Abhishek Kumar
  9. Peter W Winter
  10. Nicole Tashakkori
  11. Evan McCreedy
  12. Huafeng Liu
  13. Matthew McAuliffe
  14. William Mohler
  15. Daniel A Colon-Ramos
  16. Zhirong Bao
  17. Hari Shroff
  1. National Institutes of Health, United States
  2. Sloan-Kettering Institute, United States
  3. Yale University, United States
  4. Zhejiang University, China
  5. University of Connecticut Health Center, United States

Abstract

The nematode Caenorhabditis elegans possesses a simple embryonic nervous system comprising 222 neurons, a number small enough that the growth of each cell could be followed to provide a systems-level view of development. However, studies of single cell development have largely been conducted in fixed or pre-twitching live embryos, because of technical difficulties associated with embryo movement in late embryogenesis. We present open source untwisting and annotation software which allows the investigation of neurodevelopmental events in post-twitching embryos, and apply them to track the 3D positions of seam cells, neurons, and neurites in multiple elongating embryos. The detailed positional information we obtained enabled us to develop a composite model showing movement of these cells and neurites in an "average" worm embryo. The untwisting and cell tracking capability we demonstrate provides a foundation on which to catalog C. elegans neurodevelopment, allowing interrogation of developmental events in previously inaccessible periods of embryogenesis.

Article and author information

Author details

  1. Ryan Patrick Christensen

    National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Bethesda, United States
    For correspondence
    ryan.christensen@nih.gov
    Competing interests
    The authors declare that no competing interests exist.
  2. Alexandra Bokinsky

    Center for Information Technology, National Institutes of Health, Bethesda, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Anthony Santella

    Developmental Biology Program, Sloan-Kettering Institute, New York, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. Yicong Wu

    National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Bethesda, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Javier Marquina-Solis

    Program in Cellular Neuroscience, Neurodegeneration, and Repair, Department of Cell Biology, Yale University, New Haven, United States
    Competing interests
    The authors declare that no competing interests exist.
  6. Min Guo

    State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou, China
    Competing interests
    The authors declare that no competing interests exist.
  7. Ismar Kovacevic

    Developmental Biology Program, Sloan-Kettering Institute, New York, United States
    Competing interests
    The authors declare that no competing interests exist.
  8. Abhishek Kumar

    Program in Cellular Neuroscience, Neurodegeneration, and Repair, Department of Cell Biology, Yale University, New Haven, United States
    Competing interests
    The authors declare that no competing interests exist.
  9. Peter W Winter

    National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Bethesda, United States
    Competing interests
    The authors declare that no competing interests exist.
  10. Nicole Tashakkori

    National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Bethesda, United States
    Competing interests
    The authors declare that no competing interests exist.
  11. Evan McCreedy

    Center for Information Technology, National Institutes of Health, Bethesda, United States
    Competing interests
    The authors declare that no competing interests exist.
  12. Huafeng Liu

    State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou, China
    Competing interests
    The authors declare that no competing interests exist.
  13. Matthew McAuliffe

    Center for Information Technology, National Institutes of Health, Bethesda, United States
    Competing interests
    The authors declare that no competing interests exist.
  14. William Mohler

    Department of Genetics and Developmental Biology, University of Connecticut Health Center, Farmington, United States
    Competing interests
    The authors declare that no competing interests exist.
  15. Daniel A Colon-Ramos

    Cell Biology, Yale University, New Haven, United States
    Competing interests
    The authors declare that no competing interests exist.
  16. Zhirong Bao

    Developmental Biology Program, Sloan-Kettering Institute, New York, United States
    Competing interests
    The authors declare that no competing interests exist.
  17. Hari Shroff

    National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Bethesda, United States
    Competing interests
    The authors declare that no competing interests exist.

Copyright

This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.

Metrics

  • 4,651
    views
  • 747
    downloads
  • 31
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Ryan Patrick Christensen
  2. Alexandra Bokinsky
  3. Anthony Santella
  4. Yicong Wu
  5. Javier Marquina-Solis
  6. Min Guo
  7. Ismar Kovacevic
  8. Abhishek Kumar
  9. Peter W Winter
  10. Nicole Tashakkori
  11. Evan McCreedy
  12. Huafeng Liu
  13. Matthew McAuliffe
  14. William Mohler
  15. Daniel A Colon-Ramos
  16. Zhirong Bao
  17. Hari Shroff
(2015)
Untwisting the Caenorhabditis elegans embryo
eLife 4:e10070.
https://doi.org/10.7554/eLife.10070

Share this article

https://doi.org/10.7554/eLife.10070

Further reading

    1. Computational and Systems Biology
    Mayalen Etcheverry, Clément Moulin-Frier ... Michael Levin
    Research Article

    Many applications in biomedicine and synthetic bioengineering rely on understanding, mapping, predicting, and controlling the complex behavior of chemical and genetic networks. The emerging field of diverse intelligence investigates the problem-solving capacities of unconventional agents. However, few quantitative tools exist for exploring the competencies of non-conventional systems. Here, we view gene regulatory networks (GRNs) as agents navigating a problem space and develop automated tools to map the robust goal states GRNs can reach despite perturbations. Our contributions include: (1) Adapting curiosity-driven exploration algorithms from AI to discover the range of reachable goal states of GRNs, and (2) Proposing empirical tests inspired by behaviorist approaches to assess their navigation competencies. Our data shows that models inferred from biological data can reach a wide spectrum of steady states, exhibiting various competencies in physiological network dynamics without requiring structural changes in network properties or connectivity. We also explore the applicability of these ‘behavioral catalogs’ for comparing evolved competencies across biological networks, for designing drug interventions in biomedical contexts and synthetic gene networks for bioengineering. These tools and the emphasis on behavior-shaping open new paths for efficiently exploring the complex behavior of biological networks. For the interactive version of this paper, please visit https://developmentalsystems.org/curious-exploration-of-grn-competencies.

    1. Cancer Biology
    2. Computational and Systems Biology
    Rosalyn W Sayaman, Masaru Miyano ... Mark A LaBarge
    Research Article Updated

    Effects from aging in single cells are heterogenous, whereas at the organ- and tissue-levels aging phenotypes tend to appear as stereotypical changes. The mammary epithelium is a bilayer of two major phenotypically and functionally distinct cell lineages: luminal epithelial and myoepithelial cells. Mammary luminal epithelia exhibit substantial stereotypical changes with age that merit attention because these cells are the putative cells-of-origin for breast cancers. We hypothesize that effects from aging that impinge upon maintenance of lineage fidelity increase susceptibility to cancer initiation. We generated and analyzed transcriptomes from primary luminal epithelial and myoepithelial cells from younger <30 (y)ears old and older >55 y women. In addition to age-dependent directional changes in gene expression, we observed increased transcriptional variance with age that contributed to genome-wide loss of lineage fidelity. Age-dependent variant responses were common to both lineages, whereas directional changes were almost exclusively detected in luminal epithelia and involved altered regulation of chromatin and genome organizers such as SATB1. Epithelial expression variance of gap junction protein GJB6 increased with age, and modulation of GJB6 expression in heterochronous co-cultures revealed that it provided a communication conduit from myoepithelial cells that drove directional change in luminal cells. Age-dependent luminal transcriptomes comprised a prominent signal that could be detected in bulk tissue during aging and transition into cancers. A machine learning classifier based on luminal-specific aging distinguished normal from cancer tissue and was highly predictive of breast cancer subtype. We speculate that luminal epithelia are the ultimate site of integration of the variant responses to aging in their surrounding tissue, and that their emergent phenotype both endows cells with the ability to become cancer-cells-of-origin and represents a biosensor that presages cancer susceptibility.