Development of pacemaker properties and rhythmogenic mechanisms in the mouse embryonic respiratory network

  1. Marc Chevalier
  2. Natalia Toporikova
  3. John Simmers
  4. Muriel Thoby-Brisson  Is a corresponding author
  1. Institut de Neurosciences Cognitives et Intégratives d'Aquitaine, CNRS UMR 5287, France
  2. Washington and Lee University, United States

Abstract

Breathing is a vital rhythmic behavior generated by hindbrain neuronal circuitry, including the preBötzinger complex network (preBötC) that controls inspiration. The emergence of preBötC network activity during prenatal development has been described but little is known regarding inspiratory neurons expressing pacemaker properties at embryonic stages. Here, we combined calcium imaging and electrophysiological recordings in mouse embryo brainstem slices together with computational modeling to reveal the existence of heterogeneous pacemaker oscillatory properties relying on distinct combinations of burst-generating INaP and ICAN conductances. The respective proportion of the different inspiratory pacemaker subtypes changes during prenatal development. Concomitantly, network rhythmogenesis switches from a purely INaP/ICAN-dependent mechanism at E16.5 to a combined pacemaker/network-driven process at E18.5. Our results provide the first description of pacemaker bursting properties in embryonic preBötC neurons and indicate that network rhythmogenesis undergoes important changes during prenatal development through alterations both in circuit properties and the biophysical characteristics of pacemaker neurons.

Article and author information

Author details

  1. Marc Chevalier

    Institut de Neurosciences Cognitives et Intégratives d'Aquitaine, CNRS UMR 5287, Bordeaux, France
    Competing interests
    The authors declare that no competing interests exist.
  2. Natalia Toporikova

    Department of Biology, Washington and Lee University, Lexington, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. John Simmers

    Institut de Neurosciences Cognitives et Intégratives d'Aquitaine, CNRS UMR 5287, Bordeaux, France
    Competing interests
    The authors declare that no competing interests exist.
  4. Muriel Thoby-Brisson

    Institut de Neurosciences Cognitives et Intégratives d'Aquitaine, CNRS UMR 5287, Bordeaux, France
    For correspondence
    muriel.thoby-brisson@u-bordeaux.fr
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3214-1724

Funding

Agence Nationale pour le Développement de la Recherche en Santé (ANR12-BSV4-0011-01)

  • Muriel Thoby-Brisson

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Ethics

Animal experimentation: All experiments were performed in accordance with the guidelines of the European and French National legislation on animal experimentation and the local ethics committee of the University of Bordeaux (permit number 5012031A)

Copyright

© 2016, Chevalier et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 1,384
    views
  • 301
    downloads
  • 28
    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. Marc Chevalier
  2. Natalia Toporikova
  3. John Simmers
  4. Muriel Thoby-Brisson
(2016)
Development of pacemaker properties and rhythmogenic mechanisms in the mouse embryonic respiratory network
eLife 5:e16125.
https://doi.org/10.7554/eLife.16125

Share this article

https://doi.org/10.7554/eLife.16125

Further reading

    1. Neuroscience
    Geoffrey W Meissner, Allison Vannan ... FlyLight Project Team
    Research Article

    Techniques that enable precise manipulations of subsets of neurons in the fly central nervous system (CNS) have greatly facilitated our understanding of the neural basis of behavior. Split-GAL4 driver lines allow specific targeting of cell types in Drosophila melanogaster and other species. We describe here a collection of 3060 lines targeting a range of cell types in the adult Drosophila CNS and 1373 lines characterized in third-instar larvae. These tools enable functional, transcriptomic, and proteomic studies based on precise anatomical targeting. NeuronBridge and other search tools relate light microscopy images of these split-GAL4 lines to connectomes reconstructed from electron microscopy images. The collections are the result of screening over 77,000 split hemidriver combinations. Previously published and new lines are included, all validated for driver expression and curated for optimal cell-type specificity across diverse cell types. In addition to images and fly stocks for these well-characterized lines, we make available 300,000 new 3D images of other split-GAL4 lines.

    1. Neuroscience
    Hyun Jee Lee, Jingting Liang ... Hang Lu
    Research Advance

    Cell identification is an important yet difficult process in data analysis of biological images. Previously, we developed an automated cell identification method called CRF_ID and demonstrated its high performance in Caenorhabditis elegans whole-brain images (Chaudhary et al., 2021). However, because the method was optimized for whole-brain imaging, comparable performance could not be guaranteed for application in commonly used C. elegans multi-cell images that display a subpopulation of cells. Here, we present an advancement, CRF_ID 2.0, that expands the generalizability of the method to multi-cell imaging beyond whole-brain imaging. To illustrate the application of the advance, we show the characterization of CRF_ID 2.0 in multi-cell imaging and cell-specific gene expression analysis in C. elegans. This work demonstrates that high-accuracy automated cell annotation in multi-cell imaging can expedite cell identification and reduce its subjectivity in C. elegans and potentially other biological images of various origins.