From behavior to circuit modeling of light-seeking navigation in zebrafish larvae

  1. Sophia Karpenko
  2. Sebastien Wolf
  3. Julie Lafaye
  4. Guillaume Le Goc
  5. Thomas Panier
  6. Volker Bormuth
  7. Raphaël Candelier
  8. Georges Debrégeas  Is a corresponding author
  1. Laboratoire Jean Perrin, France
  2. Laboratoire de Physique de l'Ecole Normale Supérieure, France

Abstract

Bridging brain-scale circuit dynamics and organism-scale behavior is a central challenge in neuroscience. It requires the concurrent development of minimal behavioral and neural circuit models that can quantitatively capture basic sensorimotor operations. Here we focus on light-seeking navigation in zebrafish larvae. Using a virtual reality assay, we first characterize how motor and visual stimulation sequences govern the selection of discrete swim-bout events that subserve the fish navigation in the presence of a distant light source. These mechanisms are combined into a comprehensive Markov-chain model of navigation that quantitatively predict the stationary distribution of the fish's body orientation under any given illumination profile. We then map this behavioral description onto a neuronal model of the ARTR, a small neural circuit involved in the orientation-selection of swim bouts. We demonstrate that this visually-biased decision-making circuit can similarly capture the statistics of both spontaneous and contrast-driven navigation.

Data availability

Data and analysis codes are available at Dryad Digital: Data DOI: doi:10.5061/dryad.v9s4mw6qx

The following data sets were generated

Article and author information

Author details

  1. Sophia Karpenko

    IBPS, CNRS, Sorbonne Université, Laboratoire Jean Perrin, Paris, France
    Competing interests
    The authors declare that no competing interests exist.
  2. Sebastien Wolf

    PSL, ENS, CNRS, IBENS, INSERM, Laboratoire de Physique de l'Ecole Normale Supérieure, Paris, France
    Competing interests
    The authors declare that no competing interests exist.
  3. Julie Lafaye

    IBPS, CNRS, Sorbonne Université, Laboratoire Jean Perrin, Paris, France
    Competing interests
    The authors declare that no competing interests exist.
  4. Guillaume Le Goc

    IBPS, CNRS, Sorbonne Université, Laboratoire Jean Perrin, Paris, France
    Competing interests
    The authors declare that no competing interests exist.
  5. Thomas Panier

    IBPS, CNRS, Sorbonne Université, Laboratoire Jean Perrin, Paris, France
    Competing interests
    The authors declare that no competing interests exist.
  6. Volker Bormuth

    IBPS, CNRS, Sorbonne Université, Laboratoire Jean Perrin, Paris, France
    Competing interests
    The authors declare that no competing interests exist.
  7. Raphaël Candelier

    IBPS, CNRS, Sorbonne Université, Laboratoire Jean Perrin, Paris, France
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1523-6249
  8. Georges Debrégeas

    IBPS, CNRS, Sorbonne Université, Laboratoire Jean Perrin, Paris, France
    For correspondence
    georges.debregeas@upmc.fr
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3698-4497

Funding

Human Frontier Science Program (RGP0060/2017)

  • Georges Debrégeas

H2020 European Research Council (71598)

  • Volker Bormuth

Agence Nationale de la Recherche (ANR-16-CE16-0017)

  • Raphaël Candelier
  • Georges Debrégeas

Fondation pour la Recherche Médicale (FDT201904008219)

  • Sophia Karpenko

ATIP-Avenir program

  • Volker Bormuth

Fondation pour la Recherche Médicale (SPF201809007064)

  • Sebastien Wolf

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 approved by Le Comité d'Éthique pour l'Expérimentation Animale Charles Darwin C2EA-05 (02601.01).

Copyright

© 2020, Karpenko 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

  • 3,221
    views
  • 459
    downloads
  • 21
    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. Sophia Karpenko
  2. Sebastien Wolf
  3. Julie Lafaye
  4. Guillaume Le Goc
  5. Thomas Panier
  6. Volker Bormuth
  7. Raphaël Candelier
  8. Georges Debrégeas
(2020)
From behavior to circuit modeling of light-seeking navigation in zebrafish larvae
eLife 9:e52882.
https://doi.org/10.7554/eLife.52882

Share this article

https://doi.org/10.7554/eLife.52882

Further reading

    1. Computational and Systems Biology
    2. Neuroscience
    Anna Cattani, Don B Arnold ... Nancy Kopell
    Research Article

    The basolateral amygdala (BLA) is a key site where fear learning takes place through synaptic plasticity. Rodent research shows prominent low theta (~3–6 Hz), high theta (~6–12 Hz), and gamma (>30 Hz) rhythms in the BLA local field potential recordings. However, it is not understood what role these rhythms play in supporting the plasticity. Here, we create a biophysically detailed model of the BLA circuit to show that several classes of interneurons (PV, SOM, and VIP) in the BLA can be critically involved in producing the rhythms; these rhythms promote the formation of a dedicated fear circuit shaped through spike-timing-dependent plasticity. Each class of interneurons is necessary for the plasticity. We find that the low theta rhythm is a biomarker of successful fear conditioning. The model makes use of interneurons commonly found in the cortex and, hence, may apply to a wide variety of associative learning situations.

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

    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 >55y 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 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.