High-throughput automated methods for classical and operant conditioning of Drosophila larvae

  1. Elise C Croteau-Chonka
  2. Michael S Clayton
  3. Lalanti Venkatasubramanian
  4. Samuel N Harris
  5. Benjamin M W Jones
  6. Lakshmi Narayan
  7. Michael Winding
  8. Jean-Baptiste Masson
  9. Marta Zlatic  Is a corresponding author
  10. Kristina T Klein  Is a corresponding author
  1. University of Cambridge, United Kingdom
  2. MRC Laboratory of Molecular Biology, United Kingdom
  3. Janelia Research Campus, United States
  4. Institut Pasteur, France

Abstract

Learning which stimuli (classical conditioning) or which actions (operant conditioning) predict rewards or punishments can improve chances of survival. However, the circuit mechanisms that underlie distinct types of associative learning are still not fully understood. Automated, high-throughput paradigms for studying different types of associative learning, combined with manipulation of specific neurons in freely behaving animals, can help advance this field. The Drosophila melanogaster larva is a tractable model system for studying the circuit basis of behaviour, but many forms of associative learning have not yet been demonstrated in this animal. Here, we developed a high-throughput (i. e. multi-larva) training system that combines real-time behaviour detection of freely moving larvae with targeted opto- and thermogenetic stimulation of tracked animals. Both stimuli are controlled in either open- or closed-loop, and delivered with high temporal and spatial precision. Using this tracker, we show for the first time that Drosophila larvae can perform classical conditioning with no overlap between sensory stimuli (i. e. trace conditioning). We also demonstrate that larvae are capable of operant conditioning by inducing a bend direction preference through optogenetic activation of reward-encoding serotonergic neurons. Our results extend the known associative learning capacities of Drosophila larvae. Our automated training rig will facilitate the study of many different forms of associative learning and the identification of the neural circuits that underpin them.

Data availability

All data used to generate figures 2-5, as well as all supplemental figures, are now submitted as source data files. We also now submit CAD drawings for the multi-larva tracker.

Article and author information

Author details

  1. Elise C Croteau-Chonka

    Department of Zoology, University of Cambridge, Cambridge, United Kingdom
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5116-3772
  2. Michael S Clayton

    MRC Laboratory of Molecular Biology, Cambridge, United Kingdom
    Competing interests
    No competing interests declared.
  3. Lalanti Venkatasubramanian

    Department of Zoology, University of Cambridge, Cambridge, United Kingdom
    Competing interests
    No competing interests declared.
  4. Samuel N Harris

    MRC Laboratory of Molecular Biology, Cambridge, United Kingdom
    Competing interests
    No competing interests declared.
  5. Benjamin M W Jones

    MRC Laboratory of Molecular Biology, Cambridge, United Kingdom
    Competing interests
    No competing interests declared.
  6. Lakshmi Narayan

    Janelia Research Campus, Ashburn, United States
    Competing interests
    No competing interests declared.
  7. Michael Winding

    Department of Zoology, University of Cambridge, Cambridge, United Kingdom
    Competing interests
    No competing interests declared.
  8. Jean-Baptiste Masson

    Department of Computational Biology and Neuroscience, Institut Pasteur, Paris, France
    Competing interests
    No competing interests declared.
  9. Marta Zlatic

    MRC Laboratory of Molecular Biology, Cambridge, United Kingdom
    For correspondence
    mzlatic@mrc-lmb.cam.ac.uk
    Competing interests
    Marta Zlatic, Reviewing editor, eLife.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-3149-2250
  10. Kristina T Klein

    Department of Zoology, University of Cambridge, Cambridge, United Kingdom
    For correspondence
    kristina.t.klein@gmail.com
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8772-3628

Funding

Gates Cambridge Trust

  • Marta Zlatic

Cambridge Trust

  • Marta Zlatic

HHMI Janelia Visiting Scientist Program

  • Marta Zlatic

University of Cambridge, Trinity College

  • Marta Zlatic

HHMI Janelia

  • Marta Zlatic

European Research Council

  • Marta Zlatic

Wellcome Trust

  • Marta Zlatic

Medical Research Council

  • Marta Zlatic

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

Reviewing Editor

  1. Aravinthan D. T. Samuel, Harvard University, United States

Version history

  1. Received: May 4, 2021
  2. Preprint posted: June 14, 2021 (view preprint)
  3. Accepted: October 26, 2022
  4. Accepted Manuscript published: October 28, 2022 (version 1)
  5. Accepted Manuscript updated: October 31, 2022 (version 2)
  6. Version of Record published: November 21, 2022 (version 3)

Copyright

© 2022, Croteau-Chonka 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.

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  1. Elise C Croteau-Chonka
  2. Michael S Clayton
  3. Lalanti Venkatasubramanian
  4. Samuel N Harris
  5. Benjamin M W Jones
  6. Lakshmi Narayan
  7. Michael Winding
  8. Jean-Baptiste Masson
  9. Marta Zlatic
  10. Kristina T Klein
(2022)
High-throughput automated methods for classical and operant conditioning of Drosophila larvae
eLife 11:e70015.
https://doi.org/10.7554/eLife.70015

Share this article

https://doi.org/10.7554/eLife.70015

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