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.

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.

Metrics

  • 1,919
    views
  • 248
    downloads
  • 8
    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. 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

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.