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

  • 1,952
    views
  • 250
    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. Evolutionary Biology
    Pierre Barrat-Charlaix, Richard A Neher
    Research Article

    As pathogens spread in a population of hosts, immunity is built up, and the pool of susceptible individuals are depleted. This generates selective pressure, to which many human RNA viruses, such as influenza virus or SARS-CoV-2, respond with rapid antigenic evolution and frequent emergence of immune evasive variants. However, the host’s immune systems adapt, and older immune responses wane, such that escape variants only enjoy a growth advantage for a limited time. If variant growth dynamics and reshaping of host-immunity operate on comparable time scales, viral adaptation is determined by eco-evolutionary interactions that are not captured by models of rapid evolution in a fixed environment. Here, we use a Susceptible/Infected model to describe the interaction between an evolving viral population in a dynamic but immunologically diverse host population. We show that depending on strain cross-immunity, heterogeneity of the host population, and durability of immune responses, escape variants initially grow exponentially, but lose their growth advantage before reaching high frequencies. Their subsequent dynamics follows an anomalous random walk determined by future escape variants and results in variant trajectories that are unpredictable. This model can explain the apparent contradiction between the clearly adaptive nature of antigenic evolution and the quasi-neutral dynamics of high-frequency variants observed for influenza viruses.

    1. Computational and Systems Biology
    2. Medicine
    Xin Zhou, Zhinuo Jenny Wang ... Blanca Rodriguez
    Research Article

    Sudden death after myocardial infarction (MI) is associated with electrophysiological heterogeneities and ionic current remodelling. Low ejection fraction (EF) is used in risk stratification, but its mechanistic links with pro-arrhythmic heterogeneities are unknown. We aim to provide mechanistic explanations of clinical phenotypes in acute and chronic MI, from ionic current remodelling to ECG and EF, using human electromechanical modelling and simulation to augment experimental and clinical investigations. A human ventricular electromechanical modelling and simulation framework is constructed and validated with rich experimental and clinical datasets, incorporating varying degrees of ionic current remodelling as reported in literature. In acute MI, T-wave inversion and Brugada phenocopy were explained by conduction abnormality and local action potential prolongation in the border zone. In chronic MI, upright tall T-waves highlight large repolarisation dispersion between the border and remote zones, which promoted ectopic propagation at fast pacing. Post-MI EF at resting heart rate was not sensitive to the extent of repolarisation heterogeneity and the risk of repolarisation abnormalities at fast pacing. T-wave and QT abnormalities are better indicators of repolarisation heterogeneities than EF in post-MI.