High-throughput automated methods for classical and operant conditioning of Drosophila larvae
- University of Cambridge, United Kingdom
- MRC Laboratory of Molecular Biology, United Kingdom
- Janelia Research Campus, United States
- 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
Elise C Croteau-Chonka
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.
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High-throughput automated methods for classical and operant conditioning of Drosophila larvae
eLife 11:e70015.
https://doi.org/10.7554/eLife.70015
