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

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.

Operant conditioning of bend direction using high-throughput tracker: Sample sizes for each genotype and bend direction read-out are shown below the corresponding data in Figure 4, panels c-h, and Figure 5, panels a-d.

Replicates
• You should report how often each experiment was performed • You should include a definition of biological versus technical replication • The data obtained should be provided and sufficient information should be provided to indicate the number of independent biological and/or technical replicates • If you encountered any outliers, you should describe how these were handled • Criteria for exclusion/inclusion of data should be clearly stated • High-throughput sequence data should be uploaded before submission, with a private link for reviewers provided (these are available from both GEO and ArrayExpress) Please outline where this information can be found within the submission (e.g., sections or figure legends), or explain why this information doesn't apply to your submission: Proof-of-principle optogenetic and thermogenetic stimulation experiments: Criteria for data exclusion/inclusion is detailed in the Methods and Materials subsection titled, "Verification of optogenetic and thermogenetic stimulation efficiency".
Aversion to fictive Or42b develops after forward-paired trace conditioning: Criteria for data exclusion/inclusion is detailed in the Methods and Materials subsection titled, "High-throughput classical conditioning: Data analysis".
Operant conditioning of bend direction using high-throughput tracker: Criteria for data exclusion/inclusion is detailed in the Methods and Materials subsection titled, "High-throughput operant conditioning: Data analysis".
Visual analysis of Ddc-Gal4 expression pattern The number of confocal imaging stack replicates for each visual analysis are detailed in Figure 4 -Figure supplement 1.

Statistical reporting
• Statistical analysis methods should be described and justified • Raw data should be presented in figures whenever informative to do so (typically when N per group is less than 10) • For each experiment, you should identify the statistical tests used, exact values of N, definitions of center, methods of multiple test correction, and dispersion and precision measures (e.g., mean, median, SD, SEM, confidence intervals; and, for the major substantive results, a measure of effect size (e.g., Pearson's r, Cohen's d) • Report exact p-values wherever possible alongside the summary statistics and 95% confidence intervals. These should be reported for all key questions and not only when the p-value is less than 0.05.
Please outline where this information can be found within the submission (e.g., sections or figure legends), or explain why this information doesn't apply to your submission: Proof-of-principle optogenetic and thermogenetic stimulation experiments: Statistical analysis tests and exact values of N are described and identified in the Figure 2 caption.
Aversion to fictive Or42b develops after forward-paired trace conditioning: Raw data is shown in Figure 3, panel f. Statistical analysis tests are described and identified in the Figure 3 caption and in the Methods and Materials subsection titled, "High-throughput classical conditioning: Data analysis".
Operant conditioning of bend direction using high-throughput tracker: Statistical analysis tests and precision measures are described and identified in the Figure 4 and Figure 4 captions and in the Methods and Materials subsection titled, "High-throughput operant conditioning: Data analysis". Exact values of N are displayed in Figure 4, panels c-h, and Figure 5, panels a-d.
(For large datasets, or papers with a very large number of statistical tests, you may upload a single table file with tests, Ns, etc., with reference to sections in the manuscript.)