High-resolution analysis of individualDrosophila melanogasterlarvae within groups uncovers inter- and intra-individual variability in locomotion and its neurogenetic modulation

Abstract

Neuronally orchestrated muscular movement and locomotion are defining faculties of multicellular animals. Due to its numerically simple brain and neuromuscular system and its genetic accessibility, the larva of the fruit fly Drosophila melanogaster is an established model to study these processes at tractable levels of complexity. However, although the faculty of locomotion clearly pertains to the individual animal, present studies of locomotion in larval Drosophila mostly use group assays and measurements aggregated across individual animals. The alternative is to measure animals one at a time, an extravagance for larger-scale analyses. In principle or in practice, this in particular rules out grasping the inter- and intra-individual variability in locomotion and its genetic and neuronal determinants. Here we present the IMBA (Individual Maggot Behaviour Analyser) for tracking and analysing the behaviour of individual larvae within groups. Using a combination of computational modelling and statistical approaches, the IMBA reliably resolves individual identity across collisions. It does not require specific hardware and can therefore be used in non-expert labs. We take advantage of the IMBA first to systematically describe the inter- and intra-individual variability in free, unconstrained locomotion in wild-type animals. We then report the discovery of a novel, complex locomotion phenotype of a mutant lacking an adhesion-type GPCR. The IMBA further allows us to determine, at the level of individual animals, the modulation of locomotion across repeated activations of dopamine neurons. Strikingly, IMBA can also be used to analyse 'silly walks', that is patterns of locomotion it was not originally designed to investigate. This is shown for the transient backward locomotion induced by brief optogenetic activation of the brain-descending 'mooncrawler' neurons, and the variability in this behaviour. Thus, the IMBA is an easy-to-use toolbox allowing an unprecedentedly rich view of the behaviour and behavioural variability of individual Drosophila larvae, with utility in multiple biomedical research contexts.

Prediction error drives associative learning and conditioned behavior in a spiking model of Drosophila larva

“…Additionally, parameters of the larval body could be considered to enhance inter-individual differences, such as the larval size, which affects speed. 60 , 119 , 120 …”

LarvaTagger: Manual and automatic tagging ofDrosophilalarval behaviour

“…Furthermore, the choice of the number of larvae per behavioural assay represents a trade-off between increasing statistics and minimising crossing events. Indeed, the larvae have highly deformable bodies and are challenging to properly differentiate and track when they interact while being filmed at low resolutions (recent progress have been made at high resolution [18]). All the tracking solutions we know of require precise calibration and may fail to detect moving objects with minor variations of experimental parameters.…”

High-resolution analysis of individual Drosophila melanogaster larvae uncovers individual variability in locomotion and its neurogenetic modulation

“…Data accessibility. All raw data used for analyses and figures in this study are available as electronic supplementary material to this article [107]. The complete code of the IMBA software is available via https:// github.com/mthane/IMBA.…”