An FIM-Based Long-Term In-Vial Monitoring System for Drosophila Larvae

Megapixel camera arrays enable high-resolution animal tracking in multiwell plates

Tracking small laboratory animals such as flies, fish, and worms is used for phenotyping in neuroscience, genetics, disease modelling, and drug discovery. An imaging system with sufficient throughput and spatiotemporal resolution would be capable of imaging a large number of animals, estimating their pose, and quantifying detailed behavioural differences at a scale where hundreds of treatments could be tested simultaneously. Here we report an array of six 12-megapixel cameras that record all the wells of a 96-well plate with sufficient resolution to estimate the pose of C. elegans worms and to extract high-dimensional phenotypic fingerprints. We use the system to study behavioural variability across wild isolates, the sensitisation of worms to repeated blue light stimulation, the phenotypes of worm disease models, and worms' behavioural responses to drug treatment. Because the system is compatible with standard multiwell plates, it makes computational ethological approaches accessible in existing high-throughput pipelines.

Resolving Colliding Larvae by Fitting ASM to Random Walker-Based Pre-Segmentations

Drosophila melanogaster is an important model organism for research in neuro- and behavioral biology. Automated studies of their locomotion are crucial to link sensory input and neural processing to motor output which has led to numerous vision-based tracking systems. However, most of these approaches share the inability to segment the contours of colliding animals causing identity losses, appearing and disappearing animals, and the absence of posture and motion related measurements during the time of the collision. We present a novel collision resolution algorithm enabling an accurate contour segmentation of multiple touching Drosophila larvae. Our algorithm utilizes an adapted active shape model (ASM) to learn a low dimensional posture space which is fitted to random-walker generated pre-segmentations. We evaluate our collision resolution algorithm using three publicly available datasets and compare it with the current state-of-the-art methods. In addition, we introduce a refined dataset enabling a segmentation evaluation on the level of pixel accuracy. The results demonstrate that our approach outperforms the state-of-the-art approaches in both accuracy and computational time. We will incorporate this algorithm into our widely used tracking program to improve the statistical strength of the behavioral quantification and allow marker-free studies of interacting Drosophila larvae.

An RJMCMC-Based Method for Tracking and Resolving Collisions of Drosophila Larvae

Drosophila melanogaster is an important model organism for ongoing research in neuro- and behavioral biology. Especially the locomotion analysis has become an integral part of such studies and thus elaborated automated tracking systems have been proposed in the past. However, most of these approaches share the inability to precisely segment the contours of colliding animals leading to the absence of model and motion-related features during collisions. Here, we translate the task of tracking and resolving colliding animals into a filtering problem solvable by Markov Chain Monte Carlo methods and elaborate an adequate larva model. By comparing our method with state-of-the-art approaches, we demonstrate that our algorithm produces significantly better results in a fraction of time and facilitates the analysis of animal behavior during interaction in more detail.

Anatomy and behavioral function of serotonin receptors in Drosophila melanogaster larvae

The biogenic amine serotonin (5-HT) is an important neuroactive molecule in the central nervous system of the majority of animal phyla. 5-HT binds to specific G protein-coupled and ligand-gated ion receptors to regulate particular aspects of animal behavior. In Drosophila, as in many other insects this includes the regulation of locomotion and feeding. Due to its genetic amenability and neuronal simplicity the Drosophila larva has turned into a useful model for studying the anatomical and molecular basis of chemosensory behaviors. This is particularly true for the olfactory system, which is mostly described down to the synaptic level over the first three orders of neuronal information processing. Here we focus on the 5-HT receptor system of the Drosophila larva. In a bipartite approach consisting of anatomical and behavioral experiments we describe the distribution and the implications of individual 5-HT receptors on naïve and acquired chemosensory behaviors. Our data suggest that 5-HT_1A, 5-HT_1B, and 5-HT₇ are dispensable for larval naïve olfactory and gustatory choice behaviors as well as for appetitive and aversive associative olfactory learning and memory. In contrast, we show that 5-HT/5-HT_2A signaling throughout development, but not as an acute neuronal function, affects associative olfactory learning and memory using high salt concentration as a negative unconditioned stimulus. These findings describe for the first time an involvement of 5-HT signaling in learning and memory in Drosophila larvae. In the longer run these results may uncover developmental, 5-HT dependent principles related to reinforcement processing possibly shared with adult Drosophila and other insects.