Dataset Information

Monitoring brain activity and behaviour in freely moving Drosophila larvae using bioluminescence.

ABSTRACT: We present a bioluminescence method, based on the calcium-reporter Aequorin (AEQ), that exploits targeted transgenic expression patterns to identify activity of specific neural groups in the larval Drosophila nervous system. We first refine, for intact but constrained larva, the choice of Aequorin transgene and method of delivery of the co-factor coelenterazine and assay the luminescence signal produced for different neural expression patterns and concentrations of co-factor, using standard photo-counting techniques. We then develop an apparatus that allows simultaneous measurement of this neural signal while video recording the crawling path of an unconstrained animal. The setup also enables delivery and measurement of an olfactory cue (CO₂) and we demonstrate the ability to record synchronized changes in Kenyon cell activity and crawling speed caused by the stimulus. Our approach is thus shown to be an effective and affordable method for studying the neural basis of behavior in Drosophila larvae.

SUBMITTER: Marescotti M

PROVIDER: S-EPMC6006295 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

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Monitoring brain activity and behaviour in freely moving Drosophila larvae using bioluminescence.

Marescotti Manuela M Lagogiannis Konstantinos K Webb Barbara B Davies R Wayne RW Armstrong J Douglas JD

Scientific reports 20180618 1

We present a bioluminescence method, based on the calcium-reporter Aequorin (AEQ), that exploits targeted transgenic expression patterns to identify activity of specific neural groups in the larval Drosophila nervous system. We first refine, for intact but constrained larva, the choice of Aequorin transgene and method of delivery of the co-factor coelenterazine and assay the luminescence signal produced for different neural expression patterns and concentrations of co-factor, using standard phot ...[more]

PMID: 29915372

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Project description:Bioluminescence imaging (BLI) of gene expression in live animals is a powerful method for monitoring development, tumor growth, infections, healing, and other progressive, long-term biological processes. BLI remains an effective approach for reducing the number of animals needed to monitor dynamic changes in gene activity because images can be captured repeatedly from the same animals. When examining these ongoing changes, it is sometimes necessary to remove rhythmic effects on the bioluminescence signal caused by the circadian clock's daily modulation of gene expression. Furthermore, BLI using freely moving animals remains limited because the standard procedures can alter normal behaviors. Another obstacle with conventional BLI of animals is that luciferin, the firefly luciferase substrate, is usually injected into mice that are then imaged while anesthetized. Unfortunately, the luciferase signal declines rapidly during imaging as luciferin is cleared from the body. Alternatively, mice are imaged after they are surgically implanted with a pump or connected to a tether to deliver luciferin, but stressors such as this surgery and anesthesia can alter physiology, behavior, and the actual gene expression being imaged. Consequently, we developed a strategy that minimizes animal exposure to stressors before and during sustained BLI of freely moving unanesthetized mice. This technique was effective when monitoring expression of the Per1 gene that serves in the circadian clock timing mechanism and was previously shown to produce circadian bioluminescence rhythms in live mice. We used hairless albino mice expressing luciferase that were allowed to drink luciferin and engage in normal behaviors during imaging with cooled electron-multiplying-CCD cameras. Computer-aided image selection was developed to measure signal intensity of individual mice each time they were in the same posture, thereby providing comparable measurements over long intervals. This imaging procedure, performed primarily during the animal's night, is compatible with entrainment of the mouse circadian timing system to the light cycle while allowing sampling at multi-day intervals to monitor long-term changes. When the circadian expression of a gene is known, this approach provides an effective alternative to imaging immobile anesthetized animals and can removing noise caused by circadian oscillations and body movements that can degrade data collected during long-term imaging studies.

Project description:Monitoring and describing the physical movements and body postures of animals is one of the most fundamental tasks of ethology. The more precise the observations are the more sophisticated the interpretations can be about the biology of a certain individual or species. Animal-borne data loggers have recently contributed much to the collection of motion-data from individuals, however, the problem of translating these measurements to distinct behavioural categories to create an ethogram is not overcome yet. The objective of the present study was to develop a "behaviour tracker": a system composed of a multiple sensor data-logger device (with a tri-axial accelerometer and a tri-axial gyroscope) and a supervised learning algorithm as means of automated identification of the behaviour of freely moving dogs. We collected parallel sensor measurements and video recordings of each of our subjects (Belgian Malinois, N=12; Labrador Retrievers, N=12) that were guided through a predetermined series of standard activities. Seven behavioural categories (lay, sit, stand, walk, trot, gallop, canter) were pre-defined and each video recording was tagged accordingly. Evaluation of the measurements was performed by support vector machine (SVM) classification. During the analysis we used different combinations of independent measurements for training and validation (belonging to the same or different individuals or using different training data size) to determine the robustness of the application. We reached an overall accuracy of above 90% perfect identification of all the defined seven categories of behaviour when both training and validation data belonged to the same individual, and over 80% perfect recognition rate using a generalized training data set of multiple subjects. Our results indicate that the present method provides a good model for an easily applicable, fast, automatic behaviour classification system that can be trained with arbitrary motion patterns and potentially be applied to a wide range of species and situations.

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Dataset Information

Monitoring brain activity and behaviour in freely moving Drosophila larvae using bioluminescence.

Publications

Monitoring brain activity and behaviour in freely moving Drosophila larvae using bioluminescence.

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