Investigating behavioural correlates of sleep states in drosophila
File(s)
Author(s)
Blackhurst, Laurence
Type
Thesis
Abstract
Sleep is a universal yet diverse phenomenon across the animal kingdom. Despite its evolutionary conservation, sleep varies significantly among mammals and even more so when compared to birds, reptiles, fish, cephalopods, and insects. Recent studies have bridged the understanding of sleep across different animal classes, revealing that multiphasic sleep, characterised by distinct stages like REM and NREM in mammals, may be present across a wider range of species, though not necessarily in a structurally or functionally equivalent manner. To generate a general theory of sleep, it is essential to accurately identify and classify sleep types across species to facilitate meaningful comparisons.
In the past two decades, Drosophila melanogaster has emerged as a leading model for studying sleep. However, most research has treated Drosophila sleep as monophasic, typically defined by the cessation of movement for five minutes or more. These metrics have become outdated, as recent studies have uncovered evidence of multiphasic sleep patterns in Drosophila, including brain activity indicative of different sleep phases, behavioural markers of deeper sleep, and shorter sleep latencies. To understand sleep in Drosophila, it need to integrate these new findings into our analysis toolbox without compromising the high-throughput nature of Drosophila research.
This study investigates behavioural markers of sleep, utilising both high-throughput, low-resolution machine vision tracking data and individual, high-resolution limb tracking to identify behavioural correlates of sleep states. Our findings reveal that low-resolution data cannot currently classify sleep states or depth more accurately than the traditional five-minute inactivity rule. However, it does allow for more detailed analysis of sleep patterns, particularly when characterising sleep in new Drosophila species or mutants. High-resolution tracking, while capturing sleep postures, has limited applicability for standard sleep research due to experimental setup constraints. This work aims to be a foundation for developing more refined metrics for understanding sleep in Drosophila.
In the past two decades, Drosophila melanogaster has emerged as a leading model for studying sleep. However, most research has treated Drosophila sleep as monophasic, typically defined by the cessation of movement for five minutes or more. These metrics have become outdated, as recent studies have uncovered evidence of multiphasic sleep patterns in Drosophila, including brain activity indicative of different sleep phases, behavioural markers of deeper sleep, and shorter sleep latencies. To understand sleep in Drosophila, it need to integrate these new findings into our analysis toolbox without compromising the high-throughput nature of Drosophila research.
This study investigates behavioural markers of sleep, utilising both high-throughput, low-resolution machine vision tracking data and individual, high-resolution limb tracking to identify behavioural correlates of sleep states. Our findings reveal that low-resolution data cannot currently classify sleep states or depth more accurately than the traditional five-minute inactivity rule. However, it does allow for more detailed analysis of sleep patterns, particularly when characterising sleep in new Drosophila species or mutants. High-resolution tracking, while capturing sleep postures, has limited applicability for standard sleep research due to experimental setup constraints. This work aims to be a foundation for developing more refined metrics for understanding sleep in Drosophila.
Version
Open Access
Date Issued
2024-10-01
Date Awarded
01/06/2025
License URL
Advisor
Gilestro, Giorgio
Sponsor
Biotechnology and Biological Sciences Research Council (Great Britain)
Publisher Department
Department of Life Sciences
Publisher Institution
Imperial College London
Qualification Level
Doctoral
Qualification Name
Doctor of Philosophy (PhD)