Experimentally induced active and quiet sleep engage non-overlapping transcriptional programs in Drosophila

There is increasing evidence that sleep is a complex phenomenon in most animals, comprising of distinct stages that are characterized by dramatically different physiological processes and brain activity signatures (Jaggard et al., 2021; Van De Poll and van Swinderen, 2021). This suggests that different sleep stages, such as rapid eye movement (REM) and slow-wave sleep (SWS) in humans and other mammals (Dijk et al., 1990), are accomplishing distinct functions that are nevertheless collectively important for adaptive behavior and survival (Tononi and Cirelli, 2014). While REM and SWS appear to be restricted to a subset of vertebrates (e.g., mammals, birds, and possibly some reptiles; Siegel, 2011; Shein-Idelson et al., 2016; Rattenborg et al., 2019), a broader range of animals, including invertebrates, demonstrate evidence of ‘active’ vs. ‘quiet’ sleep (Jaggard et al., 2021; Van De Poll and van Swinderen, 2021; Libourel and Herrel, 2016). Whether active and quiet sleep represent evolutionary antecedents of REM and SWS, respectively, remains speculative (Jaggard et al., 2021; Van De Poll and van Swinderen, 2021). However, during active sleep, although animals are less responsive, brain recordings reveal a level of neural activity that is similar to wakefulness, in contrast to quiet sleep, which is characterized by significantly decreased neural activity in invertebrates (Yap et al., 2017; Tainton-Heap et al., 2021) as well as certain fish (Leung et al., 2019), mollusks (Iglesias et al., 2019), and reptiles (Shein-Idelson et al., 2016).

Although it is likely that even insects such as fruit flies and honeybees sleep in distinct stages (Sauer et al., 2003; van Alphen et al., 2013), sleep studies using the genetic model Drosophila melanogaster still mostly measure sleep as a single phenomenon, defined by 5 min (or more) of inactivity (Shaw et al., 2000; Hendricks et al., 2000). As sleep studies increasingly employ Drosophila to investigate molecular and cellular processes underpinning potential sleep functions, this simplified approach to measuring sleep in flies carries the risk of overlooking different functions accomplished by distinct kinds of sleep. Sleep physiology and functions are increasingly being addressed in the fly model by imposing experimentally controlled sleep regimes, either pharmacologically or via transient control of sleep-promoting circuits by using opto- or thermogenetic tools (Shafer and Keene, 2021). Yet, there is little knowledge available on whether these different approaches are producing qualitatively similar sleep. For example, sleep can be induced genetically in flies by activating sleep-promoting neurons in the central complex (CX) – a part of the insect brain that has been found to be involved in multimodal sensory processing (Wolff and Rubin, 2018). In particular, the dorsal fan-shaped body (dFB) of the CX has been proposed to serve as a discharge circuit for the insect’s sleep homeostat, whereby increased sleep pressure (e.g., due to extended wakefulness) alters the physiological properties of dFB neurons, causing them to fire more readily and thereby promote decreased behavioral responsiveness (Troup et al., 2018) and thus sleep (Donlea et al., 2011; Donlea et al., 2014; Donlea et al., 2018). Crucially, activation of sleep-promoting circuits including the dFB has been shown to be sleep-restorative (Tainton-Heap et al., 2021; Dissel et al., 2015), but confusingly, brain recordings during optogenetically induced sleep, via electrophysiology, as well as whole-brain calcium imaging techniques, reveal wake-like levels of brain activity (Yap et al., 2017; Tainton-Heap et al., 2021). This suggests that some approaches to optogenetically induced sleep might be promoting a form of sleep akin to the ‘active’ sleep stage detected during spontaneous sleep (Yap et al., 2017; Tainton-Heap et al., 2021).

An alternate way to induce sleep in Drosophila is by feeding flies drugs that have been designed to treat insomnia in humans, such as the GABA-agonist 4,5,6,7-tetrahyrdoisoxazolopyridin-3-ol (THIP), also known as gaboxadol (Harrison, 2007). Several studies have shown that THIP-induced sleep in flies is also restorative and achieves key functions ranging from memory consolidation to cellular repair and waste clearance (Dissel et al., 2015; Berry et al., 2015; Stanhope et al., 2020; van Alphen et al., 2021). This pharmacological approach centered on GABA function has a solid foundation based on better-understood sleep processes: in mammals, many sleep-inducing drugs also target GABA receptors, and this class of drugs tends to promote SWS (Lundahl et al., 2012). In contrast, there are no obvious drugs that promote REM sleep, although local infusion of cholinergic agonists (e.g., carbachol) to the brainstem has been shown to induce REM-like states in cats (Mitler and Dement, 1974).

In this study, we compare THIP-induced sleep with optogenetically induced sleep in Drosophila using behavior, brain activity, and transcriptomics. To ensure the validity of our comparisons, we performed all of our experiments in the same genetic background, employing a canonical Gal4 strain that expresses a transgenic cation channel in a sleep-promoting circuit: R23E10-Gal4>UAS-Chrimson (Jenett et al., 2012; Klapoetke et al., 2014). When these flies are fed all-trans-retinal (ATR) and then exposed to red light, they are put to sleep optogenetically. When these flies are instead fed THIP, they are put to sleep pharmacologically. By using the same genetic background, we were thus able to contrast the effects of either kind of sleep at the level of behavior, brain activity, and gene expression (Figure 1). We questioned how similar either form of induced sleep was.

Figure 1

Download asset Open asset

One of the key advantages of studying sleep in Drosophila is that this versatile model provides a variety of reliable approaches for inducing and controlling sleep. By being able to induce sleep on demand, either genetically or pharmacologically, researchers have been able to manipulate sleep as an experimental variable, and in this way be better able to assess causality when probing potential sleep functions. However, this approach has often sidestepped the question of whether different sleep induction methods are equivalent or whether distinct forms of sleep might be engaged by different genetic or pharmacological treatments. In mammals, GABA agonists typically promote SWS, which has been associated with cellular homeostasis and repair process in the brain (Tononi and Cirelli, 2014; Xie et al., 2013). In contrast, drugs targeting acetylcholine receptors, such as carbachol, have been found to promote brain states more reminiscent of REM sleep (Kubin, 2001; Torterolo et al., 2016). Although these drugs all induce sedative (or dissociative) states, they are clearly producing dissimilar forms of sleep in mammals, with likely different functions or consequences for the brain. In Drosophila, evidence suggests that the GABA agonist THIP promotes a form of deep or ‘quiet’ sleep, which may be functionally analogous to mammalian SWS (Yap et al., 2017; van Alphen et al., 2021; Kirszenblat and van Swinderen, 2015). In contrast, optogenetic activation of the dFB as well as other neurons may promote a form of active sleep, which we have suggested could be achieving some similar sleep functions as REM (Van De Poll and van Swinderen, 2021). THIP-induced sleep in flies has been associated with waste clearance from the brain (van Alphen et al., 2021), just as SWS has been associated with clearance of waste metabolites via the mammalian brain’s glymphatic system (Xie et al., 2013). Such functional homology suggests that the transcriptional changes we uncovered for THIP-induced sleep in Drosophila might also be relevant for mammalian SWS, with these largely centered on reduced metabolic processes and stress regulation (Muheim et al., 2019). In contrast, except for the upregulation of cholinergic signaling (Vazquez and Baghdoyan, 2001), there is little to compare to test hypotheses potentially linking active sleep in flies with REM sleep in mammals, except for the potential upregulation of cholinergic signaling. Even this cholinergic connection seems odd, seeing that the predominant excitatory neurotransmitters are reversed in the brains of insects and mammals: glutamate in mammals and acetylcholine in insects (Smarandache-Wellmann, 2016). Additionally, only nicotinic receptor subunits were identified in our analyses, whereas muscarinic receptors have been more commonly associated with REM sleep in mammals (Bourgin et al., 1995; Kashiwagi and Hayashi, 2018). Nevertheless, it is clear from our results that optogenetic-induced active sleep upregulates the expression of multiple nicotinic acetylcholine subunits, and that knocking these out individually has profound (and opposing) effects on sleep architecture in flies, and that a subset might function intrinsically within sleep-promoting neurons. This supports other studies showing a role for nAchRα’s in sleep regulation (Shi et al., 2014; Dai et al., 2021). It will be especially interesting in future brain imaging studies to see whether a knockout such as nAchRα3 is eliminating one kind of sleep (e.g., active sleep) as predicted by our behavioral data, and whether this is associated with any functional consequences. Similarly, it will be telling to see whether the opposite sleep phenotypes observed in nAchRα1, for example, result in a distinct class of functional consequences. A previous study has shown that nAchRα1 knockout animals have significantly shorter survivorship compared to controls, with flies dying almost 20 d earlier (Somers et al., 2017). One reason could be because of impaired or insufficient deep sleep functions (e.g., brain waste clearance; van Alphen et al., 2021). The nAchRα knockouts provide an opportunity to further examine mutant animals potentially lacking either kind of sleep, although this will have to be confirmed by brain imaging or electrophysiology.

We found little similarity between two different approaches to inducing sleep in flies at the level of gene expression as well as acute effects on brain activity. It may, however, not be surprising that these entirely different sleep induction methods produce dissimilar physiological effects. After all, one method requires flies to ingest a drug, which then must make its way to the brain, while the other method directly activates a subset of neurons in the central brain, as well as in the ventral nerve cord (Jones et al., 2023). Yet both methods yield similarly increased sleep duration profiles and consolidated sleep architecture (Figure 2). One underlying assumption with focusing on sleep duration as the most relevant metric for understanding sleep function in Drosophila is that sleep is a unitary phenomenon in the fly model, meaning that primarily one set of functions and one form of brain activity are occurring when flies sleep. There is now substantial evidence that this is unlikely to be true, and that like other animals flies probably also experience distinct sleep stages that accomplish different functions (Yap et al., 2017; Tainton-Heap et al., 2021; van Alphen et al., 2021; van Alphen et al., 2013; Wiggin et al., 2020; Gong et al., 2022). This does not mean that these functions are mutually exclusive; for example, both THIP provision and neuronal activation of central brain circuits have been found to promote memory consolidation in Drosophila (Dissel et al., 2015). Indeed, it seems reasonable to propose that different sleep stages could be synergistic, accomplishing a variety of homeostatic functions that might be required for adaptive behaviors in an animal. Our results suggest that THIP provision promotes a ‘quiet sleep’ stage in flies, which induces a brain-wide downregulation of metabolism-related genes. This is consistent with studies in flies showing that metabolic rate is decreased in longer sleep bouts, especially at night, and that this is recapitulated by THIP-induced sleep (Stahl et al., 2017). Our findings also align with a previous study showing that mitochondrial metabolic processes are upregulated in the absence of sleep (Kempf et al., 2019). One argument for why metabolism-related genes are downregulated during THIP-induced sleep might be that flies are starved (because they are sleeping more, and thus perhaps not eating). However, flies induced to sleep by optogenetic activation are also sleeping more (indeed, their average sleep bouts are longer), yet these did not reveal a similar downregulation of metabolic processes. Another view might be that our sampling was done after flies had achieved 10 hr of induced sleep, so sleep functions might have already been achieved by that time. Thus, we might not be uncovering genes required for achieving ‘quiet’ sleep functions as much as identifying exactly the opposite: genetic pathways that have been satisfied by 10 hr of induced quiet sleep. Other studies using THIP to induce sleep have examined longer time frames (e.g., 2 d; Dissel et al., 2015), so it remains unclear whether changes in gene regulation relate to sleep functions that have been achieved or that are still being engaged. For our transcriptomic study, both sleep induction protocols were conducted during the day (thus in presumably well-rested flies) to deliberately avoid any sleep pressure confounds. Since the dFB has been proposed as a regulator of sleep homeostasis (Donlea et al., 2011; Donlea et al., 2014; Donlea et al., 2018), one could question why its prolonged activation did not incur a deep sleep debt, which might have been detectable at a transcriptomic level by some overlap with our SD controls. That we did not see this suggests induced daytime active sleep might not accrue any sleep debt. We have shown previously that daytime sleep is qualitatively different than night-time sleep in flies, which is ‘deeper’ (van Alphen et al., 2013). It is, for example, possible that most quiet sleep happens at night in flies, so a daytime regime of induced active or quiet sleep might not incur any sleep debt for either. Following this logic, our RNA analyses simply revealed the transcriptomic changes resulting from either manipulation in well-rested flies, with the key result being that there are zero shared genes after 10 daytime hours of either manipulation.

In contrast to THIP, our optogenetic manipulation induced a brain-wide upregulation of a variety of neural mechanisms unrelated to metabolic processes, suggesting a different kind of sleep was engaged. Although many studies have shown that the R23E10-Gal4 circuit is sleep promoting (e.g., Troup et al., 2018; Donlea et al., 2014; Gong et al., 2022), and we have shown that acute activation of these neurons produces a form of active sleep (Tainton-Heap et al., 2021), it seems unlikely that active sleep regulation is limited to the dFB neurons alone (Jones et al., 2023). Other circuits in the fly central brain are also sleep-promoting, including in the ellipsoid body (Liu et al., 2016) and the ventral fan-shaped body (vFB) (Lei et al., 2022), although it remains unknown whether activation of these other circuits also promotes an active sleep stage or whether a similar transcriptome might be engaged by these alternate approaches to optogenetic sleep induction in flies. This again highlights a variant of the same problem we have uncovered in this study comparing pharmacology with optogenetics: different circuit-based approaches could all be increasing sleep duration but achieving entirely different functions by engaging distinct transcriptomes and thus different sleep functions. How many different kinds of functions are engaged by sleep remains unclear: is it roughly two functional categories linked to quiet and active sleep, or is it a broader range of subcategories that are not so tightly linked to these obviously different brain activity states?

A compelling argument could nevertheless be made for two kinds of sleep in most animals, with two distinct sets of functions (Jaggard et al., 2021; Kirszenblat and van Swinderen, 2015). Most animals have been shown to require a form of ‘quiet’ sleep to ensure survival, suggesting that these might encompass an evolutionarily conserved set of cellular processes that promote neural health and development (Zimmerman et al., 2008a), and that operate best during periods of behavioral quiescence. Nematode worms thus experience a form of quiet sleep when they pause to molt (‘lethargus’) into different life stages during their development (Raizen et al., 2008) or when cellular repair processes are needed following environmental stress (Hill et al., 2014). In flies, quiet sleep seems to be similarly required for neuronal repair (Stanhope et al., 2020) or waste clearance (van Alphen et al., 2021), and there is evidence that glia might play a key role in these cellular homeostatic processes in flies (Stanhope et al., 2020), as well as other animals (Artiushin and Sehgal, 2020). Thus, SWS in mammals and birds might present a narrow neocortical view of a more ancient set of sleep functions centered on quiescence and decreased metabolic rate. Indeed, neural quiescence is also a feature of SWS, both at the level of pulsed inhibition (down-states) and in other parts of the brain beyond the cortex (Jaggard et al., 2021). Similar to findings in flies that are induced into a quiet sleep stage with THIP (Stahl et al., 2017), metabolic rate also decreases during SWS in mammals (Caron and Stephenson, 2010). In contrast, metabolic rate is similar to waking during REM sleep in mammals (Maquet, 1995), suggesting an alternate set of functions not linked to cellular homeostasis. What might these functions be, and could some of these be conserved between active sleep in invertebrates and REM sleep in mammals? A REM-like sleep stage has now been identified in a variety of invertebrate species, including cephalopods (Iglesias et al., 2019) and jumping spiders (Rößler et al., 2022). In humans, REM sleep has been implicated in emotion regulation (Hutchison and Rathore, 2015), and cognitive disorders where emotions are dysregulated, such as depression, are often associated with REM sleep dysfunction (Harrington et al., 2018). While it is not evident how to study emotions in insects (but see Anderson and Adolphs, 2014), it could be argued that arousal systems more generally are employed to detect prediction errors and thereby promote learning (Seth and Friston, 2016). Thus, we and others have suggested that REM sleep might be crucial for optimizing prediction, and attention, and learning (Van De Poll and van Swinderen, 2021; Kirszenblat and van Swinderen, 2015; Hobson, 2009), and this may involve different kinds of homeostatic mechanisms centered on brain circuits rather than cells. Whether active sleep in flies plays a similar homeostatic role remains to be determined. Our finding that optogenetically induced sleep in Drosophila upregulates different nAchRα subunits is consistent with new findings that these subunits regulate appetitive memories in flies (Pribbenow et al., 2022) and that cholinergic systems more generally underpin learning and memory in this animal (Barnstedt et al., 2016). Yet learning and memory in flies clearly also benefit from quiet sleep, as evidenced by multiple studies using THIP as a sleep-promoting agent (Dissel et al., 2015; Berry et al., 2015; Melnattur et al., 2021). One view consistent with our findings and previous studies is that both kinds of sleep are crucial for optimal behavior: quiet sleep for cellular homeostasis and active sleep for circuit-level homeostasis. Manipulating these separately, alongside the non-overlapping pathways engaged by either kind of sleep, should help further disambiguate the functions potentially associated with these distinct sleep stages.

All data generated or analysed during this study are included in the manuscript and supporting files. The data and analysis tools underpinning this study (brain imaging as well as RNA sequencing) are available online via Dryad: https://doi.org/10.5061/dryad.1rn8pk10x.

1. 1. Niki A
   2. Lucy T-H
   3. Hang L
   4. Eleni N
   5. Amber K
   6. Qiongyi Z
   7. Trent P
   8. Philip B
   9. Paul S
   10. Bruno VS

   (2023) Dryad Digital Repository

   Data from: Experimentally induced active and quiet sleep engage non-overlapping transcriptional programs in Drosophila.

   https://doi.org/10.5061/dryad.1rn8pk10x

1. 1. Anderson DJ
   2. Adolphs R

   (2014) A framework for studying emotions across species

   Cell 157:187–200.

   https://doi.org/10.1016/j.cell.2014.03.003

   • PubMed
   • Google Scholar
2. 1. Andretic R
   2. Shaw PJ

   (2005) Essentials of sleep recordings in Drosophila: moving beyond sleep time

   Methods in Enzymology 393:759–772.

   https://doi.org/10.1016/S0076-6879(05)93040-1

   • PubMed
   • Google Scholar
3. 1. Artiushin G
   2. Sehgal A

   (2020) The glial perspective on sleep and circadian rhythms

   Annual Review of Neuroscience 43:119–140.

   https://doi.org/10.1146/annurev-neuro-091819-094557

   • PubMed
   • Google Scholar
4. 1. Barnstedt O
   2. Owald D
   3. Felsenberg J
   4. Brain R
   5. Moszynski J-P
   6. Talbot CB
   7. Perrat PN
   8. Waddell S

   (2016) Memory-relevant mushroom body output synapses are cholinergic

   Neuron 89:1237–1247.

   https://doi.org/10.1016/j.neuron.2016.02.015

   • PubMed
   • Google Scholar
5. 1. Berry JA
   2. Cervantes-Sandoval I
   3. Chakraborty M
   4. Davis RL

   (2015) Sleep facilitates memory by blocking dopamine neuron-mediated forgetting

   Cell 161:1656–1667.

   https://doi.org/10.1016/j.cell.2015.05.027

   • PubMed
   • Google Scholar
6. 1. Bharucha KN
   2. Tarr P
   3. Zipursky SL

   (2008) A glucagon-like endocrine pathway in Drosophila modulates both lipid and carbohydrate homeostasis

   The Journal of Experimental Biology 211:3103–3110.

   https://doi.org/10.1242/jeb.016451

   • PubMed
   • Google Scholar
7. 1. Bourgin P
   2. Escourrou P
   3. Gaultier C
   4. Adrien J

   (1995) Induction of rapid eye movement sleep by carbachol infusion into the pontine reticular formation in the rat

   Neuroreport 6:532–536.

   https://doi.org/10.1097/00001756-199502000-00031

   • PubMed
   • Google Scholar
8. 1. Caron AM
   2. Stephenson R

   (2010) Energy expenditure is affected by rate of accumulation of sleep deficit in rats

   Sleep 33:1226–1235.

   https://doi.org/10.1093/sleep/33.9.1226

   • PubMed
   • Google Scholar
9. 1. Chen C
   2. Buhl E
   3. Xu M
   4. Croset V
   5. Rees JS
   6. Lilley KS
   7. Benton R
   8. Hodge JJL
   9. Stanewsky R

   (2015) Drosophila Ionotropic Receptor 25a mediates circadian clock resetting by temperature

   Nature 527:516–520.

   https://doi.org/10.1038/nature16148

   • Google Scholar
10. 1. Chen W
    2. Gu X
    3. Yang YT
    4. Batterham P
    5. Perry T

    (2022) Dual nicotinic acetylcholine receptor subunit gene knockouts reveal limits to functional redundancy

    Pesticide Biochemistry and Physiology 184:105118.

    https://doi.org/10.1016/j.pestbp.2022.105118

    • PubMed
    • Google Scholar
11. 1. Dai X
    2. Zhou E
    3. Yang W
    4. Mao R
    5. Zhang W
    6. Rao Y

    (2021) Molecular resolution of a behavioral paradox: sleep and arousal are regulated by distinct acetylcholine receptors in different neuronal types in Drosophila

    Sleep 44:zsab017.

    https://doi.org/10.1093/sleep/zsab017

    • PubMed
    • Google Scholar
12. 1. Dijk DJ
    2. Brunner DP
    3. Borbély AA

    (1990) Time course of EEG power density during long sleep in humans

    The American Journal of Physiology 258:R650–R661.

    https://doi.org/10.1152/ajpregu.1990.258.3.R650

    • PubMed
    • Google Scholar
13. 1. Dissel S
    2. Angadi V
    3. Kirszenblat L
    4. Suzuki Y
    5. Donlea J
    6. Klose M
    7. Koch Z
    8. English D
    9. Winsky-Sommerer R
    10. van Swinderen B
    11. Shaw PJ

    (2015) Sleep restores behavioral plasticity to Drosophila mutants

    Current Biology 25:1270–1281.

    https://doi.org/10.1016/j.cub.2015.03.027

    • PubMed
    • Google Scholar
14. 1. Donlea JM
    2. Thimgan MS
    3. Suzuki Y
    4. Gottschalk L
    5. Shaw PJ

    (2011) Inducing sleep by remote control facilitates memory consolidation in Drosophila

    Science 332:1571–1576.

    https://doi.org/10.1126/science.1202249

    • PubMed
    • Google Scholar
15. 1. Donlea JM
    2. Pimentel D
    3. Miesenböck G

    (2014) Neuronal Machinery of Sleep Homeostasis in Drosophila

    Neuron 81:1442.

    https://doi.org/10.1016/j.neuron.2014.03.008

    • PubMed
    • Google Scholar
16. 1. Donlea JM
    2. Pimentel D
    3. Talbot CB
    4. Kempf A
    5. Omoto JJ
    6. Hartenstein V
    7. Miesenböck G

    (2018) Recurrent circuitry for balancing sleep need and sleep

    Neuron 97:378–389.

    https://doi.org/10.1016/j.neuron.2017.12.016

    • PubMed
    • Google Scholar
17. 1. Faville R
    2. Kottler B
    3. Goodhill GJ
    4. Shaw PJ
    5. van Swinderen B

    (2015) How deeply does your mutant sleep? Probing arousal to better understand sleep defects in Drosophila

    Scientific Reports 5:8454.

    https://doi.org/10.1038/srep08454

    • PubMed
    • Google Scholar
18. 1. Ferguson L
    2. Petty A
    3. Rohrscheib C
    4. Troup M
    5. Kirszenblat L
    6. Eyles DW
    7. van Swinderen B

    (2017) Transient Dysregulation of Dopamine Signaling in a developing Drosophila arousal circuit permanently impairs behavioral responsiveness in Adults

    Frontiers in Psychiatry 8:22.

    https://doi.org/10.3389/fpsyt.2017.00022

    • PubMed
    • Google Scholar
19. 1. Gong NN
    2. Luong HNB
    3. Dang AH
    4. Mainwaring B
    5. Shields E
    6. Schmeckpeper K
    7. Bonasio R
    8. Kayser MS

    (2022) Intrinsic maturation of sleep output neurons regulates sleep ontogeny in Drosophila

    Current Biology 32:4025–4039.

    https://doi.org/10.1016/j.cub.2022.07.054

    • PubMed
    • Google Scholar
20. 1. Harrington MO
    2. Johnson JM
    3. Croom HE
    4. Pennington K
    5. Durrant SJ

    (2018) The influence of REM sleep and SWS on emotional memory consolidation in participants reporting depressive symptoms

    Cortex; a Journal Devoted to the Study of the Nervous System and Behavior 99:281–295.

    https://doi.org/10.1016/j.cortex.2017.12.004

    • PubMed
    • Google Scholar
21. 1. Harrison NL

    (2007)

    Mechanisms of sleep induction by GABA(A) receptor agonists

    The Journal of Clinical Psychiatry 68 Suppl 5:6–12.

    • PubMed
    • Google Scholar
22. 1. He Q
    2. Du J
    3. Wei L
    4. Zhao Z

    (2020) AKH-FOXO pathway regulates starvation-induced sleep loss through remodeling of the small ventral lateral neuron dorsal projections

    PLOS Genetics 16:e1009181.

    https://doi.org/10.1371/journal.pgen.1009181

    • PubMed
    • Google Scholar
23. 1. Hendricks JC
    2. Finn SM
    3. Panckeri KA
    4. Chavkin J
    5. Williams JA
    6. Sehgal A
    7. Pack AI

    (2000) Rest in Drosophila is a sleep-like state

    Neuron 25:129–138.

    https://doi.org/10.1016/s0896-6273(00)80877-6

    • PubMed
    • Google Scholar
24. 1. Hill AJ
    2. Mansfield R
    3. Lopez JMNG
    4. Raizen DM
    5. Van Buskirk C

    (2014) Cellular stress induces a protective sleep-like state in C. elegans

    Current Biology 24:2399–2405.

    https://doi.org/10.1016/j.cub.2014.08.040

    • PubMed
    • Google Scholar
25. 1. Hobson JA

    (2009) REM sleep and dreaming: towards a theory of protoconsciousness

    Nature Reviews. Neuroscience 10:803–813.

    https://doi.org/10.1038/nrn2716

    • PubMed
    • Google Scholar
26. 1. Huang DW
    2. Sherman BT
    3. Lempicki RA

    (2009a) Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists

    Nucleic Acids Research 37:1–13.

    https://doi.org/10.1093/nar/gkn923

    • PubMed
    • Google Scholar
27. 1. Huang DW
    2. Sherman BT
    3. Lempicki RA

    (2009b) Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources

    Nature Protocols 4:44–57.

    https://doi.org/10.1038/nprot.2008.211

    • PubMed
    • Google Scholar
28. 1. Huber R
    2. Hill SL
    3. Holladay C
    4. Biesiadecki M
    5. Tononi G
    6. Cirelli C

    (2004) Sleep homeostasis in Drosophila melanogaster

    Sleep 27:628–639.

    https://doi.org/10.1093/sleep/27.4.628

    • PubMed
    • Google Scholar
29. 1. Hutchison IC
    2. Rathore S

    (2015) The role of REM sleep theta activity in emotional memory

    Frontiers in Psychology 6:1439.

    https://doi.org/10.3389/fpsyg.2015.01439

    • PubMed
    • Google Scholar
30. 1. Iglesias TL
    2. Boal JG
    3. Frank MG
    4. Zeil J
    5. Hanlon RT

    (2019) Cyclic nature of the REM sleep-like state in the cuttlefish Sepia officinalis

    The Journal of Experimental Biology 222:jeb174862.

    https://doi.org/10.1242/jeb.174862

    • PubMed
    • Google Scholar
31. 1. Jaggard JB
    2. Wang GX
    3. Mourrain P

    (2021) Non-REM and REM/paradoxical sleep dynamics across phylogeny

    Current Opinion in Neurobiology 71:44–51.

    https://doi.org/10.1016/j.conb.2021.08.004

    • PubMed
    • Google Scholar
32. 1. Jenett A
    2. Rubin GM
    3. Ngo T-TB
    4. Shepherd D
    5. Murphy C
    6. Dionne H
    7. Pfeiffer BD
    8. Cavallaro A
    9. Hall D
    10. Jeter J
    11. Iyer N
    12. Fetter D
    13. Hausenfluck JH
    14. Peng H
    15. Trautman ET
    16. Svirskas RR
    17. Myers EW
    18. Iwinski ZR
    19. Aso Y
    20. DePasquale GM
    21. Enos A
    22. Hulamm P
    23. Lam SCB
    24. Li H-H
    25. Laverty TR
    26. Long F
    27. Qu L
    28. Murphy SD
    29. Rokicki K
    30. Safford T
    31. Shaw K
    32. Simpson JH
    33. Sowell A
    34. Tae S
    35. Yu Y
    36. Zugates CT

    (2012) A GAL4-driver line resource for Drosophila neurobiology

    Cell Reports 2:991–1001.

    https://doi.org/10.1016/j.celrep.2012.09.011

    • PubMed
    • Google Scholar
33. 1. Jones JD
    2. Holder BL
    3. Eiken KR
    4. Vogt A
    5. Velarde AI
    6. Elder AJ
    7. McEllin JA
    8. Dissel S

    (2023) Regulation of sleep by cholinergic neurons located outside the central brain in Drosophila

    PLOS Biology 21:e3002012.

    https://doi.org/10.1371/journal.pbio.3002012

    • PubMed
    • Google Scholar
34. 1. Kashiwagi M
    2. Hayashi Y

    (2018) Life Without Dreams: Muscarinic Receptors Are Required to Regulate REM Sleep in Mice

    Cell Reports 24:2211–2212.

    https://doi.org/10.1016/j.celrep.2018.08.044

    • PubMed
    • Google Scholar
35. 1. Kempf A
    2. Song SM
    3. Talbot CB
    4. Miesenböck G

    (2019) A potassium channel β-subunit couples mitochondrial electron transport to sleep

    Nature 568:230–234.

    https://doi.org/10.1038/s41586-019-1034-5

    • PubMed
    • Google Scholar
36. 1. Kim D
    2. Langmead B
    3. Salzberg SL

    (2015) HISAT: a fast spliced aligner with low memory requirements

    Nature Methods 12:357–360.

    https://doi.org/10.1038/nmeth.3317

    • PubMed
    • Google Scholar
37. 1. Kirszenblat L
    2. van Swinderen B

    (2015) The yin and yang of sleep and attention

    Trends in Neurosciences 38:776–786.

    https://doi.org/10.1016/j.tins.2015.10.001

    • PubMed
    • Google Scholar
38. 1. Kirszenblat L
    2. Yaun R
    3. van Swinderen B

    (2019) Visual experience drives sleep need in Drosophila

    Sleep 42:zsz102.

    https://doi.org/10.1093/sleep/zsz102

    • PubMed
    • Google Scholar
39. 1. Klapoetke NC
    2. Murata Y
    3. Kim SS
    4. Pulver SR
    5. Birdsey-Benson A
    6. Cho YK
    7. Morimoto TK
    8. Chuong AS
    9. Carpenter EJ
    10. Tian Z
    11. Wang J
    12. Xie Y
    13. Yan Z
    14. Zhang Y
    15. Chow BY
    16. Surek B
    17. Melkonian M
    18. Jayaraman V
    19. Constantine-Paton M
    20. Wong GK-S
    21. Edward S Boyden ESB

    (2014) Addendum: independent optical excitation of distinct neural populations

    Nature Methods 11:338–346.

    https://doi.org/10.1038/nmeth0914-972

    • PubMed
    • Google Scholar
40. 1. Kubin L

    (2001)

    Carbachol models of REM sleep: recent developments and new directions

    Archives Italiennes de Biologie 139:147–168.

    • PubMed
    • Google Scholar
41. 1. Legland D
    2. Arganda-Carreras I
    3. Andrey P

    (2016) MorphoLibJ: integrated library and plugins for mathematical morphology with ImageJ

    Bioinformatics 32:3532–3534.

    https://doi.org/10.1093/bioinformatics/btw413

    • Google Scholar
42. 1. Lei Z
    2. Henderson K
    3. Keleman K

    (2022) A neural circuit linking learning and sleep in Drosophila long-term memory

    Nature Communications 13:609.

    https://doi.org/10.1038/s41467-022-28256-1

    • PubMed
    • Google Scholar
43. 1. Leung LC
    2. Wang GX
    3. Madelaine R
    4. Skariah G
    5. Kawakami K
    6. Deisseroth K
    7. Urban AE
    8. Mourrain P

    (2019) Neural signatures of sleep in zebrafish

    Nature 571:198–204.

    https://doi.org/10.1038/s41586-019-1336-7

    • PubMed
    • Google Scholar
44. 1. Li H
    2. Handsaker B
    3. Wysoker A
    4. Fennell T
    5. Ruan J
    6. Homer N
    7. Marth G
    8. Abecasis G
    9. Durbin R
    10. 1000 Genome Project Data Processing Subgroup

    (2009) The Sequence Alignment/Map format and SAMtools

    Bioinformatics 25:2078–2079.

    https://doi.org/10.1093/bioinformatics/btp352

    • PubMed
    • Google Scholar
45. 1. Libourel PA
    2. Herrel A

    (2016) Sleep in amphibians and reptiles: a review and a preliminary analysis of evolutionary patterns

    Biological Reviews of the Cambridge Philosophical Society 91:833–866.

    https://doi.org/10.1111/brv.12197

    • PubMed
    • Google Scholar
46. 1. Liu S
    2. Liu Q
    3. Tabuchi M
    4. Wu MN

    (2016) Sleep drive is encoded by Neural Plastic changes in a dedicated circuit

    Cell 165:1347–1360.

    https://doi.org/10.1016/j.cell.2016.04.013

    • PubMed
    • Google Scholar
47. 1. Lun ATL
    2. Chen Y
    3. Smyth GK

    (2016) It’s DE-licious: A Recipe for Differential Expression Analyses of RNA-seq Experiments Using Quasi-Likelihood Methods in edgeR

    Methods in Molecular Biology 1418:391–416.

    https://doi.org/10.1007/978-1-4939-3578-9_19

    • PubMed
    • Google Scholar
48. 1. Lundahl J
    2. Deacon S
    3. Maurice D
    4. Staner L

    (2012) EEG spectral power density profiles during NREM sleep for gaboxadol and zolpidem in patients with primary insomnia

    Journal of Psychopharmacology 26:1081–1087.

    https://doi.org/10.1177/0269881111424457

    • PubMed
    • Google Scholar
49. 1. Maquet P

    (1995) Sleep function(s) and cerebral metabolism

    Behavioural Brain Research 69:75–83.

    https://doi.org/10.1016/0166-4328(95)00017-n

    • PubMed
    • Google Scholar
50. 1. McCarthy DJ
    2. Chen Y
    3. Smyth GK

    (2012) Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation

    Nucleic Acids Research 40:4288–4297.

    https://doi.org/10.1093/nar/gks042

    • PubMed
    • Google Scholar
51. 1. Melnattur K
    2. Kirszenblat L
    3. Morgan E
    4. Militchin V
    5. Sakran B
    6. English D
    7. Patel R
    8. Chan D
    9. van Swinderen B
    10. Shaw PJ

    (2021) A conserved role for sleep in supporting Spatial Learning in Drosophila

    Sleep 44:zsaa197.

    https://doi.org/10.1093/sleep/zsaa197

    • PubMed
    • Google Scholar
52. 1. Mitler MM
    2. Dement WC

    (1974) Cataplectic-like behavior in cats after micro-injections of carbachol in pontine reticular formation

    Brain Research 68:335–343.

    https://doi.org/10.1016/0006-8993(74)90402-8

    • PubMed
    • Google Scholar
53. 1. Muheim CM
    2. Spinnler A
    3. Sartorius T
    4. Dürr R
    5. Huber R
    6. Kabagema C
    7. Ruth P
    8. Brown SA

    (2019) Dynamic- and frequency-specific regulation of sleep oscillations by cortical potassium channels

    Current Biology 29:2983–2992.

    https://doi.org/10.1016/j.cub.2019.07.056

    • PubMed
    • Google Scholar
54. 1. Perry T
    2. Chen W
    3. Ghazali R
    4. Yang YT
    5. Christesen D
    6. Martelli F
    7. Lumb C
    8. Bao Luong HN
    9. Mitchell J
    10. Holien JK
    11. Parker MW
    12. Sparks TC
    13. Batterham P

    (2021) Role of nicotinic acetylcholine receptor subunits in the mode of action of neonicotinoid, sulfoximine and spinosyn insecticides in Drosophila melanogaster

    Insect Biochemistry and Molecular Biology 131:103547.

    https://doi.org/10.1016/j.ibmb.2021.103547

    • Google Scholar
55. 1. Pfeiffer BD
    2. Truman JW
    3. Rubin GM

    (2012) Using translational enhancers to increase transgene expression in Drosophila

    PNAS 109:6626–6631.

    https://doi.org/10.1073/pnas.1204520109

    • Google Scholar
56. 1. Pribbenow C
    2. Chen YC
    3. Heim MM
    4. Laber D
    5. Reubold S
    6. Reynolds E
    7. Balles I
    8. Fernández-D V Alquicira T
    9. Suárez-Grimalt R
    10. Scheunemann L
    11. Rauch C
    12. Matkovic T
    13. Rösner J
    14. Lichtner G
    15. Jagannathan SR
    16. Owald D

    (2022) Postsynaptic plasticity of cholinergic synapses underlies the induction and expression of appetitive and familiarity memories in Drosophila

    eLife 11:e80445.

    https://doi.org/10.7554/eLife.80445

    • PubMed
    • Google Scholar
57. 1. Raizen DM
    2. Zimmerman JE
    3. Maycock MH
    4. Ta UD
    5. You Y
    6. Sundaram MV
    7. Pack AI

    (2008) Lethargus is a Caenorhabditis elegans sleep-like state

    Nature 451:569–572.

    https://doi.org/10.1038/nature06535

    • PubMed
    • Google Scholar
58. 1. Rattenborg NC
    2. van der Meij J
    3. Beckers GJL
    4. Lesku JA

    (2019) Local Aspects of Avian Non-REM and REM Sleep

    Frontiers in Neuroscience 13:567.

    https://doi.org/10.3389/fnins.2019.00567

    • PubMed
    • Google Scholar
59. 1. Robinson MD
    2. McCarthy DJ
    3. Smyth GK

    (2010) edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

    Bioinformatics 26:139–140.

    https://doi.org/10.1093/bioinformatics/btp616

    • PubMed
    • Google Scholar
60. 1. Robinson MD
    2. Oshlack A

    (2010) A scaling normalization method for differential expression analysis of RNA-seq data

    Genome Biology 11:R25.

    https://doi.org/10.1186/gb-2010-11-3-r25

    • PubMed
    • Google Scholar
61. 1. Rosenthal JS
    2. Yuan Q

    (2021) Constructing and Tuning Excitatory Cholinergic Synapses: The Multifaceted Functions of Nicotinic Acetylcholine Receptors in Drosophila Neural Development and Physiology

    Frontiers in Cellular Neuroscience 15:720560.

    https://doi.org/10.3389/fncel.2021.720560

    • PubMed
    • Google Scholar
62. 1. Rößler DC
    2. Kim K
    3. De Agrò M
    4. Jordan A
    5. Galizia CG
    6. Shamble PS

    (2022) Regularly occurring bouts of retinal movements suggest an REM sleep-like state in jumping spiders

    PNAS 119:e2204754119.

    https://doi.org/10.1073/pnas.2204754119

    • PubMed
    • Google Scholar
63. 1. Sauer S
    2. Kinkelin M
    3. Herrmann E
    4. Kaiser W

    (2003) The dynamics of sleep-like behaviour in honey bees

    Journal of Comparative Physiology. A, Neuroethology, Sensory, Neural, and Behavioral Physiology 189:599–607.

    https://doi.org/10.1007/s00359-003-0436-9

    • PubMed
    • Google Scholar
64. 1. Seth AK
    2. Friston KJ

    (2016) Active interoceptive inference and the emotional brain

    Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 371:1708.

    https://doi.org/10.1098/rstb.2016.0007

    • PubMed
    • Google Scholar
65. 1. Seugnet L
    2. Galvin JE
    3. Suzuki Y
    4. Gottschalk L
    5. Shaw PJ

    (2009) Persistent short-term memory defects following sleep deprivation in a Drosophila model of Parkinson disease

    Sleep 32:984–992.

    https://doi.org/10.1093/sleep/32.8.984

    • PubMed
    • Google Scholar
66. 1. Shafer OT
    2. Keene AC

    (2021) The Regulation of Drosophila Sleep

    Current Biology 31:R38–R49.

    https://doi.org/10.1016/j.cub.2020.10.082

    • PubMed
    • Google Scholar
67. 1. Shao L
    2. Shuai Y
    3. Wang J
    4. Feng S
    5. Lu B
    6. Li Z
    7. Zhao Y
    8. Wang L
    9. Zhong Y

    (2011) Schizophrenia susceptibility gene dysbindin regulates glutamatergic and dopaminergic functions via distinctive mechanisms in Drosophila

    PNAS 108:18831–18836.

    https://doi.org/10.1073/pnas.1114569108

    • Google Scholar
68. 1. Shaw PJ
    2. Cirelli C
    3. Greenspan RJ
    4. Tononi G

    (2000) Correlates of sleep and waking in Drosophila melanogaster

    Science 287:1834–1837.

    https://doi.org/10.1126/science.287.5459.1834

    • PubMed
    • Google Scholar
69. 1. Shein-Idelson M
    2. Ondracek JM
    3. Liaw H-P
    4. Reiter S
    5. Laurent G

    (2016) Slow waves, sharp waves, ripples, and REM in sleeping dragons

    Science 352:590–595.

    https://doi.org/10.1126/science.aaf3621

    • PubMed
    • Google Scholar
70. 1. Shi M
    2. Yue Z
    3. Kuryatov A
    4. Lindstrom JM
    5. Sehgal A

    (2014) Identification of Redeye, a new sleep-regulating protein whose expression is modulated by sleep amount

    eLife 3:e01473.

    https://doi.org/10.7554/eLife.01473

    • PubMed
    • Google Scholar
71. 1. Siegel JM

    (2011) REM sleep: a biological and psychological paradox

    Sleep Medicine Reviews 15:139–142.

    https://doi.org/10.1016/j.smrv.2011.01.001

    • PubMed
    • Google Scholar
72. 1. Smarandache-Wellmann CR

    (2016) Arthropod neurons and nervous system

    Current Biology 26:R960–R965.

    https://doi.org/10.1016/j.cub.2016.07.063

    • PubMed
    • Google Scholar
73. 1. Somers J
    2. Luong HNB
    3. Mitchell J
    4. Batterham P
    5. Perry T

    (2017) Pleiotropic Effects of Loss of the Dα1 Subunit in Drosophila melanogaster: Implications for Insecticide Resistance

    Genetics 205:263–271.

    https://doi.org/10.1534/genetics.116.195750

    • PubMed
    • Google Scholar
74. 1. Stahl BA
    2. Slocumb ME
    3. Chaitin H
    4. DiAngelo JR
    5. Keene AC

    (2017) Sleep-Dependent Modulation of Metabolic Rate in Drosophila

    Sleep 40:zsx084.

    https://doi.org/10.1093/sleep/zsx084

    • PubMed
    • Google Scholar
75. 1. Stanhope BA
    2. Jaggard JB
    3. Gratton M
    4. Brown EB
    5. Keene AC

    (2020) Sleep regulates glial plasticity and expression of the engulfment receptor draper following neural injury

    Current Biology 30:1092–1101.

    https://doi.org/10.1016/j.cub.2020.02.057

    • PubMed
    • Google Scholar
76. 1. Tainton-Heap LAL
    2. Kirszenblat LC
    3. Notaras ET
    4. Grabowska MJ
    5. Jeans R
    6. Feng K
    7. Shaw PJ
    8. van Swinderen B

    (2021) A Paradoxical Kind of Sleep in Drosophila melanogaster

    Current Biology 31:578–590.

    https://doi.org/10.1016/j.cub.2020.10.081

    • PubMed
    • Google Scholar
77. 1. Tononi G
    2. Cirelli C

    (2014) Sleep and the price of plasticity: from synaptic and cellular homeostasis to memory consolidation and integration

    Neuron 81:12–34.

    https://doi.org/10.1016/j.neuron.2013.12.025

    • PubMed
    • Google Scholar
78. 1. Torterolo P
    2. Castro-Zaballa S
    3. Cavelli M
    4. Chase MH
    5. Falconi A

    (2016) Neocortical 40 Hz oscillations during carbachol-induced rapid eye movement sleep and cataplexy

    The European Journal of Neuroscience 43:580–589.

    https://doi.org/10.1111/ejn.13151

    • PubMed
    • Google Scholar
79. 1. Troup M
    2. Yap MH
    3. Rohrscheib C
    4. Grabowska MJ
    5. Ertekin D
    6. Randeniya R
    7. Kottler B
    8. Larkin A
    9. Munro K
    10. Shaw PJ
    11. van Swinderen B

    (2018) Acute control of the sleep switch in Drosophila reveals a role for gap junctions in regulating behavioral responsiveness

    eLife 7:e37105.

    https://doi.org/10.7554/eLife.37105

    • PubMed
    • Google Scholar
80. 1. Troup M
    2. Tainton-Heap LAL
    3. van Swinderen B

    (2023) Neural Ensemble Fragmentation in the Anesthetized Drosophila Brain

    The Journal of Neuroscience 43:2537–2551.

    https://doi.org/10.1523/JNEUROSCI.1657-22.2023

    • PubMed
    • Google Scholar
81. 1. van Alphen B
    2. Yap MHW
    3. Kirszenblat L
    4. Kottler B
    5. van Swinderen B

    (2013) A dynamic deep sleep stage in Drosophila

    The Journal of Neuroscience 33:6917–6927.

    https://doi.org/10.1523/JNEUROSCI.0061-13.2013

    • PubMed
    • Google Scholar
82. 1. van Alphen B
    2. Semenza ER
    3. Yap M
    4. van Swinderen B
    5. Allada R

    (2021) A deep sleep stage in Drosophila with A functional role in waste clearance

    Science Advances 7:eabc2999.

    https://doi.org/10.1126/sciadv.abc2999

    • PubMed
    • Google Scholar
83. 1. Van De Poll MN
    2. van Swinderen B

    (2021) Balancing prediction and surprise: A role for active sleep at the dawn of consciousness?

    Frontiers in Systems Neuroscience 15:768762.

    https://doi.org/10.3389/fnsys.2021.768762

    • PubMed
    • Google Scholar
84. 1. Van Swinderen B
    2. Andretic R

    (2011) Dopamine in Drosophila: setting arousal thresholds in a miniature brain

    Proceedings. Biological Sciences 278:906–913.

    https://doi.org/10.1098/rspb.2010.2564

    • PubMed
    • Google Scholar
85. 1. Vazquez J
    2. Baghdoyan HA

    (2001) Basal forebrain acetylcholine release during REM sleep is significantly greater than during waking

    American Journal of Physiology. Regulatory, Integrative and Comparative Physiology 280:R598–R601.

    https://doi.org/10.1152/ajpregu.2001.280.2.R598

    • PubMed
    • Google Scholar
86. 1. Watson CJ
    2. Baghdoyan HA
    3. Lydic R

    (2010) Neuropharmacology of sleep and wakefulness

    Sleep Medicine Clinics 5:513–528.

    https://doi.org/10.1016/j.jsmc.2010.08.003

    • PubMed
    • Google Scholar
87. 1. Wiggin TD
    2. Goodwin PR
    3. Donelson NC
    4. Liu C
    5. Trinh K
    6. Sanyal S
    7. Griffith LC

    (2020) Covert sleep-related biological processes are revealed by probabilistic analysis in Drosophila

    PNAS 117:10024–10034.

    https://doi.org/10.1073/pnas.1917573117

    • PubMed
    • Google Scholar
88. 1. Wolff T
    2. Rubin GM

    (2018) Neuroarchitecture of the Drosophila central complex: A catalog of nodulus and asymmetrical body neurons and A revision of the protocerebral bridge catalog

    The Journal of Comparative Neurology 526:2585–2611.

    https://doi.org/10.1002/cne.24512

    • PubMed
    • Google Scholar
89. 1. Xie L
    2. Kang H
    3. Xu Q
    4. Chen MJ
    5. Liao Y
    6. Thiyagarajan M
    7. O’Donnell J
    8. Christensen DJ
    9. Nicholson C
    10. Iliff JJ
    11. Takano T
    12. Deane R
    13. Nedergaard M

    (2013) Sleep drives metabolite clearance from the adult brain

    Science 342:373–377.

    https://doi.org/10.1126/science.1241224

    • PubMed
    • Google Scholar
90. 1. Yap MHW
    2. Grabowska MJ
    3. Rohrscheib C
    4. Jeans R
    5. Troup M
    6. Paulk AC
    7. van Alphen B
    8. Shaw PJ
    9. van Swinderen B

    (2017) Oscillatory brain activity in spontaneous and induced sleep stages in flies

    Nature Communications 8:1815.

    https://doi.org/10.1038/s41467-017-02024-y

    • PubMed
    • Google Scholar
91. 1. Zimmerman JE
    2. Naidoo N
    3. Raizen DM
    4. Pack AI

    (2008a) Conservation of sleep: insights from non-mammalian model systems

    Trends in Neurosciences 31:371–376.

    https://doi.org/10.1016/j.tins.2008.05.001

    • PubMed
    • Google Scholar
92. 1. Zimmerman JE
    2. Raizen DM
    3. Maycock MH
    4. Maislin G
    5. Pack AI

    (2008b) A video method to study Drosophila sleep

    Sleep 31:1587–1598.

    https://doi.org/10.1093/sleep/31.11.1587

    • PubMed
    • Google Scholar

Author details

1. Niki Anthoney

   Queensland Brain Institute, The University of Queensland, Brisbane, Australia

   Contribution

   Data curation, Investigation, Visualization, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

2. Lucy Tainton-Heap

   Queensland Brain Institute, The University of Queensland, Brisbane, Australia

   Contribution

   Software, Formal analysis, Validation, Investigation, Visualization, Methodology

   Competing interests

   No competing interests declared

3. Hang Luong

   School of BioSciences, The University of Melbourne, Melbourne, Australia

   Contribution

   Formal analysis, Investigation, Methodology

   Competing interests

   No competing interests declared

4. Eleni Notaras

   Queensland Brain Institute, The University of Queensland, Brisbane, Australia

   Contribution

   Formal analysis, Investigation, Methodology

   Competing interests

   No competing interests declared

5. Amber B Kewin

   Queensland Brain Institute, The University of Queensland, Brisbane, Australia

   Contribution

   Investigation, Methodology

   Competing interests

   No competing interests declared

6. Qiongyi Zhao

   Queensland Brain Institute, The University of Queensland, Brisbane, Australia

   Contribution

   Data curation, Software, Formal analysis, Investigation, Methodology

   Competing interests

   No competing interests declared

7. Trent Perry

   School of BioSciences, The University of Melbourne, Melbourne, Australia

   Contribution

   Resources, Supervision, Methodology

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-8045-0487

8. Philip Batterham

   School of BioSciences, The University of Melbourne, Melbourne, Australia

   Contribution

   Resources, Supervision, Methodology

   Competing interests

   No competing interests declared

9. Paul J Shaw

   Department of Neuroscience, School of Medicine, Washington University in St. Louis, St Louis, United States

   Contribution

   Conceptualization, Resources, Funding acquisition, Investigation, Methodology, Project administration, Writing – review and editing

   Competing interests

   No competing interests declared

10. Bruno van Swinderen

    Queensland Brain Institute, The University of Queensland, Brisbane, Australia

    Contribution

    Conceptualization, Resources, Data curation, Formal analysis, Supervision, Funding acquisition, Investigation, Visualization, Methodology, Writing – original draft, Project administration, Writing – review and editing

    For correspondence

    b.vanswinderen@uq.edu.au

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0001-6552-7418

• 1,436

  views

• 143

  downloads

• 7

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Citations by DOI

• 6

  citations for umbrella DOI https://doi.org/10.7554/eLife.88198

• 1

  citation for Reviewed Preprint v1 https://doi.org/10.7554/eLife.88198.1