DOI: 10.5061/dryad.1rn8pk10x
title: Data from: Experimentally induced active and quiet sleep engage non-overlapping transcriptional programs in Drosophila

description: Sleep in mammals can be broadly classified into two different
 physiological categories: rapid eye movement (REM) sleep and slow wave
 sleep (SWS), and accordingly REM and SWS are thought to achieve a
 different set of functions. The fruit fly Drosophila melanogaster is
 increasingly being used as a model to understand sleep functions, although
 it remains unclear if the fly brain also engages in different kinds of
 sleep as well. Here, we compare two commonly used approaches for studying
 sleep experimentally in Drosophila: optogenetic activation of
 sleep-promoting neurons and provision of a sleep-promoting drug,
 Gaboxadol. We find that these different sleep-induction methods have
 similar effects on increasing sleep duration, but divergent effects on
 brain activity. Transcriptomic analysis reveals that drug-induced deep
 sleep (‘quiet’ sleep) mostly downregulates metabolism genes, whereas
 optogenetic ‘active’ sleep upregulates a wide range of genes relevant to
 normal waking functions. This suggests that optogenetics and
 pharmacological induction of sleep in Drosophila promote different
 features of sleep, which engage different sets of genes to achieve their
 respective functions.
description: 2-photon
 imaging 2-photon imaging was performed as described
 previously using a ThorLabs Bergamo series 2 multiphoton microscope.
 Fluorescence was detected with a High Sensitivity GaAsP photomultiplier
 tube (ThorLabs, PMT2000). GCaMP fluorescence was filtered through the
 microscope with a 594 dichroic beam splitter and a 525/25nm band pass
 filter. For imaging experiments, flies were secured to
 a custom-built holder. Extracellular fluid (ECF) containing 103 NaCl, 10.5
 trehalose, 10 glucose, 26 NaHCO3, 5 C6H15NO6S, 5 MgCl2 (hexa-hydrate), 2
 Sucrose, 3 KCl, 1.5 CaCl (dihydrate), and 1 NaH2PO4 (in mM) at room
 temperature was used to fill a chamber over the head of the fly. The brain
 was accessed by removing the cuticle of the fly with forceps, and the
 perineural sheath was removed with a microlance. Flies were allowed to
 recover from this for one hour before commencement of experiments. Imaging
 was performed across 18 z-slices, separated by 6µm, with two additional
 flyback frames. The entire nlsGCaMP6f signal was located within a 256 x
 256px area, corresponding to 667 x 667µm. Fly behavior was recorded with a
 Firefly MV 0.3MP camera (FMVU-03MTM-CS, FLIR Systems), which was mounted
 to a 75mm optical lens and an infrared filter. Camera illumination was
 provided by a custom-built infrared array consisting of 24 3mm infrared
 diodes. Behavioral data was collected for the duration of all experiments.
 For THIP experiments, an initial five minutes of baseline activity was
 captured, followed by perfusion of 0.2mg/ml THIP in ECF onto the brain at
 a rate of 1.25ml/minute for five minutes. An additional twenty minutes of
 both brain and behavioral activity were recorded to allow visualization of
 the fly falling asleep on the ball as a result of THIP exposure. All flies
 were removed from imaging after an experiment and confirmed to have awoken
 by visual inpection. A subset of THIP-exposed flies (n=3) remained in the
 imaging setup to measure brain activity upon recovery. Behavioral
 responsiveness probing For probing behavioral
 responsiveness in the brain imaging preparation, flies walking on an
 air-supported ball were subjected to a 50ms long, 10psi air puff stimulus,
 which was generated using a custom-built apparatus and delivered through a
 3mm-diameter tube onto the front of the fly. Flies were subjected to 10
 pre-THIP stimuli at a rate of one puff/minute, to characterize the
 baseline response rate. Flies were then perfused with 0.2mg/ml THIP in ECF
 for five minutes, followed by continuous ECF perfusion for the remaining
 experimental time. Five minutes after the fly had fallen asleep on the
 ball, a further 20 air puff stimuli were delivered, at a rate of one
 puff/minute. Behavioral responses to the air puff were noted as a ‘yes’
 (1) or ‘no’ (0), which were characterized as the fly rapidly walking on
 the ball immediately following the air puff. For statistical analysis, the
 pre-THIP condition was compared to either the first or last 10 minutes of
 the post-THIP condition. Imaging analysis
 Preprocessing of images was carried out using custom written
 Matlab scripts and ImageJ. Motion artifacts of the images were corrected
 as described previously. Image registration was achieved using efficient
 sub-pixel image registration by cross-correlation. Each z-slice in a
 volume (18 z-slices and 2 flyback slices) is acquired at a slightly
 different time point compared to the rest of the slices. Hence to perform
 volume (x,y,z) analysis of images, all the slices within a volume need to
 be adjusted for timing differences. This was achieved by using the
 9<sup>th</sup> z-slice as the reference slice and temporal
 interpolation was performed for all the other z-slices using ‘sinc’
 interpolation. The timing correction approach implemented here is
 conceptually similar to the methods using in fMRI for slice timing
 correction. For each individual z-slice, a standard
 deviation projection of the entire time series was used for watershed
 segmentation with the ‘Morphological segmentation’ ImageJ plugin. Using a
 custom-written MatLab (Mathworks) code, the mean fluorescent value of all
 pixels within a given ROI were extracted for the entire time series,
 resulting in a n x t array for each slice of each experiment, where n
 refers to the number of neurons in each Z-slice, and t refers to the
 length of the experiment in time frames. These greyscale values were
 z-scored for each neuron, and the z-scored data was transformed into a
 binary matrix where a value of &gt; 3 standard deviations of the mean
 was allocated a ‘1’, and every value &lt; 3 standard deviations was
 allocated a ‘0’. To determine whether a neuron fired during the entire
 time series, a rolling sum of the binary matrix was performed, where ten
 consecutive time frames were summed together. If the value of any of these
 summing events was greater or equal to seven (indicating a fluorescent
 change of &gt; 3 standard deviations in 7/10 time frames), a neuron
 was deemed to be active. For THIP sleep experiments, the five minutes of
 inactivity occurring after an initial 30 seconds of behavioral inactivity
 were used. After identifying firing neurons for each condition (wake vs
 sleep), the percentage of active neurons was calculated in each slice by
 taking the number of active neurons and dividing it by the total number of
 neurons. Traces of active neurons were used to
 calculate the number of firing events. This was done using the ‘findpeaks’
 matlab function on the zscored fluorescent traces, with the parameters
 ‘minpeakheight’ of 3, and ‘minpeakdistance’ of 30. Data resulting from
 this was crosschecked by taking the binary matrices of the time traces and
 finding the number of times each neuron met the activity threshold
 described above. Graph-theory analyses of neural connectivity were
 performed as described previously. RNA-Sequencing Flies
 collected for RNA-sequencing analysis were first housed in vials
 containing either 0.5mg/ml all-trans retinal (ATR) or 0.1mg/ml THIP for
 sleep induction, along with their genetically identical controls on
 standard food medium. Flies undergoing sleep induction by optogenetic
 activation with ATR and their controls were placed under constant
 red-light from 8AM until 6PM to coincide with normal 12:12 light/dark
 cycles. Flies were collected after 1 hour (ZT 1) and 10 hours (ZT 10) post
 induction for immediate brain dissection and RNA extraction. For analysis
 of pharmacological sleep induction, flies were placed on THIP or normal
 food medium at 8AM (ZT 0) and collected for dissection at 6PM (ZT
 10). Whole fly brains were dissected in ice cold
 RNA<em>later (Sigma-Aldrich)</em> with 0.1% PBST as per
 previously published protocol. The dissected brains were immediately
 pooled into five 1.5-mL Eppendorf tubes containing 5 brains
 (<em>n</em> = 25) each. Total RNA was immediately purified
 using TRIzol according to the manufacturer’s protocols (Sigma-Aldrich) and
 stored at −80°C until commencement of RNA-sequencing.
 cDNA libraries were prepared using the Illumina TruSeq stranded
 mRNA library prep kit. Image processing and sequence data extraction were
 performed using the standard Illumina Genome Analyzer software and CASAVA
 (version 1.8.2) software. Cutadapt (version 1.8.1) was used to cut the
 adaptor sequences as well as low quality nucleotides at both ends. When a
 processed read is shorter than 36 bp, the read was discarded by cutadapt,
 with the parameter setting of “-q 20,20 --minimum-length=36”. Processed
 reads were aligned to the <em>Drosophila melanogaster</em>
 reference genome (dm6) using HISAT2 (version 2.0.5), with the parameter
 setting of “--no-unal --fr --rna-strandness RF --known-splicesite-infile
 dm6_splicesites.txt”. This setting is to i) suppress SAM records for reads
 that failed to align (“--no-unal”), ii) specify the Illumina’s paired-end
 sequencing assay and the strand-specific information (“--fr
 --rna-strandness RF”) and iii) provide a list of known splice sites
 in <em>Drosophila melanogaster</em>
 (“--known-splicesite-infile dm6_splicesites.txt”). Samtools (version 1.3)
 was then used to convert “SAM” files to “BAM” files, sort and index the
 “BAM” files. The “htseq-count” module in the HTSeq package (v0.7.1) was
 used to quantitate the gene expression level by generating a raw count
 table for each sample (i.e. counting reads in gene features for each
 sample). Based on these raw count tables, edgeR (version 3.16.5) was
 adopted to perform the differential expression analysis between treatment
 groups and controls. EdgeR used a trimmed mean of M-values to compute
 scale factors for library size normalization. It used the Cox-Reid
 profile-adjusted likelihood method to estimate dispersions and the
 quasi-likelihood F-test to determine differential expression. Lowly
 expressed genes in both groups (the mean CPM &lt; 5 in both groups)
 were removed. Differentially expressed genes were identified using the
 following criteria: i) FDR &lt; 0.05 and ii) fold changes &gt; 1.5
 (or logfc &gt;0.58). Gene ontology enrichment analysis for
 differentially expressed genes was performed using the functional
 annotation tool in DAVID Bioinformatics Resources (version 6.8).
description: <b>Paper:</b> Experimentally induced active and quiet sleep
 engage non-overlapping transcriptional programs in
 <i>Drosophila</i> <b>Authors:</b> Niki Anthoney
 Lucy A.L. Tainton-Heap Hang Luong Eleni Notaras Amber B. Kewin Qiongyi
 Zhao Trent Perry Philip Batterham Paul J. Shaw Bruno van Swinderen
 <b>This README file describes the data package accompanying the
 above publication.</b> <b>Files:</b> <b>2 Photon
 Data</b> Files named: 2photon\_FlyX\_23E10Chrimson contain the raw
 2photon data and .avi behavioural data for each fly. There are 5 files for
 each of the 9 flies. These files are affixed with: \_Experiment
 \_ChanB\_Preview \_Image\_0001\_0001 \_ROIMask \_ROIs These files relate
 to Figure 3C of the above publication. Files named: 2photon\_flyX\_THIP
 contain the raw 2photon data and .avi behavioural data for each fly. There
 are 5 files for each of the 6 flies. These files are affixed with:
 \_Experiment \_ChanB\_Preview \_Image\_0001\_0001 \_ROIMask \_ROIs These
 files relate to Figure 3D and 4D-4H of the above publication.
 <b>THIP</b> responsiveness Files named:
 THIP\_responsiveness\_FlyX contains the behavioural recordings of flies
 during behavioural responsiveness experiments. There are 7 files in total,
 relating to 6 flies. Fly 3 was filed in 2 parts. These files relate to
 Figure 4C of the above publication. <b>MatlabCodes</b> File
 named: preprocessing.m contains the script used for preprocessing data two
 photon data. It performs filtering of ROIs based on ROI area, extraction
 of calcium signal over experiment and zscoring of calcium signal for
 analysis. Identifies which neurons are active in an experiment and creates
 masks of active ROIs. This file relates to two photon data found in
 figures 3 and 4 of the above paper. File named: graph\_theory\_analysis.m
 contains the script which takes input from preprocessing.m. Calcium
 signals are temporally shuffled and correlated together 1000 times and the
 95<sup>th</sup> percentile value of shuffled correlations is
 used to identify neurons that are significantly correlated together over
 an experiment. This file relates to two photon data found in figures 3 and
 4 of the above paper. File named: Figures.m was used to create Figure 4f
 of the above paper. <b>RNA Seq Data</b> File named: THIP
 samples contains the fastq files for both R1 and R2 reads generated by RNA
 Sequencing for samples that had been treated with THIP. There are 40 files
 in total relating to the five biological replicates for each experiment
 (n=4) and 2 subsequent reads for each. These files relate to figure 6 of
 the above paper. Files are named in the following convention:
 experimentX\_Y\_read1fastq.gz where X would be replaced by the experiment
 number, and Y would be replaced by the sample number eg: 1-5. 1\.
 Experiment 7: which corresponds to the following experimental conditions\.
 R23E10Gal4\&gt;UASChrimson \- THIP 2\. Experiment 8: which corresponds
 to the following experimental conditions\. R23E10Gal4\&gt;UASChrimson
 \+ THIP 3\. Experiment 9: which corresponds to the following experimental
 conditions\. R23E10Gal4\&gt;UASChrimson \+ THIP \+ Sleep deprivation
 4\. Experiment 10: which corresponds to the following experimental
 conditions\. R23E10Gal4\&gt;UASChrimson \+ THIP \+ Sleep deprivation
 File named: ATR samples contains the fastq files for both R1 and R2 reads
 generated by RNA Sequencing for samples that had been treated with ATR.
 There are 40 files in total relating to the five biological replicates for
 each experiment (n=4) and 2 subsequent reads for each. These files relate
 to figure 7 of the above paper. Files are named in the following
 convention: experimentX\_Y\_read1fastq.gz where X would be replaced by the
 experiment number, and Y would be replaced by the sample number eg: 1-5.
 1\. Experiment 13: which corresponds to the following experimental
 conditions\. R23E10Gal4\&gt;UASChrimson * ATR (1hr) 2\. Experiment 14:
 which corresponds to the following experimental conditions\.
 R23E10Gal4\&gt;UASChrimson * ATR (1hr) 3\. Experiment 15: which
 corresponds to the following experimental conditions\.
 R23E10Gal4\&gt;UASChrimson * ATR (10hr) 4\. Experiment 16: which
 corresponds to the following experimental conditions\.
 R23E10Gal4\&gt;UASChrimson * ATR (10hr)