Chapter Seventeen - RNA-seq Profiling of Small Numbers of Drosophila Neurons
Introduction
Circadian clocks allow organisms to predict and respond to daily fluctuations in their environments. In most organisms, these clocks oscillate with an ~ 24-h period and are entrained by environmental cues such as light. In Drosophila, the clock is driven by a several well-defined transcriptional feedback loops, one of which is focused on a heterodimer of the transcriptional factors CLK and CYC. CLK/CYC drives the transcription of the repressors PER and TIM in the early evening. PER and TIM levels accumulate and repress CLK/CYC-driven transcription in the late night. This negative feedback loop contributes to oscillating gene expression, which has a major impact on the circadian outputs including locomotor activity rhythms.
In Drosophila, there are ~ 75 pairs of neurons in the brain that express high levels of these clock components (CLK, CYC, PER, TIM) and are therefore considered circadian neurons. They have been divided into two main subgroups: dorsal neurons (DNs) and the lateral neurons (LNs). DNs are further subdivided into four groups based primarily on their location in the brain: DN1a, DN1p, DN2, and DN3. LNs have been divided into two main groups based on their expression of the neuropeptide pigment-dispersing factor (PDF; Helfrich-Forster, 1995): LPNs, LNds, and the fifth small LNv are PDF−, whereas the large and four of the five small LNvs are PDF+. Further experiments have shown that the PDF+ s- and l-LNvs are critical for driving the morning activity period in Drosophila and are known as the morning cells (M-cells). The PDF− LNds and the 5th small LNv are important for driving evening behavior and are known as the evening cells (E-cells; Grima et al., 2004, Stoleru et al., 2004). Immunostaining studies have begun to suggest the function(s) of these different groups, by revealing different expression patterns. For example, the circadian photoreceptor Cryptochrome (CRY) and different neuropeptides that impact the circadian system are differentially expressed within the circadian network (reviewed in Yoshii, Rieger, & Helfrich-Forster, 2012). In addition, different cell-specific drivers from the GAL4/UAS system have been used to manipulate subsets of these neurons with different UAS proteins to determine changes in circadian behavior. For example, electrical silencing of the M-cells causes a severe deficit in free-running locomotor rhythms (Depetris-Chauvin et al., 2011, Nitabach et al., 2002).
Studies in the last decade have provided further evidence that the control of circadian rhythms is not a simple case of attributing a specific task to a single group of neurons. Evidence indicates that interactions between different circadian neurons are necessary to achieve the complex regulation that drives circadian behaviors. M-cells are considered the master pacemakers since they can keep time in constant darkness, but they communicate with both the DN1s and the E-cells via PDF signaling (Guo et al., 2014, Zhang, Chung, et al., 2010, Zhang, Liu, Bilodeau-Wentworth, Hardin and Emery, 2010). Moreover, manipulating the E-cells as well as the DN1s impacts the morning peak (Guo et al., 2014, Zhang, Chung, et al., 2010, Zhang, Liu, Bilodeau-Wentworth, Hardin and Emery, 2010). The data indicate that the DN1s and E-cells are downstream of PDF signaling, but they may also feed back to influence the M-cells.
This complexity indicates that it will be important to characterize subgroups of neurons and eventually single neurons. Although immunostaining has been a valuable tool to start to decipher the unique expression patterns of circadian neurons, a genome-wide view of differential gene expression patterns would greatly expand our vision. To this end, we and others manually sorted subgroups of neurons from dissociated Drosophila brains and used microarrays to assay neuron-specific gene expression (Kula-Eversole et al., 2010, Mizrak et al., 2012, Nagoshi et al., 2010). We have now used deep-sequencing technologies to sequence the mRNA and miRNA populations of these M-cells. We also show here a bit of data from a large group of noncircadian neurons (e.g., dopaminergic: ~ 130 neurons per brain; Mao & Davis, 2009) as well as the smaller group of M neurons (M-cells; l- and s-LNvs; ~ 14 neurons/brain). By identifying mRNAs that are enriched and/or undergo cycling in particular groups of neurons, we hope to learn more about the roles of these neurons in contributing to particular aspects of circadian rhythms.
Section snippets
Isolating neurons of interest
To isolate neurons of interest, we express a fluorescent protein in a specific subset of neurons and then manually sort these fluorescent neurons from dissociated brains. One of the key steps of this procedure is ensuring that the fluorescent protein (1) is sufficiently bright to make cell sorting possible and (2) has no leaky expression outside of the neurons of interest. Although we have used UAS-MCD8-GFP (Lee & Luo, 1999; Bloomington Stock Center #56168) and UAS-EGFP (Bloomington stock
Discussion
Recent studies suggest that the control and regulation of circadian behavior is due to the coordinated response of several different groups of circadian neurons acting as a network. One critical part of dissecting this network (or any network) is learning more about the role of specific cells and groups of cells. We present here a method for isolating specific groups of neurons from the Drosophila brain and using deep sequencing to profile their miRNA and mRNA populations present in these
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Allatostatin-C/AstC-R2 Is a Novel Pathway to Modulate the Circadian Activity Pattern in Drosophila
2019, Current BiologyCitation Excerpt :The brains were then recorded in the same parameters as above, with AHL+TTX (1 uM) as a baseline recording before exposing the brains to AstC (10 uM) + TTX (1 uM). For the distribution of the differential sequencing of AstC, the mRNA transcripts were averaged across the entire AstC gene for each of the twelve datasets previously described in [15, 47]. The twelve independent datasets originate from two biological replicates of each of the six time-of-day collections.
NonA and CPX Link the Circadian Clockwork to Locomotor Activity in Drosophila
2018, NeuronCitation Excerpt :GFP labeled M- and E-neurons (dvpdf-gal4 > UAS-GFP, nonA RNAi and dvpdf-gal > UAS-GFP) were manually sorted by dissociating fly brains with papain and followed by pipette trituration. RNA-seq libraries from M- and E-neurons were generated as described in (Abruzzi et al., 2015). For NonA TRIBE, fly brains were dissected from young flies (elev-gs-gal4>UAS-nonA-ADAR).
TRIBE: Hijacking an RNA-Editing Enzyme to Identify Cell-Specific Targets of RNA-Binding Proteins
2016, CellCitation Excerpt :The neuronal groups examined were the core circadian PDF neuropeptide expressing cells (pdf-Gal4, ∼16 cells/brain), dopaminergic neurons (Tyrosine hydroxylase, TH-Gal4, ∼1,000 cells/brain) and all neurons (pan-neuronal driver, elav-Gal4, ∼100,000 cells/brain). A fluorescent protein (UAS-eGFP) was co-expressed to allow manual cell sorting of TRIBE protein-expressing and control neurons from dissociated Drosophila brains (Nagoshi et al., 2010; Abruzzi et al., 2015). Similar to the cell culture result, neuronal expression of the Hrp48-TRIBE protein caused a large increase in the number of editing sites, far more than the level of endogenous editing (range of endogenous editing sites, ∼300–2,000; range of TRIBE editing sites, ∼8,000–11,000; Figure 6A).
Rapid Changes in the Translatome during the Conversion of Growth Cones to Synaptic Terminals
2016, Cell ReportsCitation Excerpt :Several different approaches have been described for isolating RNA from neurons for mRNA sequencing studies. These include using fluorescent cell tags for isolating neurons by manual picking after tissue dissociation or through fluorescence-activated cell sorting (FACs) (Abruzzi et al., 2015). The INTACT technique utilizes cell-specific expression of a nuclear envelope tag followed by affinity purification of nuclei (Henry et al., 2012; Steiner et al., 2012).
Standardization of Single-Cell RNA-Sequencing Analysis Workflow to Study Drosophila Ovary
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