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Selective dendritic localization of mRNA in Drosophila mushroom body output neurons

  1. Jessica Mitchell
  2. Carlas S Smith
  3. Josh Titlow
  4. Nils Otto
  5. Pieter van Velde
  6. Martin Booth
  7. Ilan Davis
  8. Scott Waddell  Is a corresponding author
  1. Centre for Neural Circuits and Behaviour, University of Oxford, United Kingdom
  2. Delft Center for Systems and Control, Delft University of Technology, Netherlands
  3. Department of Biochemistry, University of Oxford, United Kingdom
  4. Department of Engineering Science, University of Oxford, United Kingdom
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Cite this article as: eLife 2021;10:e62770 doi: 10.7554/eLife.62770

Abstract

Memory-relevant neuronal plasticity is believed to require local translation of new proteins at synapses. Understanding this process requires the visualization of the relevant mRNAs within these neuronal compartments. Here, we used single-molecule fluorescence in situ hybridization to localize mRNAs at subcellular resolution in the adult Drosophila brain. mRNAs for subunits of nicotinic acetylcholine receptors and kinases could be detected within the dendrites of co-labeled mushroom body output neurons (MBONs) and their relative abundance showed cell specificity. Moreover, aversive olfactory learning produced a transient increase in the level of CaMKII mRNA within the dendritic compartments of the γ5β'2a MBONs. Localization of specific mRNAs in MBONs before and after learning represents a critical step towards deciphering the role of dendritic translation in the neuronal plasticity underlying behavioral change in Drosophila.

Introduction

Memories are believed to be encoded as changes in the efficacy of specific synaptic connections. Dendritic localization of mRNA facilitates specificity of synaptic plasticity by enabling postsynaptic synthesis of new proteins where and when they are required (Holt et al., 2019). Visualizing individual dendritically localized mRNAs in memory-relevant neurons is therefore crucial to understanding this process of neuronal plasticity.

Single-molecule fluorescence in situ hybridization (smFISH) enables cellular mRNAs to be imaged at single-molecule resolution through the hybridization of a set of complementary oligonucleotide probes, each labeled with a fluorescent dye. Recent improvements in smFISH permit mRNA transcripts to be visualized in the dense heterogenous tissue of intact Drosophila brains (Long et al., 2017; Yang et al., 2017). Combining whole fly brain smFISH with neuron-specific co-labeling makes Drosophila an ideal model to investigate cell-specific mRNA localization and whether it is regulated in response to experience.

Olfactory learning in Drosophila depresses cholinergic synaptic connections between odor-specific mushroom body Kenyon cells (KCs) and mushroom body output neurons (MBONs) (Barnstedt et al., 2016; Cohn et al., 2015; Handler et al., 2019; Hige et al., 2015; Owald et al., 2015; Perisse et al., 2016; Séjourné et al., 2011). This plasticity is driven by dopaminergic neurons whose presynaptic terminals innervate anatomically discrete compartments of the mushroom body, where they overlap with the dendrites of particular MBONs (Aso et al., 2010; Aso et al., 2014; Burke et al., 2012; Claridge-Chang et al., 2009; Li et al., 2020; Lin et al., 2014; Liu et al., 2012). Dopamine-driven plasticity is mediated by cAMP-dependent signaling and associated kinases such as calcium/calmodulin-dependent protein kinase II (CaMKII) and protein kinase A (PKA) (Boto et al., 2014; Handler et al., 2019; Hige et al., 2015; Kim et al., 2007; Qin et al., 2012; Tomchik and Davis, 2009; Yu et al., 2006; Zhang and Roman, 2013). Here, we demonstrate localization of mRNAs in the 3D volumes of MBON dendrites by registering smFISH signals with co-labeled neurons using a custom image analysis pipeline. Moreover, we find that aversive learning transiently elevates dendritic CaMKII transcript levels within γ5β'2a MBONs.

Results and discussion

mRNA localization in the intact adult Drosophila brain

Mammalian CaMKII mRNA is transported to neuronal dendrites, where it is locally translated in response to neuronal activity (Bagni et al., 2000; Miller et al., 2002; Ouyang et al., 1999). Drosophila CAMKII is critical for behavioral plasticity (Griffith, 1997; Malik et al., 2013) and is also thought to be locally translated (Ashraf et al., 2006). However, fly CAMKII mRNAs have not been directly visualized within individual neurons. We therefore first hybridized CaMKII smFISH probes to whole-mount brains and imaged the mushroom body (MB) calyx (Figure 1A, B), a recognizable neuropil containing the densely packed dendrites of ~2000 KCs and their presynaptic inputs from ~350 cholinergic olfactory projection neurons (Bates et al., 2020a) using a standard spinning disk confocal microscope. To detect and quantify mRNA within the 3D volume of the brain, we developed a FIJI-compatible custom-built image analysis tool that segments smFISH image data and identifies spots within the 3D volume using a probability-based hypothesis test. This enabled detection of mRNAs with a false discovery rate of 0.05. CaMKII smFISH probes labeled 56 ± 5 discrete puncta within each calyx (Figure 1B, C). In comparison, smFISH probes directed to the α1 nicotinic acetylcholine receptor (nAChR) subunit labeled 33 ± 2 puncta in the calyx (Figure 1B, C). Puncta were diffraction limited and the signal intensity distribution was unimodal (Figure 1D–D'), indicating that they represent single mRNA molecules.

CaMKII and nAChR α1 mRNA visualized in the mushroom body (MB) calyx and γ5β'2a mushroom body output neuron (MBON) dendrites with single-molecule fluorescence in situ hybridization (smFISH).

(A) Schematic of Drosophila MB. smFISH signal was imaged in the calyx, indicated by the dashed box. (B) CaMKII and nAChRα1 mRNAs labeled with smFISH in the MB calyx. Images are maximum intensity projections of ten 0.2 µm z-sections. (C) More CaMKII mRNAs are detected in the MB calyx relative to nAChRα1 (unpaired t-test: p=0.0003, t = 4.727, df = 15). (D) smFISH spot size distribution (full width half maximum, bottom) in MB calyx. (D'). Unimodal smFISH spot intensity distribution (signal/background) indicates imaging at single-molecule resolution. (E) Reconstruction of a γ5β'2a MBON (black) showing the dendritic field (blue) and MB (light gray). The projection to the contralateral MB is truncated. (F) Alignment of dendrite and smFISH imaging channels using co-labeling with dsDNA Vybrant DyeCycle Violet (VDV) dye. VDV is excited with 405 nm and emission is collected in the dendritic and smFISH imaging channels, which were then aligned in x, y, and z planes. (G, G') CaMKII smFISH within the γ5β'2a MBON dendrite co-labeled with R66C08-GAL4-driven UAS-myr::SNAP and visualized with JF547SNAP dye. Images are maximum intensity projections of ten 0.2 μm z-sections. (H, H') nAchRα1 smFISH in γ5β'2aMBONs. Images are maximum intensity projections of ten 0.2 μm z-sections. (I) Single CaMKII smFISH puncta localized within a γ5β'2a MBON dendrite (green arrowhead). Images are single z-sections of 0.2 μm. (J) Single CaMKII smFISH puncta localized outside of the γ5β'2a MBON dendrite (red arrowhead). Images are single z-sections of 0.2 μm.

mRNA localization within MBON dendrites

Drosophila learning is considered to be implemented as plasticity of cholinergic KC-MBON synapses. To visualize and quantify mRNA specifically within the dendritic field of the γ5β'2a and γ1pedc>α/β MBONs, we expressed a membrane-tethered UAS-myr::SNAP reporter transgene using MBON-specific GAL4 drivers. This permitted simultaneous fluorescent labeling of mRNA with smFISH probes and the MBON using the SNAP Tag (Figure 1E). To correct for chromatic misalignment (Matsuda et al., 2018) that results from imaging heterogenous tissue at depth, we also co-stained brains with the dsDNA-binding dye Vybrant DyeCycle Violet (VDV). VDV dye has a broad emission spectrum so labeled nuclei can be imaged in both the SNAP MBON and smFISH mRNA channels. This triple-labeling approach allowed quantification and correction of any spatial mismatch between MBON and smFISH channels in x, y, and z planes, which ensures that smFISH puncta are accurately assigned within the 3D volume of the MBON dendritic field (Figure 1F).

Using this smFISH approach, we detected an average of 32 ± 2 CaMKII mRNAs (Figure 1G, G') within the dendrites of γ5β'2a MBONs. However, in contrast to the calyx, we did not detect nAChRα1 in γ5β'2a MBON dendrites (Figure 1H, H'). This differential localization of the CaMKII and nAChRα1 mRNAs within neurons of the mushroom body is indicative of cell specificity. To probe mRNA localization in MBONs more broadly, we used a single YFP smFISH probe set and a collection of fly strains harboring YFP insertions in endogenous genes (Lowe et al., 2014). We selected YFP insertions in the CaMKII, PKA-R2, and Ten-m genes as test cases and compared the localization of their YFP-tagged mRNAs between γ5β'2a MBON and γ1pedc>α/β MBON dendrites.

The CaMKII::YFP allele is heterozygous in flies also expressing myr::SNAP in MBONs. Therefore, YFP smFISH probes detected half the number of CaMKII mRNAs in γ5β'2a MBON dendrites compared to CaMKII-specific probes (Figure 2A, A', C). Importantly, YFP probes hybridized to YFP-negative control brains produced background signal (Figure 2B, B') that was statistically distinguishable in brightness from genuine smFISH puncta (Figure 2D). Comparing data from YFP-negative and YFP-positive samples allowed us to define the false discovery rate to be 14% when using YFP-directed probes (Figure 2D, Figure 2—figure supplement 1). These results indicate that the YFP probes are specific and that the YFP insertion does not impede localization of CaMKII mRNA. We detected a similar abundance of CaMKII::YFP in the dendritic field of γ5β'2a (Figure 2E) and the γ1 dendritic region of γ1pedc>α/β (Figure 2F) MBONs (Figure 2G). In contrast, more PKA-R2 mRNAs were detected in the dendrites of γ5β'2a MBONs compared to γ1pedc>α/β MBONs (Figure 2G). Importantly, the relative abundance of dendritically localized CaMKII and PKA-R2 mRNAs did not simply reflect the levels of these transcripts detected in the MBON somata (Figure 2H). In addition, we did not detect Ten-m mRNAs in either γ5β'2a or γ1pedc>α/β MBON dendrites (Figure 2G, I), although they were visible in neighboring neuropil and at low levels in the MBON somata (Figure 2H). These results suggest that CaMKII and PKA-R2 mRNAs are selectively localized to MBON dendrites.

Figure 2 with 1 supplement see all
Differential localization of mRNAs in γ5β'2a and γ1pedc>α/β mushroom body output neuron (MBON) dendrites.

(A, A'). CaMKII::YFP mRNA visualized in γ5β'2a MBON dendrites using YFP single-molecule fluorescence in situ hybridization (smFISH) probes. The γ5β'2a MBON is labeled by R66C08-GAL4-driven UAS-myr::SNAP and visualized with JF547SNAP dye. Images are maximum intensity projections of ten 0.2 µm z-sections. (B, B'). YFP smFISH signal in a γ5β'2a MBON in a negative control fly. Images are maximum intensity projections of ten 0.2 µm z-sections. (C) The CaMKII::YFP allele is heterozygous, resulting in detection of half as many CaMKII mRNAs in γ5β'2a MBONs using YFP probes relative to that detected with CaMKII gene-specific probes. (D) Signal/background intensity distribution of YFP probe signals in CaMKII::YFP brains relative to control brains with no threshold on signal detection. The signal/background intensity threshold for quantitative analyses (dotted red line) resulted in a false discovery rate of ≤14% (indicated by the overlap of the histograms on the right side of the dotted red line) (see also Figure 2—figure supplement 1). (E) Reconstruction of a γ5β'2a MBON. Individual postsynapses (turquoise spheres) and presynapses (red spheres) are labeled. The projection to the contralateral mushroom body (MB) is truncated. (F) Reconstruction of a γ1pedc>α/β MBON. Individual postsynapses (turquoise spheres) and presynapses (red spheres) are labeled. The projection to the contralateral MB is truncated. (G) Quantification of mRNA localization in γ5β'2a and γ1pedc>α/β MBON dendrites with YFP smFISH probes and gene-specific nicotinic acetylcholine receptor (nAChR) subunit smFISH probes. More PKA-R2 transcripts localize within the dendrites of γ5β'2a MBONs relative to γ1pedc>α/β MBONs (unpaired t-test: p=0.004, t = 5.069, df = 11). Ten-m mRNAs did not localize to either MBON dendritic field. CaMKII mRNAs were detected in equal abundance. nAchRα1 mRNAs did not localize to the dendrites of either γ5β'2a or γ1pedc>α/β MBONs. More nAchRα5 (unpaired t-test: p=0.004, t = 3.368, df = 15) and nAchRα6 (unpaired t-test: p=0.046, t = 2.274, df = 10) mRNAs localized to γ5β'2a MBON dendrites relative to γ1pedc>α/β MBON dendrites. (H) Quantification of mRNA in γ5β'2a and γ1pedc>α/β MBON somata with YFP smFISH probes and gene-specific nAChR subunit smFISH probes. More CaMKII transcripts were present within γ5β'2a MBON somata relative to γ1pedc>α/β MBON somata (unpaired t-test: p=0.0061, t = 3.103, df = 18). More Ten-m (Mann–Whitney test: p=0.0093, Mann–Whitney U = 120) and nAchRα1 (unpaired t-test: p=0.0359, t = 2.250, df = 20) transcripts were detected in γ1pedc>α/β MBON somata relative to γ5β'2a MBON somata. (I) Example smFISH images of mRNAs localized in γ5β'2a (R66C08-GAL4>UAS-myr::SNAP) and γ1pedc>α/β MBON (MB112C-GAL4>UAS-myr::SNAP) dendrites. Images are maximum intensity projections of ten 0.2 μm z-sections. Asterisks denote significant difference (p<0.05). Data are means ± standard error of mean. Individual data points are displayed.

Although we did not detect nAChRα1 mRNA within γ5β'2a MBON dendrites, prior work has shown that nAChR subunits, including nAChRα1, are required in γ5β'2a MBON postsynapses to register odor-evoked responses and direct odor-driven behaviors (Barnstedt et al., 2016). Since the YFP insertion collection does not include nAChR subunits, we designed nAChRα5 and nAChRα6-specific smFISH probes. These probes detected nAchRα5 and nAchRα6 mRNAs within γ5β'2a and γ1pedc>α/β MBON dendrites, with nAchRα6 being most abundant (Figure 2G). Importantly, we detected nAchRα1, nAchRα5, and nAchRα6 at roughly equivalent levels in the γ5β'2a and γ1pedc>α/β MBON somata (Figure 2H). Therefore, the selective localization of nAchRα5 and nAchRα6α6 mRNA to MBON dendrites indicates that these receptor subunits may be locally translated to modify the subunit composition of postsynaptic nAChR receptors.

Localized mRNAs were on average 2.8× more abundant in γ5β'2a relative to the γ1 region of γ1pedc>α/β MBON dendrites (Figure 2G). We therefore tested whether this apparent differential localization correlated with dendritic volume and/or the number of postsynapses between these MBONs. Using the recently published electron microscope volume of the Drosophila ‘hemibrain’ (Scheffer et al., 2020Figure 2E, F), we calculated the dendritic volume of the γ5β'2a MBON to be 1515.36 nm3 and the γ1 region of the γ1pedc>α/β MBON to be 614.20 nm3. In addition, the γ5β'2a regions of the γ5β'2a MBON dendrite contain 30,625 postsynapses, whereas there are only 17,020 postsynapses in the γ1 region of the γ1pedc>α/β MBON. Larger dendritic field volume and synapse number is therefore correlated with an increased number of localized nAchRα5, nAchRα6, and PKA-R2 mRNAs. The correlation, however, does not hold for CaMKII mRNA abundance. Selective localization of mRNAs to MBON dendrites therefore appears to be more nuanced than simply reflecting the size of the dendritic arbor, the number of synapses, or the level of transcripts detected throughout the cell.

Learning transiently changes CAMKII mRNA abundance in γ5β'2a MBON dendrites

We tested whether CaMKII::YFP mRNA abundance in γ5β'2a and γ1pedc>α/β MBONs was altered following aversive learning (Figure 3A, B). We also quantified mRNA in the somata and nuclei of these MBONs (Figure 3A, B'). Transcriptional activity is indicated by a bright nuclear transcription focus (Figure 3C, Figure 3—figure supplement 1). We initially subjected flies to four conditions (Figure 3D): (1) an ‘untrained’ group that was loaded and removed from the T-maze but not exposed to odors or shock; (2) an ‘odor only’ group, exposed to the two odors as in training but without shock; (3) a ‘shock only’ group that was handled as in training and received the shock delivery but no odor exposure; and (4) a ‘trained’ group that was aversively conditioned by pairing one of the two odors with shock. Fly brains were extracted 10 min, 1 hr, or 2 hr after training and processed for smFISH.

Figure 3 with 2 supplements see all
Learning alters CaMKII mRNA abundance in the γ5β'2a mushroom body output neurons (MBONs).

(A, A'). CaMKII::YFP single-molecule fluorescence in situ hybridization (smFISH) in γ5β'2a MBON dendrites and soma (R66C08-GAL4>UAS-myr::SNAP). Images are maximum intensity projections of ten 0.2 μm z-sections. (B, B'). CaMKII::YFP smFISH in γ1pedc>α/β MBON dendrites and soma (MB112C-GAL4>UAS-myr::SNAP). Nuclear transcription foci are indicated (red arrowheads). Images are maximum intensity projections of ten 0.2 µm z-sections. (C) CaMKII::YFP smFISH signal/background in transcriptionally active γ5β'2a somata. Transcription foci are readily distinguished as the brightest puncta in the soma/nucleus (red data points). Note that only one transcription focus can be visualized per cell since the CaMKII::YFP allele is heterozygous. (D) Schematic of aversive training and control protocols followed by smFISH. The yellow and red circles represent the two odors. (E) CaMKII::YFP mRNA numbers in γ5β'2a MBON dendrites increase 10 min after odor–shock pairing, relative to control groups (one-way ANOVA: untrained-10 min p=0.001; odor only-10 min p=0.016; shock only-10 min p=0.002), and decrease to baseline by 2 hr (one-way ANOVA: 10 min-2 h p<0.001; 1–2 h p=0.004). CaMKII::YFP mRNA numbers in γ5β'2a MBON somata increase 1 hr after odor–shock pairing, relative to untrained (one-way ANOVA: p=0.001), odor only (one-way ANOVA: p=0.002), and 10 min post training (one-way ANOVA: p=0.025). The proportion of transcriptionally active γ5β'2a MBON somata is unchanged (X2=2.064, df = 5, p=0.840). (F) CaMKII::YFP mRNA numbers are not changed by aversive odor–shock pairing in γ1pedc>α/β MBON dendrites (one-way ANOVA: f = 1.473, p=0.212), their somata (one-way ANOVA: f = 2.183, p=0.067), and there is no detected change in CaMKII::YFP transcription (X2=3.723, df = 5, p=0.59). (G) Signal/background ratio of CaMKII::YFP transcription foci in γ5β'2a MBON somata. (H) Signal/background ratio of CaMKII::YFP mRNA localized in γ5β'2a MBON dendrites. Asterisks denote significant difference (p<0.05). Data are means ± standard error of mean. Individual data points are displayed.

CaMKII mRNA increased significantly in γ5β'2a MBON dendrites 10 min after training (Figure 3E) compared to all control groups. Including an additional ‘unpaired’ experiment, where odor and shock presentation was staggered, confirmed that the increase at 10 min after training requires coincident pairing of odor and shock (Figure 3—figure supplement 2). Moreover, levels returned to baseline by 1 hr and remained at that level 2 hr after training (Figure 3E). CaMKII mRNAs in γ5β'2a MBON somata showed a different temporal dynamic, with transcripts peaking 1 hr after training, albeit only relative to untrained and odor only controls (Figure 3E). The proportion of γ5β'2a nuclei containing a CaMKII transcription focus did not differ between treatments (Figure 3E), suggesting that the transcript increase in the somata is not correlated with the number of actively transcribing γ5β'2a nuclei, at least at the timepoints measured. In addition, the mean brightness of γ5β'2a transcription foci did not change across treatments (Figure 3G), although the variation was substantial. An increase of dendritically localized CaMKII mRNAs could result from enhanced trafficking or through the release of transcripts from protein bound states, which would increase smFISH probe accessibility and hence spot brightness (Buxbaum et al., 2014). Since the brightness of CaMKII mRNA spots detected in the dendrites of γ5β'2a MBONs did not change with treatment (Figure 3H), we conclude that the increased abundance likely results from altered traffic.

Assessing CaMKII mRNA abundance in γ1pedc>α/β MBONs after learning did not reveal a change in mRNA abundance in the dendrites or somata between trained flies and all control groups at all timepoints measured (Figure 3F). These results indicate specificity to the response observed in the γ5β'2a MBONs.

Since CaMKII protein is also labeled with YFP in CaMKII::YFP flies, we assessed protein expression by measuring YFP fluorescence intensity specifically within the MBON dendrites. This analysis did not reveal a significant difference in fluorescence intensity across treatments (Figure 3—figure supplement 2). However, since smFISH provides single-molecule estimates of mRNA abundance, a similar level of single-molecule sensitivity may be required to detect subcellular resolution changes in protein copy number. Moreover, new synthesis and replacement of specific isoforms of CaMKII could radically change local kinase activity (Kuklin et al., 2017; Zalcman et al., 2018), even without an observable change in overall abundance.

Early studies in Drosophila demonstrated that broad disruption of CAMKII function impaired courtship learning (Broughton et al., 2003; Griffith et al., 1994; Griffith et al., 1993; Joiner and Griffith, 1997). In contrast, later studies that manipulated activity more specifically in olfactory projection neurons or particular classes of KCs reported a preferential loss of middle-term or long-term olfactory memory (Akalal et al., 2010; Ashraf et al., 2006; Malik et al., 2013). Here, we focused our analyses on two subtypes of MBONs, which are known to exhibit changes in odor-evoked activity after a single trial of aversive olfactory conditioning. Whereas γ1pedc>α/β MBON responses to the previously shock-paired odor are depressed immediately after aversive learning (Hige et al., 2015; Perisse et al., 2016), prior studies observed a learning-related increase of the conditioned odor response of γ5β'2a MBONs (Bouzaiane et al., 2015; Owald et al., 2015), likely resulting from a release of feedforward inhibition from γ1pedc>α/β MBONs (Felsenberg et al., 2018; Perisse et al., 2016). We therefore speculate that the specific change in CaMKII mRNA abundance in the γ5β'2a MBONs after aversive learning might be a consequence of network-level potentiation of their activity, such as that that would result from a release from inhibition. Since CAMKII local translation-dependent plasticity is expected to underlie more extended forms of memory (Giese and Mizuno, 2013; Miller et al., 2002), it will be interesting to investigate whether the training-evoked change in CaMKII mRNA abundance in the γ5β'2a MBON dendrites contributes to later aversive memory formation and maintenance. This may be possible with MBON-specific targeting of CAMKII mRNAs that contain the long 3'UTR, which is essential for dendritic localization and activity-dependent local translation (Aakalu et al., 2001; Kuklin et al., 2017; Mayford et al., 1996; Rook et al., 2000).

Materials and methods

Key resources table
Reagent type
(species) or resource
DesignationSource or referenceIdentifiersAdditional information
Gene (Drosophila melanogaster)CaMKIINCBIGene ID: 43828
Gene (Drosophila melanogaster)PKA-R2NCBIGene ID: 36041
Gene (Drosophila melanogaster)Ten-mNCBIGene ID: 40464
Gene (Drosophila melanogaster)nAChRα1NCBIGene ID: 42918
Gene (Drosophila melanogaster)nAChRα5NCBIGene ID: 34826
Gene (Drosophila melanogaster)nAChRα6NCBIGene ID: 34304
Genetic reagent (Drosophila melanogaster)R66C08-GAL4Bloomington Drosophila Stock Center (Owald et al., 2015)RRID:BDSC_49412
Genetic reagent (Drosophila melanogaster)MB112c-GAL4Bloomington Drosophila Stock Center (Perisse et al., 2016)RRID:BDSC_68263
Genetic reagent (Drosophila melanogaster)UAS-myr::SNAPfBloomington Drosophila Stock CenterRRID:BDSC_58376
Genetic reagent (Drosophila melanogaster)CaMKII::YFPKyoto Stock Centre (Lowe et al., 2014)RRID:DGGR_115127
Genetic reagent (Drosophila melanogaster)PKA-R2::YFPKyoto Stock Centre (Lowe et al., 2014)RRID:DGGR_115174
Genetic reagent (Drosophila melanogaster)Ten-m::YFPKyoto Stock Centre (Lowe et al., 2014)RRID:DGGR_115131
Chemical compound20% v/v paraformaldehydeThermo Fisher ScientificCat#15713S
Chemical compoundRNase-free 10× PBSThermo Fisher ScientificCat#AM9625
Chemical compoundTriton X-100Sigma-AldrichCat#T8787
Chemical compound20× RNase-free SSCThermo Fisher ScientificCat#AM9763
Chemical compoundDeionized formamideThermo Fisher ScientificCat#AM9342
Chemical compound50% dextran sulphateMilliporeCat#S4030
Chemical compoundVybrant DyeCycle Violet StainThermo Fisher ScientificCat#V35003
Chemical compoundVectashield anti-fade mounting mediumVector LaboratoriesCat#H-1000-10
Chemical compoundJF549-SNAPTagGrimm et al., 2015
Chemical compoundMineral oilSigma-AldrichCat#M5904
Chemical compound4-Methocyclohexanol (98%)Sigma-AldrichCat#218405
Chemical compound3-Octanol (99%)Sigma-AldrichCat#153095
Software, algorithmFIJINIH (Schindelin et al., 2012)http://fiji.sc/
Software, algorithmMATLAB R2019bThe MathWorks, Natick, MAhttps://www.mathworks.com/products/matlab.html
Software, algorithmGraphPad Prism 8GraphPad Software, La Jolla, CAhttps://www.graphpad.com/scientific-software/prism/
Software, algorithmDrosophila brain smFISH analysisThis paper (Mitchell, 2021)see Data availability section
Software, algorithmBlenderBlender Foundation, Amsterdamhttps://www.blender.org
Software, algorithmNAVis 0.2.0Bates et al., 2020bhttps://pypi.org/project/navis/

Fly strains

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Flies were raised on standard cornmeal agar food in plastic vials at 25°C and 40–50% relative humidity on a 12 hr:12 hr light:dark cycle. Details of fly strains are listed in the Key Resources Table.

smFISH probes

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Oligonucleotide probe sets were designed using the web-based probe design software https://www.biosearchtech.com/stellaris-designer. The YFP smFISH probe set was purchased from LGC BioSearch Technologies (CA, USA) prelabeled with Quasar-670 dye. CaMKII, nAChRα1, nAChRα5, and nAChRα6 DNA oligonucleotide sets were synthesized by Sigma-Aldrich (Merck) and enzymatically labeled with ATTO-633 according to Gaspar et al., 2017. DNA oligonucleotide sequences for each smFISH probe set are provided in Supplementary file 1.

Whole Drosophila brain smFISH

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Whole adult brain smFISH was performed essentially as described (Yang et al., 2017). The 2–4-day-old adult Drosophila brains were dissected in 1× phosphate buffered saline (PBS) and fixed in 4% v/v paraformaldehyde for 20 min at room temperature. Brains were washed 2× with PBS, followed by 20 min in 0.3% v/v Triton X-100 in PBS (PBTX) to permeabilize the tissue, then 15 min in PBTX with 500 nM JF549-SNAPTag (Grimm et al., 2015) for neuronal labeling. Then, 3 × 10 min washes in PBTX removed excess dye. Samples were then incubated in wash buffer (2× RNase-free SSC + 10% v/v deionized formamide) for 10 min at 37°C, wash buffer was replaced with hybridization buffer (2× RNase-free SSC, 10% v/v deionized formamide, 5% w/v dextran sulphate, 250 nM smFISH probes), and samples incubated overnight at 37°C. Hybridization buffer was removed before samples were washed 2× in freshly prepared wash buffer and incubated 40 min in wash buffer containing Vybrant DyeCycle Violet Stain (1:1000) to label nuclei. Samples were then washed 3× times in wash buffer, mounted on a glass slide covered with Vectashield anti-fade mounting medium (refractive index 1.45), and immediately imaged.

Olfactory conditioning

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Aversive olfactory conditioning was performed essentially as described by Tully and Quinn, 1985. 3-Octanol (OCT) was used as the shock-paired odor. 4-Methylcyclohexanol (MCH) was used as the unpaired odor. Odors were prepared at concentrations of 9 μl OCT in 8 ml mineral oil, and 8 μl MCH in 8 ml mineral oil. Groups of ~100 flies were aliquoted into plastic vials containing standard cornmeal agar food and a 2 × 6 cm piece of filter paper. Flies were conditioned as follows: 1 min OCT paired with 12 × 90 V shocks at 5 s interstimulus interval; 45 s clean air; 1 min MCH. Control groups were handled in the same way except for the differing presentation of either odors or shock. Untrained flies experienced no odor or shock, the odor only group experienced the two odor presentations without shock, and the shock only group received the shock presentations but no odors. Aversive olfactory conditioning was performed at 23 °C and 70% relative humidity. Following training, flies were returned to food vials and brains were dissected either 10 min, 1 hr, or 2 hr later, and smFISH analyses performed.

For the 'unpaired' experiment, the interval between presentations was extended from 45 to 180 s to avoid trace conditioning of the unpaired odor. In the trained group, flies were presented with 1 min OCT paired with 12 × 90 V shocks at 5 s interstimulus interval, 180 s clean air, and then 1 min MCH. In the unpaired group, flies received 12 × 90 V shocks at 5 s interstimulus interval (no odor pairing), 180 s clean air, and then 1 min MCH. Other control groups were handled in the same way except that the odor only group experienced the two odor presentations without shock and the shock only group received the shock presentations but no odors. Following training, flies were returned to food vials and brains were dissected 10 min later for smFISH analyses.

Microscopy

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Samples were imaged on a spinning disk confocal microscope (Perkin Elmer UltraView VoX) with a 60× 1.35 N.A. oil immersion UPlanSApo objective (Olympus) and a filter set to image fluorophores in DAPI, FITC, TRITC, and CY5 channels (center/bandwidth; excitation: 390/18, 488/24, 542/27, 632/22 nm; emission: 435/48, 594/45, 676/34 nm), the corresponding laser lines (488/4.26, 561/6.60, 640/3.2, 405/1.05, 440/2.5, 514/0.8 nm/mW), and an EMCCD camera (ImagEM, Hamamatsu Photonics). The camera pixel size is 8.34 µm, resulting in a pixel size in image space of approximately 139 nm. Optical sections were acquired with 200 nm spacing along the z-axis within a 512 × 512 pixel (71.2 × 71.2 µm) field of view.

Deconvolution

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Deconvolution was carried out using commercially available software (Huygens Professional v19.10.0p1, SVI Delft, The Netherlands). Raw image data generated in .mvd2 file format were converted to OME.tiff format using FIJI (Schindelin et al., 2012) (convert_mvd2_to_tif.ijm). Spherical aberration was estimated from the microscope parameters (see Microscopy). Z-dependent momentum preserving deconvolution (CLME algorithm, theoretical high-NA PSF, iteration optimized with quality change threshold 0.1% and iterations 40 maximum, signal-to-noise ratio 20, area radius of background estimation is 700 nm, a brick mode is 1 PSF per brick, single array detector with reduction mode SuperXY) was then applied to compensate for the depth-dependent distortion in point spread function, thereby reducing artifacts and increasing image sharpness.

Multi-channel alignment

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Misalignment between channels was corrected for using Chromagnon (v. 0.81) (Matsuda et al., 2018). To estimate channel misalignment, nuclei were labeled with the broad emission spectrum dye (Vybrant DyeCycle Violet Stain, Thermo Fisher) (Smith et al., 2015). The dye was excited at 405 nm, and emission was recorded using the appropriate filters for each imaging channel. Chromatic shift was estimated by finding the affine transformation that delivers a minimum mean square error between the nuclear stain in the various channels. Nuclear calibration channels for chromatic shift correction were separated using ImageJ (see macro Split_ometiff_channels_for_chromcorrect.ijm). The affine transformation was estimated and alignment was performed by calling Chromagnon from Python (see script chromagnon_bash.py). The resulting aligned and deconvolved images were saved in .dv format for further downstream analysis.

Calculating postsynaptic abundance and volume of γ5β'2a and γ1pedc>α/β MBON dendrites

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Neuromorphological calculations were performed with NAVis 0.2.0 library functions in Python (https://pypi.org/project/navis/) (Bates et al., 2020a) using data obtained from the Drosophila hemibrain dataset (v.1.1) (https://neuprint.janelia.org) (Scheffer et al., 2020). To calculate the dendritic volume and postsynaptic abundance of γ5β'2a and γ1pedc>α/β MBONs, neuron skeletons, neuropil meshes, and synapse data were first imported. Neural skeletons were then used to generate 3D neuron reconstructions. Dendritic processes of the γ5β'2a MBON were determined by intersecting neuronal skeletons with the MB mesh containing the γ5 and β'2a compartments. Dendritic processes of the γ1pedc>α/β MBON were determined by intersecting the skeleton within the γ1 MB compartment mesh. The available γ1 MB compartment mesh did not encompass the entirety of the γ1pedc>α/β MBON dendrites in the γ1 MB compartment, so the volume of the mesh was scaled up 1.35×. This intersects with almost all γ1pedc>α/β MBON dendrites in the γ1 MB compartment, but not any other substantial part of the neuron. Dendritic volume (nm3) was calculated as the sum of the neurite voxels multiplied by 83 since the resolution of each voxel is 8 nm3. The number of postsynapses within these compartments was also determined using the synapse data that accompany the neuron skeletons (Scheffer et al., 2020).

Data visualization smFISH data were visualized in FIJI (Schindelin et al., 2012). Maximum intensity projections representing 2 μm sections are presented for visualization purposes. Figure 1I and J are single z-sections (representing a 0.2 μm section). The 3D reconstructions of γ5β'2a and γ1pedc>α/β MBONs were created in Blender v.2.8.2 with NAVis 0.2.0 plug-in and using data obtained from http://www.neuprint.janelia.org.

mRNA detection

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An smFISH spot detection MATLAB script based on Smith et al., 2015 was written to quantify localized mRNA transcripts in Drosophila brains. Software for processing smFISH datasets is available as Supplementary Software. The smFISH channel was extracted and stored as a 3D grayscale image. mRNA signal was detected using 3D generalized likelihood ratio test (Smith et al., 2015). The false detection rate is 0.05, and the spot width is σx,y = 1.39 and σz = 3.48. After 3D detection, the intensity, background, width, and subpixel position of the detected mRNA spots are estimated using maximum likelihood estimation (MLE) (Smith et al., 2010).To reduce the impact of overlapping spots in 3D, only a 2D cross section is used from the z-plane where the spot is detected. To filter out spurious detections, all spots with a width >5 pixels are discarded.

mRNA-dendrite co-localization

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To quantify calyx and dendritic localized smFISH puncta, the calyx and dendritic area were first segmented manually. The contour of the calyx and dendritic area is converted to a mask (M1) using the MATLAB R2019b function roipoly. To quantify smFISH puncta co-localizing with dendrite label, a mask of the dendrite label is created by enhancing the image using a difference of Gaussians filter (width of 1 and 5 pixels) and then thresholding the product between the enhanced image (A) and masked area (M1) to obtain a mask (M2):

M2=AM1>mean(AM1)+std(AM1)

where mean() and std() are the sample mean and sample standard deviation of the image intensity values, and AB is the Hadamard product between A and B. The sample standard deviation is calculated as

std(x)=1N1i=1N(xix¯)2

where N is the number of data points. smFISH signal within γ5β'2a MBON dendrites innervating the γ5 and β'2a MB compartments was analyzed. smFISH signal within the γ1pedc>α/β dendrites innervating the γ1 MB compartment was analyzed. Sections of 10 × 0.2 μm individual z-slices of MB calyx, γ5β'2a MBON dendrites, or γ1pedc>α/β MBON dendrites were analyzed. smFISH puncta overlapping with the calyx or dendrite mask were considered co-localizing and therefore localized within that neuronal compartment.

Spot brightness and full width half maximum (FWHM) analysis

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For each detection, a region of interest (ROI) is extracted as a 2D box in the x–y plane with a size of 2×(3σx,y+1). For each ROI, the MLE of the x and y position, the number of photons, the number of background photons, and the width of the 2D Gaussian, σx,y, is computed. The FWHM of the spots is calculated as FWHM=22ln(2) σx,y.

Verification of transcription foci

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Soma containing bright nuclear transcription foci were selected to quantify the difference in intensity relative to diffraction-limited smFISH puncta. The nuclear localization of the smFISH puncta with the highest photon count was validated by visual inspection and considered to correspond to the transcription site. The width (σx,y) of the transcription foci significantly differs from the sparse smFISH signal and is estimated by fitting a 2D Gaussian to the transcription site using the MATLAB 2019b nonlinear least-squares routine lsqcurvefit. Transcription foci brightness and background were computed using the same MLE protocol as for diffraction-limited spots, but with the estimated σx,y.

YFP fluorescence intensity

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To quantify YFP fluorescence intensity within co-labeled neurons, we developed a FIJI-compatible macro plug-in. Depth-dependent bleaching was first corrected for over the z-stack using an exponential fit. Background signal was then subtracted in each z-section using a rolling ball filter with a width of 60 pixels. Five z-sections above and below the center of the image were cropped for analysis. YFP fluorescence intensity was recorded within the dendrites or soma of the co-labeled neuron using the mask described above (mRNA-dendrite co-localization). Fluorescence intensity was calculated as analog digital units (adu)/volume (dendrites or soma) to give adu/voxel. Software for analyzing fluorescent protein expression in single neurons is available as Supplementary Software.

Statistical analyses

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Data were visualized and analyzed statistically using GraphPad Prism version 8.3.1 (332). The distribution of a dataset was assessed with a Shapiro–Wilk test. Gaussian distributed smFISH abundance was compared between two groups using an unpaired t-test. Gaussian distributed smFISH abundance between multiple groups was compared using a one-way ANOVA followed by Tukey’s post hoc test. Non-Gaussian distributed smFISH abundance was compared between two groups using a Mann–Whitney U test. Proportions of transcriptionally active soma were compared to transcriptionally inactive soma using a chi-square test. YFP-positive and -negative smFISH intensity distributions were compared with a two-sided Wilcoxon rank-sum test. YFP fluorescence intensity across treatments was compared using a one-way ANOVA for Gaussian distributed data and a Kruskal–Wallis test for non-Gaussian distributed data. Statistical significance is defined as p<0.05.

References

    1. Bagni C
    2. Mannucci L
    3. Dotti CG
    4. Amaldi F
    (2000)
    Chemical stimulation of synaptosomes modulates alpha -Ca2+/calmodulin-dependent protein kinase II mRNA association to polysomes
    The Journal of Neuroscience 20:RC76.

Decision letter

  1. Mani Ramaswami
    Reviewing Editor; Trinity College Dublin, Ireland
  2. K VijayRaghavan
    Senior Editor; National Centre for Biological Sciences, Tata Institute of Fundamental Research, India

In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.

Acceptance summary:

This work describes the implementation of smFISH to visualise mRNAs in memory-encoding, mushroom-body output neurons (MBONs) in vivo. Using this technique, the authors report that levels of a CaMKII reporter mRNA are increased after associative training in dendrites of these MBONs known to mediate this form of memory. This is unexpected because gene expression changes are thought to occur after treatments that induce long-term memory and the increase is seen to occur after a single training trial, which should not induce LTM. Thus, the work points to unexpected and previously undocumented changes in the state or levels of dendritic mRNA after training regimens that do not induced long-term memory. Together, this work describes a very useful technology to study memory-associated changes in RNA transport, localisation, or accessibility in vivo and points to unexpected regulation of these processes by experiences that do not cause LTM. Additional analyses to establish the function and mechanisms for this learning-dependent CamKII localization localization will be required to establish the importance of these intriguing observations. The current work provides a foundation for such future studies.

Decision letter after peer review:

[Editors’ note: the authors submitted for reconsideration following the decision after peer review. What follows is the decision letter after the first round of review.]

Thank you for submitting your work entitled "Dendritic localization of mRNA in Drosophila Mushroom Body Output Neurons" for consideration by eLife. Your article has been reviewed by three peer reviewers, and the evaluation has been overseen by a Reviewing Editor and a Senior Editor. The reviewers have opted to remain anonymous.

Our decision has been reached after consultation between the reviewers. Based on these discussions and the individual reviews below, we genuinely regret to inform you that this potentially important work will not be considered further for publication in eLife.

All of the reviewers agreed that this was a technically impressive paper working towards an exciting and important goal. However there was a clear consensus not only that the observations made required more controls to firm up, but also and more critically, need to be extended further, to establish the origin of the increased smFISH signal (how), as well as its biological significance (why).

Reviewer #1:

Targeting of mRNAs to synapses, combined with activity-dependent local translation, has been proposed to underlie various forms of synaptic plasticity, as well as formation of long-term memories. How this process is regulated in vivo during physiological learning and memory has remained unclear.

Here, the authors aimed at addressing this question by studying dendritic mRNA localization in Drosophila brains. This is relevant, as imaging, conditioning and manipulation of neuronal activity can be efficiently combined in this system, yet no quantitative analysis of dendritic RNA localization has so far been reported. In this study, the authors describe that distinct mRNAs localize to different extent to the dendrites of neurons undergoing learning and memory-dependent plasticity (γ5β'2a MBON and γ1pedc>α/β MBON). Furthermore, they uncover that aversive olfactory conditioning induces within minutes a transient increase in the amount of camkII mRNA molecules localizing to the dendrites of γ5β'2a MBON neurons. This last observation is interesting, as it suggests the existence of plasticity-dependent regulatory mechanisms controlling dendritic mRNA localization. However, both the origin of such a regulatory process and its biological implications remain unclear (see major points below) and should be investigated further.

1) In Figure 2, the authors compare the dendritic localization profile of different RNAs in different neuron types (γ5β'2a and γ1pedc>α/β MBONs).

– In 2F, they compare the accumulation of RNAs encoding subunits of the nAchR receptor. This is interesting as it points to local and specific regulation of receptor composition.

– In 2E, the rationale to compare the distribution of CamkII, PKA-R2 and Ten-m mRNAs, however, is less clear: why are PKA-R2 and Ten-m specifically analyzed/interesting? Why is it surprising to not see Ten-m RNAs in the dendrites of γ5β'2a MBON? Also, it is difficult (impossible?) to interpret the observed differences in dendritic localization without knowing 1- if the transcripts are at all expressed in the neurons under consideration, and 2- their expression levels. Addressing this last point could be done by counting the number of transcripts found in the corresponding cell bodies (as done in Figure 3).

– Last, the authors compare the amount of RNAs in γ1pedc>α/β and γ5β'2a MBONs and try to make the point i- that the amount of dendritically-localized mRNAs correlate with dendritic volume and synapse number, and ii- that these factors may thus be "important determinants of localized mRNA copy number". Clearly, however, 2 of the 4 RNAs they identify as localized do not follow this principle (CamkII is equally abundant in both neuron types and the fold difference observed for PKA-R2 is not in the range of the observed difference in dendritic volume/synapse number). This makes the correlation quite weak. Determining whether the amount of RNAs present in dendrites correlate with the total amount of RNA (or at least the amount in cell bodies) for each species in the two populations under consideration may make a stronger case and highlight trends and/or specific behaviors.

2) The increase in the amount of dendritically-localized Camk2 RNA seen 10 minutes after conditioning is the most interesting observation of this study. This observation should however be consolidated as both the biological meaning of the transient increase, and the mechanisms underlying this regulatory process remain unclear.

– Investigating further if the observed changes are linked to local translation (and not only changes in global protein levels), or are specific to short-term/long-term memory paradigms would significantly strengthen the manuscript. This may also allow the authors to explain how their data fit with previous experiments demonstrating that the training protocol used in this study induces translation-independent short-term memory.

– Alternatively, understanding how this process is regulated (activity-dependent? 3'UTR-dependent? transport vs degradation…) may help the authors point to interesting underlying mechanisms.

Reviewer #2:

Mitchell et al. applied smFISH on the mushroom body circuit to visualize seemingly single- mRNA signals localized to the dendrites of single MBONs. They further found that aversive olfactory learning increases CamKII mRNA levels in the dendrites. The topic is interesting, and the imaging technique is high-level. But there are concerns regarding the evidence to support their claims.

1) For example in Figure 2D, there is a considerable amount of false detection of background signal in YFP- condition. The authors should estimate the false detection rate. Importantly, the intensity of the signals and the background would be different with different probes. It would be necessary to verify the signal by using mutants or genetic knockdown for each probe.

2) To claim selective localization of mRNA (Figure 2F), it is not enough to count the mRNA puncta in the dendrites because that may simply reflect the transcription level. Therefore, it is necessary to count mRNA in reference regions (e.g. soma as they did in Figure 3) and quantify the relative localization.

3) In Figure 3E and F, unpaired training control is necessary to claim that the increase is learning-dependent. Also, it is not fair to compare the 10 min-odor only to 1 hr-trained (Figure 3E). The authors should compare the trained and the unpaired at the respective time points.

Reviewer #3:

This technically accomplished and well-written manuscript documents dendritic localization of subsets of mRNAs in specific neurons within the Drosophila brain and its modulation by learning paradigms. The development of a robust system for studying mRNA localization in a tractable physiological setting is valuable. However, the biological insights provided by this study fall short of what I would expect for a short communication in eLife. Specifically, the authors are unable to find a correlation between learning-associated changes in mRNA distribution and the synthesis or distribution of the protein product. In the absence of such data, or other functional data, the relevance of mRNA localization in this system is unclear.

The authors provide evidence that changes in mRNA distribution are not accompanied by changes in the proportion of cells transcribing the mRNA. This suggests that localization rather than synthesis of the mRNA is primarily being affected, which is a key distinction. Given the importance of this part of the work the authors need to provide additional evidence that the large foci are indeed sites of transcription. For example, endogenous autosomal genes might be expected to have two foci, whereas X-linked genes should have one focus in males and two in females. The authors should also quantify the fluorescence intensity of presumptive nascent transcript foci with and without learning as it seems conceivable that changes in mRNA distribution are due to the same subset of cells transcribing more of the mRNA (rather than an increase in the proportion of transcriptionally active cells).

[Editors’ note: further revisions were suggested prior to acceptance, as described below.]

Thank you for submitting your article "Selective dendritic localization of mRNA in Drosophila mushroom body output neurons" for consideration by eLife. Your article has been reviewed by two peer reviewers, and the evaluation has been overseen by Mani Ramaswami as Reviewing Editor and K VijayRaghavan as the Senior Editor. The reviewers have opted to remain anonymous.

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.

Essential Revisions:

1) One thing still unclear is the calculation of false detection rate (previous point #1). The authors claim that the rate is 5%, but how was this calculated? In the corresponding data (Figure 2D), “contamination” counts look much larger than 5%. If the authors mean the q-value of by the 5% of FDR, what was the multiple hypothesis testing? How is the value of "signal/background" in Figure 2D calculated? Justification is critical to interpret data of fewer counts (e.g. dendritic CaMKII). This needs to be clarified.

2) The Discussion should include a clear consideration of the implications and observed increase in reporter RNA levels in MBON dendrites. Why is it observed after training trials that should not induce LTM and how does this observation impact or fit with previous observations and conclusions? Are there any specific experiments that could test the authors models? In addition, a discussion of potential mechanisms that could underlie this phenomenon and experiments required to address them would be valuable to the reader.

https://doi.org/10.7554/eLife.62770.sa1

Author response

[Editors’ note: The authors appealed the original decision. What follows is the authors’ response to the first round of review.]

Reviewer #1:

Targeting of mRNAs to synapses, combined with activity-dependent local translation, has been proposed to underlie various forms of synaptic plasticity, as well as formation of long-term memories. How this process is regulated in vivo during physiological learning and memory has remained unclear.

Here, the authors aimed at addressing this question by studying dendritic mRNA localization in Drosophila brains. This is relevant, as imaging, conditioning and manipulation of neuronal activity can be efficiently combined in this system, yet no quantitative analysis of dendritic RNA localization has so far been reported. In this study, the authors describe that distinct mRNAs localize to different extent to the dendrites of neurons undergoing learning and memory-dependent plasticity (γ5β'2a MBON and γ1pedc>α/β MBON). Furthermore, they uncover that aversive olfactory conditioning induces within minutes a transient increase in the amount of camkII mRNA molecules localizing to the dendrites of γ5β'2a MBON neurons. This last observation is interesting, as it suggests the existence of plasticity-dependent regulatory mechanisms controlling dendritic mRNA localization. However, both the origin of such a regulatory process and its biological implications remain unclear (see major points below) and should be investigated further.

1) In Figure 2, the authors compare the dendritic localization profile of different RNAs in different neuron types (γ5β'2a and γ1pedc>α/β MBONs).

– In 2F, they compare the accumulation of RNAs encoding subunits of the nAchR receptor. This is interesting as it points to local and specific regulation of receptor composition.

– In 2E, the rationale to compare the distribution of CamkII, PKA-R2 and Ten-m mRNAs, however, is less clear: why are PKA-R2 and Ten-m specifically analyzed/interesting? Why is it surprising to not see Ten-m RNAs in the dendrites of γ5β'2a MBON? Also, it is difficult (impossible?) to interpret the observed differences in dendritic localization without knowing 1- if the transcripts are at all expressed in the neurons under consideration, and 2- their expression levels. Addressing this last point could be done by counting the number of transcripts found in the corresponding cell bodies (as done in Figure 3).

As previously stated in the manuscript, we studied CAMKII, PKA-R2 and Ten-m mRNAs because we wished to determine the utility of using the YFP insertion library with the same YFP-targeted smFISH probes. PKA has of course been implicated in neuronal plasticity and Ten-m is another neuronally expressed gene, and therefore a potentially interesting thing to compare CAMKII and PKA-R2 to. As requested by the reviewer, we now provide quantification of expression of these mRNAs in the somata of the relevant neurons.

– Last, the authors compare the amount of RNAs in γ1pedc>α/β and γ5β'2a MBONs and try to make the point i- that the amount of dendritically-localized mRNAs correlate with dendritic volume and synapse number, and ii- that these factors may thus be "important determinants of localized mRNA copy number". Clearly, however, 2 of the 4 RNAs they identify as localized do not follow this principle (CamkII is equally abundant in both neuron types and the fold difference observed for PKA-R2 is not in the range of the observed difference in dendritic volume/synapse number). This makes the correlation quite weak. Determining whether the amount of RNAs present in dendrites correlate with the total amount of RNA (or at least the amount in cell bodies) for each species in the two populations under consideration may make a stronger case and highlight trends and/or specific behaviors.

The reviewer raises an excellent point. We have now assessed the distribution of all the mRNAs we look at in both the somata and dendrites. We have now better described these results. Whereas the abundance of the nAchRα5, nAchRα6 and PKA-R2 mRNAs correlates to the size of the dendritic arbor and synpase number, the correlation does not hold for CaMKII mRNA abundance. We have therefore altered the conclusion accordingly.

2) The increase in the amount of dendritically-localized Camk2 RNA seen 10 minutes after conditioning is the most interesting observation of this study. This observation should however be consolidated as both the biological meaning of the transient increase, and the mechanisms underlying this regulatory process remain unclear.

– Investigating further if the observed changes are linked to local translation (and not only changes in global protein levels), or are specific to short-term/long-term memory paradigms would significantly strengthen the manuscript. This may also allow the authors to explain how their data fit with previous experiments demonstrating that the training protocol used in this study induces translation-independent short-term memory.

– Alternatively, understanding how this process is regulated (activity-dependent? 3'UTR-dependent? transport vs degradation…) may help the authors point to interesting underlying mechanisms.

We agree this is an interesting result that raises several questions. As we said in the manuscript there is currently no method available to assess local translation with a similar resolution to smFISH in the fly brain. Since this is currently impossible, we think it reasonable that it is beyond the scope of a Short Report. We already show that the increased signal is transient following a single training trial, going up at 10 min and down again by 1 h. Moreover, this does not happen in naïve (mock trained) flies, or those exposed to the odor or shock alone. In comparison signals in the somata go up transiently at 1h and are back down at 2h. Our data are therefore consistent with these changes occurring after learning. I think it’s potentially endless to request that we look after spaced training given that we see a transient increase following a single trial. At present this is a technically very demanding set of experiments conducted at multiple time points on flies that have been handled in four different ways. In the revision we have added an “unpaired control” which supports the notion that the increase in dendritic CaMKII mRNA is the result of learning.

Re: translation independent short-term memory. We are not suggesting that this CAMKII mRNA response is necessarily causal for memory. However, we show that it specifically occurs after training (as mentioned above we now show that it also does not occur in flies trained in an unpaired manner). As an aside, I am sure the reviewers and editors are aware that all experiments claiming protein synthesis independence of biological process are undermined by being unable to totally block it and maintain cellular and/or organismal viability.

Reviewer #2:

Mitchell et al. applied smFISH on the mushroom body circuit to visualize seemingly single- mRNA signals localized to the dendrites of single MBONs. They further found that aversive olfactory learning increases CamKII mRNA levels in the dendrites. The topic is interesting, and the imaging technique is high-level. But there are concerns regarding the evidence to support their claims.

1) For example in Figure 2D, there is a considerable amount of false detection of background signal in YFP- condition. The authors should estimate the false detection rate. Importantly, the intensity of the signals and the background would be different with different probes. It would be necessary to verify the signal by using mutants or genetic knockdown for each probe.

We have now determined the false detection rate for YFP smFISH probes to be <5%. We are aware that things could be different using different probes and that is one reason why this study aimed to utilise the same YFP probe set with the different flies carrying YFP insertions in interesting genes, eg. CAMKII, PKA-R2 and Ten-m. Flies lacking YFP have been used as controls. Our control for other things we probed such as nAChR subunits is that their signals are differentially localised. For example, nAChRa1 was detected in somata but not in dendrites whereas nAChRa5 and nAChRa6 are in somata and differentially localized in dendrites. Mutants for nAChR receptors are unavailable/ lethal and RNAi would be hypomorphic, poorly controlled, and therefore likely unconvincing.

2) To claim selective localization of mRNA (Figure 2F), it is not enough to count the mRNA puncta in the dendrites because that may simply reflect the transcription level. Therefore, it is necessary to count mRNA in reference regions (e.g. soma as they did in Figure 3) and quantify the relative localization.

We agree and have now included data collected from the somata of the relevant neurons.

3) In Figure 3E and F, unpaired training control is necessary to claim that the increase is learning-dependent. Also, it is not fair to compare the 10 min-odor only to 1 hr-trained (Figure 3E). The authors should compare the trained and the unpaired at the respective time points.

We state that the increase occurs “after training”. We currently compare it to mock trained (untrained) flies and other flies exposed to odor alone, or shock alone. No changes are observed in these other flies. The only observed change we see is a transient increase of CAMKII that is specific to the γ5β'2a MBON after training. This suggests the increase results from pairing since it does not occur after either odor or shock exposure alone, that are presumably able to drive “activity”. Nevertheless, we have now performed another set of experiments (presented in Figure 3—figure supplement 2A) that include an “unpaired” control and show that the increase in dendritic CaMKII only occurs in trained flies.

Reviewer #3:

This technically accomplished and well-written manuscript documents dendritic localization of subsets of mRNAs in specific neurons within the Drosophila brain and its modulation by learning paradigms. The development of a robust system for studying mRNA localization in a tractable physiological setting is valuable. However, the biological insights provided by this study fall short of what I would expect for a short communication in eLife. Specifically, the authors are unable to find a correlation between learning-associated changes in mRNA distribution and the synthesis or distribution of the protein product. In the absence of such data, or other functional data, the relevance of mRNA localization in this system is unclear.

We are not sure what “insights” one expects for a Short Report. We show dendritic localization of several mRNAs in memory-relevant locations in the fly brain for the first time. We also show that CAMKII localization is altered after training. It is true that the reason for the change in mRNA localization is currently unclear. However, “relevance” is a criticism that is easy to deliver and hard to contend with. Moreover, the fact that we did not observe significant changes in YFP fluorescence does not mean that there is no correlation between learning-associated changes in mRNA abundance and changes in protein abundance. Fluorescence intensity measurements for the protein are extremely crude, compared with the single molecule mRNA resolution that we have achieved using smFISH. The technology required to image translation events at molecular resolution in a whole brain does not yet exist.

The authors provide evidence that changes in mRNA distribution are not accompanied by changes in the proportion of cells transcribing the mRNA. This suggests that localization rather than synthesis of the mRNA is primarily being affected, which is a key distinction. Given the importance of this part of the work the authors need to provide additional evidence that the large foci are indeed sites of transcription. For example, endogenous autosomal genes might be expected to have two foci, whereas X-linked genes should have one focus in males and two in females. The authors should also quantify the fluorescence intensity of presumptive nascent transcript foci with and without learning as it seems conceivable that changes in mRNA distribution are due to the same subset of cells transcribing more of the mRNA (rather than an increase in the proportion of transcriptionally active cells).

These are good points. We have now provided information regarding fluorescence intensity of transcription foci, and show that we do not observe any changes. It is important to note that we already show that CAMKII-YFP flies have only one focus, which is expected as these flies are heterozygous for the allele. This is of course equivalent to the suggestion of X-linked, etc. We have also now included more data in Figure 3—figure supplement 1 that illustrates monoallelic and biallelic labelling of transcription foci.

[Editors’ note: what follows is the authors’ response to the second round of review.]

Essential Revisions:

1) One thing still unclear is the calculation of false detection rate (previous point #1). The authors claim that the rate is 5%, but how was this calculated? In the corresponding data (Figure 2D), “contamination” counts look much larger than 5%. If the authors mean the q-value of by the 5% of FDR, what was the multiple hypothesis testing? How is the value of "signal/background" in Figure 2D calculated? Justification is critical to interpret data of fewer counts (e.g. dendritic CaMKII). This needs to be clarified.

We thank the reviewer for catching this important point. In the analysis of single molecule imaging data, there are two possible sources of false detection: (1) in the analytical detection of diffraction limited spots, and (2) from background signal of any unbound probes in the sample. The false detection rate (FDR) owing from spot detection (1) is assessed by performing a hypothesis test in each pixel i.e. is there a molecule present or not. If we would consider this as a single hypothesis testing problem with 5% false positive probability we would expect that 5% of all pixels are false positives. In order to assess the FDR owing from unbound probes (2), we ran the same detection algorithm on YFP positive, and negative control brains (2D). Since only spots with signal/background >6 are counted, the overlap between detections in the YFP positive and negative controls is 14%. As the reviewer notes, this is represented by the overlap to the right of the signal detection threshold line (red dotted line) of the YFP+ and YFP- histograms in Figure 2D. We have now included an additional supplementary figure (Figure 2—figure supplement 1) which shows how varying the signal detection threshold changes the overlap (i.e. the false detection rate) and also the number of spots which are included/excluded (i.e. counted/discarded). Increasing the spot detection threshold reduces the false detection rate, but also increases the number of detections that are discarded, including real spots. We chose the signal/background threshold >6 in an effort to balance the trade-off between throwing away real spots and falsely counting background noise. We believe the inclusion of this additional figure clarifies the reviewer comments regarding the false detection rate. The legend for Figure 2 and the main text have also been adjusted to clarify this issue.

2) The Discussion should include a clear consideration of the implications and observed increase in reporter RNA levels in MBON dendrites. Why is it observed after training trials that should not induce LTM and how does this observation impact or fit with previous observations and conclusions? Are there any specific experiments that could test the authors models? In addition, a discussion of potential mechanisms that could underlie this phenomenon and experiments required to address them would be valuable to the reader.

We have included the following paragraph in the Discussion:

“Early studies in Drosophila demonstrated that broad disruption of CAMKII function impaired courtship learning (Broughton et al., 2003; Griffith et al., 1994, 1993; Joiner and Griffith, 1997). […] This may be possible with MBON-specific targeting of CAMKII mRNAs that contain the long 3'UTR, which is essential for dendritic localization and activity-dependent local translation (Aakalu et al., 2001; Kuklin et al., 2017; Mayford et al., 1996; Rook et al., 2000).”

https://doi.org/10.7554/eLife.62770.sa2

Article and author information

Author details

  1. Jessica Mitchell

    Centre for Neural Circuits and Behaviour, University of Oxford, Oxford, United Kingdom
    Contribution
    Conceptualization, Data curation, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing - original draft, Writing - review and editing
    Competing interests
    No competing interests declared
  2. Carlas S Smith

    1. Centre for Neural Circuits and Behaviour, University of Oxford, Oxford, United Kingdom
    2. Delft Center for Systems and Control, Delft University of Technology, Delft, Netherlands
    Contribution
    Conceptualization, Resources, Data curation, Software, Formal analysis, Supervision, Validation, Investigation, Visualization, Methodology, Project administration, Writing - review and editing
    Competing interests
    No competing interests declared
  3. Josh Titlow

    Department of Biochemistry, University of Oxford, Oxford, United Kingdom
    Contribution
    Formal analysis, Investigation, Methodology, Writing - review and editing
    Competing interests
    No competing interests declared
  4. Nils Otto

    Centre for Neural Circuits and Behaviour, University of Oxford, Oxford, United Kingdom
    Contribution
    Formal analysis, Investigation, Visualization, Methodology, Writing - review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9713-4088
  5. Pieter van Velde

    Delft Center for Systems and Control, Delft University of Technology, Delft, Netherlands
    Contribution
    Software, Formal analysis, Investigation
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7281-8026
  6. Martin Booth

    1. Centre for Neural Circuits and Behaviour, University of Oxford, Oxford, United Kingdom
    2. Department of Engineering Science, University of Oxford, Oxford, United Kingdom
    Contribution
    Supervision, Funding acquisition
    Competing interests
    No competing interests declared
  7. Ilan Davis

    Department of Biochemistry, University of Oxford, Oxford, United Kingdom
    Contribution
    Resources, Funding acquisition, Writing - review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5385-3053
  8. Scott Waddell

    Centre for Neural Circuits and Behaviour, University of Oxford, Oxford, United Kingdom
    Contribution
    Conceptualization, Resources, Supervision, Funding acquisition, Writing - original draft, Project administration, Writing - review and editing
    For correspondence
    scott.waddell@cncb.ox.ac.uk
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4503-6229

Funding

Wellcome Trust (200846/Z/16/Z)

  • Scott Waddell

Wellcome Trust (203261/Z/16/Z)

  • Scott Waddell

European Research Council

  • Scott Waddell

Netherlands Organisation for Scientific Research (NWO START-UPproject no. 740.018.015)

  • Carlas S Smith
  • Pieter van Velde

Wellcome Trust (107457)

  • Ilan Davis
  • Martin Booth

Wellcome Trust (096144)

  • Ilan Davis

Wellcome Trust (209412)

  • Ilan Davis

Wellcome Trust (091911)

  • Ilan Davis

BBSRC

  • Jessica Mitchell

NWO (Veni project no. 16761)

  • Carlas S Smith
  • Pieter van Velde

MRC/EPSRC/BBSRC (MR/K01577X/1)

  • Martin Booth

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

We are grateful for the microscopy facilities and expertise provided by Micron Advanced Bioimaging Unit (supported by Wellcome Strategic Awards 091911 and 107457). We thank Jeff Lee for assistance with smFISH probe generation and members of the Waddell group for discussion. JM was funded through the BBSRC Interdisciplinary Bioscience Doctoral Training Programme. CSS and PVV were funded by the Netherlands Organisation for Scientific Research (NWO), under NWO START-UP project no. 740.018.015 and NWO Veni project no. 16761. CSS was initially supported by grants to SW and MB and acknowledges a research fellowship through Merton College, Oxford, UK. MB was funded by Wellcome Strategic Awards (095927 and 107457) and the MRC/EPSRC/BBSRC (MR/K01577X/1). ID was supported by a Wellcome Senior Research Fellowship (096144), Wellcome Trust Investigator Award (209412), and Wellcome Strategic Awards (091911 and 107457). SW was funded by a Wellcome Principal Research Fellowship (200846/Z/16/Z), an ERC Advanced Grant (789274), and a Wellcome Collaborative Award (203261/Z/16/Z).

Senior Editor

  1. K VijayRaghavan, National Centre for Biological Sciences, Tata Institute of Fundamental Research, India

Reviewing Editor

  1. Mani Ramaswami, Trinity College Dublin, Ireland

Publication history

  1. Received: September 3, 2020
  2. Accepted: March 15, 2021
  3. Accepted Manuscript published: March 16, 2021 (version 1)
  4. Accepted Manuscript updated: March 18, 2021 (version 2)
  5. Version of Record published: March 26, 2021 (version 3)

Copyright

© 2021, Mitchell et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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