Neuropeptides (NPs) exert an important but complex influence on neural function and behavior (Hökfelt et al., 2000; Insel and Young, 2000; Nässel and Winther, 2010; Bargmann and Marder, 2013). A major lacuna in the study of NPs is the lack of a method for imaging NP release in vivo, with subcellular spatial resolution and subsecond temporal resolution. Available techniques for measuring NP release include microdialysis (Kendrick, 1990), antibody-coated microprobes (Schaible et al., 1990) and GFP-tagged propeptides visualized either by standard fluorescence microscopy (van den Pol, 2012), or by TIRF imaging of cultured neurons (Xia et al., 2009). In Drosophila, a fusion between rat Atrial Natriuretic Peptide/Factor (ANP/F) and GFP was used to investigate neuropeptide trafficking at the fly neuromuscular junction (NMJ) (Rao et al., 2001). Release was measured indirectly, as a decrease in ANP-GFP fluorescence intensity at nerve terminals reporting residual unreleased peptide, on a time-scale of seconds (Wong et al., 2015). None of these methods combines NP specificity, genetically addressable cell type-specificity, high temporal resolution and applicability to in vivo preparations (Supplementary file 1). A major challenge is to develop a tool that encompasses all these features for direct, robust measurement of NP release in vivo.

Neuropeptides are synthesized as precursors, sorted into dense core vesicles (DCVs), post-translationally modified and cleaved into active forms prior to release (Taghert and Veenstra, 2003). We reasoned that an optimal in vivo real-time NP release reporter should include (1) a reporter domain that reflects the physico-chemical contrast between the intravesicular milieu and the extracellular space (Figure 1—figure supplement 1A); and (2) a sorting domain that ensures its selective trafficking into DCVs (Figure 1—figure supplement 1b). The NP precursor may function as the sorting domain, suggested by studies of DCV fusion using pIAPP-EGFP (Barg et al., 2002) and NPY-pHluorin (Zhu et al., 2007) in cultured neurons, or ANP-GFP in Drosophila (Rao et al., 2001). We therefore developed a pipeline to screen various transgenes comprising NP precursors fused at different sites to fluorescent reporters, in adult flies (Figure 1—figure supplement 1B–C). A total of 54 constructs were tested. We found that optimal trafficking was achieved by substituting the reporter for the NP precursor C-terminal domain that follows the final peptide (Figure 1—figure supplement 1B). In order to maintain covalent linkage with the reporter domain, we removed the dibasic cleavage site C-terminal to the final peptide.

The DCV lumen has lower pH and free calcium (pH = 5.5–6.75, [Ca²⁺]~30 µM) compared to the extracellular space (pH = 7.3, [Ca²⁺]~2 mM) (Mitchell et al., 2001; Sturman et al., 2006). These differences prompted us to test validated sorting domains in a functional ex vivo screen using either pH-sensitive fluorescent proteins (Miesenböck et al., 1998) or genetically-encoded calcium indicators (GECIs) (Tian et al., 2012; Lin and Schnitzer, 2016) (Figure 1—figure supplement 1A–D). Reporters based on pHluorins (Miesenböck et al., 1998) did not perform well in our hands, therefore we focused on GCaMP6s (Chen et al., 2013). The calcium sensitivity threshold of GCaMP6s is below the calcium concentration in both DCVs and the extracellular space. However, GCaMP6s fluorescence is quenched in the acidic DCV lumen (Barykina et al., 2016), enabling it to function as a dual calcium/pH indicator (Figure 1A). These key properties should boost the contrast between GCaMP6s fluorescence in unreleased vs. released DCVs, potentially allowing us to trace NP release at the cellular level in vivo.

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Raw data for Immuno-EM experiments (ANP).

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Raw data for Immunno-EM experiments (control group).

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We sought to test several NP precursor-GCaMP6s fusion proteins, called NPRRs (NeuroPeptide Release Reporters; unless otherwise indicated all NPRRs refer to fusions with GCaMP6s), in an intact preparation using electrical stimulation to evoke release. Initially for proof-of-principle experiments, we used the Drosophila larval NMJ to test NPRR^ANP, a GCaMP6s fusion with rat ANP (Burke et al., 1997). NMJ terminals are large, individually identifiable, and easy to image and record. In particular, boutons on muscle 12/13 are diverse -- Type Ib and Type Is boutons contain mostly synaptic vesicles and few DCVs, while Type III boutons contain an abundance of DCVs but no synaptic vesicles (Menon et al., 2013); moreover, Type III-specific GAL4 drivers are available (Koon and Budnik, 2012) (Figure 1B).

Expression of NPRR^ANP pan-neuronally (under the control of nsyb-GAL4) followed by double immuno-staining for ANP and GCaMP (anti-GFP) indicated that the sorting domain and the reporter domains showed a similar localization in Type III neurons (Figure 1—figure supplement 2). Moreover, the distribution of NPRR^ANP overlapped that of Bursicon (Figure 1—figure supplement 3D), an NP that is endogenously expressed in Type III neurons (Loveall and Deitcher, 2010). Both GCaMP and Bursicon immunoreactivity were strongest within boutons, consistent with the known subcellular localization of DCVs (Gorczyca and Budnik, 2006).

Glutamate is the only known canonical neurotransmitter used at the larval NMJ (Menon et al., 2013). This allowed visualization of the subcellular localization of small synaptic vesicles (SV) by immuno-staining for vGluT, a vesicular glutamate transporter (Fremeau et al., 2001; Kempf et al., 2013). In Type Ib neurons (which contain relatively few DCVs relative to SVs [Menon et al., 2013]), vGluT staining was observed as patches with a dim center, which may reflect clustered SVs, while NPRR^ANP immunoreactivity was seen in dispersed, non-overlapping punctae (Figure 1C, α-GFP, inset). In Type III neurons, NPRRs were strongly expressed but no vGluT immunoreactivity was detected (Figure 1C). The subcellular distribution of this NPRR in larval NMJ neurons, therefore, is similar to that of other DCV-targeted markers previously used in this system (Rao et al., 2001; Shakiryanova et al., 2006), and appears to reflect exclusion from SVs.

The diffraction limit of light microscopy precluded definitive co-localization of NPRRs in DCVs. Therefore, we employed Immuno-Electron microscopy (Immuno-EM) to investigate the subcellular localization of NPRRs at the nanometer scale. To maximize antigenicity for Immuno-EM, we generated constructs that replaced GCaMP6s with GFP (NPRR^ANP-GFP;). NPRR^ANP-GFP showed dense labeling in association with DCVs (Figure 1D, arrows), where the average number of gold particles/µm² was substantially and significantly higher than in neighboring bouton cytoplasm (DCV/Bouton ~ 14.26) (Figure 1E, Supplementary file 2). Taken together, these data indicate that NPRR^ANP-GFP is localized to DCVs. By extension, they suggest that NPRR^ANP-GCaMP6s (which has an identical structure to NPRR^ANP-GFP except for the modifications that confer calcium sensitivity) is similarly packaged in DCVs. While these two reporters show indistinguishable distributions by immunofluorescence (Figure 1—figure supplement 4), we cannot formally exclude that the substitution of GCaMP for GFP may subtly alter subcellular localization of the NPRR in a manner undetectable by light microscopy.

To measure the release of NPRRs from DCVs, we next expressed NPRR^ANP in Type III neurons using a specific GAL4 driver for these cells (Koon and Budnik, 2012) (Figure 2E and Figure 1—figure supplement 3D). We delivered 4 trials of 70 Hz electrical stimulation to the nerve bundle, a frequency reported to trigger NP release as measured by ANF-GFP fluorescence decrease (Rao et al., 2001; Shakiryanova et al., 2006), and used an extracellular calcium concentration that promotes full fusion mode (Alés et al., 1999). This stimulation paradigm produced a relative increase in NPRR^ANP fluorescence intensity (ΔF/F), whose peak magnitude increased across successive trials (Figure 2A, red bars and 2D; Video 1; Figure 2—figure supplement 1, A₁ vs. A₇). Responses in each trial showed a tri-phasic temporal pattern: (1) In the ‘rising’ phase, NPRR^ANP ∆F/F peaked 0.5–5 secs after stimulation onset, in contrast to the virtually instantaneous peak seen in positive control specimens expressing conventional GCaMP6s in Type III neurons (Figure 2A–B). The NPRR^ANP latency to peak was similar to the reported DCV fusion latency following depolarization in hippocampal neurons (Xia et al., 2009). This delay is thought to reflect the kinetic difference between calcium influx and DCV exocytosis due to the loose association between DCVs and calcium channels (Xia et al., 2009). (2) In the ‘falling’ phase, NPRR^ANP ∆F/F began to decline 1–5 s before the termination of each stimulation trial, presumably reflecting depletion of the available pool of releasable vesicles. In contrast, GCaMP6s fluorescence did not return to baseline until after stimulation offset (Figure 2A–B). (3) Finally, unlike GCaMP6s, NPRR^ANP exhibited an ‘undershoot’ (∆F/F below baseline) during the post-stimulation intervals, followed by a ‘recovering’ phase (Figure 2A; Figures 2C,I1–4). This undershoot may reflect dilution of released fluorescent NPRR molecules by diffusion into the synaptic cleft (van den Pol, 2012), while recovery may reflect DCV replenishment in the boutons from vesicles proximal to the imaged release site.

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Because NPRR^ANP fluorescence was preferentially accumulated within boutons, we asked whether these regions contributed to ∆F/F peaks more significantly than the inter-bouton intervals (IBIs). To do this, we partitioned the processes into boutons and IBI fields (Figure 2—figure supplement 2A), and compared the ∆F/F in these regions during stimulation trials. The time-averaged ratio of bouton/IBI ΔF/F (see Materials and Methods) was significantly higher for NPRR^ANP than for GCaMP6s, particularly during later stimulation trials (Figure 2—figure supplement 2B, green bars, S2-4). This contrast indicates that NPRR^ANP signals are preferentially observed in boutons, where DCVs are located, and do not reflect differences in cytoplasmic free Ca²⁺ levels between these regions as detected by GCaMP6s.

To test definitively if NPRR^ANP ∆F/F signals are dependent upon NP release, we blocked vesicle fusion at terminals of Type III neurons using expression of tetanus toxin light chain (TNT) (Sweeney et al., 1995), a protease that cleaves n-synaptobrevin, a v-snare required for DCV fusion (Figure 2—figure supplement 3) (Xu et al., 1998). As a control, we used impotent TNT (TNT^imp), a reduced activity variant (Sweeney et al., 1995). TNT expression completely abolished stimulation-induced ∆F/F increases from NPRR^ANP, while TNT^imp did not (Figure 2F). Further analysis revealed that both the ∆F/F peaks and inter-stimulation undershoots were diminished by TNT (Figure 2G–H). In contrast, neither TNT nor TNT^imp affected the kinetics of GCaMP6s signals in Type III neurons (Figure 2—figure supplement 2C), which report cytosolic Ca²⁺ influx. Taken together, these data support the idea that NPRR^ANP signals specifically reflect DCV release.

ANP is a rat NP that lacks a Drosophila homolog (Rao et al., 2001). To determine whether our method could be applied to detect the release of a specific, endogenous fly NP, we tested NPRR^dTK, one of 6 different reporter variants we initially generated from the Drosophila neuropeptide precursor, DTK (Figure 1—figure supplement 1B). In contrast to ANP which encodes a single peptide, DTK yields multiple NP derivatives (Winther et al., 2003). Light microscopy (Figure 3A) and Immuno-EM (Figure 3B, arrows) confirmed that NPRR^dTK, like NPRR^ANP, was localized to DCVs (DCV/bouton ~ 22.19, Figure 3C). Using the Type III-specific GAL4 driver to express NPRR^dTK and the same stimulation protocol as used for NPRR^ANP, the basic tri-phasic response profile was also observed (Figure 3D). However, peak heights and baseline fluorescence fell progressively with successive stimulation trials (Figure 3E), in contrast to NPRR^ANP where the first peak and undershoot were lower (Figure 2C–D). The reason for this difference is currently unclear.

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We next investigated the relationship between NPRR signal and stimulation intensity, by delivering to the Type III neurons a series of low to high frequency electrical stimuli (1–70 Hz; Levitan et al., 2007) while imaging the nerve terminals. For direct comparison of NPRR responses across different preparations, we applied a posteriori normalization of fluorescent peaks in each trial to the highest response obtained among all trials. For both NPRR^ANP and NPRR^dTK (Figure 4A–B), the peak responses showed a positive correlation with stimulation frequency, analogous to that observed using cytosolic GCaMP6s (Figure 4C). In Type III neurons, the responses of both NPRRs to stimulation frequencies < 30 Hz (1,5,10,20 Hz) were not statistically significant from zero. NPRR^ANP showed a higher sensitivity to high stimulation frequencies (30 Hz: 18.14%, 50 Hz: 82.40% Normalized peak ∆F/F), while NPRR^dTK showed a higher stimulation threshold and lower sensitivity (30 Hz: 3.57%, 50 Hz: 24.67% Normalized peak ∆F/F).

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We next investigated whether the relatively high stimulation frequency required to observe significant responses with NPRRs was a function of the reporters, or rather of the cell class in which they were tested. To do this, we expressed both NPRRs in Type Ib neurons, a class of motor neurons that contains both SVs and DCVs (Figure 1B, Figure 4D–F), and performed stimulation frequency titration experiments. Strikingly, in Type Ib neurons, significant increases in ∆F/F could be observed at frequencies as low as 10 Hz (Figure 4D,E; NPRR^ANP @ 20 Hz: 12.50%, NPRR^dTK @ 20 Hz: 17.67% normalized peak ∆F/F). The reason for the difference in NPRR threshold between Type III and Type Ib neurons is unknown, but parallels their difference in GCaMP6s response to electrical stimulation (Figure 4C vs. Figure 4F).

Notably, although NPRR^ANP and NPRR^dTK presented distinct response profiles in Type III neurons, their performance in Type Ib neurons was more similar (Figure 4A vs. Figure 4B; cf. Figure 4D vs. Figure 4E). In summary, the differences in performance we observed between the two NPRRs appeared to be specific to Type III neurons, and were minor in comparison to the differences in performance of both reporters between the two cell classes. The reason for the differences between NPRR^ANP and NPRR^dTK sensitivity and kinetics in Type III neurons is unknown but may reflect differences in how well these reporters compete with the high levels of endogenous neuropeptide (Bursicon) for packaging, transport or release.

Here we present proof-of-principle for a method to detect the release of different neuropeptides in intact neural tissue, with subcellular spatial and sub-second temporal resolution. By exploiting the fluorescent change of GCaMP in response to a shift in pH and [Ca²⁺], we visualized the release of neuropeptides by capturing the difference between the intravesicular and extracellular microenvironment. NPRR responses exhibited triphasic kinetics, including rising, falling and recovering phases. In the falling phase, a post-stimulus ‘undershoot’, was observed in which the fluorescent intensity fell below pre-stimulation baseline. This undershoot presumably reflect the slow kinetics of DCV replenishment relative to release.

The molecular mechanisms of NP release are incompletely understood (Xu and Xu, 2008). It is possible that individual DCVs only unload part of their cargo during stimulation, in which case many DCVs that underwent fusion may still contain unreleased NPRR molecules following a stimulus pulse. Although we are convinced that NPRR signals do indeed reflect NP release, due to the presence of the recovering phase, we cannot formally exclude that unreleased NPRRs may contribute to the signal change due to their experience of intravesicular [Ca²⁺]/pH changes that occur during stimulation. To resolve this issue in the future, an ideal experiment would be to co-express an NPRR together with a [Ca²⁺]/pH-invariant NP-reporter fusion. Multiple attempts to generate such fusions with RFP were unsuccessful, due to cryptic proteolytic cleavage sites in the protein which presumably result in degradation by DCV proteases during packaging.

To test if NPRR^ANP ∆F/F signals are dependent on NP release, we expressed the light chain of tetanus toxin (TNT), a reagent shown to effectively block NP release in many (McNabb and Truman, 2008; Hentze et al., 2015; Zandawala et al., 2018), if not all (Umezaki et al., 2011), systems. We observed a striking difference in NPRR kinetics in flies co-expressing TNT vs. its proteolytically inactive ‘impotent’ control form TNT^imp (Figure 2F). The strong reduction of NPRR signals by TNT-mediated n-syb cleavage is consistent with the idea that these signals reflect the release of NPRRs from DCVs.

We have tested the generalizability of the principles used to generate NPRRs by (1) constructing a surrogate NP reporter NPRR^ANP as well as a multi-peptide-producing endogenous Drosophila NP reporter NPRR^dTK (Figures 2–3); (2) characterized NPRR signals in response to varying intensities of electrical stimulation; and (3) recorded NPRR signals in two different classes of NMJ motor neurons containing DCVs with or without SVs, respectively (Figure 4). These experiments revealed, to our surprise, that NPRR responses exhibit cell-type specific characteristics (Figure 4). As NPRRs are applied to other neuropeptides and cell types, a systematic characterization of neuropeptide release properties in different peptidergic neurons should become possible, furthering our understanding of neuropeptide biology.

The method described here can, in principle, be extended to an in vivo setting. This would open the possibility of addressing several important unresolved issues in the study of NP function in vivo. These include the ‘which’ problem (which neuron(s) release(s) NPs under particular behavioral conditions?); the ‘when’ problem (when do these neurons release NPs relative to a particular behavior or physiological event?); the ‘where’ problem (are NPs released from axons, dendrites or both?); and the ‘how’ problem (how is NP release regulated?). The application of NPRRs to measuring NP release dynamics in awake, freely behaving animals may yield answers to these important long-standing questions.

Source data of EM for Figure 1 and 3, and codes used for Figure 2 and 3 have been provided.

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Author details

1. Keke Ding

   Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, United States

   Contribution

   Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Writing—original draft, Writing—review and editing

   Contributed equally with

   Yifu Han

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-5261-4843

2. Yifu Han

   1. Department of Neurobiology, University of Southern California, Los Angeles, United States
   2. Neuroscience Graduate Program, University of Southern California, Los Angeles, United States

   Contribution

   Data curation, Formal analysis, Investigation, Methodology, Writing—review and editing

   Contributed equally with

   Keke Ding

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-1201-654X

3. Taylor W Seid

   Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, United States

   Contribution

   Validation, Investigation, Methodology

   Competing interests

   No competing interests declared

4. Christopher Buser

   Oak Crest Institute of Science, Monrovia, United States

   Contribution

   Data curation, Formal analysis, Investigation, Methodology

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-4379-3878

5. Tomomi Karigo

   Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, United States

   Contribution

   Investigation, Methodology

   Competing interests

   No competing interests declared

6. Shishuo Zhang

   Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, United States

   Contribution

   Validation, Investigation

   Competing interests

   No competing interests declared

7. Dion K Dickman

   Department of Neurobiology, University of Southern California, Los Angeles, United States

   Contribution

   Resources, Formal analysis, Supervision, Funding acquisition, Methodology, Writing—review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-1884-284X

8. David J Anderson

   1. Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, United States
   2. Howard Hughes Medical Institute, California Institute of Technology, Pasadena, United States
   3. Tianqiao and Chrissy Chen Institute for Neuroscience, California Institute of Technology, Pasadena, United States

   Contribution

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

   For correspondence

   wuwei@caltech.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-6175-3872

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