Neuropeptides are the largest class of signaling molecules in the central nervous system where they act as neurotransmitters, neuromodulators, and hormones (Wotjak et al., 2008). In vivo neuropeptide detection is technically challenging as neuropeptides can be rapidly cleaved by peptidases and undergo posttranslational modifications. To further complicate detection, neuropeptides are found at orders of magnitude lower concentrations compared to classical neurotransmitters (i.e. glutamate, GABA, and the biogenic amines) and adsorb to a variety of surfaces during sample handling (Zhou et al., 2015; Maes et al., 2014).

For decades, neuropeptides have been assayed with immunoaffinity-based techniques due to their high sensitivity (Maidment et al., 1989; Maidment et al., 1991). However, selectivity remains a concern due to poor antibody specificity for full length vs. truncated peptides that contain identical binding epitopes. Nanoflow liquid chromatography-mass spectrometry (nLC-MS) is a powerful alternative for peptide detection because it provides high sensitivity and specificity without the need for an immunocapture step. To this end, nLC-MS has been used successfully to detect a number of endogenous peptides derived from intracranial sampling techniques such as microdialysis (Van Wanseele et al., 2016).

Opioid peptides are prominent neuromodulators for regulating motivated behaviors. Though understanding their endogenous release properties is critical for dissecting the neural circuits that mediate these behaviors it has been extremely difficult to reliably achieve thus far. The reason primarily being that opioid peptides are very similar in structure and origin making selective detection of peptides and their subtype fragments virtually impossible. All opioid peptides, except nociceptin share a common N-terminal Tyr-GlyGly-Phe signature sequence, which interacts with opioid receptors. The preproenkephalin gene encodes several copies of the pentapeptide Met-enkephalin and one copy of Leu-enkephalin. The preprodynorphin gene also encodes multiple opioid peptides, including dynorphin A, dynorphin B, and neo-endorphin. The preprodynorphin-derived peptides all contain the Leu-enkephalin N-terminal sequence. To further complicate things the enkephalins preferentially bind to the δ over μ opioid receptor whereas dynorphin preferentially binds to the κ opioid receptor.

Despite these complexities some studies have reliably detected opioid peptides in rat models (DiFeliceantonio et al., 2012; Mabrouk et al., 2011; Lam and Gianoulakis, 2011a; Lam and Gianoulakis, 2011b; Lam et al., 2008; Lam et al., 2010), however, it has not been previously possible to reliably detect evoked neuropeptide release in a cell-type selective manner using transgenic mouse models. However, with the advent of optogenetic approaches allowing researchers to spatially target and manipulate neuronal firing patterns the specific neuronal properties of neuropeptide release can now more likely be empirically determined.

We and others have recently shown that discrete targeting of the kappa opioid system in the nucleus accumbens (NAc) can modulate both rewarding and aversive behaviors (Al-Hasani et al., 2015; Castro and Berridge, 2014). We showed that photostimulation of dynorphin (dyn) cells in the ventral nucleus accumbens shell (vNAcSh) elicited robust aversive behavior, while photostimulation of dorsal NAcSh dyn (dNAcSh) cells induced a place preference that was positively reinforcing, however, no successful measurements of optogenetically-evoked neuropeptide release in vivo have been reported to date. Furthermore, few reports have extensively investigated regional distinctions within the NAc shell and no reports to our knowledge have successfully measured optogenetically-evoked neuropeptide release in vivo.

To quantify evoked-neuropeptide release in anatomically and behaviorally distinct regions, we pursued a method to reliably detect neuropeptides in the nucleus accumbens during photostimulation of opioid-containing neurons. We developed a custom optogenetic-microdialysis (opto-dialysis) probe that simultaneously provides photostimulation with the ability to sample local neurochemical release in awake, freely moving mice. We established a targeted nLC-MS method to analyze the opioid peptides dyn (fragment dynorphin A_1-8) and enkephalins (leu- and met-), as well as dopamine, GABA and glutamate. We applied these techniques to precisely investigate regional differences between the vNAcSh and dNAcSh neuropeptide release dynamics. This system allows quantification of neuropeptide release while directly controlling cell-type selective neuronal firing in the NAcSh.

To establish a quantitative assay, we used a custom synthesized isotopically labeled dyn (DYN*, YGGFLRRI with isotope ¹³C₆¹⁵N₁-leucine) with a mass shift of +7 and a +3.5 m/z shift (+2 charge state) as an internal standard (IS) to account for variability during the LC injection, surface adsorption, and ionization efficiency. High concentration injections of DYN* were fully resolved by the mass spectrometer and did not result in cross talk with endogenous dyn (Figure 1a and b), demonstrating that the addition of labeled DYN* does not contribute to or interfere with the endogenous dyn signal, while maintaining a similar retention time. To further validate the use of DYN* as an internal standard when detecting opioid peptides we prepared a linear calibration curve with a mixture of dyn, Leu-Enkephalin (LE) and Met-Enkephalin (ME) standards spiked with DYN* and detected all four compounds within 5 min following loading and desalting the sample on the column. Four distinct peaks are shown in the reconstructed ion chromatographic trace, confirming the separation and reliable detection of all three opioid peptides with isotopically labeled DYN* in one sample (Figure 1c). The addition of the DYN* isotope to our assay further improves quantification by improving relative standard deviation for repeated injections of standards (Figure 1d–f). Ratios of dyn, LE, and ME to a consistent DYN* were used for calibrations and analysis of standards for quantitative analysis for all experiments shown here. No effect of dialysate matrix for any targeted analyte was observed (Figure 1g–j).

Figure 1

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For in vivo detection in freely moving mice, we developed a customizable microdialysis probe (i.e. customizable length, depth and sampling area) with an integrated fiber optic to locally sample proximal to the site of photostimulation in the brain (Figure 2a, Figure 2—figure supplemenrt 1a–e). Traditional dialysis probes incorporate inlet and outlet tubing encased in a semi-permeable membrane enclosed with epoxy, and further encased in a stiff cannula for rigidity and robustness. To minimize the size of the opto-dialysis probe we did not include the final external casing and instead took advantage of the natural rigidity of the fiber optic to support the dialysis inlet-outlet assembly (Figure 2a, Figure 2—figure supplemenrt 1a–e). This resulted in a maximal probe diameter of 480 μm. To maximize concentrations of peptides entering the probe, we used a 60 kDa molecular weight cutoff, polyacrylonitrile membrane with a slight negative charge for optimal peptide recovery (AN69, Hospal, Bologna Italy) (Zhou et al., 2015; Maidment et al., 1989) and showed we can measure changes in peptide stock concentration within the 15 min fraction collection time (Figure 2b).

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To evoke and measure in vivo peptide release we injected AAV5-EF1α-DIO-ChR2-eYFP in to either the vNAcSh or the dNAcSh of preprodynorphin-IRES-cre (dyn-cre) mice and implanted the custom opto-dialysis probes 3 weeks later (Figure 3a). Following recovery artificial cerebrospinal fluid (aCSF) was perfused through the device at 0.8 μL/min and fractions were collected on ice every 15 min, generating 12 μL volumes, 2 μL of which were aliquoted and used for a small molecule detection assay. Three baseline fractions were collected prior to a fraction capturing 15 min of 10 Hz (10 ms pulse width) photostimulation, and six additional fractions were collected following photostimulation. As a positive control for detecting dynamic changes in vivo by nLC-MS and to establish sampled neuron responsivity to stimuli, we infused 100 mM K⁺ aCSF at the end of each collection experiment. The influx of K⁺ ions causes depolarization, resulting in vesicular exocytosis, which is expected to evoke a large increase in peptide concentration. Mice were included in the study if 100 mM K⁺ stimulation resulted in a positive increase in analytes at the end of the experiment (Figure 3—figure supplement 2a–c) and if correct anatomical probe placement and viral expression were confirmed (Figure 3—figure supplement 3a and b). In the current study 81.1% (15/18) of the mice were included in the analysis.

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A significant increase in dyn was detected in dyn-cre positive mice during photostimulation in both the vNAcSh (interaction effect; t = 3.941, p<0.001) and dNAcSh (interaction effect; t = 3.012, p=0.003), compared to control mice (Figure 3b). Interestingly, in the vNAcSh there was also a sustained increase in dyn after photostimulation (interaction effect; t = 2.499, p=0.014) (Figure 3b). Dyn release during photostimulation was also significantly higher in the vNAcSh compared to the dNAcSh (interaction effect; t = 2.749, p=0.007) and post stimulation (interaction effect; t = 2.806, p=0.006) (Figure 3b). Photostimulation of vNAcSh dyn neurons was previously shown to cause aversive behavior, consistent with early pharmacological studies which linked dyn with negative emotional states (Al-Hasani et al., 2015; Bals-Kubik et al., 1993). Here, we demonstrate sufficiently discrete dynorphin detection to measure different levels of peptide in two regions of the NAc shell separated by 1 mm.

We simultaneously detected robust release of LE and ME in both the vNAcSh and dNAcSh. During photostimulation of dyn-containing cells in the dNAcSh, LE levels were significantly elevated compared to controls (interaction effect; t = 5.384, p<0.0001) (Figure 3c). In contrast, no changes in LE were detected during photostimulation of dyn-containing cells in the vNAcSh (Figure 3c). Converse to this, we observed a significant increase in ME during photostimulation of dyn-containing cells in the vNAcSh (comparing cre + and cre-, interaction effect; t = 2.824, p=0.006). However, the same effect was not significant in the dNAcSh (comparing cre + and cre-, t = 1.78, p=0.053) (Figure 3d). Importantly, we observed a significant change in ME in the dNAcSh when compared to its baseline (t = 1.94, p=0.033), and there was no significant difference between the effect of photostimulation in ventral and dorsal (i.e. both are increased). However, the lack of a significance between Cre + and Cre- in dNAcSh is likely due, at least in part, to the fact the baseline levels of opioid peptides in dNAcSh are higher than the vNAcSh (7.114 pM versus 2.71 pM, respectively). Though data are represented as % of baseline the range of absolute dialysate concentration detected for dyn was 0.28–0.44 pM in dNAcSh and 0.13–0.28 pM in vNAcSh; LE 1.39–3.28 pM in dNAcSh and 1.30–2.24 pM in vNAcSh; ME 6.19–8.69 pM in dNAcSh and 2.57–4.11 pM.

To determine if peptide release corresponded with small molecule neurotransmitter release, we applied a benzoyl chloride (BzCl) derivatization LC-MS method to monitor three small molecules, dopamine, GABA and glutamate in dialysis samples (Song et al., 2012; Wong et al., 2016). Interestingly, dopamine increased in the dNAcSh during photostimulation (interaction effect; t = 5.007, p<0.0001), which persisted following stimulation (interaction effect; t = 2.081, p=0.039) (Figure 3e). Levels of GABA also increased in the vNAcSh following photostimulation (t = 2.363, p=0.020) and had a prolonged response (t = 4.744, p<0.0001) (Figure 3—figure supplement 3a). There were no significant changes in glutamate levels during photostimulation in either vNAcSh or dNAcSh (Figure 3—figure supplement 3b).

The data show that photostimulation of dyn-containing cells in the vNAcSh and dNAcSh results in a detectable increase in dyn release, which is greater in the vNAcSh. This result correlates with previous behavioral data demonstrating that dyn release likely drives a region dependent preference and aversion behavior (Al-Hasani et al., 2015). We observed a significant increase in LE and dopamine during photostimulation in the dNAcSh. Importantly, we previously observed a preference behavior when photostimulating dyn cells in the dNAcSh and these data suggest that the preference may be related, at least in part, to the detected increases in dopamine and LE. Furthermore, many studies have shown that levels of both dopamine and LE increase during preference or reward (Olds, 1982; Spanagel et al., 1990; Bruijnzeel, 2009). Importantly, using this method it is not possible to know whether LE is derived from pDyn or pENK but in future experiments we can combine this with conditional knockout approaches to selectively delete the peptides in specific cell types, so that we can later determine what happens to these signals in the absence of expression of particular peptides in D1 vs D2 cells and subregions. In addition, we cannot preclude interactions between MSN cell types, specifically that optogenetic activation of D1 cells could impart changes on D2 cell activity thereby releasing Met-Enk, indirectly, and/or via indirect circuits outside the NAc. In addition, dopamine release via D1 MSN stimulation, could be via two mechanisms 1) via indirect suppression of D1 projections back to VTA GABA neurons (Edwards et al., 2017), which acts via GABA-B to disinhibit VTA DA neurons. This would then result in dopamine release in the NAc following D1 stimulation. Furthermore, an alternative, presynaptic regulation of DA release from VTA to NAc projections is also plausible, and recently been investigated by multiple groups (Lemos et al., 2016; Dobbs et al., 2016).

The observed changes in GABA in both vNAcSh and dNAcSh are as predicted due to the fact that dyn-containing medium spiny neurons are GABAergic. The observed increase in ME is intriguing, as ME is not released from the same cells as dynorphin. There has been some evidence to suggest that enkephalins play a role in regulating disruptions in homeostasis following chronic exposure to drugs of abuse (Kreek and Koob, 1998; Shoblock and Maidment, 2007). Specifically, increases in enkephalin release during drug withdrawal has been shown through peptide tissue content, mRNA levels and microdialysis of extracellular enkephalins in a number of brain regions including the nucleus accumbens (Nylander et al., 1995). Concurrently, studies have measured increases in dynorphin expression during withdrawal (Isola et al., 2008; McCarthy et al., 2010; Przewłocka et al., 1997; Rylkova et al., 2009; Lindholm et al., 2000). It seems possible therefore that when we are stimulating the release of dynorphin (perhaps mimicking some aspects of withdrawal), levels of met-enkephalin increase in response to this, as a homeostatic mechanism to relieve this withdrawal-like state. This release profile is unique to the vNAcSh suggesting that this region is critical in the regulation of hedonic homeostasis. Though we have previously shown that dyn mRNA expression is similar in the vNAcSh and dNAcSh (Al-Hasani et al., 2015), it would be ideal to also verify the expression pattern and distribution of the enkephalins in the vNAcSh and dNAcSh. Finally, it should be noted that this detection technique maintains the poor temporal resolution of peptide and other microdialysis approaches. Therefore, conclusions regarding the differential transmitter and peptide release profiles should be tempered by the long time course of collection.

The ability to detect multiple peptides and small molecules in vivo with high sensitivity, as shown here demonstrates the potential of this method. It is, however, important to acknowledge limitations and future considerations with this methodology. Due to the structural similarity between ME/LE and dyn, we included DYN* as our only internal standard. An ideal assay would include isotopically labeled internal standards for each analyte of interest, however, the addition of analytes to detect in the ion trap reduces points per peak and complicates the chromatogram.

In this current method the 3 peaks of interest, LE/ME/dyn, all appear in the chromatogram in close proximity between 4–5.5 min, which may not seem ideal. A way to resolve this would be to flatten out the solvent gradient to better resolve the peaks chromatographically, however the length of the run would be longer, therefore reducing overall throughput. Here it takes 18 min from injection-to-injection and it is important to note that the peaks, while not fully resolved in time, were fully resolved by mass-to-charge ratio, so the separation was sufficient in this case. It is important to consider this delicate balance going forward to detect more peptides in an extended experimental paradigm and for concurrent detection of other peptides within individual samples.

Here, we only detected dyn1-8, LE and ME, but as mentioned in the introduction there are many more fragments that could be involved or altered that we have not measured. We primarily focused on dyn1-8 as this was the fragment that was the most reliable to detect in developing this methodology. Since we were at the early stages of method development it was crucial to limit variability where possible. The limitations to in vivo detection of neuropetides by microdialysis with LC-MS has been thoroughly explored and are largely due to absorption and lack of adequate recovery (Zhou et al., 2015). In this current method, organic modifiers are not added to the perfusate for dialysis or to the sample in the vial, as we validated that there was no peptide carryover/stickiness in the LC-MS lines without such agents. However, it has been shown that the addition of an organic modifier, like acetonitrile, to a sample could be optimized for each peptide fragment and improve quantitation by reducing stickiness to the vial and LC-MS fluidic pathway (Zhou et al., 2015). This approach, however, appears to be dependent on each individual peptide.

This new approach, we describe here allows for detection of cell-type-specific evoked in vivo neuropeptide release within neural circuits to be observed during freely moving behavior. Neuropeptide biology remains a challenging territory in neuroscience. Methods to detect in vivo release of endogenous peptides are limited, preventing the full understanding of their properties and dynamics. The method described here is a first step in closing this gap in our understanding of neuropeptide-containing circuits during behavior.

All data generated or analysed during this study are included in the manuscript and supporting files.

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Author details

1. Ream Al-Hasani

   1. Department of Anesthesiology, Division of Basic Research, Washington University School of Medicine, St. Louis, United States
   2. Department of Pharmaceutical and Administrative Sciences, St. Louis College of Pharmacy, St. Louis, United States
   3. Center for Clinical Pharmacology, Washington University School of Medicine and St. Louis College of Pharmacy, St. Louis, United States

   Contribution

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

   Contributed equally with

   Jenny-Marie T Wong

   For correspondence

   al-hasanir@wustl.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-8781-6234

2. Jenny-Marie T Wong

   Department of Chemistry, University of Michigan, Ann Arbor, United States

   Contribution

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

   Contributed equally with

   Ream Al-Hasani

   Competing interests

   No competing interests declared

3. Omar S Mabrouk

   1. Department of Chemistry, University of Michigan, Ann Arbor, United States
   2. Department of Pharmacology, University of Michigan, Ann Arbor, United States

   Contribution

   Conceptualization, Data curation, Formal analysis, Supervision, Validation, Investigation, Visualization, Methodology, Writing—original draft, Project administration

   Competing interests

   No competing interests declared

4. Jordan G McCall

   1. Department of Anesthesiology, Division of Basic Research, Washington University School of Medicine, St. Louis, United States
   2. Department of Pharmaceutical and Administrative Sciences, St. Louis College of Pharmacy, St. Louis, United States
   3. Center for Clinical Pharmacology, Washington University School of Medicine and St. Louis College of Pharmacy, St. Louis, United States
   4. Washington University Pain Center, Washington University School of Medicine, St. Louis, United States

   Contribution

   Data curation, Validation, Investigation, Visualization, Methodology, Writing—original draft, Project administration

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-8295-0664

5. Gavin P Schmitz

   1. Department of Anesthesiology, Division of Basic Research, Washington University School of Medicine, St. Louis, United States
   2. Center for Clinical Pharmacology, Washington University School of Medicine and St. Louis College of Pharmacy, St. Louis, United States

   Contribution

   Validation, Investigation, Visualization

   Competing interests

   No competing interests declared

6. Kirsten A Porter-Stransky

   Department of Psychology, University of Michigan, Ann Arbor, United States

   Contribution

   Investigation

   Competing interests

   No competing interests declared

7. Brandon J Aragona

   Department of Psychology, University of Michigan, Ann Arbor, United States

   Contribution

   Conceptualization

   Competing interests

   No competing interests declared

8. Robert T Kennedy

   1. Department of Chemistry, University of Michigan, Ann Arbor, United States
   2. Department of Pharmacology, University of Michigan, Ann Arbor, United States

   Contribution

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

   Contributed equally with

   Michael R Bruchas

   For correspondence

   rtkenn@umich.edu

   Competing interests

   No competing interests declared

9. Michael R Bruchas

   1. Department of Anesthesiology, Division of Basic Research, Washington University School of Medicine, St. Louis, United States
   2. Washington University Pain Center, Washington University School of Medicine, St. Louis, United States
   3. Department of Neuroscience, Washington University School of Medicine, St. Louis, United States
   4. Department of Anesthesiology and Pain Medicine, Center for the Neurobiology of Addiction, Pain, and Emotion, University of Washington, Washington, United States

   Contribution

   Conceptualization, Resources, Data curation, Supervision, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing—original draft, Project administration, Writing—review and editing

   Contributed equally with

   Robert T Kennedy

   For correspondence

   bruchasm@wustl.edu

   Competing interests

   Co-founder of Neurolux, Inc, a company that is making wireless optogenetic probes. None of the work in this manuscript used these devices or is related to any of the company's activities, but we list this information here in full disclosure

   "This ORCID iD identifies the author of this article:" 0000-0003-4713-7816

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