Unlocking opioid neuropeptide dynamics with genetically-encoded biosensors

doi:10.21203/rs.3.rs-2871083/v1

Neuropeptides are ubiquitous in the nervous system. Research into neuropeptides has been limited by a lack of experimental tools that allow for the precise dissection of their complex and diverse dynamics in a circuit-specific manner. Opioid peptides comprise a clinically relevant family that modulates pain, reward, and aversion. To illuminate the spatiotemporal dynamics of endogenous opioid signaling in the brain, we developed a class of genetically-encoded fluorescent sensors based on kappa, delta, and mu opioid receptors: κLight, δLight, and µLight, respectively. We used κLight to identify electrical stimulation parameters that trigger endogenous opioid release and the spatiotemporal scale of dynorphin volume transmission ex vivo. Using in vivo fiber photometry, we characterized optogenetically-driven opioid release, and observed differential opioid signaling in response to fearful and rewarding conditions in the nucleus accumbens. These sensors reveal the dynamics of endogenous opioid neuropeptide release in vivo, in awake freely moving behavior.

Biological sciences/Neuroscience/Synaptic transmission/Neurotransmitters

Biological sciences/Biological techniques/Imaging/Fluorescence imaging

Neuropeptides (NPs) are small proteins that modify neural activity, regulate brain states, and control blood flow in the nervous system^1-5. Neurons synthesize and release neuropeptides (NP) in addition to fast-acting neurotransmitters (NT) such as glutamate and GABA⁶. NPs activate select G protein-coupled receptors to modulate synaptic strength, neuronal excitability, and circuit dynamics. Unlike small molecule NTs, NPs are hypothesized to be released into the extra-synaptic space and thought to be cleared by proteolysis and diffusion over a range of 100 micrometers to millimeters to affect neurons, leading to long-lasting modulatory effects ^6,7^,⁸. A comprehensive understanding of the conditions that trigger NP release from neurons and the spatiotemporal extent of peptide release has been lacking, and yet is critical for understanding the actions of NPs at the molecular, cellular, circuit and network levels to their influence on animal behavioral states.

Among all known NPs, the opioid system is the most functionally diverse and clinically relevant family ^9-15. The opioid receptor family contains distinct receptor subtypes—kappa, delta, and mu (κOR, δOR, and µOR, respectively), as well as nociception receptors — which can be activated by at least 20 endogenous opioid peptides with differential affinity and selectivity ^12,16,17. Kappa, delta, and mu and nociception opioid receptors activate inhibitory G_i/o G proteins, which leads to reductions in cellular excitability and neurotransmitter secretion in receptor-expressing neurons. Opioid peptides and their receptors are widely distributed across cortical and subcortical brain regions ^18,19. It is thought, that the diversity of opioid peptides is essential for modulating complex behavior and physiological processes, such as pain, reward, substance abuse/dependence, and stress ²⁰. Opioids drugs targeting these receptors are used to treat severe pain, but prolonged use can lead to addiction and overdose ²¹. Newer efforts have isolated opioid receptors as potential targets for anxiety, depression, and addiction ^22,23. Some of these efforts have been hindered by a lack of high-resolution methods for studying endogenous neuropeptide release in vivo.

Studies into neuropeptide systems, especially opioid systems, have been historically challenging due to a lack of sensitive experimental tools in the spatial temporal domain which can facilitate understanding the complexity and diversity of NP signaling in a circuit-specific manner. The endogenous opioid peptides have similar structures and bind to different opioid receptors with relatively lower selectivity than some neuropeptide molecules at their cognate receptors¹⁶. Physiologically relevant NP release by neurons are thought to be difficult to trigger and the released concentration may also be at orders of magnitude lower than classical neurotransmitters (nanomolar vs. micromolar or even submillimolar) ²⁴, making it extremely difficult to adequately probe the conditions to trigger the endogenous peptide release and measure the released concentration ex vivo and in vivo²⁵. As a result, it has been exceedingly difficult to study the processes that regulate opioid neuropeptide release. Recent technological advances have begun to reveal the anatomical and spatiotemporal features of opioid signaling ^26,27. Transcriptomics studies have documented the distribution of opioid peptide-receptor pairs across cell types in the cortex, highlighting the functional significance of opioid signaling in mediating trans-cellular communication in neural circuits ²⁸. Features of peptide diffusion and clearance have been revealed by combining light-triggered photo-release of caged enkephalin with electrophysiological measurements of peptide-evoked currents in brain slices ²⁹. In vivo, optogenetically-driven peptide release has been detected using high-speed micro-dialysis ³⁰. Despite these successes, it remains challenging to quantify behaviorally relevant endogenous opioid peptide release with subsecond and subregional resolution.

To bridge this gap in technology, we developed a class of genetically encoded opioid peptide indicators, κLight, δLight, and µLight, based on κOR, δOR, and µOR respectively. We used these sensors to systematically evaluate ligand binding-induced conformational changes at all three receptors and thereby established the binding specificity and efficacy of 14 opioid peptides and 8 opioid drugs. In acute hippocampal slice, we used κLight to determine electrical parameters that can trigger endogenous opioid peptide release and quantified the diffusion rate of dynorphin using photoactivatable peptide. Using optogenetics to stimulate opioid peptide release, we detected circuit-specific endogenous opioid signaling in vivo. Finally, we used these sensors to reveal rapid opioid peptide release in a subregion-specific manner in response to fear and reward conditions within the nucleus accumbens of awake, behaving mice.

Design and engineering of opioid biosensors

We replaced amino acids between R2.57 and R6.24 of the human κ receptor, S2.47 and K6.24 of the human δ receptor, and S6.23 and K6.24 of human µ receptors, respectively, with a circularly permuted green fluorescent protein (cpGFP), to generate κLight, δLight, and µLight sensors (Fig.1A-B, Supplementary Fig.1A-B). The dynamic range of each sensor was optimized by screening linker compositions. In total, the dynamic range of 698 κLight variants, 64 δLight variants, and 233 µLight variants were examined in response to U50,488, met-enkephalin (ME) and DAMGO, respectively (Supplementary Fig.1C). To promote excellent membrane localization; we fused a telencephalin (TlcnC) tag ³¹ or endoplasmic reticulum (ER) export motif (FCYENEV) ³² followed by a chain of GS linker and the proximal restriction and clustering (PRC) tag ³³ to the c-terminus of κLight, δLight, and µLight. We named these new variants κLight1.3, δLight1, and µLight1, respectively. In addition, we mutated D3.22 of KOR and D3.32 in DOR in the binding pockets to attenuate the ligand-binding, which led to two control sensors κLight0 and δLight0.

When transiently expressed in mammalian HEK293 cells and dissociated neuronal culture, we observed excellent membrane expression of κLight1.3, δLight, and µLight. All three sensors were activated by their endogenous receptor agonists (100 µM), dynorphin A1-8 (DynA8), ME and β-endorphin, respectively (Signal to noise ratios (SNR) values for κLight1 (HEK) = 7.5 ± 0.45, κLight1.3 (neuron) = 5.6 ± 0.2, δLight (HEK) = 16 ± 0.62, δLight (neuron) = 8.9 ± 0.43, µLight (HEK) = 4.7 ± 0.26) (Fig.1C-D, Supplementary Fig.1D). The ligand-induced responses (κLight1.3 ΔF/F (neuron) = 151 ± 5.1%; δLight ΔF/F (neuron) = 123 ± 19.4%; µLight ΔF/F (neuron) = 19.6 ± 3.2%) were blocked by naloxone (1 mM), which is an antagonist for all three receptors (Supplementary Fig.1E-G).

To eliminate response variability due to inconsistent expression level of sensors via transient transfection, we developed HEK293T cell lines stably expressing κLight1.3, δLight, and µLight, respectively (Supplementary Fig.1H). Using these cell lines, we characterized the promiscuity of endogenous opioid peptides on activating sensors ³⁴. First, all three sensors have consistent excitation peak wavelength at 495 nm and emission peaks at 515 nm (Supplementary Fig.1I). Second, in situ titration showed that all three sensors can be activated by three distinct endogenous opioid peptides but with different potency and efficacy. κLight1.3 responded to dynorphin A1-13 (DynA13) with an apparent EC50 of 89.8 pM, which is three magnitudes higher than β-endorphin and ME. However, at higher concentrations (>10 µM), κLight1.3 displayed higher fluorescent changes to β-endorphin, followed by DynA13 and ME (ΔF/F (κLight - DynA13) = 93.6 ± 3.9%; ΔF/F (κLight - β-endorphin) = 126.9 ± 8.6%; ΔF/F (κLight - ME) = 80.3 ± 1.8%) (Fig.1E). δLight is activated by ME with an EC50 of 6.5 nM, which is two orders of magnitude greater than DynA13 and β-endorphin, and has higher fluorescent efficacy compared to these two peptides(ΔF/F (δLight - DynA13) = 232.6 ± 6.8%; ΔF/F (δLight - β-endorphin) = 147.9 ± 4.1%; ΔF/F (δLight - ME) = 246.1 ± 4.6%) (Fig. 1F). Together, at presumed physiological conditions (pM-100 nM), both κLight1.3 and δLight are selective and sensitive to endogenous opioid peptides. In contrast, all three endogenous opioid peptides showed similar sensor potency and efficacy for µLight activation (Supplementary Fig.1J).

By running the in-situ titration in antagonist mode ³⁵, we were able to determine the selectivity of antagonists acting on κLight and δLight. In addition to naloxone, we chose nor-binaltorphimine (Nor-BNI), ICI 174864, and CTAP, which selectively antagonize κOR, δOR, and µOR, respectively. As expected, increasing the concentration of naloxone (100 nM to 10 µM) shifted the apparent EC50 to the right for DynA13 and ME for κLight1.3 and δLight, respetively: naloxone inhibited δLight with 2-fold greater affinity than κLight (p2A (δLight - naloxone) = 7.64, pA2 (κLight - naloxone) = 5.68). Nor-BNI displayed slightly higher affinity in blocking κLight than δLight (pA2 = 8.28 and 7.3 respectively) (Fig.1G-J, O-P). We did not observe apparent antagonism of κLight by ICI 174864, whereas it effectively inhibited activation of δLight by ME (pA2 δLight – ICI 174864 = 7.17) (Fig.1K, L, O-P). The µOR-selective antagonist CTAP did not affect the EC50 of DynA13 or ME in either κLight or δLight, respectively (Fig.1M, N, O-P).

Selectivity and pharmacology of the opioid biosensors.

We next used a low concentration (10 nM) of a broad panel of endogenous and synthetic ligands to evaluate their rank order of response for inducing sensor fluorescence. We found that known κOR selective endogenous peptides induced significantly greater fluorescence changes at κLight compared to δOR- or µOR-selective ligands. Among the dynorphin peptides, the shorter form dynorphin DynA1-8 induced lower activation of κLight compared to DynA1-13. Interestingly, nalfurafine, a synthetic κOR agonist, elicited an almost two-fold greater fluorescent change compared to the dynorphins (Fig.2A). For δLight cells, enkephalins and δOR-selective agonists elicited larger responses compared to other ligands; deltorphin I displayed similar efficacy as ME and LE for δLight activation (Fig.2B). Endogenous opioid peptide agonists at µOR, including β-endorphin, endomorphin, metorphinamide, and BAM18, displayed various efficacies for κLight1.3 and δLight activation, although to a much smaller extent compared to κOR- and δOR-specific peptides. Notably, U50,488 and U69,593 selectively activated κLight over δLight, while SNC80 and SNC162 activated δLight over κLight, confirming the sensors’ specificity to receptor-specific small molecule agonists (Fig.2A, B).

We then used radar plots to compare the proportionality constant (s-slope) of various receptor-selective ligands for activating each sensor (Fig.2C-E, Supplementary Table 1). The s-slope is a constant that links the variables of dynamic range (ΔF/F_max) and EC50 of a given sensor response to a drug, defined as ΔF/F_max / EC50. It highlights both the efficacy and potency of drugs on sensor responses ³⁶. By plotting s-slope values of individual ligands on three sensors as a radar plot, we found that the long forms of Dyn are more potent in activating κLight1.3 than the short forms, the latter of which displayed considerable activity at δLight as well. Both nalfurafine and U50,488 were selective for κLight1.3 (Fig.2C). The enkephalins (both ME and Leu-Enk (LE)), as well as β-endorphin, were highly selective for δLight, whereas deltorphin I and DPDPE displayed similar s-slopes for κLight1.3 and δLight. Despite low efficacy at κLight1.3, the s-slope of SNC80 was slightly higher at κLight1.3 than that at δLight (Fig.2D). Notably, µLight was insensitive to morphine, whereas the latter induced slight fluorescent increases at κLight1.3 and δLight. In contrast, methadone activated all three sensors with similar efficacy and potency. Buprenorphine activates all three sensors but showed higher potency for µLight and δLight. On the other hand, other µOR-selective synthetic drugs, including DAMGO, fentanyl, and oxycodone, engaged µLight with higher s-slopes compared to κLight1.3 and δLight (Fig.2E). Interestingly, oxycodone and buprenorphine suppressed, rather than enhanced, µLight fluorescence; thus, the s-slope was calculated using the absolute ΔF/F_max (Supplementary Fig.1K).

To determine whether the insertion of cpGFP perturbs the ligand-binding properties of these receptor-based opioid sensors, we conducted a radioligand binding assay using cells expressing each sensor and a panel of ligands that includes several endogenous peptides ^16,37,38. For µLight cells, endogenous opioid peptides displaced [³H] diprenorphine binding with nM IC_{50 \}except for metorphamide (µM IC₅₀). Specific binding detected in the presence of these peptides ranges from 34 ± 2% for peptide F to 82 ± 2% with BAM18. In the case of synthetic agonists, we see that DAMGO and oxycodone have nM IC₅₀ while morphine and fentanyl have µM IC₅₀. Interestingly, in the case of fentanyl we find that it exhibits nM IC₅₀in CHO cells stably expressing µ receptors (Supplementary Table 2). For δLight cells, the endogenous opioid peptides and the synthetic agonists displace [³H] diprenorphine binding with nM IC₅₀except for peptide E (µM IC₅₀). Specific binding detected in the presence of the endogenous peptides ranges from 32 ± 3% for BAM18 to 77 ± 4% with ME (Supplementary Table 2). For κLight1.3 cells, the endogenous opioid peptides and the synthetic agonist, U69,593 displace [³H] diprenorphine binding with nM IC_50.Specific binding detected in the presence of the endogenous peptides ranges from 10 ± 5% for ME to 76 ± 2% with DynB13 (Supplementary Table 2). For the peptides DynA8, β-endorphin, and ME, we used s-slope analysis to compare binding parameters with the sensors to those reported for opioid receptors under similar assay conditions ¹⁶. We found that they correlate in all three cases (Supplementary Fig.2A-C). Similarly, the s-slope determined by the fluorescence assay was also correlated to that determined by radio-ligand binding (Supplementary Fig.2D-F). Most importantly, we found that the effects of these peptides on receptors’ confirmation changes can be differentiated by the fluorescence assay (ΔF/F_max,) but not by radio-ligand binding approach (E_max) (Supplementary Fig.2G-I).

Together, these data suggest that the cpGFP insert is not likely to perturb the binding pockets of the patent receptor. Our studies demonstrate that peptide binding to an opioid sensor triggers fluorescence changes that correlate with the binding of the peptide to the receptor, making the sensors serve as useful tools to quantify differences in ligand-driven conformational changes between peptides.

Probing dynorphin release by simultaneous photo-stimulation and κLight dynamics

Photoactivatable or “caged” synthetic variants of opioid NPs or photosensitive nano-vesicles (nVs) can be activated with millisecond precision using short flashes of light and have been optimized for spectrally orthogonal use with GFP-based probes ³⁹. The spatiotemporal scale over which NP volume transmission occurs in brain tissue has been determined by combining photoactivatable NPs or nVs, electrophysiological recording or cell-based NP biosensors. We thus asked whether κLight is capable of reporting opioid peptide volume transmission in brain tissue using photo-uncaging experiments.

To choose the most appropriate κLight variant that balances dynamic range and sensitivity, we first examined the responses and kinetics of various κLight variants using photoactivable Dyn8 (CYD8) ²⁹. We injected AAV9-hSyn-κLight1.x (top kLight variants including 1.2a, 1.2b, 1.2c, 1.3) into the dorsal striatum (dStr) of C57 mouse pups (P0 – P3) and prepared the brain slices after 3 weeks of expression (Fig.3A). On the day of imaging, CYD8 was circulated in the bath and photo-uncaged with 50 ms flash of 355 nm laser light over an area of 700 µm², while imaging the responses of κLight with a 473 nm LED within the same region (Fig.3B). Among all the κLight variants tested (Supplementary Fig.3A), κLight1.3 yielded the greatest response (ΔF/F=11 ± 1.4 %) (Fig.3C, D), followed by κLight1.2a (ΔF/F=9.09 ± 0.81 %), κLight1.2c (ΔF/F=6.84 ± 0.65 %), and κLight1.2b (ΔF/F=5.1 ± 0.51 %) (Supplementary Fig. 3B, C). The uncaging response was completely blocked by the presence of naloxone (0.5 ± 0.1 %) (Fig.3D), confirming that the fluorescence change is due to ligand-dependent sensor activation, as opposed to being an artifact of the UV light flash. While κLight1.3 had the greatest ΔF/F, we noticed that its response was slow to decay in comparison to most of the other variants (tau_off - κLight1.3 = 202.1 sec, tau_off - κLight1.2a = 179.7 sec, tau_off - κLight1.2b = 246.1 sec, tau_off - κLight1.2c = 165.0 sec) (Fig.3C, Supplementary Fig.3B), presumably due to the higher affinity for dynorphins that results in slower peptide dissociation (Supplementary Fig.3D).

We next examined whether sensor expression might alter the ability of peptide ligands to engage endogenous opioid receptors. For this experiment, we used κLight1.2a, which exhibited faster decay kinetics than κLight1.3 upon DynA8 photorelease, yet still produced a relatively large ΔF/F. AAVs encoding κLight1.2a or GFP control were injected into the hippocampus of C57 pups (P0 – P3) and allowed to express for a minimum of 3 weeks before acute slices were prepared for electrophysiology (Supplementary Fig3E). Parvalbumin interneurons in the CA1 region of the hippocampus express MOR and DOR, which act presynaptically to suppress synaptic transmission ⁴⁰. Although DynA8’s primary target is KOR, it also binds to MOR and DOR (e.g. Fig 2B and Supplementary Table 1) ⁴¹. This allowed us to ask whether the activation of MOR and DOR by DynA1-8 is altered by the expression of κLight1.2a. To assay opioid receptor function, we recorded inhibitory currents (IPSCs) in pyramidal cells, evoked with a stimulation protocol that favors MOR- and DOR-sensitive parvalbumin synapses ⁴⁰ (Supplementary Fig.3F). Photorelease of DynA1-8 using 5 ms flashes of 355 nm light produced a rapid, power-dependent reduction in IPSC amplitude that reversed over the course of several minutes (Supplementary Fig.3G,H). Compared to GFP control, κLight1.2a expression altered neither the degree of IPSC suppression, nor the time-course of IPSC recovery in response to DynA8 photorelease across all light powers densities examined (Supplementary Fig.3I,J). These results suggest that κLight1.2a expression does not result in sufficient ligand buffering as to perturb the activation of endogenous opioid receptors.

We next measured the spread of DynA1-8 in space and time. AAV1-hSyn-κLight1.2a was injected into dStr and imaging was performed three weeks post-injection (Fig.3A). Small volumes of DynA1-8 were rapidly photoreleased using a focused 25 µm diameter spot of 355 nm light (Fig.3E) while monitoring sensor activation at distances of up to 125 µm away. We observed that the peak ΔF/F decreased with increased time from uncaging and with distance from the uncaging site (Supplementary Fig.4A). For each video frame post-uncaging, we plotted the fluorescence profile as a function of distance from the uncaging spot and extracted the ΔF/F half-width, which was used to compute an effective diffusion coefficient (D*) of 1.4 ± 0.4 µm²/s (n = 7 slices from 4 mice) for DynA8 in dStr (Supplementary Fig.4B-D). These results suggest that DynA1-8 can reach receptors over 100 µm away from release sites within several seconds of release in the hippocampus.

Two-photon imaging of endogenous dynorphin release triggered by electrical stimulation

It has been historically difficult to determine the electrical parameters that can effectively trigger the release of endogenous opioid peptides in brain tissue. We thus examined if κLight is capable of detecting endogenous opioid peptide release triggered by electrical stimulation ex vivo. To do so, we first improved the basal fluorescence of κLight1.3 by integrating CYKIWRNFKGK as linker 1 and SVISKAKIRTV as linker 2 derived from the oxytocin sensor MTRIA_OT⁴² (Supplementary Fig.3A). This new variant, named κLight1.3a, displayed a similar dynamic range (κLight1.3 at 155 ± 11.6%, κLight1.3a at 152 ± 29.5%, p=0.92, unpaired t test), but >2x the basal brightness compared to κLight1.3 (κLight1.3 at 25 ± 0.08, κLight1.3a at 61.8 ± 7.6, p=0.0075, unpaired t test) (Supplementary Fig.4E, F). Immunoreactivity studies have shown abundant dynorphin stored in dentate granule cells, dynorphin dynamics in CA3 have also been shown to have an association with stress under various behavior paradigms, and dynorphins have been shown to inhibit excitatory neurotransmission and prevent the induction of long-term potentiation (LTP) in hippocampus ^43-45. We sparsely expressed κLight1.3a in CA3 by delivering AAV1-CAG-DIO-κLight1.3a in combination with AAV1-hSyn-Cre (Fig.3G). After 3 weeks of expression, we observed bright labeling of neurons in CA3 and dentate gyrus with clear processes in the basal state using two-photon imaging (Fig.3H).

Next, we evaluated the responses of κLight1.3a to a range of electrical stimuli parameters applied locally via a stimulating electrode in CA3. Trains of electrical stimuli (1 s, 50 Hz, 0.5 sec inter-stimulus-interval) produced sustained fluorescence increases that rapidly decayed upon cessation of the stimulus (Fig.3I), with increasing number of stimuli driving larger maximum fluorescence responses (15 stimulations: 14.3 ± 2.4%, 10 stimulations: 8.39 ± 1.9%, 5 stimulations: 4.28 ± 0.6%, 1 stimulation: 2.12 ± 3.3%) (Fig.3K). The response to 15 stimuli was strongly attenuated by the addition of the KOR antagonist nor-BNI (100 μM, ∆F/F=1.57 ± 1.2%), consistent with the observed fluorescence increase resulting from activation by endogenous peptide. In the presence of DOR antagonist ICI 174864 (100 μM), the responses were decreased but not statistically significant (Fig.3J, K) (∆F/F=6.44 ± 0.3%).

Detecting the dynamics of opioid receptor-selective ligand binding in vivo

We next determined if κLight and δLight can be activated by systemic administration of exogenous small molecule drugs in vivo. We injected AAV encoding κLight1.3 or δLight in the hypothalamus (ARC) ⁴⁶, CA3 ⁴³, and NAc ³⁰, areas abundant in KOR and DOR. We next implanted fiber optic ferrules above each injection site and recorded the fluorescence of κLight and δLight upon intraperitoneal (i.p) injection of opioid receptor selective ligands using fiber photometry (Fig.4A, Supplementary Fig.4G).

In each case, we observed dose-dependent fluorescence increases in response to systemic drug i.p. treatment which were blocked by the non-selective opioid receptor antagonist naloxone. In ARC, κLight1.3 responded to the KOR-selective agonist U69,593 with a robust increase in fluorescence within a few minutes of drug injection (1 mg/kg: z-score_peak = 7.0 ± 1.9, 3 mg/kg: z-score_peak= 15.9 ± 3.05). Co-injection of naloxone (4 mg/kg) drastically attenuated the response to U69,593 (3 mg/kg) (U69,593+naloxone z-score_peak= 0.39 ± 0.59) (Fig.4B). In CA3, the KOR selective agonist U50,488, similarly activated κLight1.3 in a dose-dependent manner. Again, the response to U50,488 (10 mg/kg) was completely blocked by co-injecting naloxone (10 mg/kg) (5 mg/kg: z-score_peak = 2.68 ± 1.8; 10 mg/kg: z-score_peak = 11.1 ± 3.2; U50,488+Naloxone: z-score_peak = -2.86 ± 0.83) (Fig.4C).

In ARC, SNC162 administration produced increases δLight fluorescence that were blocked by naloxone (4 mg/kg co-injected with SNC162 (5 mg/kg)). (2.25 mg/kg: z-score_peak = 2.4 ± 1.0; 5 mg/kg: z-score_peak = 7.28 ± 2.4; SNC162+naloxone: z-score_peak = 0.19 ± 0.72) (Fig. 4D). In NAc, the administration SNC162 (5 mg/kg) also increased δLight fluorescence, and this was again blocked by naloxone (4 mg/kg) (SNC162: z-score_peak = 7.45 ± 2.2; SNC162+naloxone: z-score_peak = -1.66 ± 0.11) (Fig. 4E).

Importantly, we did not observe fluorescent changes in response to agonist when then non-functional mutant sensors κLight0 or δLight0 were expressed in ARC, CA3, and NAc (Supplementary Fig.4H-K). These results suggest that both sensors can be faithfully activated by receptor-specific agonists in vivo.

Measuring evoked endogenous dynorphin release induced by photo-stimulation of neural circuits

Though optogenetics has been broadly used to trigger neuromodulator release and neural activity, direct monitoring of peptide release triggered by optogenetic stimulation in vivo, especially in a circuit-specific manner with high temporal resolution, has not been measured optically. NAc contains abundant dynorphin and previous studies have demonstrated that targeting the Dyn-KOR system in the NAcSh can modulate both rewarding and aversive behaviors ^47,48. Furthermore, previous work has demonstrated the ability to measure the optogenetically-evoked release of dynorphin in the NAcSh using in vivo opto-dialysis ³⁰. Studies have also shown that the basolateral amygdala (BLA) sends dense, functional excitatory projections to the NAcSh and that these terminals are sensitive to modulation by Dyn-KOR ^49,50. We, therefore, set out to determine if κLight can detect photo-stimulated release in vivo in BLA to NAcSh projection.

To detect dynorphin signaling at KOR-expressing neurons, we injected KOR-Cre mice with AAV1-CAG-DIO-κLight1.3a and implanted optical fibers in the NAcSh. A subset of mice were also injected with the red-shifted opsin ChRimson (AAV5-DIO-EF1a-ChRimson-tdTomato) in the BLA (Fig.5A-C, Supplementary Fig.5A); ChRimson-lacking mice served as a negative control to determine if optical stimulation produced artifactual dynamics in κLight1.3a fluorescence. To ensure a good dynamic range, adequate expression, and fiber-expression alignments as a foundation for the following optogenetic stimulation experiments, we first examined the response of κLight1.3a to the agonist U50,488 in these mice (Fig.5D). U50488 (10 mg/kg; i.p) administration resulted in a rapid, sustained, and robust increase in the fluorescence of κLight1.3a. This increase was significantly attenuated when the animals were pre-treated with the short-acting, reversible KOR antagonist, JNJ-67953964 ⁵¹ (aticaprant, 3 mg/kg; i.p) (p =0.034, paired t test), demonstrating the selectivity of κLight1.3a responses in vivo (Norm peak, p =0.0344, paired t test. Norm AUC, p =0.0138, paired t test) (Fig.5E-H).

Next, we tested whether κLight1.3a can detect endogenous dynorphin release in the NAc evoked via stimulation of glutamatergic BLA terminals, known to densely innervate the NAc ⁴⁹. A 1 s, 20 Hz, 5 ms pulse-width stimulation produced a brief artifact, followed by a significant increase in κLight1.3 fluorescence (Supplementary Fig.5B, C). Importantly, this stimulus artifact was present to the same extent in all animals, with and without ChRimson expression in the BLA terminals (Supplementary Fig.5D). However, the subsequent increase in κLight1.3a fluorescence was present only in the animals expressing ChRimson in BLA, suggesting that this elevation is due to the BLA terminal stimulation-evoked release of dynorphin (p < 0.0001, Welch’s t test) (Supplementary Fig.5E). To determine the appropriate stimulation parameters for stimulation-evoked dynorphin release, we performed a battery of experiments modulating stimulation number (1-5 stim), laser intensity (0.5 – 5 mW), and stimulation time (1-30 sec) within the same session in a randomized order (Supplementary Fig.5F-H). Varying the length of stimulation from 1-5 sec revealed, somewhat paradoxically, that 1 sec of photo-stimulation produced the most κLight1.3a activation, while the magnitude of the artifact (fluorescence minimum) remained constant throughout (p =0.0082, Brown-Forsythe and Welch ANOVA test) (Supplementary Fig.5I-J). Based on these results, we performed all our subsequent experiments using 1s, 20 Hz, 5 ms pulse-width stimulation.

We then determined the pharmacological selectivity of BLA terminal stimulation-evoked κLight1.3a activation. We first pre-treated animals with vehicle or aticaprant (3 mg/kg; i.p), followed by 10 trials/animal of BLA terminal stimulation, while simultaneously monitoring κLight1.3a fluorescence. We observed that KOR antagonism significantly decreased stimulation-evoked κLight1.3a activity in vivo (Norm peak, p =0.0365, paired t test. Norm AUC, p < 0.0001, paired t test) (Fig.5I-L). We then posited that if this is due to KOR antagonism, wherein the antagonist prevents endogenous dynorphin from binding κLight1.3a, we should obtain a similar result following KOR agonism due to κLight1.3 occupancy by U50,488. Hence, we injected animals with vehicle or U50,488 (10 mg/kg; i.p) and performed the aforementioned recordings of stimulation-evoked κLight1.3 activity. As with aticaprant, we found that U50,488 significantly blunted evoked-κLight1.3a activation(Norm peak, p =0.0022, paired t test. Norm AUC, p = 0.0072, paired t test) (Fig.5M-P). This suggests that U50,488 occupied and competed for the binding of evoked endogenous dynorphin to κLight1.3a. Altogether, these results demonstrate that we can use optogenetics to trigger and measure terminal dynorphin release with κLight in a circuit-specific manner.

Monitoring dynorphin and enkephalin release dynamics during fear and reward seeking behavior

After successfully detecting optogenetically-evoked dynorphin release, we next sought to use κLight and δLight to monitor longitudinal opioid peptide signaling dynamics in behaving animals under fear-inducing and rewarding conditions. Previous studies have demonstrated that dynorphin neurons in ventral and dorsal NAcSh subregions (vNAc and dNAc, respectively) have a distinct role in aversive and reward behavior ⁴⁷. Furthermore, sub-region-specific dynorphin and enkephalin release have been measured in vNAc versus dNAc using an opto-dialysis method ³⁰. We thus decided to examine the utility of κLight1.3 and δLight in probing subregion-specific release of opioid peptides in NAc during fear-learning. To do so, AAV9-hSyn-κLight1.3 or AAV9-hSyn-δLight was injected in dNAc and vNAc, followed by fiber implantation. Three weeks post-surgery, we measured peptide transients during an auditory fear conditioning experiment consisting of 30 presentations of a 30 s tone co-terminating with a 1.5 sec foot-shock (0.5 mA) (Fig.6A, Supplementary Fig.6A). In the case of κLight, both dNAc and vNAc, we observed a quick rise in fluorescence intensity after the onset of the tone, which was sustained during tone presentation, followed by a small dip at the onset of the shock and a large rise immediately after the foot-shock. The fluorescence signal then gradually decreased to the baseline after ~40 sec (Tau - κLight1.3 in dNAc = 28.7 sec; Tau - κLight1.3 in vNAc = 21.7 sec) (Fig. 6B-C). To assess differences in release between NAc subregions, we calculated the area under the curve (AUC) of individual trials. The AUC to the tone was similar between dNAc and vNAc, whereas the AUC of the post-shock response was significantly higher in dNAc compared to vNAc (AUC dNAc; 194 ± 24, AUC vNAc; 135 ± 15, p = 0.0355, unpaired t test) (Fig.6D). We did not observe fluorescent changes during fear-learning when AAV1-hSyn-κLight0 was expressed either in dNAc or vNAc. (Supplementary Fig.6B-D).

In the case of δLight in dNAc, we observed a brief increase in fluorescence triggered by the tone that gradually decreased to the baseline during the course of tone presentation. The foot shock also triggered a large fluorescence increase followed by a sharp decay over 10 sec after the shock (Tau - δLight in dNAc = 9.9 sec; Tau - δLight in vNAc = 3.6 sec) (Fig.6E-F). Although the AUC of the tone-evoked response in vNAc was slightly larger in amplitude than in dNAc, the difference was not significant. Again, the AUC of the shock-evoked response in dNAc was significantly higher than in vNAc (AUC dNAc; 18 ± 1.8, AUC vNAc; 13 ± 1.4, p = 0.0276, unpaired t test) (Fig.6G). We observed significantly attenuated fluorescence changes to the tone and shock in the animals expressing the control sensor δLight0 (Supplementary Fig.6E, F).

Together, these data suggest κLight and δLight can faithfully report the sub-regional differences in endogenous opioid peptide release triggered during fear learning. More interestingly, the post-shock signals from κLight are much larger and longer lasting in early trials, and the response gradually shifted from the shock to tone as the number of trials increased (Fig.6B-C, heatmap).However, we did not observe this pattern signal shift from shock to tone in δLight (Fig.6E-F, heatmap). This result suggests that dynorphin, but not enkephalin, might actively track fear state in NAcSh.

To determine the utility of κLight to probe reward-trigger endogenous dynorphin release, we first recorded the response of κLight1.3a to Pavlovian conditioning in NAc (Fig.6H). To target KOR-expressing neurons, we again injected CAG-DIO-κLight1.3a into NAc of KOR-Cre mice and trained these animals using classical reward conditioning. Although reward delivery during early trials did not produce fluorescence increases, we found a significant increase in κLight1.3a fluorescence in during reward delivery and consumption following conditioning, as animals increased their reward consumption across training (AUC early: 8.4 ± 0.739, AUC trained: 10.51 ± 0.77, p < 0.0001, paired t test) (Fig. 6I). These results suggest that endogenous dynorphin is released during reward reinforcement, supporting our prior work showing that subpopulations of dynorphin neurons in the NAcSh are reinforcing ⁴⁷.

Similarly, we monitored δLight fluorescence in ARC while mice retrieved caramel rewards (Fig.6J). We observed elevated δLight signals in animals injected with saline following caramel retrieval, and this response was blocked when naloxone (4 mg/kg) was injected prior to caramel retrieval (AUC saline: 20 ± 2.3, AUC naloxone: 6 ± 2.7, p = 0.0197, unpaired t test) (Fig.6K). We did not observe an increase of δLight0 in response to caramel retrieval under either condition (Supplementary Fig.6G). Together, these results suggest that κLight and δLight can faithfully track dynamic changes in endogenous opioid release during the full course of aversive and rewarding behaviors in vivo.

In this study, we develop, and characterize genetically encoded opioid receptor sensors for high-resolution tracking of opioid peptides under various experimental settings. GPCR-based sensors have been valuable in monitoring neuromodulator signals in awake animals^52,53, initially for biogenic amines and acetylcholine^35,54-60, and more recently for neuropeptides including oxytocin, orexin, and others^42,61-63. The development of opioid sensors addresses a crucial need in the neuroscience toolkit due to opioids' widespread significance.

All three sensors, µLight, κLight, and δLight, collectively respond to a wide range of opioid ligands, including and endogenous opioid neuropeptides, with κLight and δLight retaining the pharmacological selectivity of the parent receptor. These sensors can detect and differentiate conformational changes of the receptor induced by various peptiides, which is difficult to do using traditional radio-ligand binding assays. However, µLight is weakly activated by small molecule drugs like morphine and fentanyl and has a lower binding efficacy for endogenous peptides. In fact, oxycodone was observed to suppress µLight fluorescence. Structural studies of µOR in active and inactive state revealed that conformational changes of TM5 and TM6 depends on an allosteric coupling between ligand-binding pockets and G-protein ⁶⁴. As cpGFP was inserted into intracellular loop3, it is possible that cpGFP insertion decreased such coupling. Future optimization of µLight is crucial for reliably detecting MOR-selective neuropeptide β-endorphin.

Neuropeptide receptors can be expressed at a significant distance(µm-mm) from putative peptide release sites, suggesting volume transmission as one mode of neuropeptidergic transmission, enabling small amounts of neuropeptides to widely impact brain function. We used κLight with spatially restricted peptide photorelease to measure DynA1-8 in the dorsal striatum, indicating that it can signal via volume transmission to activate receptors over 100 µm away within seconds, with an apparent diffusion coefficient of 1.4 µm²/s. Diffusion coefficients for neuropeptides and similar molecules vary greatly depending on peptide type and brain region, peptidase content, as well as tissue tortuosity^8,62,65-67. We measured diffusion in the striatum, a tortuous region with myelinated fiber bundles and patch-matrix microcircuits; peptides may exhibit higher mobility in less tortuous regions. Although peptide uncaging has advantages, it doesn't target endogenous release sites and may release larger quantities than dense core vesicles. Additionally, confining sensor expression to KOR-expressing cells could improve sensitivity to endogenous peptide release by minimizing background fluorescence from neurons potentially unexposed to locally released peptide which can further enhance the accuracy of measurement. Further studies on endogenously released peptide spread are needed.

Understanding the neural activity patterns required for evoking neuropeptide release remains a decades long challenge. Monitoring neuropeptide release in response to electrical or optogenetic stimulation ex vivo or in vivo offers a powerful method for identifying these activity patterns. We demonstrated κLight's utility in determining electrical parameters to trigger endogenous release in hippocampal slices; this overcomes the challenge of using electrophysiological assays for endogenous receptor activation.

Identifying conditions that support endogenous peptide release may be most appropriately addressed in vivo, where neural circuits remain fully intact and endogenous neuromodulatory tone remains unaltered by brain slicing. To demonstrate circuit and cell-type specific release, rather than stimulating dynorphinergic cells within NAc directly, we optogenetically stimulated their glutamatergic inputs arising from the BLA. Prior work has established that optogenetic stimulation of BLA terminals in the NAc reliably drives action potentials in striatal neurons in brain slices, as well as facilitate reward seeking behavior ⁴⁹. In addition, synaptic stimulation of action potentials in peptidergic neurons via strong glutamatergic drive can activate metabotropic glutamate receptors, which have been implicated as gatekeepers for dynorphin secretion from striatal neurons in brain slices ⁶⁸. Using this optogenetic approach, we successfully identified stimulation conditions that result in κLight activation, presumably via dynorphin secretion from striatal neurons, as BLA neurons themselves express little to no prodynorphin mRNA (Allen Brain Atlas ISH data). Paradoxically, we found that increasing the duration of the stimulus beyond 1 sec decreased the degree of κLight activation. This is likely due to the stimulation artifact generated by red-light, as evidenced by the comparable minima observed in the animals expressing ChRimson and the controls that lack it (Supplementary Fig. 5B-D). Furthermore, increasing stimulation number also increased the width of the artifact (Supplementary Fig. 5F-H). While our data strongly suggest that this paradoxical suppression is likely due to stimulation artifacts induced by red light, future studies are required to explore the possibility of recruitment of additional neurochemical signaling processes during sustained stimulation that may suppress dynorphin release. Moreover, KOR-mediated suppression of BLA synaptic output, via dyn-KOR signaling at BLA terminals following sustained stimulation, resulting in the dampening of further synaptic activation of NAc dynorphin neurons requires further study. Additionally, whether prolonged stimulation of opioid release, either via optogenetics, or in response to strong behavioral stimuli such as footshock, results in the transient quenching of sensor activity also warrants future exploration.

In this study, we further demonstrated utility of κLight and δLight sensors in tracking rapid dynamic changes in endogenous opioid peptide release triggered by both reward and aversion, which can vary between subregions. Collectively, κLight and δLight respond to most endogenous opioid neuropeptides, including various dynorphin forms and enkephalins. However, the promiscuity among opioid receptors and peptides present a disadvantage in specificity, as the sensors cannot reliably distinguish between the endogenous peptides that might activate them – indeed many brain areas are rich in multiple opioid neuropeptides. Further engineering effort combined with structural analysis may make it possible to reduce such promiscuity. We expect broad application of these opioid sensors to enable a new understanding of how endogenous opioid peptide signaling contributes to various physiological and pathological conditions, including pain, stress, reward, and drug addiction. Monitoring circuit-specific peptide release in behaving animals may reveal new opioid functions in behavioral state transitions and associative learning. Detecting discrete peptide release events, evoked optogenetically or behaviorally, can help identify differences in opioid secretion under various pharmacological, behavioral, or disease states. While pharmacology, photopharmacology, and optogenetics have contributed significantly to opioid receptor signaling knowledge, these sensors enable a shift from focusing on receptor activation consequences to exploring endogenous neuropeptide secretion's impact on the brain's complex and diverse functions.

ACKNOWLEDGEMENTS

This work is funded by NIH Brain Initiative (U01NS103522 and U01NS120820 to L.T., U01NS113295 to M.B.& L.T.). We thank NIDA Grants R37DA033396 and R61DA051489 to M.R.B. This research was supported, in part, by NIH grants DA008863 and DA058300 to L.A.D., 2R01MH101214-06 to B.L and L.T., and DK126740 to D.A. We thank the support from NIMH Intramural Research Program (ZIA-MH002970-04) to H.A.D. We thank Mark Von Zastrow (UCSF) and John Williams (Vollum) for providing key input to design and characterize early versions of sensors. We thank Sarah Gooding for assistance in drug injection experiments in vivo. We thank Brian Trainor (UC Davis) for insightful comments.

AUTHOR CONTRIBUTIONS

L.T. conceived and led the project. K.M. and C.D. developed and optimized the opioid sensors. C.D. designed and performed key experiments, including in vitro sensor characterization, drug screening assays, part of the in vivo drug injection assay, and in vivo fear conditioning behavior experiments. C.D. also performed rodent surgeries for two-photon electrical stimulation experiments and in vivo fear conditioning experiments. R.G. worked with M.R. Bruchas to design and perform the optogenetics and pavlovian conditioning experiments. Y.J. led the design and implementation of the two-photon electrical stimulation experiments. X.J.H. worked closely with M.R. Banghart to design and execute the uncaging experiments and dynorphin diffusion constant measurement. H.W. and R.F. characterized sensor variants and control sensors using in vivo fear conditioning experiments with guidance from H.T. N.S.A played a crucial role in performing the majority of the in vivo drug injection and caramel retrieval experiments with guidance from D.A. A.G. and I.G. performed the radioligand binding assay under the guidance of L.A.D. R. L engineered viral vectors encoding sensors. D.K.L assisted in vitro characterization. J.L.W. provided compounds and helped design the sensor drug screening assay. L.T., C.D., and M.R.Banghart wrote the manuscript with input from other authors.

ETHICS DECLARATIONS

L.T. is a co-founder of Seven Biosciences. All other authors declare no competing interests.

Data and Code Availability

All source data present in this manuscript and custom MATLAB code are available from https://github.com/lintianlab/opioid_sensorsV1.

RESOURCE AVAILABILITY

Lead Contact

Further information and requests for resources and reagents should be directed and will be fulfilled by lead contact, Lin Tian, [email protected].

Materials Availability

The following plasmids and viral constructs have been deposited in Addgene and UNC neurotools:

Constructs	Deposited at	Identifier
pCMV- κLight1.3	Addgene	201223
pAAV-hSyn-κLight1.3	Canadian Neurophotonics	Lot 1952
pAAV-CAG-DIO-κLight1.3a	UNC Neurotools	NT-23-724
pCMV- δLight	Addgene	201224
pAAV-hSyn-δLight	UNC Neurotools	NT-23-485
pCMV- µLight	Addgene	201225

κLight1.3, δLight and µLight stable cell lines will be available upon request via MTA with UCD.

Key Resources Table

Reagent or Resource	Source	Identifier
Bacterial and Virus Strains
NEB 5-alpha competent E. coli (high efficiency)	NEB	C2987I
NEB10-beta competent E. coli (high efficiency)	NEB	C3019I
AAV1-hSyn-κLight1.2a	UC Davis viral core	N/A
AAV1-hSyn-κLight1.2b	UC Davis viral core	N/A
AAV1-hSyn-κLight1.2c	UC Davis viral core	N/A
AAV9-hSyn-κLight1.3	Canadian Neurophotonics	N/A
AAV1-hSyn-κLight0	Canadian Neurophotonics	N/A
AAV9-hSyn-δLight	Canadian Neurophotonics	N/A
AAV1-hSyn-δLight0	Canadian Neurophotonics	N/A
AAV1-CAG-DIO-κLight1.3a	UC Davis viral core	N/A
AAV5-DIO-ChrimsonR-tdTomato	UW NAPE Center viral vector core	N/A
AAV-DJ-CAG-GFP	Addgene	Plasmid #37825
Chemicals, Peptides, and Recombinant Proteins
Dynorphin A17	sigma aldrich	D8147-1MG
Dynorphin A13	Phoenix Pharmaceuticals, Inc.	021-21
Dynorphin A8	Phoenix Pharmaceuticals, Inc.	021-10
Dynorphin B9	Phoenix Pharmaceuticals, Inc.	021-38
Met-enkephalin	Phoenix Pharmaceuticals, Inc.	024-35
Leu-enkephalin	Phoenix Pharmaceuticals, Inc.	024-21
Deltorphin I	Phoenix Pharmaceuticals, Inc.	050-05
DPDPE	Phoenix Pharmaceuticals, Inc.	024-16
DAMGO	Phoenix Pharmaceuticals, Inc.	024-10
β-endorphin	Phoenix Pharmaceuticals, Inc.	022-14
Endomorphin I	Phoenix Pharmaceuticals, Inc.	044-10
Metorphinamide	Phoenix Pharmaceuticals, Inc.	024-54
β-neo-endorphin	Phoenix Pharmaceuticals, Inc.	021-44
BAM18	Phoenix Pharmaceuticals, Inc.	024-06
U50488	Fisher scientific	04-952-5
SNC80	Fisher scientific	50-816-10001
Nalfurafine hydrochloride	AdooQ Bioscience	A12579
Morphine Sulfate	Mallinckrodt	0406-1521-53
Fentanyl citrate salt	Sigma-Aldrich	F3886-25MG
Oxycodone hydrochloride	Sigma-Aldrich	O1378-500MG
(±)-Methadone hydrochloride	Sigma-Aldrich	M0267-1G
Buprenorphine hydrochloride	Sigma-Aldrich	B9275-50MG
Naloxone	Tocris	05-991-00
Norbinaltorphimine	Millipore Sigma	5.08017.0001
ICI174864	Tocris	0820
CTAP	Tocris	1560/1
CYD8	Banghart Lab and NIDA Drug Supply Program	N/A
NBQX	HelloBio	Cat #HB0443
CPP	Tocris	Cat #0247
U69593	Millipore Sigma	U103-5MG
SNC162	Tocris	1529
Aticaprant	Research Triangle Institute	14240-115
Critical Commercial Assays
DNA miniprep kit	QIAGEN	27104
PCR purification kitDNA miniprep kit	QIAGEN	27104
Endo-free plasmid maxi kitPCR purification kit	QIAGEN	28104
Lipofectamine2000 transfection reagentEndo-free plasmid maxi kit	QIAGEN	12362
Lipofectamine2000 transfection reagent	ThermoFisher	11668019
Experimental Models: Cell lines
κLight stable line	Tian Lab	N/A
δLight stable line	Tian Lab	N/A
µLight stable line	Tian Lab	N/A
Experimental Models: Organisms/Strains
C57/BL6J	The Jackson Laboratory	N/A
KOR-Cre	Bruchas Lab	N/A
Recombinant DNA
pLVX_EF1a- vector	Tian Lab	N/A
pCMV_delta8.2	Addgene	12263
pAAV_hSyn- vector	Addgene	111068
pCMV-vector	Addgene	111054
pCMV_κLight1.3	Tian Lab	N/A
pCMV_δLight	Tian Lab	N/A
pCMV_µLight	Tian Lab	N/A
pCMV_µLight	Tian Lab	N/A
Software and Algorithms
Matlab	Mathworks Inc	https://www.mathworks.com
ScanImage 5	Pologruto et al, 2003	http://scanimage.vidriotechnologies.com/
Ocular	Qimaging	https://www.qimaging.com/ocular
Igor Pro	WaveMetrics	https://www.wavemetrics.com
ImageJ	Schneider et al, 2012	https://imagej.nih.gov/ij/index.html
Illustrator CC	Adobe Systems Inc.	https://www.adobe.com/
Prism 9	GraphPad Inc	https://www.graphpad.com
Excel	Microsoft	https://www.microsoft.com/en-us/microsoft-365/excel
Equipment
2-photon Microscope	Scientifica
Small Animal Stereotax	David Kopf Instruments	1900
Leica Stellaris 8 Confocal	Leica	Stellaris 8
2-photon Microscope	Scientifica

EXPERIMENTAL MODEL AND SUBJECT DETAILS

Animals

All experimental procedures involving animals were approved by the Institutional Animal Care and Use Committee (IACUC) at the University of California, Davis, the University of California San Diego, the University of Washington, the University of Iowa Cold Spring Harbor Laboratory, the National Institute of Mental Health, or Icahn School of Medicine, and adhered to principles described in the National Institutes of Health Guide for the Care and Use of Laboratory Animals. The University of California, Davis, the University of California San Diego, the University of Washington, the University of Iowa, Cold Spring Harbor Laboratory, the National Institute of Mental Health, and Icahn School of Medicine are accredited by the Association for Assessment and Accreditation of Laboratory Animal Care International (AAALAC).

METHOD DETAILS

Sensor Development and Characterization

Development of κLight, δLight, and µLight.

All constructs were designed using circular polymerase extension cloning (CPEC), restriction cloning, and gBlock gene fragments (Integrated DNA Technologies) ⁶⁹. Sequences coding for a FLAG epitope were placed at the 5¢-end of the construct as previously described ⁷⁰. HindIII and NotI cut sites were placed at the 5¢- and 3¢-ends, respectively, for cloning into pCMV (Addgene) to generate all pCMV constructs. BamHI and HindIII sites were introduced via PCR for final subcloning onto pAAV.hSynapsin1 vectors (Addgene). To maximize coupling between conformational changes and chromophore fluorescence, we chose to use a cpGFP module (LSS-LE-cpGFP-LP-DQL) from GCaMP6 ⁷¹ for insertion into the human OPRK1 (KOR), OPRD1 (DOR) and OPRM1 (MOR) using circular polymerase extension cloning (CPEC).

For screening linker variants, we generated linker libraries by first creating an insert DNA carrying a randomized 2 amino acid linker on each side of cpGFP (LSS-xx-cpGFP-xx-DQL). Cloned constructs were amplified and purified with the Qiagen PCR purification kit prior to NEB^® 5-α competent E. coli transformation. Competent cells were plated onto kanamycin-containing agar plates. After allowing for 24 hours of growth at 37 °C, single colonies were manually picked and grown overnight as described previously ⁷². Plasmids from the colonies were purified using the Qiagen miniprep kit. Top variants were sequenced by Genewiz. For the iteration of κLight variants: κLight1.1 was discovered after linker screen, resulted in linker GI-PH. κLight1.2a: V164K from κLight1.1. κLight1.2b is κLight1.2a with ER2 tag. κLight1.2c is κLight1.2b with T603K. κLight1.3 is κLight1.2a with TlcnC, PRC, and ER2 tag. κLight0 is κLight1.2a with D128N. δLight has linker GI-PH, V154K mutation with PRC and ER2 tag. δLight0 has D128N mutation. µLight has linker sequence CI-SH, V175Q mutation with PRC and ER2 tag. To make AAV plasmids, NEB® stable competent cells were transformed with pAAV plasmids. After growth on an agar plate at 30°C, a single colony was selected. After sequencing confirmed the presence of the sensor gene, the cells were expanded at 30°C in 100 mL of growth medium (2xYT) and purified with a Qiagen Endo-free Plasmid Maxi kit and send to the UC DAVIS Virus Packaging Core for virus production.

Tissue Culture

HEK293T cells were grown in DMEM, supplemented with fetal bovine serum (FBS) and penicillin-streptomycin. Cells were transfected with Effectene according to the manufacturer's instructions. Prior to imaging, cells were washed with Hank’s Balanced Salt Solution (HBSS) supplemented with 2 mM MgCl₂ and 2 mM CaCl₂. All images were collected in HBSS containing Mg²⁺ and Ca²⁺ (HBSS+).

Transient Transfection

HEK293T cells were plated and transfected concurrently 24 h prior to each experiment using the Qiagen Effectene Transfection Reagent kit according to the manufacturer’s protocol.

Displacement binding assays

Membranes were prepared from mLight, dLight, kLight cells or CHO-MOR cells as described previously³⁷. Displacement binding assays were carried out with membranes (100 mg), [³H]diprenorphine (3 nM final concentration) and different ligands (0-10 mM final concentration) as described previously ^16,38, except that the assay buffer consisted of 50 mM Tris-Cl (pH 7.4) containing 100 mM NaCl, 10 mM MgCl₂, 0.2 mM EGTA and protease inhibitor cocktail (Sigma Aldrich; cat No.P2714), and incubation was carried out for 1 h at 30 °C.

Micro-confocal High-throughput Imaging Experiments

Glass bottom 96-well plates (P96-1.5H-N, Cellvis) were coated with 50 µg/mL of poly-D-lysine (Sigma, P6407-5MG) and 10 µg/mL of laminin (Sigma, L2020) overnight in an incubator (37°C, 5% CO₂). Plates were washed with Dulbecco’s PBS (ThermoFisher, 14190-250) and PSYLI2 cells were suspended in DMEM (Fisher, 11995073) containing 10% FBS (Fisher, 26-140-079) with 5% penicillin-streptomycin (Fisher, 15140-163) and plated at a density of 40,000 cells/well 24 h prior to each experiment. Immediately prior to an experiment, stock solutions of drugs in DMSO (10 mM) were diluted 1:100 in imaging media distributed across an empty 96-well plate (treatment plate) in triplicate following a randomized plate map. The imaging media consisted of 1 x HBSS (Fisher, 14175103) containing 0.5 M MgCl₂ (Sigma, M8266-1KG) and 0.5 M CaCl₂ (Sigma, C5670-50G). Cells grown in a separate 96-well plate (assay plate) were gently washed 3x with imaging media, and the wells were filled with an appropriate volume of imaging media for the respective experiment (vide infra).

HEK cell titration

For titration experiments, 50 µL of imaging media was added to each well of the assay plate. Wells were then imaged with ImageXpress Micro Confocal High-Content Imaging system at 40x (N.A. = 0.6) with 4 regions of interest (ROI) taken per well with no bias to location and no overlap of the ROIs (exposure = 300 ms) with MetaXpress software. Next, 50 µL from the treatment plate was transferred to the assay plate containing a doubled desired final concentrations. As for titration dose and controls, ligand of interest from 1pM to 100 µM (final) dissolved in HBSS+ as vehicle were used. Blank controls with vehicles were present on every plate with randomized locations. After 5 min of incubation, the same sites were re-imaged using the same settings.

Once imaging was complete, the images were exported and analyzed using a self-written MATLAB script. The script will be deposited onto Github. In short, segmentation was performed on individual images and a mask highlighting the membrane of the HEK293T cells was generated. Pixel intensities were obtained from the mask-highlighted area and exported into Excel. The ΔF/F values for each well were calculated using the following equation:

These values were then used to obtain the triplicate mean (n = 3).

SNR values are calculated by:

Schild Regression Analysis

A treatment plate was prepared by pre-mixing various concentrations of antagonists with increasing concentrations of the sensors’ specific agonist. The agonist and antagonist were premixed in doubled concentrations in a treatment plate in HBSS+. Wells were first imaged with 50uL HBSS+ in the well, and 50uL of the mix of ligands from the treatment plate was then added to the imaging plate, and the wells were imaged again under the same settings.

Slice Experiments

Stereotaxic Intracranial Injection

Male and female C57/B6J mice (postnatal day 0–3) were anesthetized with isoflurane and placed in a small animal stereotaxic frame (David Kopf Instruments). After puncturing the skin and skull under aseptic conditions, AAVs were injected (0.5–1 µl total volume) bilaterally through a pulled glass pipette at a rate of 100 nl/min using a UMP3 microsyringe pump (World Precision Instruments). Depending on the size of the mouse, injection coordinates ranged between 0 to +0.5 mm from bregma, 0.5 to 1.0 mm lateral, and 1.8 to 2.3 mm below pia for dorsal striatum. For targeting hippocampus to study buffering, injection coordinates ranged from +0.3-0.5 mm from lambda, 2.2-2.5 mm lateral, and 1.4 to 2.0 below pia. After surgical procedures, mice were returned to their home cage for >30 days to allow for maximal gene expression. For CA3 injection in hippocampus for electrical stimulation, we used the following coordinates [-1.7 mm AP, 1.75 mm ML, -2.3 mm DV]. To achieve sparse labeling of neurons in CA3, we injected AAV1-CAG-DIO-κLight1.3 into CA3 with a 1:1000 dilution of AAV1-hSyn-Cre virus. Male and female C57/B6J mice were injected 8-10 weeks postnatal.

Brain Slice Preparation

Postnatal day 30-60 mice were anesthetized with isoﬂurane and killed, and the brain was removed, blocked, and mounted in a VT1000S vibratome (Leica Instruments).For striatal imaging experiments,coronal slices (300 μm) were prepared in 34°C ACSF containing (in mM), 127 NaCl, 2.5 KCl, 25 NaHCO3, 1.25 NaH2PO4, 2 CaCl2, 1 MgCl2, and 25 glucose, osmolarity 307,equilibrated with 95% O2/5% CO2. For hippocampal electrophysiology recordings, horizontal slices (300 μm) were prepared in ice-cold choline-ACSF containing (in mM) 25 NaHCO3, 1.25 NaH2PO4, 2.5 KCl, 7 MgCl2, 25 glucose, 1 CaCl2, 110 choline chloride, 11.6 ascorbic acid, and 3.1 pyruvic acid, equilibrated with 95% O2/5% CO2. Slices were transferred to a holding chamber with oxygenated ACSF and incubated at 32 °C for 30 min and then left at room temperature until recordings were performed.

Fluorescence Imaging with Peptide Uncaging

All video recordings were performed within 5 h of slicing in a submerged slice chamber perfused with ACSF warmed to 32 °C and equilibrated with 95% O2/5% CO2. Sensor-expressing tissue in the dorsal striatum was located and imaged through an eGFP filter cube (Semrock GFP-3035D-OMF) under a 60x, 0.8 NA objective using a SciCam CCD camera (Scientifica, Uckfield, UK) and illumination with the 470 nm LED (CoolLED, Andover, UK). Ocular image acquisition software (Qimaging) was used to acquire videos using a 100 ms exposure times at a frame rate of 1 Hz. For uncaging trials, 5 µM of CYD8 was circulated in the bath prior to beginning video acquisition. During uncaging trials, ScanImage was used to trigger video acquisition and the UV laser. Uncaging was carried out using 50 ms ﬂashes of light from a 355 nm laser (DPSS Lasers, Santa Clara, CA). For full-field uncaging (Figure 3C-D and Supplementary Figure 3B-C), a 70 µm diameter area of tissue was illuminated with collimated UV light at a power density of 5 µW/µm², as measured in the sample plane. When measuring DynA8 diffusion, a 25 µm diameter area of focused 355 nm light at a power density of 39 µW/µm² was applied near the edge of the imaging field

Electrophysiology

All recordings were performed within 5 h of slicing in a submerged slice chamber perfused with ACSF warmed to 32 °C and equilibrated with 95% O2/5% CO2. Whole-cell voltage clamp recordings were obtained with an Axopatch 700B ampliﬁer (Molecular Devices, San Jose, CA). Data were sampled at 10 kHz, ﬁltered at 3 kHz, and acquired using National Instruments acquisition boards and a custom version of ScanImage written in MATLAB (Mathworks, Natick, MA). Cells were rejected if holding currents exceeded −200 pA or if the series resistance (<25 MΩ) changed during the experiment by more than 20%.For recordings measuring inhibitory synaptic transmission in mouse hippocampus, patch pipets (2.8−3.5 MΩ) were ﬁlled with an internal solution containing (in mM) 135 CsMeSO3, 10 HEPES, 1 EGTA, 3.3 QX-314 (Cl − salt), 4 Mg-ATP, 0.3 Na-GTP, and 8 Na 2 phosphocreatine (pH 7.3, 295 mOsm/kg). Cells were held at 0 mV to produce outward currents. Excitatory transmission was blocked by the addition to the ACSF of NBQX (10 μM) and CPP (10 μM).To electrically evoke IPSCs, stimulating electrodes pulled from theta glass with ∼5 μm tip diameters were placed at the border between stratum pyramidale and straum oriens nearby the recorded cell (∼50−150 μm) and a two brief pulses (0.5 ms, 50−300 μA, 50 ms interval) were delivered every 20 s. Uncaging was carried out using 5 ms ﬂashes of collimated full-ﬁeld illumination with a 355 nm laser at different power densities, which were measured at the sample plane.

Data Analysis

Video acquisition data were first analyzed in ImageJ and subsequently plotted in Igor Pro (Wavemetrics). The mean brightness of each frame was divided by the average baseline fluorescence of the first minute to calculate ΔF/F. Then, the first minute before uncaging was fit with a biexponential curve to estimate the rate of bleaching during the video acquisition. The fitted bleaching curve was then subtracted from the recorded traces to correct for bleaching. A 700 µm² circle ROI was drawn at the center of the uncaging field and the mean brightness of this ROI was plotted per frame. Electrophysiology data were analyzed in Igor Pro (Wavemetrics). Peak current amplitudes were calculated by averaging over a 2 ms window around the peak IPSC. To determine magnitude of modulation by DynA8 photorelease (% IPSC suppression), the IPSC peak amplitude measured immediately after a ﬂash was divided by the average peak amplitude of the three IPSCs preceding the light ﬂash. To determine the time constant of recovery (tau off), the IPSC amplitudes were fit to a monoexponential function starting at the point of maximal IPSC suppression to the point at which the IPSC amplitude returned to baseline.

Diffusion coefficient calculation

Based on a derivation of Fick’s law of diffusion that yields ⁶⁶, D* is the slope of the linear regression between , where gamma is the half-width of the spatial fluorescence profile, and time (t), as demonstrated by diffusion of dextrans molecules or quantum dots in the cortex ⁷³. To reduce noise, we averaged 50 pixels in the y-axis around the center line of the image plane (parallel to the uncaging spot). .

Brain Slices for Two-Photon Imaging

3 to 4 weeks after viral injection, samples from adult mice were anesthetized with 2.5% avertin and perfused in ice-cold carborgen (95% O2 and 5% CO2) gassed cutting NMDG-HEPES artificial cerebrospinal fluid (aCSF) solution that contained (in mM): 92 NMDG, 2.5 KCl, 1.25 NaH2PO4, 30 NaHCO3, 20 HEPES, 24 D-Glucose, 2mM Thiourea, 5 sodium ascorbate, 3 sodium pyruvate, pH adjusted to 7.3-7.4 and supplemented with 0.5 CaCl2 and 10 MgCl2, before decapitated. Brains were quickly extracted and were cut (300 μm) with a vibratome (V1200, Leica) in ice-cold oxygenated NMDG-HEPES aCSF. Brain slices were incubated at 34-36ºC for 10 min before transferring to HEPES holding aCSF that contained (in mM): 92 NaCl, 2.5 KCl, 1.25 NaH2PO4, 30 NaHCO3, 20 HEPES, 25 D-Glucose, 2 Thiourea, 5 sodium ascorbate and 3 sodium pyruvate, pH adjusted to 7.3-7.4 and supplemented with and 2 CaCl2 and 2 MgCl2, saturated with 95% O2 and 5% CO2⁷⁴. Imaging was carried out at room temperature using a 2-photon microscope. The sensor was excited at 920 nm with a Ti: sapphire laser (Ultra II, Coherent) that was focused by an Olympus 40×, 0.8NA water immersion objective. Emitted fluorescence was separated by a 525/50 nm filter set, and detected by a photomultiplier (H7422PA-40, Hamamatsu). Data were acquired and collected with ScanImage5 software. Electrical stimulation was performed with a bipolar stimulating electrode (Array of 2 SNEX-100 PI concentric electrodes epoxied side-by-side, MicroProbes). The area within approximately 20 μm of the electrode was imaged. Rectangular voltage pulses were applied through a 9-channel programmable pulse stimulator (Master-9, A.M.P. Instruments LTD) and a stimulus isolation unit (Analog Stimulus Isolator, A-M SYSTEMS). Imaging and electrical stimulation were controlled by an Axon Digidata 1550B. Field potentials were applied at 1, 5, 10, and 15 trains with inter stimulation interval of 0.5 s, where 1 train at 5V, 50Hz with a duration of 1s. Experiments were carried out at a scan rate of 30 (512×512 pixels) Hz. Image analysis was performed with ImageJ, data analyses were calculated using MATLAB and SigmaPlot 12.0. Drugs were dissolved as a stock solution in imaging HBSS buffer and diluted to final concentration prior to application in the perfusion system.

In vivo Sensor Recordings

Experimental Subjects and Stereotaxic Surgery

Adult (25-35 g), 12-16 week old KOR-Cre mice or C57/B6J mice were group-housed, given access to food pellets and water ad libitum, and maintained on a 12 hr:12 hr light:dark cycle (lights on at 7:00 AM). All animals were kept in a sound-attenuated, isolated holding facility in the lab 1 week prior to surgery, post-surgery, and throughout the duration of the behavioral assays to minimize stress.

For surgery, mice were anesthetized in an induction chamber (2-4% isoflurane) and placed into a stereotaxic frame (Kopf Instruments, Model 1900) where they were maintained at 1%-2% isoflurane. Male and female mice were anesthetized, following which we performed a craniotomy and unilaterally injected as described below, using a blunt neural syringe (65457-01, Hamilton Company). For photo-stimulation experiments and κLight Pavlovian conditioning experiments: 300-400 nL of AAV5-DIO-ChrimsonR-tdTomato (UW NAPE Center Viral Vector Core, viral titer 5 x 10¹² vg/mL) into the BLA (stereotaxic coordinates from bregma: -1.3 mm [AP], +- 3.2 mm [ML], -4.6 mm [DV]), and AAV-DIO-κLight1.3 (UC Davis Viral Core, viral titer 3.6 x 10¹³ vg/mL) followed immediately by fiber optic implantation into the NAcSh (stereotaxic coordinates from bregma: +1.3 mm [AP], +- 0.5 mm [ML], -4.5 mm [DV]). For fear conditioning experiments: 300-400 nL of AAV9-hSyn-κLight1.3 (Canadian Neurophotonics, viral titer 1 x 10¹³ vg/mL) and AAV9-hSyn-δLight (Canadian Neurophotonics, viral titer 3.3 x 10¹² vg/mL) separately into the dorsal NAcsh (dNAcsh, +1.3 mm [AP], +-0.5 mm [ML], -4.5 mm [DV]) and ventral NAcsh (vNAcsh, +1.3 mm [AP], +-0.5 mm [ML], -5 mm [DV]). For fear conditioning control experiments: 300-400 nL of AAV1-hSyn-κLight0 (Canadian Neurophotonics, viral titer 7.8 x 10¹² vg/mL) and AAV1-hSyn-δLight0 (Canadian Neurophotonics, viral titer 9.5 x 10¹² vg/mL) were injected into vNAcsh as controls. For δLight caramel reward retrieval experiments: 300- 500 nL virus (AAV9-hSyn-δLight, AAV-syn-δLight0) was injected bilaterally in the medio-basal hypothalamus (ARC, -1.25 mm [AP], +-0.25 mm [ML], -5.6 mm [DV] from surface of the brain) using a pulled glass pipette (Drummond Scientific, Wiretrol, Broomall, PA) controlled by a micromanipulator (Narishige, East Meadow, NY). Fiber cannula was then implanted at the injection site and secured the implants using two bone screws and a dental cement head cap (Lang Dental). ([AP] values are measured from bregma, and [ML] values are measured from the skull at bregma unless otherwise noted.)

Fiber Photometry

For fiber photometry studies, recordings were obtained throughout the entirety of drug injection, Pavlovian conditioning and head-fixed sessions as previously described ⁷⁵. Prior to recording, an optic fiber was attached to the implanted fiber using a ferrule sleeve (Doric, ZR_2.5). Two LEDs were used to excite κLight1.3. A 531-Hz sinusoidal LED light (Thorlabs, LED light: M470F3; LED driver: DC4104) was bandpass filtered (470 ± 20 nm, Doric, FMC4) to excite κLight1.3 and evoke emission. A 211-Hz sinusoidal LED light (Thorlabs, LED light: M405FP1; LED driver: DC4104) was bandpass filtered (405 ± 10 nm, Doric, FMC4) to evoke isosbestic control emission. Laser intensity for the 470 nm and 405 nm wavelength bands were measured at the tip of the optic fiber and adjusted to 50 uW before each day of recording. κLight1.3 fluorescence traveled through the same optic fiber before being bandpass filtered (525 ± 25 nm, Doric, FMC4), transduced by a femtowatt silicon photoreceiver (Newport, 2151) and recorded by a real-time processor (TDT, RZ5P). The envelopes of the 531-Hz and 211-Hz signals were extracted in real-time by the TDT program Synapse at a sampling rate of 1017.25 Hz. For the ChrimsonR stimulation experiments, a 625 nm laser was used at 2 mW intensity to deliver red light through the tip of the same optic fiber used to excite BLA terminals for stimulation-evoked dynorphin release.

Drug Injection

Mice were pre-treated with either vehicle (17:1:1:1 – Saline:DMSO:Corn Oil, EtOH) or Aticaprant (Eli Lilly) at 3 mg/kg of body weight i.p for 30 minutes. Mice were then tethered to a photometry cable and placed in a chamber. Following a 5-minute baseline recording, mice were injected with either saline or U50,488 (Sigma Aldrich) at 10 mg/kg body weight i.p. Recordings were conducted for a total of 1 hour.

Pavlovian Behavior Paradigm

Mice were initially food deprived to 90% of their body weight and trained in a Pavlovian behavioral paradigm for a total of 7 days with a modular test chamber (17.8 x 15.2 x 18.4 cm) (Med Associates Inc.), as previously described ⁷⁵. Mice were tethered to a photometry cable and habituated to an operant chamber in which there is a house light and pellet receptacle. The house light illuminates as the conditioned stimulus (CS) and sucrose pellets (20 mg, BioServe) are the unconditioned stimulus (US). Each trial consists of 5 seconds of CS presentation and a single sucrose pellet dropped 7 seconds after CS onset. Inter-trial interval was randomized between 60-120 seconds. Total session was 1 hour.

Stimulation-evoked release

Mice were head-restrained in a custom-made head-fixation device ⁷⁶ and tethered to a photometry cable. For initial parameter determination experiments, mice received 20 Hz, 5 ms pulse-width laser stimulation in a randomized order varying the stimulus intensity, duration or pulse number, separated by an inter-trial-interval of 5 minutes resulting in 5 trials per mouse per condition, every session. For drug pre-treatment experiments, mice were injected with aticaprant at 3 mg/kg or U50,488 at 10 mg/kg body weight i.p using the aforementioned vehicle 30 minutes prior to stimulation sessions. Mice received 10 trials/mouse with an inter-trial interval of 5 minutes.

Fear Conditioning Paradigm

Mice were placed into a fear conditioning chamber (Med Associates) with a patch cord connected for photometric recordings. A Doric fiber photometry system was used in this study with 465 nm and 405 nm light (LED, ~30 mW) used for generating the signal and as an isosbestic control, respectively. Each animal received 15 presentations of a 27 sec tone (3,000 Hz) co-terminating with a foot-shock (0.5 mA for 1.5 s) delivered at 2 min intervals. Each animal received 15 tone/foot-shock pairings over the course of 40 min, and the responses for these trials were averaged to create a single trace per animal. Data analysis was performed with custom-written script in MATLAB. In brief, 405 nm traces were fit with a bi-exponential curve, and then the fit was subtracted from the signal to correct for baseline drift. ∆F/F% was calculated as [100*(465 signal - fitted signal) / fitted signal)]. Traces were then z-scored. A heatmap was plotted using a custom MATLAB script by plotting normalized single trials of traces from all animals tested per brain region.

Photometry Analysis

Custom MATLAB scripts were developed for analyzing fiber photometry data in context of mouse behavior. The isosbestic 405 nm excitation control signal was subtracted from the 470 nm excitation signal to remove movement artifacts. Baseline drift was evident in the signal due to slow photobleaching artifacts, particularly during the first several minutes of each recording session. A double exponential curve was fit to the raw trace and subtracted to correct for baseline drift. After baseline correction, the photometry trace was z-scored relative to the mean and standard deviation of the session. The post-processed fiber photometry signal was analyzed in the context of animal behavior during Pavlovian conditioning and operant task performance. Pearson correlations, one sample t-tests, two sample t-tests and two-way ANOVAs were performed using standard MATLAB functions “corr”, “ttest”, “ttest2” and “anovan”, respectively. Peak, mean and minimum fluorescence was determined during pre-determined time windows for the injection period (0-5 min), reward period (5-20 sec), release period (0-20 sec) or artifact period (0-20 sec) subtracted from peak, mean or minimum fluorescence values in a baseline window (-5-0 sec). Code that supports the analysis will be made available from the corresponding author upon reasonable request.

Perfusion and Histology

Stock avertin was made by mixing 10 g of 2,2,2-tribromoethyl alcohol and 10 ml of tert-amyl alcohol. The working stock was diluted to 1.2% (v/v) with water and shielded from light. Animals were euthanized with 125 mg/kg 1.2% Avertin (i.p.) followed by transcardial perfusion with ice-cold 1x phosphate buffered saline (PBS) and subsequently perfused with ice-cold 4% paraformaldehyde (PFA) in 1x PBS. After extraction of the mouse brains, samples were post-fixed in 4% PFA at 4°C overnight. The mouse brains were cryo-protected by immersion in 10% sucrose in a 1x PBS solution overnight. Samples were next placed in 30% sucrose in a 1x PBS solution for >1 day, before embedding the samples in O.C.T. Samples were then transferred to a -80°C freezer for long-term storage or were sliced into 50 µm sections on a cryostat (Leica Biosystems) for histology. Histology samples were imaged on Leica Stellaris 8 confocal microscope.

QUANTIFICATION AND STATISTICAL ANALYSIS

Treatments were randomized, and the data were analyzed by experimenters blinded to the treatment conditions. Statistical analyses were performed using GraphPad Prism 9 unless noted otherwise. Measurements are taken from distinct samples, and the sample size is indicated as n numbers. All comparisons were planned prior to performing each experiment. A p<0.05 was considered significant. Data are represented as mean ± SEM, unless otherwise noted, with asterisks indicating *p < 0.05, **p < 0.01, ***p < 0.001, and ****p < 0.0001. Measurements are taken from distinct samples, and sample size is indicated as n numbers.

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There is NO Competing Interest.

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Unlocking opioid neuropeptide dynamics with genetically-encoded biosensors

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