We assayed opioid-induced presynaptic inhibition by adapting a widely used pHluorin-based unquenching assay (Sankaranarayanan et al., 2000) to monitor opioid effects on presynaptic activity in cultured neurons. In this assay, neurons were expressing opioid receptors together with VAMP2-SEP, imaged using a widefield microscope, and were electrically stimulated to induce synaptic vesicle exocytosis. The super-ecliptic pHluorin (SEP) is a pH sensitive GFP whose fluorescence increases as the synaptic vesicle protein VAMP2-SEP relocalizes from acidic synaptic vesicles to the terminal plasma membrane. This fluorescence increase provides a readout for presynaptic activity, which is typically lower when neurons are perfused with agonist for opioid receptors, reflecting opioid mediated presynaptic inhibition. The basic hardware configuration is summarized in Figure 1A. Details of a lab-built apparatus and an automated data analysis pipeline, including specific code modules, are included in Appendix 1. In the adult striatum, a large fraction of medium spiny neurons express MOR or DOR endogenously. However, in our primary neuron cultures, only a small proportion of neurons express opioid receptors endogenously, as assessed functionally and by immunocytochemistry (Jullié et al., 2020). Therefore, co-expression of recombinant receptors together with the synaptic vesicle exocytosis reporter is necessary to detect presynaptic inhibition using the aggregate readout. Figure 1B shows an example of a recording from the analysis and illustrates how the degree of presynaptic inhibition was defined. We believe this simple assay offers a number of advantages for mechanistic interrogation, relative to more complex models that offer advantages for relating presynaptic inhibition to physiology. First, optical measurement of presynaptic activity provides a direct and reliable readout of the degree of inhibition that is independent of compounded postsynaptic effects. Second, the cultured neuron system is highly amenable to genetic and pharmacological manipulations. Third, the hardware and analysis pipeline are simple and largely open-source, facilitating rapid and economical deployment.

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To examine the effect of prolonged opioid exposure in this system, we measured the presynaptic inhibition mediated by [D-Ala², N-MePhe⁴, Gly-ol]-enkephalin (DAMGO), a peptide full agonist of MOR. We compared inhibition of the electrically-evoked pHluorin response observed in opioid-naïve neurons (we define this as the acute condition) to that observed in neurons pre-exposed to DAMGO for 18 hr (we define this as the chronic condition). Significant inhibition was observed in both conditions (unpaired t-test compared to control, acute p<1 e^–5, chronic p<1 e^–5), but the degree of inhibition was reduced in the chronic condition (Figure 1C, unpaired t-test p<1 e^–5). These results indicate that prolonged agonist exposure promotes tolerance to presynaptic inhibition by opioids. We further assessed this by concentration-response analysis, verifying decreased efficacy of presynaptic inhibition and also revealing a decrease in potency (Figure 1D, EC50 acute 1.13 nM, EC50 after induction of tolerance 33.85 nM).

Presynaptic inhibition by opioids is well known to be resistant to rapid desensitization processes which attenuate signaling typically over several minutes (Blanchet and Lüscher, 2002; Fyfe et al., 2010; Jullié et al., 2020; Lowe and Bailey, 2015; Pennock et al., 2012; Rhim et al., 1993), suggesting that presynaptic tolerance represents a distinct regulatory process. In addition, after the induction of opioid tolerance in vivo, presynaptic inhibition remains resistant to rapid desensitization while desensitization of the postsynaptic response is enhanced (Arttamangkul et al., 2018; Fyfe et al., 2010). In our in vitro system, we did not detect any evidence for desensitization of the DAMGO response over a 10-min interval of sequential stimulation after the induction of tolerance (Figure 1E, left. Mean inhibition at 2 min 36.98 ± 2.80%, mean inhibition at 12 min 37.44 ± 3.34%, paired t-test, p=0.87). Rather, time course analysis revealed that tolerance develops gradually over multiple hours (Figure 1F). This extended time course is reminiscent of the process of receptor downregulation, described previously in other systems and associated with a depletion of the total receptor reserve (Chavkin and Goldstein, 1982; Christie, 2008; Law et al., 1984). Supporting the hypothesis that presynaptic tolerance involves a similar process, we found that reducing receptor reserve using the irreversible antagonist β-Chlornaltrexamine (β-CNA) accelerated the development of presynaptic tolerance (Figure 1F, unpaired t-test, p=0.075, 0.002, 0.047 for acute, 2 and 4 hr, respectively). This effect was quite sensitive, with significant acceleration evident even under alkylation conditions that have only a small impact on the maximal opioid response and which produce no detectable rapid desensitization of the response (Figure 1E, right, mean inhibition at 2 min 65.79 ± 3.41%, mean inhibition at 12 min 62.23 ± 3.39%, paired t-test, p=0.35). To directly test if long-term agonist exposure induces a depletion of surface receptors in axons, we imaged SEP-tagged MOR and quantified the fluorescence over thousands of synapses over multiple microscopic fields (Figure 1G). This analysis revealed that prolonged DAMGO exposure indeed reduces the presynaptic surface MOR pool (Figure 1G, MOR no DAMGO mean normalized fluorescence 1.60, 95% confidence interval 1.54–1.66, MOR +DAMGO 18 hours mean normalized fluorescence 1.17, 95% CI 1.08–1.27, two samples Kolmogorov-Smirnov test p<1e^–5). Together, these results indicate that presynaptic MOR tolerance is a process distinct from rapid desensitization and likely mediated by a net reduction of surface receptors on axons.

We have previously shown that MOR undergoes rapid agonist-induced endocytosis in terminals and accumulates in endosomes, located both in terminals and in the adjacent axon shaft, which are marked by retromer complex associated with their limiting membrane (Jullié et al., 2020). We verified this by labeling surface MOR in axons and monitoring DAMGO-induced redistribution of surface-labeled MOR to endosomes marked by GFP-tagged VPS29, a core retromer component (Figure 2A, Figure 2—video 1). Application of DAMGO produced a significant, time-dependent accumulation of surface-labeled MOR in retromer-marked endosomes over several minutes (Figure 2B, repeated measure ANOVA p=0.0048).

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Rapid endocytosis of MOR in axons is known to require phosphorylation of Ser and Thr residues in the MOR cytoplasmic tail (Jullié et al., 2020). Using the same assay, we verified that mutation of all Ser and Thr residues in the MOR tail (MOR S/T to A) abolished rapid internalization (Figure 2C, DAMGO compared to vehicle, repeated measure ANOVA p=0.57). MOR S/T to A strongly inhibited synaptic vesicle exocytosis acutely but mutation of all phosphorylation sites prevented the development of presynaptic tolerance after chronic treatment with DAMGO (Figure 2D, unpaired t-test p=0.29). Key residues that regulate phosphorylation-dependent endocytosis of MOR in other systems are localized into a cluster within the C-terminal tail called the STANT motif (Arttamangkul et al., 2019; Just et al., 2013; Lau et al., 2011). Consistent with this, mutation of the 3 phosphorylatable residues sites in this motif strongly inhibited endocytosis of presynaptic receptors (Figure 2E, DAMGO compared to vehicle, repeated measure ANOVA p=0.17). The STANT mutant potently inhibited presynaptic activity acutely and little tolerance was observed after 18 hr of treatment with DAMGO (Figure 2F, unpaired t-test p=0.033). It is known that the GPCR kinases 2 and 3 (GRK2/3) are key regulators of MOR phosphorylation and endocytosis (Jullié et al., 2020; Leff et al., 2020; Lowe and Bailey, 2015; Møller et al., 2020). Consistent with this, compound 101 (Cmpd101), a pharmacological inhibitor of GRK2/3 activity, blocked wild-type MOR accumulation in retromer marked endosomes (Cmpd101 compared to DMSO vehicle, repeated measure ANOVA p=0.0018). Further, Cmpd101 significantly blocked the development of tolerance, verifying that phosphorylation is required for the attenuation of presynaptic MOR signaling under conditions of chronic activation (Figure 2H, unpaired t-test p=0.0012). Cmpd101 also blocked the loss of surface receptors induced by chronic treatment of neurons with DAMGO (Figure 2I, two samples Kolmogorov-Smirnov test: DMSO only mean normalized fluorescence 1.33, 95% CI 1.28–1.38, compared to DMSO +DAMGO mean normalized fluorescence 0.77, 95% CI 0.74–0.79, p<1e^–5. DMSO +DAMGO compared to Cmpd101 +DAMGO mean normalized fluorescence 1.23, 95% CI 1.19–1.28 p<1e^–5. DMSO only compared to Cmpd101 +DAMGO p=0.051). Together, these results suggest that GRK2/3-dependent phosphorylation of the MOR tail, by driving the rapid endocytosis of receptors, initiates the process of presynaptic tolerance by reducing the density of receptors present on the axon surface under conditions of prolonged opioid exposure.

DAMGO efficiently promotes phosphorylation of the MOR tail, and this is a key determinant of β-arrestin recruitment driving subsequent receptor endocytosis. Non-peptide partial agonists such as morphine are less efficacious than DAMGO for promoting receptor phosphorylation as well as endocytosis (Just et al., 2013; Lau et al., 2011). Morphine has been shown to induce presynaptic tolerance in chronically treated animals (Fyfe et al., 2010) but, to our knowledge, its effect on surface MOR availability on axons has not been tested. We were unable to detect significant rapid internalization of MOR in axons, measured after 30 min of morphine exposure, using our endosomal recruitment assay (Figure 3A, repeated measure ANOVA p=0.36). However, significant functional tolerance was detected after prolonged (18 hr) morphine exposure (Figure 3B, unpaired t-test, acute compared to morphine +DMSO p=0.0018). Morphine induced presynaptic tolerance to a reduced degree relative to DAMGO, but it remained dependent on GRK2/3-mediated phosphorylation because it was blocked by Cmpd101 (Figure 3B, unpaired t-test, morphine +DMSO compared to morphine +Cmpd101 p=0.0016). Accordingly, and despite morphine not producing detectable rapid internalization in axons, we asked whether chronic exposure to morphine is also associated with a phosphorylation-dependent reduction of the overall density of MOR on the axon surface. To test this, we imaged SEP-MOR at synapses after chronic treatment with morphine +Cmpd101 or morphine +DMSO. We found that morphine +DMSO vehicle significantly reduced the amount of receptors at the surface of axons (morphine +DMSO mean normalized fluorescence 1.01, 95% CI 0.96–1.06, compared to DMSO only, two samples Kolmogorov-Smirnov test p<1e^–5), but this effect was not as pronounced as when neurons were treated with DAMGO +DMSO (Figure 3C, two samples Kolmogorov-Smirnov test p<1e^–5). Furthermore, the morphine-induced reduction of surface receptor number was inhibited by Cmpd101 (Figure 3C, morphine +Cmpd101 mean normalized fluorescence 1.23, 95% CI 1.18–1.27, compared to morphine +DMSO two samples Kolmogorov-Smirnov test p<1e^–5). These observations suggest that morphine, despite promoting MOR endocytosis only weakly compared to DAMGO, is indeed able to produce presynaptic tolerance after chronic exposure through a similar phosphorylation-dependent mechanism.

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G protein-biased agonists are thought to stimulate MOR internalization even less strongly than morphine. We therefore tested two such compounds, PZM21 and TRV130 (DeWire et al., 2013; Manglik et al., 2016). We could not detect any significant internalization induced by bath application of PZM21 (repeated measure ANOVA, P=0.68), nor tolerance to DAMGO mediated presynaptic inhibition after 18 hr of incubation with the biased agonist (Figure 3D and E, unpaired t-test, acute compared to PZM21 +DMSO p=0.37, PZM21 +DMSO compared to PZM21 +Cmpd101 p=0.30). Similarly, TRV130 failed to produce significant MOR internalization (repeated measure ANOVA p=0.39) or tolerance (Figure 3F and G, unpaired t-test, acute compared to TRV130 +DMSO p=0.37, TRV130 +DMSO compared to TRV130 +Cmpd101 p=0.091). Together, these results establish a positive correlation between the endocytic efficacy of chemically diverse MOR agonists and the observed degree of tolerance that they produce.

MOR is not the only receptor mediating presynaptic neuromodulation by opioids. DOR is another well-known example that mediates Gi-coupled inhibition of neurotransmitter release in response to opioids (Bardoni et al., 2014; He et al., 2021; Piskorowski and Chevaleyre, 2013). Physiological tolerance to DOR-mediated effects is well established (DiCello et al., 2019; Pradhan et al., 2010), and agonist-induced internalization of DOR has been clearly demonstrated in the soma and dendrites of neurons (Pradhan et al., 2009; Scherrer et al., 2006). Recent evidence indicates that DOR does not rapidly desensitize at the presynapse (He et al., 2021). However, DOR trafficking in axons has not been studied previously, and it is not known if longer-term tolerance develops to DOR-mediated presynaptic inhibition. We found that, similar to MOR, surface labeled DOR is diffusely distributed in axons of striatal neurons and does not detectably accumulate at synapses marked with syp-mCh under basal conditions (Figure 4A, baseline). After stimulation of DOR with the peptide agonist [D-Ala², D-Leu⁵]-Enkephalin (DADLE), there was a redistribution of surface receptors in punctate structures that colocalized with the retromer marker VPS29-GFP (Figure 4A and B, Figure 4—video 1. DADLE compared to vehicle repeated measure ANOVA, p=0.076). This indicates that, as for MOR, presynaptic DOR undergoes ligand dependent endocytosis and accumulates in a similar population of presynaptic endosomes. Using our optical assay to probe presynaptic inhibition, we found that DADLE-induced activation of DOR produces a potent inhibition of synaptic vesicle exocytosis (Figure 4C and D). We could not detect significant attenuation of this response after 10 min of agonist application, suggesting that presynaptic inhibition mediated by DOR is resistant to acute desensitization (Figure 1C, mean inhibition at 2 min 81.90 ± 2.76%, mean inhibition at 12 min 82.96 ± 4.55%, paired t-test, p=0.80). We probed presynaptic DOR tolerance by measuring inhibition after continuous agonist exposure for 18 hr. DOR-mediated inhibition of synaptic vesicle exocytosis was barely detectable after this chronic treatment, indicating robust tolerance (Figure 4D, unpaired t-test, acute compared to chronic p<1e^–5). Accordingly, while both DOR and MOR -mediated presynaptic inhibition are resistant to acute desensitization yet become tolerant after chronic agonist exposure, the degree of tolerance development is greater degree for DOR when assessed under comparable conditions (Figure 4D, unpaired t-test, MOR compared to DOR after chronic treatment p=1.01e^–5).

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DOR is co-expressed with MOR in some neurons, and it has been proposed that such co-expression can underlie functional cross-talk and cross-regulation between these distinct opioid receptor types (He et al., 2021; Wang et al., 2018). This motivated us to ask if presynaptic tolerance elicited by chronic agonist exposure is receptor-specific or if chronic activation of one receptor type promotes the development of tolerance at the other. Neurons co-expressing MOR and DOR were probed in our optical assay after chronic treatment with either the MOR-selective full agonist DAMGO or the DOR-selective full agonist [D-Pen^2,5]-Enkephalin (DPDPE). MOR tolerance was induced by DAMGO but not by DPDPE (Figure 4E, mean MOR inhibition after DAMGO chronic 45.85 ± 4.19%, mean MOR inhibition after DPDPE chronic 66.09 ± 4.80%, unpaired t-test p=0.0039). Conversely, DOR tolerance was induced by DPDPE but not by DAMGO (mean DOR inhibition after DAMGO chronic 59.10 ± 4.59%, mean DOR inhibition after DPDPE chronic 39.72 ± 5.58%, unpaired t-test p=0.012). Together, these results indicate that both DOR and MOR mediate presynaptic inhibition when co-expressed at the same terminals and that both responses develop significant tolerance after chronic agonist exposure. However, tolerance to each opioid response is induced in a homologous manner, indicating that its development is receptor-specific.

As our results establish a link between the development of presynaptic tolerance and a reduction in the surface pool of opioid receptors, we anticipated from the above results that loss of surface receptors occurs to a greater degree for DOR than MOR. To test this, we imaged surface DOR fluorescence using a N-terminally SEP-tagged construct. We found that 18 hr of incubation with DADLE led to reduction in the number of surface receptors, indicating that DOR tolerance is paralleled by a reduction of the receptor pool in axons (DOR no DADLE mean normalized fluorescence 1.43, 95% CI 1.38–1.48, compared to DOR +18 hr DADLE mean normalized fluorescence 0.32, 95% CI 0.31–0.34, two samples Kolmogorov-Smirnov test p<1e^–5). Also, the loss of receptor induced by chronic treatment was much more pronounced than for SEP-MOR, in agreement with our measurements of presynaptic inhibition after induction of tolerance (Figure 5A, MOR +DAMGO compared to DOR +DADLE, two samples Kolmogorov-Smirnov test p<1e^–5). Enhanced down-regulation of surface DOR relative to MOR has been observed previously in non-neural models, and it results from a reduced efficiency of DOR to enter the recycling pathway compared to MOR (Tanowitz and von Zastrow, 2003). To test if this is the case in axons, we co-expressed syp-mCh together with SEP-tagged receptors and exposed neurons to agonist for 20 min, a time sufficient to strongly drive receptors into the endocytic pathway. We then imaged axons at an acquisition rate sufficiently high (10 Hz) to resolve individual receptor-containing vesicular fusion events mediating receptor recycling to the axon surface; these appear as bursts of fluorescence due to rapid SEP dequenching upon exposure to the neutral extracellular milieu (Figure 5B). Such insertion events were detected for both SEP-DOR and SEP-MOR, with no significant difference in amplitude (Figure 5C, unpaired t-test, p=0.93). While recycling events were rare for both receptors in neurons not pretreated with agonist, their frequency was significantly higher after agonist treatment consistent with ligand-induced trafficking and recycling (unpaired t-test, MOR p=0.00023, DOR p=0.0015). More importantly, we found that after agonist treatment, the frequency of SEP-DOR surface insertion events was about half of what was observed for SEP-MOR (unpaired t-test, p=0.0231). Together, these data suggest that, while both MOR and DOR undergo robust ligand-dependent endocytosis in axons, DOR recycles less efficiently than MOR. We propose that, when iterated over the course of 18 hr of agonist treatment, this produces a difference in the degree of progressive receptor depletion from the axon surface which underlies the observed difference in magnitude of functional tolerance development.

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Whereas both MOR and DOR appear to rely on endocytosis-dependent loss of axonal receptors for the development of presynaptic tolerance, we found the biochemical requirements for this control to be remarkably different between the two opioid receptor types. Specifically, while MOR internalization, surface receptor loss, and tolerance clearly require phosphorylation of the receptor’s cytoplasmic tail, this was not the case for DOR. First, the degree of DADLE-induced DOR tolerance observed in the presence of Cmpd101 was indistinguishable from the DMSO vehicle control (Figure 6A, unpaired t-test, p=0.71). We also observed rapid internalization of DOR in the presence of Cmpd101 (Figure 6B, Cmpd101 compared to DMSO vehicle, repeated measure ANOVA, p=0.70). These results indicate that DOR tolerance and internalization do not require GRK2/3 activity, in contrast to MOR. Second, mutating all Ser and Thr residues in the DOR C-terminal tail (DOR S/T to A) did not prevent the development of presynaptic tolerance (Figure 6C, unpaired t-test, p=0.00036). We also observed significant rapid internalization of DOR S/T to A, as indicated by the mutant receptor undergoing DADLE-induced accumulation at retromer marked endosomes (Figure 6D, Figure 6—video 1, repeated measure ANOVA, P=0.0335). These results indicate that DOR tolerance and internalization do not require phosphorylation of the receptor’s cytoplasmic tail, in contrast to MOR. Third, assay of surface receptor fluorescence indicated that the S/T to A mutation does not prevent DADLE-induced reduction of the surface pool of SEP-DOR (Figure 6E, DOR S/T to A no DADLE mean normalized fluorescence 1.30, 95% CI 1.27–1.32, compared to DOR S/T to A+18 hr DADLE mean normalized fluorescence 0.45, 95% CI 0.44–0.46, two samples Kolmogorov-Smirnov test p<1e^–5). This indicates that agonist-induced reduction of the axonal surface DOR pool can occur in the complete absence of phosphorylation of the cytoplasmic tail.

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Altogether our data suggest that endocytosis is responsible for long-term surface receptor depletion for both MOR and DOR, but that significant endocytosis of DOR can occur in the absence of phosphorylation of the receptor’s cytoplasmic tail. To confirm this result, we used a different assay that leverages the properties of SEP. Ammonium chloride (NH₄Cl) can titrate acidic intracellular compartments and reveal SEP fluorescence from internal receptors. In neurons expressing SEP-DOR S/T to A, and in basal conditions, application of NH₄Cl induced a modest fluorescence increase (Δ1, mean = 7.39 ± 0.75%), suggesting that SEP-DOR S/T to A mostly resides at the surface of the axon. After 30 min of DADLE bath application, NH₄Cl application led to a significantly larger fluorescence increase (Δ2, mean = 16.45 ± 2.27%, paired t-test p=0.0017), indicating that a proportion of mutant receptors had relocalized from the surface to internal acidic organelles (Figure 6F and G). These results independently confirm that phosphorylation of the DOR tail is not essential for endocytosis in axons.

All procedures were performed according to the National Institutes of Health Guide for Care and Use of Laboratory Animals and approved by the University of California San Francisco Institutional Animal Care and Use Committee (protocol number AN185688). Briefly, after euthanasia of the pregnant Sprague-Dawley rat (CO₂ and bilateral thoracotomy), the brains of embryonic day 18 rats of both sexes were extracted from the skull. The striatum, including the caudate-putamen and nucleus accumbens, were identified (Banker and Goslin, 1999). The structures were dissected in ice cold Hank’s buffered saline solution Calcium/magnesium/phenol red free (Gibco). Striatum were dissociated in 0.05% trypsin/EDTA (Gibco) for 15 min at 37 °C before 2 washes in Dulbecco’s modified Eagle’s medium (DMEM, Gibco) supplemented with 10% fetal bovine serum (University of California, San Francisco, Cell Culture Facility) and 30 mM HEPES (Gibco). Neurons were mechanically separated with a flame-polished Pasteur pipette. Nucleofected striatal neurons were transfected using manufacturer’s instructions (Rat Neuron Nucleofector Kit, Lonza) for rat hippocampal neurons before plating. Cells were plated on poly-D-lysine coated 35 mm glass bottom dishes (Matek) in DMEM supplemented with 10% fetal bovine serum. Medium was exchanged 1–4 days after plating for phenol red free Neurobasal (Gibco) supplemented with Glutamax 1 x (Gibco) and B27 1 x (Thermo Fisher). Half of the culture medium was exchanged every week with fresh, equilibrated medium. Cytosine arabinosine 2 mM (Sigma-Aldrich) was added at 8 days in vitro (DIV). For transfection using lipofectamine 2000 (Thermo Fisher), transfection was performed on DIV 8, using 1 ml of lipofectamine and 1 μg DNA in 1 ml of medium per 35 mm imaging dish, and medium was exchanged 6 hr later. Neurons were maintained in a humidified incubator with 5% CO₂ at 37 °C and imaged after 13–21 days in vitro. All experiments were performed on at least 2 independent cultures.

To generate SSF-STANT in PCAGGS-SE, SSF-STANT sequence was amplified by PCR and inserted into pCAGGS-SE after digestion and ligation using EcoRI and XhoI sites. To generate SEP-DOR, SEP and DOR sequences were amplified by PCR and inserted into PCAGGS-SE using EcoRI and XhoI sites with a two fragments In-Fusion (Takara Bio) strategy. SSF-DOR S/T to A in PCAGGS-SE was generated with two fragments In-Fusion cloning in the NheI site. First fragment was a PCR amplification of the DOR sequence, and the second a codon optimized gene block that encodes the C-terminus tail of DOR with S and T amino acids sequences mutated to encode for A. SEP-DOR S/T to A was generated by PCR amplification of SEP and DOR S/T to A sequences and insertion into PCAGGS-SE using NheI sites with a two fragments In-Fusion strategy. All constructs were verified using sequencing, sequences for primers and gene block can be found in the extended methods section.

Imaging of VAMP2-SEP for measurement of presynaptic activity, as well as imaging of SEP-DOR S/T to A for endocytosis was performed with a S Fluor 40x1.30 NA objective on a Nikon TE-2000 inverted microscope. System was controlled by Micromanager 1.4.10 software equipped with an Andor iXon EM +EMCCD camera, and a Bioptechs objective warmer. Illumination, perfusion and electrical field stimulation devices were custom built (see extended methods and files on the repository https://doi.org/10.5281/zenodo.6954811). Briefly, an insert was 3D printed and equipped with two platinum wires distant of 1 cm used for electrical field stimulation (100 action potential at 10 Hz, 10 V/cm). Illumination in the green channel (VAMP2-SEP, SEP-DOR S/T to A) was controlled and synchronized with an Arduino uno, with a blue LED replacing the mercury bulb of a Nikon lamp and an appropriate combination of filters and dichroic mirror. Illumination in the red channel was achieved with a 543 nm HeNe laser (Spectra Physics) and a micrometer-guided illuminator (Nikon), and syp-mCh fluorescence imaged with an appropriate set of filters and dichroic mirror. Neurons in glass bottom culture dishes were transferred from the incubator onto the system, culture medium removed and exchanged for HEPES buffered saline solution (HBS) imaging solution. HBS contained, in mM: NaCL 120, KCl 2, CaCl2 2, MgCl2 2, Glucose 5, HEPES 10 and osmolarity was adjusted to 270mOsm and pH to 7.4. This insert left a dead volume of 300 μl inside the imaging dish and was used to perfuse solutions with a debit of 1.5 ml/min.

To measure presynaptic inhibition neurons were nucleofected with VAMP2-SEP and opioid receptors. 2 min acquisitions at 1 Hz were performed sequentially and electrical field stimulation starting at frame 59. First acquisition was always in HBS only to obtain a baseline response, ammonium chloride solution (HBS containing 50 mM NH₄Cl with NaCl adjusted to 80 mM) was added with a pipette for the last 20 frames of the last acquisition. To monitor stability of the recording in SSF-MOR expressing neurons (‘control’, Figure 1C), the second acquisition was started 1 min after the end of the first one and was in HBS only. To monitor acute inhibition at MOR, solution was shifted to HBS +DAMGO (Sigma-Aldrich) 10 μM after the end of the first acquisition and the second acquisition was started 1 min later. To monitor tolerance, neurons were subject to the same protocol except that they were pretreated with DAMGO 10 μM directly in the cell culture medium for 18 hr in the incubator before imaging. Because our perfusion system allows for only 3 different solutions, we generated the concentration-response curves using only two concentrations of DAMGO per acquisition. After the baseline acquisition in control solution, a first concentration of DAMGO was perfused for 1 min and through the second acquisition. Solution was shifted to a higher concentration of DAMGO for 1 min before starting the third acquisition, and ammonium chloride solution added for the last 20 frames. Using this protocol, acute inhibition or inhibition after 18 hr of incubation with DAMGO 10 μM was measured in the presence of, sequentially, 0.1–1 nM, 10–100 nM, or 30–300 nM DAMGO. When assessing desensitization after tolerance, the protocol was the same except that neurons were kept in DAMGO for 8 more minutes at the end of the second acquisition, and a third acquisition was performed still in the presence of DAMGO, with ammonium chloride solution added at the end of the last acquisition. For β-CNA experiments the protocol was the same as for the measure of desensitization after induction of tolerance except that neurons were treated with β-CNA 50 nM for 5 min and washed three times with HBS before imaging, not incubated with DAMGO. To obtain time course inhibition neurons were imaged as described when measuring tolerance except incubation time changed, and for the β-CNA conditions neurons were incubated with β-CNA 50 nM for 5 min and washed three times with media before incubation with DAMGO 10 μM for the time indicated. Acute inhibition and tolerance measurement for neurons nucleofected with VAMP2-SEP and SSF-MOR S/T to A and SSF-STANT was assessed as described for SSF-MOR. For measure of tolerance in the presence of Cmpd101 neurons were incubated with Cmpd101 30 μM (HelloBio) or DMSO control in the culture medium in the incubator for 10 min before adding DAMGO 10 μM for 18 hr and imaging as described previously. Tolerance to morphine 10 μM (Sigma-Aldrich), PZM21 10 μM and TRV130 10 μM (generous gifts from Aashish Manglik, UCSF) with either Cmpd101 30 μM or DMSO vehicle was assessed as described with DAMGO. Measure of acute inhibition, acute desensitization, tolerance, Cmpd101 effect for SSF-DOR and SSF-DOR S/T to A was assessed as described for SSF-MOR except the agonist used was DADLE 10 μM (Sigma-Aldrich). To monitor the lack of cross tolerance between SSF-DOR and SSF-MOR, both receptors were nucleofected together with VAMP2-SEP and DAMGO 10 μM or DPDPE 10 μM (Sigma-Aldrich) added to the culture medium for 18 hr in the incubator before imaging. Neurons incubated with DAMGO were imaged in HBS, solution was changed to HBS +DPDPE 10 μM and a second acquisition started one minute later, last acquisition was started 1 min after switching the solution to HBS +DAMGO 10 μM. Same was done for neurons incubated with DPDPE except the order of solution exchange was switched.

To monitor SEP-DOR S/T to A endocytosis, neurons were nucleofected with syp-mCh and SEP-DOR S/T to A and imaged in HBS for one frame in the syp-mCh channel and one frame in the SEP channel. Perfusion was switched to ammonium chloride solution and a second image taken 1 min later. Perfusion was switched to HBS +DADLE 10 μM and a third image taken 30 min later, the fourth frame was taken after 1 min of perfusion with ammonium chloride solution.

Imaging of surface labeled opioid receptors at retromer marked endosomes, insertion events as well as quantification of surface fluorescence of SEP-tagged opioid receptors in axons was performed on a Nikon Ti-E TIRF microscope controlled by NIS-Elements 4.1 software. Microscope was equipped with an Andor iXon DU897 EMCCD camera, a perfect focus system and an objective and stage heater set to 37 °C. Oblique illumination was achieved with 488, 561, and 647 nm solid-state lasers (Keysight Technologies) coming at an oblique incident angle from an Apo TIRF 100x1.49 NA objective, and all channels imaged with an appropriate set of dichroic mirror and emission filters.

To image the recruitment of surface labeled receptors at endosomes, neurons that were transfected with VPS29-GFP, syp-mCh and SSF-tagged opioid receptors using the lipofectamine method were incubated for 15 min with Alexa-647 conjugated anti-FLAG antibody (1/1000, M1 antibody from Sigma-Aldrich Cat# F-3040, RRID: AB_439712, Alexa Fluor 647 Protein Labeling Kit from Thermo Fisher) before neurons were washed three times with HBS and mounted on the microscope in HBS. One image was acquired in the syp-mCh channel, rest of the acquisition was one frame in the Alexa-647 channel and one frame in the GFP channel every minute for a total length of the time lapse of 35 min. Agonist was added by pipetting 100 μl of agonist-containing solution into the glass bottom dish after 5 min of baseline to a final concentration of 10 μM, as indicated in figure legends. When neurons were treated with Cmpd101 or DMSO, neurons were incubated with Cmpd101 30 μM or DMSO vehicle together with M1-Alexa647 1/1000 for 15 min. Cells were washed three times with HBS and mounted on the stage with HBS +Cmpd101 30 μM or HBS +DMSO vehicle, rest of the protocol was the same as described previously.

For imaging of insertion events, striatal neurons were transfected using the lipofectamine method with SEP-tagged opioid receptors and syp-mCh. Cells were incubated for 20 min in the culture medium in the incubator with DAMGO 10 μM (for SEP-MOR) or DADLE 10 μM (for SEP-DOR), before three washes with HBS and mounting on the microscope stage in HBS +agonist at 10 μM concentration. Cells in the no agonist conditions were washed in HBS and mounted in HBS without agonist. One frame was acquired in the red channel, and the green channel was imaged at 10 Hz in stream mode for 5 min.

For imaging of surface fluorescence from SEP-tagged opioid receptors, neurons nucleofected with SEP-tagged opioid receptor and syp-mCh were washed three times with HBS and mounted onto the microscope stage in HBS. 30–40 random regions of interest were selected per dish in the syp-mCh channel only (experimenter was blinded to the green channel), and one image acquired in the green and red channel for each region. When neurons were chronically treated, cells were incubated for 18 hr with agonist at a concentration of 10 μM in the culture medium in the incubator, the rest of the protocol was the same. If Cmpd101 or DMSO vehicle were present, Cmpd101 30 μM or DMSO vehicle were added to the culture medium for 10 min before agonist 10 μM was added for 18 hr, rest of the protocol was the same. Experiments were performed on the same culture in parallel for the conditions that are presented on the same graphs.

Image analysis was performed on unprocessed 16 bits TIFF images using custom written scripts in MATLAB (Mathworks, R2019b). Scripts are provided on an open repository at the following address: https://doi.org/10.5281/zenodo.6954811. See extended methods for details of the procedures.

For the quantification of presynaptic activity, putative synapses were detected using an automated image classifier on an average baseline (defined as 6 frames before stimulation) subtracted ammonium chloride average image (defined as 5 last frames of the last acquisition). Fluorescence was quantified in a 5 pixels radius circle around each synapse, baseline fluorescence for each acquisition subtracted and normalized to the fluorescence in ammonium chloride. Synapses were validated if no pixel was saturated and the amplitude of the response during the first acquisition (in HBS only) was five times greater than the standard deviation of the baseline. For each condition and each acquisition, fluorescence from validated synapses were averaged across conditions and displayed in the inset curves (± standard error of the mean). To calculate the degree of inhibition, only acquisitions that had more than 50 validated synapses were considered and their fluorescence averaged across each acquisition. The ratio of the amplitude (defined as the maximum value of the 5 frames after stimulation) over the amplitude of the first acquisition was used to define the degree of inhibition. Normalized concentration-response curves were generated by calculating the mean inhibition for each data point, subtracting the average inhibition observed in control solution (Figure 1C) and normalizing by the average acute inhibition in 10 μM DAMGO.

For the quantification of SEP tagged opioid receptors and syp-mCh fluorescence at synapses, synapses in focus were identified based on syp-mCh signal and manually picked, only exceptions were synapses that were over glial background autofluorescence or overlapping with somato-dendritic SEP signal. For each synapse, background subtracted average fluorescence within a 3 pixels radius was calculated for both channels. Values for each synapse and channel were normalized by the median fluorescence of the ‘no-agonist’ condition of the same experimental day and data pooled between experiments.

To quantify receptor recruitment at retromer marked endosomes, a mask of VPS29-GFP endosomes was generated for each frame of the time lapse. To do so, regions of interest were manually selected on the image and refined by thresholding a maximal temporal projection of the receptor channel. Endosomes within this region were defined by a VPS29-GFP fluorescence value above a threshold set manually and objects larger than 5 pixels. For each region, background subtracted receptor fluorescence was calculated for each segmented endosomes for all frames. Average receptor fluorescence was calculated at all segmented endosomes of the same region, for the ‘no agonist’, baseline bin value. Receptor fluorescence at each endosome was normalized by this baseline value and averaged across all regions for 5 min intervals as indicated in the figures. Binned values were averaged across acquisitions for the same condition.

To quantify the frequency and fluorescence of single insertion events, bursts of fluorescence were manually selected on the image series. Fluorescence was quantified in a 2.2 pixels radius circle and the average baseline fluorescence of the 10 preceding frames was subtracted. Amplitude of fluorescence events is defined as the maximal fluorescence in the 10 frames following the detection. Fluorescence curves were averaged for all events of the same conditions. Frequency of events were normalized for each acquisition by the number of syp-mCh marked synapses in the field of view.

To quantify SEP-DOR S/T to A endocytosis using ammonium chloride unquenching, the four images were manually aligned to the first image to compensate for drift over the acquisition and lines drawn on axons identified based on syp-mCh signal. Background subtracted average fluorescence from a 3-pixel wide linescan was calculated for all images and normalized to the fluorescence value of the first image for each acquisition.

Quantifications of data are presented as either mean ± standard error of the mean, cumulative frequency curves, paired measurements or box and whisker plots (4 quartiles with inclusive median, outliers are not displayed, average is marked as a ‘X’). Graphs were generated with Excel (Microsoft office, 2016). Look up tables used can be found in the extended method section. All experiments were performed on at least independent neuronal cultures and sample size indicated in the figure legends. When performing two tailed Student’s t-test the software used was Excel, two samples Kolmogorov-Smirnov test were performed with MATLAB. 95% confidence intervals were estimated by calculation of the mean over 50,000 random bootstraps using MATLAB. Fitting of the concentration-response curves and repeated measure ANOVA were performed using Prism 9 (GraphPad). For receptor enrichment at endosomes, we excluded cells with an enrichment value >3 fold from the statistical analysis because such high enrichment values result from very low baseline and thus introduce excessive variability in the ∆F/F calculation. 3 cells in total were excluded in this manner, for the conditions DOR +agonist (#3318) compared to vehicle (p-value 0.0763 if included), MOR +agonist (#2782) compared to vehicle (p-value 0.0071 if included), DOR +DMSO (#3120) compared to DOR +Cmpd101 (p-value 0.48 if included), and are highlighted in the accompanying figure dataset.

All the workflow presented here is setup for analysis in batch (software handles multiple acquisitions at the same time). User will need to install the different scripts provided and add them to the path in MATLAB. In addition, it requires the image processing toolbox, the machine learning toolbox and the parallel processing toolbox (optional but slower, see notes). Sample data are available for running the analysis.

It is likely that some of the scripts will not function on mac computers. One reason is indexing of folders that uses “ \ ” instead of “ / ”, user can edit the code manually and exchange the symbols to solve these issues. Another reason is that the “xlswrite” function that is not compatible with the OS, to solve this convert the cell array to table and use “writetable” function instead. An example of the syntax is provided below:

sheet = array2table(RESULT);writetable(sheet,['myfile.xlsx'],'Sheet',[’sheetName'],'WriteVariableNames',0);

Set the current folder as described previously (and as orgamized in the sample dataset), with the multiple conditions folders and acquisitions subfolders (2 movies +1 “ZAP” synapse +background coordinate file). Code looks for “ZAP” in the filename to identify the coordinate file, for “2_1.tif” to identify the second file with NH4 at the end, and pick the other “.tif” file as default for the first control movie. You will to rename the files or change the code accordingly (lines 92–94). Enter “zap100Poolstdev” in the command window (“zap100Pool3stdev” if 3 movies). Code requires “xlswrite” function that (to my knowledge) does not work on MAC OS version of MATLAB. User can convert the corresponding cells to tab format and write excel or csv file. I will expand here considering the 2 acquisitions and a note can be found for 3 acquisitions paradigm at the end. Code needs “numinputdlg” script to run a prompt for parameters, alternatively delete lines 50–59 and input parameters manually in lines 41–47. While a number of parameters are embedded in the code for the sake a more friendly user interface, I will provide line numbers for these.

Code will analyze each acquisition, normalize and pool the data for the whole condition, and normalize again. Details of the operations are found below:

For each acquisition, code will generate a.xlsx file containing multiple tabs

First tab contains the parameters, the defaults are:

• First stimulation: default 59, in our setting, marks the beginning of the stimulation

• Size of the synapse: default 5, radius in pixels around synapse coordinates in which the fluorescence measurement is done

• baseline: default 6, number of frames before stimulation to define fluorescence baseline

• Amplitude: default 5, number of frames to define the amplitude of fluorescence increase after stimulation

• Stimulation: default is 10, length of stimulation in frames.

• Frames in NH4Cl: default 5, to define fluorescence in NH4Cl.

• Threshold Amplitude: default 5, to select only responsive synapses.

How these numbers are used for quantification is explained below.

For each coordinates, the script will calculate raw fluorescence values (average fluorescence in a circle of radius “Size of synapse”) and subtract the average fluorescence in the user defined background region. This appears in two tabs with “_raw” added at the end of the movie file name.

The script then normalizes the fluorescence values for each synapse. To do so it defines the baseline fluorescence as the median fluorescence value between the frames “first stimulation – baseline” and “first stimulation”. Baseline fluorescence value is subtracted from raw values. Script will calculate the average raw fluorescence of the last 5 frames of the NH4 application, and this value is used to divide the baseline subtracted raw fluorescence values, for the two movies. This appears in two tabs with “_NH4norm” at the end, normalized fluorescence over fluorescence in NH4.

In these tabs there are two extra columns at the very end that are labelled “amplitude” and “STAMP”. These values are only used for classifying synapses as responsive. Amplitude is the average of _NH4norm fluorescence between the frames (First stim +Stim) and (First stim +Stim + Amplitude), with the 20% highest and 20% lowest values excluded (line 247) to minimize noise. “STAMP” corresponds to this amplitude value divided by the standard deviation of the baseline _NH4norm fluorescence between the frames “first stimulation – baseline” and “first stimulation”.

Based on these calculations, a number of synapses are excluded.

• Synapses that have values above the NH4 average value before NH4 is applied (20 frames, line 215).

• Synapses that contain 1 or more saturated pixel (value >16,300 based on 14 bits, line 215) during the last NH4 frames.

These two categories of synapses will appear in the tab “_removed” of the excel file with a value 1 for “saturation”.

-Synapses for which STAMP <Threshold for the first acquisition, this is to remove structures detected as synapses that do not respond to the stimulation (low signal to noise). This will appear in the tab “_removed” of the excel file with a value 1 for “amplitude”.

The two remaining tabs are labeled “_final” for each movie and contain the quantifications only for the synapse that passed these exclusion criteria.

For each condition, the script will pool data from this single-acquisition analysis and perform another normalization for the whole condition folder, the results are found in a “_std_Pool.xlsx” file.

All quantifications for synapses that have been validated are pooled into two tabs, one for each movie (1^st Stim, 2^Nd Stim), with the name of the corresponding acquisition file in the first column. Last column represents the amplitude of the response as defined previously. Average curves +/-SEM presented in the manuscript were obtained from this dataset.

For each acquisition/movie, an average of fluorescence and amplitude for validated synapses is calculated, this appears in the tabs “1^st cell, 2^nd cell”, as well as the number of validated synapses for each acquisition “n”. Reminder, the average amplitude here is not what was used for final quantification, we choose to quantify the amplitude of the average fluorescence - not the average of the amplitudes, to improve signal to noise. This is explained below.

For each acquisition, we therefore obtain two normalized over NH4 fluorescence curves for validated synapses (1^st movie baseline, 2^nd movie +drug, usually). We here define the amplitude of the average as the maximum fluorescence value between the frames (First stim +Stim) and (First stim +Stim + Amplitude), that is calculated for each movie. This appears into the “final” tab of the excel file under “Max Amp”, for each movie (1^st STIM, 2^Nd STIM). Average fluorescence curves for each movie are normalized to the Max Amp value of the first (control) acquisition and displayed into this “final” tab, as well as averages and SEM value for the whole condition. To define the degree of inhibition in the present manuscript, we only considered acquisitions with at least 50 validated synapses and used the ratio such that the percentage of inhibition is given by:

100*(1-(Max Amp(2^nd movie) / Max Amp (1^st movie, control))).

In the 3 movies/acquisition paradigm, we used “zap100Pool3stdev”, with folder containing 1 baseline movie “1_1.tif”, 1 drug movie “1_2.tif”, 1 drug movie +NH4 at the end “1_3.tif”, 1 synapse.txt coordinate file “ZAP”. Script will process as previously for 2 acquisitions except that it quantifies another movie but all synapse validation and normalizations steps remain the same.

The code runs by entering “Manon” in the command window of MATLAB.

If you run the code multiple times on the same movies, make sure that the previous files generated by the program are in a different directory or code will overwrite and edit.

Code asks for “size of the structure”, this is the minimal size in pixels of the structure you would be segmenting. Default is 5, and this is what was used in the study.

Code asks for synapse movie, this should be a multi-tif single channel. Code does no operation on this image; it is for display only. If only two channels have been aquired, provide a movie of the same size (repeat marker or receptor for example). It will appear in blue in the image display.

Code asks for marker movie, this should be the movie you try to segment, it will appear in green in the image display.

Code asks for receptor movie, this is the signal you try to quantify, it will appear in red in the image display.

This is the user interface for this analysis that will pop up (Appendix 1—figure 7).

Appendix 1—figure 7

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If not: make sure all your movies are the same size (frame number in particular), make sure you set the path correctly (for the code and for your working directory), make sure you have the proper toolbox installed (image processing toolbox)

Set contrast and look at your images to check for quality using the controls. Zoom button can be used to zoom on some area of the image, use right click/reset to original view to get back to original image.

Quantification starts by clicking on “select ROI”.

Program asks to select a rectangular background region, draw the rectangle with the cursor and double click on the rectangle to validate the background region.

Program returns control of a cursor, use it to draw a polygon around a ROI (left click), double click to close and validate the polygon. Another interface will pop up (Appendix 1—figure 8).

Appendix 1—figure 8

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Use the controls to define the segmentation

The receptor mask is made on the maximal projection of the receptor movie (red, image on the left). User can control a threshold above which you define the mask, and the number of regions to include in the mask starting from the biggest. This mask appears in the “mask” window as red over blue background (the polygon ROI). This mask is used to refine the segmentation in the marker channel.

The marker mask is generated using two controls, I recommend you use the threshG control only that sets the threshold for the segmentation. Other parameter for segmentation would be the size of the object (inputed at the beginning of the code, 5 in this study) and dilG that lets you soothe the structures (1 in this study). The segmented structures within the receptor mask (middle, green) appears on the “mask” window as green dots over red background.

When satisfied with the segmentation, click on “create Mask”. The window closes and the polygon you drew appears stably on the image. If you’re not satisfied with the ROI you selected, click on “Edit Poly”, it will erase the polygon like nothing ever happened.

When you have selected all the ROIs on the image, click on export data. Leave some time for MATLAB to do its job as writing on excel takes a bit of time, script will display the message “done”, only then you can explore your results in the excel file. The file is added to the current directory and is called “name of your synapse movie_Manon.xlsx”. This file contains the quantifications:

In the first tab, every column is a frame of the movie.

back Fluo is the average fluo in the background region.

Average Fluo Receptor is the background subtracted average fluorescence of the receptor in the max projection mask, per ROI (multiple lines, per ROI).

Average per region Fluo at marker is the background subtracted average fluorescence of receptor at endosomes within the same ROI. Values at individual segmented endosomes within that ROI are averaged. (multiple lines, per ROI)

Average per region norm Fluo at marker is the background subtracted average fluorescence of receptor at endosomes normalized by the background subtracted average fluorescence of the receptor within the same ROI. Normalized values at individual segmented endosomes within that ROI are averaged. (multiple lines, per ROI)

Total Av per region Fluo at marker is the background subtracted average fluorescence of receptor at endosomes within the same ROI. This averaging is done by averaging all pixels for endosomes (compared to averaging individual endosomes) within the ROI.

Total Av per region Norm Fluo at marker is the background subtracted average fluorescence of receptor at endosomes normalized by the background subtracted average fluorescence of the receptor within the same ROI. This averaging is done by averaging all pixels for endosomes (compared to averaging individual endosomes) within the ROI.

Per endosome Fluo at marker is the background subtracted average fluorescence of receptor at endosomes across all ROIs. SEM is provided.

Per endosome Norm at marker is the background subtracted average fluorescence of receptor at endosomes across all ROIs, normalized by the background subtracted average fluorescence of the receptor in the corresponding ROI. SEM is provided.

Fluorescence at individual endosomes is found in sheet 2. First column will be the region ID, first line is average with sem below (if missing endosomes in one region this line gets buggy, sorry I did not fix this…but basically it is the average of all endosome values across all regions and associated sem). This is the dataset that was used for further analysis in this manuscript with PoolManonBin.

Normalized Fluorescence at individual endosomes is found in sheet 3. is the background subtracted average fluorescence of receptor at every endosomes across all ROIs, normalized by the background subtracted average fluorescence of the receptor in the corresponding ROI.

Sheet 4 contains all parameters used for the quantification, including thresholds and polygon coordinates.

Program also generates a.tif 8 bit movie “name of your synapse movie_mask.tif” that contains the generated segmentation as follow: polygon value = 1;

Receptor mask value = 2;

Endosome mask value = 3;

In the current manuscript, data were binned by time intervals, normalized and pooled. To do so, Excel files generated by the “Manon” script are then sorted into separate folders per condition. Set the current directory in the directory that contains these folders and enter “PoolManonBin” in the command window.

Code goes directly into “sheet 2” of each excel file, for each region it will obtain the average value from all detected structures of the first 6 frames. All values for this region are normalized by this average, and this is repeated for all regions. Normalized values are then averaged per bins frame 0–5, 6–10, 11–15, 16–20, 21–25, 26–30, 31–35 (time lapse were 35 minutes in these experiments). This is repeated across Excel files present in the folder, and code will output a “NameofFolderManon_PoolBin.xlsx” file that contains in the first column the name of the excel file pooled and the binned values for each file, together with an average and sem.

The 4 images per acquisition were assembled into a multi.tif stack. In a folder that contains this stack together with the first image, run “ManuAllign” in the command window. Code asks for the reference image (first image in our case), then for the stack to align (the 4 images stack). Reference images appear in green on the user interface, red is the stack to align. You will find controls to set the contrast and the frame, as well X and Y adjust that allow you to move each frame of the stack to it is corrected for drift compared to the reference image. When satisfied with the drift correction click on “export” and script will generate a.tif file “originalstackname_alligned”.

Code will erase pixels (value set to 0) as you move the image to keep dimensions consistent. These areas were excluded from quantification by staying away from the edges.

To do the linescan analysis on the stack, we used a 4 images stack of the original syp-mCh image (duplicated 4 times) and the aligned stack. Set the current directory in a folder that contains these two files, enter “Linescan2” in the command window. Code asks for the stack to quantify first (in green) then for the synapse image (in red) that will appear color coded as indicated on the user interface.

This interface has been my “do everything” interface and contains many buttons, most of which will be buggy when transferred from one code to another, therefore I will not develop here what these buttons do and will focus on the ones that are relevant to this study.

You will find controls to set the contrast and frame as usual for this interface. Click on “axon” button to start manually drawing a linescan using the cross to select points with left click, to stop drawing use the right click for the last point. You might want to draw other lines, just repeat using the “axon” button. Do not change frame, and do not try to interact with the interface when drawing. When done selecting new lines, click on “Quantax” button, code will ask for the width of the linescan (3 in the current study), then select a rectangular background region and double click on it to validate. When done the code will display all kind of boxes around the lines that were visual controls for the linescan operation (no option to set the width of linescan with MATLAB, we had to rotate a rectangular matrix etc.). Script generates an Excel file “line_nameofthefile.xlsx” that contains the average fluorescence value of the linescan for each frame of the stack. Here is how it is calculated:

For each pixel along the line, an average is done along the width of the linescan. Then all linescans values are averaged by the total length of all the different linescans.

In a current directory that contains your marker multi-tif file (synapse) and your surface fluorescence multi-tif file of the same number of images (SEP channel), enter “CircleQuant2” in the command window. Code asks for the SEP stack first (in green) then for the synapse image (in red) that will appear color coded as indicated on the user interface. To select synapses click on “Pick …”, script will ask to select the background region, select synapses with left click, use right click when you are done selecting all synapses on the image. Move on to the next frame using the bottom left slider, repeat operation for each frame by clicking on “Pick …”. When the whole stack is reviewed, click on “Quant …”, code asks for the diameter of the circle in which to quantify the fluorescence (default is 3, what we used in the current study). An Excel file “nameofgreenchannel_CircleQuant2.xlsx” is generated. Every line of the file displays name of the green stack, frame, synapse number, X coordinate, Y coordinate, average fluorescence in the green channel, average fluorescence in the red channel.

To estimate confidence intervals we used the script “PermutToxls”. Variables for each measurement (list of fluorescence values) need to be saved beforehand as a.mat file. When running the code, enter the number of iterations N (50,000 in this study) and the name of the two variables you want to compare. The script generates N random bootstraps from each sample distribution and calculate the mean, and outputs the 2.5% (CI low) and 97.5% (CI high) (95% confidence interval values) in a excel file. The code can also generate random permutation statistics on a N permutations using code retrieved from Laurens R Krol (2022). Permutation Test (https://github.com/lrkrol/permutationTest; Krol, 2021), GitHub. Retrieved May 24, 2022 (not used in the present study).

This interface has been our “do everything” interface and contains many buttons, most of which will be buggy when transferred from one code to another. If you need to save the quantification before you are done reviewing the whole stack, you can save the data and pick up where you left by going directly to the frame where you stopped. In this case, make sure to change the name of the first saved.xlsx file or it will be edited and the quantification lost.

Movies are reviewed manually in a directory containing your synapse image and the SEP channel for the apparition of fluorescence bursts in the green channel using the “CircleQuant2” script as described above. Using the “Pick” button (lower one), events are selected for the frame where the burst of fluorescence appear. When done reviewing the stack, click on “save” and program will output a “nameofthegreenstack_annotate.txt” file that contains the events coordinate (event ID, frame, X coordinate, Y coordinate). To review the events and quantify the fluorescence, enter “FT1cTIF” in the command window. This script is adapted from Jullié et al., 2014. Code will ask to input parameters and displays a user interface the allows to browse selected events and displays quantifications. Click “next” to review all events, click “writeXLS” to export the quantification in a “nameofthegreenchannel_data.xls” file. The quantification is to be found in the “green sheet”, are displayed the quantification parameters, the integrated fluorescence intensity in a ”radius” defined circle around the selected event with the baseline (average fluorescence in the same area for the “baseline” frames before). Area (for average fluorescence) is found in the excel sheet. We used an unreasonably high “threshold parameter” (50) in this study to maintain the size/location of the fluorescence quantification. We obtained the average fluorescence by dividing the integrated intensity by the corresponding area value.

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

We thank Aashish Manglik for providing aliquots of PZM21 and TRV130. We thank Miriam Stoeber for valuable discussions. We thank members of the von Zastrow lab for valuable discussion, suggestions, and technical help throughout this project. Oblique illumination experiments were performed at the Center for Advanced Light Microscopy at UCSF and we thank its expert staff for providing advice and maintaining the equipment. We thank the Center for Advanced Technology at UCSF for providing access to the 3D printer.

All procedures were performed according to the National Institutes of Health Guide for Care and Use of Laboratory Animals and approved by the University of California San Francisco Institutional Animal Care and Use Committee (protocol number AN185688).

1. Preprint posted: June 16, 2022 (view preprint)
2. Received: June 22, 2022
3. Accepted: November 14, 2022
4. Accepted Manuscript published: November 15, 2022 (version 1)
5. Version of Record published: November 29, 2022 (version 2)

© 2022, Jullié et al.

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