Neural circuit-selective, multiplexed pharmacological targeting of prefrontal cortex-projecting locus coeruleus neurons drives antinociception

Abstract

Selective manipulation of neural circuits using optogenetics and chemogenetics holds great translational potential but requires genetic access to neurons. Here, we demonstrate a general framework for identifying genetic tool-independent, pharmacological strategies for neural circuit-selective modulation. We developed an economically accessible calcium imaging-based approach for large-scale pharmacological scans of endogenous receptor-mediated neural activity. As a testbed for this approach, we used the mouse locus coeruleus due to the combination of its widespread, modular efferent neural circuitry and its wide variety of endogenously expressed GPCRs. Using machine learning-based action potential deconvolution and retrograde tracing, we identified an agonist cocktail that selectively inhibits medial prefrontal cortex-projecting locus coeruleus neurons. In vivo, this cocktail produces synergistic antinociception, consistent with selective pharmacological blunting of this neural circuit. This framework has broad utility for selective targeting of other neural circuits under different physiological and pathological states, facilitating non-genetic translational applications arising from cell type-selective discoveries.

Figure 1.. Deconvolution of individual spikes in LC-NE neurons using calcium imaging.

(A) A schematic cartoon illustrating the viral strategy for selective expression of GCaMP8f in LC. (B) Left: A DIC image under 40x objective lens showing the spatial information of extracted ROIs; Middle & Right: Live DIC and fluorescent images, respectively, showing the simultaneous electrophysiological and imaging recordings. (C) A scatter plot that displays the distribution of firing rate in LC-NE neurons under normal slice preparation by cell-attached recordings. (D) An example of aligned simultaneous cell-attached and calcium imaging recording, note the complete coincidence between action potentials and peaks of calcium waveform. (E) A schematic cartoon depicting the machine learning-based network training and spike deconvolution using trained model. (F) An example of model evaluation using the simultaneous recording on a LC-NE neuron with around 2 Hz spontaneous firing, note the perfect inference of action potential firing. (G) A plot showing the predictive accuracy for spike deconvolution, excellent accuracy remains with lower firing rate and drops along the firing rate of recorded cells. Proportions of correct and failed predictions are denoted by circle in different colors (correct: black; miss: blue; incorrect: red).

Figure 2.. Ex vivo multiplexed pharmacological scan of GPCRs in LC.

(A) A schematic cartoon displaying differential expression of multiple GPCR in LC-NE neurons. (B) Top, a schematic diagram illustrating the protocol of pharmacological scan. Bottom, representative calcium traces from an ROI showing the pharmacological effects upon application of DAMGO and Substance P, agonists targeting MOR and Neurokinin-1 receptor (NK1R), respectively. Note the stable calcium fluctuations among the baseline traces representing a complete wash between pharmacological effects. (C) Firing rate plots showing the pharmacological effects of DAMGO using cell-attached (left) and imaging (right) recordings. Cell-attached: Paired-t test, t = 11.99, ****p<0.0001. Imaging: Wilcoxon matched-pairs signed rank test, W = −5747, ****p<0.0001. (D) Plots showing the percentile of changes in firing rate from data shown in C (left: cell-attached recording, right: imaging recording). (E-G) Summarized results of changes in firing rate led by pharmacological activations of GPCRs in LC-NE across three groups of GPCR agonists. Repeated measures two-way ANOVA followed by Bonferroni test, ****p<0.0001, ns = not significant, please see Table 2 for detailed statistics. (H) A plot demonstrating the examination of presynaptic modulation from GPCRs targeted by Group I agonists, only phenylephrine, an agonist targeting alpha1-adrenergic receptor, shows a presynaptic modulation as the significantly different pharmacological effect from the continuous pre-administration of synaptic blockers (5mM kynurenic acid + 1μM strychnine + 100μM picrotoxin) in bath. Repeated measures two-way ANOVA, ****p<0.0001, ns = not significant, please see Supplementary Datasheet 1 for detailed statistics. All of data are represented as mean ± SD Abbreviations: Phe: phenylephrine, McN: McN-A-343, Mus: muscarine, Pir: pirenzepine, Calc: calcitonin, U50: U50488, SNC: SNC-162, N/OFQ: Nociceptin, SubP: SubstanceP, Aden: adenosine, Bac: baclofen, OXA: orexin A, 8OH: 8OH-DPAT, Ana: anandamide, SST: somatostatin, SB: synaptic blockers.

Figure 3.. Pharmacological scan of GPCRs targeting the LC-mPFC module.

(A) A schematic cartoon describing the viral and modular tracing strategies from mPFC to LC. (B) A schematic cartoon demonstrating the recording setup. (C) A representative fluorescent image showing the distribution of mPFC-projecting LC-NE neurons indicated by the colocalization of TH (cyan) and Ctb (magenta) immunoreactive signals. (D-F) Representative examples from 7 extracted ROIs showing their spatial information in D, and modular identity indicated by post-hoc morphological reconstruction in E, F shows pharmacological responses of orexin A that activates orexin receptors. Note the consistent numerical naming of ROIs across figures. GCaMP8f and Ctb immunoreactive signals are shown by cyan and magenta color in E, and pink backgrounds in F denote the mPFC-projecting modular identity. (G-I) Plots showing the firing rate results of DAMGO, 8OH-DPAT and McN-A-343 across mPFC- and non-mPFC-projecting LC-NE neurons. DAMGO: Student’s t-test, t = 3.260, **p<0.01. 8OH-DPAT: Mann-Whitney test, U = 106, *p<0.05. McN-A-343: Mann-Whitney test, U = 209, *p<0.05. (J) A color plot demonstrating the visualized results of modular pharmacological scan, pharmacological effects are displayed by color and the targeted GPCRs by each agonist are listed. The red rectangles indicate significant difference between pharmacological effects in different LC modules. Please see Table 3 and Table 4 for statistical details. All of data are represented as mean ± SD. Abbreviations: 5HTR2a/c: serotonin receptor 2a/c, α1-AR: alpha1-adrenergic receptor, mAChR1/3: muscarinic receptor 1/3, CalcitoninR: calcitonin receptor, KOR: kappa opioid receptor, DOR: delta opioid receptor, MOR: mu opioid receptor, NOPR: nociceptin receptor, CRFR: corticotropin-releasing factor receptor, NK1R: neurokinin 1 receptor, AdenosineRs: adenosine receptors, GABABR: GABAB receptor, Orexin1/2R: orexin receptors 1/2, 5HTR1a: serotonin receptor 1a, CB1/2R: cannabinoid receptor 1/2, SomatostatinR: somatostatin receptor.

Figure 4.. Multiple pharmacological approaches drive antinociception by shifting activity away from the LC-mPFC module.

(A) A schematic cartoon illustrating the experimental design of Hargreaves thermal plantar assay in concert with LC local pharmacological infusions. (B) A fluorescent image showing a representative cannula implantation toward bilateral LC, which is denoted by TH immunoreactive signals (Red). The dashed rectangles indicate the trajectory of cannula. (C-G) Plots demonstrating the analgesia led by pharmacological infusions of DAMGO, 8OH-DPAT, McN-A-343, Cocktail A and Cocktail B toward LC with different dosages. The data are calculated from ratios between paw withdrawal latencies under baseline and pharmacological conditions. DAMGO: Repeated measures one-way ANOVA followed by Dunnett’s test, F = 20.77, **p<0.01, ****p<0.0001. 8OH-DPAT: Repeated measures one-way ANOVA followed by Dunnett’s test, F = 11.90, **p<0.01. McN-A-343: Friedman test followed by Dunn’s test, Friedman statistic = 22.82, *p<0.05. Cocktail A: Repeated measures one-way ANOVA followed by Dunnett’s test, F = 22.07, **p<0.01, ***p<0.001. Cocktail B: Repeated measures one-way ANOVA followed by Dunnett’s test, F = 33.86, ***p<0.001, ****p<0.0001. (H) A plot showing pharmacological dose-response curves from data shown in C. The rectangle denotes the pharmacological response with 10μM in dosage. (I) An isobobogram showing the synergistic interaction within Cocktail A, the experimental and theoretical EC₅₀ of Cocktail A as well as the EC₅₀ of DAMGO and 8OH-DPAT are indicated by black dots, the experimental EC₅₀s of individual mice are indicated by red cross and the black line denotes the additive isobole. The upper right plot shows a statistic evaluation of synergism. One-sample Wilcoxon Signed Rank Test, *p<0.05. (J) A plot showing the comparison of antinociceptive effects upon infusions of different pharmacological approaches in fixed dosage shown in D (10μM). Note the greater analgesia led by Cocktail B. One-way ANOVA followed by Holm-Šídák’s test, F = 13.52, *p<0.05, **p<0.01, ***p<0.001, ****p<0.0001. (K) A plot demonstrating the percentile of changes in firing rate by administration of Cocktail B using ex vivo pharmacological scan. Student’s t-test, t = 2.150, *p<0.05. All data are represented as mean ± SD except SEMs used in the isologram in I.

Figure 5.. Modular, conditional LC-mPFC MOR knockout disrupts Cocktail B-mediated antinociception

(A) A schematic cartoon illustrating the viral strategies for the modular deletion of MOR with fluorescent labeling in LC. (B) A fluorescent image showing the injection site of CAV-mCherry into mPFC. (C) A representative fluorescent image demonstrating the distribution of mPFC innervating LC-NE neurons by the colocalization of TH (cyan) and mCherry (magenta) immunoreactive signals. (D) An example of live DIC and fluorescent images during a cell-attached recording (left & middle), the right panel shows a DIC image under 5x objective lens. (E) A representative cell-attached recording demonstrating a clear genetic deletion of modular MOR in LC. The recorded cell without functional MORs failed to respond to the application of DAMGO. (F) A schematic cartoon illustrating the viral strategies for the modular deletion of MOR in LC. (G) A timeline showing the experimental design for Hargreaves task in concert with pharmacological infusions. (H) A plot showing the impact of modular knockout of MOR in LC on pharmacological analgesia. Note that this genetic deletion disrupts the pharmacological effects led by DAMGO and Cocktail B but no other applications. Repeated measures two-way ANOVA followed by Bonferroni’s test, **p<0.01, ***p<0.001, ****p<0.0001. Only selective statistical comparisons are shown in H, please see Table 5 for detailed statistics. All of data are represented as mean ± SD.