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Rabies screen reveals GPe control of cocaine-triggered plasticity

Nature volume 549, pages 345–350 (2017)Cite this article

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Abstract

Identification of neural circuit changes that contribute to behavioural plasticity has routinely been conducted on candidate circuits that were preselected on the basis of previous results. Here we present an unbiased method for identifying experience-triggered circuit-level changes in neuronal ensembles in mice. Using rabies virus monosynaptic tracing, we mapped cocaine-induced global changes in inputs onto neurons in the ventral tegmental area. Cocaine increased rabies-labelled inputs from the globus pallidus externus (GPe), a basal ganglia nucleus not previously known to participate in behavioural plasticity triggered by drugs of abuse. We demonstrated that cocaine increased GPe neuron activity, which accounted for the increase in GPe labelling. Inhibition of GPe activity revealed that it contributes to two forms of cocaine-triggered behavioural plasticity, at least in part by disinhibiting dopamine neurons in the ventral tegmental area. These results suggest that rabies-based unbiased screening of changes in input populations can identify previously unappreciated circuit elements that critically support behavioural adaptations.

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Figure 1: Cocaine-induced changes to VTA neuron inputs.
Figure 2: Cocaine triggers increase in GPe-PV neuron activity and excitability.
Figure 3: Bidirectional modulation of rabies labelling by activity manipulations.
Figure 4: GPe-PV neuron activity is required for cocaine-induced LMS and CPP.
Figure 5: GPe-PV neurons disinhibit VTA-DA neurons.

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Acknowledgements

This study was supported by grants from the Howard Hughes Medical Institute (Hughes Collaborative Innovation Award), National Institutes of Health (R01-NS50835, PO1 DA008227, TR01-MH099647, F32-DA038913, K99-DC013059 and K99-DA041445), and the Stanford Neurosciences Institute.

Author information

Author notes

  1. Timothy J. Mosca

    Present address: Department of Neuroscience, Thomas Jefferson University, Philadelphia, Pennsylvania, 19107, USA

Authors and Affiliations

  1. Department of Psychiatry and Behavioral Sciences, Nancy Pritzker Laboratory, Stanford University School of Medicine, Stanford, 94305, California, USA

    Kevin T. Beier, Paul Hoerbelt, Lin Wai Hung, Boris D. Heifets, Sophie Neuner & Robert C. Malenka

  2. Department of Biology, Stanford University, Stanford, 94305, California, USA

    Kevin T. Beier, Katherine E. DeLoach, Timothy J. Mosca & Liqun Luo

  3. Neurosciences Program, Stanford University, Stanford, 94305, California, USA

    Christina K. Kim

  4. Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, 94305, California, USA

    Boris D. Heifets

  5. Department of Bioengineering, Stanford University, Stanford, 94305, California, USA

    Karl Deisseroth

  6. Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, 94305, California, USA

    Karl Deisseroth

  7. Howard Hughes Medical Institute, Stanford University, Stanford, 94305, California, USA

    Katherine E. DeLoach, Karl Deisseroth & Liqun Luo

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Contributions

K.T.B. performed the majority of experiments and data analysis; C.K.K. assisted with fibre photometry experiments and data analysis with support from K.D; P.H. assisted with electrophysiological recordings and data analysis; L.W.H. performed surgeries for ChR2 stimulation and Fos counting; B.D.H. assisted with terminal inhibition experiments; T.J.M. assisted with assay design for puncta quantification; K.E.D. and S.N. provided technical support; K.T.B., L.L. and R.C.M. designed the experiments, interpreted the results and wrote the paper, which was edited by all authors.

Corresponding authors

Correspondence to Liqun Luo or Robert C. Malenka.

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Competing interests

R.C.M. and K.D. are on the scientific advisory board of Circuit Therapeutics, Inc., a biotech dedicated to development of novel therapeutics for brain disorders. All other authors declare no competing financial interests.

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Reviewer Information Nature thanks P. Kenny, M. Wolf and the other anonymous reviewer(s) for their contribution to the peer review of this work.

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Extended data figures and tables

Extended Data Figure 1 Changes to VTA-DA inputs induced by drugs of abuse.

a, Quantification of monosynaptic inputs to VTA-DA neurons labelled in animals receiving single dose administration of cocaine, amphetamine, nicotine, morphine, saline, or fluoxetine one day before injection of RVdG into the VTA. Data were combined to generate Fig. 1c. b, Sample images of GPe neurons labelled by RVdG and co-stained for parvalbumin. Pie graph shows proportion of labelled cells that co-stained for parvalbumin. c, Sample images of NAcMedS neurons labelled by RVdG in DAT-Cre;D1-tdTomato mice. Pie graph shows proportion of labelled cells that were D1+ as defined by presence of tdTomato (scale bars, 50 μm).

Extended Data Figure 2 Axonal projections of GPe-PV neurons.

a, AAV-FLExloxP-mGFP was injected into the GPe of Pvalb-Cre animals, and mGFP+ axons were quantified throughout the brain. b, Quantification of fraction of mGFP+ axons in the indicated brain regions. c, Sample image of mGFP+ axons in the ventral midbrain (scale bar, 500 μm). d, Quantification of fraction of mGFP+ axons in the indicated ventral midbrain brain regions. The schematics of the mouse brain in this figure were adapted from ref. 33. DLStr, dorsolateral striatum; DMStr, dorsomedial striatum.

Extended Data Figure 3 Inhibition of GPe-PV neuron activity modestly affects cocaine-induced locomotion.

a, Cre-dependent AAVs expressing YFP, hM4Di, Kir2.1 or TeTxLc were injected into the GPe of Pvalb-Cre animals. b, Quantification of effects of CNO on basal locomotion in animals expressing YFP or hM4Di (compared to YFP + saline: YFP + CNO, P = 0.36; hM4Di + CNO, P = 0.59). c, Quantification of basal locomotion during GPe-PV neuron inhibition (hM4Di, P = 0.54; Kir2.1, P = 0.66; TeTxLc, P = 0.27). d, Quantification of cocaine-induced locomotion during GPe-PV neuron inhibition (hM4Di, P = 0.37; Kir2.1, P = 0.12; TeTxLc, P = 0.002). The schematics of the mouse brain in this figure were adapted from ref. 33.

Extended Data Figure 4 Labelled GPe inputs to the VTA correlated with LMS.

a, AAV-FLExloxP-TC and AAV-FLExloxP-G were injected into the VTA of DAT-Cre mice. Eleven days later, animals were habituated for two days to an open field chamber, and given a drug injection the following day. RVdG was injected one day after the drug. Five days after RVdG injection, the animal was given a second injection of the same drug in the open field. b, Normalized labelled GPe inputs plotted against the relative locomotion in session 2 vs. session 1 for cocaine (n = 34), amphetamine (n = 5), nicotine (n = 5) and morphine (n = 5). Regression line is plotted for all drugs combined. c–e, Labelled GPe inputs after a single dose of cocaine significantly correlated with LMS (c) but not total locomotion after the first (d) or second (e) dose of cocaine. f–h, Plots of labelled GPe inputs vs. LMS for amphetamine (f; 1 mg kg−1), nicotine (g; 0.5 mg kg−1), or morphine (h; 10 mg kg−1). The schematics of the mouse brain in this figure were adapted from ref. 33.

Extended Data Figure 5 Inhibition of the GPe prevents morphine LMS and CPP.

a, A Cre-dependent AAV expressing either YFP or hM4Di was injected into the GPe of Pvalb-Cre mice. b, c, Quantification of LMS (b; P = 0.022) and CPP (c; P = 0.0005) in animals in which YFP or hM4Di (activated by CNO) were expressed in GPe-PV neurons. The schematics of the mouse brain in this figure were adapted from ref. 33.

Extended Data Figure 6 Inhibition of GPe-PV neurons that project to the midbrain blocks cocaine-induced CPP and LMS.

a, CAV-FLExloxP-Flp was injected into the ventral midbrain, and AAV-FLExFRT-Kir2.1 or AAV-FLExFRT-YFP was injected into the GPe of Pvalb-Cre mice. b, c, Quantification of LMS (b; P = 0.019) and CPP (c; P = 0.0067) in animals in which YFP or Kir2.1 were expressed in GPe-PV neurons projecting to the ventral midbrain. The schematics of the mouse brain in this figure were adapted from ref. 33.

Extended Data Figure 7 GPe-PV neurons that project to the midbrain collateralize to multiple subcortical targets.

a, CAV-FLExloxP-Flp was injected into the ventral midbrain, and AAV-FLExFRT-mGFP was injected into in the GPe of Pvalb-Cre mice. b, Representative image of mGFP+ collaterals in the thalamus and subthalamic nucleus (STN) (scale bar, 500 μm). c, Quantification of projection fraction of collaterals to indicated target regions. The schematics of the mouse brain in this figure were adapted from ref. 33.

Extended Data Figure 8 Dopamine neuron activity is required for the development of LMS and CPP.

a, Breeding scheme for experiments. LSL, loxP stop loxP. b, c, Quantification of LMS (b; P = 0.001) and CPP (c; P = 0.005) in control animals or animals expressing hM4Di in dopamine neurons receiving CNO.

Extended Data Figure 9 Map of anatomical location of ventral midbrain cells from which whole cell recordings were made.

Individual dots indicate location of cells in which ChR2-evoked IPSCs due to ChR2 expression in GPe-PV neurons could be detected (connected) or not (not connected) in NAcLat-projecting (a) or NAcMed-projecting (b) VTA-DA cells, and SNr-GABA (c) or VTA-GABA (d) cells. The schematics of the mouse brain in this figure were adapted from ref. 33.

Extended Data Figure 10 GPe-PV neurons mediate their effects through SNr-GABA neurons.

a, Procedure to test LMS and CPP during SNr-GABA activation. b, c, Activating SNr-GABA neurons with CNO prevented LMS (b; P = 0.010) and CPP (c; P = 0.015). d, Injection strategy to test whether SNr-GABA neurons are downstream of GPe-PV neurons. e, f, While expression of Kir2.1 in GPe-PV neurons prevented LMS and CPP, this suppression was overcome by concurrent inhibition of SNr-GABA neurons (e; P = 0.035, f; P = 0.036). The schematics of the mouse brain in this figure were adapted from ref. 33.

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Beier, K., Kim, C., Hoerbelt, P. et al. Rabies screen reveals GPe control of cocaine-triggered plasticity. Nature 549, 345–350 (2017). https://doi.org/10.1038/nature23888

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