Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Pain modulates dopamine neurons via a spinal–parabrachial–mesencephalic circuit

Nature Neuroscience volume 24, pages 1402–1413 (2021)Cite this article

Subjects

Abstract

Pain decreases the activity of many ventral tegmental area (VTA) dopamine (DA) neurons, yet the underlying neural circuitry connecting nociception and the DA system is not understood. Here we show that a subpopulation of lateral parabrachial (LPB) neurons is critical for relaying nociceptive signals from the spinal cord to the substantia nigra pars reticulata (SNR). SNR-projecting LPB neurons are activated by noxious stimuli and silencing them blocks pain responses in two different models of pain. LPB-targeted and nociception-recipient SNR neurons regulate VTA DA activity directly through feed-forward inhibition and indirectly by inhibiting a distinct subpopulation of VTA-projecting LPB neurons thereby reducing excitatory drive onto VTA DA neurons. Correspondingly, ablation of SNR-projecting LPB neurons is sufficient to reduce pain-mediated inhibition of DA release in vivo. The identification of a neural circuit conveying nociceptive input to DA neurons is critical to our understanding of how pain influences learning and behavior.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Functional neuroanatomy of LPB projections to the ventral midbrain.
Fig. 2: Spinal cord, dorsal horn neurons target LPB → SNR neurons.
Fig. 3: LPB → SNR neurons represent diverse painful stimuli.
Fig. 4: Inhibition of the LPB–SNR pathway reduces behavioral responses to pain.
Fig. 5: LPB-targeted SNR neurons are excited by noxious stimuli.
Fig. 6: LPB-targeted SNR neurons innervate VTA DA neurons directly and indirectly.
Fig. 7: LPB contributes to pain-induced inhibition of DA release in vivo.
Fig. 8: Circuit model.

Similar content being viewed by others

Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request. Source data are provided with this paper.

Code availability

All custom code used for analysis in this paper is available on request.

References

  1. Berridge, K. C. & Kringelbach, M. L. Pleasure systems in the brain. Neuron 86, 646–664 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Schultz, W. Dopamine reward prediction-error signalling: a two-component response. Nat. Rev. Neurosci. 17, 183–195 (2016).

    Article  PubMed Central  CAS  Google Scholar 

  3. Watabe-Uchida, M., Eshel, N. & Uchida, N. Neural circuitry of reward prediction error. Annu. Rev. Neurosci. 40, 373–394 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Wise, R. A. & Rompre, P. P. Brain dopamine and reward. Annu Rev. Psychol. 40, 191–225 (1989).

    Article  CAS  PubMed  Google Scholar 

  5. Borsook, D. et al. Reward deficiency and anti-reward in pain chronification. Neurosci. Biobehav. Rev. 68, 282–297 (2016).

    Article  CAS  PubMed  Google Scholar 

  6. Porreca, F. & Navratilova, E. Reward, motivation and emotion of pain and its relief. PAIN 158, S43–S49 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  7. Taylor, A. M. W., Becker, S., Schweinhardt, P. & Cahill, C. Mesolimbic dopamine signaling in acute and chronic pain: implications for motivation, analgesia and addiction. PAIN 157, 1194–1198 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  8. Wood, P. B. Role of central dopamine in pain and analgesia. Expert Rev. Neurother. 8, 781–797 (2008).

    Article  CAS  PubMed  Google Scholar 

  9. Coizet, V., Dommett, E. J., Klop, E. M., Redgrave, P. & Overton, P. G. The parabrachial nucleus is a critical link in the transmission of short latency nociceptive information to midbrain dopaminergic neurons. Neuroscience 168, 263–272 (2010).

    Article  CAS  PubMed  Google Scholar 

  10. de Jong, J. W. et al. A neural circuit mechanism for encoding aversive stimuli in the mesolimbic dopamine system. Neuron 101, 133–151 (2019).

    Article  PubMed  CAS  Google Scholar 

  11. Maeda, H. & Mogenson, G. J. Effects of peripheral stimulation on the activity of neurons in the ventral tegmental area, substantia nigra and midbrain reticular formation of rats. Brain Res. Bull. 8, 7–14 (1982).

    Article  CAS  PubMed  Google Scholar 

  12. Mirenowicz, J. & Schultz, W. Preferential activation of midbrain dopamine neurons by appetitive rather than aversive stimuli. Nature 379, 449–451 (1996).

    Article  CAS  PubMed  Google Scholar 

  13. Ungless, M. A. Uniform inhibition of dopamine neurons in the ventral tegmental area by aversive stimuli. Science 303, 2040–2042 (2004).

    Article  CAS  PubMed  Google Scholar 

  14. Navratilova, E. et al. Pain relief produces negative reinforcement through activation of mesolimbic reward-valuation circuitry. Proc. Natl Acad. Sci. USA 109, 20709–20713 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Defazio, G. et al. Pain as a nonmotor symptom of Parkinson disease: evidence from a case–control study. Arch. Neurol. 65, 1191–1194 (2008).

    Article  PubMed  Google Scholar 

  16. Huang, S., Borgland, S. L. & Zamponi, G. W. Peripheral nerve injury-induced alterations in VTA neuron firing properties. Mol. Brain 12, 89 (2019).

  17. Ren, W. et al. Adaptive alterations in the mesoaccumbal network following peripheral nerve injury. Pain https://doi.org/10.1097/j.pain.0000000000002092 (2020).

  18. Schwartz, N. et al. Decreased motivation during chronic pain requires long-term depression in the nucleus accumbens. Science 345, 535–542 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Finan, P. H. & Smith, M. T. The comorbidity of insomnia, chronic pain and depression: dopamine as a putative mechanism. Sleep. Med. Rev. 17, 173–183 (2013).

    Article  PubMed  Google Scholar 

  20. Cechetto, D. F., Standaert, D. G. & Saper, C. B. Spinal and trigeminal dorsal horn projections to the parabrachial nucleus in the rat. J. Comp. Neurol. 240, 153–160 (1985).

    Article  CAS  PubMed  Google Scholar 

  21. Deng, J. et al. The parabrachial nucleus directly channels spinal nociceptive signals to the intralaminar thalamic nuclei, but not the amygdala. Neuron 107, 909–923 (2020).

    Article  CAS  PubMed  Google Scholar 

  22. Gauriau, C. & Bernard, J.-F. Pain pathways and parabrachial circuits in the rat. Exp. Physiol. 87, 251–258 (2002).

    Article  PubMed  Google Scholar 

  23. Bernard, J. F., Huang, G. F. & Besson, J. M. The parabrachial area: electrophysiological evidence for an involvement in visceral nociceptive processes. J. Neurophysiol. 71, 1646–1660 (1994).

    Article  CAS  PubMed  Google Scholar 

  24. Campos, C. A., Bowen, A. J., Roman, C. W. & Palmiter, R. D. Encoding of danger by parabrachial CGRP neurons. Nature 555, 617–622 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Bernard, J. F., Bester, H. & Besson, J. M. Involvement of the spino–parabrachio–amygdaloid and –hypothalamic pathways in the autonomic and affective emotional aspects of pain. Prog. Brain Res. 107, 243–255 (1996).

  26. Chiang, M. C. et al. Divergent neural pathways emanating from the lateral parabrachial nucleus mediate distinct components of the pain response. Neuron 106, 927–939 (2020).

    Article  CAS  PubMed  Google Scholar 

  27. Han, S., Soleiman, M. T., Soden, M. E., Zweifel, L. S. & Palmiter, R. D. Elucidating an affective pain circuit that creates a threat memory. Cell 162, 363–374 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Palmiter, R. D. The parabrachial nucleus: CGRP neurons function as a general alarm. Trends Neurosci. 41, 280–293 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Saper, C. B. & Loewy, A. D. Efferent connections of the parabrachial nucleus in the rat. Brain Res. 197, 291–317 (1980).

    Article  CAS  PubMed  Google Scholar 

  30. Morales, M. & Margolis, E. B. Ventral tegmental area: cellular heterogeneity, connectivity and behaviour. Nat. Rev. Neurosci. 18, 73–85 (2017).

    Article  CAS  PubMed  Google Scholar 

  31. Yang, H. et al. Nucleus accumbens subnuclei regulate motivated behavior via direct inhibition and disinhibition of VTA dopamine subpopulations. Neuron 97, 434–449 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Barrot, M. Tests and models of nociception and pain in rodents. Neuroscience 211, 39–50 (2012).

    Article  CAS  PubMed  Google Scholar 

  33. Magarinos-Ascone, C., García-Austt, E. & Bun˜o, W. Polymodal sensory and motor convergence in substantia nigra neurons of the awake monkey. Brain Res. 646, 299–302 (1994).

    Article  CAS  PubMed  Google Scholar 

  34. Mailly, P., Charpier, S., Menetrey, A. & Deniau, J.-M. Three-dimensional organization of the recurrent axon collateral network of the substantia nigra pars reticulata neurons in the rat. J. Neurosci. 23, 5247–5257 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Abbott, F. V., Franklin, K. B. & Westbrook, R. F. The formalin test: scoring properties of the first and second phases of the pain response in rats. Pain 60, 91–102 (1995).

    Article  PubMed  Google Scholar 

  36. Rossi, M. A. et al. A GABAergic nigrotectal pathway for coordination of drinking behavior. Nat. Neurosci. 19, 742–748 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Johansen, J. P. & Fields, H. L. Glutamatergic activation of anterior cingulate cortex produces an aversive teaching signal. Nat. Neurosci. 7, 398–403 (2004).

    Article  CAS  PubMed  Google Scholar 

  38. Häusser, M. Optogenetics: the age of light. Nat. Methods 11, 1012–1014 (2014).

    Article  PubMed  CAS  Google Scholar 

  39. Yang, C. F. et al. Sexually dimorphic neurons in the ventromedial hypothalamus govern mating in both sexes and aggression in males. Cell 153, 896–909 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Patriarchi, T. et al. Ultrafast neuronal imaging of dopamine dynamics with designed genetically encoded sensors. Science 360, eaat4422 (2018).

  41. Bernard, J. F., Huang, G. F. & Besson, J. M. The parabrachial area: electrophysiological evidence for an involvement in visceral nociceptive processes. J. Neurophysiol. 71, 1646–1660 (1994).

    Article  CAS  PubMed  Google Scholar 

  42. Han, W. et al. A neural circuit for gut-induced reward. Cell 175, 665–678 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Fernandes, A. B. et al. Postingestive modulation of food seeking depends on vagus-mediated dopamine. Neuron 106, 778–788 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Matsumoto, M. & Hikosaka, O. Two types of dopamine neuron distinctly convey positive and negative motivational signals. Nature 459, 837–841 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Lammel, S. et al. Input-specific control of reward and aversion in the ventral tegmental area. Nature 491, 212–217 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Stamatakis, A. M. & Stuber, G. D. Activation of lateral habenula inputs to the ventral midbrain promotes behavioral avoidance. Nat. Neurosci. 15, 1105–1107 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Gao, D. M., Jeaugey, L., Pollak, P. & Benabid, A. L. Intensity-dependent nociceptive responses from presumed dopaminergic neurons of the substantia nigra, pars compacta in the rat and their modification by lateral habenula inputs. Brain Res. 529, 315–319 (1990).

    Article  CAS  PubMed  Google Scholar 

  48. Tian, J. & Uchida, N. Habenula lesions reveal that multiple mechanisms underlie dopamine prediction errors. Neuron 87, 1304–1316 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Brown, M. T., Henny, P., Bolam, J. P. & Magill, P. J. Activity of neurochemically heterogeneous dopaminergic neurons in the substantia nigra during spontaneous and driven changes in brain state. J. Neurosci. 29, 2915–2925 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Henny, P. et al. Structural correlates of heterogeneous in vivo activity of midbrain dopaminergic neurons. Nat. Neurosci. 15, 613–619 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Lerner, T. N. et al. Intact-brain analyses reveal distinct information carried by SNc dopamine subcircuits. Cell 162, 635–647 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Brischoux, F., Chakraborty, S., Brierley, D. I. & Ungless, M. A. Phasic excitation of dopamine neurons in ventral VTA by noxious stimuli. Proc. Natl Acad. Sci. USA 106, 4894–4899 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Hikosaka, O. & Wurtz, R. H. Visual and oculomotor functions of monkey substantia nigra pars reticulata. IV. Relation of substantia nigra to superior colliculus. J. Neurophysiol. 49, 1285–1301 (1983).

    Article  CAS  PubMed  Google Scholar 

  54. Liu, D. et al. A common hub for sleep and motor control in the substantia nigra. Science 367, 440–445 (2020).

    Article  CAS  PubMed  Google Scholar 

  55. Brown, J., Pan, W.-X. & Dudman, J. T. The inhibitory microcircuit of the substantia nigra provides feedback gain control of the basal ganglia output. eLife 3, e02397 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  56. Deniau, J. M. & Chevalier, G. The lamellar organization of the rat substantia nigra pars reticulata: distribution of projection neurons. Neuroscience 46, 361–377 (1992).

    Article  CAS  PubMed  Google Scholar 

  57. Wang, S. & Redgrave, P. Microinjections of muscimol into lateral superior colliculus disrupt orienting and oral movements in the formalin model of pain. Neuroscience 81, 967–988 (1997).

    Article  CAS  PubMed  Google Scholar 

  58. Corder, G. et al. An amygdalar neural ensemble that encodes the unpleasantness of pain. Science 363, 276–281 (2019).

    Article  CAS  PubMed  Google Scholar 

  59. Jääskeläinen, S. K. et al. Role of the dopaminergic system in chronic pain–a fluorodopa-PET study. Pain 90, 257–260 (2001).

    Article  PubMed  Google Scholar 

  60. Jarcho, J. M., Mayer, E. A., Jiang, Z. K., Feier, N. A. & London, E. D. Pain, affective symptoms and cognitive deficits in patients with cerebral dopamine dysfunction. Pain 153, 744–754 (2012).

    Article  CAS  PubMed  Google Scholar 

  61. Zhang, S.-J. et al. Optogenetic dissection of entorhinal-hippocampal functional connectivity. Science 340, 1232627 (2013).

    Article  PubMed  CAS  Google Scholar 

  62. Jhou, T. C., Fields, H. L., Baxter, M. G., Saper, C. B. & Holland, P. C. The rostromedial tegmental nucleus, a GABAergic afferent to midbrain dopamine neurons, encodes aversive stimuli and inhibits motor responses. Neuron 61, 786–800 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Beyeler, A. et al. Divergent routing of positive and negative information from the amygdala during memory retrieval. Neuron 90, 348–361 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Kim, C. K. et al. Simultaneous fast measurement of circuit dynamics at multiple sites across the mammalian brain. Nat. Methods 13, 325–328 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  65. Tye, K. M. et al. Amygdala circuitry mediating reversible and bidirectional control of anxiety. Nature 471, 358–362 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Rosland, J. H., Tjølsen, A., Maehle, B. & Hole, K. The formalin test in mice: effect of formalin concentration. Pain 42, 235–242 (1990).

    Article  CAS  PubMed  Google Scholar 

  67. Dubuisson, D. & Dennis, S. G. The formalin test: a quantitative study of the analgesic effects of morphine, meperidine and brain stem stimulation in rats and cats. Pain 4, 161–174 (1977).

    Article  CAS  PubMed  Google Scholar 

  68. Tjølsen, A., Berge, O. G., Hunskaar, S., Rosland, J. H. & Hole, K. The formalin test: an evaluation of the method. Pain 51, 5–17 (1992).

    Article  PubMed  Google Scholar 

  69. Deuis, J. R., Dvorakova, L. S. & Vetter, I. Methods used to evaluate pain behaviors in rodents. Front. Mol. Neurosci. 10, 284 (2017).

  70. Chaplan, S. R., Bach, F. W., Pogrel, J. W., Chung, J. M. & Yaksh, T. L. Quantitative assessment of tactile allodynia in the rat paw. J. Neurosci. Methods 53, 55–63 (1994).

    Article  CAS  PubMed  Google Scholar 

  71. Yang, H.-B. et al. cAMP-dependent protein kinase activated Fyn in spinal dorsal horn to regulate NMDA receptor function during inflammatory pain. J. Neurochem. 116, 93–104 (2011).

    Article  CAS  PubMed  Google Scholar 

  72. Gong, H. et al. High-throughput dual-colour precision imaging for brain-wide connectome with cytoarchitectonic landmarks at the cellular level. Nat. Commun. 7, 12142 (2016).

  73. Osakada, F. & Callaway, E. M. Design and generation of recombinant rabies virus vectors. Nat. Protoc. 8, 1583–1601 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  74. Lammel, S. et al. Unique properties of mesoprefrontal neurons within a dual mesocorticolimbic dopamine system. Neuron 57, 760–773 (2008).

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

S.L. is a Weill Neurohub Investigator, John P. Stock Faculty Fellow and Rita Allen Scholar. This work was supported by the National Institutes of Health (R01DA042889 to S.L.; R01MH107742 to B.K.L.; U01MH114829 to B.K.L.), a NARSAD Young Investigator Award (23543 to S.L.), the Brain Research Foundation (BRFSG-2015-7 to S.L.), Wayne and Gladys Valley Foundation (to S.L.), One Mind Foundation (to S.L.) and the National Natural Science Foundation of China (61721092 and 61890953 to H.G.). The funders had no role in the study design, data collection and analysis, decision to publish or preparation of the manuscript. We thank J. Roeper (Goethe University) for critical reading of the manuscript.

Author information

Author notes

  1. Hongbin Yang

    Present address: MOE Frontier Science Center for Brain Research and Brain-Machine Integration, Zhejiang University Medical Center, School of Brain Science and Brain Medicine, Zhejiang University, Hangzhou, China

Authors and Affiliations

  1. Department of Molecular and Cell Biology and Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA

    Hongbin Yang, Johannes W. de Jong, Ignas Cerniauskas, James R. Peck & Stephan Lammel

  2. Neurobiology Section, Division of Biological Sciences, University of California, San Diego, La Jolla, CA, USA

    Byung Kook Lim

  3. Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China

    Hui Gong

  4. HUST-Suzhou Institute for Brainsmatics, JITRI Institute for Brainsmatics, Suzhou, China

    Hui Gong

  5. Alcohol and Addiction Research Group, Department of Neurology, University of California, San Francisco, San Francisco, CA, USA

    Howard L. Fields

Authors
  1. Hongbin Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Johannes W. de Jong
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ignas Cerniauskas
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. James R. Peck
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Byung Kook Lim
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Hui Gong
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Howard L. Fields
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Stephan Lammel
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Stereotaxic injections were performed by H.Y. Immunohistochemistry was performed by H.Y. and J.R.P. Ex vivo electrophysiology was performed by H.Y. In vivo electrophysiology was performed by J.W.J., I.C. and H.Y. Fiber photometry was performed by J.W.J. Critical viral reagents were provided by B.K.L. fMOST was performed by H.G. Behavioral experiments and optogenetics were performed by H.Y. and J.W.J. The study was designed by H.Y., H.L.F. and S.L. Results were analyzed and interpreted by H.Y., J.W.J., H.L.F. and S.L. The manuscript was written by H.Y. and S.L. and edited by all authors.

Corresponding author

Correspondence to Stephan Lammel.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Neuroscience thanks Richard Palmiter, Sarah Ross and Kay Tye for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Neuroanatomical characterization of LPB → SNR and LPB → VTA neurons.

(a) eYFP-expressing LPBVGLUT2 neurons. (b-g) LPBVGLUT2 terminals expressing eYFP in different brain regions. Bregma: 0.14 mm (b), −1.22 mm (c), −1.46 mm (d), −1.46 mm (e), −3.40 mm (f), −3.40 mm (g). Note fibers of passage in VTA (red arrow) (scale bar 50 μm). (h) Mean eYFP fluorescence intensity in different brain areas (n = 5 mice; Data represent mean ± SEM). (i) Synaptophysin-mCherry expression in LPB (left) and ventral midbrain (right) from VGLUT2-Cre mice (scale bars 50 μm). (j) High resolution images of synaptophysin-expressing LPBVGLUT2 terminals close to lateral VTA (lVTA) TH-immunopositive and SNR GABA-immunopositive neurons (scale bar 20 μm). (k) Mean synaptophysin intensity (quantified as number of particles in a defined region) for different ventral midbrain subregions (n = 3 mice). (l) Experimental design. (m) Sample of a recorded and neurobiotin (NB)-filled, mCherry-positive SNR cell (scale bar 50 μm). (n) Light-evoked EPSC recorded in mCherry-positive SNR cell (black) in response to stimulation of LPB inputs (red trace: 20 µM CNQX). (o) Mean EPSC amplitude before (ACSF) and after CNQX application. (p) eYFP-expressing LPBGAD2 neurons at bregma −5.30 mm. (q-s) LPBGAD2 terminals expressing eYFP in (q) DLPAG (Bregma: −4.60 mm), but not in (r) ventral midbrain (Bregma: −3.30 mm) or (s) amygdala (Bregma: −1.50 mm; scale bar 200 μm). (t) Left: Retrobead injection site in NAcLat of a VGLUT2-Cre mouse for experiment in Fig. 1g (scale bar 200 µm). Middle/Right: lVTA cell that was recorded for experiment in Fig. 1g and filled with neurobiotin (NB). It is retrogradely labeled (that is, projects to NAcLat) and TH-immunopositive (scale bar 50 µm). (u) Same as in (t) but cell is in SNR and TH-immunonegative (scale bar 50 µm). (v) Recorded VTA cell from a GAD2tdTomato mouse and filled with NB (Refers to Fig. 1k; scale bar 50 µm). Data represent mean ± SEM. Significance was calculated by means of one-way RM ANOVA with Tukey’s post-hoc test (k) or paired t-test (o). * p < 0.05, ** p < 0.01, *** p < 0.001.

Source data

Extended Data Fig. 2 Whole brain mapping of monosynaptic inputs to LPB → SNR and LPB → VTA neurons.

(a) Schematic showing AAV helper virus (DIO-TVA and DIO-RVG) injections into LPB and EvA-RV-GFP into SNR or VTA of VGLUT2-Cre mice. (b) Sample coronal brain section showing VTA-projecting starter cells in LPB (green: EnvA-ΔG-GFP; red: TVA-mCherry, blue: DAPI; scale bar 100 µm). (c) Bar graph showing quantification of TVA-expressing cells (red) in LPB. (d) Bar graph showing number of starter cells in LPB. (e) Total number of EnvA-ΔG-GFP labeled cells across all brain regions analyzed. (f) Sample images showing GFP-expressing cells (green) that make monosynaptic inputs onto LPB → SNR or LPB → VTA neurons for different brain areas (scale bar 100 µm). (g) Quantification of inputs to LPB → SNR and LPB → VTA neurons. Data are presented as a percentage of total input neurons counted in each individual brain region (BNST: bed nucleus of the stria terminalis, CeA: central nucleus of the amygdala, BLA: basolateral amygdala, LH: lateral hypothalamic area, ZI: zona incerta, VTA: ventral tegmental area, SNR: substantia nigra, reticular part, MG: medial geniculate nucleus, PAG: periaqueductal grey, vlPAG: ventrolateral periaqueductal gray, SC: superior colliculus, LL: lateral lemniscus, DR: dorsal raphe nucleus, CnF: cuneiform nucleus, PPT: pedunculopontine tegmental nucleus, LDT: laterodorsal tegmental nucleus, MPB: medial parabrachial nucleus, LPBi: ipsilateral LPB, PACRt:parvicellular reticular nucleus, Gi: gigantocellular reticular nucleus; n = 4-5 mice). Data represent mean ± SEM. Significance was calculated by means of unpaired t-test. * p < 0.05.

Source data

Extended Data Fig. 3 Heterogenous responses to noxious stimuli in LPB neurons.

(a) Schematic of experimental design showing in vivo electrophysiological recordings of non-projection defined LPB neurons. Animals were exposed to tail pinch, heat, and electrical shock. (b) Pie graphs represent the proportion of LPB cells that are excited (green), inhibited (red) and do not respond (grey) to tail pinch, heat, or electrical tail shock (n = 63-97 cells from n = 5 mice). (c) Samples of spike raster plots and firing patterns for two different LPB cells that either excited (top) or inhibited (bottom) in response to noxious stimuli. Note that the top and bottom graphs are from the same LPB cell (that is, an LPB cell that was excited by all three noxious stimuli (top) and another LPB cell that was inhibited by all three noxious stimuli (bottom)). (d,e) Analysis for LPB cells that were recorded in response to all three noxious stimuli (n = 60 cells). Overlap between the proportion of LPB cells that were excited (top, green, n = 30 cells) or inhibited (bottom, red, n = 16 cells) in response to individual noxious stimuli (d) and LPB cells that show both excitatory and inhibitory responses for individual noxious stimuli (left, n = 9 cells) or did not respond at all (right, n = 5 cells) (e).

Source data

Extended Data Fig. 4 Noxious stimuli activate LPB → SNR neurons and alter excitatory and inhibitory transmission.

(a) Mice were subjected to a hot plate test (c,e,f) or received unilateral intraplantar injection of 1% formalin or saline (d,g,h). (b) LPB subregions are highlighted in different colors to demonstrate location of LPB subpopulations projecting to SNR (blue), VTA (red) or CeA (yellow). (c) Mean number of c-Fos-immunopositive cells in animals exposed to 37˚C (white triangle) or 55˚C (red triangle) heat and mean number of FG labeled cells for each temperature level (white and red circle, respectively) for different LPB subregions described in (b) (37˚C: n = 3 mice; 55˚C: n = 4 mice). (d) Same as in (c), but for animals that received intraplantar injections of 1% formalin or saline (n = 4 mice). (e) SNR-projecting LPB cells (green, FG) and c-Fos immunoreactivity (red) in response to 37˚C (left) or 55˚C (right) heat (scale bar 50 µm). (f) Animals that have been exposed to 55˚C heat display significantly increased c-Fos immunoreactivity in LPB cells (left) and in SNR-projecting LPB neurons (right, FG-positive cells) when compared to 37˚C heat. No difference in mean number of FG-labeled LPB cells between 37˚C and 55˚C heat (middle). (g,h) Same as in (e,f), but for animals that received intraplantar injections of 1% formalin or saline. (i-k) Sample traces of miniature inhibitory postsynaptic currents (mIPSCs) from SNR-projecting LPB neurons recorded from animals that received intraplantar injections of 1% formalin (red) or saline (grey; cells recorded in 1 µM TTX, 20 µM CNQX, 50 µM D-AP5) (i). Cumulative probability plots and bar graphs of the means from the frequencies (j) and amplitudes (k) of mIPSCs recorded from SNR-projecting LPB neurons (saline: n = 13 cells; formalin: n = 14 cells). (l-n) Same as in (i-k), but for recordings of miniature excitatory postsynaptic currents (mEPSCs; cells recorded in 1 µM TTX, 100 µM picrotoxin; saline: n = 11 cells; formalin: n = 12 cells). Data represent mean ± SEM. Significance was calculated by means of unpaired t-test for across group comparison (f,h,j,k,m,n). * p < 0.05, ** p < 0.01.

Source data

Extended Data Fig. 5 Optogenetic silencing of the LPB–SNR pathway does not affect locomotor activity and general licking behavior.

(a) Serial reconstruction of viral injection sites in LPB. Right panels show representative examples of NpHR-eYFP (green) injection sites across the rostro-caudal extent of the LPB (scale bar 100 µm). Left panels show schematics of the corresponding brain regions in which NpHR-eYFP was detected. Each color represents the expression profile from a single mouse that was used for the experiments shown in Fig. 4a-f. (b) Corresponding serial reconstructions of optical fiber locations in the SNR (TH: red; scale bar 100 µm). (c) Schematic design of open field test for assessing the effects of optogenetic silencing of the LPB–SNR pathway on general locomotor activity. (d) Representative trajectories of animals expressing NpHR (top) or eYFP (bottom). (e,f) Optogenetic silencing of the LPB–SNR pathway does not have a significant effect on the (e) mean distance traveled (a measure for locomotor activity) or (f) time spent in the center of the box in NpHR- (n = 9 mice) and eYFP-expressing (n = 5 mice) mice. (g) Schematic showing control experiment for studying general licking behavior in response to optogenetic silencing of LPB → SNR neurons. (h) Bar graphs showing mean number (left) and duration (right) of licks for sucrose solution in NpHR- (n = 8 mice) and eYFP-expressing (n = 6 mice) mice. Data represent mean ± SEM. Significance was calculated by means of one-way RM ANOVA with Tukey’s post-hoc test (e,f) or unpaired t-test (h).

Source data

Extended Data Fig. 6 Genetic ablation of LPB → SNR neurons reduces behavioral responses to formalin-induced pain.

(a) Injection of retrogradely transported pseudotyped equine infectious anemia virus expressing Cre-recombinase (RG-EIAVCre) into the right SNR and AAV carrying Cre-dependent Caspase 3 (CASP) or eYFP into the right LPB of C57BL/6 mice. 5 weeks later, mice received bilateral intraplantar injections of 1% formalin into the hind paws. (b) Comparison between animals in which SNR-projecting LPB neurons were genetically ablated using CASP (left) and animals that express a control vector (eYFP) in SNR-projecting LPB neurons (middle). Sections were stained using an eYFP antibody (green; scale bar 50 µm). CASP animals show significantly reduced number of eYFP-positive SNR-projecting LPB cells when compared to control animals (right; CASP: n = 5 mice, eYFP: n = 5 mice). (c) Mean total distance traveled was not significantly different between CASP and eYFP animals in the open field test (CASP: n = 10 mice, eYFP: n = 7 mice). (d) Number of licks in response to formalin injection in CASP mice (n = 10 mice) for the left (blue) and right (grey) hind paws (left). Mean number of licks during phase I and phase II of the formalin test for comparison of left and right hind paws (right). (e) Same as in (d) but for analysis of lick duration. (f,g) Same as in (d,e) but for eYFP control animals (n = 7 mice). (h) Comparison of mean total number of licks (left) and mean total lick duration (right) for CASP and eYFP mice for individual hind paws. Data represent mean ± SEM. Significance was calculated by means of unpaired t-test (a,b,d,e,f,g) and one-way RM ANOVA with Tukey’s post-hoc test (h). * p < 0.05, ** p < 0.01, *** p < 0.001.

Source data

Extended Data Fig. 7 Comparison of transsynaptic distribution of AAV1-Cre versus CAV2-Cre.

(a,b) Sample fluorescent images showing tdTomato labeled neurons (red) in different brain regions in response to (a) CAV2-Cre or (b) AAV1-Cre injection into the LPB of Ai14 mice (n = 2 mice for each condition) (DAPI: blue; scale bars 100 µm). (c) Left: Schematic showing injection of AAV1-Cre into SNR and AAV-DIO-eYFP into LPB of C57BL/6 mice (n = 2 mice). Right: Sample fluorescent image showing that eYFP-expressing cells are predominantly located in the LPBc (DAPI: blue; scale bar 100 µm).

Extended Data Fig. 8 Whole-brain projections of LPB-targeted SNR neurons.

(a) Schematic showing unilateral targeting of anterogradely transported AAV1-Cre to LPB and AAV carrying Cre-dependent eYFP to SNR of C57BL/6 mice. (b) Representative fluorescent images showing coronal brain sections of eYFP-expressing cells in the SNR (upper left image) and eYFP-expressing terminals across different brain regions (scale bar 100 µm). (c) Quantification of fluorescence intensity of eYFP-expressing terminals in different brain regions (n = 3 mice). (d) Schematic showing injection of fluorescent retrobeads and AAV-DIO-ChR2 into the LPB of VGLUT2-Cre mice. Whole-cell patch-clamp recordings were performed from retrogradely labeled (that is, beads-positive) cells in the lateral SNR. (e) Left: Sample trace showing light-evoked EPSC from LPB-projecting SNR neurons (black trace) in response to light stimulation of LPB inputs. Light-evoked EPSCs are blocked by bath application of 20 µM CNQX (red trace). Right: Bar graph showing mean EPSC amplitudes before (ACSF) and after bath application of CNQX (n = 5 cells). (f) Sample image of retrogradely labeled (beads, red) cells in the lateral SNR that were filled with neurobiotin (NB, green) during whole-cell patch-clamp recordings (scale bar 50 µm). Significance was calculated by means of paired t-test (e). * p < 0.05. Data represent mean ± SEM.

Source data

Extended Data Fig. 9 SNR → LPB neurons have very few collaterals to NAcLat-projecting VTA DA neurons.

(a) Schematic showing injection of fluorogold (FG) into the LPB and AAVs encoding the cellular receptor for subgroup A avian leukosis viruses (TVA) and rabies virus glycoprotein (RG) in the VTA of DAT-Cre mice. In the same animals, EnvA-pseudotyped, glycoprotein-deficient rabies virus expressing GFP (EnvA-RV-GFP) was targeted to the NAcLat. (b) Representative example of coronal section of the ventral midbrain showing retrogradely labeled cells in the lateral SNR (that is, LPB-projecting, FG-positive, red). GFP-positive cells (green) make monosynaptic connections onto VTA DA neurons projecting to NAcLat and are mainly located in the substantia nigra pars compacta (SNc), ventral SNR (vSNR) and lateral VTA, but do not overlap with the lateral SNR (lSNR; scale bar 100 µm). (c) Pie chart showing proportion of analyzed cells (n = 4035 cells from n = 3 mice) in the ventral midbrain that express GFP (green, 19.7%) or are labeled by FG (red, 79.4%) or contain both GFP and FG (yellow, 0.9%).

Source data

Extended Data Fig. 10 Optogenetic stimulation of LPB → VTA DA neurons does not affect locomotor activity but promotes reward-related behavior.

(a) Targeting of AAVs encoding the cellular receptor for subgroup A avian leukosis viruses (TVA) and rabies virus glycoprotein (RG) as well as EnvA-pseudotyped, glycoprotein-deficient rabies virus expressing ChR2 or GFP (EnvA-RV-ChR2/-GFP) to VTA of DAT-Cre mice. Patch-clamp recordings were performed from LPB neurons (d-f) or bilateral optical fibers were implanted above the LPB for assessment of locomotor activity in open field test (g-j). (b) Injection site in VTA (scale bar 100 µm). (c) ChR2-expressing cells in LPB, which make monosynaptic connections onto VTA DA neurons (scale bar 100 µm). (d) Sample patch-clamp recordings from LPB neurons showing light-evoked action potentials in response to 5 Hz, 10 Hz, 20 Hz or 40 Hz stimulation (scale bars 20 mV/0.5 sec). (e) Mean number of spikes in response to different stimulation frequencies (5 Hz: n = 5 cells, 10 Hz: n = 5 cells, 20 Hz: n = 5 cells, 40 Hz: n = 5 cells). (f) Neurobiotin (NB)-filled LPB cell expressing ChR2 (scale bar 20 µm). (g) Experimental design. (h) Representative trajectories of animals expressing ChR2 (top) or GFP (bottom) in LPB → VTA DA neurons. (i) 20 Hz stimulation of LPB → VTA DA neurons does not have significant effect on the mean distance traveled between ChR2- (left) and GFP-expressing (right) mice. (j) No significant difference in time spent in center area between ChR2 and GFP mice. (k) Schematic of real-time (RT) place preference assay. (l) Trajectories of sample ChR2- and GFP-expressing mice during RT place preference test. (m) ChR2- (left) but not GFP-expressing (right) mice spent significantly more time on the side of the chamber paired with light stimulation of LPB → VTA DA neurons (ChR2: n = 9 mice, GFP: n = 7 mice). Data represent mean ± SEM. Significance was calculated by means of one-way RM ANOVA with Tukey’s post-hoc test (i) and two-way RM ANOVA with Holm-Sidak’s post-hoc test (j) or paired t-tests (m). * p < 0.05.

Source data

Supplementary information

Supplementary Information

Supplementary Figs. 1–3, Supplementary Methods, Supplementary Notes and Supplementary Table 1.

Reporting Summary

Supplementary Video 1

fMOST whole-brain imaging of LPBVGLUT2 projections; related to Fig. 1.

Supplementary Video 2

3D reconstruction of the VTA showing LPBVGLUT2 terminals and fibers of passage; related to Fig. 1.

Supplementary Data 1

Source data for experiment shown in Supplementary Fig. 1g.

Source data

Source Data Fig. 1

Source data file for experiments.

Source Data Fig. 2

Source data file for experiments.

Source Data Fig. 3

Source data file for experiments.

Source Data Fig. 4

Source data file for experiments.

Source Data Fig. 5

Source data file for experiments.

Source Data Fig. 6

Source data file for experiments.

Source Data Fig. 7

Source data file for experiments.

Source Data Extended Data Fig. 1

Source data file for experiments.

Source Data Extended Data Fig. 2

Source data file for experiments.

Source Data Extended Data Fig. 3

Source data file for experiments.

Source Data Extended Data Fig. 4

Source data file for experiments.

Source Data Extended Data Fig. 5

Source data file for experiments.

Source Data Extended Data Fig. 6

Source data file for experiments.

Source Data Extended Data Fig. 8

Source data file for experiments.

Source Data Extended Data Fig. 9

Source data file for experiments.

Source Data Extended Data Fig. 10

Source data file for experiments.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yang, H., de Jong, J.W., Cerniauskas, I. et al. Pain modulates dopamine neurons via a spinal–parabrachial–mesencephalic circuit. Nat Neurosci 24, 1402–1413 (2021). https://doi.org/10.1038/s41593-021-00903-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41593-021-00903-8

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing