A fluorescent sensor for spatiotemporally resolved imaging of endocannabinoid dynamics in vivo

A fluorescent sensor for spatiotemporally resolved imaging of endocannabinoid dynamics in vivo

Abstract

Endocannabinoids (eCBs) are retrograde neuromodulators with important functions in a wide range of physiological processes, but their in vivo dynamics remain largely uncharacterized. Here we developed a genetically encoded eCB sensor called GRABeCB2.0. GRABeCB2.0 consists of a circular-permutated EGFP and the human CB1 cannabinoid receptor, providing cell membrane trafficking, second-resolution kinetics with high specificity for eCBs, and shows a robust fluorescence response at physiological eCB concentrations. Using GRABeCB2.0, we monitored evoked and spontaneous changes in eCB dynamics in cultured neurons and acute brain slices. We observed spontaneous compartmentalized eCB transients in cultured neurons and eCB transients from single axonal boutons in acute brain slices, suggesting constrained, localized eCB signaling. When GRABeCB2.0 was expressed in the mouse brain, we observed foot shock-elicited and running-triggered eCB signaling in the basolateral amygdala and hippocampus, respectively. In a mouse model of epilepsy, we observed a spreading wave of eCB release that followed a Ca2+ wave through the hippocampus. GRABeCB2.0 is a robust probe for eCB release in vivo.

Access options

Subscribe to Journal

Get full journal access for 1 year

99,00 €

only 8,25 € per issue

Tax calculation will be finalised during checkout.

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Data availability

Plasmids for expressing eCB2.0 and eCBmut used in this study have been deposited to Addgene (164604–164612). Source data are provided with this paper.

References

  1. 1.

    Zuardi, A. W. History of cannabis as a medicine: a review. Braz. J. Psychiatry 28, 153–157 (2006).

    PubMed 
    Article 

    Google Scholar
     

  2. 2.

    Piomelli, D. The molecular logic of endocannabinoid signalling. Nat. Rev. Neurosci. 4, 873–884 (2003).

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  3. 3.

    Wilson, R. I. & Nicoll, R. A. Endocannabinoid signaling in the brain. Science 296, 678–682 (2002).

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  4. 4.

    Kano, M., Ohno-Shosaku, T., Hashimotodani, Y., Uchigashima, M. & Watanabe, M. Endocannabinoid-mediated control of synaptic transmission. Physiol. Rev. 89, 309–380 (2009).

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  5. 5.

    Hebert-Chatelain, E. et al. A cannabinoid link between mitochondria and memory. Nature 539, 555–559 (2016).

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  6. 6.

    Benard, G. et al. Mitochondrial CB1 receptors regulate neuronal energy metabolism. Nat. Neurosci. 15, 558–564 (2012).

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  7. 7.

    Jimenez-Blasco, D. et al. Glucose metabolism links astroglial mitochondria to cannabinoid effects. Nature 583, 603–608 (2020).

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  8. 8.

    Stella, N. Cannabinoid signaling in glial cells. Glia 48, 267–277 (2004).

    PubMed 
    Article 

    Google Scholar
     

  9. 9.

    Navarrete, M., Diez, A. & Araque, A. Astrocytes in endocannabinoid signalling. Phil. Trans. R Soc. Lond. B 369, 20130599 (2014).

    Article 
    CAS 

    Google Scholar
     

  10. 10.

    Chevaleyre, V., Takahashi, K. A. & Castillo, P. E. Endocannabinoid-mediated synaptic plasticity in the CNS. Annu. Rev. Neurosci. 29, 37–76 (2006).

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  11. 11.

    Oddi, S., Scipioni, L. & Maccarrone, M. Endocannabinoid system and adult neurogenesis: a focused review. Curr. Opin. Pharmacol. 50, 25–32 (2020).

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  12. 12.

    Moreira, F. A. & Lutz, B. The endocannabinoid system: emotion, learning and addiction. Addict. Biol. 13, 196–212 (2008).

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  13. 13.

    Guindon, J. & Hohmann, A. G. The endocannabinoid system and pain. CNS Neurol. Disord. Drug Targets 8, 403–421 (2009).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  14. 14.

    Kesner, A. J. & Lovinger, D. M. Cannabinoids, endocannabinoids and sleep. Front. Mol. Neurosci. 13, 125 (2020).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  15. 15.

    Silvestri, C. & Di Marzo, V. The endocannabinoid system in energy homeostasis and the etiopathology of metabolic disorders. Cell Metab. 17, 475–490 (2013).

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  16. 16.

    Katona, I. & Freund, T. F. Endocannabinoid signaling as a synaptic circuit breaker in neurological disease. Nat. Med. 14, 923–930 (2008).

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  17. 17.

    Fernandez-Espejo, E., Viveros, M. P., Nunez, L., Ellenbroek, B. A. & Rodriguez de Fonseca, F. Role of cannabis and endocannabinoids in the genesis of schizophrenia. Psychopharmacology 206, 531–549 (2009).

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  18. 18.

    Fraguas-Sanchez, A. I., Martin-Sabroso, C. & Torres-Suarez, A. I. Insights into the effects of the endocannabinoid system in cancer: a review. Br. J. Pharmacol. 175, 2566–2580 (2018).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  19. 19.

    Patel, S., Hill, M. N., Cheer, J. F., Wotjak, C. T. & Holmes, A. The endocannabinoid system as a target for novel anxiolytic drugs. Neurosci. Biobehav. Rev. 76, 56–66 (2017).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  20. 20.

    Ligresti, A., Petrosino, S. & Di Marzo, V. From endocannabinoid profiling to ‘endocannabinoid therapeutics’. Curr. Opin. Chem. Biol. 13, 321–331 (2009).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar
     

  21. 21.

    Wilson, R. I. & Nicoll, R. A. Endogenous cannabinoids mediate retrograde signalling at hippocampal synapses. Nature 410, 588–592 (2001).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar
     

  22. 22.

    Zoerner, A. A. et al. Quantification of endocannabinoids in biological systems by chromatography and mass spectrometry: a comprehensive review from an analytical and biological perspective. Biochim. Biophys. Acta 1811, 706–723 (2011).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar
     

  23. 23.

    Marchioni, C. et al. Recent advances in LC–MS/MS methods to determine endocannabinoids in biological samples: application in neurodegenerative diseases. Anal. Chim. Acta 1044, 12–28 (2018).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar
     

  24. 24.

    Kreitzer, A. C. & Regehr, W. G. Retrograde inhibition of presynaptic calcium influx by endogenous cannabinoids at excitatory synapses onto Purkinje cells. Neuron 29, 717–727 (2001).

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  25. 25.

    Maejima, T., Hashimoto, K., Yoshida, T., Aiba, A. & Kano, M. Presynaptic inhibition caused by retrograde signal from metabotropic glutamate to cannabinoid receptors. Neuron 31, 463–475 (2001).

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  26. 26.

    Ohno-Shosaku, T., Maejima, T. & Kano, M. Endogenous cannabinoids mediate retrograde signals from depolarized postsynaptic neurons to presynaptic terminals. Neuron 29, 729–738 (2001).

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  27. 27.

    Wiskerke, J. et al. Characterization of the effects of reuptake and hydrolysis inhibition on interstitial endocannabinoid levels in the brain: an in vivo microdialysis study. ACS Chem. Neurosci. 3, 407–417 (2012).

    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar
     

  28. 28.

    Walker, J. M., Huang, S. M., Strangman, N. M., Tsou, K. & Sanudo-Pena, M. C. Pain modulation by release of the endogenous cannabinoid anandamide. Proc. Natl Acad. Sci. USA 96, 12198–12203 (1999).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  29. 29.

    Sun, F. et al. A genetically encoded fluorescent sensor enables rapid and specific detection of dopamine in flies, fish, and mice. Cell 174, 481–496 (2018).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  30. 30.

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

  31. 31.

    Jing, M. et al. A genetically encoded fluorescent acetylcholine indicator for in vitro and in vivo studies. Nat. Biotechnol. 36, 726–737 (2018).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  32. 32.

    Feng, J. et al. A genetically encoded fluorescent sensor for rapid and specific in vivo detection of norepinephrine. Neuron 102, 745–761 (2019).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  33. 33.

    Peng, W. et al. Regulation of sleep homeostasis mediator adenosine by basal forebrain glutamatergic neurons. Science 369, eabb05566 (2020).

  34. 34.

    Patriarchi, T. et al. An expanded palette of dopamine sensors for multiplex imaging in vivo. Nat. Methods 17, 1147–1155 (2020).

  35. 35.

    Jing, M. et al. An optimized acetylcholine sensor for monitoring in vivo cholinergic activity. Nat. Methods 17, 1139–1146 (2020).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  36. 36.

    Sun, F. et al. Next-generation GRAB sensors for monitoring dopaminergic activity in vivo. Nat. Methods 17, 1156–1166 (2020).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  37. 37.

    Wan, J. et al. A genetically encoded sensor for measuring serotonin dynamics. Nat. Neurosci. 24, 746–752 (2021).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  38. 38.

    Howlett, A. C. et al. International Union of Pharmacology. XXVII. Classification of cannabinoid receptors. Pharmacol. Rev. 54, 161–202 (2002).

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  39. 39.

    Hua, T. et al. Crystal structures of agonist-bound human cannabinoid receptor CB1. Nature 547, 468–471 (2017).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  40. 40.

    Hua, T. et al. Crystal structure of the human cannabinoid receptor CB1. Cell 167, 750–762 (2016).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  41. 41.

    Krishna Kumar, K. et al. Structure of a signaling cannabinoid receptor 1–G protein complex. Cell 176, 448–458 (2019).

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  42. 42.

    Li, X. et al. Crystal structure of the human cannabinoid receptor CB2. Cell 176, 459–467 (2019).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  43. 43.

    Shao, Z. et al. Structure of an allosteric modulator bound to the CB1 cannabinoid receptor. Nat. Chem. Biol. 15, 1199–1205 (2019).

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  44. 44.

    Shao, Z. et al. High-resolution crystal structure of the human CB1 cannabinoid receptor. Nature 540, 602–606 (2016).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  45. 45.

    Kroeze, W. K. et al. PRESTO-Tango as an open-source resource for interrogation of the druggable human GPCRome. Nat. Struct. Mol. Biol. 22, 362–369 (2015).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  46. 46.

    Wu, J. et al. Genetically encoded glutamate indicators with altered color and topology. ACS Chem. Biol. 13, 1832–1837 (2018).

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  47. 47.

    Alger, B. E. Retrograde signaling in the regulation of synaptic transmission: focus on endocannabinoids. Prog. Neurobiol. 68, 247–286 (2002).

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  48. 48.

    Ogasawara, D. et al. Rapid and profound rewiring of brain lipid signaling networks by acute diacylglycerol lipase inhibition. Proc. Natl Acad. Sci. USA 113, 26–33 (2016).

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  49. 49.

    Long, J. Z. et al. Selective blockade of 2-arachidonoylglycerol hydrolysis produces cannabinoid behavioral effects. Nat. Chem. Biol. 5, 37–44 (2009).

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  50. 50.

    Mor, M. et al. Cyclohexylcarbamic acid 3′- or 4′-substituted biphenyl-3-yl esters as fatty acid amide hydrolase inhibitors: synthesis, quantitative structure–activity relationships, and molecular modeling studies. J. Med. Chem. 47, 4998–5008 (2004).

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  51. 51.

    Brenowitz, S. D. & Regehr, W. G. Associative short-term synaptic plasticity mediated by endocannabinoids. Neuron 45, 419–431 (2005).

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  52. 52.

    Soler-Llavina, G. J. & Sabatini, B. L. Synapse-specific plasticity and compartmentalized signaling in cerebellar stellate cells. Nat. Neurosci. 9, 798–806 (2006).

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  53. 53.

    Gerdeman, G. L., Ronesi, J. & Lovinger, D. M. Postsynaptic endocannabinoid release is critical to long-term depression in the striatum. Nat. Neurosci. 5, 446–451 (2002).

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  54. 54.

    Kreitzer, A. C. & Malenka, R. C. Endocannabinoid-mediated rescue of striatal LTD and motor deficits in Parkinson’s disease models. Nature 445, 643–647 (2007).

    CAS 
    Article 

    Google Scholar
     

  55. 55.

    Peterfi, Z. et al. Endocannabinoid-mediated long-term depression of afferent excitatory synapses in hippocampal pyramidal cells and GABAergic interneurons. J. Neurosci. 32, 14448–14463 (2012).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  56. 56.

    Ohno-Shosaku, T. et al. Presynaptic cannabinoid sensitivity is a major determinant of depolarization-induced retrograde suppression at hippocampal synapses. J. Neurosci. 22, 3864–3872 (2002).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  57. 57.

    Siksou, L. et al. Three-dimensional architecture of presynaptic terminal cytomatrix. J. Neurosci. 27, 6868–6877 (2007).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  58. 58.

    Li, B. Central amygdala cells for learning and expressing aversive emotional memories. Curr. Opin. Behav. Sci. 26, 40–45 (2019).

    PubMed 
    Article 

    Google Scholar
     

  59. 59.

    Katona, I. et al. Distribution of CB1 cannabinoid receptors in the amygdala and their role in the control of GABAergic transmission. J. Neurosci. 21, 9506–9518 (2001).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  60. 60.

    Morena, M., Patel, S., Bains, J. S. & Hill, M. N. Neurobiological interactions between stress and the endocannabinoid system. Neuropsychopharmacology 41, 80–102 (2016).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar
     

  61. 61.

    Gunduz-Cinar, O., Hill, M. N., McEwen, B. S. & Holmes, A. Amygdala FAAH and anandamide: mediating protection and recovery from stress. Trends Pharmacol. Sci. 34, 637–644 (2013).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  62. 62.

    Marshel, J. H. et al. Cortical layer-specific critical dynamics triggering perception. Science 365, eaaw5202 (2019).

  63. 63.

    Dana, H. et al. Sensitive red protein calcium indicators for imaging neural activity. eLife 5, e12727 (2016).

  64. 64.

    Soltesz, I. et al. Weeding out bad waves: towards selective cannabinoid circuit control in epilepsy. Nat. Rev. Neurosci. 16, 264–277 (2015).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar
     

  65. 65.

    Farrell, J. S. et al. In vivo assessment of mechanisms underlying the neurovascular basis of postictal amnesia. Sci. Rep. 10, 14992 (2020).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  66. 66.

    Heinbockel, T. et al. Endocannabinoid signaling dynamics probed with optical tools. J. Neurosci. 25, 9449–9459 (2005).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  67. 67.

    Masuho, I. et al. Distinct profiles of functional discrimination among G proteins determine the actions of G protein-coupled receptors. Sci. Signal. 8, ra123 (2015).

    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar
     

  68. 68.

    Hollins, B., Kuravi, S., Digby, G. J. & Lambert, N. A. The C-terminus of GRK3 indicates rapid dissociation of G protein heterotrimers. Cell. Signal. 21, 1015–1021 (2009).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  69. 69.

    Li, H. et al. Experience-dependent modification of a central amygdala fear circuit. Nat. Neurosci. 16, 332–339 (2013).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  70. 70.

    Penzo, M. A. et al. The paraventricular thalamus controls a central amygdala fear circuit. Nature 519, 455–459 (2015).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  71. 71.

    Yu, K. et al. The central amygdala controls learning in the lateral amygdala. Nat. Neurosci. 20, 1680–1685 (2017).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  72. 72.

    Kaifosh, P., Lovett-Barron, M., Turi, G. F., Reardon, T. R. & Losonczy, A. Septo-hippocampal GABAergic signaling across multiple modalities in awake mice. Nat. Neurosci. 16, 1182–1184 (2013).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  73. 73.

    Lovett-Barron, M. et al. Dendritic inhibition in the hippocampus supports fear learning. Science 343, 857–863 (2014).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  74. 74.

    Farrell, J. S. et al. Postictal behavioural impairments are due to a severe prolonged hypoperfusion/hypoxia event that is COX-2 dependent. eLife 5, e19352 (2016).

  75. 75.

    Kaifosh, P., Zaremba, J. D., Danielson, N. B. & Losonczy, A. SIMA: Python software for analysis of dynamic fluorescence imaging data. Front. Neuroinform. 8, 80 (2014).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China (31925017 and 31871087), the Beijing Municipal Science & Technology Commission (Z181100001318002 and Z181100001518004), the NIH BRAIN Initiative (1U01NS113358), the Shenzhen-Hong Kong Institute of Brain Science (NYKFKT2019013), the Science Fund for Creative Research Groups of the National Natural Science Foundation of China (81821092) and grants from the Peking-Tsinghua Center for Life Sciences and the State Key Laboratory of Membrane Biology at Peking University School of Life Sciences (Y.L.); the NIAAA (ZIA AA000416) (D.M.L.); the NIH BRAIN Initiative (1U01NS113358) (J. Ding); the NIH (R01MH101214 and R01NS104944) (B.L.); the American Epilepsy Society (postdoctoral fellowship) and the NIH (K99NS117795) (B.D.); the Canadian Institutes for Health Research (postdoctoral fellowship) (J.S.F.); and the NIH (NS99457) (I.S.). We thank members and alumni of the Li laboratory for helpful discussions. We thank Y. Rao at PKU for use of the 2P microscope, Y. Xue at PKU Health Science Center for assistance with the Δ-9-THC experiment and X. Lei at PKU-CLS and the National Center for Protein Sciences at Peking University in Beijing, China, for support and assistance with the Opera Phenix high-content screening system and imaging platform.

Author information

Affiliations

  1. State Key Laboratory of Membrane Biology, Peking University School of Life Sciences, Beijing, China

    Ao Dong, Kaikai He, Ruyi Cai, Huan Wang, Jiali Duan & Yulong Li

  2. PKU-IDG/McGovern Institute for Brain Research, Beijing, China

    Ao Dong, Kaikai He, Ruyi Cai, Huan Wang, Jiali Duan & Yulong Li

  3. Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China

    Ao Dong & Yulong Li

  4. Department of Neurosurgery, Stanford University, Palo Alto, CA, USA

    Barna Dudok, Jordan S. Farrell & Ivan Soltesz

  5. Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA

    Wuqiang Guan & Bo Li

  6. Laboratory for Integrative Neuroscience, National Institute on Alcohol Abuse and Alcoholism, Bethesda, MD, USA

    Daniel J. Liput & David M. Lovinger

  7. Laboratory of Molecular Physiology, National Institute on Alcohol Abuse and Alcoholism, Bethesda, MD, USA

    Daniel J. Liput & Henry L. Puhl

  8. Neuroscience PhD Program, Stanford University, Palo Alto, CA, USA

    Eddy Albarran

  9. Department of Neurosurgery, Department of Neurology, Stanford University School of Medicine, Palo Alto, CA, USA

    Jun Ding

  10. Chinese Institute for Brain Research, Beijing, China

    Yulong Li

Contributions

Y.L. conceived the project. A.D., K.H., H.L.P., R.C., H.W. and J. Duan performed experiments related to developing, optimizing and characterizing sensors in cultured HEK293T cells and neurons. D.J.L. performed surgery and photometry recording experiments related to the validation of the sensor in DLS brain slices under the supervision of D.M.L. A.D. performed the surgery and 2P imaging in hippocampal brain slices. E.A. performed the surgery and electrophysiological recordings in striatal brain slices under the supervision of J. Ding. W.G. performed fiber photometry recordings in freely moving mice during foot shock under the supervision of B.L. B.D. and J.S.F. performed in vivo 2P imaging in the hippocampus in mice during running and seizure under the supervision of I.S. All authors contributed to data interpretation and analysis. A.D. and Y.L. wrote the manuscript with input from other authors.

Corresponding author

Correspondence to
Yulong Li.

Ethics declarations

Competing interests

Y.L. and H.W. have filed patent applications (international patent no. PCT/CN2018/107533), the value of which might be affected by this publication. The remaining authors declare no competing interests.

Additional information

Peer review information Nature Biotechnology thanks Daniele Piomelli, Pablo Castillo and the other, anonymous, reviewer(s) 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 Strategy for optimizing and screening the GRABeCB sensors.

a, A flowchart showing the development process of the eCB2.0 sensor. Responses to 10 μM 2-AG of candidate sensors were shown alongside each step. b, Schematic diagram depicting the structure of the GRABeCB2.0 sensor. The IgK leader sequence and the sequence derived from GRABNE are shown. c, Amino acids sequence of the eCB2.0 sensor. The phenylalanine residue at position 1772.64 in the CB1R was mutated to an alanine to generate the eCBmut sensor (indicated by the gray box). Note that the numbering used in the figure corresponds to the start of the IgK leader sequence.

Extended Data Fig. 2 Dose–response curves of GRABeCB2.0 to synthetic CB1R agonists and the phytocannabinoid Δ-9-THC.

a, Dose–response curve of eCB2.0 to WIN55212-2; n = 3 wells each, mean ± s.e.m. b, Dose–response curve of eCB2.0 to CP55940; n = 3 wells each, mean ± s.e.m. c, Dose–response curve of eCB2.0 to Δ-9-THC; n = 3 wells each, mean ± s.e.m.

Source data

Extended Data Fig. 3 Photostability and intracellular signaling couplings of GRABeCB2.0 sensor.

a, Normalized fluorescence of EGFP-CAAX and eCB2.0 (in the absence and presence of 2-AG) in HEK293T cells during 1 P (confocal) bleaching. b, Integrated fluorescence of EGFP-CAAX and eCB2.0 (in the absence and presence of 2-AG) shown in a; n = 29, 27, 28 cells from 3 cultures. Boxes show the first and third quartiles as well as the median (line), and the whiskers extend to the most extreme data point that is no more than 1.5× the interquartile range from the box. Two-tailed Mann-Whitney tests were performed: P = 1.44E-10 (between EGFP and eCB2.0 in saline) and 1.37E-6 (between EGFP and eCB2.0 with 2-AG). c, Fast and slow time constants and slow component amplitudes of EGFP-CAAX and eCB2.0 (in the absence and presence of 2-AG) traces fit by double exponentials. d, Normalized fluorescence of EGFP-CAAX and eCB2.0 (in the absence and presence of 2-AG) in HEK293T cells during 2 P bleaching. e, Time constants of EGFP-CAAX and eCB2.0 (in the absence and presence of 2-AG) traces fit by single exponentials; n = 79, 48, 104 cells from 3 cultures. Boxes show the first and third quartiles as well as the median (line), and the whiskers extend to the most extreme data point that is no more than 1.5× the interquartile range from the box. Two-tailed Mann-Whitney tests were performed: P = 0.0049 (between EGFP and eCB2.0 in saline) and 0.0581 (between EGFP and eCB2.0 with 2-AG). f, Schematic diagram depicting the strategy for measuring G protein activation using the chimeric Gαq-i protein. g, Representative traces showing the jRGECO1a responses to 2-AG perfusion in cells expressing CB1R, CB1R + eCB2.0 or eCB2.0. h, Dose-response curves of peak jRGECO1a ΔF/F0 measured in cells expressing CB1R, CB1R + eCB2.0, or eCB2.0; n = 4, 4 and 3 cultures, mean ± s.e.m. Two concentrations of 2-AG were used for eCB2.0 expressed cells. Two-tailed Student’s t tests were performed: P = 0.2392, 0.1455, 0.6711, 0.9191 and 0.8371 (between CB1R and CB1R + eCB2.0); P = 0.0156 and 0.0015 (between CB1R and eCB2.0). i, G protein coupling was measured using a BRET Gβγ sensor in cells expressing CB1R, eCB2.0, or eCBmut; n = 3 experiments, mean ± s.e.m. Two-tailed Student’s t tests were performed: P = 3.84E-05, 0.4082, 0.0699 and 0.2961. j, β-arrestin coupling was measured using the Tango assay in cells expressing CB1R, eCB2.0, or eCBmut; n = 3 wells each, mean ± s.e.m. k, Dose-response curves of eCB2.0 to 2-AG measured in cells expressing eCB2.0 or eCB2.0 + CB1R; n = 3 wells each, mean ± s.e.m. Two-tailed Student’s t tests were performed: P = 0.3036, 0.3231, 0.7697, 0.7900, 0.9723, 0.5482 and 0.1383. , p < 0.001; , p < 0.01; *, p < 0.05; n.s., not significant.

Source data

Extended Data Fig. 4 Expression of GRABeCB2.0 has no significant effect on electrically evoked glutamate release in cultured neurons.

a, Fluorescence microscopy images of neurons expressing Rncp-iGluSnFR (upper) and neurons co-expressing Rncp-iGluSnFR and eCB2.0 (bottom). Similar results were observed for more than 20 neurons. Scale bar, 30 μm. b, Example traces showing the electrical stimulation evoked glutamate signals. c, Pseudocolor change in Rncp-iGluSnFR fluorescence in neurons expressing Rncp-iGluSnFR (upper) and co-expressing Rncp-iGluSnFR and eCB2.0 (bottom) before and after the electrical stimulation. Shown are 25 regions of interest (ROIs) in one culture each. d, Summary of peak Rncp-iGluSnFR ΔF/F0 measured in neurons expressing Rncp-iGluSnFR (upper) or co-expressing Rncp-iGluSnFR and eCB2.0 (bottom); n = 100 ROIs from 4 cultures each. Boxes show the first and third quartiles as well as the median (line), and the whiskers extend to the most extreme data point that is no more than 1.5× the interquartile range from the box. Two-tailed Mann-Whitney test was performed: P = 0.2564. n.s., not significant.

Source data

Extended Data Fig. 5 Estimated concentrations of electrically evoked 2-AG release in cultured neurons.

a, An example trace of ΔF/F0 measured in eCB2.0 expressed neurons; the indicated concentrations of 2-AG were applied. b, An example dose-response curve measured in neurons expressing eCB2.0. eCB2.0 signals evoked by 1–100 electrical pulses at 20 Hz and corresponding estimated 2-AG concentrations were indicated (green dots). c, Summary of estimated 2-AG release concentrations evoked by 1–100 electrical pulses at 20 Hz; n = 3 cultures, mean ± s.e.m.

Source data

Extended Data Fig. 6 Spontaneous eCB transients in cultured neurons are sensitive to the CB1R neutral antagonist but not the action potential blocker.

a, Cumulative transient change in eCB2.0 fluorescence measured during 20 mins of recording in the absence (left) or presence (right) of 1 μM NESS0327. Pseudocolor images were calculated as the average temporal projection subtracted from the maximum temporal projection. Similar results were observed for 3 cultures. Scale bar, 100 μm. b, Summary of the frequency of transient changes in eCB2.0 fluorescence measured in saline (Ctrl) and after NESS0327 application; n = 18 & 18 sessions from 3 cultures with 10-min recording/session. Boxes show the first and third quartiles as well as the median (line), and the whiskers extend to the most extreme data point that is no more than 1.5× the interquartile range from the box. Two-tailed Student’s t test was performed: P = 1.68E-5. c, Cumulative transient change in eCB2.0 fluorescence measured during 20 mins of recording in the absence (left) or presence (right) of 1 μM TTX. Pseudocolor images were calculated as the average temporal projection subtracted from the maximum temporal projection. Similar results were observed for 3 cultures. Scale bar, 100 μm. d, Summary of the frequency of transient changes in eCB2.0 fluorescence measured in saline (Ctrl) and after TTX application; n = 12 & 14 sessions from 3 cultures with 10-min recording/session. Boxes show the first and third quartiles as well as the median (line), and the whiskers extend to the most extreme data point that is no more than 1.5× the interquartile range from the box. Two-tailed Student’s t test was performed: P = 0.5972. , p < 0.001; n.s., not significant.

Source data

Extended Data Fig. 7 Detection of 2-AG, AEA, and DSI in GRABeCB2.0 expressed acute striatal slices.

a, Schematic diagram depicting the strategy for virus injection in DLS, followed by the preparation of acute brain slices used for electrical stimulation and recording. b, Schematic diagram depicting the quantification of F0 and decay time constant of the evoked eCB2.0 signal. c, Quantification of relative F0 and decay time constant of evoked eCB2.0 signals before and after JZL184 or URB597 treatment. n = 3 slices, mean ± s.e.m. d, Fluorescence microscopy images of control and eCB2.0 expressed striatal slices. Recorded MSN neurons were loaded with Alexa 594. Similar results were observed for more than 10 neurons. Scale bar, 10 μm. e, Depolarizing neurons in control and eCB2.0 expressed striatum caused similar depression on sIPSC. Three shadow regions correspond to baseline, early and late in c. f, Summary of normalized charge recorded in MSNs in control and eCB2.0 expressed striatum during baseline, right after depolarization (early) and 16 s after depolarization (late); n = 8 and 13 neurons, mean ± s.e.m. Two-tailed Wilcoxon matched-pairs signed rant tests were performed: P = 0.0078 (upper left), 0.0234 (upper right), 0.0134 (bottom left) and 0.0266 (bottom right). , p < 0.01; *, p < 0.05.

Source data

Extended Data Fig. 8 Detection of eCB signals in acute hippocampal and BLA slices using 2 photon imaging.

a, Schematic diagram depicting the strategy for virus injection in the hippocampal CA1 region, followed by the preparation of acute slices for electrical stimulation and 2-photon imaging. b, (Left) fluorescence image of eCB2.0 expressed in the hippocampal CA1 region, showing the position of the stimulating electrode. (Right) pseudocolor images showing the change in eCB2.0 fluorescence at baseline and after 10 or 50 pulses applied at 20 Hz. The dashed circle shows the ROI for quantification. Similar results were observed for 5 slices. Scale bar, 100 μm. c, Representative traces and summary of the peak change in eCB2.0 fluorescence evoked by electrical pulses applied at the indicated frequencies; n = 5 slices, mean ± s.e.m. d, Time course of the change in eCB2.0 fluorescence; where indicated, AEA and AM251 were applied. e, Representative traces of the change in eCB2.0 fluorescence evoked by electrical stimulation in the absence and presence of AM251. f, Schematic diagram depicting the strategy for virus injection in the BLA region, followed by the preparation of acute slices for electrical stimulation and 2-photon imaging. g, Pseudocolor images showing the change in eCB2.0 fluorescence after 20, 50 or 100 pulses applied at 20 Hz. Similar results were observed for 3 slices. Scale bar, 100 μm. h, Traces of eCB2.0 fluorescence evoked by electrical pulses applied at the indicated frequencies. i, Representative pseudocolor image, trace, and summary of peak change in eCB2.0 fluorescence upon 75 mM K+ ACSF perfusion. Scale bar, 100 μm. n = 3 slices, mean ± s.e.m.

Source data

Extended Data Fig. 9 Expression of GRABeCB sensors has minimal effect on animal behaviors.

a, Fluorescence images of coronal slices prepared from mice expressing GFP or GRABeCB2.0 in BLA. Similar results were observed for 6 mice. Scale bar, 1 mm. b, Schematic diagrams showing the open field test (OFT) and the elevated plus maze test (EPMT). c, Quantification of behavioral parameters in the OFT. n = 6 mice, mean ± s.e.m. Two-tailed Student’s t tests were performed: P = 0.2084, 0.8737, 0.5858 and 0.4464. d, Quantification of behavioral parameters in the EPMT. n = 6 mice, mean ± s.e.m. Two-tailed Student’s t tests were performed: P = 0.2912, 0.5377, 0.6007, 0.3386, 0.3748, 0.4958 and 0.1411. e, Schematic diagram showing the fear conditioning test. f, Quantification of freezing behavior before, during and after conditioning. n = 6 mice, mean ± s.e.m. Two-way ANOVA test was performed: P = 0.3799 (between two animal groups during conditioning); two-tailed Student’s t tests were performed: P = 0.3297 and 0.8669 (during retrieval). g, Quantification of averaged speed, running speed and averaged distance in control, eCB2.0 and eCBmut expressing mice; n = 19, 8 and 6 mice. Boxes show the first and third quartiles as well as the median (line), and the whiskers extend to the most extreme data point that is no more than 1.5× the interquartile range from the box. One-way ANOVA tests were performed: P = 0.9017, 0.0681 and 0.4197. n.s., not significant.

Source data

Extended Data Fig. 10 eCB and Ca2+ waves in mouse hippocampal CA1 region during seizure activity.

In vivo two-photon fluorescence images of eCB2.0 and jRGECO1a expressed in the mouse hippocampal CA1 region before and after stimulus evoked seizure activity. Frames were extracted from those shown in Supplementary Video 1. Seconds (s) after the stimulus are indicated. Similar results were observed for 6 mice. Scale bar, 100 μm.

Supplementary information

Supplementary Video 1

eCB and Ca2+ signals in the mouse hippocampal CA1 during seizures. Fluorescence videos of eCB2.0 and jRGECO1a in the mouse hippocampal CA1 region during seizure activity, which is indicated by the LFP recording. The video is played at three times the speed. Similar results were observed for six mice.

About this article

Cite this article

Dong, A., He, K., Dudok, B. et al. A fluorescent sensor for spatiotemporally resolved imaging of endocannabinoid dynamics in vivo.
Nat Biotechnol (2021). https://doi.org/10.1038/s41587-021-01074-4

Download citation

Read More

Author: admin

Leave a Reply

Your email address will not be published. Required fields are marked *