Imaging neuromodulators with high spatiotemporal resolution using genetically encoded indicators

Abstract

Multiple aspects of neural activity, from neuronal firing to neuromodulator release and signaling, underlie brain function and ultimately shape animal behavior. The recently developed and constantly growing toolbox of genetically encoded sensors for neural activity, including calcium, voltage, neurotransmitter and neuromodulator sensors, allows precise measurement of these signaling events with high spatial and temporal resolution. Here, we describe the engineering, characterization and application of our recently developed dLight1, a suite of genetically encoded dopamine (DA) sensors based on human inert DA receptors. dLight1 offers high molecular specificity, requisite affinity and kinetics and great sensitivity for measuring DA release in vivo. The detailed workflow described in this protocol can be used to systematically characterize and validate dLight1 in increasingly intact biological systems, from cultured cells to acute brain slices to behaving mice. For tool developers, we focus on characterizing five distinct properties of dLight1: dynamic range, affinity, molecular specificity, kinetics and interaction with endogenous signaling; for end users, we provide comprehensive step-by-step instructions for how to leverage fiber photometry and two-photon imaging to measure dLight1 transients in vivo. The instructions provided in this protocol are designed to help laboratory personnel with a broad range of experience (at the graduate or post-graduate level) to develop and utilize novel neuromodulator sensors in vivo, by using dLight1 as a benchmark.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: General strategy for GPCR-based sensor development.
Fig. 2: Outline of the workflow for sensor characterization and validation.
Fig. 3: Experimental setup for in vivo dopamine imaging with fiber photometry.
Fig. 4: Representative traces of dopamine dynamics during reward-based learning.
Fig. 5: In vivo validation of dLight1 with optogenetic control of dopamine release.
Fig. 6: In vivo two-photon imaging of dopamine dynamics in behaving mice.

Data availability

All DNA plasmids and viruses mentioned in this protocol can be obtained from either the Tian laboratory at UC Davis or Addgene under a materials transfer agreement. All data present in this article are available from the authors upon request. Implemented and curated computer codes have been deposited in github (https://github.com/GradinaruLab/dLight1/).

References

  1. 1.

    Marder, E. Neuromodulation of neuronal circuits: back to the future. Neuron 76, 1–11 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  2. 2.

    Lee, S. H. & Dan, Y. Neuromodulation of brain states. Neuron 76, 209–222 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  3. 3.

    Tritsch, N. X. & Sabatini, B. L. Dopaminergic modulation of synaptic transmission in cortex and striatum. Neuron 76, 33–50 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  4. 4.

    Rusakov, D. A., Savtchenko, L. P., Zheng, K. & Henley, J. M. Shaping the synaptic signal: molecular mobility inside and outside the cleft. Trends Neurosci. 34, 359–369 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. 5.

    Cachope, R. & Cheer, J. F. Local control of striatal dopamine release. Front. Behav. Neurosci. 8, 188 (2014).

    PubMed  PubMed Central  Google Scholar 

  6. 6.

    Kehr, J. & Yoshitake, T. Monitoring molecules in neuroscience: historical overview and current advancements. Front. Biosci. (Elite Ed.) 5, 947–954 (2013).

    Google Scholar 

  7. 7.

    Wightman, R. M. Detection technologies. Probing cellular chemistry in biological systems with microelectrodes. Science 311, 1570–1574 (2006).

    CAS  PubMed  Google Scholar 

  8. 8.

    Darvesh, A. S. et al. In vivo brain microdialysis: advances in neuropsychopharmacology and drug discovery. Expert Opin. Drug Discov. 6, 109–127 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. 9.

    Park, J., Takmakov, P. & Wightman, R. M. In vivo comparison of norepinephrine and dopamine release in rat brain by simultaneous measurements with fast-scan cyclic voltammetry. J. Neurochem. 119, 932–944 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. 10.

    Bucher, E. S. & Wightman, R. M. Electrochemical analysis of neurotransmitters. Annu. Rev. Anal. Chem. (Palo Alto Calif.) 8, 239–261 (2015).

    CAS  Google Scholar 

  11. 11.

    Jaquins-Gerstl, A. & Michael, A. C. A review of the effects of FSCV and microdialysis measurements on dopamine release in the surrounding tissue. Analyst 140, 3696–3708 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. 12.

    Muller, A., Joseph, V., Slesinger, P. A. & Kleinfeld, D. Cell-based reporters reveal in vivo dynamics of dopamine and norepinephrine release in murine cortex. Nat. Methods 11, 1245–1252 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. 13.

    Lee, D. et al. Temporally precise labeling and control of neuromodulatory circuits in the mammalian brain. Nat. Methods 14, 495–503 (2017).

    CAS  Google Scholar 

  14. 14.

    Chen, T. W. et al. Ultrasensitive fluorescent proteins for imaging neuronal activity. Nature 499, 295–300 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Tian, L. et al. Imaging neural activity in worms, flies and mice with improved GCaMP calcium indicators. Nat. Methods 6, 875–881 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. 16.

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

    PubMed  PubMed Central  Google Scholar 

  17. 17.

    Venkatakrishnan, A. J. et al. Molecular signatures of G-protein-coupled receptors. Nature 494, 185–194 (2013).

    CAS  PubMed  Google Scholar 

  18. 18.

    Manglik, A. & Kruse, A. C. Structural basis for G protein-coupled receptor activation. Biochemistry 56, 5628–5634 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. 19.

    Svoboda, K. & Yasuda, R. Principles of two-photon excitation microscopy and its applications to neuroscience. Neuron 50, 823–839 (2006).

    CAS  PubMed  Google Scholar 

  20. 20.

    Dana, H. et al. High-performance GFP-based calcium indicators for imaging activity in neuronal populations and microcompartments. Nat. Methods 16, 649–657 (2019).

    CAS  PubMed  Google Scholar 

  21. 21.

    Abdelfattah, A. S. et al. Bright and photostable chemigenetic indicators for extended in vivo voltage imaging. Science 365, 699–704 (2019).

    CAS  PubMed  Google Scholar 

  22. 22.

    Piatkevich, K. D. et al. Population imaging of neural activity in awake behaving mice in multiple brain regions. Nature 574, 413–417 (2019).

    CAS  PubMed  Google Scholar 

  23. 23.

    Broussard, G. J. et al. In vivo measurement of afferent activity with axon-specific calcium imaging. Nat. Neurosci. 21, 1272–1280 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. 24.

    Marvin, J. S. et al. An optimized fluorescent probe for visualizing glutamate neurotransmission. Nat. Methods 10, 162–170 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. 25.

    Rasmussen, S. G. et al. Crystal structure of the β2 adrenergic receptor-Gs protein complex. Nature 477, 549–555 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. 26.

    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  Google Scholar 

  27. 27.

    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.e19 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. 28.

    Feng, J. et al. A genetically encoded fluorescent sensor for rapid and specific in vivo detection of norepinephrine. Neuron 102, 745–761.e8 (2019).

    CAS  PubMed  Google Scholar 

  29. 29.

    Mingote, S. et al. Functional connectome analysis of dopamine neuron glutamatergic connections in forebrain regions. J. Neurosci. 35, 16259–16271 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. 30.

    de Jong, J. W. et al. A neural circuit mechanism for encoding aversive stimuli in the mesolimbic dopamine system. Neuron 101, 133–151.e7 (2018).

    PubMed  Google Scholar 

  31. 31.

    Corre, J. et al. Dopamine neurons projecting to medial shell of the nucleus accumbens drive heroin reinforcement. eLife 7, e39945 (2018).

    PubMed  PubMed Central  Google Scholar 

  32. 32.

    Dong, H. et al. Dorsal striatum dopamine levels fluctuate across the sleep-wake cycle and respond to salient stimuli in mice. Front. Neurosci. 13, 242 (2019).

    PubMed  PubMed Central  Google Scholar 

  33. 33.

    Zhang, Y. et al. Capping of the N-terminus of PSD-95 by calmodulin triggers its postsynaptic release. EMBO J. 33, 1341–1353 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. 34.

    Thomas, P. & Smart, T. G. HEK293 cell line: a vehicle for the expression of recombinant proteins. J. Pharmacol. Toxicol. Methods 51, 187–200 (2005).

    CAS  PubMed  Google Scholar 

  35. 35.

    Tsvetanova, N. G. & von Zastrow, M. Spatial encoding of cyclic AMP signaling specificity by GPCR endocytosis. Nat. Chem. Biol. 10, 1061–1065 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. 36.

    Thorne, N., Inglese, J. & Auld, D. S. Illuminating insights into firefly luciferase and other bioluminescent reporters used in chemical biology. Chem. Biol. 17, 646–657 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. 37.

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

    PubMed  PubMed Central  Google Scholar 

  38. 38.

    Goodman, O. B. et al. Beta-arrestin acts as a clathrin adaptor in endocytosis of the beta2-adrenergic receptor. Nature 383, 447–450 (1996).

    CAS  PubMed  Google Scholar 

  39. 39.

    Vickery, R. G. & von Zastrow, M. Distinct dynamin-dependent and -independent mechanisms target structurally homologous dopamine receptors to different endocytic membranes. J. Cell Biol. 144, 31–43 (1999).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. 40.

    Irannejad, R. et al. Conformational biosensors reveal GPCR signalling from endosomes. Nature 495, 534–538 (2013).

    CAS  PubMed  Google Scholar 

  41. 41.

    Lee, Y. B., Glover, C. P., Cosgrave, A. S., Bienemann, A. & Uney, J. B. Optimizing regulatable gene expression using adenoviral vectors. Exp. Physiol. 90, 33–37 (2005).

    CAS  PubMed  Google Scholar 

  42. 42.

    Schnütgen, F. et al. A directional strategy for monitoring Cre-mediated recombination at the cellular level in the mouse. Nat. Biotechnol. 21, 562–565 (2003).

    PubMed  Google Scholar 

  43. 43.

    Aschauer, D. F., Kreuz, S. & Rumpel, S. Analysis of transduction efficiency, tropism and axonal transport of AAV serotypes 1, 2, 5, 6, 8 and 9 in the mouse brain. PLoS ONE 8, e76310 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. 44.

    Grimm, D. et al. In vitro and in vivo gene therapy vector evolution via multispecies interbreeding and retargeting of adeno-associated viruses. J. Virol. 82, 5887–5911 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. 45.

    Tervo, D. G. et al. A designer AAV variant permits efficient retrograde access to projection neurons. Neuron 92, 372–382 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. 46.

    Chan, K. Y. et al. Engineered AAVs for efficient noninvasive gene delivery to the central and peripheral nervous systems. Nat. Neurosci. 20, 1172–1179 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. 47.

    Challis, R. C. et al. Systemic AAV vectors for widespread and targeted gene delivery in rodents. Nat. Protoc. 14, 379–414 (2019).

    CAS  PubMed  Google Scholar 

  48. 48.

    Aurnhammer, C. et al. Universal real-time PCR for the detection and quantification of adeno-associated virus serotype 2-derived inverted terminal repeat sequences. Hum. Gene Ther. Methods 23, 18–28 (2012).

    CAS  PubMed  Google Scholar 

  49. 49.

    Nassi, J. J., Cepko, C. L., Born, R. T. & Beier, K. T. Neuroanatomy goes viral. Front. Neuroanat. 9, 80 (2015).

    PubMed  PubMed Central  Google Scholar 

  50. 50.

    Wickersham, I. R. et al. Monosynaptic restriction of transsynaptic tracing from single, genetically targeted neurons. Neuron 53, 639–647 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. 51.

    Lo, L. & Anderson, D. J. A Cre-dependent, anterograde transsynaptic viral tracer for mapping output pathways of genetically marked neurons. Neuron 72, 938–950 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. 52.

    Chatterjee, S. et al. Nontoxic, double-deletion-mutant rabies viral vectors for retrograde targeting of projection neurons. Nat. Neurosci. 21, 638–646 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  53. 53.

    Helmchen, F. & Denk, W. Deep tissue two-photon microscopy. Nat. Methods 2, 932–940 (2005).

    CAS  PubMed  Google Scholar 

  54. 54.

    Palij, P. & Stamford, J. A. Real-time monitoring of endogenous noradrenaline release in rat brain slices using fast cyclic voltammetry: 3. Selective detection of noradrenaline efflux in the locus coeruleus. Brain Res. 634, 275–282 (1994).

    CAS  PubMed  Google Scholar 

  55. 55.

    John, C. E. & Jones, S. R. in Electrochemical Methods for Neuroscience (eds Michael, A. C. & Borland, L. M.) (CRC Press/Taylor & Francis, Boca Raton, FL, 2007).

  56. 56.

    Bull, D. R. et al. Application of fast cyclic voltammetry to measurement of electrically evoked dopamine overflow from brain slices in vitro. J. Neurosci. Methods 32, 37–44 (1990).

    CAS  PubMed  Google Scholar 

  57. 57.

    Courtney, N. A. & Ford, C. P. The timing of dopamine- and noradrenaline-mediated transmission reflects underlying differences in the extent of spillover and pooling. J. Neurosci. 34, 7645–7656 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  58. 58.

    Xie, T., McCann, U. D., Kim, S., Yuan, J. & Ricaurte, G. A. Effect of temperature on dopamine transporter function and intracellular accumulation of methamphetamine: implications for methamphetamine-induced dopaminergic neurotoxicity. J. Neurosci. 20, 7838–7845 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  59. 59.

    Gunaydin, L. A. et al. Natural neural projection dynamics underlying social behavior. Cell 157, 1535–1551 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  60. 60.

    Sparta, D. R. et al. Construction of implantable optical fibers for long-term optogenetic manipulation of neural circuits. Nat. Protoc. 7, 12–23 (2011).

    PubMed  Google Scholar 

  61. 61.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  62. 62.

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

    PubMed  PubMed Central  Google Scholar 

  63. 63.

    Goldey, G. J. et al. Removable cranial windows for long-term imaging in awake mice. Nat. Protoc. 9, 2515–2538 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  64. 64.

    Asaad, W. F., Santhanam, N., McClellan, S. & Freedman, D. J. High-performance execution of psychophysical tasks with complex visual stimuli in MATLAB. J. Neurophysiol. 109, 249–260 (2013).

    PubMed  Google Scholar 

  65. 65.

    Owen, S. F. & Kreitzer, A. C. An open-source control system for in vivo fluorescence measurements from deep-brain structures. J. Neurosci. Methods 311, 170–177 (2019).

    PubMed  Google Scholar 

  66. 66.

    Chen, Y. et al. NS21: re-defined and modified supplement B27 for neuronal cultures. J. Neurosci. Methods 171, 239–247 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  67. 67.

    Franklin, K. & Paxinos, G. Paxinos and Franklin’s the Mouse Brain in Stereotaxic Coordinates, Compact: The Coronal Plates and Diagrams (Academic Press, 2019).

  68. 68.

    Menegas, W., Babayan, B. M., Uchida, N. & Watabe-Uchida, M. Opposite initialization to novel cues in dopamine signaling in ventral and posterior striatum in mice. eLife 6, e21886 (2017).

    PubMed  PubMed Central  Google Scholar 

  69. 69.

    al, J. R. C. E. Dorsal raphe dopamine neurons modulate arousal and promote wakefulness by salient stimuli. Neuron 94, 1205–1219 (2017).

    Google Scholar 

  70. 70.

    Klapoetke, N. C. et al. Independent optical excitation of distinct neural populations. Nat. Methods 11, 338–346 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  71. 71.

    Kibbe, W. A. OligoCalc: an online oligonucleotide properties calculator. Nucleic Acids Res. 35, W43–W46 (2007).

    PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

This work was supported by NIH BRAIN Initiative grants U01NS090604, U01NS013522, DP2MH107056 and U01NS103571 (L.T.); grants DP2NS083038, R01NS085938 and P30CA014195 (A.N.); BRAIN Initiative grants U01NS013522 (J.T.W. and M.v.Z.), and NIH grant DP2NS087949 and NIH/NIA grant R01AG047664 (V.G.). K.M. is a DFG research fellow and recipient of a Catharina Foundation postdoctoral scholar award. V.G. is a Heritage Principal Investigator supported by the Heritage Medical Research Institute.

Author information

Affiliations

Authors

Contributions

T.P. and L.T. wrote the manuscript with contributions from J.R.C. and V.G. (fiber photometry and optogenetics), G.J.B. (rAAV preparation and cloning), R.L. (structural modeling), A.M. and M.v.Z. (TIRF microscopy, FACS and cAMP measurements), K.M. and A.N. (in vivo two-photon imaging in behaving mice), and J.W. (ex vivo two-photon imaging).

Corresponding authors

Correspondence to John Williams or Axel Nimmerjahn or Mark von Zastrow or Viviana Gradinaru or Lin Tian.

Ethics declarations

Competing interests

T.P. and L.T. are co-inventors on a patent application (WO/2018/098262A1) for the technology described in this paper. L.T. is the co-founder of Seven Biosciences.

Additional information

Peer review information Nature Protocols thanks Thomas Knopfel and 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.

Related link

Key references using this protocol

Patriarchi, T. et al. Science 360, eaat4422 (2018) https://science.sciencemag.org/content/360/6396/eaat4422/

Integrated supplementary information

Supplementary Fig. 1 Design of GPCR-based sensors using the universal cpGFP module.

Shown is a sequence alignment of the contiguous regions of transmembrane helixes 5 and 6 (TM5, TM6) used to develop fluorescent sensors from 10 different GPCRs. The third intracellular loop of the receptors is deleted in the sensors (region shown in orange shading) and replaced with the universal cpGFP module consisting of the indicated residues surrounding cpGFP (indicated in the green box). The complete sequence for the cpGFP module is available from Addgene or in our original publication16). Adapted with permission from16, American Association for the Advancement of Science.

Supplementary Fig. 2 Comparison of locomotion-related dopamine transients using dLight1.1 or dLight1.2 in behaving mice with two-photon microscopy.

a, Schematics of two-photon imaging of head-fixed mouse during treadmill locomotion. b, Mean ΔF/F for all significant positive-going transients in mice expressing either dLight1.1 (n = 2 mice, 131 transients) or dLight1.2 (n = 2 mice, 31 transients). c-d, Mean transient ΔF/F during rest and run for all fields in dLight1.1 (n = 2 mice, 5 fields) and dLight1.2 (n = 2 mice, 8 fields). Adapted with permission from16, American Association for the Advancement of Science.

Supplementary Fig. 3 Comparison between dLight1.3b and GRAB-DA1m in neurons.

a, Representative images of primary cultured neurons (DIV15) expressing either dLight1.3b or GRAB-DA1m. Cells were transduced by directly applying ~1010 viral particles either AAV9.Synapsin.dLight1.3b or AAV9.Synapsin.GRAB-DA1m onto the medium 14 d prior to imaging (AAVs produced by the UC Davis Viral Vector Core Facility). Cells were imaged under identical conditions (laser intensity, pinhole size, etc.) with a 40x oil-based objective on a Zeiss 710 confocal microscope. Intensity profile plots representative of the lines drawn across the neuronal dendrites are show as insets. Scale bars, 20 µm. b, Basal fluorescence and signal to noise calculations were performed on Fiji (Image J) from regions of interest manually drawn on the neuronal membrane. At least n=3 neurons from 2 separate experiments were included in the analysis. Data are shown as individual ROI values ± SEM. ****p<0.0001, n.s.= not significant, unpaired student’s t-test. c, Time-lapse images were acquired at ~1 second intervals. Drugs (dopamine, DA; haloperidol, Halo; SCH23390, SCH) were directly applied on the cells at the time points and concentrations indicated in the graphs. n≥2 neurons for each sensor. Data are shown as mean ± SEM.

Supplementary Fig. 4 Equipment setup for photometry recordings.

a, Photograph of optical components in the fiber photometry setup described in 51 and 18. b, Photograph of data processor with inputs from photodetector and external TTL and with outputs for LED modulation. Optical components in (a) are located inside the rackbox below. c, Photograph of a mouse expressing dLight1.1 during freely moving behavior inside an operant box. This water-deprived mouse is obtaining sucrose water reward from lickometer. All procedures were performed in accordance with the guidelines of the National Institutes of Health and were approved by the Institutional Animal Care and Use Committee (IACUC) and by the Office of Laboratory Animal Resources at the California Institute of Technology.

Supplementary Fig. 5 Example raw photometry traces.

a, Raw photometry signals demodulated from 490-nm (blue) and 405-nm (violet) channel. Notice photobleaching in both channels. b, Photometry signals after 405-nm signal is fitted to 490-nm signal by applying a least-squares fit. c, ΔF/F trace is calculated for each recording session as. (490-nm signal – fitted 405-nm signal)/fitted 405-nm signal. Notice this can effectively remove the contribution of photobleaching and potential contamination from motion artifacts. All procedures were performed in accordance with the guidelines of the National Institutes of Health and were approved by the Institutional Animal Care and Use Committee (IACUC) and by the Office of Laboratory Animal Resources at the California Institute of Technology.

Supplementary Fig. 6 Equipment setup of perfusion system for titrations on cells.

a, The perfusion system setup for performing neuromodulator titrations during one-photon imaging of sensor fluorescence is based on an inverted confocal microscope (7). A display (1) connected with a digital controller (2) for the eight-valve perfusion system (3), an eight-channel perfusion inlet (4) and a single channel outlet (5) connected to a peristaltic pump (6) for removal of excess buffer from the dish. b, Close up view of the perfusion inlet and outlet setup on the stage adaptor containing the imaged dish.

Supplementary information

Supplementary Information

Supplementary Figures 1–6

Reporting Summary

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Patriarchi, T., Cho, J.R., Merten, K. et al. Imaging neuromodulators with high spatiotemporal resolution using genetically encoded indicators. Nat Protoc 14, 3471–3505 (2019). https://doi.org/10.1038/s41596-019-0239-2

Download citation

Further reading

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

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