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Near-infrared catecholamine nanosensors for high spatiotemporal dopamine imaging

Nature Protocols volume 16, pages 3026–3048 (2021)Cite this article

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Abstract

Dopamine neuromodulation of neural synapses is a process implicated in a number of critical brain functions and diseases. Development of protocols to visualize this dynamic neurochemical process is essential to understanding how dopamine modulates brain function. We have developed a non-genetically encoded, near-IR (nIR) catecholamine nanosensor (nIRCat) capable of identifying ~2-µm dopamine release hotspots in dorsal striatal brain slices. nIRCat is readily synthesized through sonication of single walled carbon nanotubes with DNA oligos, can be readily introduced into both genetically tractable and intractable organisms and is compatible with a number of dopamine receptor agonists and antagonists. Here we describe the synthesis, characterization and implementation of nIRCat in acute mouse brain slices. We demonstrate how nIRCat can be used to image electrically or optogenetically stimulated dopamine release, and how these procedures can be leveraged to study the effects of dopamine receptor pharmacology. In addition, we provide suggestions for building or adapting wide-field microscopy to be compatible with nIRCat nIR fluorescence imaging. We discuss strategies for analyzing nIR video data to identify dopamine release hotspots and quantify their kinetics. This protocol can be adapted and implemented for imaging other neuromodulators by using probes of this class and can be used in a broad range of species without genetic manipulation. The synthesis and characterization protocols for nIRCat take ~5 h, and the preparation and fluorescence imaging of live brain slices by using nIRCats require ~6 h.

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Fig. 1: Overview of the procedure.
Fig. 2: nIRCat synthesis.
Fig. 3: nIRCat characterization.
Fig. 4: Preparation of nIRCat-labeled acute brain slices.
Fig. 5: Acquiring and processing evoked dopamine response from nIRCat-labeled acute brain slices.

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Data availability

All materials are available from commercial sources or can be derived by using methods described in this protocol. All primary data underlying the figures reported in the article are publicly available via the Dryad repository at https://doi.org/10.6078/D1VH87. Additional data can be obtained from the corresponding author upon reasonable request.

Code availability

Code for analyzing and processing nIR images is freely available online at https://github.com/jtdbod/Nanosensor-Imaging-App and released under an MIT license.

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Acknowledgements

The authors acknowledge prior work from A.G.B. presented in this protocol and previously published by Beyene et al.19. We acknowledge support of NIH MIRA award R35 (to M.P.L.), a Burroughs Wellcome Fund Career Award at the Scientific Interface (CASI) (to M.P.L.), the Simons Foundation (to M.P.L.), a Stanley Fahn PDF Junior Faculty Grant with Award No. PF-JFA-1760 (to M.P.L.), a Beckman Foundation Young Investigator Award (to M.P.L.), a CZI Deep-Tissue Imaging Award (to M.P.L.) and a DARPA Young Investigator Award (to M.P.L.). M.P.L. is a Chan Zuckerberg Biohub investigator. S.J.Y. acknowledges the support of NSF Graduate Research Fellowships (NSF DGE 1752814). J.T.D.B.-O. is supported by the Department of Defense office of the Congressionally Directed Medical Research Programs (CDMRP) Parkinson’s Research Program (PRP) Early Investigator Award.

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Author notes

  1. These authors contributed equally to this work: Sarah J. Yang, Jackson Travis Del Bonis-O’Donnell.

Authors and Affiliations

  1. Department of Chemical and Biomolecular Engineering, University of California, Berkeley, Berkeley, CA, USA

    Sarah J. Yang, Jackson Travis Del Bonis-O’Donnell & Markita P. Landry

  2. Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA

    Abraham G. Beyene

  3. Innovative Genomics Institute (IGI), Berkeley, CA, USA

    Markita P. Landry

  4. California Institute for Quantitative Biosciences, QB3, University of California, Berkeley, Berkeley, CA, USA

    Markita P. Landry

  5. Chan-Zuckerberg Biohub, San Francisco, CA, USA

    Markita P. Landry

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  1. Sarah J. Yang
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  3. Abraham G. Beyene
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Contributions

A.G.B. and M.P.L. designed the experiments. J.T.D.B.-O. designed and built the microscope system and wrote the analysis software package. A.G.B., S.J.Y. and J.T.D.B.-O. performed the experiments. A.G.B., S.J.Y. and J.T.D.B.-O. analyzed the data and created the figures. S.J.Y. and J.T.D.B.-O. wrote the manuscript. All authors approved the manuscript.

Corresponding author

Correspondence to Markita P. Landry.

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The authors declare no competing interests.

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Peer review information Nature Protocols thanks the anonymous reviewers for their contribution to the peer review of this work.

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Related links

Key references using this protocol

Beyene, A. G. et al. Sci. Adv. 5, eaaw3108 (2019): https://doi.org/10.1126/sciadv.aaw3108

Jeong, S. et al. Sci. Adv. 5, 3771–3789 (2019): https://doi.org/10.1126/sciadv.aay3771

Yang, D. et al. ACS Nano. 14, 13794–13805 (2020): https://doi.org/10.1021/acsnano.0c06154

Beyene, A. G. et al. Nano Lett. 18, 6995–7003 (2018): https://doi.org/10.1021/acs.nanolett.8b02937

Supplementary information

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Yang, S.J., Del Bonis-O’Donnell, J.T., Beyene, A.G. et al. Near-infrared catecholamine nanosensors for high spatiotemporal dopamine imaging. Nat Protoc 16, 3026–3048 (2021). https://doi.org/10.1038/s41596-021-00530-4

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