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.

A tissue-like neurotransmitter sensor for the brain and gut

Nature volume 606, pages 94–101 (2022)Cite this article

Subjects

Abstract

Neurotransmitters play essential roles in regulating neural circuit dynamics both in the central nervous system as well as at the peripheral, including the gastrointestinal tract1,2,3. Their real-time monitoring will offer critical information for understanding neural function and diagnosing disease1,2,3. However, bioelectronic tools to monitor the dynamics of neurotransmitters in vivo, especially in the enteric nervous systems, are underdeveloped. This is mainly owing to the limited availability of biosensing tools that are capable of examining soft, complex and actively moving organs. Here we introduce a tissue-mimicking, stretchable, neurochemical biological interface termed NeuroString, which is prepared by laser patterning of a metal-complexed polyimide into an interconnected graphene/nanoparticle network embedded in an elastomer. NeuroString sensors allow chronic in vivo real-time, multichannel and multiplexed monoamine sensing in the brain of behaving mouse, as well as measuring serotonin dynamics in the gut without undesired stimulations and perturbing peristaltic movements. The described elastic and conformable biosensing interface has broad potential for studying the impact of neurotransmitters on gut microbes, brain–gut communication and may ultimately be extended to biomolecular sensing in other soft organs across the body.

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

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Learn more

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

Fig. 1: NeuroString for sensing neurotransmitters in the brain and gut.
Fig. 2: Electrochemical sensing performance of NeuroString electrode in solution.
Fig. 3: Neurochemical sensing in the brain.
Fig. 4: Neurochemical sensing in the GI system.

Data availability

The datasets generated during and/or analysed in this study are available from the corresponding author on reasonable request. The method and source code for the ex vivo gut segment diameter mapping recipe is provided in the Supplementary Information. Source data are provided with this paper.

References

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  2. Nakatsuka, N. et al. Aptamer–field-effect transistors overcome Debye length limitations for small-molecule sensing. Science 362, 319–324 (2018).

    Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 

  3. Kishida, K. T. et al. Subsecond dopamine fluctuations in human striatum encode superposed error signals about actual and counterfactual reward. Proc. Natl Acad. Sci. 113, 200–205 (2016).

    Article  CAS  PubMed  ADS  Google Scholar 

  4. Wise, R. A. Dopamine, learning and motivation. Nat. Rev. Neurosci. 5, 483–494 (2004).

    Article  CAS  PubMed  Google Scholar 

  5. Naughton, M., Mulrooney, J. B. & Leonard, B. E. A review of the role of serotonin receptors in psychiatric disorders. Hum. Psychopharmacol. 15, 397–415 (2000).

    Article  CAS  PubMed  Google Scholar 

  6. Hyman, S. E. & Malenka, R. C. Addiction and the brain: the neurobiology of compulsion and its persistence. Nat. Rev. Neurosci. 2, 695–703 (2001).

    Article  CAS  PubMed  Google Scholar 

  7. Russo, S. J. & Nestler, E. J. The brain reward circuitry in mood disorders. Nat. Rev. Neurosci. 14, 609–625 (2013).

    Article  CAS  PubMed  Google Scholar 

  8. Cummings, J. L. Depression and Parkinson’s disease: a review. Am. J. Psychiatry 149, 443–454 (1992).

    Article  CAS  PubMed  Google Scholar 

  9. Spohn, S. N. & Mawe, G. M. Non-conventional features of peripheral serotonin signalling-the gut and beyond. Nat. Rev. Gastroenterol. Hepatol. 14, 412–420 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Furness, J. B. The enteric nervous system and neurogastroenterology. Nat. Rev. Gastroenterol. Hepatol. 9, 286–294 (2012).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Robinson, D. L., Venton, B. J., Heien, M. L. A. V. & Wightman, R. M. Detecting subsecond dopamine release with fast-scan cyclic voltammetry in vivo. Clin. Chem. 49, 1763–1773 (2003).

    Article  CAS  PubMed  Google Scholar 

  14. Hashemi, P., Dankoski, E. C., Petrovic, J., Keithley, R. B. & Wightman, R. M. Voltammetric detection of 5-hydroxytryptamine release in the rat brain. Anal. Chem. 81, 9462–9471 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Schwerdt, H. N. et al. Long-term dopamine neurochemical monitoring in primates. Proc. Natl Acad. Sci. 114, 13260–13265 (2017).

    Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 

  16. Taylor, I. M. et al. Enhanced dopamine detection sensitivity by PEDOT/graphene oxide coating on in vivo carbon fiber electrodes. Biosens. Bioelectron. 89, 400–410 (2017).

    Article  CAS  PubMed  Google Scholar 

  17. Clark, J. J. et al. Chronic microsensors for longitudinal, subsecond dopamine detection in behaving animals. Nat. Methods 7, 126–129 (2010).

    Article  CAS  PubMed  Google Scholar 

  18. Moran, R. J. et al. The protective action encoding of serotonin transients in the human brain. Neuropsychopharmacology 43, 1425–1435 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Bang, D. et al. Sub-second dopamine and serotonin signaling in human striatum during perceptual decision-making. Neuron 108, 999–1010.e6 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Minev, I. R. et al. Electronic dura mater for long-term multimodal neural interfaces. Science 347, 159–163 (2015).

    Article  CAS  PubMed  ADS  Google Scholar 

  21. Vázquez-Guardado, A., Yang, Y., Bandodkar, A. J. & Rogers, J. A. Recent advances in neurotechnologies with broad potential for neuroscience research. Nat. Neurosci. 23, 1522–1536 (2020).

    Article  CAS  PubMed  Google Scholar 

  22. Hong, G. & Lieber, C. M. Novel electrode technologies for neural recordings. Nat. Rev. Neurosci. 20, 330–345 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Deng, J. et al. Electrical bioadhesive interface for bioelectronics. Nat. Mater. 20, 229–236 (2021).

    Article  CAS  PubMed  ADS  Google Scholar 

  24. Frank, J. A., Antonini, M. J. & Anikeeva, P. Next-generation interfaces for studying neural function. Nat. Biotechnol. 37, 1013–1023 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Lee, S. et al. Ultrasoft electronics to monitor dynamically pulsing cardiomyocytes. Nat. Nanotechnol. 14, 156–160 (2019).

    Article  CAS  PubMed  ADS  Google Scholar 

  26. Terem, I. et al. Revealing sub-voxel motions of brain tissue using phase-based amplified MRI (aMRI). Magn. Reson. Med. 80, 2549–2559 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Spencer, K. C. et al. Characterization of mechanically matched hydrogel coatings to improve the biocompatibility of neural implants. Sci. Rep. 7, 1952 (2017).

    Article  PubMed  PubMed Central  ADS  CAS  Google Scholar 

  28. Schwerdt, H. N. et al. Subcellular probes for neurochemical recording from multiple brain sites. Lab Chip 17, 1104–1115 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Mazzuoli-Weber, G. & Schemann, M. Mechanosensitivity in the enteric nervous system. Front. Cell. Neurosci. 9, 408 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  30. Patel, B. A., Bian, X., Quaiserová-Mocko, V., Galligan, J. J. & Swain, G. M. In vitro continuous amperometric monitoring of 5-hydroxytryptamine release from enterochromaffin cells of the guinea pig ileum. Analyst 132, 41–47 (2007).

    Article  CAS  PubMed  ADS  Google Scholar 

  31. Osorio, N. & Delmas, P. Patch clamp recording from enteric neurons in situ. Nat. Protoc. 6, 15–27 (2011).

    Article  PubMed  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  33. Xu, C., Wu, F., Yu, P. & Mao, L. In vivo electrochemical sensors for neurochemicals: recent update. ACS Sens. 4, 3102–3118 (2019).

    Article  CAS  PubMed  Google Scholar 

  34. Kinloch, I. A., Suhr, J., Lou, J., Young, R. J. & Ajayan, P. M. Composites with carbon nanotubes and graphene: an outlook. Science 362, 547–553 (2018).

    Article  CAS  PubMed  ADS  Google Scholar 

  35. Sun, D. et al. Electrodeposition synthesis of a NiO/CNT/PEDOT composite for simultaneous detection of dopamine, serotonin, and tryptophan. Sens. Actuators B Chem. 259, 433–442 (2018).

    Article  CAS  Google Scholar 

  36. Yang, W. et al. Enhancing electrochemical detection of dopamine via dumbbell-like FePt–Fe3O4 nanoparticles. Nanoscale 9, 1022–1027 (2017).

    Article  CAS  PubMed  Google Scholar 

  37. Fernandes, D. M. et al. Novel electrochemical sensor based on N-doped carbon nanotubes and Fe3O4 nanoparticles: simultaneous voltammetric determination of ascorbic acid, dopamine and uric acid. J. Colloid Interface Sci. 432, 207–213 (2014).

    Article  CAS  PubMed  ADS  Google Scholar 

  38. Salinas-Hernández, X. I. et al. Dopamine neurons drive fear extinction learning by signaling the omission of expected aversive outcomes. eLife 7, e38818 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  39. Bertrand, P. P., Hu, X., Mach, J. & Bertrand, R. L. Serotonin (5-HT) release and uptake measured by real-time electrochemical techniques in the rat ileum. Am. J. Physiol. Gastrointest. Liver Physiol. 295, G1228–G1236 (2008).

    Article  CAS  PubMed  Google Scholar 

  40. Bertrand, P. P. Real-time measurement of serotonin release and motility in guinea pig ileum. J. Physiol. 577, 689–704 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Bertrand, P. P. & Bertrand, R. L. Serotonin release and uptake in the gastrointestinal tract. Auton. Neurosci. 153, 47–57 (2010).

    Article  CAS  PubMed  Google Scholar 

  42. Bertrand, P. P. Real-time detection of serotonin release from enterochromaffin cells of the guinea-pig ileum. Neurogastroenterol. Motil. 16, 511–514 (2004).

    Article  CAS  PubMed  Google Scholar 

  43. Mawe, G. M. & Hoffman, J. M. Serotonin signalling in the gut—functions, dysfunctions and therapeutic targets. Nat. Rev. Gastroenterol. Hepatol. 10, 473–486 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Gershon, M. D. & Tack, J. The serotonin signaling system: from basic understanding to drug development for functional GI disorders. Gastroenterology 132, 397–414 (2007).

    Article  CAS  PubMed  Google Scholar 

  45. Okayasu, I. et al. A novel method in the induction of reliable experimental acute and chronic ulcerative colitis in mice. Gastroenterology 98, 694–702 (1990).

    Article  CAS  PubMed  Google Scholar 

  46. Linden, D. R., Chen, J. X., Gershon, M. D., Sharkey, K. A. & Mawe, G. M. Serotonin availability is increased in mucosa of guinea pigs with TNBS-induced colitis. Am. J. Physiol. Gastrointest. Liver Physiol. 285, 761–768 (2003).

    Article  Google Scholar 

  47. Padmanabhan, P., Grosse, J., Asad, A. B. M. A., Radda, G. K. & Golay, X. Gastrointestinal transit measurements in mice with 99mTc-DTPA-labeled activated charcoal using NanoSPECT-CT. EJNMMI Res. 3, 60 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  48. Singh, M. K. & Bandyopadhyay, D. Design and synthesis of nanoporous perylene bis-imide linked metalloporphyrin frameworks and their catalytic activity. J. Chem. Sci. 128, 1–8 (2016).

    Article  CAS  Google Scholar 

  49. Lin, J. et al. Laser-induced porous graphene films from commercial polymers. Nat. Commun. 5, 5714 (2014).

    Article  CAS  PubMed  ADS  Google Scholar 

  50. Metscher, B. D. MicroCT for comparative morphology: Simple staining methods allow high-contrast 3D imaging of diverse non-mineralized animal tissues. BMC Physiology 9, 11 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  51. Roberts, J. G. & Sombers, L. A. Fast-scan cyclic voltammetry: chemical sensing in the brain and beyond. Anal. Chem. 90, 490–504 (2018).

    Article  CAS  PubMed  Google Scholar 

  52. Howard, C. D., Li, H., Geddes, C. E. & Jin, X. Dynamic nigrostriatal dopamine biases action selection. Neuron 93, 1436–1450.e8 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Stamford, J. A., Kruk, Z. L. & Millar, J. Striatal dopamine terminals release serotonin after 5-HTP pretreatment: in vivo voltammetric data. Brain Res. 515, 173–180 (1990).

    Article  CAS  PubMed  Google Scholar 

  54. Swamy, B. E. K. & Venton, B. J. Carbon nanotube-modified microelectrodes for simultaneous detection of dopamine and serotonin in vivo. Analyst 132, 876–884 (2007).

    Article  CAS  PubMed  ADS  Google Scholar 

  55. Jackson, B. P. & Mark Wightman, R. Dynamics of 5-hydroxytryptamine released from dopamine neurons in the caudate putamen of the rat. Brain Res. 674, 163–166 (1995).

    Article  CAS  PubMed  Google Scholar 

  56. Tatem, K. S. et al. Behavioral and locomotor measurements using an open field activity monitoring system for skeletal muscle diseases. J. Vis. Exp. 29, 51785 (2014).

    Google Scholar 

  57. Lynch, J. J., Castagné, V., Moser, P. C. & Mittelstadt, S. W. Comparison of methods for the assessment of locomotor activity in rodent safety pharmacology studies. J. Pharmacol. Toxicol. Methods 64, 74–80 (2011).

    Article  CAS  PubMed  Google Scholar 

  58. Hoibian, E., Florens, N., Koppe, L., Vidal, H. & Soulage, C. O. Distal colon motor dysfunction in mice with chronic kidney disease: putative role of uremic toxins. Toxins 10, 204 (2018).

    Article  PubMed Central  CAS  Google Scholar 

  59. Wirtz, S. et al. Chemically induced mouse models of acute and chronic intestinal inflammation. Nat. Protoc. 12, 1295–1309 (2017).

    Article  CAS  PubMed  Google Scholar 

  60. Tainaka, K. et al. Chemical landscape for tissue clearing based on hydrophilic reagents. Cell Rep. 24, 2196–2210.e9 (2018).

    Article  CAS  PubMed  Google Scholar 

  61. Swaminathan, M. et al. Video imaging and spatiotemporal maps to analyze gastrointestinal motility in mice. J. Vis. Exp. 2016, e53828 (2016).

    Google Scholar 

  62. Spear, E. T. et al. Altered gastrointestinal motility involving autoantibodies in the experimental autoimmune encephalomyelitis model of multiple sclerosis. Neurogastroenterol. Motil. 30, e13349 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

This work was partly supported by the Stanford Bio-X Interdisciplinary Initiatives Seed Grants Program (IIP award) and the Wu Tsai Neuroscience Institute Big Idea Award. Part of this work was performed at the Stanford Nano Shared Facilities (SNSF), supported by the National Science Foundation under award ECCS-1542152. X.C. and L.Y. are partially supported by National Institutes of Health grants R01DA045664, R01MH116904 and R01HL150566, and X.C. is also supported by the Firmenich Next Generation Fund. Y.L. is supported by National Science Scholarship (A*STAR, Singapore). Part of the electrochemical measurement experiments during revision was conducted at Michigan State University. We thank N. G. Hollon and X. Jin from the Salk Institute for their help with the FSCV technique; G. Hennig (University of Vermont) for the design and use of Volumetry software; S. Baker and A. Zhang for their help on pig animal surgery; S. Das from the Julia Kaltschmidt lab for the help on the colonic motility assay setup; S. Rogalla for the endoscope imaging; T. Z. Gao for comments on the manuscript; and Y. Tsao, S. Chen and Y. Liu for their help on materials characterization.

Author information

Author notes

  1. These authors contributed equally: Jinxing Li, Yuxin Liu, Lei Yuan

Authors and Affiliations

  1. Department of Chemical Engineering, Stanford University, Stanford, CA, USA

    Jinxing Li, Yu-Qing Zheng, Simiao Niu, Levent Beker, Vittorio Mottini, Jeffrey B.-H. Tok & Zhenan Bao

  2. Department of Biomedical Engineering and Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, USA

    Jinxing Li & Vittorio Mottini

  3. Department of Bioengineering, Stanford University, Stanford, CA, USA

    Yuxin Liu & James C. Y. Dunn

  4. Department of Biology, Stanford University, Stanford, CA, USA

    Lei Yuan, Baibing Zhang & Xiaoke Chen

  5. Division of Gastroenterology and Hepatology, Stanford University School of Medicine, Stanford, CA, USA

    Estelle Spear Bishop & Aida Habtezion

  6. Department of Materials Science and Engineering, Stanford University, Stanford, CA, USA

    Kecheng Wang, Jing Tang, Wenhui Xu, Gan Chen & Yi Cui

  7. Department of Chemistry, Stanford University, Stanford, CA, USA

    Thomas L. Li & Bianxiao Cui

  8. Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA

    Thomas L. Li & Sergiu P. Pașca

  9. Department of Surgery/Pediatric Surgery, Stanford University, Stanford, CA, USA

    Modupeola Diyaolu, Anne-Laure Thomas & James C. Y. Dunn

  10. Stanford Brain Organogenesis, Wu Tsai Neurosciences Institute and Bio-X, Stanford University, Stanford, CA, USA

    Sergiu P. Pașca

Authors
  1. Jinxing Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yuxin Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lei Yuan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Baibing Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Estelle Spear Bishop
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Kecheng Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Jing Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Yu-Qing Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Wenhui Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Simiao Niu
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Levent Beker
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Thomas L. Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Gan Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Modupeola Diyaolu
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Anne-Laure Thomas
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Vittorio Mottini
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Jeffrey B.-H. Tok
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. James C. Y. Dunn
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Bianxiao Cui
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Sergiu P. Pașca
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Yi Cui
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. Aida Habtezion
    View author publications

    You can also search for this author in PubMed Google Scholar

  23. Xiaoke Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  24. Zhenan Bao
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

J.L., Y.L., X.C. and Z.B. conceived the project. J.L., Y.L., L.Y., X.C., Z.B., E.S.B. and A.H. designed the experiments. J.L. and Y.L. designed and fabricated the device and performed the mechanical, electrical and electrochemical measurements. K.W. synthesized the metalloporphyrin. J.L., Y.L. and Y.-Q.Z. performed the SEM, CT and photography. J.T. and G.C. performed the EELS, HRTEM, SEM and Raman characterizations. J.L., Y.L., L.Y., B.Z. and E.S.B. carried out the ex vivo experiments and animal studies. S.N. and L.B. contributed to the initial materials selection. M.D., A.-L.T. and J.C.Y.D. developed the pig animal model and protocol. W.X. helped to prepare the schematics. V.M. helped to analyse the voltammograms. T.L.L., B.C. and S.P.P. contributed to characterization of tissue responses. J.L., Y.L. and E.S.B. analysed the results. J.L., Y.L., X.C., Z.B. and J.B.-H.T. wrote the manuscript. All authors discussed the results and commented on the manuscript. All authors have given approval to the final version of the manuscript. Z.B. and X.C. directed the project.

Corresponding authors

Correspondence to Xiaoke Chen or Zhenan Bao.

Ethics declarations

Competing interests

J.L., Y.L. and Z.B. are inventors on a patent application (no. 63/085,720) submitted by the Board of Trustees of Stanford University.

Peer review

Peer review information

Nature thanks Giovanni Traverso and the other, anonymous, reviewers for their contribution to the peer review of this work.

Additional information

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

Extended data figures and tables

Extended Data Fig. 1 Fabrication process of the NeuroString sensors.

a, Scheme showing the chemical structures of the polymer precursors and the laser carbonization process. b, Layout showing the dimensions of the multichannel NeuroString electrodes for the sensing in the brain. c, Fabrication process of the NeuroString for brain neurochemical sensing. (1) A polymer precursor solution containing a polyamic acid mixed with a metalloporphyrin (2.3 × 10−1 mM) was uniformly drop-casted (50 μl cm−2) as a film on a polyimide substrate. (2) The precursor film was annealed in air at 250 °C for 1 h to form the polyimide film. (3) The film surface was laser engraved by an Epilog Fusion M2 laser (power 6 W) to generate the graphene network with Fe3O4 or NiO nanoparticles. A HPDFO lens with a focal point of 0.001” (25.4 μm) can be used improve the resolution of the engraving. (4) A SEBS solution (H1062, 0.1 g ml−1 in toluene) was drop-casted on the graphene networks, which was then peeled off from the substrate and attached to another glass substrate when the SEBS side was in contact with glass. (5) Another SEBS layer (H1062, 0.1 g ml−1 in toluene) was spin-coated on top at 1,000 rpm, to form an encapsulation layer. (6) A high laser power of 30 W was subsequently used to cut into the desired size and shape and isolate the individual electrode strings with width 90 µm. (7) The electrode area is dip-coated in another elastomer solution (Kuraray LA3320, 0.1 g ml−1 in acetone) to fully encapsulate the graphene electrodes. (8) For implantation in the brain, the NeuroStrings were dip-coated in a pullulan solution (0.3 g ml−1) and dried overnight to form a rigid coating. (9) The tips of electrodes were cut by a razor blade to expose the cross sections of the graphene. To fully expose the graphene, the cross-sectional surfaces of electrodes were oxygen plasma-treated for 2 min (Technics Micro-RIE Series 800, 150 W, 200 mTorr). To avoid any electrochemical interference of the ascorbic acid from the biological fluids, the tips of the electrodes were dipped in a Nafion solution (0.5% in water/ethanol) to form a Nafion coating before use. (10) Dissolving of the pullulan in tissue finally releases the NeuroString as a soft implant. d, Layout showing the dimensions of the NeuroString electrodes for sensing in the gut. e, Fabrication process of the NeuroString for gut neurochemical sensing. (1) A polymer precursor solution containing a polyamic acid mixed with a metalloporphyrin (2.3 × 10−1 mM) was uniformly drop-casted (50 μl cm−2) as a film on a polyimide substrate. (2) The precursor film was annealed in air at 250 °C for 1 h to form the polyimide film. (3) The film surface was laser engraved by an Epilog Fusion M2 Laser (power 6 W) to generate the graphene network with Fe3O4 or NiO nanoparticles. (4) SEBS solution (H1062, 0.1 g ml−1 in toluene) was drop casted on the graphene networks to form graphene–SEBS composite, which was then delaminated and transferred from the substrate and flipped on another glass substrate. (5) Another SEBS layer (H1062, 0.1 g ml−1 in toluene) was spin-coated on top at 1,000 rpm, to form an encapsulation layer. (6) A high-power laser with power 30 W and 20% speed was used to cut the undesired part of the device. (7) For easier placing the mouse gut, a pullulan solution (0.1 g ml−1) was dip-coated on the electrodes and dried overnight to form the shuttle layer. (8) The ends of electrodes were cut using a razor blade to expose the cross sections of the graphene. To fully expose the graphene, the cross-sectional surfaces of electrodes were oxygen plasma-treated for 2 min. To avoid any interference of the ascorbic acid from the biological fluids, the tips of the electrodes were finally dipped in a Nafion solution (0.5% in water/ethanol) to form a Nafion coating before use. (9) Dissolving of the pullulan in tissue will release the NeuroString as a soft implant.

Extended Data Fig. 2 Characterization of the laser-induced graphene used in the NeuroString sensor.

a, SEM images showing the resolution of the laser fabrication process (laser power 6 W). Direct laser writing can achieve a resolution of about 100 μm, whereas individual structures smaller than 50 μm can be fabricated by the laser engraving (etching) process: for example, two laser cutting lines with a distance of 150 μm will lead to a free-standing electrode of width 50 μm. b–d, SEM images showing the graphene networks made by different laser powers. The graphene layer thicknesses are: 40–50 μm (laser power 6 W), 50–80 μm (laser power 9 W) and 120–150 μm (laser power 12 W). Thicker polyimide film will be carbonized at a higher laser power, so that the graphene layer increases. The SEM characterization was repeated and reproduced six times. e, High-resolution TEM of laser-induced graphene showing the characteristic 0.34-nm d-spacing between graphene sheets (laser power 6 W). The TEM characterization was repeated and reproduced three times. f, Normalized confocal Raman spectra (633-nm laser excitation) of a laser-induced graphene film made by different laser powers. The intensity profile indicates that higher laser power induced more defects in graphene. The higher D band at lower laser power is mainly owing to the more oxygen and graphene oxide present in the laser-induced graphene.

Source data

Extended Data Fig. 3 Characterization of the transition metal oxide nanoparticles involved in the NeuroString sensor.

a, TEM characterization of the laser-induced graphene decorated with Fe3O4 nanoparticles (laser power 6 W). The TEM characterization was repeated and reproduced three times. b, TEM intensity profile of the Fe3O4 nanocrystal shown in a. c, EELS analysis of the laser-induced graphene decorated with Fe3O4 nanoparticles (laser power 6 W). d, EELS mapping showing decoration of Fe3O4 nanoparticles on the graphene. e, TEM characterization of the laser-induced graphene decorated with NiO nanoparticles (laser power 6 W). The TEM characterization was repeated and reproduced three times. f, TEM intensity profile of the NiO nanocrystal shown in e. g, EELS analysis of the laser-induced graphene decorated with NiO nanoparticles (laser power 6 W).

Source data

Extended Data Fig. 4 Selectivity and sensitivity characterization of the NeuroString sensors.

a, b, Cyclic voltammetry backgrounds of different electrodes in PBS buffer (pH 7.4) (scan rate 400 V s−1). NeuroString has lower background current than the carbon fibre. c, Reaction mechanism of the dopamine and serotonin oxidation during FSCV measurement. d, Comparison of the selectivity of different electrodes for simultaneous dopamine and serotonin sensing using cyclic voltammetry. The cyclic voltammetry is performed in a solution with 500 nM dopamine and 500 nM serotonin in PBS (pH 7.4), with a scan rate of 10 V s−1. e, f, Normalized oxidation current (nA) of NeuroString (without and with Fe3O4 nanoparticles) and carbon nanofibres for dopamine and serotonin sensing (scan rate 400 V s−1). As the background current varies from 100 to 500 nA depending on the dimension, the \({\rm{normalized\; oxidation\; current}}=\frac{{\rm{Background\; current}}}{{\rm{Faradic\; current}}}\times 1,000\,{\rm{nA}}\); the background current is defined as the current value at 0.5 V. n = 5 different NeuroString electrodes were examined in independent measurements, box range: 25–75%. g, Concentration-dependent calibration response of NeuroString electrode to 5-HT using FSCV and chronoamperometry. (PBS buffer pH 7.4; chronoamperometry potential: 0.6 V, error bars are obtained from n = 6 different NeuroString electrodes examined in independent measurements.) Simultaneous and selective detection of DA and 5-HT. h, FSCV of various concentrations of 5-HT (100 nM, 250 nM, 500 nM, 750 nM, 1,000 nM) in 200 nM DA solution (dissolved in PBS buffer, pH 7.4); inset shows the linear plot of currents against concentrations of 5-HT. i, FSCV of various concentrations of DA (100 nM, 250 nM, 500 nM, 750 nM, 1,000 nM) in 200 nM 5-HT solution (dissolved in PBS buffer, pH 7.4); inset shows the linear plot of currents against concentrations of DA. Simultaneous and selective detection of NP, EP and 5-HT. j, FSCV of various concentrations of EP (100 nM, 250 nM, 500 nM, 750 nM, 1,000 nM) in 200 nM 5-HT and 200 nM EP solution (dissolved in PBS buffer, pH 7.4); inset shows the linear plot of currents against concentrations of EP. k, FSCV of various concentrations of 5-HT (100 nM, 250 nM, 500 nM, 750 nM, 1,000 nM) in 200 nM NP and 200 nM EP solution (dissolved in PBS buffer, pH 7.4); inset shows the linear plot of currents against concentrations of 5-HT. l, FSCV of various concentrations of NP (100 nM, 250 nM, 500 nM, 750 nM, 1,000 nM) in 200 nM 5-HT and 200 nM EP solution (dissolved in PBS buffer, pH 7.4); inset shows the linear plot of currents against concentrations of NP.

Source data

Extended Data Fig. 5 Measurement of the neurotransmitter release during fear conditioning and extinction training, and chronic stability of the NeuroString for neurotransmitter sensing.

a–d, Dopamine release in the NAc measured during various phases of a fear extinction task (NeuroStrings were implanted at least two weeks before the behaviour assay started). a, Trial structure for the fear extinction training. b, Percentage of freezing to the CS during the early extinction phase (E-Ext, 1–8 trials) and the late extinction phase (L-Ext, 9–15 trials) (n = 5 mice). The CS evoking lower freezing levels during the late phase indicated successful extinction learning. P-value 0.0005. c, Quantification of dopamine responses to the CS during habituation (before Cond.), the fear conditioning (an electric shock after the tone) and fear extinction phase (n = 5 mice). P-values: 0.0018 (shock), 0.003 (E-Ext), 0.0706 (L-Ext). d, Exemplar time-aligned dopamine signals from a mouse during each phase. e, Chronic measurement of dopamine in the NAc evoked by optogenetic stimulation of dopamine neurons in the VTA of DAT-Cre mice expressing ChR2 (error bars are obtained from measurements from n = 6 biologically independent mice). P-values: 0.9011 (week 4), 0.9926 (week 8), 0.6946 (week 12), 0.4462 (week 16). f, g, Representative measurements from a NeuroString electrode in a mouse across 16 weeks in the form of background-subtracted cyclic voltammogram (f) and the corresponding electrochemical impedance (g). P-values are calculated by paired two-tailed Student’s t-test: NS, P > 0.05; *P ≤ 0.05; **P ≤ 0.01; ***P ≤ 0.001.

Source data

Extended Data Fig. 6 Evaluation of NeuroString performance for serotonin sensing in the GI tract of rodents and large animals.

a, Setup of the colonic motility assay providing video imaging and spatiotemporal maps to analyse colonic motility of mice inserted with NeuroString ex vivo. Control is a mouse colon without anything inside the lumen. b, Representative H&E staining images showing the colon tissue of mice placed with a flexible Kapton (length 2 cm, width 800 μm, thickness 120 μm) film and a NeuroString with the same dimensions. The materials were placed in the colon of freely moving mice for 3 h before collecting the tissue. Damage to the tissue was clearly observed with flexible polyimide film implant. The H&E staining was repeated and reproduced for five mice. Open-field activity (average velocity) (c, d) and pellet output (e) over a 5-h period for mice with the colon acutely placed with a NeuroString or a flexible Kapton polyimide as control (n = 6). P-values: NeuroString (0.9689), polyimide (0.0004) in d; NeuroString (0.4895), polyimide (0.0015) in e. The dotted lines in c show 5-min trajectories of the mice. f, Photos showing NeuroString wrapped around a probe extended from one working channel of an endoscope (left) and the obtained endoscopy photo showing the NeuroString entering the colon lumen for serotonin sensing in a rat model (right). g, Representative serotonin concentration mapping in the rat colon (5 cm from the anus) collected by delivering the NeuroString into the colon lumen and slowly taking it out (error bar denotes ±standard deviation of the measurement results obtained from three channels). h, μ-CT images showing the NeuroString conformally loaded in the mouse colon. i, Representative H&E staining images showing the colon tissue of control mouse treated with saline water and colitis mouse with inflammation induced by DSS after 10 days of colitis development. The H&E staining was repeated and reproduced for five mice. j, Measured serotonin concentration in the colon tissue of control and DSS mice using enzyme-linked immunosorbent assay. P-value 0.0171. k, Layout of the NeuroString sensor for serotonin sensing in the pig colon. The sensor fabrication process is the same as that illustrated in Extended Data Fig. 1, except for the use of a large polyimide sheet (12” × 12”) as a substrate. l, Scheme and photo showing the multiple-site serotonin measurement in the intestine of a pig by NeuroString. m, Simultaneous serotonin sensing in different segments of the intestinal tract by multiple-channel NeuroString. n, o, Drug-induced luminal serotonin concentration change using fluoxetine (SSRI), MB, fluoxetine (SSRI) + MB and saline as control (n = 6 pigs). P-values: SSRI (0.5553), MB (0.3567), SSRI + MB (0.0099). P-values are calculated by paired two-tailed Student’s t-test: NS, P > 0.05; *P ≤ 0.05; **P ≤ 0.01; ***P ≤ 0.001.

Source data

Supplementary information

Supplementary Information

This file contains supplementary figures, code examples, legends for supplementary videos 1 and 2 and supplementary references.

Reporting Summary

Supplementary Data for Supplementary Figure 2

This file contains supplementary data for Supplementary Figure 2.

Supplementary Data for Supplementary Figure 3

This file contains supplementary data for Supplementary Figure 3.

Supplementary Data for Supplementary Figure 4

This file contains supplementary data for Supplementary Figure 4.

Supplementary Video 1

Supplementary Video 2

Source data

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, J., Liu, Y., Yuan, L. et al. A tissue-like neurotransmitter sensor for the brain and gut. Nature 606, 94–101 (2022). https://doi.org/10.1038/s41586-022-04615-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41586-022-04615-2

This article is cited by

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

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