The use of graphene-based materials to engineer sophisticated biosensing interfaces that can adapt to the central nervous system requires a detailed understanding of how such materials behave in a biological context. Graphene’s peculiar properties can cause various cellular changes, but the underlying mechanisms remain unclear. Here, we show that single-layer graphene increases neuronal firing by altering membrane-associated functions in cultured cells. Graphene tunes the distribution of extracellular ions at the interface with neurons, a key regulator of neuronal excitability. The resulting biophysical changes in the membrane include stronger potassium ion currents, with a shift in the fraction of neuronal firing phenotypes from adapting to tonically firing. By using experimental and theoretical approaches, we hypothesize that the graphene–ion interactions that are maximized when single-layer graphene is deposited on electrically insulating substrates are crucial to these effects.

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  1. 1.

    Geim, A. K. & Novoselov, K. S. The rise of graphene. Nat. Mater. 6, 183–191 (2007).

  2. 2.

    Yang, Y. et al. Graphene based materials for biomedical applications. Mat. Today 16, 365–373 (2013).

  3. 3.

    Li, X. et al. Transfer of large-area graphene films for high-performance transparent conductive electrodes. Nano Lett. 9, 4359–4363 (2009).

  4. 4.

    Shin, S. R. et al. Graphene-based materials for tissue engineering. Adv. Drug Deliv. Rev. 105, 255–274 (2016).

  5. 5.

    Lu, Y. et al. Flexible neural electrode array based-on porous graphene for cortical microstimulation and sensing. Sci. Rep. 6, 33526 (2016).

  6. 6.

    Fabbro, A. et al. Graphene-based interfaces do not alter target nerve cells. ACS Nano 10, 615–623 (2016).

  7. 7.

    Rauti, R. et al. Graphene oxide nanosheets reshape synaptic function in cultured brain networks. ACS Nano 10, 4459–4471 (2016).

  8. 8.

    Famm, K. et al. Drug discovery: a jump-start for electroceuticals. Nature 496, 159–161 (2013).

  9. 9.

    Rivnay, J. et al. Next-generation probes, particles, and proteins for neural interfacing. Sci. Adv. 3, e1601649 (2017).

  10. 10.

    Cançado, L. G. et al. Quantifying defects in graphene via Raman spectroscopy at different excitation energies. Nano Lett. 11, 3190–3196 (2011).

  11. 11.

    Kim, J. et al. Monolayer graphene-directed growth and neuronal differentiation of mesenchymal stem cells. J. Biomed. Nanotechnol. 11, 2024–2033 (2015).

  12. 12.

    Baldrighi, M. et al. Carbon nanomaterials interfacing with neurons: an in vivo perspective. Front. Neurosci. 10, 250 (2016).

  13. 13.

    Lovat, V. et al. Carbon nanotube substrates boost neuronal electrical signaling. Nano Lett. 5, 1107–1110 (2005).

  14. 14.

    Cellot, G. et al. Carbon nanotubes might improve neuronal performance by favouring electrical shortcuts. Nat. Nanotech. 4, 126–133 (2009).

  15. 15.

    Cellot, G. et al. Carbon nanotube scaffolds tune synaptic strength in cultured neural circuits: novel frontiers in nanomaterial-tissue interactions. J. Neurosci. 31, 12945–12953 (2011).

  16. 16.

    Raastad, M. et al. Putative single quantum and single fibre excitatory postsynaptic currents show similar amplitude range and variability in rat hippocampal slices. Eur. J. Neurosci. 4, 113–117 (1992).

  17. 17.

    Pampaloni, N. P. et al. Sculpting neurotransmission during synaptic development by 2D nanostructured interfaces. Nanomedicine https://doi.org/10.1016/j.nano.2017.01.020 (2017).

  18. 18.

    Arosio, D. & Ratto, G. M. Twenty years of fluorescence imaging of intracellular chloride. Front. Cell. Neurosci. 8, 258 (2014).

  19. 19.

    Cherubini, E. GABA mediated excitation in immature rat CA3 hippocampal neurons. Int J. Dev. Neurosci. 8, 481–490 (1990).

  20. 20.

    Marandi, N., Konnerth, A. & Garaschuk, O. Two-photon chloride imaging in neurons of brain slices. Pflug. Arch. 445, 357–365 (2002).

  21. 21.

    Ruscheweyh, R. & Sandkuhler, J. Lamina-specific membrane and discharge properties of rat spinal dorsal horn neurones in vitro. J. Physiol. 541, 231–244 (2002).

  22. 22.

    Chang, Y. M. & Luebke, J. I. Electrophysiological diversity of layer 5 pyramidal cells in the prefrontal cortex of the rhesus monkey: in vitro slice studies. J. Neurophysiol. 98, 2622–2632 (2007).

  23. 23.

    Routh, B. N. et al. Anatomical and electrophysiological comparison of CA1 pyramidal neurons of the rat and mouse. J. Neurophysiol. 102, 2288–2302 (2009).

  24. 24.

    Renganathan, M., Cummins, T. R. & Waxman, S. G. Contribution of Na(v)1.8 sodium channels to action potential electrogenesis in DRG neurons. J. Neurophysiol. 86, 629–640 (2001).

  25. 25.

    Kress, G. J. et al. Axonal sodium channel distribution shapes the depolarized action potential threshold of dentate granule neurons. Hippocampus 20, 558–571 (2016).

  26. 26.

    Sah, P. & Faber, E. S. Channels underlying neuronal calcium-activated potassium currents. Prog. Neurobiol. 66, 345–353 (2002).

  27. 27.

    Furlan, F. et al. ERG conductance expression modulates the excitability of ventral horn GABAergic interneurons that control rhythmic oscillations in the developing mouse spinal cord. J. Neurosci. 27, 919–928 (2007).

  28. 28.

    Marom, S. & Shahaf, G. Development, learning and memory in large random networks of cortical neurons: lessons beyond anatomy. Q. Rev. Biophys. 35, 63–87 (2002).

  29. 29.

    Sterratt, D. Principles of Computational Modelling in Neuroscience (Cambridge Univ. Press, Cambridge, 2011).

  30. 30.

    Kumpf, R. A. & Dougherty, D. A. A mechanism for ion selectivity in potassium channels: computational studies of cation-pi interactions. Science 261, 1708–1710 (1993).

  31. 31.

    Shi, G. et al. Ion enrichment on the hydrophobic carbon-based surface in aqueous salt solutions due to cation–π interactions. Sci. Rep. 3, 3436 (2013).

  32. 32.

    Pham, T. A. et al. Salt solutions in carbon nanotubes: the role of cation−π interactions. J. Phys. Chem. C. 120, 7332–7338 (2016).

  33. 33.

    Williams, C. D. et al. Effective polarization in pairwise potentials at the graphene–electrolyte interface. J. Phys. Chem. Lett. 8, 703–708 (2017).

  34. 34.

    Dong, X. et al. Doping single-layer graphene with aromatic molecules. Small 5, 1422–1426 (2009).

  35. 35.

    Chacón-Torres, J. C., Wirtz, L. & Pichler, T. Manifestation of charged and strained graphene layers in the Raman response of graphite intercalation compounds. ACS Nano. 7, 9249–9259 (2013).

  36. 36.

    Novák, M. et al. Solvent effects on ion-receptor interactions in the presence of an external electric field. Phys. Chem. Chem. Phys. 18, 30754–30760 (2016).

  37. 37.

    Chen, K. et al. Electronic properties of graphene altered by substrate surface chemistry and externally applied electric field. J. Phys. Chem. C. 116, 6259–6267 (2012).

  38. 38.

    Novoselov, K. et al. Two-dimensional gas of massless Dirac fermions in graphene. Nature 438, 197–200 (2005).

  39. 39.

    Gigante, G. et al. Network events on multiple space and time scales in cultured neural networks and in a stochastic rate model. PLoS Comput. Biol. 11, e1004547 (2015).

  40. 40.

    Gambazzi, L. et al. Diminished activity-dependent brain-derived neurotrophic factor expression underlies cortical neuron microcircuit hypoconnectivity resulting from exposure to mutant huntingtin fragments. J. Pharmacol. Exp. Ther. 335, 13–22 (2010).

  41. 41.

    González-Herrero, H. et al. Graphene tunable transparency to tunneling electrons: a direct tool to measure the local coupling. ACS Nano. 10, 5131–5144 (2016).

  42. 42.

    Praveen, C. S. et al. Adsorption of alkali adatoms on graphene supported by the Au/Ni(111) surface. Phys. Rev. B 92, 075403 (2015).

  43. 43.

    Kang, Y.-J. et al. Electronic structure of graphene and doping effect on SiO2. Phys. Rev. B 78, 115404 (2008).

  44. 44.

    Miwa, R. H. et al. Doping of graphene adsorbed on the a-SiO2 surface. Appl. Phys. Lett. 99, 163108 (2011).

  45. 45.

    Ao, Z. et al. Density functional theory calculations on graphene/α-SiO2(0001) interface. Nanoscale Res. Lett. 7, 158 (2012).

  46. 46.

    Fan, X. F. et al. Interaction between graphene and the surface of SiO2. J. Phys. Condens. Matter 24, 305004 (2012).

  47. 47.

    Hille, B. Ion Channels of Excitable Membranes (Sinauer, Sunderland, MA, 2001).

  48. 48.

    Slomowitz, E. et al. Interplay between population firing stability and single neuron dynamics in hippocampal networks. eLife 4, e04378 (2015).

  49. 49.

    Bogaard, A. et al. Interaction of cellular and network mechanisms in spatiotemporal pattern formation in neuronal networks. J. Neurosci. 29, 1677–1687 (2009).

  50. 50.

    Radulescu, R. A. Mechanisms explaining transitions between tonic and phasic firing in neuronal populations as predicted by a low dimensional firing rate model. PLoS One 5, e12695 (2010).

  51. 51.

    Wrobel, G. et al. Transmission electron microscopy study of the cell-sensor interface. J. R. Soc. Interface 5, 213–222 (2008).

  52. 52.

    Braun, D. & Fromherz, P. Fluorescence interference-contrast microscopy of cell adhesion on oxidized silicon. Appl. Phys. A 65, 341–348 (1997).

  53. 53.

    Ferrari, A. C. et al. Raman spectrum of graphene and graphene layers. Phys. Rev. Lett. 97, 187401 (2006).

  54. 54.

    Cançado, L. G. et al. Measuring the degree of stacking order in graphite by Raman spectroscopy. Carbon 46, 272–275 (2008).

  55. 55.

    Alagem, N. et al. Mechanism of Ba(2+) block of a mouse inwardly rectifying K+ channel: differential contribution by two discrete residues. J. Physiol. 534, 381–393 (2001).

  56. 56.

    Alger, B. E. & Nicoll, R. A. Epileptiform burst afterhyperpolarization: calcium-dependent potassium potential in hippocampal CA1 pyramidal cells. Science 210, 1122–1124 (1980).

  57. 57.

    Jiang, Y. & MacKinnon, R. The barium site in a potassium channel by X-ray crystallography. J. Gen. Physiol. 115, 269–272 (2000).

  58. 58.

    Drieschner, S. et al. Frequency response of electrolyte-gated graphene electrodes and transistors. J. Phys. D 50, 095304 (2017).

  59. 59.

    Drieschner, S. et al. High surface area graphene foams by chemical vapor deposition. 2D Mater. 3, 045013 (2016).

  60. 60.

    Matruglio, A. et al. Contamination-free suspended graphene structures by a Ti-based transfer method. Carbon 103, 305–310 (2016).

  61. 61.

    Sontheimer, H . & Ransom, C. in Patch-Clamp Analysis (ed. Walz, W.) 35–67 (Humana Press, New York, 2007).

  62. 62.

    Usmani, S. et al. 3D meshes of carbon nanotubes guide functional reconnection of segregated spinal explants. Sci. Adv. 2, e1600087 (2016).

  63. 63.

    D’Amico, F. et al. UV resonant Raman scattering facility at Elettra. Nucl. Instrum. Methods Phys. Res. 703, 33–37 (2013).

  64. 64.

    Wilcox, R. R. & Rousselet, G. A. A guide to robust statistical methods in neuroscience. Curr. Protoc. Neurosci. 82, 8.42.1–8.42.30 (2018).

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We thank M. Lazzarino, S. Dal Zilio and the Facility of NanoFabrication of IOM of Trieste for experimental assistance in the fabrication of suspended SLG and FIB analysis, and N. Secomandi and R. Rauti for assistance in imaging. We thank A. Laio, G. Scoles, A. Nistri and B. Cortés-Llanos for discussion. This paper is based on work supported by the European Union Seventh Framework Program under grant agreement no. 696656 Graphene Flagship and no. 720270 Human Brain Project Flagship, and by the Flanders Research Foundation (grant no. G0F1517N). M.P., as the recipient of the AXA Chair, is grateful to the AXA Research Fund for financial support. M.P. was also supported by the Spanish Ministry of Economy and Competitiveness MINECO (project CTQ2016-76721-R), by the University of Trieste and by Diputación Foral de Gipuzkoa program Red (101).

Author information

Author notes

    • Alessia Matruglio

    Present address: CERIC-ERIC (Central European Research Infrastructure Consortium), Trieste, Italy

  1. These authors contributed equally: Niccolò Paolo Pampaloni, Martin Lottner


  1. International School for Advanced Studies (SISSA), Trieste, Italy

    • Niccolò Paolo Pampaloni
    • , Laura Ballerini
    •  & Denis Scaini
  2. Walter Schottky Institut and Physik-Department, Technische Universität München, Garching, Germany

    • Martin Lottner
  3. Molecular, Cellular, and Network Excitability, Department of Biomedical Sciences, Universiteit Antwerp, Antwerp, Belgium

    • Michele Giugliano
  4. Department of Computer Science, University of Sheffield, Sheffield, UK

    • Michele Giugliano
  5. Lab of Neural Microcircuitry, Brain Mind Institute, EPFL, Lausanne, Switzerland

    • Michele Giugliano
  6. CNR-IOM—Istituto Officina dei Materiali, Trieste, Italy

    • Alessia Matruglio
  7. Elettra Sincrotrone Trieste S.C.p.A., Trieste, Italy

    • Francesco D’Amico
    •  & Denis Scaini
  8. Department of Chemical and Pharmaceutical Sciences, University of Trieste, Trieste, Italy

    • Maurizio Prato
  9. Nanobiotechnology Laboratory, CIC biomaGUNE, San Sebastiàn, Spain

    • Maurizio Prato
  10. Ikerbasque, Basque Foundation for Science, Bilbao, Spain

    • Maurizio Prato
  11. Catalan Institute of Nanoscience and Nanotechnology (ICN2), CSIC and The Barcelona Institute of Science and Technology, Campus UAB, Bellaterra, Spain

    • Josè Antonio Garrido
  12. ICREA3, Barcelona, Spain

    • Josè Antonio Garrido


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N.P.P. performed electrophysiological experiments, imaging, immunochemistry, confocal microscopy and all the related analysis. M.L. fabricated supported SLG and MLG and performed all material characterization. M.G. performed mathematical simulations and analysis and contributed to the writing of the manuscript; A.M. fabricated suspended SLG and gold-plated samples. F.D.A. and A.M. performed Raman experiments and data analysis on SLG and MLG in wet and dried conditions. M.P., D.S. and L.B. conceived the study. D.S., L.B. and J.A.G. designed the experimental strategy, interpreted the results and wrote the manuscript. All authors discussed the results and commented on the manuscript.

Competing interests

The authors declare no competing interests.

Corresponding authors

Correspondence to Josè Antonio Garrido or Laura Ballerini or Denis Scaini.

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