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Time for NanoNeuro

Nature Methods volume 18, pages 1287–1293 (2021)Cite this article

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Abstract

The study of electronic properties of materials at the nanoscale has unveiled physical laws and generated materials such as nanoparticles, quantum dots, nanodiamonds, nanoelectrodes, and nanoprobes. Independently, large-scale public and private neuroscience programs have been launched to develop methods to measure and manipulate neural circuits in living animals and humans. Here, we review an upcoming field, NanoNeuro, defined as the intersection of nanoscience and neuroscience, that aims to develop nanoscale methods to record and stimulate neuronal activity. Because of their unique physical properties, nanomaterials have intrinsic advantages as biosensors and actuators, and they may be applicable to humans without the need for genetic modifications. Thus, nanoscience could make major methodological contributions to the future of neuroscience and, more generally, to biomedical sciences.

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Fig. 1: Nanoprobes and nanoelectrodes.
Fig. 2: Plasmonic nanoparticles.
Fig. 3: Nanoparticle-based recording and manipulation of neuronal activity.

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References

  1. Insel, T. R., Landis, S. C. & Collins, F. S. Research priorities. The NIH BRAIN Initiative. Science 340, 687–688 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Yuste, R. & Bargmann, C. Toward a Global BRAIN Initiative. Cell 168, 956–959 (2017).

    Article  CAS  PubMed  Google Scholar 

  3. International Brain Initiative.International Brain Initiative: an innovative framework for coordinated global brain research efforts. Neuron 105, 947 (2020).

    Article  CAS  Google Scholar 

  4. Yager, T. D., Nickerson, D. A. & Hood, L. E. The Human Genome Project: creating an infrastructure for biology and medicine. Trends Biochem. Sci. 16, 454 (1991).

    Article  CAS  PubMed  Google Scholar 

  5. Helmchen, F. & Konnerth, A. Imaging in neuroscience: a laboratory manual. in Imaging Series (eds. Helmchen, F. & Konnerth, A.) 1084 (Cold Spring Harbor Laboratory Press, 2011).

  6. Ogawa, S., Lee, T. M., Kay, A. R. & Tank, D. W. Brain magnetic resonance imaging with contrast dependent on blood oxygenation. Proc. Natl Acad. Sci. USA 87, 9868–9872 (1990).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Alivisatos, A. P. et al. The Brain Activity Map Project and the challenge of functional connectomics. Neuron 74, 970–974 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Lozano, A. M. et al. Deep brain stimulation: current challenges and future directions. Nat. Rev. Neurol. 15, 148–160 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  9. Nagel, G. et al. Channelrhodopsin-2, a directly light-gated cation-selective membrane channel. Proc. Natl Acad. Sci. USA 100, 13940–13945 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Zayat, L., Baraldo, L. & Etchenique, R. Inorganic caged compounds: uncaging with visible light. CSH Protoc. 2007, db.ip39 (2007).

    Google Scholar 

  11. Kosslyn, S. M. et al. The role of area 17 in visual imagery: convergent evidence from PET and rTMS. Science 284, 167–170 (1999).

    Article  CAS  PubMed  Google Scholar 

  12. Feynman, R. P. There’s plenty of room at the bottom [data storage]. J. Microelectromech. Syst. 1, 60–66 (1992). In this prescient lecture that opened the field, Feynman describes the potential for physics at the nanoscale.

    Article  Google Scholar 

  13. Binnig, G. & Rohrer, H. Scanning tunneling microscopy — from birth to adolescence. Rev. Mod. Phys. 59, 615–625 (1987).

    Article  CAS  Google Scholar 

  14. Brus, L. Quantum crystallites and nonlinear optics. Appl. Phys. A 53, 465–474 (1991).

    Article  Google Scholar 

  15. Murthy, S. K. Nanoparticles in modern medicine: state of the art and future challenges. Int. J. Nanomed. 2, 129–141 (2007).

    CAS  Google Scholar 

  16. Silva, G. A. Neuroscience nanotechnology: progress, opportunities and challenges. Nat. Rev. Neurosci. 7, 65–74 (2006).

    Article  CAS  PubMed  Google Scholar 

  17. Kotov, N. A. et al. Nanomaterials for neural interfaces. Adv. Mater. 21, 3970–4004 (2009).

    Article  CAS  Google Scholar 

  18. Rust, M. J., Bates, M. & Zhuang, X. Sub-diffraction-limit imaging by stochastic optical reconstruction microscopy (STORM). Nat. Methods 3, 793–795 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Jun, J. J. et al. Fully integrated silicon probes for high-density recording of neural activity. Nature 551, 232–236 (2017). This articles describes the generation of Neuropixel probes, an increasingly popular nanoelectronic device for electrical recordings in vivo.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Lei, N. et al. High-resolution extracellular stimulation of dispersed hippocampal culture with high-density CMOS multielectrode array based on non-Faradaic electrodes. J. Neural Eng. 8, 044003 (2011). The authors describe a large-scale multielectrode array for recording and stimulating neuronal activity.

    Article  CAS  PubMed  Google Scholar 

  21. Segev, E. et al. In Conference on Lasers and Electro-Optics (OSA, 2016).

  22. Robinson, J. T. et al. Vertical nanowire electrode arrays as a scalable platform for intracellular interfacing to neuronal circuits. Nat. Nanotechnol. 7, 180–184 (2012). This article describes the manufacturing of nanopillar arrays and how they’re used to record intracellular activity from cultured neurons.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Alivisatos, A. P. et al. Nanotools for neuroscience and brain activity mapping. ACS Nano 7, 1850–1866 (2013). A review about a series of nanomethods with potential applications in neuroscience.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Seo, D. et al. Wireless recording in the peripheral nervous system with ultrasonic neural dust. Neuron 91, 529–539 (2016).

    Article  CAS  PubMed  Google Scholar 

  25. Choi, J. et al. A 512-pixel, 51-kHz-frame-rate, dual-shank, lens-less, filter-less single photon avalanche diode CMOS neural imaging probe. IEEE J. Solid-State Circuits 54, 2957–2968 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  26. Shekar, S. et al. A miniaturized multi-clamp CMOS amplifier for intracellular neural recording. Nat. Electron. 2, 343–350 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  27. Abbott, J. et al. A nanoelectrode array for obtaining intracellular recordings from thousands of connected neurons. Nat. Biomed. Eng. 4, 232–241 (2020).

    Article  CAS  PubMed  Google Scholar 

  28. Jayant, K. et al. Flexible nanopipettes for minimally invasive intracellular electrophysiology in vivo. Cell Rep. 26, 266–278 (2019). The authors introduce quartz nanoelectrodes for stable intracellular recordings in vivo.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Anzar, N., Hasan, R., Tyagi, M., Yadav, N. & Narang, J. Carbon nanotube — a review on synthesis, properties and plethora of applications in the field of biomedical science. Sens. Int. 1, 100003 (2020).

    Article  Google Scholar 

  30. Schuss, Z., Singer, A. & Holcman, D. The narrow escape problem for diffusion in cellular microdomains. Proc. Natl Acad. Sci. USA 104, 16098–16103 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Holcman, D. & Yuste, R. The new nanophysiology: regulation of ionic flow in neuronal subcompartments. Nat. Rev. Neurosci. 16, 685–692 (2015).

    Article  CAS  PubMed  Google Scholar 

  32. Maier, S. A. Plasmonics: Fundamentals and Applications. (Springer Science & Business Media, 2007).

  33. Bharadwaj, P., Deutsch, B. & Novotny, L. Optical antennas. Adv. Opt. Photonics 1, 438 (2009).

    Article  Google Scholar 

  34. Novotny, L. & van Hulst, N. Antennas for light. Nat. Photonics 5, 83–90 (2011). The authors review applications of plasmonic nanoparticles in diverse fields of science.

    Article  CAS  Google Scholar 

  35. Pitarke, J. M., Silkin, V. M., Chulkov, E. V. & Echenique, P. M. Theory of surface plasmons and surface-plasmon polaritons. Rep. Prog. Phys. 70, 1–87 (2007).

    Article  CAS  Google Scholar 

  36. Atwater, H. A. The promise of plasmonics. Sci. Am. 17, 56–63 (2007).

    Article  Google Scholar 

  37. Xu, H., Bjerneld, E. J., Käll, M. & Börjesson, L. Spectroscopy of single hemoglobin molecules by surface enhanced Raman scattering. Phys. Rev. Lett. 83, 4357–4360 (1999).

    Article  CAS  Google Scholar 

  38. Zhang, R. et al. Chemical mapping of a single molecule by plasmon-enhanced Raman scattering. Nature 498, 82–86 (2013).

    Article  CAS  PubMed  Google Scholar 

  39. Anger, P., Bharadwaj, P. & Novotny, L. Enhancement and quenching of single-molecule fluorescence. Phys. Rev. Lett. 96, 113002 (2006).

    Article  PubMed  CAS  Google Scholar 

  40. Quidant, R. In Gold Nanoparticles for Physics, Chemistry and Biology 483–508 (World Scientific, 2017).

  41. Neubrech, F. et al. Resonant plasmonic and vibrational coupling in a tailored nanoantenna for infrared detection. Phys. Rev. Lett. 101, 157403 (2008).

    Article  PubMed  CAS  Google Scholar 

  42. García-Etxarri, A. & Dionne, J. A. Surface-enhanced circular dichroism spectroscopy mediated by nonchiral nanoantennas. Phys. Rev. B Condens. Matter 87, 235409 (2013).

    Article  CAS  Google Scholar 

  43. Kneipp, K. et al. Single molecule detection using surface-enhanced raman scattering (SERS). Phys. Rev. Lett. 78, 1667–1670 (1997).

    Article  CAS  Google Scholar 

  44. Nie, S. & Emory, S. R. Probing single molecules and single nanoparticles by surface-enhanced raman scattering. Science 275, 1102–1106 (1997).

    Article  CAS  PubMed  Google Scholar 

  45. Liz-Marzán, L. M. In Colloidal Synthesis of Plasmonic Nanometals 1–13 (Jenny Stanford Publishing, 2020).

  46. Lee, S.-W. et al. Highly sensitive biosensing using arrays of plasmonic Au nanodisks realized by nanoimprint lithography. ACS Nano 5, 897–904 (2011).

    Article  CAS  PubMed  Google Scholar 

  47. Horák, M. et al. Comparative study of plasmonic antennas fabricated by electron beam and focused ion beam lithography. Sci. Rep. 8, 9640 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  48. Baffou, G., Bordacchini, I., Baldi, A. & Quidant, R. Simple experimental procedures to distinguish photothermal from hot-carrier processes in plasmonics. Light. Sci. Appl. 9, 108 (2020).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  49. Cohen, M. R. & Moiseenkova-Bell, V. Y. Structure of thermally activated TRP channels. Curr. Top. Membr. 74, 181–211 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Bruchez, M. Jr. Semiconductor nanocrystals as fluorescent biological labels. Science 281, 2013–2016 (1998).

    Article  CAS  PubMed  Google Scholar 

  51. Habib, A. et al. Electro-plasmonic nanoantenna: a nonfluorescent optical probe for ultrasensitive label-free detection of electrophysiological signals. Sci. Adv. 5, eaav9786 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. de Boer, W. D. A. M. et al. Neuronal photoactivation through second-harmonic near-infrared absorption by gold nanoparticles. Light. Sci. Appl. 7, 100 (2018). The authors use plasmonic nanoparticles to activate neurons in vitro and in vivo using two-photon lasers.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  53. García-Etxarri, A. et al. Strong magnetic response of submicron silicon particles in the infrared. Opt. Express 19, 4815–4826 (2011).

    Article  PubMed  CAS  Google Scholar 

  54. Jiang, Y. et al. Heterogeneous silicon mesostructures for lipid-supported bioelectric interfaces. Nat. Mater. 15, 1023–1030 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Fang, Y. et al. Texturing silicon nanowires for highly localized optical modulation of cellular dynamics. Nano Lett. 18, 4487–4492 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Chen, H.-T., Taylor, A. J. & Yu, N. A review of metasurfaces: physics and applications. Rep. Prog. Phys. 79, 076401 (2016).

    Article  PubMed  CAS  Google Scholar 

  57. Segev, E. et al. In Conference on Lasers and Electro-Optics JTh4B.2 (Optical Society of America, 2016).

  58. Chan, W. C. & Nie, S. Quantum dot bioconjugates for ultrasensitive nonisotopic detection. Science 281, 2016–2018 (1998).

    Article  CAS  PubMed  Google Scholar 

  59. Larson, D. R. et al. Water-soluble quantum dots for multiphoton fluorescence imaging in vivo. Science 300, 1434–1436 (2003).

    Article  CAS  PubMed  Google Scholar 

  60. Lidke, K., Rieger, B., Jovin, T. & Heintzmann, R. Superresolution by localization of quantum dots using blinking statistics. Opt. Express 13, 7052–7062 (2005).

    Article  PubMed  Google Scholar 

  61. Ebenstein, Y. et al. Lighting up individual DNA binding proteins with quantum dots. Nano Lett. 9, 1598–1603 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Efros, A. L. et al. Evaluating the potential of using quantum dots for monitoring electrical signals in neurons. Nat. Nanotechnol. 13, 278–288 (2018). The authors review the potential application of Quantum Dots as reporters of neural activity.

    Article  CAS  PubMed  Google Scholar 

  63. Liu, Y., Lu, Y., Chen, G. & Wang, Q. Recent progress of hybrid optical probes for neural membrane potential imaging. Biotechnol. J. e2000086 (2020).

  64. Park, K., Deutsch, Z., Li, J. J., Oron, D. & Weiss, S. Single molecule quantum-confined Stark effect measurements of semiconductor nanoparticles at room temperature. ACS Nano 6, 10013–10023 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Bar-Elli, O. et al. Rapid voltage sensing with single nanorods via the quantum confined stark effect. ACS Photonics 5, 2860–2867 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Marshall, J. D. & Schnitzer, M. J. Optical strategies for sensing neuronal voltage using quantum dots and other semiconductor nanocrystals. ACS Nano 7, 4601–4609 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Park, K. & Weiss, S. Design rules for membrane-embedded voltage-sensing nanoparticles. Biophys. J. 112, 703–713 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Caglar, M. et al. All-optical detection of neuronal membrane depolarization in live cells using colloidal quantum dots. Nano Lett. 19, 8539–8549 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Ludwig, A. et al. Development of lipid-coated semiconductor nanosensors for recording of membrane potential in neurons. ACS Photonics 7, 1141–1152 (2020).

    Article  CAS  Google Scholar 

  70. Chen, G. et al. Glutathione-capped quantum dots for plasma membrane labeling and membrane potential imaging. Nano Res. 12, 1321–1326 (2019).

    Article  CAS  Google Scholar 

  71. Nag, O. K. et al. Quantum dot–peptide–fullerene bioconjugates for visualization of in vitro and in vivo cellular membrane potential. ACS Nano 11, 5598–5613 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Karaveli, S. et al. Modulation of nitrogen vacancy charge state and fluorescence in nanodiamonds using electrochemical potential. Proc. Natl Acad. Sci. USA 113, 3938–3943 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Huang, Y.-A. et al. The effect of fluorescent nanodiamonds on neuronal survival and morphogenesis. Sci. Rep. 4, 6919 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Barbiero, M. et al. Nanoscale magnetic imaging enabled by nitrogen vacancy centres in nanodiamonds labelled by iron–oxide nanoparticles. Nanoscale 12, 8847–8857 (2020).

    Article  CAS  PubMed  Google Scholar 

  75. Le Sage, D. et al. Optical magnetic imaging of living cells. Nature 496, 486–489 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  76. Glenn, D. R. et al. Single-cell magnetic imaging using a quantum diamond microscope. Nat. Methods 12, 736–738 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Haziza, S. et al. Fluorescent nanodiamond tracking reveals intraneuronal transport abnormalities induced by brain-disease-related genetic risk factors. Nat. Nanotechnol. 12, 322–328 (2017).

    Article  CAS  PubMed  Google Scholar 

  78. Barry, J. F. et al. Optical magnetic detection of single-neuron action potentials using quantum defects in diamond. Proc. Natl Acad. Sci. USA 113, 14133–14138 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Lay, A. et al. Optically robust and biocompatible mechanosensitive upconverting nanoparticles. ACS Cent. Sci. 5, 1211–1222 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Ma, Y. et al. Mammalian near-infrared image vision through injectable and self-powered retinal nanoantennae. Cell 177, 243–255.e15 (2019).

    Article  CAS  PubMed  Google Scholar 

  81. Liu, J. et al. Near-infrared voltage nanosensors enable real-time imaging of neuronal activities in mice and zebrafish. J. Am. Chem. Soc. 142, 7858–7867 (2020).

    Article  CAS  PubMed  Google Scholar 

  82. Christiansen, M. G., Senko, A. W. & Anikeeva, P. Magnetic strategies for nervous system control. Annu. Rev. Neurosci. 42, 271–293 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Saurabh, S. et al. Multiplexed modular genetic targeting of quantum dots. ACS Nano 8, 11138–11146 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Mehlenbacher, R. D., Kolbl, R., Lay, A. & Dionne, J. A. Nanomaterials for in vivo imaging of mechanical forces and electrical fields. Nat. Rev. Mater. 3, 17080 (2018).

    Article  CAS  Google Scholar 

  85. Choi, H. S. & Frangioni, J. V. Nanoparticles for biomedical imaging: fundamentals of clinical translation. Mol. Imaging 9, 291–310 (2010).

    Article  CAS  PubMed  Google Scholar 

  86. Acarón Ledesma, H. et al. An atlas of nano-enabled neural interfaces. Nat. Nanotechnol. 14, 645–657 (2019).

    Article  PubMed  CAS  Google Scholar 

  87. Alivisatos, A. P. et al. The brain activity map. Science 339, 1284–1285 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Park, J. et al. Improved surface functionalization and characterization of membrane-targeted semiconductor voltage nanosensors. J. Phys. Chem. Lett. 10, 3906–3913 (2019).

    Article  CAS  PubMed  Google Scholar 

  89. Park, K. et al. Membrane insertion of — and membrane potential sensing by — semiconductor voltage nanosensors: feasibility demonstration. Sci. Adv. 4, e1601453 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  90. Nag, O. K. et al. Nanoparticle-mediated visualization and control of cellular membrane potential: strategies, progress, and remaining issues. ACS Nano 14, 2659–2677 (2020). A comprehensive review on the use of nanoparticles for neural sensing and actuation.

    Article  CAS  PubMed  Google Scholar 

  91. Xu, H., Aizpurua, J., Kall, M. & Apell, P. Electromagnetic contributions to single-molecule sensitivity in surface-enhanced raman scattering. Phys. Rev. E Stat. Phys. Plasmas Fluids Relat. Interdiscip. Top. 62, 4318–4324 (2000).

    CAS  Google Scholar 

  92. Wu, D. M., García-Etxarri, A., Salleo, A. & Dionne, J. A. Plasmon-enhanced upconversion. J. Phys. Chem. Lett. 5, 4020–4031 (2014).

    Article  CAS  PubMed  Google Scholar 

  93. Hamill, O. P., Marty, A., Neher, E., Sakmann, B. & Sigworth, F. J. Improved patch–clamp techniques for high-resolution current recording from cells and cell-free membrane patches. Pflug. Arch. 391, 85–100 (1981).

    Article  CAS  Google Scholar 

  94. Yuste, R. & Katz, L. C. Control of postsynaptic Ca2+ influx in developing neocortex by excitatory and inhibitory neurotransmitters. Neuron 6, 333–344 (1991).

    Article  CAS  PubMed  Google Scholar 

  95. Bando, Y., Sakamoto, M., Kim, S., Ayzenshtat, I. & Yuste, R. Comparative evaluation of genetically encoded voltage indicators. Cell Rep. 26, 802–813.e4 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. Prakash, R. et al. Two-photon optogenetic toolbox for fast inhibition, excitation and bistable modulation. Nat. Methods 9, 1171–1179 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  97. Fino, E. et al. RuBi-glutamate: two-photon and visible-light photoactivation of neurons and dendritic spines. Front. Neural Circuits 3, 2 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  98. Carvalho-de-Souza, J. L. et al. Photosensitivity of neurons enabled by cell-targeted gold nanoparticles. Neuron 86, 207–217 (2015). The authors use gold nanoparticles to activate cultured neurons with visible light and conclude that this effect is temperature-mediated.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  99. Garcia-Etxarri, A. Modelling of plasmonic nanoantennas for optical microscopy and surface enhanced spectroscopy, PhD thesis, UPV/EHU (2010).

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Acknowledgements

This article originated from group discussions at the DIPC/Columbia University ‘NanoNeuro2020’ meeting, which took place online on 24–25 June 2020. We thank all participants for their input and the Kavli Foundation for funding. R.Y. is supported by NEI (R01EY011787), NINDS (R01NS110422; R34NS116740), NIMH (R01MH115900), NSF (CRCNS 1822550), and the Vannevar Bush Faculty Award (ONR N000142012828). A.G.E. acknowledges support from the Spanish Ministerio de Ciencia e Innovación (PID2019-109905GA-C22) and from E. Jaurlaritza (IT1164-19, KK-2019/00101 and KK-2021/00082).

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  1. Donostia International Physics Center, Donostia-San Sebastián, Spain

    Aitzol Garcia-Etxarri & Rafael Yuste

  2. IKERBASQUE, Bilbao, Spain

    Aitzol Garcia-Etxarri & Rafael Yuste

  3. Kavli Institute of Brain Sciences, Dept. Biological Sciences, Columbia University, New York, USA

    Rafael Yuste

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Peer review information Nature Methods thanks Marcel Bruchez, Gabriel Silva, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Nina Vogt was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Garcia-Etxarri, A., Yuste, R. Time for NanoNeuro. Nat Methods 18, 1287–1293 (2021). https://doi.org/10.1038/s41592-021-01270-9

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