Skip to main content
SpringerLink
Log in

Estimating the Effects of Nanoparticles on Neuronal Field Potentials Based on Their Effects on Single Neurons In Vitro

  • Protocol
  • First Online:
Use of Nanoparticles in Neuroscience

Part of the book series: Neuromethods ((NM,volume 135))

  • 709 Accesses

Abstract

The application of nanoparticles in medicine requires a rigorous examination of their safety in order to determine and predict their benefits and potential side effects.

The aim of this study was to examine in vitro effects of coated silver-nanoparticles (cAg-NPs) on the excitability of single neuronal cells and to integrate those findings into an in silico model to predict their effects on neuronal circuits and finally on field potentials generated by those circuits.

As a first step, patch-clamp experiments were performed on single cells to investigate the effects of nano-sized silver particles surrounded by an organic coating. The parameters that were altered by exposure to those nanoparticles were then determined through using the Hodgkin & Huxley model of the sodium current. As a next step, to predict possible changes in network signaling due to the applied cAg-NPs, those findings were integrated into a well-defined neuronal circuit of thalamocortical interactions in silico. The model was then extended to observe neural fields originating from activity of neurons exhibiting Hodgkin & Huxley type action potentials. As a last step, the loop between field potentials and its generators was closed to investigate how the neural field potentials influence the spike generation in neurons that are physically located within these fields, if this feedback causes relevant changes in the underlying neuronal signaling within the circuit, and most importantly if the cAg-NPs effects on single neurons of the network are strong enough to cause observable changes in the generated field potentials themselves.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.00
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 119.00
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Zhang Y, Satterlee A, Huang L (2012) In vivo gene delivery by nonviral vectors: overcoming hurdles? Mol Ther 20(7):1298–1304

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Lamarre B, Ryadnov MG (2011) Self-assembling viral mimetics: one long journey with short steps. Macromol Biosci 11(4):503–513

    Article  CAS  PubMed  Google Scholar 

  3. Chithrani B, Ghazani A, Chan W (2006) Determining the size and shape dependence of gold nanoparticle uptake into mammalian cells. Nano Lett 6:662–668

    Article  CAS  PubMed  Google Scholar 

  4. Xu L, Zhao J, Zhang T, Ren G, Yang ZL (2009) In vitro study on influence of nanoparticles of CuO on Ca1 pyramidal neurons of rat hippocampus potassium currents. Environ Toxicol 24:211–217

    Article  CAS  PubMed  Google Scholar 

  5. Zhao J, Xu L, Zhang T, Ren G, Yang ZL (2009b) Influences of nanoparticles zinc oxide on acutely isolated rat hippocampal Ca3 pyramidal neurons. Neurotoxicology 30:220–230

    Article  CAS  PubMed  Google Scholar 

  6. Liu Z, Ren G, Zhang T, Yang ZL (2009) Action potential changes associated with the inhibitory effects on voltage-gated sodium current of hippocampal ca1 neurons by silver nanoparticles. Toxicology 264:179–174

    Article  CAS  PubMed  Google Scholar 

  7. Hodgkin AL, Huxley AF (1952) A quantitative description of membrane current and its application to conduction and excitation in nerve. J Physiol 117:500–544

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Llinás R, Ribary U (1993) Coherent 40-Hz oscillation characterizes dream state in humans. Proc Natl Acad Sci U S A 90:2078–2081

    Article  PubMed  PubMed Central  Google Scholar 

  9. Llinás R, Ribary U, Joliot M, Wang XJ (1994) Content and context in temporal thalamocortical binding. In: Buzski G et al (eds) Temporal coding in the brain. Springer, Berlin

    Google Scholar 

  10. Llinás R, Ribary U, Contreras D, Pedroarena C (1998) The neuronal basis for consciousness. Philos Trans R Soc Lond B 353:1841–1849

    Article  Google Scholar 

  11. Llinás R, Leznik E, Urbano FJ (2002) Temporal binding via cortical coincidence detection of specific and nonspecific thalamocortical inputs: a voltage-dependent dye-imaging study in mouse brain slices. Proc Natl Acad Sci 99(1):449–454

    Article  PubMed  PubMed Central  Google Scholar 

  12. Amari S (1977) Dynamics of pattern formation in lateral inhibition type neural fields. Biol Cybern 27:77–87

    Article  CAS  PubMed  Google Scholar 

  13. Busse M, Kraegeloh A, Arzt E, Strauss D (2011) Modeling the influences of nanoparticles on neural field oscillations in thalamocortical networks. Conf Proc IEEE Eng Med Biol Soc 2011:136–139

    Google Scholar 

  14. Pellegrino T, Manna L, Kudera S, Liedl T, Koktysh D, Rogach AL, Keller S, Rdler J, Natile G, Parak WJ (2004) Hydrophobic nanocrystals coated with an amphiphilic polymer shell: a general route to water soluble nanocrystals. Nano Lett 127:703–707

    Article  Google Scholar 

  15. Koch M, Kiefer S, Cavelius C, Kraegeloh A (2012) Use of a silver ion selective electrode to assess mechanisms responsible for biological effects of silver nanoparticles. J Nanopart Res 14(2):646. https://doi.org/10.1007/s11051-011-0646-y

    Article  Google Scholar 

  16. Fenwick EM, Marty A, Neher E (1982) A patch-clamp study of bovine chromaffin cells and of their sensitivity to acetylcholine. J Physiol 331:577–597

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Kobayashi H, Shiraishi S, Yanagita T, Yokoo H, Yamamoto R, Minami S, Saitoh T, Wada A (2002) Regulation of voltage-dependent sodium channel expression in adrenal chromaffin cells: involvement of multiple calcium signaling pathways. Ann N Y Acad Sci 971:127–134

    Article  CAS  PubMed  Google Scholar 

  18. Tischler AS (2002) Chromaffin cells as models of endocrine cells and neurons. Ann N Y Acad Sci 971:366–370

    Article  PubMed  Google Scholar 

  19. Goldin AL, Barchi RL, Caldwell J, Hofmann F, Howe JR, Hunter JC et al (2000) Nomenclature of voltage-gated sodium channels. Neuron 28:365–368

    Article  CAS  PubMed  Google Scholar 

  20. Catterall WA, Goldin AL, Waxman SG (2005) International union of pharmacology. xlvii. nomenclature and structure-function relationships of voltage-gated sodium channels. Pharmacol Rev 57:397–409

    Article  CAS  PubMed  Google Scholar 

  21. Goldin AL (2001) Resurgence of sodium channel research. Annu Rev Physiol 63:871–894

    Article  CAS  PubMed  Google Scholar 

  22. Lorincz A, Nusser ZA (2010) Molecular identity of dendritic voltage-gated sodium channels. Science 328(5980):906–909

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Royeck M, Horstmann M, Remy S, Reitze M, Yaari Y, Beck HA (2008) Role of axonal nav1.6 sodium channels in action potential initiation of ca1 pyramidal neurons. J Neurophysiol 100(4):2361–2380

    Article  CAS  PubMed  Google Scholar 

  24. Toledo-Aral JJ, Moss BL, He Z, Koszowski AG, Whisenand T et al (1997) Identification of pn1, a predominant voltage-dependent sodium channel expressed principally in peripheral neurons. Proc Natl Acad Sci U S A 94:1527–1532

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Busse M, Stevens D, Kraegeloh A, Cavelius C, Vukelic M, Arzt E, Strauss D (2013) Estimating the modulatory effects of nanoparticles on neuronal circuits using computational upscaling. Int J Nanomed 8:3559–3572

    Google Scholar 

  26. Malmivuo J, Plonsey R (1995) Bioelectromagnetism. Oxford University Press, New York, NY

    Google Scholar 

  27. Price KV, Storn RM, Lampinen JA (2005) Differential evolution: a prictical approach to global optimization. Springer, Berlin

    Google Scholar 

  28. Shepherd GM (2001) The synaptic organization of the brain, 5th edn. Oxford University Press, Inc., Oxford

    Google Scholar 

  29. Jones EG (2002) Thalamic circuitry and thalamocortical synchrony. Philos Trans R Soc Lond B Biol Sci 357(1428):1659–1673

    Article  PubMed  PubMed Central  Google Scholar 

  30. Singer W (1999) Neuronal synchrony: a versatile code for the definition of relations? Neuron 21(1):49–65

    Article  Google Scholar 

  31. Hughes SW, Errington A, Lorincz ML, Kkesi KA, Juhsz G, Orbn G, Cope DW, Crunelli V (2008) Novel modes of rhythmic burst firing at cognitively-relevant frequencies in thalamocortical neurons. Brain Res 1235:12–20

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Gray CM, Singer W (1989) Stimulus-specific neuronal oscillations in orientation columns of cat visual cortex. Proc Natl Acad Sci U S A 86:1698–1702

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Ribary U, Ioannides AA, Singh KD, Hasson R, Bolton JPR, Lado F, Mogilner A, Llinás R (1991b) Magnetic field tomography of coherent thalamocortical 40-Hz oscillations in humans. Proc Natl Acad Sci U S A 8:11037–11041

    Article  Google Scholar 

  34. Singer W (1993) Synchronization of cortical activity and its putative role in information processing and learning. Annu Rev Physiol 55:349–374

    Article  CAS  PubMed  Google Scholar 

  35. Gregoriou GG, Gotts SJ, Zhou H, Desimone R (2009) High-frequency, long-range coupling between prefrontal and visual cortex during attention. Science 324:1207–1210

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Ribary U, Cappel J, Yamamoto T, Suk J, Llinás R (1991a) Anatomical localizaiton revealed by meg recordings of the human somatosensory system. Electroencephalogr Clin Neurophysiol 78:185–196

    Article  PubMed  Google Scholar 

  37. Gray CM, McCormick DA (1996) Chattering cells: superficial pyramidal neurons contributing to the generation of synchronous oscillations in the visual cortex. Science 274:109–113

    Article  CAS  PubMed  Google Scholar 

  38. Steriade M, Timofeev I, Drmller N, Grenier F (1998) Dynamic properties of corticothalamic neurons and local cortical interneurons generating fast rhythmic (30–40 Hz) spike bursts. J Neurophysiol 79:483–490

    Article  CAS  PubMed  Google Scholar 

  39. Brumberg JC, Nowak LG, McCormick DA (2000) Ionic mechanisms underlying repetitive high-frequency burst firing in supragranular cortical neurons. J Neurosci 20:4829–4843

    CAS  PubMed  Google Scholar 

  40. Wang XJ, Golomb D, Rinzel J (1995) Emergent spindle oscillations and intermittent burst firing in a thalamic model: specific neuronal mechanisms. Proc Natl Acad Sci U S A 92:5577–5581

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Wang XJ (1998) Calcium coding and adaptive temporal computation in cortical pyramidal neurons. J Neurophysiol 79:1549–1566

    Article  CAS  PubMed  Google Scholar 

  42. Golomb D, Shedmi A, Curt R, Ermentrout GB (2006) Persistent synchronized bursting activity in cortical tissues with low magnesium concentration: a modeling study. J Neurophysiol 95:1049–1067

    Article  CAS  PubMed  Google Scholar 

  43. Golomb D, Wang XJ, Rinzel J (1996) Propagation of spindle waves in a thalamic slice model. J Neurophysiol 75(2):750–769

    Article  CAS  PubMed  Google Scholar 

  44. Wang XJ, Buz’saki G (1996) Gamma oscillation by synaptic inhibition in a hippocampal interneuronal network model. J Neurosci 16(20):6402–6413

    CAS  PubMed  Google Scholar 

  45. Destexhe A, Bal T, McCormick DA, Sejnowski TJ (1996a) Ionic mechanisms underlying synchronized oscillations and propagating waves in a model of ferret thalamic slices. J Neurophysiol 76(3):2049–2070

    Article  CAS  PubMed  Google Scholar 

  46. Bazhenov M, Timofeev I, Steriade M, Sejnowski TJ (1998) Cellular and network models for intrathalamic augmenting responses during 10-Hz stimulation. J Neurophysiol 79:2730–2748

    Article  CAS  PubMed  Google Scholar 

  47. Golomb D, Amitai Y (1997) Propagating neuronal discharges in neocortical slices: computational and experimental study. J Neurophysiol 78:1199–1211

    Article  CAS  PubMed  Google Scholar 

  48. Destexhe A, Contreras D, Sejnowski TJ, Steriade M (1994a) A model of spindle rhythmicity in the isolated thalamic reticular nucleus. J Neurophysiol 72(2):803–818

    Article  CAS  PubMed  Google Scholar 

  49. Destexhe A, Contreras D, Steriade M, Sejnowski TJ, Huguenard JR (1996b) In vivo, in vitro, and computational analysis of dendritic calcium currents in thalamic reticular neurons. J Neurosci 16:999–1016

    Google Scholar 

  50. Ferguson KA, Campbell SA (2009) A two compartment model of a ca1 pyramidal neuron. Can Appl Math Q 17(2):293–307

    Google Scholar 

  51. Destexhe A, Contreras D, Steriade M (1998a) Mechanisms underlying the synchronizing action of corticothalamic feedback through inhibition of thalamic relay cells. J Neurophysiol 79:999–1016

    Article  CAS  PubMed  Google Scholar 

  52. Bazhenov M, Timofeev I, Steriade M, Sejnowski TJ (2002) Model of thalamocortical slow-wave sleep oscillations and transitions to activated states. J Neurosci 22(19):8691–8704

    CAS  PubMed  Google Scholar 

  53. Destexhe A, Mainen ZF, Sejnowski TJ (1998b) Kinetic models of synaptic transmission. In: Koch C, Segev I (eds) Methods in neuronal modeling, 2nd edn. The MIT Press, Cambridge

    Google Scholar 

  54. Wang XJ, Rinzel J (1992) Alternating and synchronous rhythms in reciprocally inhibitory model neurons. Neural Comput 4:84–97

    Article  Google Scholar 

  55. Golomb D, Wang XJ, Rinzel J (1994) Synchronization properties of spindle oscillations in a thalamic reticular nucleus model. J Neurophysiol 72(3):1109–1126

    Article  CAS  PubMed  Google Scholar 

  56. Destexhe A, Mainen ZF, Sejnowski TJ (1994b) An efficient method for computing synaptic conductances based on a kinetic model of receptor binding. Neural Comput 6:14–18

    Article  Google Scholar 

  57. Anastassiou CA, Perin R, Markram H, Koch CK (2011) Ephaptic coupling of cortical neurons. Nat Neurosci 14(2):217–223

    Article  CAS  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Systems Neuroscience and Neurotechnology Unit, Saarland University, Saarland, Germany

    Michael Busse, Narsis Salafzoon & Daniel J. Strauss

  2. Neurocenter, Faculty of Medicine, Saarland University of Applied Sciences, Homburg/Saarbruecken, Germany

    Michael Busse, Narsis Salafzoon & Daniel J. Strauss

  3. INM—Leibniz Institute for New Materials, Saarbruecken, Germany

    Michael Busse, Annette Kraegeloh & Daniel J. Strauss

  4. Department of Physiology, Faculty of Medicine, Saarland University, Homburg/Saarbruecken, Germany

    David R. Stevens

Authors
  1. Michael Busse
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Narsis Salafzoon
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Annette Kraegeloh
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. David R. Stevens
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Daniel J. Strauss
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Michael Busse .

Editor information

Editors and Affiliations

  1. Department of Biology, The University of Texas at San Antonio, San Antonio, Texas, USA

    Fidel Santamaria

  2. Air Force Research Laboratory, 711 HPW/RHDR, Fort Sam Houston, Texas, USA

    Xomalin G. Peralta

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Science+Business Media LLC

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Busse, M., Salafzoon, N., Kraegeloh, A., Stevens, D.R., Strauss, D.J. (2018). Estimating the Effects of Nanoparticles on Neuronal Field Potentials Based on Their Effects on Single Neurons In Vitro. In: Santamaria, F., Peralta, X. (eds) Use of Nanoparticles in Neuroscience. Neuromethods, vol 135. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7584-6_10

Download citation

  • DOI: https://doi.org/10.1007/978-1-4939-7584-6_10

  • Published:

  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-7582-2

  • Online ISBN: 978-1-4939-7584-6

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation