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Brain Structure and Function

, Volume 223, Issue 7, pp 3383–3410 | Cite as

Electrotonic signal processing in AII amacrine cells: compartmental models and passive membrane properties for a gap junction-coupled retinal neuron

  • Bas-Jan Zandt
  • Margaret Lin Veruki
  • Espen Hartveit
Original Article

Abstract

Amacrine cells are critical for processing of visual signals, but little is known about their electrotonic structure and passive membrane properties. AII amacrine cells are multifunctional interneurons in the mammalian retina and essential for both rod- and cone-mediated vision. Their dendrites are the site of both input and output chemical synapses and gap junctions that form electrically coupled networks. This electrical coupling is a challenge for developing realistic computer models of single neurons. Here, we combined multiphoton microscopy and electrophysiological recording from dye-filled AII amacrine cells in rat retinal slices to develop morphologically accurate compartmental models. Passive cable properties were estimated by directly fitting the current responses of the models evoked by voltage pulses to the physiologically recorded responses, obtained after blocking electrical coupling. The average best-fit parameters (obtained at − 60 mV and ~ 25 °C) were 0.91 µF cm−2 for specific membrane capacitance, 198 Ω cm for cytoplasmic resistivity, and 30 kΩ cm2 for specific membrane resistance. We examined the passive signal transmission between the cell body and the dendrites by the electrotonic transform and quantified the frequency-dependent voltage attenuation in response to sinusoidal current stimuli. There was significant frequency-dependent attenuation, most pronounced for signals generated at the arboreal dendrites and propagating towards the soma and lobular dendrites. In addition, we explored the consequences of the electrotonic structure for interpreting currents in somatic, whole-cell voltage-clamp recordings. The results indicate that AII amacrines cannot be characterized as electrotonically compact and suggest that their morphology and passive properties can contribute significantly to signal integration and processing.

Keywords

Retina Amacrine cell Compartmental model Electrotonic Passive membrane properties Dendrites 

Notes

Acknowledgements

Financial support from The Research Council of Norway (NFR 182743, 189662, 214216 to E.H.; NFR 213776, 261914 to M.L.V.) is gratefully acknowledged.

Author contributions

BJZ performed morphological reconstructions and compartmental modeling. MLV and EH conceived and designed the experiments and performed electrophysiological recording and MPE microscopic imaging. BJZ, MLV and EH interpreted data, made the figures, wrote the manuscript, and approved the final version of the manuscript. The experiments were done in the Department of Biomedicine, University of Bergen.

Compliance with ethical standards

Ethical approval

The use of animals in this study was carried out under the approval of and in accordance with the regulations of the Animal Laboratory Facility at the Faculty of Medicine at the University of Bergen (accredited by AAALAC International).

Conflict of interest

The authors declare that they have no conflict of interest.

References

  1. Abrahamsson T, Cathala L, Matsui K, Shigemoto R, DiGregorio DA (2012) Thin dendrites of cerebellar interneurons confer sublinear synaptic integration and a gradient of short-term plasticity. Neuron 73:1159–1172CrossRefPubMedGoogle Scholar
  2. Balakrishnan V, Puthussery T, Kim M-H, Taylor WR, von Gersdorff H (2015) Synaptic vesicle exocytosis at the dendritic lobules of an inhibitory interneuron in the mammalian retina. Neuron 87:563–575CrossRefPubMedPubMedCentralGoogle Scholar
  3. Bloomfield SA, Völgyi B (2004) Function and plasticity of homologous coupling between AII amacrine cells. Vis Res 44:3297–3306CrossRefPubMedGoogle Scholar
  4. Boos R, Schneider H, Wässle H (1993) Voltage- and transmitter-gated currents of AII-amacrine cells in a slice preparation of the rat retina. J Neurosci 13:2874–2888CrossRefPubMedGoogle Scholar
  5. Brent RP (1973) A new algorithm for minimizing a function of several variables without calculating derivatives. In: Algorithms for minimization without derivatives. Prentice Hall, Englewood Cliffs, pp 116–167Google Scholar
  6. Cajal SRy (1893) La rétine des vertébrés. La Cellule 9:119–255Google Scholar
  7. Cajal SRy (1909) Histologie du Système Nerveux de l’Homme et des Vertébrés, vol I. Maloine, ParisGoogle Scholar
  8. Cajal SRy (1911) Histologie du Système Nerveux de l’Homme et des Vertébrés, vol II. Maloine, ParisGoogle Scholar
  9. Carnevale NT, Hines ML (2006) The NEURON book. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  10. Carnevale NT, Tsai KY, Claiborne BJ, Brown TH (1995) The electrotonic transformation: a tool for relating neuronal form to function. In: Tesauro G, Touretzky DS, Leen TK (eds) Advances in neural information processing systems, vol 7. MIT Press, Cambridge, pp 69–76Google Scholar
  11. Castilho Á, Ambrósio AF, Hartveit E, Veruki ML (2015) Disruption of a neural microcircuit in the rod pathway of the mammalian retina by diabetes mellitus. J Neurosci 35:5422–5433CrossRefPubMedGoogle Scholar
  12. Cembrowski MS, Logan SM, Tian M, Jia L, Li W, Kath WL, Riecke H, Singer JH (2012) The mechanisms of repetitive spike generation in an axonless retinal interneuron. Cell Rep 1:155–166CrossRefPubMedPubMedCentralGoogle Scholar
  13. Choi H, Zhang L, Cembrowski MS, Sabottke CF, Markowitz AL, Butts DA, Kath WL, Singer JH, Riecke H (2014) Intrinsic bursting of AII amacrine cells underlies oscillations in the rd1 mouse retina. J Neurophysiol 112:1491–1504CrossRefPubMedPubMedCentralGoogle Scholar
  14. Clements JD, Redman SJ (1989) Cable properties of cat spinal motoneurones measured by combining voltage clamp, current clamp and intracellular staining. J Physiol 409:63–87CrossRefPubMedPubMedCentralGoogle Scholar
  15. De Schutter E, Steuber V (2001) Modeling simple and complex active neurons. In: De Schutter E (ed) Computational neuroscience: realistic modeling for experimentalists. CRC Press, Boca Raton, pp 233–257Google Scholar
  16. De Schutter E, van Geit W (2010) Modeling complex neurons. In: De Schutter E (ed) Computational modeling methods for neuroscientists. MIT Press, Cambridge, pp 259–283Google Scholar
  17. Deans MR, Völgyi B, Goodenough DA, Bloomfield SA, Paul DL (2002) Connexin36 is essential for transmission of rod-mediated visual signals in the mammalian retina. Neuron 36:703–712CrossRefPubMedPubMedCentralGoogle Scholar
  18. Destexhe A, Huguenard JR (2010) Modeling voltage-dependent channels. In: De Schutter E (ed) Computational modeling methods for neuroscientists. MIT Press, Cambridge, pp 107–137Google Scholar
  19. Diamond JS (2017) Inhibitory interneurons in the retina: types, circuitry, and function. Annu Rev Vis Sci 3:1–24CrossRefPubMedGoogle Scholar
  20. Ding JB, Takasaki KT, Sabatini BL (2009) Supraresolution imaging in brain slices using stimulated-emission depletion two-photon laser scanning microscopy. Neuron 63:429–437CrossRefPubMedPubMedCentralGoogle Scholar
  21. Doll CJ, Hochachka PW, Reiner PB (1993) Reduced ionic conductance in turtle brain. Am J Physiol 265:R929-R933Google Scholar
  22. Gill SB, Veruki ML, Hartveit E (2006) Functional properties of spontaneous IPSCs and glycine receptors in rod amacrine (AII) cells in the rat retina. J Physiol 575:739–759CrossRefPubMedPubMedCentralGoogle Scholar
  23. Golding NL, Mickus TJ, Katz Y, Kath WL, Spruston N (2005) Factors mediating powerful voltage attenuation along CA1 pyramidal neuron dendrites. J Physiol 568:69–82CrossRefPubMedPubMedCentralGoogle Scholar
  24. Habermann CJ, O’Brien BJ, Wässle H, Protti DA (2003) AII amacrine cells express L-type calcium channels at their output synapses. J Neurosci 23:6904–6913CrossRefPubMedGoogle Scholar
  25. Hampson ECGM., Vaney DI, Weiler R (1992) Dopaminergic modulation of gap junction permeability between amacrine cells in mammalian retina. J Neurosci 12:4911–4922CrossRefPubMedGoogle Scholar
  26. Hartveit E, Veruki ML (2010) Accurate measurement of junctional conductance between electrically coupled cells with dual whole-cell voltage-clamp under conditions of high series resistance. J Neurosci Meth 187:13–25CrossRefGoogle Scholar
  27. Hartveit E, Veruki ML (2012) Electrical synapses between AII amacrine cells in the retina: function and modulation. Brain Res 1487:160–172CrossRefPubMedGoogle Scholar
  28. Hartveit E, Zandt B-J, Madsen E, Castilho Á, Mørkve SH, Veruki ML (2018) AMPA receptors at ribbon synapses in the mammalian retina: kinetic models and molecular identity. Brain Struct Funct 223:769–804CrossRefPubMedGoogle Scholar
  29. Helmstaedter M, Briggman KL, Turaga SC, Jain V, Seung HS, Denk W (2013) Connectomic reconstruction of the inner plexiform layer in the mouse retina. Nature 500:168–174CrossRefPubMedGoogle Scholar
  30. Hille B (2001) Ion channels of excitable membranes, 3rd edn. Sinauer, SunderlandGoogle Scholar
  31. Holmes WR (2010) Passive cable modeling. In: De Schutter E (ed) Computational modeling methods for neuroscientists. MIT Press, Cambridge, MA, pp 233–258Google Scholar
  32. Horikawa K, Armstrong WE (1988) A versatile means of intracellular labeling: injection of biocytin and its detection with avidin conjugates. J Neurosci Meth 25:1–11CrossRefGoogle Scholar
  33. Jackson MB (2006) Molecular and cellular biophysics. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  34. Jacobs G, Claiborne B, Harris K (2010) Reconstruction of neuronal morphology. In: De Schutter E (ed) Computational modeling methods for neuroscientists. MIT Press, Cambridge, pp 187–210Google Scholar
  35. Jaeger D (2001) Accurate reconstruction of neuronal morphology. In: De Schutter E (ed) Computational neuroscience: realistic modeling for experimentalists. CRC Press, Boca Raton, pp 159–178Google Scholar
  36. Jaffe DB, Carnevale NT (1999) Passive normalization of synaptic integration influenced by dendritic architecture. J Neurophysiol 82:3268–3285CrossRefPubMedGoogle Scholar
  37. Kita H, Armstrong W (1991) A biotin-containing compound N-(2- aminoethyl)biotinamide for intracellular labeling and neuronal tracing studies: comparison with biocytin. J Neurosci Meth 37:141–150CrossRefGoogle Scholar
  38. Koch C (1999) Biophysics of computation: information processing in single neurons. Oxford University Press, New YorkGoogle Scholar
  39. Koch C, Rapp M, Segev I (1996) A brief history of time (constants). Cereb Cortex 6:93–101CrossRefPubMedGoogle Scholar
  40. Kolb H, Famiglietti EV (1974) Rod and cone pathways in the inner plexiform layer of cat retina. Science 186:47–49CrossRefPubMedGoogle Scholar
  41. Kole MHP, Stuart GJ (2012) Signal processing in the axon initial segment. Neuron 73:235–247CrossRefPubMedGoogle Scholar
  42. Kothmann WW, Trexler EB, Whitaker CM, Li W, Massey SC, O’Brien J (2012) Nonsynaptic NMDA receptors mediate activity-dependent plasticity of gap junctional coupling in the AII amacrine cell network. J Neurosci 32:6747–6759CrossRefPubMedPubMedCentralGoogle Scholar
  43. Mainen ZF, Sejnowski TJ (1996) Influence of dendritic structure on firing pattern in model neocortical neurons. Nature 382:363–366CrossRefPubMedGoogle Scholar
  44. Major G (2001) Passive cable modeling—a practical introduction. In: De Schutter E (ed) Computational neuroscience: realistic modeling for experimentalists. CRC Press, Boca Raton, pp 209–232Google Scholar
  45. Major G, Larkman AU, Jonas P, Sakmann B, Jack JJB (1994) Detailed passive cable models of whole-cell recorded CA3 pyramidal neurons in rat hippocampal slices. J Neurosci 14:4613–4638CrossRefPubMedGoogle Scholar
  46. Manookin MB, Beaudoin DL, Ernst ZR, Flagel LJ, Demb JB (2008) Disinhibition combines with excitation to extend the operating range of the OFF visual pathway in daylight. J Neurosci 28:4136–4150CrossRefPubMedPubMedCentralGoogle Scholar
  47. Marc RE, Anderson JR, Jones BW, Sigulinsky CL, Lauritzen JS (2014) The AII amacrine cell connectome: a dense network hub. Front Neural Circ 8:104.  https://doi.org/10.3389/fncir.2014.00104 CrossRefGoogle Scholar
  48. Masland RH (2012) The tasks of amacrine cells. Vis Neurosci 29:3–9CrossRefPubMedPubMedCentralGoogle Scholar
  49. Meyer A, Tetenborg S, Greb H, Segelken J, Dorgau B, Weiler R, Hormuzdi SG, Janssen- Bienhold U, Dedek K (2016) Connexin30.2: in vitro interaction with connexin36 in HeLa cells and expression in AII amacrine cells and intrinsically photosensitive ganglion cells in the mouse retina. Front Mol Neurosci 9:36.  https://doi.org/10.3389/fnmol.2016.00036 CrossRefPubMedPubMedCentralGoogle Scholar
  50. Mills SL, Massey SC (1995) Differential properties of two gap junctional pathways made by AII amacrine cells. Nature 377:734–737CrossRefPubMedGoogle Scholar
  51. Mørkve SH, Veruki ML, Hartveit E (2002) Functional characteristics of non-NMDA- type ionotropic glutamate receptor channels in AII amacrine cells in rat retina. J Physiol 542:147–165CrossRefPubMedPubMedCentralGoogle Scholar
  52. Münch TA, da Silveira RA, Siegert S, Viney TJ, Awatramani GB, Roska B (2009) Approach sensitivity in the retina processed by a multifunctional neural circuit. Nat Neurosci 12:1308–1316CrossRefPubMedGoogle Scholar
  53. Murphy GJ, Rieke F (2008) Signals and noise in an inhibitory interneuron diverge to control activity in nearby retinal ganglion cells. Nat Neurosci 11:318–326CrossRefPubMedPubMedCentralGoogle Scholar
  54. Nörenberg A, Hu H, Vida I, Bartos M, Jonas P (2010) Distinct nonuniform cable properties optimize rapid and efficient activation of fast-spiking GABAergic interneurons. Proc Natl Acad Sci USA 107:894–899CrossRefPubMedGoogle Scholar
  55. Oltedal L, Veruki ML, Hartveit E (2009) Passive membrane properties and electrotonic signal processing in retinal rod bipolar cells. J Physiol 587:829–849CrossRefPubMedGoogle Scholar
  56. Perreault M-C, Raastad M (2006) Contribution of morphology and membrane resistance to integration of fast synaptic signals in two thalamic cell types. J Physiol 577:205–220CrossRefPubMedPubMedCentralGoogle Scholar
  57. Peters F, Gennerich A, Czesnik D, Schild D (2000) Low frequency voltage clamp: recording of voltage transients at constant average command voltage. J Neurosci Meth 99:129–135CrossRefGoogle Scholar
  58. Pologruto TA, Sabatini BL, Svoboda K (2003) ScanImage: flexible software for operating laser scanning microscopes. Biomed Eng Online 2:13CrossRefPubMedPubMedCentralGoogle Scholar
  59. Schaefer AT, Helmstaedter M, Sakmann B, Korngreen A (2003) Correction of conductance measurements in non-space-clamped structures: 1. Voltage-gated K+ channels. Biophys J 84:3508–3528CrossRefPubMedPubMedCentralGoogle Scholar
  60. Schmidt-Hieber C, Jonas P, Bischofberger J (2007) Subthreshold dendritic signal processing and coincidence detection in dentate gyrus granule cells. J Neurosci 27:8430–8441CrossRefPubMedGoogle Scholar
  61. Schubert T, Euler T (2010) Retinal processing: global players like it local. Curr Biol 20:R486-488CrossRefGoogle Scholar
  62. Spruston N, Jaffe DB, Johnston D (1994) Dendritic attenuation of synaptic potentials and currents: the role of passive membrane properties. Trends Neurosci 17:161–166CrossRefPubMedGoogle Scholar
  63. Spruston N, Stuart G, Häusser M (2016) Principles of dendritic integration. In: Stuart G, Spruston N, Häusser M (eds) Dendrites, 3rd edn. Oxford University Press, Oxford, pp 351–398CrossRefGoogle Scholar
  64. Stincic T, Smith RG, Taylor WR (2016) Time course of EPSCs in ON-type starburst amacrine cells is independent of dendritic location. J Physiol 594:5685–5694CrossRefPubMedPubMedCentralGoogle Scholar
  65. Strettoi E, Masland RH (1996) The number of unidentified amacrine cells in the mammalian retina. Proc Natl Acad Sci USA 93:14906–14911CrossRefPubMedGoogle Scholar
  66. Strettoi E, Raviola E, Dacheux RF (1992) Synaptic connections of the narrow-field, bistratified rod amacrine cell (AII) in the rabbit retina. J Comp Neurol 325:152–168CrossRefPubMedGoogle Scholar
  67. Strettoi E, Dacheux RF, Raviola E (1994) Cone bipolar cells as interneurons in the rod pathway of the rabbit retina. J Comp Neurol 347:139–149CrossRefPubMedGoogle Scholar
  68. Szoboszlay M, Lörincz A, Lanore F, Vervaeke K, Silver RA, Nusser Z (2016) Functional properties of dendritic gap junctions in cerebellar Golgi cells. Neuron 90:1043–1056CrossRefPubMedPubMedCentralGoogle Scholar
  69. Thompson SM, Masukawa LM, Prince DA (1985) Temperature dependence of intrinsic membrane properties and synaptic potentials in hippocampal CA1 neurons in vitro. J Neurosci 5:817–824CrossRefPubMedGoogle Scholar
  70. Tian M, Jarsky T, Murphy GJ, Rieke F, Singer JH (2010) Voltage-gated Na channels in AII amacrine cells accelerate scotopic light responses mediated by the rod bipolar cell pathway. J Neurosci 30:4650–4659CrossRefPubMedPubMedCentralGoogle Scholar
  71. Trevelyan AJ, Jack J (2002) Detailed passive cable models of layer 2/3 pyramidal cells in rat visual cortex at different temperatures. J Physiol 539:623–636CrossRefPubMedPubMedCentralGoogle Scholar
  72. Trexler EB, Li W, Mills SL, Massey SC (2001) Coupling from AII amacrine cells to ON cone bipolar cells is bidirectional. J Comp Neurol 437:408–422CrossRefPubMedGoogle Scholar
  73. Vaney DI (1991) Many diverse types of retinal neurons show tracer coupling when injected with biocytin or Neurobiotin. Neurosci Lett 125:187–190CrossRefPubMedGoogle Scholar
  74. Vardi N, Smith RG (1996) The AII amacrine network: coupling can increase correlated activity. Vis Res 36:3743–3757CrossRefPubMedGoogle Scholar
  75. Veruki ML, Hartveit E (2002a) AII (rod) amacrine cells form a network of electrically coupled interneurons in the mammalian retina. Neuron 33:935–946CrossRefPubMedGoogle Scholar
  76. Veruki ML, Hartveit E (2002b) Electrical synapses mediate signal transmission in the rod pathway of the mammalian retina. J Neurosci 22:10558–10566CrossRefPubMedGoogle Scholar
  77. Veruki ML, Hartveit E (2009) Meclofenamic acid blocks electrical synapses of retinal AII amacrine and ON-cone bipolar cells. J Neurophysiol 101:2339–2347CrossRefPubMedGoogle Scholar
  78. Veruki ML, Mørkve SH, Hartveit E (2003) Functional properties of spontaneous EPSCs and non-NMDA receptors in rod amacrine (AII) cells in the rat retina. J Physiol 549:759–774CrossRefPubMedPubMedCentralGoogle Scholar
  79. Veruki ML, Oltedal L, Hartveit E (2010) Electrical coupling and passive membrane properties of AII amacrine cells. J Neurophysiol 103:1456–1466CrossRefPubMedGoogle Scholar
  80. Vervaeke K, Lörincz A, Nusser Z, Silver RA (2012) Gap junctions compensate for sublinear dendritic integration in an inhibitory network. Science 335:1624–1628CrossRefPubMedPubMedCentralGoogle Scholar
  81. Vlasits AL, Morrie RD, Tran-Van-Minh A, Bleckert A, Gainer CF, DiGregorio DA, Feller MB (2016) A role for synaptic input distribution in a dendritic computation of motion direction in the retina. Neuron 89:1317–1330CrossRefPubMedPubMedCentralGoogle Scholar
  82. Wu C, Ivanova E, Cui J, Lu Q, Pan Z-H (2011) Action potential generation at an axon initial segment-like process in the axonless retinal AII amacrine cell. J Neurosci 31:14654–14659CrossRefPubMedPubMedCentralGoogle Scholar
  83. Yasuda R, Nimchinsky EA, Scheuss V, Pologruto TA, Oertner TG, Sabatini BL, Svoboda K (2004) Imaging calcium concentration dynamics in small neuronal compartments. Sci STKE 2004:p15Google Scholar
  84. Zandt B-J, Liu JH, Veruki ML, Hartveit E (2017) AII amacrine cells: quantitative reconstruction and morphometric analysis of electrophysiologically identified cells in live rat retinal slices imaged with multi-photon excitation microscopy. Brain Struct Funct 222:151–182CrossRefPubMedGoogle Scholar

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© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of BiomedicineUniversity of BergenBergenNorway

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