Advertisement

Multiphoton Excitation Microscopy for the Reconstruction and Analysis of Single Neuron Morphology

  • Espen HartveitEmail author
  • Bas-Jan Zandt
  • Margaret Lin Veruki
Protocol
Part of the Neuromethods book series (NM, volume 148)

Abstract

Neurons are the main cellular components of the circuits of the central nervous system (CNS). The dendritic and axonal morphology of individual neurons display marked variability between neurons in different regions of the CNS, and there is evidence that the morphology of a neuron has a strong impact on its function. For studies of structure-function relationships of specific types of neurons, it is important to visualize and quantify the complete neuronal morphology. In addition, realistic and detailed morphological reconstruction is essential for developing compartmental models that can be used for studying neuronal computation and signal processing. Here we describe in detail how multiphoton excitation (MPE) microscopy of dye-filled neurons can be used for visualization and imaging of neuronal morphology, followed by a workflow with digital deconvolution and manual or semiautomatic morphological reconstruction. The specific advantages of MPE structural imaging are low phototoxicity, the ease with which it can be combined with parallel physiological measurements from the same neurons, and the elimination of tissue post-processing and fixation-related artifacts. Because manual morphological reconstruction can be very time-consuming, this chapter also includes a detailed, step-by-step description of a workflow for semiautomatic morphological reconstruction (using freely available software developed in our laboratory), exemplified by reconstruction of a retinal amacrine cell (AII).

Keywords

Computational neuroanatomy Morphology Neuronal reconstruction Multiphoton excitation microscopy 3D microscopy Dendrites Morphometry Retina 

Notes

Acknowledgments

This research was supported by The Research Council of Norway (NFR 182743, 189662, 214216 to EH; NFR 213776, 261914 to MLV).

References

  1. 1.
    Waldeyer W (1891) Ueber einige neuere Forschungen im Gebiete der Anatomie des Centralnervensystems. Sonderabdruck aus der “Deutschen Medicinischen Wochenschrift”, 1891, No. 44 u. ff. Georg Thieme, LeipzigGoogle Scholar
  2. 2.
    Shepherd GM (2016) Foundations of the neuron doctrine. 25th Anniversary Edition. Oxford University Press, New YorkGoogle Scholar
  3. 3.
    McKenna T, Davis J, Zornetzer SF (eds) (1992) Single neuron computation. Academic Press, BostonGoogle Scholar
  4. 4.
    Cuntz H, Remme MWH, Torben-Nielsen B (eds) (2014) The computing dendrite. From structure to function. Springer series in computational neuroscience, vol. 11. Series eds, Destexhe A, Brette R. Springer, New YorkGoogle Scholar
  5. 5.
    Shepherd GM, Grillner S (eds) (2018) Handbook of brain microcircuits, 2nd edn. Oxford University Press, New YorkGoogle Scholar
  6. 6.
    Golgi C (1873) Sulla struttura della sostanza grigia del cervello (Communicazione preventiva). Gazzetta Medica Italiana 33:244–246. Reprinted as: Sulla sostanza grigia del cervello, Opera Omnia, 1903, Vol. 1, Istologia Normale, pp. 91–98. Ulrico Hoepli, MilanGoogle Scholar
  7. 7.
    Cajal S Ramón y (1909) Histologie du Système Nerveux de l'Homme et des Vertébrés, vol. I. Maloine, ParisGoogle Scholar
  8. 8.
    Cajal S Ramón y (1911) Histologie du Système Nerveux de l’Homme et des Vertébrés, vol. II. Maloine, ParisGoogle Scholar
  9. 9.
    Segev I, Rinzel J, Shepherd GM (eds) (1995) The theoretical foundation of dendritic function. MIT Press, CambridgeGoogle Scholar
  10. 10.
    Rall W (2016) Modeling dendrites: a personal perspective. In: Stuart G, Spruston N, Häusser M (eds) Dendrites, 3rd edn. Oxford University Press, New York, pp 429–438CrossRefGoogle Scholar
  11. 11.
    Mainen ZF, Sejnowski TJ (1996) Influence of dendritic structure on firing pattern in model neocortical neurons. Nature 382:363–366PubMedCrossRefPubMedCentralGoogle Scholar
  12. 12.
    Soltesz I (2006) Diversity in the neuronal machine. Order and variability in interneuronal microcircuits. Oxford University Press, New YorkCrossRefGoogle Scholar
  13. 13.
    Meredith GE, Arbuthnott GW (eds) (1993) Morphological investigations of single neurons in vitro. IBRO handbook series: Methods in the neurosciences. General ed: Smith AD. Wiley, ChichesterGoogle Scholar
  14. 14.
    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
  15. 15.
    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
  16. 16.
    Evers JF, Duch C (2014) Quantitative geometric three-dimensional reconstruction of neuronal architecture and mapping of labeled proteins from confocal image stacks. In: Bakota L, Brandt R (eds) Laser scanning microscopy and quantitative image analysis of neuronal tissue, Neuromethods, vol 87. Springer, New York, pp 219–237CrossRefGoogle Scholar
  17. 17.
    Cline HT (2016) Dendrite development. In: Stuart G, Spruston N, Häusser M (eds) Dendrites, 3rd edn. Oxford University Press, New York, pp 77–94CrossRefGoogle Scholar
  18. 18.
    Parekh R, Ascoli GA (2013) Neuronal morphology goes digital: a research hub for cellular and system neuroscience. Neuron 77:1017–1038PubMedPubMedCentralCrossRefGoogle Scholar
  19. 19.
    Glaser JR, Glaser EM (1990) Neuron imaging with Neurolucida – a PC-based system for image combining microscopy. Comput Med Imaging Graph 14:307–317PubMedCrossRefPubMedCentralGoogle Scholar
  20. 20.
    Turner DA, Wheal HV, Stockley E, Cole H (1991) Three-dimensional reconstructions and analysis of the cable properties of neurons. In: Chad J, Wheal H (eds) Cellular neurobiology. A practical approach. IRL Press at Oxford University Press, Oxford, pp 225–246Google Scholar
  21. 21.
    Meijering E (2010) Neuron tracing in perspective. Cytometry A 77:693–704PubMedCrossRefPubMedCentralGoogle Scholar
  22. 22.
    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
  23. 23.
    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
  24. 24.
    Dumitriu D, Rodriguez A, Morrison JH (2011) High-throughput, detailed, cell-specific neuroanatomy of dendritic spines using microinjection and confocal microscopy. Nat Prot 6:1391–1411CrossRefGoogle Scholar
  25. 25.
    Blackman A, Grabuschnig S, Legenstein R, Sjöström PJ (2014) A comparison of manual reconstruction from biocytin histology or 2-photon imaging: morphometry and computer modeling. Front Neuroanat 8:65PubMedPubMedCentralCrossRefGoogle Scholar
  26. 26.
    Murphy DB, Davidson MW (2013) Fundamentals of light microscopy and electronic imaging, 2nd edn. Wiley-Blackwell, HobokenGoogle Scholar
  27. 27.
    Denk W, Strickler JH, Webb WW (1990) Two-photon laser scanning fluorescence microscopy. Science 248:73–76PubMedCrossRefPubMedCentralGoogle Scholar
  28. 28.
    Denk W (2011) Introduction to multiphoton-excitation fluorescence microscopy. In: Yuste R (ed and series ed) Imaging. A laboratory manual. Cold Spring Harbor Laboratory Press, Cold Spring Harbor, pp 105–110Google Scholar
  29. 29.
    Tashiro A, Aaron G, Aronov D, Cossart R, Dumitriu D, Fenstermaker V, Goldberg J, Hamzei-Sichani F, Ikegaya Y, Konur S, MacLean J, Nemet B, Nikolenko V, Portera-Cailliau C, Yuste R (2006) Imaging brain slices. In: Pawley JB (ed) Handbook of biological confocal microscopy, 3rd edn. Springer, New York, pp 722–735CrossRefGoogle Scholar
  30. 30.
    Groh A, Krieger P (2011) Structure-function analysis of genetically defined neuronal populations. In: Helmchen F, Konnerth A (eds) Yuste R (series ed) Imaging in neuroscience. A laboratory manual. Cold Spring Harbor Laboratory Press, Cold Spring Harbor, pp 377–386Google Scholar
  31. 31.
    Zandt B-J, Veruki ML, Hartveit E (2018) Electrotonic signal processing in AII amacrine cells: compartmental models and passive membrane properties for a gap junction-coupled retinal neuron. Brain Struct Funct 223:3383–3410PubMedCrossRefPubMedCentralGoogle Scholar
  32. 32.
    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
  33. 33.
    Holmes WR (2010) Passive cable modeling. In: De Schutter E (ed) Computational modeling methods for neuroscientists. MIT Press, Cambridge, pp 233–258Google Scholar
  34. 34.
    Donohue DE, Ascoli GA (2011) Automated reconstruction of neuronal morphology: an overview. Brain Res Rev 67:94–102PubMedCrossRefPubMedCentralGoogle Scholar
  35. 35.
    Acciai L, Soda P, Iannello G (2016) Automated neuron tracing methods: an updated account. Neuroinformatics 14:353–367PubMedCrossRefPubMedCentralGoogle Scholar
  36. 36.
    Losavio BE, Liang Y, Santamaría-Pang A, Kakadiaris IA, Colbert CM, Saggau P (2008) Live neuron morphology automatically reconstructed from multiphoton and confocal imaging data. J Neurophysiol 100:2422–2429PubMedCrossRefPubMedCentralGoogle Scholar
  37. 37.
    Cuntz H, Forstner F, Borst A, Häusser M (2010) One rule to grow them all: a general theory of neuronal branching and its practical application. PLoS Comput Biol 6:1–14CrossRefGoogle Scholar
  38. 38.
    Cuntz H, Forstner F, Borst A, Häusser M (2011) The TREES toolbox – probing the basis of axonal and dendritic branching. Neuroinformatics 9:91–96PubMedCrossRefPubMedCentralGoogle Scholar
  39. 39.
    Myatt DR, Hadlington T, Ascoli GA, Nasuto SJ (2012) Neuromantic – from semi-manual to semi-automatic reconstruction of neuron morphology. Front Neuroinform 6:4PubMedPubMedCentralCrossRefGoogle Scholar
  40. 40.
    Feng L, Zhao T, Kim J (2014) neuTube 1.0: a new design for efficient neuron reconstruction software based on the SWC format. eNeuro.  https://doi.org/10.1523/ENEURO.0049-14
  41. 41.
    Zandt B-J, Losnegård A, Hodneland E, Veruki ML, Lundervold A, Hartveit E (2017) Semi-automatic 3D morphological reconstruction of neurons with densely branching morphology: Application to retinal AII amacrine cells imaged with multi-photon excitation microscopy. J Neurosci Meth 279:101–118CrossRefGoogle Scholar
  42. 42.
    Neher E (1992) Correction for liquid junction potentials in patch clamp experiments. In: Rudy B, Iverson LE (eds) Ion channels, Methods in enzymology, vol 207. Academic Press, San Diego, pp 123–131CrossRefGoogle Scholar
  43. 43.
    Heintzmann R (2006) Band limit and appropriate sampling in microscopy. In: Celis JE (ed) Cell biology. A laboratory handbook, vol 3. Elsevier, Amsterdam, pp 29–36Google Scholar
  44. 44.
    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 222:151–182PubMedCrossRefPubMedCentralGoogle Scholar
  45. 45.
    Pologruto TA, Sabatini BL, Svoboda K (2003) ScanImage: flexible software for operating laser scanning microscopes. Biomed Eng Online 2:13PubMedPubMedCentralCrossRefGoogle Scholar
  46. 46.
    Dorostkar MM, Dreosti E, Odermatt B, Lagnado L (2010) Computational processing of optical measurements of neuronal and synaptic activity in networks. J Neurosci Meth 188:141–150CrossRefGoogle Scholar
  47. 47.
    Cannell MB, McMorland A, Soeller C (2006) Image enhancement by deconvolution. In: Pawley JB (ed) Handbook of biological confocal microscopy, 3rd edn. Springer, New York, pp 488–500CrossRefGoogle Scholar
  48. 48.
    van der Voort HTM, Strasters KC (1995) Restoration of confocal images for quantitative image analysis. J Microsc 178:165–181Google Scholar
  49. 49.
    Scorcioni R, Polavaram S, Ascoli GA (2008) L-measure: a web-accessible tool for the analysis, comparison and search of digital reconstructions of neuronal morphologies. Nat Prot 3:866–876CrossRefGoogle Scholar
  50. 50.
    Wouterlood FG, Beliën JAM (2014) Translation, touch, and overlap in multi-fluorescence confocal laser scanning microscopy to quantitate synaptic connectivity. In: Bakota L, Brandt R (eds) Laser scanning microscopy and quantitative image analysis of neuronal tissue, Neuromethods, vol 87. Springer, New York, pp 1–36CrossRefGoogle Scholar
  51. 51.
    Cox G, Sheppard CJR (2004) Practical limits of resolution in confocal and non-linear microscopy. Microsc Res Tech 63:18–22PubMedCrossRefPubMedCentralGoogle Scholar
  52. 52.
    Sigal YM, Speer CM, Babcock HP, Zhuang X (2015) Mapping synaptic input fields of neurons with super-resolution imaging. Cell 163:493–505PubMedPubMedCentralCrossRefGoogle Scholar
  53. 53.
    Tsukamoto Y, Omi N (2013) Functional allocation of synaptic contacts in microcircuits from rods via rod bipolar to AII amacrine cells in the mouse retina. J Comp Neurol 521:3541–3555PubMedPubMedCentralCrossRefGoogle Scholar
  54. 54.
    Sethian JA (1996) A fast marching level set method for monotonically advancing fronts. Proc Natl Acad Sci U S A 93:1591–1595PubMedPubMedCentralCrossRefGoogle Scholar
  55. 55.
    Hodneland E, Kögel T, Frei DM, Gerdes HH, Lundervold A (2013) CellSegm – a MATLAB toolbox for high-throughput 3D cell segmentation. Source Code Biol Med 8:16Google Scholar
  56. 56.
    Schindelin J, Arganda-Carreras I, Frise E, Kaynig V, Longair M, Pietzsch T, Preibisch S, Rueden C, Saalfeld S, Schmid B, Tinevez JY, White DJ, Hartenstein V, Eliceiri K, Tomancak P, Cardona A (2012) Fiji: an open-source platform for biological-image analysis. Nat Methods 9:676–682CrossRefGoogle Scholar
  57. 57.
    Weickert J (1997) A review of nonlinear diffusion filtering. In: ter Haar Romeny B, Florack L, Koenderink J, Viergever M (eds) Scale-space theory in computer vision, Lecture notes in computer science, vol 1252. Springer, Berlin, pp 3–28Google Scholar
  58. 58.
    Cannon RC, Turner DA, Pyapali GK, Wheal HV (1998) An on-line archive of reconstructed hippocampal neurons. J Neurosci Meth 84:49–54CrossRefGoogle Scholar
  59. 59.
    Lee T-C, Kashyap RL, Chu C-N (1994) Building skeleton models via 3-D medial surface/axis thinning algorithms. CVGIP: Graph Models Image Process 56:462–478Google Scholar
  60. 60.
    Prim RC (1957) Shortest connection networks and some generalizations. Bell Syst Technol J 36:1389–1401CrossRefGoogle Scholar
  61. 61.
    Paglieroni DW (1992) Distance transforms: properties and machine vision applications. CVGIP: Graph Models Image Process 54:56–74Google Scholar
  62. 62.
    Maurer CR, Qi R, Raghavan V (2003) A linear time algorithm for computing exact Euclidean distance transforms of binary images in arbitrary dimensions. IEEE Trans Pattern Analysis and Machine Intelligence 25:265–270CrossRefGoogle Scholar
  63. 63.
    Peng H, Ruan Z, Long F, Simpson JH, Myers EW (2010) V3D enables real-time 3D visualization and quantitative analysis of large-scale biological image data sets. Nature Biotech 28:348–353CrossRefGoogle Scholar
  64. 64.
    Peng H, Long F, Zhao T, Myers G (2011) Proof-editing is the bottleneck of 3D neuron reconstruction: the problem and solutions. Neuroinformatics 9:103–105PubMedCrossRefPubMedCentralGoogle Scholar
  65. 65.
    Peng H, Bria A, Zhou Z, Iannello G, Long F (2014) Extensible visualization and analysis for multidimensional images using Vaa3D. Nat Prot 9:193–208CrossRefGoogle Scholar
  66. 66.
    Koch C, Segev I (2000) The role of single neurons in information processing. Nat Neurosci 3 Suppl:1171–1177PubMedCrossRefPubMedCentralGoogle Scholar
  67. 67.
    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
  68. 68.
    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
  69. 69.
    Carnevale NT, Hines ML (2006) The NEURON Book. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  70. 70.
    Schneider CJ, Cuntz H, Soltesz I (2014) Linking macroscopic with microscopic neuroanatomy using synthetic neuronal populations. PLoS Comput Biol 10:e1003921PubMedPubMedCentralCrossRefGoogle Scholar
  71. 71.
    Evers JF, Schmitt S, Sibila M, Duch C (2005) Progress in functional neuroanatomy: precise automatic geometric reconstruction of neuronal morphology from confocal image stacks. J Neurophysiol 93:2331–2342PubMedCrossRefPubMedCentralGoogle Scholar
  72. 72.
    Helmstaedter M, Briggman KL, Denk W (2011) High-accuracy neurite reconstruction for high-throughput neuroanatomy. Nat Neurosci 14:1081–1088PubMedCrossRefPubMedCentralGoogle Scholar
  73. 73.
    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 500:168–174CrossRefGoogle Scholar
  74. 74.
    Kroon DJ (2009) Accurate Fast Marching toolbox for MATLAB. www.mathworks.com/matlabcentral/fileexchange/24531-accurate-fast-marching

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Espen Hartveit
    • 1
    Email author
  • Bas-Jan Zandt
    • 1
  • Margaret Lin Veruki
    • 1
  1. 1.Department of BiomedicineUniversity of BergenBergenNorway

Personalised recommendations