Skip to main content

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

  • Protocol
  • First Online:
Multiphoton Microscopy

Part of the book series: Neuromethods ((NM,volume 148))

  • 1139 Accesses

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).

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

Access this chapter

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 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
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

References

  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, Leipzig

    Google Scholar 

  2. Shepherd GM (2016) Foundations of the neuron doctrine. 25th Anniversary Edition. Oxford University Press, New York

    Google Scholar 

  3. McKenna T, Davis J, Zornetzer SF (eds) (1992) Single neuron computation. Academic Press, Boston

    Google Scholar 

  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 York

    Google Scholar 

  5. Shepherd GM, Grillner S (eds) (2018) Handbook of brain microcircuits, 2nd edn. Oxford University Press, New York

    Google Scholar 

  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, Milan

    Google Scholar 

  7. Cajal S Ramón y (1909) Histologie du Système Nerveux de l'Homme et des Vertébrés, vol. I. Maloine, Paris

    Google Scholar 

  8. Cajal S Ramón y (1911) Histologie du Système Nerveux de l’Homme et des Vertébrés, vol. II. Maloine, Paris

    Google Scholar 

  9. Segev I, Rinzel J, Shepherd GM (eds) (1995) The theoretical foundation of dendritic function. MIT Press, Cambridge

    Google Scholar 

  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 429–438

    Chapter  Google Scholar 

  11. Mainen ZF, Sejnowski TJ (1996) Influence of dendritic structure on firing pattern in model neocortical neurons. Nature 382:363–366

    Article  CAS  PubMed  Google Scholar 

  12. Soltesz I (2006) Diversity in the neuronal machine. Order and variability in interneuronal microcircuits. Oxford University Press, New York

    Book  Google Scholar 

  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, Chichester

    Google Scholar 

  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 159–178

    Google Scholar 

  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 187–210

    Google Scholar 

  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 219–237

    Chapter  Google Scholar 

  17. Cline HT (2016) Dendrite development. In: Stuart G, Spruston N, Häusser M (eds) Dendrites, 3rd edn. Oxford University Press, New York, pp 77–94

    Chapter  Google Scholar 

  18. Parekh R, Ascoli GA (2013) Neuronal morphology goes digital: a research hub for cellular and system neuroscience. Neuron 77:1017–1038

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Glaser JR, Glaser EM (1990) Neuron imaging with Neurolucida – a PC-based system for image combining microscopy. Comput Med Imaging Graph 14:307–317

    Article  CAS  PubMed  Google Scholar 

  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 225–246

    Google Scholar 

  21. Meijering E (2010) Neuron tracing in perspective. Cytometry A 77:693–704

    Article  PubMed  Google Scholar 

  22. Horikawa K, Armstrong WE (1988) A versatile means of intracellular labeling: injection of biocytin and its detection with avidin conjugates. J Neurosci Meth 25:1–11

    Article  CAS  Google Scholar 

  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 37:141–150

    Article  CAS  Google Scholar 

  24. Dumitriu D, Rodriguez A, Morrison JH (2011) High-throughput, detailed, cell-specific neuroanatomy of dendritic spines using microinjection and confocal microscopy. Nat Prot 6:1391–1411

    Article  CAS  Google Scholar 

  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 8:65

    Article  PubMed  PubMed Central  Google Scholar 

  26. Murphy DB, Davidson MW (2013) Fundamentals of light microscopy and electronic imaging, 2nd edn. Wiley-Blackwell, Hoboken

    Google Scholar 

  27. Denk W, Strickler JH, Webb WW (1990) Two-photon laser scanning fluorescence microscopy. Science 248:73–76

    Article  CAS  PubMed  Google Scholar 

  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 105–110

    Google Scholar 

  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 722–735

    Chapter  Google Scholar 

  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 377–386

    Google Scholar 

  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 223:3383–3410

    Article  PubMed  Google Scholar 

  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 209–232

    Google Scholar 

  33. Holmes WR (2010) Passive cable modeling. In: De Schutter E (ed) Computational modeling methods for neuroscientists. MIT Press, Cambridge, pp 233–258

    Google Scholar 

  34. Donohue DE, Ascoli GA (2011) Automated reconstruction of neuronal morphology: an overview. Brain Res Rev 67:94–102

    Article  PubMed  Google Scholar 

  35. Acciai L, Soda P, Iannello G (2016) Automated neuron tracing methods: an updated account. Neuroinformatics 14:353–367

    Article  PubMed  Google Scholar 

  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 100:2422–2429

    Article  PubMed  Google Scholar 

  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 6:1–14

    Article  CAS  Google Scholar 

  38. Cuntz H, Forstner F, Borst A, Häusser M (2011) The TREES toolbox – probing the basis of axonal and dendritic branching. Neuroinformatics 9:91–96

    Article  PubMed  PubMed Central  Google Scholar 

  39. Myatt DR, Hadlington T, Ascoli GA, Nasuto SJ (2012) Neuromantic – from semi-manual to semi-automatic reconstruction of neuron morphology. Front Neuroinform 6:4

    Article  PubMed  PubMed Central  Google Scholar 

  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. 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 279:101–118

    Article  Google Scholar 

  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 123–131

    Chapter  Google Scholar 

  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 29–36

    Google Scholar 

  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 222:151–182

    Article  PubMed  Google Scholar 

  45. Pologruto TA, Sabatini BL, Svoboda K (2003) ScanImage: flexible software for operating laser scanning microscopes. Biomed Eng Online 2:13

    Article  PubMed  PubMed Central  Google Scholar 

  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 188:141–150

    Article  Google Scholar 

  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 488–500

    Chapter  Google Scholar 

  48. van der Voort HTM, Strasters KC (1995) Restoration of confocal images for quantitative image analysis. J Microsc 178:165–181

    Google Scholar 

  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 3:866–876

    Article  CAS  Google Scholar 

  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 1–36

    Chapter  Google Scholar 

  51. Cox G, Sheppard CJR (2004) Practical limits of resolution in confocal and non-linear microscopy. Microsc Res Tech 63:18–22

    Article  PubMed  Google Scholar 

  52. Sigal YM, Speer CM, Babcock HP, Zhuang X (2015) Mapping synaptic input fields of neurons with super-resolution imaging. Cell 163:493–505

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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 521:3541–3555

    Article  PubMed  PubMed Central  Google Scholar 

  54. Sethian JA (1996) A fast marching level set method for monotonically advancing fronts. Proc Natl Acad Sci U S A 93:1591–1595

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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 8:16

    Google Scholar 

  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 9:676–682

    Article  CAS  PubMed  Google Scholar 

  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 3–28

    Google Scholar 

  58. Cannon RC, Turner DA, Pyapali GK, Wheal HV (1998) An on-line archive of reconstructed hippocampal neurons. J Neurosci Meth 84:49–54

    Article  CAS  Google Scholar 

  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 56:462–478

    Google Scholar 

  60. Prim RC (1957) Shortest connection networks and some generalizations. Bell Syst Technol J 36:1389–1401

    Article  Google Scholar 

  61. Paglieroni DW (1992) Distance transforms: properties and machine vision applications. CVGIP: Graph Models Image Process 54:56–74

    Google Scholar 

  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 25:265–270

    Article  Google Scholar 

  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 28:348–353

    Article  CAS  Google Scholar 

  64. Peng H, Long F, Zhao T, Myers G (2011) Proof-editing is the bottleneck of 3D neuron reconstruction: the problem and solutions. Neuroinformatics 9:103–105

    Article  PubMed  Google Scholar 

  65. Peng H, Bria A, Zhou Z, Iannello G, Long F (2014) Extensible visualization and analysis for multidimensional images using Vaa3D. Nat Prot 9:193–208

    Article  CAS  Google Scholar 

  66. Koch C, Segev I (2000) The role of single neurons in information processing. Nat Neurosci 3 Suppl:1171–1177

    Article  CAS  PubMed  Google Scholar 

  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 233–257

    Google Scholar 

  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 259–283

    Google Scholar 

  69. Carnevale NT, Hines ML (2006) The NEURON Book. Cambridge University Press, Cambridge

    Book  Google Scholar 

  70. Schneider CJ, Cuntz H, Soltesz I (2014) Linking macroscopic with microscopic neuroanatomy using synthetic neuronal populations. PLoS Comput Biol 10:e1003921

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  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 93:2331–2342

    Article  CAS  PubMed  Google Scholar 

  72. Helmstaedter M, Briggman KL, Denk W (2011) High-accuracy neurite reconstruction for high-throughput neuroanatomy. Nat Neurosci 14:1081–1088

    Article  CAS  PubMed  Google Scholar 

  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 500:168–174

    Article  CAS  PubMed  Google Scholar 

  74. Kroon DJ (2009) Accurate Fast Marching toolbox for MATLAB. www.mathworks.com/matlabcentral/fileexchange/24531-accurate-fast-marching

Download references

Acknowledgments

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

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Espen Hartveit .

Editor information

Editors and Affiliations

Appendix A: Recommended Parameter Values for Automated Reconstruction

Appendix A: Recommended Parameter Values for Automated Reconstruction

figure a

Rights and permissions

Reprints and permissions

Copyright information

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

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Hartveit, E., Zandt, BJ., Veruki, M.L. (2019). Multiphoton Excitation Microscopy for the Reconstruction and Analysis of Single Neuron Morphology. In: Hartveit, E. (eds) Multiphoton Microscopy. Neuromethods, vol 148. Humana, New York, NY. https://doi.org/10.1007/978-1-4939-9702-2_8

Download citation

  • DOI: https://doi.org/10.1007/978-1-4939-9702-2_8

  • Published:

  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-4939-9701-5

  • Online ISBN: 978-1-4939-9702-2

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics