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Rayburst sampling, an algorithm for automated three-dimensional shape analysis from laser scanning microscopy images

Abstract

Precise quantification of complex three-dimensional (3D) structures from laser scanning microscopy (LSM) images is increasingly necessary in understanding normal function and pathologic processes in biology. This protocol describes a versatile shape analysis algorithm, Rayburst sampling, that generates automated 3D measurements from LSM images. Rayburst defines and efficiently casts a multidirectional core of rays from an interior point to the surface of a solid, allowing precise quantification of anisotropic and irregularly shaped 3D structures. Quantization error owing to the finite voxel representation in digital images is minimized by interpolating intensity values continuously between voxels. The Rayburst algorithm provides a primitive for the development of higher level algorithms that solve specific shape analysis problems. Examples are provided of applications to 3D neuronal morphometry: (i) estimation of diameters in tubular neuronal dendritic branching structures, and (ii) measurement of volumes and surface areas for dendritic spines and spatially complex histopathologic structures.

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Figure 1: Recursive subdivision of a regular octahedron to obtain a geodesic unit sphere from which the Rayburst sampling core, with an associated triangular surface mesh, can be derived.
Figure 2: Iterative computation of face of exit of a ray.
Figure 3: Dendritic branch diameter estimation by 2D Rayburst, irrespective of orientation of branch within image stack.
Figure 4: Estimation of volume and surface area of a dendritic spine using Rayburst.
Figure 5: Application of Rayburst to reconstruction of a spiny dendritic branch from a layer III pyramidal neuron from prefrontal area 46 in a rhesus monkey (a,b), and a thioflavine S-labeled amyloid plaque from a Tg2576 mouse (c,d).

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References

  1. Vetter, P., Roth, A. & Häusser, M. Propagation of action potentials in dendrites depends on dendritic morphology. J. Neurophysiol. 85, 926–937 (2001).

    CAS  PubMed  Google Scholar 

  2. Euler, T. & Denk, W. Dendritic processing. Curr. Opin. Neurobiol. 11, 415–422 (2001).

    CAS  PubMed  Google Scholar 

  3. Stuart, G., Spruston, N. & Häusser, M. Dendrites (Oxford University Press, Oxford, 1999).

    Google Scholar 

  4. Rothnie, P., Kabaso, D., Hof, P.R., Henry, B.I. & Wearne, S.L. Functionally relevant measures of spatial complexity in neuronal dendritic arbors. J. Theor. Biol. 238, 506–526 (2004).

    Google Scholar 

  5. Mainen, Z.F. & Sejnowski, T.J. Influence of dendritic structure on firing pattern in model neocortical neurons. Nature 382, 363–366 (1996).

    CAS  PubMed  Google Scholar 

  6. Krichmar, J.L., Nasuto, S.J., Scorcioni, R., Washington, S.D. & Ascoli, G.A. Effects of dendritic morphology on CA3 pyramidal cell electrophysiology: a simulation study. Brain Res. 941, 11–28 (2002).

    CAS  PubMed  Google Scholar 

  7. Ascoli, G.A. Passive dendritic integration heavily affects spiking dynamics of recurrent networks. Neural Networks. 16, 657–663 (2003).

    PubMed  Google Scholar 

  8. Häusser, M. & Mel, B.W. Dendrites: bug or feature? Curr. Opin. Neurobiol. 13, 372–383 (2003).

    PubMed  Google Scholar 

  9. Bloom, F.E., Young, W.G., Nimchinsky, E.A., Hof, P.R. & Morrison, J.H. Neuronal vulnerability and informatics in human disease. in Neuroinformatics—An Overview of the Human Brain Project (eds. Koslow, S.H. & Huerta, M.F.) 83–123 (Mahwah, Lawrence Erlbaum, 1997).

    Google Scholar 

  10. Streekstra, G.J., Smeulders, A.W.M. & van den Boomgaard, R. Scale dependent differential geometry for the measurement of center line and diameter in 3D curvilinear structures. in 6th European Conference on Computer Vision 2000 (Dublin, Ireland, 2000).

    Google Scholar 

  11. Streekstra, G.J. & van Pelt, J. Analysis of tubular structures in three-dimensional confocal images. Network Comput. Neural Systems 13, 381–395 (2002).

    Google Scholar 

  12. Dima, A., Scholz, M. & Obermayer, K. Semi-automatic quality determination of 3D confocal microscope scans of neuronal cells denoised by 3D-wavelet-shrinkage. In: H.H. Szu, ed., Wavelet Applications VI-Proceedings of the SPIE, 3723, 446–457 (1999).

    Google Scholar 

  13. Messerli, M. NeuronTracer Reference Manual V. 1.0##1–33 (Zurich, Bitplane AG, 2000).

  14. Schmitt, S., Evers, J.F., Duch, C., Scholz, M. & Obermayer, K. New methods for the computer-assisted 3-D reconstruction of neurons from confocal image stacks. NeuroImage 23, 1283–1298 (2004).

    PubMed  Google Scholar 

  15. He, W. et al. Automated three-dimensional tracing of neurons in confocal and brightfield images. Microsc. Microanal. 9, 296–310 (2003).

    CAS  PubMed  Google Scholar 

  16. Al-Kofahi, K.A. et al. Rapid automated three-dimensional tracing of neurons from confocal image stacks. IEEE Trans. Inform. Tech. Biomed. 6, 171–187 (2003).

    Google Scholar 

  17. van Pelt, J., van Ooyen, A. & Uylings, H.B.M. The need for integrating neuronal morphology databases and computational environments in exploring neuronal structure and function. Anat. Embryol. 204, 255–265 (2001).

    CAS  Google Scholar 

  18. Ashby, N. & Brittin, W.E. Thomson's problem. Am. J. Phys. 54, 776–777 (1986).

    Google Scholar 

  19. Wearne, S.L. et al. New techniques for imaging, digitization and analysis of three-dimensional neuronal morphology on multiple scales. Neuroscience 136, 661–680 (2005).

    CAS  PubMed  Google Scholar 

  20. Hill, S. Trilinear interpolation. in Graphics Gems IV (ed. Heckbert, P.S.). 521–525 (Academic Press, San Diego, 1994).

    Google Scholar 

  21. Scorcioni, R. & Ascoli, G.A. Algorithmic extraction of morphological statistics from electronic archives of neuroanatomy. in IWANN 2001, Lecture Notes in Computer Science (eds. Mira, J. & Prieto, A.) 30–37 (Springer-Verlag, Berlin, Heidelberg, 2001).

    Google Scholar 

  22. Weaver, C.M., Hof, P.R., Wearne, S.L. & Lindquist, W.B. Automated algorithms for multiscale morphometry of neuronal dendrites. Neural Comput. 16, 1353–1383 (2004).

    PubMed  Google Scholar 

  23. Hines, M.L. The NEURON simulation program. in Neural Network Simulation Environments (ed. Skrzypek, J.) 147–163 (Kluwer, Norwell, MA, 1994).

    Google Scholar 

  24. Bower, J.M. & Beeman, D. The Book of Genesis: Exploring Realistic Neural Systems with the General Neural Simulation System 2nd edn. (Springer-Verlag, New York, NY, 1998).

    Google Scholar 

  25. Blum, H. A transformation for extracting new descriptors of shape. in Models for the Perception of Speech and Visual Form (ed. Wathen-Dunn, W.) 362–380 (MIT Pressm Cambridge, MA, 1967).

    Google Scholar 

  26. Koh, I.Y.Y., Lindquist, W.B., Zito, K., Nimchinsky, E.A. & Svoboda, K. An image analysis algorithm for the fine structure of neuronal dendrites. Neural Comput. 14, 1283–1310 (2002).

    PubMed  Google Scholar 

  27. Borgefors, G., Nystrom, I. & Sanniti Di Baja, G. Computing skeletons in three dimensions. Pattern Recognition 32, 1225–1236 (1999).

    Google Scholar 

  28. Peters, A., Palay, S.L. & Webster, H.D. The Fine Structure of the Nervous System (Oxford University Press, New York, 1991).

    Google Scholar 

  29. Jain, A.K. Fundamentals of Digital Image Processing (Prentice-Hall, Englewood Cliffs, NJ, 1989).

    Google Scholar 

  30. Rodriguez, A. et al. Automated reconstruction of 3D neuronal morphology from laser scanning microscopy images. Methods Companion Methods Enzymol. 30, 94–105 (2003).

    CAS  Google Scholar 

  31. Wu, C.-C., Reilly, J.F., Young, W.G., Morrison, J.H. & Bloom, F.E. High-throughput morphometric analysis of individual neurons. Cereb. Cortex 14, 543–554 (2004).

    PubMed  Google Scholar 

  32. Hao, J. et al. Estrogen alters spine number and morphology in prefrontal cortex of aged female rhesus monkeys. J. Neurosci. 26, 2571–2578 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Cannon, R.C., Turner, D.A., Pyapali, G.K. & Wheal, H.V. An on-line archive of reconstructed hippocampal neurons. J. Neurosci. Methods 84, 49–54 (1998).

    CAS  PubMed  Google Scholar 

  34. Hsiao, K. et al. Correlative memory deficits, Aβ elevation, and amyloid plaques in transgenic mice. Science 274, 99–102 (1996).

    CAS  PubMed  Google Scholar 

  35. Duan, H., Wearne, S.L., Morrison, J.H. & Hof, P.R. Quantitative analysis of the dendritic morphology of corticocortical projection neurons in the macaque monkey association cortex. Neuroscience 114, 349–359 (2002).

    CAS  PubMed  Google Scholar 

  36. Duan, H. et al. Age-related morphologic alterations in dendrites and spine densities of corticocortically projecting neurons in macaque monkeys. Cereb. Cortex 13, 950–961 (2003).

    PubMed  Google Scholar 

  37. Perl, D.P. et al. Practical approaches to stereology in the setting of aging and disease-related brain banks. J. Chem. Neuroanat. 20, 7–19 (2000).

    CAS  PubMed  Google Scholar 

  38. Hof, P.R., Nimchinsky, E.A. & Morrison, J.H. Neurochemical phenotype of corticocortical connections in the macaque monkey: quantitative analysis of a subset of neurofilament protein-immunoreactive projection neurons in frontal, parietal, temporal and cingulate cortices. J. Comp. Neurol. 362, 109–133 (1995).

    CAS  PubMed  Google Scholar 

  39. Nimchinsky, E.A., Hof, P.R., Young, W.G. & Morrison, J.H. Neurochemical, morphologic and laminar characterization of cortical projection neurons in the cingulate motor areas of the macaque monkey. J. Comp. Neurol. 374, 136–160 (1996).

    CAS  PubMed  Google Scholar 

  40. Romijn, H.J. et al. Double immunolabeling of neuropeptides in the human hypothalamus as analyzed by confocal laser scanning fluorescence microscopy. J. Histochem. Cytochem. 47, 229–235 (1999).

    CAS  PubMed  Google Scholar 

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Acknowledgements

This work was supported by NIH grants from NIMH, NIDCD, NIA and NCRR. The authors thank colleagues in the Wearne and Hof laboratories for their participation.

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Correspondence to Susan L Wearne.

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Rodriguez, A., Ehlenberger, D., Hof, P. et al. Rayburst sampling, an algorithm for automated three-dimensional shape analysis from laser scanning microscopy images. Nat Protoc 1, 2152–2161 (2006). https://doi.org/10.1038/nprot.2006.313

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