Computational reconstruction of cell and tissue surfaces for modeling and data analysis (original) (raw)

Nature Protocols volume 4, pages 1006–1012 (2009)Cite this article

Abstract

We present a method for the computational reconstruction of the 3-D morphology of biological objects, such as cells, cell conjugates or 3-D arrangements of tissue structures, using data from high-resolution microscopy modalities. The method is based on the iterative optimization of Voronoi representations of the spatial structures. The reconstructions of biological surfaces automatically adapt to morphological features of varying complexity with flexible degrees of resolution. We show how 3-D confocal images of single cells can be used to generate numerical representations of cellular membranes that may serve as the basis for realistic, spatially resolved computational models of membrane processes or intracellular signaling. Another example shows how the protocol can be used to reconstruct tissue boundaries from segmented two-photon image data that facilitate the quantitative analysis of lymphocyte migration behavior in relation to microanatomical structures. Processing time is of the order of minutes depending on data features and reconstruction parameters.

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References

  1. Stoll, S., Delon, J., Brotz, T.M. & Germain, R.N. Dynamic imaging of T cell-dendritic cell interactions in lymph nodes. Science 296, 1873–1876 (2002).
    Article PubMed Google Scholar
  2. Germain, R.N. et al. An extended vision for dynamic high-resolution intravital immune imaging. Semin. Immunol. 17, 431–441 (2005).
    Article CAS PubMed PubMed Central Google Scholar
  3. Xu, X., Meier-Schellersheim, M., Yan, J. & Jin, T. Locally controlled inhibitory mechanisms are involved in eukaryotic GPCR-mediated chemosensing. J. Cell Biol. 178, 141–153 (2007).
    Article CAS PubMed PubMed Central Google Scholar
  4. Couteau, B., Payan, Y. & Lavallee, S. The mesh-matching algorithm: an automatic 3D mesh generator for finite element structures. J. Biomech. 33, 1005–1009 (2000).
    Article CAS PubMed Google Scholar
  5. Lorensen, W.E. & Cline, E.C. Marching Cubes: a high resolution 3D surface construction algorithm. Comput. Graph. 21, 163–169 (1987).
    Article Google Scholar
  6. Bootsma, G.J. & Brodland, G.W. Automated 3-D reconstruction of the surface of live early-stage amphibian embryos. IEEE Trans. Bio-Med. Eng. 52, 1407–1414 (2005).
    Article Google Scholar
  7. Yu, X. et al. A novel biomedical meshing algorithm and evaluation based on revised Delaunay and Space Disassembling. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2007, 5091–5094 (2007).
    Google Scholar
  8. Baker, T.J. Mesh generation: art or science? Progress in Aerospace Sciences 41, 29–63 (2005).
    Article Google Scholar
  9. Qi, H., Cannons, J.L., Klauschen, F., Schwartzberg, P.L. & Germain, R.N. SAP-controlled T–B cell interactions underlie germinal centre formation. Nature 455, 764–769 (2008).
    Article CAS PubMed PubMed Central Google Scholar
  10. Voronoi, G. Nouvelles applications des paramètres continus à la théorie des formes quadratiques. J. Reine. Angew. Math. 133, 97–178 (1907).
    Google Scholar
  11. Lopreore, C.L. et al. Computational modeling of three-dimensional electrodiffusion in biological systems: application to the node of Ranvier. Biophys. J. 95, 2624–2635 (2008).
    Article CAS PubMed PubMed Central Google Scholar
  12. Dirichlet, G.L. Über die Reduktion der positiven quadratischen Formen mit drei unbestimmten ganzen Zahlen. J. Reine. Angew. Math. 40, 209–227 (1850).
    Article Google Scholar
  13. Aurenhammer, F. Voronoi diagrams—a survey of a fundamental geometric data structure. ACM Computing Surveys 23, 345–405 (1991).
    Article Google Scholar
  14. Coggan, J.S. et al. Evidence for ectopic neurotransmission at a neuronal synapse. Science 309, 446–451 (2005).
    Article CAS PubMed PubMed Central Google Scholar
  15. Kholodenko, B.N. Cell-signalling dynamics in time and space. Nat. Rev. 7, 165–176 (2006).
    Article CAS Google Scholar
  16. Lizana, L., Konkoli, Z., Bauer, B., Jesorka, A. & Orwar, O. Controlling chemistry by geometry in nanoscale systems. Annu. Rev. Phys. Chem. 60, 449–468 (2009).
    Article CAS PubMed Google Scholar
  17. Neves, S.R. et al. Cell shape and negative links in regulatory motifs together control spatial information flow in signaling networks. Cell 133, 666–680 (2008).
    Article CAS PubMed PubMed Central Google Scholar
  18. Jones, W.P. & Menzies, K.R. Analysis of the cell-centred finite volume method for the diffusion equation. J. Comput. Phys. 165, 45–68 (2000).
    Article Google Scholar
  19. Germain, R.N. et al. Making friends in out-of-the-way places: how cells of the immune system get together and how they conduct their business as revealed by intravital imaging. Immunol. Rev. 221, 163–181 (2008).
    Article CAS PubMed Google Scholar
  20. Schwickert, T.A. et al. In vivo imaging of germinal centres reveals a dynamic open structure. Nature 446, 83–87 (2007).
    Article CAS PubMed Google Scholar
  21. Allen, C.D., Okada, T., Tang, H.L. & Cyster, J.G. Imaging of germinal center selection events during affinity maturation. Science 315, 528–531 (2007).
    Article CAS PubMed Google Scholar
  22. Hauser, A.E. et al. Definition of germinal-center B cell migration in vivo reveals predominant intrazonal circulation patterns. Immunity 26, 655–667 (2007).
    Article CAS PubMed Google Scholar

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Acknowledgements

This research was supported by the Intramural Research Program of NIAID, NIH.

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Authors and Affiliations

  1. Program in Systems Immunology and Infectious Disease Modeling, Laboratory of Immunology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, USA
    Frederick Klauschen, Ronald N Germain & Martin Meier-Schellersheim
  2. Lymphocyte Biology Section, Laboratory of Immunology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, USA
    Hai Qi, Jackson G Egen & Ronald N Germain

Authors

  1. Frederick Klauschen
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  2. Hai Qi
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  3. Jackson G Egen
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  4. Ronald N Germain
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  5. Martin Meier-Schellersheim
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Corresponding authors

Correspondence toFrederick Klauschen or Martin Meier-Schellersheim.

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Klauschen, F., Qi, H., Egen, J. et al. Computational reconstruction of cell and tissue surfaces for modeling and data analysis.Nat Protoc 4, 1006–1012 (2009). https://doi.org/10.1038/nprot.2009.94

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