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Semiautomatic Segmentation of Plant Golgi Stacks in Electron Tomograms Using 3dmod

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Plant Protein Secretion

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1662))

Abstract

Electron tomography is a powerful tool for visualizing subcellular organelles. With the advances in cryo-fixation techniques, it is now possible to reconstruct complex structures in cells preserved close to their native states in three-dimension (3D) using electron tomography. In order to better visualize these objects, 3D models are made from outlines of organelles in individual tomographic slices, which can be used to display morphological features and quantify structural parameters. While outlines of simple organelles can be drawn by hand fairly quickly, it is possible to accelerate 3D modeling of more complex organelles by means of semiautomatic segmentation. In this chapter, we use the example of reconstructing Golgi cisternae of a plant cell into 3D models using the semiautomatic protocol.

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Acknowledgments

This work was supported by grants from the Research Grants Council of Hong Kong (GRF-14126116, AoE/M-05/12, C4011-14R) and the Cooperative Research Program for Agriculture Science and Technology Development (Project No. PJ010953092015) from the Rural Development Administration, Republic of Korea.

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Authors and Affiliations

  1. State Key Laboratory of Agrobiotechnology, Centre for Cell and Developmental Biology, School of Life Sciences, Chinese University of Hong Kong, Hong Kong, China

    Keith Ka Ki Mai & Byung-Ho Kang

Authors
  1. Keith Ka Ki Mai
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  2. Byung-Ho Kang
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Corresponding author

Correspondence to Byung-Ho Kang .

Editor information

Editors and Affiliations

  1. School of Life Sciences, The Chinese University of Hong Kong, Shatin, Hong Kong, China

    Liwen Jiang

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Mai, K.K.K., Kang, BH. (2017). Semiautomatic Segmentation of Plant Golgi Stacks in Electron Tomograms Using 3dmod. In: Jiang, L. (eds) Plant Protein Secretion. Methods in Molecular Biology, vol 1662. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7262-3_8

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  • DOI: https://doi.org/10.1007/978-1-4939-7262-3_8

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-7261-6

  • Online ISBN: 978-1-4939-7262-3

  • eBook Packages: Springer Protocols

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