AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
Powered by AMiner. Clicking this button will take you away from SciOpen.
Home Tsinghua Science and Technology Article
PDF (487.7 KB)
Cite
EndNote(RIS) BibTeX
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Open Access

AutoGDeterm: Automatic Geometry Determination for Electron Tomography

High Performance Computer Research Center, ICT, CAS, Beijing 100101, China.
University of Chinese Academy of Sciences, Beijing 100101, China.
Center for Biological Imaging, IBP, CAS and the National Key Laboratory of Biomacromolecules, CAS Center for Excellence in Biomacromolecules, Beijing 100101, China.
Show Author Information

Abstract

Electron Tomography (ET) is an important method for studying cell ultrastructure in three-dimensional (3D) space. By combining cryo-electron tomography of frozen-hydrated samples (cryo-ET) and a sub-tomogram averaging approach, ET has recently reached sub-nanometer resolution, thereby realizing the capability for gaining direct insights into function and mechanism. To obtain a high-resolution 3D ET reconstruction, alignment and geometry determination of the ET tilt series are necessary. However, typical methods for determining geometry require human intervention, which is not only subjective and easily introduces errors, but is also labor intensive for high-throughput tomographic reconstructions. To overcome these problems, we have developed an automatic geometry-determination method, called AutoGDeterm. By taking advantage of the high-contrast re-projections of the Iterative Compressed-sensing Optimized Non-Uniform Fast Fourier Transform (NUFFT) reconstruction (ICON) and a series of numerical analysis methods, AutoGDeterm achieves high-precision fully automated geometry determination. Experimental results on simulated and resin-embedded datasets show that the accuracy of AutoGDeterm is high and comparable to that of the typical “manual positioning” method. We have made AutoGDeterm available as software, which can be freely downloaded from our website http://ear.ict.ac.cn.

Keywords

electron tomographygeometry determinationhuman interventionfull automationAutoGDetermcomparable accuracy

References

[1]
S. Asano, B. D. Engel, and W. Baumeister, In situ cryo-electron tomography: A post-reductionist approach to structural biology, J. Mol. Biol., vol. 428, no. 2, pp. 332-343, 2016.
Crossref Google Scholar
[2]
J. A. G. Briggs, Structural biology in situ - the potential of subtomogram averaging, Curr. Opin. Struct. Biol., vol. 23, no. 2, pp. 261-267, 2013.
Crossref Google Scholar
[3]
T. A. M. Bharat, C. J. Russo, J. Löwe, L. A. Passmore, and S. H. W. Scheres, Advances in single-particle electron cryomicroscopy structure determination applied to sub-tomogram averaging, Structure, vol. 23, no. 9, pp. 1743-1753, 2015.
Crossref Google Scholar
[4]
S. Pfeffer, L. Burbaum, P. Unverdorben, M. Pech, Y. X. Chen, R. Zimmermann, R. Beckmann, and F. Förster, Structure of the native sec61 protein-conducting channel, Nat. Commun., vol. 6, p. 8403, 2015.
Crossref Google Scholar
[5]
R. M. Mersereau and A. V. Oppenheim, Digital reconstruction of multidimensional signals from their projections, Proc. IEEE, vol. 62, no. 10, pp. 1319-1338, 1972.
Crossref Google Scholar
[6]
H. Winkler and K. A. Taylor, Accurate marker-free alignment with simultaneous geometry determination and reconstruction of tilt series in electron tomography, Ultramicroscopy, vol. 106, no. 106, pp. 240-254, 2006.
Crossref Google Scholar
[7]
P. Penczek, M. Marko, K. Buttle, and J. Frank, Double-tilt electron tomography, Ultramicroscopy, vol. 60, no. 3, pp. 393-410, 1995.
Crossref Google Scholar
[8]
M. C. Lawrence, Least-squares method of alignment using markers, in Electron Tomography, J. Frank, ed. Springer, 1992, pp. 197-204.
Crossref
[9]
J. R. Kremer, D. N. Mastronarde, and J. R. Mcintosh, Computer visualization of three-dimensional image data using IMOD, J. Struct. Biol., vol. 116, no. 1, pp. 71-76, 1996.
Crossref Google Scholar
[10]
J. Frank, Electron Tomography: Methods for Three-Dimensional Visualization of Structures in the Cell. Springer, 2006.
[11]
F. Amat, F. Moussavi, L. R. Comolli, G. Elidan, K. H. Downing, and M. Horowitz, Markov random field based automatic image alignment for electron tomography, J. Struct. Biol., vol. 161, no. 3, pp. 260-275, 2008.
Crossref Google Scholar
[12]
R. M. Han, L. S. Wang, Z. Y. Liu, F. Sun, and F. Zhang, A novel fully automatic scheme for fiducial marker-based alignment in electron tomography, J. Struct. Biol., vol. 192, no. 3, pp. 403-417, 2015.
Crossref Google Scholar
[13]
R. Guckenberger, Determination of a common origin in the micrographs of tilt series in three-dimensional electron microscopy, Ultramicroscopy, vol. 9, nos. 1&2, pp. 167-173, 1982.
Crossref Google Scholar
[14]
Y. Liu, P. A. Penczek, B. F. McEwen, and J. Frank, A marker-free alignment method for electron tomography, Ultramicroscopy, vol. 58, nos. 3&4, pp. 393-402, 1995.
Crossref Google Scholar
[15]
H. Winkler and K. A. Taylor, Marker-free dual-axis tilt series alignment, J. Struct. Biol., vol. 182, no. 2, pp. 117-124, 2013.
Crossref Google Scholar
[16]
S. Brandt, J. Heikkonen, and P. Engelhardt, Automatic alignment of transmission electron microscope tilt series without fiducial markers, J. Struct. Biol., vol. 136, no. 3, pp. 201-213, 2001.
Crossref Google Scholar
[17]
R. M. Han, F. Zhang, X. H. Wan, J. J. Fernández, F. Sun, and Z. Y. Liu, A marker-free automatic alignment method based on scale-invariant features, J. Struct. Biol., vol. 186, no. 1, pp. 167-180, 2014.
Crossref Google Scholar
[18]
S. Phan, J. Bouwer, J. Lanman, M. Terada, and A. Lawrence, Non-linear bundle adjustment for electron tomography, computer science and information engineering, in Proc. 2009 WRI World Congress on Computer Science and Information Engineering, Los Angeles, CA, USA, 2009, pp. 604-612.
Crossref
[19]
A. F. Lawrence, S. Phan, and M. Ellisman, Electron tomography and multiscale biology, in Proc. 9@th Int. Conf. Theory and Applications of Models of Computation, Beijing, China, 2012, pp. 109-130.
Crossref
[20]
D. L. Donoho, Compressed sensing, IEEE Trans. Inf. Theory, vol. 52, no. 4, pp. 1289-1306, 2006.
Crossref Google Scholar
[21]
M. Lustig, D. Donoho, and J. M. Pauly, Sparse MRI: The application of compressed sensing for rapid MR imaging, Magn. Reson. Med., vol. 58, no. 6, pp. 1182-1195, 1999.
Crossref Google Scholar
[22]
Y. C. Deng, Y. Chen, Y. Zhang, S. L. Wang, F. Zhang, and F. Sun, ICON: 3D reconstruction with ‘missing-information’ restoration in biological electron tomography, J. Struct. Biol., vol. 195, no. 1, pp. 100-112, 2016.
Crossref Google Scholar
[23]
R. Gordon, R. Bender, and G. T. Herman, Algebraic reconstruction techniques (ART) for three-dimensional electron microscopy and x-ray photography, J. Theor. Biol., vol. 29, no. 3, pp. 471-481, 1970.
Crossref Google Scholar
[24]
P. Gilbert, Iterative methods for the three-dimensional reconstruction of an object from projections, J. Theor. Biol., vol. 36, no. 1, pp. 105-117, 1972.
Crossref Google Scholar
[25]
M. Radermacher, Weighted back-projection methods, in Electron Tomography, J. Frank, ed. Springer, 2006.
[26]
X. H. Wan, T. Katchalski, C. Churas, S. Ghosh, S. Phan, A. Lawrence, Y. Hao, Z. Y. Zhou, R. J. Chen, Y. Chen, Z. Fa, and M. H. Ellisman, Electron tomography simulator with realistic 3d phantom for evaluation of acquisition, alignment and reconstruction methods, J. Struct. Biol., vol. 198, no. 2, pp. 103-115, 2017.
Crossref Google Scholar
Tsinghua Science and Technology
Volume 23 Issue 4,
August 2018
Pages 369-376
DOI: 10.26599/TST.2018.9010036
Cite this article:
Chen Y, Wang Z, Li L, et al. AutoGDeterm: Automatic Geometry Determination for Electron Tomography. Tsinghua Science and Technology, 2018, 23(4): 369-376. https://doi.org/10.26599/TST.2018.9010036

768

Views

38

Downloads

1

Crossref

N/A

Web of Science

2

Scopus

1

CSCD

Altmetrics
Received: 06 September 2017
Accepted: 01 November 2017
Published: 16 August 2018
© The authors 2018
Return