Autogdeterm: Automatic geometry determination for electron tomography

Yu Chen, Zihao Wang, Lun Li, Jingrong Zhang, Xiaohua Wan, Fei Sun*, Fa Zhang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

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.

Original languageEnglish
Article number8421546
Pages (from-to)369-376
Number of pages8
JournalTsinghua Science and Technology
Volume23
Issue number4
DOIs
Publication statusPublished - Aug 2018
Externally publishedYes

Keywords

  • AutoGDeterm
  • comparable accuracy
  • electron tomography
  • full automation
  • geometry determination
  • human intervention

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