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SPIDER image processing for single-particle reconstruction of biological macromolecules from electron micrographs

Nature Protocols volume 3, pages 1941–1974 (2008)Cite this article

Abstract

This protocol describes the reconstruction of biological molecules from the electron micrographs of single particles. Computation here is performed using the image-processing software SPIDER and can be managed using a graphical user interface, termed the SPIDER Reconstruction Engine. Two approaches are described to obtain an initial reconstruction: random-conical tilt and common lines. Once an existing model is available, reference-based alignment can be used, a procedure that can be iterated. Also described is supervised classification, a method to look for homogeneous subsets when multiple known conformations of the molecule may coexist.

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Figure 1: Geometry for collection of conical tilt data.
Figure 2: SPIRE.
Figure 3: Simulated micrograph tilt pair.
Figure 4: Reference-free alignment.
Figure 5: Windowed particles.
Figure 6: Creation of a binary mask and filtration.
Figure 7: Factor map.
Figure 8: Importance and reconstituted images.
Figure 9: Clustering of images.
Figure 10: The missing-cone artifact.
Figure 11: 2D projections of 3D reconstructions for classes.
Figure 12: Merged reconstruction.
Figure 13
Figure 14: CTF profiles as a function of spatial frequency.
Figure 15: Processing of phantom data.
Figure 16: K-means classification.
Figure 17
Figure 18: Scatter plot of factor 1 versus factor 2, generated with scatter.py. (left) and overview plot (right).
Figure 19
Figure 20: Power spectra.
Figure 21
Figure 22
Figure 23: Multiple references.
Figure 24
Figure 25: Distribution of orientations.
Figure 26: Initial reconstruction.
Figure 27: Refined reconstruction.
Figure 28: Amplitude-enhancement profiles.
Figure 29: Effect of amplitude enhancement.
Figure 30: Reference maps used for supervised classification.
Figure 31: Distribution of particle resemblance with respect to the two references (red curve), which reveals a possible bimodel distribution.
Figure 32: Reconstructions from two subsets using supervised classification.

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Acknowledgements

This article is dedicated to the memory of our good friend and colleague Nicolas Boisset, who passed away on January 4, 2008. The authors would like to thank Jesse Brown for batch files on the common-lines approach and helpful discussions. We also thank Michael Watters for assistance with the preparation of the illustrations. Supported by HHMI and NIH grants P41 RR01219 and R37 GM29169 (to J.F.).

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Authors and Affiliations

  1. Wadsworth Center, Empire State Plaza, Albany, 12201-0509, New York, USA

    Tanvir R Shaikh, Haixiao Gao, William T Baxter, Ardean Leith & Joachim Frank

  2. Department of Cell Biology, The Scripps Research Institute, 10550 North Torrey Pines Road, CB227, La Jolla, 92037, California, USA

    Francisco J Asturias

  3. Département de Biologie Structurale, IMPMC, UMR CNRS 7590, University P. et M. Curie, 140 Rue de Lourmel, Paris, 75015, France

    Nicolas Boisset

  4. Department of Biochemistry and Molecular Biophysics, Howard Hughes Medical Institute, 650 West, 168th Street, New York, 10032, New York, USA

    Joachim Frank

  5. Department of Biological Sciences, Columbia University, 1212 Amsterdam Avenue, New York, 10027, New York, USA

    Joachim Frank

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  1. Tanvir R Shaikh
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Correspondence to Joachim Frank.

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Shaikh, T., Gao, H., Baxter, W. et al. SPIDER image processing for single-particle reconstruction of biological macromolecules from electron micrographs. Nat Protoc 3, 1941–1974 (2008). https://doi.org/10.1038/nprot.2008.156

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