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Journal of Electron Microscopy 2007 56(3):83-92; doi:10.1093/jmicro/dfm010
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© The Author 2007. Published by Oxford University Press on behalf of Japanese Society of Microscopy. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org

A Statistically Harmonized Alignment-Classification in Image Space Enables Accurate and Robust Alignment of Noisy Images in Single Particle Analysis

Masaaki Kawata1 and Chikara Sato2,*

1 Grid Technology Research Center
2 Neuroscience Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), AIST Tsukuba Central 2, Tsukuba 305-8568, Japan

* To whom correspondence should be addressed. E-mail: ti-sato{at}aist.go.jp.

In determining the three-dimensional (3D) structure of macromolecular assemblies in single particle analysis, a large representative dataset of two-dimensional (2D) average images from huge number of raw images is a key for high resolution. Because alignments prior to averaging are computationally intensive, currently available multireference alignment (MRA) software does not survey every possible alignment. This leads to misaligned images, creating blurred averages and reducing the quality of the final 3D reconstruction.

We present a new method, in which multireference alignment is harmonized with classification (multireference multiple alignment: MRMA). This method enables a statistical comparison of multiple alignment peaks, reflecting the similarities between each raw image and a set of reference images. Among the selected alignment candidates for each raw image, misaligned images are statistically excluded, based on the principle that aligned raw images of similar projections have a dense distribution around the correctly aligned coordinates in image space. This newly developed method was examined for accuracy and speed using model image sets with various signal-to-noise ratios, and with electron microscope images of the Transient Receptor Potential C3 and the sodium channel. In every data set, the newly developed method outperformed conventional methods in robustness against noise and in speed, creating 2D average images of higher quality. This statistically harmonized alignment-classification combination should greatly improve the quality of single particle analysis.

Keywords     multireference alignment, two-dimensional averaging, electron microscopy, three-dimensional reconstruction, image classification, parallel processing

Received     31 January 2007, accepted 19 March 2007


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