Automated Particle Picking based on Correlation Peak Shape Analysis and Iterative Classification

Authors: Hrabe Thomas, Beck Florian, Nickell Stephan

Abstract:

Cryo-electron microscopy (CEM) in combination with single particle analysis (SPA) is a widely used technique for elucidating structural details of macromolecular assemblies at closeto- atomic resolutions. However, development of automated software for SPA processing is still vital since thousands to millions of individual particle images need to be processed. Here, we present our workflow for automated particle picking. Our approach integrates peak shape analysis to the classical correlation and an iterative approach to separate macromolecules and background by classification. This particle selection workflow furthermore provides a robust means for SPA with little user interaction. Processing simulated and experimental data assesses performance of the presented tools.

Keywords: Cryo-electron Microscopy, Single Particle Analysis, Image Processing.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1083509

References:

[1] Frank, J., Three-dimensional electron microscopy of macromolecular assemblies: visualization of biological molecules in their native state. 2006: Oxford University Press.
[2] Adiga, P.S., et al., A binary segmentation approach for boxing ribosome particles in cryo EM micrographs. J Struct Biol, 2004. 145(1-2): p. 142- 51.
[3] Adiga, U., et al., Particle picking by segmentation: a comparative study with SPIDER-based manual particle picking. J Struct Biol, 2005. 152(3): p. 211-20.
[4] Sander, B., M.M. Golas, and H. Stark, Advantages of CCD detectors for de novo three-dimensional structure determination in single-particle electron microscopy. J Struct Biol, 2005. 151(1): p. 92-105.
[5] Nickell, S., et al., Automated cryoelectron microscopy of "single particles" applied to the 26S proteasome. FEBS Lett, 2007. 581(15): p. 2751-6.
[6] Korinek, A., et al., Computer controlled cryo-electron microscopy - TOM(2) a software package for high-throughput applications. J Struct Biol, 2011. 175(3): p. 394-405.
[7] Zhu, Y., et al., Automatic particle detection through efficient Hough transforms. IEEE Trans Med Imaging, 2003. 22(9): p. 1053-62.
[8] Sigworth, F.J., Classical detection theory and the cryo-EM particle selection problem. J Struct Biol, 2004. 145(1-2): p. 111-22.
[9] Zhu, Y., et al., Automatic particle selection: results of a comparative study. J Struct Biol, 2004. 145(1-2): p. 3-14.
[10] Roseman, A.M., Particle finding in electron micrographs using a fast local correlation algorithm. Ultramicroscopy, 2003. 94(3-4): p. 225-36.
[11] Kumar, B.V., A. Mahalanobis, and R.D. Juday, Correlation Pattern Recognition. 2005: Cambridge University Press.
[12] Russel, S.J. and P. Norwig, Artificial Intelligence. 1995: Prentice Hall.
[13] Nicholson, W.V. and R.M. Glaeser, Review: automatic particle detection in electron microscopy. J Struct Biol, 2001. 133(2-3): p. 90- 101.
[14] Caprari, R.S., Method of target detection in images by moment analysis of correlation peaks. Appl Opt, 1999. 38(8): p. 1317-24.
[15] Volkmann, N., An approach to automated particle picking from electron micrographs based on reduced representation templates. J Struct Biol, 2004. 145(1-2): p. 152-6.
[16] Woolford, D., et al., SwarmPS: rapid, semi-automated single particle selection software. J Struct Biol, 2007. 157(1): p. 174-88.
[17] Duda, R.O., P.E. Hart, and D.G. Stork, Pattern Classification. 2. ed. 2000: Wiley-Interscience.
[18] Runkler, T.A., Information Mining. 2000: Vieweg.
[19] Forsyth, D.A. and J. Ponce, Computer Vision - A modern approach. 2003: Pearson Studium.
[20] Nickell, S., et al., TOM software toolbox: acquisition and analysis for electron tomography. J Struct Biol, 2005. 149(3): p. 227-34.
[21] Forster, F., et al., Classification of cryo-electron sub-tomograms using constrained correlation. J Struct Biol, 2008. 161(3): p. 276-86.
[22] Nickell, S., et al., Structural analysis of the 26S proteasome by cryoelectron tomography. Biochem Biophys Res Commun, 2007. 353(1): p. 115-20.
[23] Scheres, S.H., et al., Image processing for electron microscopy singleparticle analysis using XMIPP. Nat Protoc, 2008. 3(6): p. 977-90.
[24] Saxton, W.O. and W. Baumeister, The correlation averaging of a regularly arranged bacterial cell envelope protein. J Microsc, 1982. 127(Pt 2): p. 127-138.
[25] Short, J.M., SLEUTH--a fast computer program for automatically detecting particles in electron microscope images. J Struct Biol, 2004. 145(1-2): p. 100-10.
[26] Bishop, C.M., Pattern recognition and machine learning. 2009: Springer.
[27] Bohn, S., et al., Structure of the 26S proteasome from Schizosaccharomyces pombe at subnanometer resolution. Proc Natl Acad Sci U S A, 2010. 107(49): p. 20992-7.