Automation of random conical tilt and orthogonal tilt data collection using feature-based correlation

Craig Yoshioka, James Pulokas, Denis Fellmann, Clinton S. Potter, Ronald A. Milligan, Bridget Carragher

Research output: Contribution to journalArticle

29 Scopus citations

Abstract

Visualization by electron microscopy has provided many insights into the composition, quaternary structure, and mechanism of macromolecular assemblies. By preserving samples in stain or vitreous ice it is possible to image them as discrete particles, and from these images generate three-dimensional structures. This 'single-particle' approach suffers from two major shortcomings; it requires an initial model to reconstitute 2D data into a 3D volume, and it often fails when faced with conformational variability. Random conical tilt (RCT) and orthogonal tilt (OTR) are methods developed to overcome these problems, but the data collection required, particularly for vitreous ice specimens, is difficult and tedious. In this paper, we present an automated approach to RCT/OTR data collection that removes the burden of manual collection and offers higher quality and throughput than is otherwise possible. We show example datasets collected under stain and cryo conditions and provide statistics related to the efficiency and robustness of the process. Furthermore, we describe the new algorithms that make this method possible, which include new calibrations, improved targeting and feature-based tracking.

Original languageEnglish (US)
Pages (from-to)335-346
Number of pages12
JournalJournal of Structural Biology
Volume159
Issue number3
DOIs
StatePublished - Sep 2007

Keywords

  • Automation
  • Cryo-electron microscopy
  • Electron microscopy
  • Orthogonal tilt reconstruction
  • Random conical tilt
  • TEM

ASJC Scopus subject areas

  • Structural Biology

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