Dataset Information

Accurate modeling of single-particle cryo-EM images quantitates the benefits expected from using Zernike phase contrast.

ABSTRACT: The use of a Zernike-type phase plate in biologic cryo-electron microscopy allows the imaging, without using defocus, of what are predominantly phase objects. It is thought that such phase-plate implementations might result in higher quality images, free from the problems of CTF correction that occur when images must be recorded at extremely high values of defocus. In single-particle cryo-electron microscopy it is hoped that these improvements in image quality will facilitate work on structures that have proved difficult to study, either because of their relatively small size or because the structures are not completely homogeneous. There is still a need, however, to quantitate how much improvement can be gained by using a phase plate for single-particle cryo-electron microscopy. We present a method for quantitatively modeling the images recorded with 200keV electrons, for single particles embedded in vitreous ice. We then investigate what difference the use of a phase-plate device could have on the processing of single-particle data. We confirm that using a phase plate results in single-particle datasets in which smaller molecules can be detected, particles can be more accurately aligned and problems of heterogeneity can be more easily addressed.

SUBMITTER: Hall RJ

PROVIDER: S-EPMC3138492 | biostudies-literature | 2011 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Accurate modeling of single-particle cryo-EM images quantitates the benefits expected from using Zernike phase contrast.

Hall R J RJ Nogales E E Glaeser R M RM

Journal of structural biology 20110402 3

The use of a Zernike-type phase plate in biologic cryo-electron microscopy allows the imaging, without using defocus, of what are predominantly phase objects. It is thought that such phase-plate implementations might result in higher quality images, free from the problems of CTF correction that occur when images must be recorded at extremely high values of defocus. In single-particle cryo-electron microscopy it is hoped that these improvements in image quality will facilitate work on structures ...[more]

PMID: 21463690

Similar Datasets

Project description:BackgroundAn important task of macromolecular structure determination by cryo-electron microscopy (cryo-EM) is the identification of single particles in micrographs (particle picking). Due to the necessity of human involvement in the process, current particle picking techniques are time consuming and often result in many false positives and negatives. Adjusting the parameters to eliminate false positives often excludes true particles in certain orientations. The supervised machine learning (e.g. deep learning) methods for particle picking often need a large training dataset, which requires extensive manual annotation. Other reference-dependent methods rely on low-resolution templates for particle detection, matching and picking, and therefore, are not fully automated. These issues motivate us to develop a fully automated, unbiased framework for particle picking.ResultsWe design a fully automated, unsupervised approach for single particle picking in cryo-EM micrographs. Our approach consists of three stages: image preprocessing, particle clustering, and particle picking. The image preprocessing is based on multiple techniques including: image averaging, normalization, cryo-EM image contrast enhancement correction (CEC), histogram equalization, restoration, adaptive histogram equalization, guided image filtering, and morphological operations. Image preprocessing significantly improves the quality of original cryo-EM images. Our particle clustering method is based on an intensity distribution model which is much faster and more accurate than traditional K-means and Fuzzy C-Means (FCM) algorithms for single particle clustering. Our particle picking method, based on image cleaning and shape detection with a modified Circular Hough Transform algorithm, effectively detects the shape and the center of each particle and creates a bounding box encapsulating the particles.ConclusionsAutoCryoPicker can automatically and effectively recognize particle-like objects from noisy cryo-EM micrographs without the need of labeled training data or human intervention making it a useful tool for cryo-EM protein structure determination.

Dataset Information

Accurate modeling of single-particle cryo-EM images quantitates the benefits expected from using Zernike phase contrast.

Publications

Accurate modeling of single-particle cryo-EM images quantitates the benefits expected from using Zernike phase contrast.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets