Single particle analysis

Single Particle Analysis of electron microscopy images

Single Particle Analysis is a group of related of computerized image processing techniques used to analyze images from transmission electron microscopy (TEM) . These methods were developed to improve and extend the information obtainable from TEM images of particulate samples, typically proteins or other large biological entities such as whole viruses. Individual images of stained or unstained particles are very noisy, and so hard to interpret. Combining several digitized images of similar particles together gives an image with stronger and more easily interpretable features. An extension of this technique uses single particle methods to build up a three dimensional reconstruction* of the particle. Using cryo-electron microscopy it is now possible to generate reconstructions with sub-nanometer resolution and near-atomic resolution in the case of highly symmetric viruses.

Technique

The image processing is carried out using specialized software programs, which are described here. Depending on the sample or the desired results, various steps of two- or three-dimensional processing can be done.

Alignment and Classification

Biological samples, and especially samples embedded in thin vitreous ice, are highly radiation sensitive, thus only low electron doses can be used to image the sample. This low dose, as well as variations in the metal stain used (if used) means images have high noise relative to the signal given by the particle being observed. By aligning several similar images to each other so they are in register and then averaging them, an image with higher signal to noise ratio can be obtained. As the noise is mostly randomly distributed and the underlying image features constant, by averaging the intensity of each pixel over several images only the constant features are reinforced. Typically, the optimal alignment (a translation and an in-plane rotation) to map one image onto another is calculated by cross-correlation.

However, a micrograph often contains particles in multiple different orientations and/or conformations, and so to get more representative image averages, a method is required to group similar particle images together into multiple sets. This is normally carried out using one of several data analysis and image classification algorithms, such as multi-variate statistical analysis* and hierarchical ascendant classification*, or K-means classification*.

Often data sets of ten of thousands of particle images are used, and to reach an optimal solution an iterative procedure of alignment and classification is used, whereby strong image averages produced by classification are used as reference images for a subsequent alignment the whole data.

Image Filtering

Image filtering (band pass filtering) is often used to reduce the influence of high and/or low spatial frequency* information in the images, which can affect the results of the alignment and classification procedures. This is particularly useful in negative stain images.

Due to the nature of image formation in the electron microscope, images are obtained using significant underfocus*. This, along with features inherent in the microscope's lens system, creates blurring of the collected images visible as a point spread function. The combined effects of the imaging conditions are known as the Contrast Transfer Function* (CTF), and can be approximated mathematically as a function in reciprocal space.