A homogeneous sample of sufficient concentration and quantity is important for high-resolution single particle 3-D reconstruction of viruses. Multiple biophysical methods including ion-exchange chromatography (e.g., Adenovirus Purification ViraKit™ from VIRAPUR), filter, and density gradient centrifugation are often used to separate particles by charge, size, and density. Dialysis is often used as the last step to exchange samples into a buffer that is more suitable for cryo-EM imaging but might not be necessarily suitable for long-term storage of the sample. To verify homogeneity, negative staining check could be used. Sample concentration is another important factor to consider. A good starting point is at approximately 10¹² particle/ml or 1.0 mg/ml level concentration. One should pay special attention to the pH, salt concentration, and the additives of the buffer solutions. Typically, 100 mM Tris or phosphate buffer is sufficient for virus particles. Sucrose/glycerol, detergent, and high salt concentration should be avoided in the buffer.

TEM grids are used to hold a thin layer of virus particles for TEM imaging. A few parameters should be considered when choosing grids.

To preserve native structure and to enhance tolerance for radiation damage, the virus samples should be embedded in vitreous ice first using fast plunge freezing and then imaged at liquid N₂ temperature [26, 27]. Sample freezing can be done in three ways: manual, semiautomatic, and automatic. In either way, the plunge freezing for high-resolution data collection needs to minimize the vitreous ice thickness (slightly thicker than the particle size), preventing crystalline ice formation, and to reduce ice contamination. A big advantage for semiautomatic and automatic cryo-plunger is the use of an environmental chamber to control humidity [28]. The FEI Vitrobot™ can also set blotting force as a parameter. Safety regulations should be abided for infectious viruses according to their biosafety classes [29]. For example, biosafety level (BSL) 2 viruses should be plunge-frozen in a biosafety hood and BSL-3 viruses can only be frozen in a certified BSL-3 lab. The typical sample freezing procedure is shown as below.

Due to radiation damage, biological samples must be imaged under cryo-conditions using low doses. Depending on target resolution, particle size, and particle distribution, the combination of dose, magnification, exposure time, gun lens, aperture size, spot size, beam spread area, and defocus range should be planned at the beginning of the session. To achieve high-resolution data collection, one should pay attention to the following conditions.

To achieve optimal optic quality, the microscope should be carefully aligned at the beginning of data collection and monitored throughout the entire imaging session. Special attention should be paid to the following several critical steps.

Low-dose imaging mode should be used to minimize exposure of samples to electrons (see Fig. 1). First, the whole grid should be surveyed at low magnification (200–5,000×) in search mode with a low-intensity beam and large defocus to find areas with thin ice and good particle distribution. Mark these positions using a corresponding function on the microscope or computer. Once this is done, go to one marked position, and set up the magnification, illumination area, off-axis focusing distance, and direction angle for focusing mode. Then, change to exposure mode to set up the low-dose imaging conditions including spot size, illumination area, defocus value, and exposure time. Make sure that the dose is measured using an open grid area (e.g., any broken area or holes without ice). To avoid the lens hysteresis problem, one should cycle through the sequence of “search → focus → exposure” a few times before actual imaging and keep this order during the entire imaging session (e.g., do not go back to focus mode after exposure). During imaging, if several marked squares are close enough (e.g., 3 × 3 squares), there is no need to adjust eucentricity or alignment. Once the new imaging area is far from the original alignment area, one should recheck the eucentricity and astigmatism to make sure that they are not too much off. Once the imaging conditions are set up, the repetitive imaging through different holes can be done manually or automatically using appropriate software such as Leginon [30].

The amount of data for different projects is different depending on sample properties (size, symmetry, surface feature, etc.) and targeted reconstruction resolution. For low-resolution (15 Å and lower) 3-D reconstruction, a few hundred icosahedral virus particles are sufficient. Higher resolution reconstruction will need more particles with subnanometer resolution needing a few thousand particles. For near-atomic resolution 3-D reconstructions, about 50,000–100,000 icosahedral virus particles should be imaged. This amount of data is equivalent to about 500–1,000 films (or 2,000–4,000 4K × 4K CCD images) for icosahedral virus of ~50 nm in diameter.

For image processing and 3-D reconstruction, the image pixel values should be linear to the density projection of target structures. It is important that the digital images are consistent with this requirement. From electron optics and image formation theory of TEM instruments, the electron wave intensities at the imaging plane are proportional to the structural densities [31] and thus the pixel values for digital camera (i.e., CCD and DDD) images and the optical densities (O.D.) of photographic films (i.e., film darkness) are also linear to the structural densities. The digital camera images can be used as is. However, extra attention should be paid to the photographic films as they must be further digitized using film scanners.

Many different scanners including both commercial products and specially designed scanners have been used in the cryo-EM field [32]. Currently, the most popular scanner is the Nikon Super CoolScan 9000 ED (see Note 4) which scans at 4,000 dpi (i.e., 6.35 μm/pixel). The scanner records the light intensities transmitted through the film and the saved image pixel values are thus the transmittance instead of the optical density values (see Note 5). The scanned images should be transformed so that the pixel values become linear to optical densities. This conversion can be done using the log transform of transmittance.

The convention for image contrast (i.e., brighter or darker pixels for particles relative to background pixels) is different for different image processing software. We adopted the positive contrast convention in which the particles should be brighter in graphic display (i.e., larger pixel values) than the background. The contrast can be inverted by multiplying each pixel value by −1. For CCD images, the contrast needs to be inverted while the scanned image by a Nikon scanner is already in suitable contrast.

The following command will perform image format and O.D. conversion of scanned images (see Note 6):

The following command will invert the contrast of CCD images:

Single particle 3-D reconstructions of icosahedral viruses (see Notes 7 and 8) were among the earliest EM reconstructions of biological structures. Many general software packages or specialized programs have been developed by different groups to perform all or a subset of the image processing and 3-D reconstruction tasks (see http://​en.​wikibooks.​org/​wiki/​Software_​Tools_​For_​Molecular_​Microscopy). Comprehensive coverage of all the software packages is beyond the scope of this protocol. Instead, we will focus on image processing strategies and a set of programs that we routinely use in our projects. We use EMAN [33] and EMAN2 [34] packages as the general image processing software and have developed many additional programs for icosahedral and asymmetric reconstruction of viruses. If not specially explained, in this protocol, the programs starting with “e2” are EMAN2 programs while programs jspr.py, jalign, j3dr, images2lst.py, nikontiff2mrc.py, filterMicrograph.py, goodImageSizes.py, batchboxer.py, fitctf2.py, flipHand.py, commonImages.py, and symreduce.py are developed in the Jiang lab.

As the entire image processing and 3-D reconstruction project consists of multiple tasks in different stages, the protocol is divided into a series of sections in an order parallel to that of image processing tasks carried out in an actual project. Each of the following sections will focus on one of the tasks and the overall image processing strategy will be summarized in the final section.

For single particle cryo-EM, the particles in a micrograph must be individually selected and then saved for subsequent processing. While it is possible that particle selection is performed directly from the micrograph of original sampling, we prefer a three-step process that includes prefiltering of micrographs to enhance contrast, particle selection using the prefiltered micrographs, and particle output from original images.

TEM images are modulated by the contrast transfer function (CTF) of the objective lens in TEM optical system [31, 36, 37]. Since the CTF functions cannot be precisely preset and can vary significantly among different images, it is critical to accurately determine the CTF parameters for each micrograph for proper corrections and to reach high-resolution 3-D reconstructions.

Among many of the parameters (defocus, B-factor, noises, etc.), the defocus value (see Note 11) is the most critical parameter to be determined. The signature of CTF is its oscillatory nature [38] which can be seen easily as the Thon rings [39] in the image power spectra (see Fig. 3). The Thon rings oscillate more frequently at larger defocuses and less at smaller defocuses (see Fig. 4). This relationship between defocus and Thon rings is the basis of both manual and automated CTF fitting methods.

The image power spectra can be computed from either selected particles [40] or directly from the micrograph using grid boxing [41]. We suggest the former approach as it will enhance the Thon rings by avoiding the micrograph background areas empty of particles. Since the background areas with vitreous ice do not have strong Thon rings, their inclusion will weaken the average power spectra and contribute negatively to the reliability of CTF fitting.

To efficiently process a large number of images needed for high-resolution 3-D reconstructions, we suggest a two-step CTF fitting process that starts with automated fitting [41–46] and then completes with interactive graphic screening/verification. The reason for the need of final manual screening in practice is due to the inevitable occasional failures of automated fitting methods [42] and lack of reliable detection of these failures by the automated methods. In our recent automated fitting method, we have integrated four different automated fitting algorithms which can cross-validate the fitting results and only prompt users with the inconsistent results among the methods for further manual screening [42]. Once CTF fitting is completed, the determined CTF parameters should be saved to the image headers that are needed for subsequent CTF correction and further refinement.

The following commands will perform each of the three tasks:

In these commands, we assume that the images are free of astigmatism, which is a reasonable assumption for a well-aligned microscope by experienced microscopists. Though many fitting methods including fitctf2.py are able to determine astigmatism, we typically leave it to later high-resolution refinement steps (see Subheading 3.15). Due to this postponed handling of astigmatism and further refinement of the focus values at the level of individual particles in later high-resolution refinement steps, we typically only record the CTF parameters fitted from power spectra as initial coarse values without CTF phase correction. Instead, CTF correction is performed as part of 2-D alignment and 3-D reconstruction tasks.

Despite careful sample preparation, microscope alignment, and data collection, it is almost always true that the qualities of a subset of images are poor and these images should be discarded. Depending on the type of quality issues, poor quality images can be identified at different steps. For example, during film digitization step, images of very thick ice, large amount of ice contamination, very few particles, or severe charging/drifting do not need to be scanned. However, stringent image quality evaluation is generally evaluated using the image power spectra and often integrated as part of the manual screening/verification of fitted CTF parameters.

A high-quality image should have isotropic Thon rings in its 2-D power spectra and the Thon rings should extend to high resolutions (i.e., closer to the edge of 2-D power spectra). Elongated Thon rings indicate a significant level of astigmatism (see Fig. 3). Astigmatic images should be discarded if the image processing and 3-D reconstruction software does not support determination and correction of astigmatism. In the image processing strategy described here, astigmatic images can be effectively utilized. When the Thon rings are significantly weaker along some direction than its perpendicular direction, the specimen has undergone significant drift or charging during exposure. These images should be discarded. Sometimes, a sharp peak can be seen at 3.7 Å in the 1-D power spectra, which is indicative of a significant level of ice contamination. Occasionally, we have also seen sharp peaks in the power spectra which were ultimately traced back to a malfunctioning scanner.

The Thon rings gradually become weaker at higher resolutions and the rate of weakening can be used to quantify the image quality. The quality factor is termed as B-factor (see Note 13) of which the value can be obtained during CTF fitting [40]. Smaller B-factors correspond to better quality images and B-factors of low-dose cryo-EM images of biological samples taken with modern TEMs with a FEG gun can reach values around 200 Ų or better. Studies of B-factor distributions have found that B-factors become slightly larger at large defocuses [40], so it is beneficial to collect images at smaller defocuses. In consideration of decreased image contrast at a smaller defocus, a defocus range of 1–2 μm should be a good compromise of the different factors for near-atomic resolution 3-D reconstruction of many viruses.

It is often observed that the image qualities can vary significantly even among images taken in same session. It is common practice to only select the best fraction of the images for inclusion in further image processing and 3-D reconstruction. The selection can be done using a graphic CTF fitting program such as ctfit in EMAN to examine the resolution of the last visible CTF peaks in the 1-D power spectra plot (see Fig. 4 and Note 14). Depending on the goal of the project, the resolution limit should be adjusted accordingly. For example, only images with CTF peaks beyond 6 Å will be included for projects aiming at near-atomic resolution (3–4 Å) reconstructions. However, it must be pointed out that the visibility of CTF peaks at high resolution is affected not only by imaging quality but also by other factors and the resolution cutoff should not be overly aggressive, especially for near-atomic resolution projects. For example, a small number of particles in a single micrograph might not have sufficient scattering power for clearly visible CTF peaks at high resolutions. Another common reason is the focus variations due to either residual astigmatism, small local tilt of the sample, or simply the particle Z-position variations in thick ice. Averaging of the power spectra of different focuses will effectively accelerate the apparent decay rate of CTF peak heights and even introduce zero CTF peak heights at a certain resolution beyond which weak CTF peaks become visible again [47]. These second groups of CTF peaks are out of sync with the first group of CTF peaks at lower resolutions, and their peak positions cannot be simultaneously fitted with a single defocus. These focus variations can be effectively determined and corrected with proper 2-D alignment methods (see Subheading 3.15). From these analyses and experiences with many datasets, we suggest that cryo-EM image qualities are often better than what the highest resolution CTF peaks or the fitted B-factors indicate. It is now a common observation that 3-D reconstructions can reach a resolution significantly beyond the apparent limit of the images.

The particle images that pass the quality screening are deemed good and will be included for subsequent image processing and 3-D reconstruction. All these particle images can be pooled into a single large dataset using the following command:

As single particle cryo-EM images are 2-D projections of the to-be-determined 3-D structure at random views, the inverse problem is to determine the 3-D structure from these 2-D images using computational image processing methods. Current image processing methods rely on iterative processes in which the 3-D reconstruction is iteratively improved. It is critical that the initial 3-D model is correctly constructed before proceeding to full refinement. If an initial model is already known from earlier studies of the same virus or its homolog, that initial model can be used (see Note 15) and the user can skip the step on building the initial model in this section. For a project on a new virus structure, several methods have been developed in the field to build a de novo initial model:

In this protocol, we will focus on the random de novo model method. In general, only a small dataset at coarse sampling (via binning of the particle images) is needed for building the initial model. We typically bin the images 4× so that the sampling of the binned images is around 4–6 Å. The binning not only reduces the image size to speed up computation but also effectively enhances the image contrast for more robust initial model building. The following command will bin the images:

A small number of particles (100–200) randomly selected from the entire dataset are typically used for initial model building:

Particles of homogenous conformation and good contrast will enhance the success rate of the random de novo model method. If the quality or contrast of the randomly selected particles consistently cause initial model building to fail, the user can manually screen the particle images and select particles at larger defocuses and with higher contrast. This can be done by running
to display the particles and then using the “Sets” tab function in its control widget.

The random de novo model construction and refinement can then be performed using the following command:

As the random de novo model method starts from random parameters and some of the starting points might be too distant for the iterative refinements to converge to the correct solution, it might be necessary to repeat the random de novo model method multiple times (with different random initial views assigned to the particles) to obtain a correct initial model. The parameter –nRepeat=<n> can be used to specify the number of repeats. The multiple independent de novo models are also useful for removing bad particles in subsequent full dataset refinement (see Subheading 3.14).

In single particle cryo-EM, the image processing programs can always produce 3-D density maps, though currently no method can either guarantee that these density maps are correct models or offer a score to accurately quantify the reliability of the models. However, for a new project with a completely unknown target structure, a critical decision on whether the model is correct must be made. Here, we list a few criteria that can help make the decision: