Towards atomic resolution structural determination by single-particle cryo-electron microscopy

Abstract

Recent advances in cryo-electron microscopy and single-particle reconstruction (collectively referred to as “cryoEM”) have made it possible to determine the three-dimensional (3D) structures of several macromolecular complexes at near-atomic resolution (~3.8 – 4.5 Å). These achievements were accomplished by overcoming challenges in sample handling, instrumentation, image processing, and model building. At near-atomic resolution, many detailed structural features can be resolved, such as the turns and deep grooves of helices, strand separation in β sheets, and densities for loops and bulky amino acid side chains. Such structural data of the cytoplasmic polyhedrosis virus (CPV), the Epsilon 15 bacteriophage and the GroEL complex have provided valuable constraints for atomic model building using integrative tools, thus significantly enhancing the value of the cryoEM structures. The CPV structure revealed a drastic conformational change from a helix to a β hairpin associated with RNA packaging and replication, coupling of RNA processing and release, and the long sought-after polyhedrin-binding domain. These latest advances in single-particle cryoEM provide exciting opportunities for the 3D structural determination of viruses and macromolecular complexes that are either too large or too heterogeneous to be investigated by conventional X-ray crystallography or nuclear magnetic resonance (NMR) methods.

Introduction

Electron imaging is rapidly emerging as an indispensible tool in biology that offers great promises for three-dimensional (3D) structural studies of macromolecular complexes or biological nano-machines. Electron diffraction data have long been successfully used to derive 3D structures of two-dimensional crystalline samples, up to a resolution of 1.9 Å [1-4]. For macromolecule complexes in their non-crystalline “native” states, single-particle cryo-electron microscopy (commonly referred to as “cryoEM”) has played an increasingly important role in determining 3D structures up to subnanometer resolution [e.g. 5,6-10]. Such large assemblies often are either too large or too heterogeneous to study by conventional methods, such as X-ray crystallography and nuclear magnetic resonance (NMR) [e.g. 11,12-16].

Four recent studies have further pushed the limit of cryoEM single-particle reconstruction to near-atomic resolution (~3.8 – 4.5 Å) [17-20] (Table 1). At this resolution, many detailed structural features such as turns and deep grooves of helices, strand separation in β sheets, densities for loops and bulky amino acid side chains can be resolved. Such structure features revealed in the cytoplasmic polyhedrosis virus (CPV) [18], the GroEL [20] and the Epsilon 15 bacteriophage [21] were used to trace the amino-acid backbones using integrative modeling tools. In the case of CPV and Epsilon 15 phage where no prior atomic structures were available, model building increased the information content of cryoEM-derived structures and significantly enhanced their overall values to address biological questions. In the cases of the rotavirus [19] and the GroEL [20], where X-ray models were available for the same complexes, an excellent agreement between the cryoEM structure and X-ray structure were demonstrated, up to the level of amino acid side chains, thus firmly establishing the validity of near-atomic resolution structures from cryoEM. Crucial to these latest developments of single-particle cryoEM are careful and systematic considerations of sample preparation and cooling strategies, instrumentation and imaging conditions, and computational methods for processing noisy cryoEM images and atomic model building. Here, I will first provide a discussion about these issues by comparing the approaches used to obtain the latest results (Table 1). Then, I will illustrate how a near-atomic resolution structure can address significant biological questions using the 3.8-Å CPV structure as an example [18].

Table 1.

Near-atomic resolution structures by single-particle cryoEM^*

Complex [ref.]	Sample temperature	CCD/film	software	CPU hrs **	Subunits averaged	effective resolution	modeling method
CPV [18]	LN	CCD	IMIRS [45]	~103	7.69×105	3.8 Å	O [57]
ε15 phage [17]	LH	film	EMAN [43]	~106	1.2×106	4.5 Å	Coot [58]
GroEL [20]	LH	film	EMAN [43]	NA	2.86×105	4.2 Å	Coot [58]
Rotavirus [19]	LN	film	FREALIG N [44]	~105	6.55×106	3.8 Å	none

^*CryoEM images were all recorded using 300keV electrons generated by field emission guns. LN: liquid nitrogen; LH: liquid helium; NA: not available.

^**CPU hours are estimated based on either information from the papers cited or through personal communication.

Sample handling

The goal of cryoEM sample preparation is to obtain frozen hydrated specimen on grids from freshly prepared samples to minimize possible deformation or damage to the macromolecular complexes being studied. In high-resolution cryoEM, structural homogeneity or integrity is more important than purity, as opposed to X-ray crystallography and NMR, in which sample purity is essential. In cryoEM images, obvious impurities or contaminations can be distinguished from particles of interest either visually or by computational classification, and thus will not severely impact or prevent subsequent data processing. However, sorting out structural deformation or flexibility computationally is much more challenging and is often a limiting factor towards obtaining a high-resolution reconstruction. In this regard, it is desirable to avoid possible structural damage caused by density gradient centrifugation. For example, a simple protocol with single-step purification was used for the structurally robust or stable CPV particles [18]. In the case of the rotavirus double-layered particle reconstruction [19], structural heterogeneity was not as large a concern because highly ordered crystals were grown from the same sample, indicating that it is likely to be structurally homogenous. In most cryoEM studies, structural heterogeneity could very well be the first limiting factor towards atomic resolution, even if the sample is chemically pure [e.g. 22]. Therefore, it is desirable to use a freshly prepared, minimally treated or disturbed sample that has never been frozen and thawed in order to increase the chance of obtaining high-resolution cryoEM reconstructions.

The choice of using liquid helium versus liquid nitrogen for sample cooling in cryoEM imaging has been a hotly debated issue. In fact, among the four near-atomic resolution structures reported to date, the 3.8-Å resolution structures of the CPV [18] and rotavirus [19] were obtained from liquid nitrogen-cooled samples, but the 4.2-Å GroEL structure [20] and the 4.5-Å Epsilon 15 bacteriophage structure [17] were from liquid helium-cooled samples (Table 1). An increase (1.4 to 2.5 times) of dose tolerance in liquid helium was estimated based on the measurement of electron diffraction spot intensity [23], but most those earlier studies were largely inconclusive, as comparisons were made using data from different instruments [24]. Recent direct comparison between imaging the same samples at liquid nitrogen and liquid helium temperatures showed that, surprisingly, liquid helium is actually a worse cryogen for cryoelectron tomography applications due to decreased low-resolution contrast between ice and biological samples at liquid helium temperature [25-27]. To address the question of which cryogen is better suited for high-resolution single-particle cryoEM imaging, we used a FEI Polara G2 dual temperature cryoelectron microscope to compare the data quality between images obtained from the same sample cooled at liquid nitrogen and liquid helium temperatures, respectively (I. Atanasov and Z. H. Zhou, unpublished data). A systematic comparative analysis of specimen charging, image contrast, dose tolerance, and potential resolution limit has led us to conclude that there is no overall benefit to using liquid helium other than liquid nitrogen for sample cooling for high-resolution imaging. When liquid helium is used, the benefit of the slightly better dose tolerance was negated by the lower image contrast due to increased ice density, the more stringent treatment requirement of support film to prevent charging, as well as the significantly higher cost of helium.

Instrumentation and imaging

When pushing towards high resolution for very large particles, the depth-of-field problem emerges as a limiting factor [28-32]. The current formulation based on the Central Projection Theorem [33,34], as implemented in most current 3D reconstruction programs, may no longer holds under this situation. Instead, the Fourier transform of a projected image corresponds to neither a slice, nor an Ewald sphere in the Fourier space, but to the sum of the values on two quadratic surfaces in 3D Fourier space [32]. Higher voltage (thus shorter electron wavelength) alleviates this problem. But for an 800 Å or larger particle, a full correction of the transfer function is necessary for pushing resolution beyond 4 Å using a 300keV electrons [28,29,32]. Indeed, 300keV electrons were used in all four near-atomic resolution structures (Table 1).

Except for the still limited field of view, CCD cameras offer a number of significant advantages over film as a recording media [35-38] and have been rigorously demonstrated to be a preferred recording device for single-particle cryoEM imaging up to subnanometer resolution[39]. First, the broad dynamic range, linearity, and low level of noise make these devices ideal for recording diffraction intensities of 2D crystals [40]. Second, a CCD camera gives instant feedback about image quality, thus allowing for the efficient optimization of sample preparation and microscope alignment. Third, without the step of loading and unloading of photographic films in the microscope vacuum, the usable time of a cryoEM grid can be significantly extended from the typical one day for a film session to days or weeks for a CCD session. Fourth, although high resolution data is more severely dampened due to worse point-spread function of CCD cameras, CCD images have better signal-to-noise ratio at low resolution region, thus providing better image “contrast” crucial for aligning single particle images [39]. Finally, compared to conventional photographic films, imaging on a CCD exposes a much smaller sample area. Thus, it minimizes possible beam-induced specimen movement or charging similar to the “spot-scan” approach [41] and reduces the effect of defocus variation within each image. For near atomic-resolution cryoEM reconstruction, the raw cryoEM images should be rigorously screened by selecting only those with visible contrast transfer function (CTF) rings up to 5 Å in their power spectra [42]. These images should also have no visible specimen drift, charging, and astigmatism in their power spectra to ensure that only high-quality images are used in the final 3D reconstruction.

Reconstruction and effective resolution

Data processing for single-particle reconstruction consists of two essential steps: orientation-center parameter determination and 3D reconstruction. Structure refinement is carried out as an iterative procedure of these two steps by gradually pushing towards higher resolution. Various software packages, including EMAN [43], FREALIGN [44], and IMIRS [45,46], were successfully used for the near-atomic resolution structure reconstructions (Table 1). The principles for orientation-center parameter determination used in these packages are similar, including searching for common lines [33] and matching computed projections [47]. For the 3D reconstruction step, EMAN and FREALIGN use direct Fourier inversion method, which is the most computationally efficient, but may have large memory requirement (e.g., ~30 GB for an 800 Å particle) and is generally more sensitive to noise. In contrast, the less computation efficient Fourier-Bessel synthesis method [33] and spherical harmonics synthesis method [48] implemented in IMIRS are less sensitive to image noise and have less memory requirement. Overall, the total computing times used in these studies differ by several orders of magnitude (Table 1) and are likely related to differences in implementation details and user procedures.

The effective resolution (or better yet, the resolving power) of a single-particle cryoEM reconstruction can be estimated by both critically evaluating the structural features or hallmarks resolved in the density map and by using statistical analysis of independent reconstructed maps. Figure 1 illustrates the structure feature-based method. Simulated densities are shown for the three types of secondary structure element -- α helix, β sheet, and connecting loop -- at different resolutions by filtering an atomic-resolution model using a Gaussian filter implemented in EMAN [20]. At ~3.8-4.0 Å resolution, an ideal density map of a helix should reveal deep grooves and clear pitches. Also resolved are strands in β sheets with an inter-strand distance of ~4.4 Å (except for regions of hydrogen-bond densities) and the zigzagging pattern of Cα atoms separated by ~3.8 Å. Densities for bulky side-chains should also begin to appear. In the CPV cryoEM density map, these features are all present, including the 3.8-Å distance between adjacent Cα atoms and convincing densities for many side-chains (Fig. 2). Commonly used statistical criteria for assessing effective resolution include Fourier shell correlation (FSC) [49] and spectral signal to noise ratio (SSNR) [50], which were shown to be equivalent [51].

Figure 1. Secondary structure elements at different resolutions.

The segment is extracted from the atomic model of HK97 capsid protein [81], including an α helix and a β hairpin joined together by a loop and filtered to different resolutions using EMAN [43]. Bulky side-chain residues are labeled. Under this idealistic situation with perfect maps, the strands in the β hairpin begin to separate and bulky side chains are clearly visible at 4 Å. At 2 Å resolution, a hole in each aromatic ring is resolved (arrow in the bottom panel). All density maps were displayed using Chimera [82].

Figure 2. Density maps superimposed with atomic models, demonstrating the quality of the CPV cryoEM map.

A, A representative region of the density map (blue) superimposed with the Cα model (red) of the CSP-B apical domain. B, C, Views of density maps of one α-helix (B) and four β-strands (C) for CSP-B superimposed with the corresponding Cα model, showing the clear turn and deep groove of the α-helix and the density for the bulky side chain in B, and the clear separation of β-strands in C (note also the densities for the bulky side chains of residues 341 and 343). D, Cα model of a β strand of TP in the density map, showing zigzagging of the main chain and the clear densities of side chains. Note that inter-consecutive-Cα distances are ~3.8 Å, which is a well established distance in polypeptide chains. Adapted with permissions from the publisher and authors [18].

It cannot be over emphasized that it is critical to evaluate map quality by examining the resolvable features in the maps, such as helix grooves, bulky side chain densities, and well separated strands in β sheets. Additionally, because self-correlating, systematic error may exist in reconstruction methods, one needs to be cautious in using the FSC curves to assess the effective resolution of a cryoEM map, particularly when the resolution is approaching near atomic scale. It has been recognized that the possible noise correlation may give rise to misleading “high resolution” FSC assessment, due to model bias in a template-matching-based refinement using cross correlation [52,53]. For this reason, it is necessary to use structural features or stereochemistry as an internal control to judge whether such model bias has occurred during refinement. When the resolution reaches near atomic scale, the aforementioned structural features can be used to judge whether the FSC assessment has been influenced by model bias. This model bias problem in FSC evaluation is more severe for image data with low signal-to-noise ratio, such as those at close-to-focus conditions, or for asymmetric or low-symmetric objects, and small particles [52]. Another way to monitor possible model bias is to refine the full image data set against two different initial models and monitor the convergence of the two reconstructions [52]. Finally, model bias can be eliminated by using the focal-pair approach [45,46,54] with model-free common lines method for orientation-center estimation [33]. Subsequent model-based orientation-center refinement can be performed as a “local” search by limiting the range of orientation or center changes [46], thus reducing the risk of model bias in resolution estimation.

The resolution of cryoEM structures can also be judged empirically by direct comparison with published structures obtained through other well established structural determination methods. For example, it is obvious that features revealed in an experimentally determined X-ray structure at 3.7-Å resolution [55] are not nearly as detailed as the simulated “perfect” structure with a Gaussian filter to only 4 Å (c.f., Fig. 1 and Fig. 3A-B). One can see that the structural features revealed in the 3.8-Å CPV map (Fig. 2) are very much comparable to those revealed in the 3.7-Å X-ray map (Fig. 3). Likewise, some regions of the CPV cryoEM map, such as the small protrusion domain of CSP, have lower resolutions than other regions, likely due to local intrinsic flexibility. This resolution variance in different regions of the same map is also common in X-ray structures (Fig. 3A and B). It can easily be seen that the structure of Dsbb (Fig. 3B) is less resolved.

Figure 3. Comparison of experimental and ideal structures at 3.7 Å resolution.

(A, B) Electron density maps of the DsbA and DsbB portions of the DsbB (Cys130Ser)-DsbA (Cys33Ala) complex. The structure was determined by X-ray crystallography to 3.7 Å resolution. Cα traces (yellow line) are superimposed on the electron density map (mesh). (C-F) Densities simulated from the atomic model by filtering to 3.7 Å using EMAN [43]. The maps are superimposed with atomic models without (C, D) and with side chains (E, F). A and B were adapted from publisher with permissions [55].

Segmentation and model building

Surface rendering of large volume data is very computationally demanding and can be a rate-limiting step in structural interpretation. Therefore it is desirable to dissect or segment individual structural components from the entire complex so that detailed features can be conveniently identified and compared. For icosahedral viruses, only structural components within an asymmetric unit are structurally unique and warrant examination. Segmentation of individual components also allow non-icosahedral averaging of structurally similar components to enhance the S/N ratio and further improve the resolution of the averaged subunits [19,56]. For example, non-icosahedral averaging of the 13 subunits of VP6 molecules in an asymmetric unit of a rotavirus reconstructed from 8400 particles significantly improved the resolution of the map (Fig. 4) [19]. The noise in the VP6 trimer was reduced by averaging, resulting in better resolved side-chain densities (Fig. 4C-D).

Figure 4. Rotavirus structure showing the power of 13 fold non-icosahedral averaging.

(A) Rotavirus DLP. (B) Trimer of VP6 after 13 fold averaging. (C-D) A slab of density (red rectangular in B) superimposed with the X-ray model before (C) and after (D) averaging. Adapted with permissions from publisher and authors [19].

Choices are limited for model building tools suitable for interpreting a near-atomic resolution cryoEM structure. Further complicating the issue is the fact that cryoEM maps often contain many subunits with over 5,000 amino-acids, yet less than 10-20% of those have identifiable, bulky side-chains. This is problematic because widely used atomic model-building tools, most notably O [57] and Coot [58], employ a bottom-up approach - that is, from side chains, to amino acids, to atomic model. Such bottom-up approaches have stringent requirements for map quality and resolution and are not readily applicable to near-atomic resolution cryoEM maps. Novel integrative tools, such as skeletonization and graph optimization, can be used together with other modeling tools for building rough Cα models [20,59] (Table 1).

Homologous structures can be of great help in model building based on near-atomic resolution data. For example, the CPV CSP and orthoreovirus λ1 protein sequences (and their structural homologs in BTV and RDV) have sequence identities of less than 10%, which is about the level expected between random sequences. Therefore, these proteins have no detectable sequence homology using current sequence alignment programs. Nevertheless the cryoEM map clearly reveals that CSP shares a similar overall topology (or fold) with λ1, although there are significant insertions and deletions of helices. Therefore, one could use the known orthoreovirus λ1 structure as a topological reference, when building models for CSP-A and CSP-B.

Uncertainties in amino acid residue registration exist in interpreting maps around 3.8-4.0 Å resolution. In order to avoid over-interpretation of less resolved regions, one should be cautious not to go into unwarranted detail such as side-chain orientation, when describing these structures and their implications. It is important to remember that at near-atomic resolution, accurate registration of residues remains difficult. For this reason, model building from cryoEM reconstructions in the absence of existing atomic models of proteins with recognizable sequence homology is a significant, yet a very challenging undertaking. The models presented by Yu et al. [18] are a combination of full-atom residue models (for helix-rich regions) and Cα models (for regions of less defined densities). Because there is no refinement process for improving the model derived from cryoEM maps, in contrast to well established model refinement schemes routinely performed in X-ray crystallography, these models are essentially Cα models. As the technology of single particle cryoEM advances further to true “atomic resolution,” it is expected that these uncertainties will be resolved.

Existing methods well-established in crystallography can be readily adapted for verifying models derived from cryoEM maps. For example, models can be submitted to the Dali server to check for similarity to any existing fold. In the case of CPV, for example, when the CSP-A model was submitted to the Dali server, the search identified Bluetongue virus (BTV) capsid protein VP3 as a top hit, consistent with their fold similarity. However, when searching the structure database for the GTase domain of CPV, Dali did not identify any known GTases, suggesting CPV GTase does not share a significant fold homology with other known GTases. CPV GTase is more like the GTase of the unrelated Paramecium bursaria Chlorella virus 1 in the sense that it clearly consists of two domains, one large and one small, between which there exists a deep and narrow cleft [18]. Even in the absence of a Cα model, a set of helices identified in a cryoEM structure can also be used as elements for probing structure databases for similar folds [8]. To this end, DejaVu and COSEC [60-62] can be used for such “spatial fold recognition”. A successful match suggests that the intermediate resolution structure under investigation has a possible homolog within the fold space. Other considerations include directions of side-chain densities, which should normally be pointing toward the N-terminus and the densities of helices. However, at the limited resolution of 3.8-4.0 Å, there could be occasional ambiguities in which side-chain densities appear sideways or horizontal, instead of pointing clearly toward the N-terminus.

Addressing biological questions at near-atomic resolution

Because no crystal structures are available for CPV and Epsilon 15 bacteriophage, atomic models derived from their cryoEM structures offered clues for functional implications [17,18]. The 3.88-Å structure of CPV illustrates how a near-atomic resolution cryoEM structure can address interesting biological questions (Fig. 5) [18]. CPV is unique within Reoviridae in that it has only a single-layered capsid contained within polyhedrin inclusion bodies, yet is still fully capable of cell entry and replication [63,64]. Previous cryoEM studies of CPV have provided a wealth of information about its architectural organization [65,66], transcription enzyme complex, and genomic RNA organization [67], as well as molecular interactions and complementarity [13]. However, in the absence of an atomic model of the full CPV capsid, many fundamental issues remained elusive, including the structural basis for RNA packing, transcription, processing and release, and the mechanism underlying CPV’s unique property of polyhedra embedding. Although the 3.88-Å resolution is still limited compared to high-resolution X-ray crystal structures, it has allowed an unambiguous tracing of the main chains and building of Cα models for CPV proteins to help resolve molecular interactions (Fig. 5B) and provide functional interpretation (Fig. 5C).

Figure 5. CPV structure at 3.88 Å illustrating structures addressing three biological questions.

(A) Full map colored according to particle radius. (B) Asymmetric unit colored by protein subunits showing molecular interactions. (C) Schematic illustration highlighting structures addressing each of the three biological questions. Shown clockwise are identification of polyhedrin binding domain in TP, CSP model and a drastic conformation change from helix in CSP-A to β hairpin in CSP-B, and GTase structure and coupling of its active site with mRNA release hole. Adapted from [18] with permissions from authors.

First, a drastic conformational change has been observed between the two CSP conformers. An α helix near the C terminal region of CSP-A changes into part of a β hairpin in CSP-B with a large rotation (~90°) and translation (backward ~20 Å) (Fig. 5C). Such a drastic local conformational change has never been described previously in orthoreovirus [68] or BTV [69] structures. Furthermore, this conformation change is located on the innermost protein region directly facing the viral RNA genome, rather than external components, suggesting the conformational change most likely results from its interaction with the RNA genome. It leads to the formation of tracks that may accommodate and facilitate packing and sliding during RNA transcription. This drastic conformational change is different from the small scale structural differences observed between the two capsid conformers of BTV, which can be considered as quasi-equivalence between non-icosahedrally related subunits [69].

Second, the structure revealed an mRNA releasing hole coupled with the active site of mRNA capping machinery. The CPV cryoEM structure revealed holes as possible conduit for mRNA release around the fivefold axis where nascent RNA is made (Fig. 5C). Holes for RNA releasing have been suggested in the X-ray structures of orthoreovirus [68] and BTV [69], and the ~30-Å cryoEM structure of rotavirus [70]. However, the CPV structure shows that the hole is coupled with GTase in a way that is different from that of the orthoreovirus in both the position of the releasing hole and the conformation of GTase, which catalyses the first step of RNA capping. Interestingly, the location of the putative CPV mRNA release hole is quite similar to that observed in the actively transcribing rotavirus double-shelled particle [70]. However, due to the lack of a TP structural homolog in rotavirus or other non-turreted members of Reoviridae, nascent mRNAs are capped within the capsid shell first, prior to their release through the mRNA releasing holes or conduits. Therefore, a completely different RNA capping mechanism has to be used for these viruses. The details of this mechanism have yet to be discovered.

For turreted Reoviridae (such as orthoreovirus and CPV), mRNA is concurrently capped and released through the holes into the turret cavity. Because CPV is architecturally similar to the inner core of the orthoreovirus, one might expect that CPV has a similar mRNA releasing and capping pathway. However, our structure clearly shows that the mRNA release hole/conduit and the conformation of GTase are different from those of the orthoreovirus [68]. This major difference in RNA capping and release is likely related to the plugging of the turret by the hemagglutinin-like A-spike, which is absent from the orthoreovirus. The discovery of structural similarity between CPV GTase and PBCV-1 GTase [71], instead of the GTase domain of orthoreovirus λ2 protein, further substantiates this difference in the RNA capping and release mechanism from other members of Reoviridae.

Finally, the near-atomic resolution structure of CPV has also resolved the long-sought-after structure of the polyhedrin-binding domain (Fig. 5C), a structure with potential to be engineered as a universal packaging molecule [72,73]. There are four pieces of evidence for the assignment of the CPV-unique polyhedrin binding domain. First, biochemical and genetic studies have firmly established that the N-terminal 79-residue domain is the polyhedrin-binding domain [64,72,74]. This domain is unique to CPV and is absent in the orthoreovirus λ2, where the GTase domain is the N-terminal domain. The cryoEM map of CPV TP (the λ2 homolog) clearly shows that the N-terminal helix of the CPV GTase domain is connected via a robust and continuous density to an additional β-sheet domain. Therefore, the only possible assignment of this additional domain is that it is the CPV-unique polyhedrin binding domain. Because fusion of proteins to this domain would lead to their incorporation into polyhedra-derived nanoscale delivery systems, containers, or protein chips, its structural elucidation opens the door to exciting prospects for exploiting this new structure for nanobiotechnology applications.

Conclusions

Electron diffraction patterns from two-dimensional crystals were successfully used to derive atomic structures by crystallography [75-77]. High-resolution single-particle electron imaging has proven to be more challenging than electron crystallography due to many more limiting factors encountered in electron optics, imaging, image correction, and structure interpretation. The milestone accomplishments in breaking the nanometer barrier occurred about ten years ago by using the structural studies of the relatively small hepatitis B virus core [5,6]. Since then, there have been great efforts at sub-nanometer resolutions en route to atomic-resolution single-particle cryoEM reconstructions [e.g. 7-15,16,78]. Four recent studies have achieved a resolution around 4 Å, allowing Cα model building, and even backbones with side chains for proteins in some segments [17-20]. The advancement of cryoEM instrumentation, the development of more sophisticated algorithms, and ever-growing computational power together provide us every reason to believe that we are entering the age of atomic resolution cryoEM reconstruction. This exciting development in single-particle cryoEM, together with the advancement of molecular-resolution cryo-electron tomography [See review 79], makes electron imaging an indispensible tool in structural biology. The integrative application of these two modalities of electron imaging in studying pleomorphic or dynamic structures [e.g. 80] will likely have a profound impact on biology, chemistry, and medicine for years to come.