We revisit the topic of common lines between projection images in single-particle cryo-electron microscopy (cryo-EM). We derive a novel low-rank constraint on a certain 2n × n matrix storing properly scaled basis vectors for the common lines between n projection images of one molecular conformation. Using this algebraic constraint and others, we give optimization algorithms to denoise common lines and recover the unknown 3D rotations associated with the images. As an application, we develop a clustering algorithm to partition a set of noisy images into homogeneous communities using common lines, in the case of discrete heterogeneity in cryo-EM. We demonstrate the methods on synthetic and experimental datasets.

Cryo-electron microscopy (cryo-EM) is an imaging technique that allows the visualization of proteins and macromolecular complexes at near-atomic resolution. The low electron doses used to prevent radiation damage to the biological samples result in images where the power of noise is 100 times stronger than that of the signal. Accurate identification of proteins from these low signal-to-noise ratio (SNR) images is a critical task, as the detected positions serve as inputs for the downstream 3D structure determination process. Current methods either fail to identify all true positives or result in many false positives, especially when analyzing images from smaller-sized proteins that exhibit extremely low contrast, or require manual labeling that can take days to complete. Acknowledging the fact that accurate protein identification is dependent upon the visual interpretability of micrographs, we propose a framework that can perform denoising and detection in a joint manner and enable particle localization under extremely low SNR conditions using self-supervised denoising and particle identification from sparsely annotated data. We validate our approach on three challenging single-particle cryo-EM datasets and projection images from one cryo-electron tomography dataset with extremely low SNR, showing that it outperforms existing state-of-the-art methods used for cryo-EM image analysis by a significant margin. We also evaluate the performance of our algorithm under decreasing SNR conditions and show that our method is more robust to noise than competing methods.

Electron cryo-tomography is an imaging technique for probing 3D structures with at the nanometer scale. This technique has been used extensively in the biomedical field to study the complex structures of proteins and other macromolecules. With the advancement in technology, microscopes are currently capable of producing images amounting to terabytes of data per day, posing great challenges for scientists as the speed of processing of the images cannot keep up with the ever-higher throughput of the microscopes. Therefore, automation is an essential and natural pathway on which image processing—from individual micrographs to full tomograms—is developing. In this paper, we present Ot2Rec, an open-source pipelining tool which aims to enable scientists to build their own processing workflows in a flexible and automatic manner. The basic building blocks of Ot2Rec are plugins which follow a unified application programming interface structure, making it simple for scientists to contribute to Ot2Rec by adding features which are not already available. In this paper, we also present three case studies of image processing using Ot2Rec, through which we demonstrate the speedup of using a semi-automatic workflow over a manual one, the possibility of writing and using custom (prototype) plugins, and the flexibility of Ot2Rec which enables the mix-and-match of plugins. We also demonstrate, in the Supplementary Material, a built-in reporting feature in Ot2Rec which aggregates the metadata from all process being run, and output them in the Jupyter Notebook and/or HTML formats for quick review of image processing quality. Ot2Rec can be found at https://github.com/rosalindfranklininstitute/ot2rec.

A common task in cryo-electron microscopy data processing is to compare three-dimensional density maps of macromolecules. In this paper, we propose an algorithm for aligning three-dimensional density maps, which exploits common lines between projection images of the maps. The algorithm is fully automatic and handles rotations, reflections (handedness), and translations between the maps. In addition, the algorithm is applicable to any type of molecular symmetry without requiring any information regarding the symmetry of the maps. We evaluate our alignment algorithm on publicly available density maps, demonstrating its accuracy and efficiency. The algorithm is available at https://github.com/ShkolniskyLab/emalign.

Different tasks in the computational pipeline of single-particle cryo-electron microscopy (cryo-EM) require enhancing the quality of the highly noisy raw images. To this end, we develop an efficient algorithm for signal enhancement of cryo-EM images. The enhanced images can be used for a variety of downstream tasks, such as two-dimensional classification, removing uninformative images, constructing ab initio models, generating templates for particle picking, providing a quick assessment of the data set, dimensionality reduction, and symmetry detection. The algorithm includes built-in quality measures to assess its performance and alleviate the risk of model bias. We demonstrate the effectiveness of the proposed algorithm on several experimental data sets. In particular, we show that the quality of the resulting images is high enough to produce ab initio models of $ \sim 10 $ Å resolution. The algorithm is accompanied by a publicly available, documented, and easy-to-use code.

Single-particle cryo-electron microscopy (cryo-EM) is a powerful imaging modality capable of visualizing proteins and macromolecular complexes at near-atomic resolution. The low electron-doses used to prevent radiation damage to the biological samples, however, result in images where the power of the noise is 100 times greater than the power of the signal. To overcome these low signal-to-noise ratios (SNRs), hundreds of thousands of particle projections are averaged to determine the three-dimensional structure of the molecule of interest. The sampling requirements of high-resolution imaging impose limitations on the pixel sizes that can be used for acquisition, limiting the size of the field of view and requiring data collection sessions of several days to accumulate sufficient numbers of particles. Meanwhile, recent image super-resolution (SR) techniques based on neural networks have shown state-of-the-art performance on natural images. Building on these advances, here, we present a multiple-image SR algorithm based on deep internal learning designed specifically to work under low-SNR conditions. Our approach leverages the internal image statistics of cryo-EM movies and does not require training on ground-truth data. When applied to single-particle datasets of apoferritin and T20S proteasome, we show that the resolution of the 3D structure obtained from SR micrographs can surpass the limits imposed by the imaging system. Our results indicate that the combination of low magnification imaging with in silico image SR has the potential to accelerate cryo-EM data collection by virtue of including more particles in each exposure and doing so without sacrificing resolution.

Cryo-electron tomography (cryo-ET) is a powerful technique that can provide unprecedented insight into protein-protein interactions and molecular machinery in a near-native state. The adoption of cryo-ET by life science research groups is hampered by the challenges associated with cryo-ET sample preparation. The current sample preparation process has many steps at which ice contamination may occur to negatively affect the final sample and data quality. A survey was conducted to better understand the effects and impact of ice contamination to the cryo-ET outcome. Over 80 cryo-electron microscopy users worldwide participated in our survey. The results are presented in this article. We furthermore discussed the currently available solutions that can alleviate the ice contamination problems to increase the sample yield and cryo-ET data output.

The application of cryo-correlative light and cryo-electron microscopy (cryo-CLEM) gives us a way to locate structures of interest in the electron microscope. In brief, the structures of interest are fluorescently tagged, and images from the cryo-fluorescent microscope (cryo-FM) maps are superimposed on those from the cryo-electron microscope (cryo-EM). By enhancing cryo-FM to include single-molecule localization microscopy (SMLM), we can achieve much better localization. The introduction of cryo-SMLM increased the yield of photons from fluorophores, which can benefit localization efforts. Dahlberg and Moerner (2021, Annual Review of Physical Chemistry, 72, 253–278) have a recent broad and elegant review of super-resolution cryo-CLEM. This paper focuses on cryo(F)PALM/STORM for the cryo-electron tomography community. I explore the current challenges to increase the accuracy of localization by SMLM and the mapping of those positions onto cryo-EM images and maps. There is much to consider: we need to know if the excitation of fluorophores damages the structures we seek to visualize. We need to determine if higher numerical aperture (NA) objectives, which add complexity to image analysis but increase resolution and the efficiency of photon collection, are better than lower NA objectives, which pose fewer problems. We need to figure out the best way to determine the axial position of fluorophores. We need to have better ways of aligning maps determined by FM with those determined by EM. We need to improve the instrumentation to be easier to use, more accurate, and ice-contamination free. The bottom line is that we have more work to do.

Cryo-electron microscopy (cryo-EM) has become the technique of choice for structural biology of macromolecular assemblies, after the ‘resolution revolution’ that has occurred in this field since 2012. With a suitable instrument, an appropriate electron detector and, last but not least, a cooperative sample it is now possible to collect images from which macromolecular structures can be determined to better than 2 Å resolution, where reliable atomic models can be built. By electron tomography and sub-tomogram averaging of cryo-samples, it is also possible to reconstruct subcellular structures to sub-nanometre resolution. This review describes the infrastructure that is needed to achieve this goal. Ideally, a cryo-EM lab will have a dedicated 300 kV electron microscope for data recording and a 200 kV instrument for screening cryo-samples, both with direct electron detectors, and at least one 120 kV EM for negative-stain screening at room temperature. Added to this should be ancillary equipment for specimen preparation, including a light microscope, carbon coater, plasma cleaner, glow discharge unit, a device for fast, robotic sample freezing, liquid nitrogen storage Dewars and a ready supply of clean liquid nitrogen. In practice, of course, the available budget will determine the number and types of microscopes and how elaborate the lab can be. The cryo-EM lab should be designed with adequate space for the electron microscopes and ancillary equipment, and should allow for sufficient storage space. Each electron microscope room should be connected to the image-processing computers by fibre-optic cables for the rapid transfer of large datasets. The cryo-EM lab should be overseen by a facility manager whose responsibilities include the day-to-day tasks to ensure that all microscopes are operating perfectly, organising service and repairs to minimise downtime, and controlling the budget. Large facilities will require additional support staff who help to oversee the operation of the facility and instruct new users.

The National Cryo-Electron Microscopy Facility (NCEF) at the National Cancer Institute was launched in May of 2017 to provide free and rapid access to high-resolution cryo-EM data collection to United States researchers working on problems of broad general relevance to cancer biology. The decision about suitability of projects for data collection is made on a first-come, first-served basis by NCEF staff and is based solely on the quality of the screening images provided, without need for a scientific proposal. Here we provide an overview of the operation of the facility, typical data collection procedures, and some insights that have emerged from the structures reported from data collected at the facility.

Cryo-electron microscopy (cryo-EM) enables the study of biological structures in situ in great detail and to solve protein structures at Ångstrom level resolution. Due to recent advances in instrumentation and data processing, the field of cryo-EM is a rapidly growing. Access to facilities and national centers that house the state-of-the-art microscopes is limited due to the ever-rising demand, resulting in long wait times between sample preparation and data acquisition. To improve sample storage, we have developed a cryo-storage system with an efficient, high storage capacity that enables sample storage in a highly organized manner. This system is simple to use, cost-effective and easily adaptable for any type of grid storage box and dewar and any size cryo-EM laboratory.

The cyclic coordinate descent (CCD) method is a popular loop closure method in protein structure modeling. It is a robotics algorithm originally developed for inverse kinematic applications. We demonstrate an effective method of building the backbone of protein structure models using the principle of CCD and a guiding trace. For medium-resolution 3-dimensional (3D) images derived using cryo-electron microscopy (cryo-EM), it is possible to obtain guiding traces of secondary structures and their skeleton connections. Our new method, constrained cyclic coordinate descent (CCCD), builds α-helices, β-strands, and loops quickly and fairly accurately along predefined traces. We show that it is possible to build the entire backbone of a protein fairly accurately when the guiding traces are accurate. In a test of 10 proteins, the models constructed using CCCD show an average of 3.91 Å of backbone root mean square deviation (RMSD). When the CCCD method is incorporated in a simulated annealing framework to sample possible shift, translation, and rotation freedom, the models built with the true topology were ranked high on the list, with an average backbone RMSD100 of 3.76 Å. CCCD is an effective method for modeling atomic structures after secondary structure traces and skeletons are extracted from 3D cryo-EM images.

Fiber diffraction data have been obtained from Narcissus mosaic virus, a potexvirus from the family Flexiviridae, and soybean mosaic virus (SMV), a potyvirus from the family Potyviridae. Analysis of the data in conjunction with cryo-electron microscopy data allowed us to determine the symmetry of the viruses and to make reconstructions of SMV at 19 Å resolution and of another potexvirus, papaya mosaic virus, at 18 Å resolution. These data include the first well-ordered data ever obtained for the potyviruses and the best-ordered data from the potexviruses, and offer the promise of eventual high resolution structure determinations.

Here we evaluate a new grid substrate developed by ProtoChips Inc. (Raleigh, NC) for cryo-transmission electron microscopy. The new grids are fabricated from doped silicon carbide using processes adapted from the semiconductor industry. A major motivating purpose in the development of these grids was to increase the low-temperature conductivity of the substrate, a characteristic that is thought to affect the appearance of beam-induced movement (BIM) in transmission electron microscope (TEM) images of biological specimens. BIM degrades the quality of data and is especially severe when frozen biological specimens are tilted in the microscope. Our results show that this new substrate does indeed have a significant impact on reducing the appearance and severity of beam-induced movement in TEM images of tilted cryo-preserved samples. Furthermore, while we have not been able to ascertain the exact causes underlying the BIM phenomenon, we have evidence that the rigidity and flatness of these grids may play a major role in its reduction. This improvement in the reliability of imaging at tilt has a significant impact on using data collection methods such as random conical tilt or orthogonal tilt reconstruction with cryo-preserved samples. Reduction in BIM also has the potential for improving the resolution of three-dimensional cryo-reconstructions in general.

Two issues that often impact the cryo-electron microscopy (cryoEM) specimen preparation process are agglomeration of particles near hole edges in holey carbon films and variations in vitreous ice thickness. In many cases, the source of these issues was identified to be the residues and topography often seen in commercially available films. To study and minimize their impact during specimen preparation, an improved holey carbon film has been developed. Rather than using a consumable template based on soft materials that must be removed prior to grid assembly, a method was developed that uses a hard template and a water-soluble release layer to replicate the template pattern into the carbon films. The advantages of this method are the improved purity and flatness of the carbon films, and these attributes are shown to have a dramatic improvement on the distribution of single particles embedded in vitreous ice suspended across the holes. Improving particle distribution is an enabling factor toward increasing the throughput of data collection for cryoEM.

The vault complex is a ubiquitous 13-MDa ribonucleoprotein assembly, composed of three proteins (TEP1, 240 kDa; VPARP, 193 kDa; and MVP, 100 kDa) that are highly conserved in eukaryotes and an untranslated RNA (vRNA). The vault has been shown to affect multidrug resistance in cancer cells, and one particular component, MVP, is thought to play a role in the transport of drug from the nucleus. To locate the position of the vRNA, vaults were treated with RNases, and cryo-electron microscopy (cryo-EM) was performed on the resulting complexes. Using single-particle reconstruction techniques, 3,476 particle images were combined to generate a 22-Å-resolution structure. Difference mapping between the RNase-treated vault and the previously calculated intact vault reconstructions reveals the vRNA to be at the ends of the vault caps. In this position, the vRNA may interact with both the interior and exterior environments of the vault. The finding of a 16-fold density ring at the top of the cap has allowed modeling of the WD40 repeat domain of the vault TEP1 protein within the cryo-EM vault density. Both stoichiometric considerations and the finding of higher resolution for the computationally selected and refined “barrel only” images indicate a possible symmetry mismatch between the barrel and the caps. The molecular architecture of the complex is emerging, with 96 copies of MVP composing the eightfold symmetric barrel, and the vRNA together with one copy of TEP1 and four predicted copies of VPARP comprising each cap.

Two recently published but independently derived structures, namely the X-ray crystallographic structure of ribosomal protein S7 and the “binding pocket” for this protein in a three-dimensional model of the 16S rRNA, have been correlated with one another. The known rRNA–protein interactions for S7 include a minimum binding site, a number of footprint sites, and two RNA–protein crosslink sites on the 16S rRNA, all of which form a compact group in the published 16S rRNA model (despite the fact that these interactions were not used as primary modeling contraints in building that model). The amino acids in protein S7 that are involved in the two crosslinks to 16S rRNA have also been determined in previous studies, and here we have used these sites to orient the crystallographic structure of S7 relative to its rRNA binding pocket. Some minor alterations were made to the rRNA model to improve the fit. In the resulting structure, the principal positively charged surface of the protein is in contact with the 16S rRNA, and all of the RNA–protein interaction data are satisfied. The quality of the fit gives added confidence as to the validity of the 16S rRNA model. Protein S7 is furthermore known to be crosslinked both to P site-bound tRNA and to mRNA at positions upstream of the P site codon; the matched S7-16S rRNA structure makes a prediction as to the location of this crosslink site within the protein molecule.