CryoCycle your grids: Plunge vitrifying and reusing clipped grids to advance cryoEM democratization.

CryoEM democratization is hampered by access to costly plunge-freezing supplies. We introduce methods, called CryoCycle, for reliably blotting, vitrifying, and reusing clipped cryoEM grids. We demonstrate that vitreous ice may be produced by plunging clipped grids with purified proteins into liquid ethane and that clipped grids may be reused several times for different protein samples. Furthermore, we demonstrate the vitrification of thin areas of cells prepared on gold-coated, pre-clipped grids.

Cryo-focused ion beam for in situ structural biology: State of the art, challenges, and perspectives.

Cryogenic-focused ion beam (cryo-FIB) instruments became essential for high-resolution imaging in cryo-preserved cells and tissues. Cryo-FIBs use accelerated ions to thin samples that would otherwise be too thick for cryo-electron microscopy (cryo-EM). This allows visualizing cellular ultrastructures in near-native frozen hydrated states. This review describes the current state-of-the-art capabilities of cryo-FIB technology and its applications in structural cell and tissue biology. We discuss recent advances in instrumentation, imaging modalities, automation, sample preparation protocols, and targeting techniques. We outline remaining challenges and future directions to make cryo-FIB more precise, enable higher throughput, and be widely accessible. Further improvements in targeting, efficiency, robust sample preparation, emerging ion sources, automation, and downstream electron tomography have the potential to reveal intricate molecular architectures across length scales inside cells and tissues. Cryo-FIB is poised to become an indispensable tool for preparing native biological systems in situ for high-resolution 3D structural analysis.

CryoCycle your grids: Plunge vitrifying and reusing clipped grids to advance cryoEM democratization.

CryoEM democratization is hampered by access to costly plunge-freezing supplies. We introduce methods, called CryoCycle, for reliably blotting, vitrifying, and reusing clipped cryoEM grids. We demonstrate that vitreous ice may be produced by plunging clipped grids with purified proteins into liquid ethane and that clipped grids may be reused several times for different protein samples. Furthermore, we demonstrate the vitrification of thin areas of cells prepared on gold-coated, pre-clipped grids.

Cryo-Electron Microscopy Screening Automation across Multiple Grids using Smart Leginon.

Advancements in cryo-electron microscopy (cryoEM) techniques over the past decade have allowed structural biologists to routinely resolve macromolecular protein complexes to near-atomic resolution. The general workflow of the entire cryoEM pipeline involves iterating between sample preparation, cryoEM grid preparation, and sample/grid screening before moving on to high-resolution data collection. Iterating between sample/grid preparation and screening is typically a major bottleneck for researchers, as every iterative experiment must optimize for sample concentration, buffer conditions, grid material, grid hole size, ice thickness, and protein particle behavior in the ice, amongst other variables. Furthermore, once these variables are satisfactorily determined, grids prepared under identical conditions vary widely in whether they are ready for data collection, so additional screening sessions prior to selecting optimal grids for high-resolution data collection are recommended. This sample/grid preparation and screening process often consumes several dozen grids and days of operator time at the microscope. Furthermore, the screening process is limited to operator/microscope availability and microscope accessibility. Here, we demonstrate how to use Leginon and Smart Leginon Autoscreen to automate the majority of cryoEM grid screening. Autoscreen combines machine learning, computer vision algorithms, and microscope-handling algorithms to remove the need for constant manual operator input. Autoscreen can autonomously load and image grids with multi-scale imaging using an automated specimen-exchange cassette system, resulting in unattended grid screening for an entire cassette. As a result, operator time for screening 12 grids may be reduced to ~10 min with Autoscreen compared to ~6 h using previous methods which are hampered by their inability to account for high variability between grids. This protocol first introduces basic Leginon setup and functionality, then demonstrates Autoscreen functionality step-by-step from the creation of a template session to the end of a 12-grid automated screening session.

Heterogeneous non-canonical nucleosomes predominate in yeast cells in situ.

Nuclear processes depend on the organization of chromatin, whose basic units are cylinder-shaped complexes called nucleosomes. A subset of mammalian nucleosomes in situ (inside cells) resembles the canonical structure determined in vitro 25 years ago. Nucleosome structure in situ is otherwise poorly understood. Using cryo-electron tomography (cryo-ET) and 3D classification analysis of budding yeast cells, here we find that canonical nucleosomes account for less than 10% of total nucleosomes expected in situ. In a strain in which H2A-GFP is the sole source of histone H2A, class averages that resemble canonical nucleosomes both with and without GFP densities are found ex vivo (in nuclear lysates), but not in situ. These data suggest that the budding yeast intranuclear environment favors multiple non-canonical nucleosome conformations. Using the structural observations here and the results of previous genomics and biochemical studies, we propose a model in which the average budding yeast nucleosome's DNA is partially detached in situ.

Fully automated multi-grid cryoEM screening using Smart Leginon.

Single-particle cryo-electron microscopy (cryoEM) is a swiftly growing method for understanding protein structure. With increasing demand for high-throughput, high-resolution cryoEM services comes greater demand for rapid and automated cryoEM grid and sample screening. During screening, optimal grids and sample conditions are identified for subsequent high-resolution data collection. Screening is a major bottleneck for new cryoEM projects because grids must be optimized for several factors, including grid type, grid hole size, sample concentration, buffer conditions, ice thickness and particle behavior. Even for mature projects, multiple grids are commonly screened to select a subset for high-resolution data collection. Here, machine learning and novel purpose-built image-processing and microscope-handling algorithms are incorporated into the automated data-collection software Leginon, to provide an open-source solution for fully automated high-throughput grid screening. This new version, broadly called Smart Leginon, emulates the actions of an operator in identifying areas on the grid to explore as potentially useful for data collection. Smart Leginon Autoscreen sequentially loads and examines grids from an automated specimen-exchange system to provide completely unattended grid screening across a set of grids. Comparisons between a multi-grid autoscreen session and conventional manual screening by 5 expert microscope operators are presented. On average, Autoscreen reduces operator time from ∼6 h to <10 min and provides a percentage of suitable images for evaluation comparable to the best operator. The ability of Smart Leginon to target holes that are particularly difficult to identify is analyzed. Finally, the utility of Smart Leginon is illustrated with three real-world multi-grid user screening/collection sessions, demonstrating the efficiency and flexibility of the software package. The fully automated functionality of Smart Leginon significantly reduces the burden on operator screening time, improves the throughput of screening and recovers idle microscope time, thereby improving availability of cryoEM services.

Learning to automate cryo-electron microscopy data collection with Ptolemy.

Over the past decade, cryo-electron microscopy (cryoEM) has emerged as an important method for determining near-native, near-atomic resolution 3D structures of biological macromolecules. To meet the increasing demand for cryoEM, automated methods that improve throughput and efficiency of microscope operation are needed. Currently, the targeting algorithms provided by most data-collection software require time-consuming manual tuning of parameters for each grid, and, in some cases, operators must select targets completely manually. However, the development of fully automated targeting algorithms is non-trivial, because images often have low signal-to-noise ratios and optimal targeting strategies depend on a range of experimental parameters and macromolecule behaviors that vary between projects and collection sessions. To address this, Ptolemy provides a pipeline to automate low- and medium-magnification targeting using a suite of purpose-built computer vision and machine-learning algorithms, including mixture models, convolutional neural networks and U-Nets. Learned models in this pipeline are trained on a large set of images from real-world cryoEM data-collection sessions, labeled with locations selected by human operators. These models accurately detect and classify regions of interest in low- and medium-magnification images, and generalize to unseen sessions, as well as to images collected on different microscopes at another facility. This open-source, modular pipeline can be integrated with existing microscope control software to enable automation of cryoEM data collection and can serve as a foundation for future cryoEM automation software.

Smart data collection for CryoEM.

This report provides an overview of the discussions, presentations, and consensus thinking from the Workshop on Smart Data Collection for CryoEM held at the New York Structural Biology Center on April 6-7, 2022. The goal of the workshop was to address next generation data collection strategies that integrate machine learning and real-time processing into the workflow to reduce or eliminate the need for operator intervention.

Architecture of the human erythrocyte ankyrin-1 complex.

The stability and shape of the erythrocyte membrane is provided by the ankyrin-1 complex, but how it tethers the spectrin-actin cytoskeleton to the lipid bilayer and the nature of its association with the band 3 anion exchanger and the Rhesus glycoproteins remains unknown. Here we present structures of ankyrin-1 complexes purified from human erythrocytes. We reveal the architecture of a core complex of ankyrin-1, the Rhesus proteins RhAG and RhCE, the band 3 anion exchanger, protein 4.2, glycophorin A and glycophorin B. The distinct T-shaped conformation of membrane-bound ankyrin-1 facilitates recognition of RhCE and, unexpectedly, the water channel aquaporin-1. Together, our results uncover the molecular details of ankyrin-1 association with the erythrocyte membrane, and illustrate the mechanism of ankyrin-mediated membrane protein clustering.

Structural basis for the mechanisms of human presequence protease conformational switch and substrate recognition.

Presequence protease (PreP), a 117 kDa mitochondrial M16C metalloprotease vital for mitochondrial proteostasis, degrades presequence peptides cleaved off from nuclear-encoded proteins and other aggregation-prone peptides, such as amyloid ß (Aß). PreP structures have only been determined in a closed conformation; thus, the mechanisms of substrate binding and selectivity remain elusive. Here, we leverage advanced vitrification techniques to overcome the preferential denaturation of one of two ~55 kDa homologous domains of PreP caused by air-water interface adsorption. Thereby, we elucidate cryoEM structures of three apo-PreP open states along with Aß- and citrate synthase presequence-bound PreP at 3.3-4.6 Å resolution. Together with integrative biophysical and pharmacological approaches, these structures reveal the key stages of the PreP catalytic cycle and how the binding of substrates or PreP inhibitor drives a rigid body motion of the protein for substrate binding and catalysis. Together, our studies provide key mechanistic insights into M16C metalloproteases for future therapeutic innovations.

Waffle Method: A general and flexible approach for improving throughput in FIB-milling.

Cryo-FIB/SEM combined with cryo-ET has emerged from within the field of cryo-EM as the method for obtaining the highest resolution structural information of complex biological samples in-situ in native and non-native environments. However, challenges remain in conventional cryo-FIB/SEM workflows, including milling thick specimens with vitrification issues, specimens with preferred orientation, low-throughput when milling small and/or low concentration specimens, and specimens that distribute poorly across grid squares. Here we present a general approach called the 'Waffle Method' which leverages high-pressure freezing to address these challenges. We illustrate the mitigation of these challenges by applying the Waffle Method and cryo-ET to reveal the macrostructure of the polar tube in microsporidian spores in multiple complementary orientations, which was previously not possible due to preferred orientation. We demonstrate the broadness of the Waffle Method by applying it to three additional cellular samples and a single particle sample using a variety of cryo-FIB-milling hardware, with manual and automated approaches. We also present a unique and critical stress-relief gap designed specifically for waffled lamellae. We propose the Waffle Method as a way to achieve many advantages of cryo-liftout on the specimen grid while avoiding the long, challenging, and technically-demanding process required for cryo-liftout.

Better, Faster, Cheaper: Recent Advances in Cryo-Electron Microscopy.

Cryo-electron microscopy (cryo-EM) continues its remarkable growth as a method for visualizing biological objects, which has been driven by advances across the entire pipeline. Developments in both single-particle analysis and in situ tomography have enabled more structures to be imaged and determined to better resolutions, at faster speeds, and with more scientists having improved access. This review highlights recent advances at each stageof the cryo-EM pipeline and provides examples of how these techniques have been used to investigate real-world problems, including antibody development against the SARS-CoV-2 spike during the recent COVID-19 pandemic.

Label-free visual proteomics: Coupling MS- and EM-based approaches in structural biology.

Combining diverse experimental structural and interactomic methods allows for the construction of comprehensible molecular encyclopedias of biological systems. Typically, this involves merging several independent approaches that provide complementary structural and functional information from multiple perspectives and at different resolution ranges. A particularly potent combination lies in coupling structural information from cryoelectron microscopy or tomography (cryo-EM or cryo-ET) with interactomic and structural information from mass spectrometry (MS)-based structural proteomics. Cryo-EM/ET allows for sub-nanometer visualization of biological specimens in purified and near-native states, while MS provides bioanalytical information for proteins and protein complexes without introducing additional labels. Here we highlight recent achievements in protein structure and interactome determination using cryo-EM/ET that benefit from additional MS analysis. We also give our perspective on how combining cryo-EM/ET and MS will continue bridging gaps between molecular and cellular studies by capturing and describing 3D snapshots of proteomes and interactomes.

In situ cryo-FIB/SEM Specimen Preparation Using the Waffle Method.

Cryo-focused ion beam (FIB) milling of vitrified specimens is emerging as a powerful method for in situ specimen preparation. It allows for the preservation of native and near-native conditions in cells, and can reveal the molecular structure of protein complexes when combined with cryo-electron tomography (cryo-ET) and sub-tomogram averaging. Cryo-FIB milling is often performed on plunge-frozen specimens of limited thickness. However, this approach may have several disadvantages, including low throughput for cells that are small, or at low concentration, or poorly distributed across accessible areas of the grid, as well as for samples that may adopt a preferred orientation. Here, we present a detailed description of the "Waffle Method" protocol for vitrifying thick specimens followed by a semi-automated milling procedure using the Thermo Fisher Scientific (TFS) Aquilos 2 cryo-FIB/scanning electron microscope (SEM) instrument and AutoTEM Cryo software to produce cryo-lamellae. With this protocol, cryo-lamellae may be generated from specimens, such as microsporidia spores, yeast, bacteria, and mammalian cells, as well as purified proteins and protein complexes. An experienced lab can perform the entire protocol presented here within an 8-hour working day, resulting in two to three cryo-lamellae with target thicknesses of 100-200 nm and dimensions of approximately 12 µm width and 15-20 µm length. For cryo-FIB/SEMs with particularly low-contamination chambers, the protocol can be extended to overnight milling, resulting in up to 16 cryo-lamellae in 24 h. Graphical abstract.

Characterization of the SARS-CoV-2 S Protein: Biophysical, Biochemical, Structural, and Antigenic Analysis.

Coronavirus disease 2019 (COVID-19) is a global health crisis caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and there is a critical need to produce large quantities of high-quality SARS-CoV-2 Spike (S) protein for use in both clinical and basic science settings. To address this need, we have evaluated the expression and purification of two previously reported S protein constructs in Expi293F and ExpiCHO-S cells, two different cell lines selected for increased protein expression. We show that ExpiCHO-S cells produce enhanced yields of both SARS-CoV-2 S proteins. Biochemical, biophysical, and structural (cryo-EM) characterizations of the SARS-CoV-2 S proteins produced in both cell lines demonstrate that the reported purification strategy yields high-quality S protein (nonaggregated, uniform material with appropriate biochemical and biophysical properties), and analysis of 20 deposited S protein cryo-EM structures reveals conformation plasticity in the region composed of amino acids 614-642 and 828-854. Importantly, we show that multiple preparations of these two recombinant S proteins from either cell line exhibit identical behavior in two different serology assays. We also evaluate the specificity of S protein-mediated host cell binding by examining interactions with proposed binding partners in the human secretome and report no novel binding partners and notably fail to validate the Spike:CD147 interaction. In addition, the antigenicity of these proteins is demonstrated by standard ELISAs and in a flexible protein microarray format. Collectively, we establish an array of metrics for ensuring the production of high-quality S protein to support clinical, biological, biochemical, structural, and mechanistic studies to combat the global pandemic caused by SARS-CoV-2.

Topaz-Denoise: general deep denoising models for cryoEM and cryoET.

Cryo-electron microscopy (cryoEM) is becoming the preferred method for resolving protein structures. Low signal-to-noise ratio (SNR) in cryoEM images reduces the confidence and throughput of structure determination during several steps of data processing, resulting in impediments such as missing particle orientations. Denoising cryoEM images can not only improve downstream analysis but also accelerate the time-consuming data collection process by allowing lower electron dose micrographs to be used for analysis. Here, we present Topaz-Denoise, a deep learning method for reliably and rapidly increasing the SNR of cryoEM images and cryoET tomograms. By training on a dataset composed of thousands of micrographs collected across a wide range of imaging conditions, we are able to learn models capturing the complexity of the cryoEM image formation process. The general model we present is able to denoise new datasets without additional training. Denoising with this model improves micrograph interpretability and allows us to solve 3D single particle structures of clustered protocadherin, an elongated particle with previously elusive views. We then show that low dose collection, enabled by Topaz-Denoise, improves downstream analysis in addition to reducing data collection time. We also present a general 3D denoising model for cryoET. Topaz-Denoise and pre-trained general models are now included in Topaz. We expect that Topaz-Denoise will be of broad utility to the cryoEM community for improving micrograph and tomogram interpretability and accelerating analysis.

Characterization of the SARS-CoV-2 S Protein: Biophysical, Biochemical, Structural, and Antigenic Analysis.

Coronavirus disease 2019 ( COVID-19 ) is a global health crisis caused by the novel severe acute respiratory syndrome coronavirus 2 ( SARS-CoV-2 ), and there is a critical need to produce large quantities of high-quality SARS-CoV-2 Spike ( S ) protein for use in both clinical and basic science settings. To address this need, we have evaluated the expression and purification of two previously reported S protein constructs in Expi293F ™ and ExpiCHO-S ™ cells, two different cell lines selected for increased expression of secreted glycoproteins. We show that ExpiCHO-S ™ cells produce enhanced yields of both SARS-CoV-2 S proteins. Biochemical, biophysical, and structural ( cryo-EM ) characterization of the SARS-CoV-2 S proteins produced in both cell lines demonstrate that the reported purification strategy yields high quality S protein (non-aggregated, uniform material with appropriate biochemical and biophysical properties). Importantly, we show that multiple preparations of these two recombinant S proteins from either cell line exhibit identical behavior in two different serology assays. We also evaluate the specificity of S protein-mediated host cell binding by examining interactions with proposed binding partners in the human secretome. In addition, the antigenicity of these proteins is demonstrated by standard ELISAs, and in a flexible protein microarray format. Collectively, we establish an array of metrics for ensuring the production of high-quality S protein to support clinical, biological, biochemical, structural and mechanistic studies to combat the global pandemic caused by SARS-CoV-2.

Family-wide Structural and Biophysical Analysis of Binding Interactions among Non-clustered δ-Protocadherins.

Non-clustered δ1- and δ2-protocadherins, close relatives of clustered protocadherins, function in cell adhesion and motility and play essential roles in neural patterning. To understand the molecular interactions underlying these functions, we used solution biophysics to characterize binding of δ1- and δ2-protocadherins, determined crystal structures of ectodomain complexes from each family, and assessed ectodomain assembly in reconstituted intermembrane junctions by cryoelectron tomography (cryo-ET). Homophilic trans (cell-cell) interactions were preferred for all δ-protocadherins, with additional weaker heterophilic interactions observed exclusively within each subfamily. As expected, δ1- and δ2-protocadherin trans dimers formed through antiparallel EC1-EC4 interfaces, like clustered protocadherins. However, no ectodomain-mediated cis (same-cell) interactions were detectable in solution; consistent with this, cryo-ET of reconstituted junctions revealed dense assemblies lacking the characteristic order observed for clustered protocadherins. Our results define non-clustered protocadherin binding properties and their structural basis, providing a foundation for interpreting their functional roles in neural patterning.

Positive-unlabeled convolutional neural networks for particle picking in cryo-electron micrographs.

Cryo-electron microscopy is a popular method for the determination of protein structures; however, identifying a sufficient number of particles for analysis can take months of manual effort. Current computational approaches find many false positives and require ad hoc postprocessing, especially for unusually shaped particles. To address these shortcomings, we develop Topaz, an efficient and accurate particle-picking pipeline using neural networks trained with a general-purpose positive-unlabeled learning method. This framework enables particle detection models to be trained with few sparsely labeled particles and no labeled negatives. Topaz retrieves many more real particles than conventional picking methods while maintaining low false-positive rates, is capable of picking challenging unusually shaped proteins (for example, small, non-globular and asymmetric particles), produces more representative particle sets and does not require post hoc curation. We demonstrate the performance of Topaz on two difficult datasets and three conventional datasets. Topaz is modular, standalone, free and open source ( http://topaz.csail.mit.edu ).

Structure of the gene therapy vector, adeno-associated virus with its cell receptor, AAVR.

Adeno-associated virus (AAV) vectors are preeminent in emerging clinical gene therapies. Generalizing beyond the most tractable genetic diseases will require modulation of cell specificity and immune neutralization. Interactions of AAV with its cellular receptor, AAVR, are key to understanding cell-entry and trafficking with the rigor needed to engineer tissue-specific vectors. Cryo-electron tomography shows ordered binding of part of the flexible receptor to the viral surface, with distal domains in multiple conformations. Regions of the virus and receptor in close physical proximity can be identified by cross-linking/mass spectrometry. Cryo-electron microscopy with a two-domain receptor fragment reveals the interactions at 2.4 Å resolution. AAVR binds between AAV's spikes on a plateau that is conserved, except in one clade whose structure is AAVR-incompatible. AAVR's footprint overlaps the epitopes of several neutralizing antibodies, prompting a re-evaluation of neutralization mechanisms. The structure provides a roadmap for experimental probing and manipulation of viral-receptor interactions.