Name: routine collection of high-resolution cryo-em datasets using 200 kv transmission electron microscope
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Sagar Khavnekar reports no conflicts of interest. The other authors are employees of Thermo Fisher Scientific, the MSD-EM division.

The described protocol assumes that the optics of the used TEM microscope is in a well-aligned state. For the 200 kV TEM used in this Protocol, such column alignments are done, verified, and saved by an experienced service engineer after microscope installation or any significant service intervention. These alignment settings can be recalled anytime in the microscope UI. Users can use the Direct Alignment procedures in the microscope UI to re-tweak critical parameters. Some alignments, such as gun tilt and gun shift, are stable and do not need to be adjusted by users on a daily basis. Checks and re-alignments (if required) of gun tilt and shift by the microscope supervisor are advised twice a year. On the other hand, some alignments are critical and must be aligned before each data collection as described in the protocol above (such as objective astigmatism and coma-free alignment). If the Autocoma function in the analysis software fails to converge, alignment of beam tilt pivot points and/or rotation center should be verified and adjusted, and the correct centering of the C2 aperture should be confirmed. Afterward, the Autostigmate function should be run as objective stigmators are used for coma correction as well. These alignments should be iterated until both Autostigmate&#160;and Autocomafunctions succeed on their first iteration. If necessary, another area may be selected (e.g., support carbon foil without ice), imaged defocus adjusted, or image acquisition time increased to optimize the signal-to-noise ratio in acquired images, and visibility of multiple Thon rings in the Fourier transform of the acquired images.
Modern cryo-EM microscopes generate vast amounts of data that often exceed 1 TB per dataset to achieve high-resolution 3D reconstructions, particularly for proteins with low symmetry. Cryo-EM data and results are also typically complemented by data and results from orthogonal methods to fully understand structure-function relationships in each scientific project. Organization of collected data, their transfer into an image processing pipeline, and sharing of a resulting cryo-EM reconstruction among collaborators put additional requirements on new adopters of cryo-EM methodology to set up their local IT infrastructure. Data management software, such as Athena, facilitate centralized storage of data acquired by any connected instrument or software operated by a registered user. Stored data and metadata are accessible using a simple web browser interface by multiple users, who can have different roles in the project with different access rights (either as the owner, collaborator, or viewer) based on their login credentials and data sharing definition in the Experiment setup. This digitization of experimental workflows provides means for data and metadata sharing among collaborators without unnecessary duplication and increases productivity and traceability of used workflows. Implementation of a general and customizable structure of projects, experiments, and workflows in data management software is universal and allows for customization and integration of orthogonal experiments using complementary methods into a single project database.
The selection of areas for the data collection on a cryo-EM grid is critical for the successful acquisition of high-resolution datasets. Cryo-EM grids produced with traditional plunge-freezing devices, such as the Vitrobot (a fully automated vitrification system), will typically display a gradient of ice thickness over the grid surface (Figure 4). This may be beneficial as the grid contains areas with different ice thicknesses; however, areas with the ideal ice thickness for data collection must be identified as described in the protocol above. An optimal cryo-EM grid should contain as little transfer ice contamination as possible and contain enough grid squares with intact holey support foil. Collection of data on grid-squares that have cracks or broken areas is not recommended as collected images will be affected by significantly stronger overall drift upon illumination by an electron beam in comparison with the grid squares with intact support foil. Excess of crystalline ice can occlude the majority of foil-holes and/or interfere with autofocusing and such grid squares should be avoided as well. Grid squares with thin ice usually exhibit large vitreous areas and many bright foil-holes that are visible in an image taken using the Atlas preset. The occurrence of thicker ice close to grid bars is to be expected and uncritical as foil-holes in these areas of the grid square are excluded during the Hole Selection procedure. The presence of several empty holes in a grid square may signify that vitreous ice in the surrounding holes is extremely thin and may contain damaged particles or no particles at all. Generally, it is wise to choose grid squares with a variety of ice thicknesses at different regions on the grid for initial screening and assessment to understand what areas have the best conditions for high-resolution data collection and exhibit the ideal particle density and orientation distribution. For the apoF and 20S proteasome samples used in this study, areas with the thinnest observable ice contain the best conditions for high-resolution imaging of these samples.
When selecting holes automatically in all selected grid squares using the data collection&#160;software, it is advised to perform Template Execution task on a representative hole in each grid square to check and ensure that neither overly thick nor overly thin nor unexpectedly non-vitreous squares were chosen for data collection. During data acquisition, key quality indicators of collected images, such as image drift and CTF fitting, can be monitored using EQM. Data collection can then be optimized by skipping areas that yield images of poor quality. However, images with high-resolution CTF fits can still contain images with particles in a few preferred orientations or particles denatured in a too-thin ice layer. Real-time particle picking and 2D classification from collected images would provide additional information about the quality of structural data in imaged particles and reveal both preferred orientations of intact particles in ice or inconsistent structure of (partly) denatured particles. Calculation of class averages can therefore help to further refine appropriate regions for data collection, as has been already implemented and shown in other software packages23,28.
Selection of the imaging settings for data acquisition, such as magnification, electron dose rate and defocus range, depend on several criteria, such as the target resolution, size of the protein, sample concentration, desired microscope throughput, etc. For the direct electron detector camera used in these experiments, the electron dose rate was chosen in the range of 4-5 e-/pix/s by selecting an appropriate SPOT size and intensity to maintain parallel illumination. As shown in Table 1, a different SPOT size may be used in the Hole/Eucentric Height preset to assure sufficient signal-to-noise ratio in the image for centering of holes during data collection. The magnification should be chosen such that the pixel size is at least 2-3x smaller than the target resolution for the cryo-EM reconstruction. However, the higher magnification (i.e., smaller pixel size) is used, the smaller field of view is captured in images, and there are less particles per image, which ultimately leads to longer data collection time to collect images with enough particles to reconstruct 3D maps to a high resolution. For the apoF sample, we used the pixel size of 0.43 &#197; as we had sufficient sample concentration for high density of particles in images and aimed sub-2 &#197; resolution of the reconstruction. For the 20S proteasome sample, we used the pixel size of 0.68 &#197; to cover a larger field of view in acquired images. Typically for 200 kV TEM microscopes, cryo-EM images are acquired in the defocus range from 0.8 to 2.0 &#181;m. However, with the improved contrast and signal-to-noise ratio using the energy filter, data acquisitions can be done much closer to focus to better preserve high-resolution information in acquired images due to smaller aberrations and correspondingly reduced decay of the CTF envelope function. We also do not use an objective aperture as the aperture may introduce additional image aberrations while image contrast is already sufficiently enhanced by using the energy filter. For the apoF and 20S proteasome samples, we used the defocus settings of 0.5 &#181;m, 0.7&#181;m, and 0.9 &#181;m. For smaller proteins (&#60;200 kDa), we used defocus settings of&#160;-0.5&#160;&#181;m, -0.7&#160;&#181;m, and -0.9&#160;&#181;m&#160;to improve the contrast of particles and facilitate easier particle picking and initial coarse alignment in the 3D refinement step of 3D reconstruction, which led to ~2.5 &#197; resolution 3D maps (unpublished results).
We have already shown that imaging with an energy filter improves signal-to-noise ratio (SNR) in cryo-EM images collected on the high-end 300-kV TEM microscopes11. In fact, when electrons pass through a sample, two main types of interactions occur: i) Elastically scattered electrons maintain their energy and contribute to image formation by interference with the non-scattered incident beam via the phase-contrast mechanism ii) inelastically scattered electrons lose some energy in the sample and contribute mainly to noise in images. Therefore, SNR can be significantly improved by filtering the inelastically scattered electrons, which have lower energy than the incident beam and elastically scattered electrons, using a narrow energy slit. However, it is critical to use a sufficiently stable energy filter, such as the Selectris or Selectris-X, to be able to use very narrow (10 eV or smaller) slits over long (12+ hours) automated data acquisition of high-resolution cryoEM datasets.
Cryo-EM images acquired with 200 kV TEM microscopes at the same conditions as with 300 kV TEM microscopes exhibit smaller SNR at high resolution (particularly &#60;4 &#197;) due to faster decay of the CTF envelope functions. Consequently, a higher number of particles (and therefore a higher number of collected images) is required to achieve a certain resolution when using 200 kV TEMs. Additionally, the depth of field (10-25 nm in the 2-3 &#197; resolution range) is also about 20% smaller in 200 kV images35, meaning that less particles in the ice layer (typically 20-50 nm thick) will be fully in focus and constructively contribute to all high-resolution features of a calculated 3D reconstruction unless defocus values are refined for each particle independently in the later stages of the 3D reconstruction procedure. For larger particles (such as icosahedral virions or other macromolecular assemblies), the particle size may exceed the depth of field at high resolutions and introduce phase errors due to planar approximation of the Ewald sphere in the standard 3D reconstruction algorithms36. These errors can be refined by advanced algorithms that are already implemented in common cryo-EM image processing packages37,28,39. As the Ewald sphere has larger curvature in 200 kV data than 300 kV data, the Ewald sphere correction is needed at relatively lower resolutions and/or for relatively smaller macromolecular assemblies when using 200 kV TEMs. On the other hand, 200 kV images exhibit higher contrast of particles in thin ice (20-50 nm) that is significantly thinner than the 200-300 keV electron mean free path (220-280 nm). The higher contrast helps to improve the correct global alignment of individual particles, particularly for weakly scattering smaller proteins whose structure is not yet known, and the 3D reference model is not yet well established.
Here, we demonstrated on the example of a 20S proteasome that image contrast and quality could be similarly improved with an energy filter when using a 200 kV TEM microscope. Using the same number of particles, data collected using the 20 eV slit were reconstructed to 2.26 &#197; resolution in comparison to data collected with the fully opened energy slit that was reconstructed only to 2.34 &#197; resolution. The best reconstruction was achieved from data collected using the 10 eV slit that was reconstructed to 2.14 &#197; resolution. These results are in agreement with the theoretical prediction that filtering of the inelastically scattered electrons increases SNR in collected images and facilitates higher resolution in cryo-EM reconstructions from the given number of particles, as summarized in Table 4. These results were further confirmed by the B-factors calculated from these datasets that indicate higher quality of images in the energy-filtered datasets.
We can therefore conclude that while 300 kV TEM microscopes deliver the highest throughput and the highest possible resolution in cryo-EM reconstructions, the 200 kV TEM microscopes also provide high-quality datasets for high-resolution cryo-EM reconstructions. We showed here that the quality of acquired images, and therefore overall time-to-structure, can be further improved by using the 200 kV TEM equipped with an energy filter and a direct electron detector. The presented Protocol describes all necessary steps on how to routinely obtain high-resolution cryo-EM data using this setup and reveal fine structural details of macromolecular 3D structures, which are essential for understanding the key structure-function relationships in structural biology and structure-based drug design.

Determination of protein structure is critical for understanding the molecular architecture, function, and regulation of protein complexes involved in key cellular processes, such as cell metabolism, signal transduction, or host-pathogen interactions. Cryo-electron microscopy (cryo-EM) has emerged as a powerful technique capable of solving the 3D structure of many proteins and their complexes that were too challenging for the traditional structural techniques, such as X-ray diffraction and NMR spectroscopy. Particularly, cryo-EM has been proven as the method of choice for membrane proteins, which cannot be readily crystallized or prepared in sufficient quantities for the traditional structural techniques, and provided new insights into the structure and function of important cellular receptors and ion channels1,2,3,4,5. Most recently, cryo-EM has played an important role in battling the Covid-19 pandemic by determining the mechanism of SARS-CoV-2 infection at the molecular level, which elucidated the origins of Covid-19 disease and provided the basis for the rapid development of efficient vaccines and therapeutics6,7,8,9,10.
Typically, high-end 300-kV transmission electron microscopes (TEM) are used for high-resolution structure determination of biomolecules by cryo-EM single-particle analysis (SPA) to reveal their conformation and interactions. Recently, the SPA technique reached a new frontier when the common benchmark cryo-EM sample apo-ferritin was reconstructed at atomic resolution (1.2 &#197;)11,12 using a 300-kV TEM equipped with the cold field emission gun (E-CFEG), a direct electron detector, and an energy filter. At this resolution, it was possible to unambiguously resolve positions of individual atoms in the structure, conformation of individual amino acid side chains as well as hydrogen bonding and other interactions, which open new possibilities for structure-based drug discovery of new targets and optimization of existing drug candidates.
Mid-range 200 kV TEM microscopes are often used for sample screening and sample optimization before final high-resolution data collection using the high-end TEM microscopes, particularly at larger cryo-EM facilities. Typically, imaged samples can be resolved in the 3-4 &#197; resolution range that is sufficient for moving to a high-end 300-kV TEM for final data collection. Consequently, data collection using the 200-kV TEM is often not further optimized for the highest-possible resolution results. Moreover, many interesting biological questions can be already answered and published at these resolutions as all amino acid side chains are already resolved, and occupancy of ligand binding sites can also be reliably determined13. It has already been shown that 200-kV TEMs can reach resolutions beyond 3 &#197; for numerous samples14,15,16,17,18. Images taken at 200 kV exhibit inherently higher contrast of imaged particles, which may even facilitate more accurate initial alignment of the particles despite more attenuated signal at high resolution in comparison with 300 kV TEM images. It is important to note that the achieved resolution of reconstructed cryo-EM maps is also limited by structural flexibility and conformational heterogeneity of imaged samples, which impacts both 200-kV and 300-kV reconstructions. In fact, much more cryo-EM reconstructions obtained using the 300-kV systems were resolved in the 3-4 &#197; resolution range than at higher resolutions19. As 200 kV TEM microscopes are less complex and fit into smaller rooms, these microscopes represent a good, lower-cost option for structure determination of biological macromolecules by cryo-EM while preserving automation of long data collections from multiple samples stored within the microscope Autoloader system.
Collection of cryo-EM datasets for high-resolution structure determination requires accurate alignment of the microscope optics. Column alignments proceed systematically from the electron source down to the condenser lens system, the objective lens, and the energy filter with an electron detector. The full sequence of alignments is not typically required. When needed, the user is guided via semi-automated procedures with a proper description of each step in a context-aware help window throughout the alignment procedure in the microscope user interface (Direct Alignments control panel). Once the microscope is fully aligned, electron optics remains stable, and the alignments do not need to be changed for at least a few months. Only the most sensitive alignments, such as parallel illumination of the sample plane, objective astigmatism, and coma-free alignment, must be refined just before starting the collection of each dataset. The quality of collected data can then be monitored during data collection using different software packages, such as EPU Quality Monitor, cryoSPARC Live20, Relion21, Scipion22, WARP23, or Appion24.
Besides accurate alignments of the microscope, the high quality of well-purified samples with minimal conformational and compositional heterogeneity is also a prerequisite for the collection of high-resolution datasets and solving high-resolution structures. More details about typical protocols, frequent challenges, and possible remedies can be found in other reviews dedicated to this topic25,26,27. Essentially, it is critical to find areas on a given cryo-EM grid that has sufficiently thin ice to preserve high-resolution information, and individual particles are densely distributed at random orientations without overlaps. However, typical cryo-EM grids have non-uniform ice thickness, and it is therefore important to find and select the optimal areas for imaging. Different means for estimation of ice thickness on the grid are available in software packages dedicated for automated collection of cryo-EM datasets, such as EPU&#160;2, Leginon28, or SerialEM29.
The advent of fast and sensitive direct electron detectors enabled the collection of images in many fractions as movies that enabled compensation of beam-induced movements and resulted in a substantial increase in the quality and quantity of data used in image processing and final 3D reconstruction30. At the same time, automation and high throughput data collection provide huge datasets with thousands of images/movies that represent challenges for data storage and access. The adopted model with large cryo-EM facilities serving tens to hundreds of users especially calls for organized data management with proper tracking and data sharing in established cryo-EM pipelines31,32.
This study describes a protocol for routine collection of high-resolution cryo-EM datasets using the 200 kV Glacios TEM microscope. Necessary alignments of the microscope optics are described together with procedures for evaluation of cryo-EM samples and selection of suitable areas for high-resolution data collection. The organization of collected data and related metadata with sample information is demonstrated in Athena - a data management platform that facilitates the review of sample information and collected data. Using the mouse apo-ferritin sample, it was possible to achieve a 3D reconstruction at 1.6 &#197; resolution13. Using the described protocol, we also reconstructed the 3D density map of the 20S proteasome from Thermoplasma acidophilum at 2.1 &#197; resolution.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>AutoGrid rings</td>
			<td>Thermo Fisher Scientific</td>
			<td>1036173</td>
			<td>Package of 100x AutoGrid rings for the standard EM grids.</td>
		</tr>
		<tr>
			<td>C-Clip</td>
			<td>Thermo Fisher Scientific</td>
			<td>1036171</td>
			<td>Package of 100 clips that secure 
			the standard EM grids inside the AutoGrid rings.</td>
		</tr>
		<tr>
			<td>Data Management Platform</td>
			<td>Thermo Fisher Scientific</td>
			<td>1160939</td>
			<td>Part of the Glacios base configuraiton; includes Athena Software</td>
		</tr>
		<tr>
			<td>EPU Quality Monitor</td>
			<td>Thermo Fisher Scientific</td>
			<td>1179770</td>
			<td></td>
		</tr>
		<tr>
			<td>EPU Software</td>
			<td>Thermo Fisher Scientific</td>
			<td>1025080</td>
			<td>Part of the Glacios base configuration</td>
		</tr>
		<tr>
			<td>Ethane 3.5</td>
			<td>Westfalen</td>
			<td>A06010110</td>
			<td>Ethane gas used for making liquid ethane (puritiy at least N35, i.e. 99.95% vol)</td>
		</tr>
		<tr>
			<td>Falcon 4 200kV</td>
			<td>Thermo Fisher Scientific</td>
			<td>1166936</td>
			<td>Direct electron detector</td>
		</tr>
		<tr>
			<td>Glacios</td>
			<td>Thermo Fisher Scientific</td>
			<td>1149551</td>
			<td>200 kV TEM</td>
		</tr>
		<tr>
			<td>GloQube Plus Glow Discharge System for TEM Grids and surface modification</td>
			<td>Quorum</td>
			<td>N/A</td>
			<td>also available via Thermo Fisher Scientific (PN 1160602)</td>
		</tr>
		<tr>
			<td>QuantiFoil grids</td>
			<td>Quantifoil</td>
			<td>N/A</td>
			<td>R-2/1, 300 mesh; carbon foil grid</td>
		</tr>
		<tr>
			<td>Relion</td>
			<td>MRC Laboratory of Molecular Biology</td>
			<td>N/A</td>
			<td>open source software: 
			https://relion.readthedocs.io/en/release-3.1/</td>
		</tr>
		<tr>
			<td>Selectris with Falcon 
			4 for 200 kV</td>
			<td>Thermo Fisher Scientific</td>
			<td>1191753</td>
			<td>Energy filter</td>
		</tr>
		<tr>
			<td>Selectris X with Falcon 
			4 for 200 kV</td>
			<td>Thermo Fisher Scientific</td>
			<td>1191755</td>
			<td>Energy filter</td>
		</tr>
		<tr>
			<td>UltrAuFoil grids</td>
			<td>Quantifoil</td>
			<td>N/A</td>
			<td>R-1.2/1.2, 300 mesh; gold foil grids</td>
		</tr>
		<tr>
			<td>Vitrobot Mk. IV</td>
			<td>Thermo Fisher Scientific</td>
			<td>1086439</td>
			<td>Automated vitrification system</td>
		</tr>
		<tr>
			<td>Whatman 595 filter paper</td>
			<td>Thermo Fisher Scientific</td>
			<td>AA00420S</td>
			<td></td>
		</tr>
	</tbody>
</table>

routine collection of high-resolution cryo-em datasets using 200 kv transmission electron microscope

Cryo-electron microscopy (cryo-EM) has been established as a routine method for protein structure determination during the past decade, taking an ever-increasing share of published structural data. Recent advances in TEM technology and automation have boosted both the speed of data collection and quality of acquired images while simultaneously decreasing the required level of expertise for obtaining cryo-EM maps at sub-3 &#197; resolutions. While most of such high-resolution structures have been obtained using state-of-the-art 300 kV cryo-TEM systems, high-resolution structures can be also obtained with 200 kV cryo-TEM systems, especially when equipped with an energy filter. Additionally, automation of microscope alignments and data collection with real-time image quality assessment reduces system complexity and assures optimal microscope settings, resulting in increased yield of high-quality images and overall throughput of data collection. This protocol demonstrates the implementation of recent technological advances and automation features on a 200 kV cryo-transmission electron microscope and shows how to collect data for the reconstruction of 3D maps that are sufficient for de novo atomic model building. We focus on best practices, critical variables, and common issues that must be considered to enable the routine collection of such high-resolution cryo-EM datasets. Particularly the following essential topics are reviewed in detail: i) automation of microscope alignments, ii) selection of suitable areas for data acquisition, iii) optimal optical parameters for high-quality, high-throughput data collection, iv) energy filter tuning for zero-loss imaging, and v) data management and quality assessment. Application of the best practices and improvement of achievable resolution using an energy filter will be demonstrated on the example of apo-ferritin that was reconstructed to 1.6 &#197;, and Thermoplasma acidophilum 20S proteasome reconstructed to 2.1-&#197; resolution using a 200 kV TEM equipped with an energy filter and a direct electron detector.

The data management portal provides efficient, structured storage of collected images, data, and metadata from multiple experimental workflows in a single software platform. Each defined experiment in a created project consists of a workflow with customer-defined steps to capture sample information, collected data, and related metadata without any constraints to provide maximal flexibility and usability for any possible experiments and all use cases (Figure 1, Figure 2). The data management portal also has a lab note functionality to illustrate workflow steps, including image processing with intermediate results, that can be all together associated with a project and provide as complete a record as possible for analysis and creation of reports and publications.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/63519/63519fig01.jpg" /> 
Figure 1: Example of possible organization of data and metadata in the data management platform. Each project can consist of multiple experiments, such as cryo-EM or mass-spectroscopy (viz. Protocol step 2.3). Each experiment can include multiple user-defined workflows (viz. Protocol step 2.5), each consisting of multiple configurable steps (viz. Protocol step 2.7). <a href="https://www.jove.com/files/ftp_upload/63519/63519fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/63519/63519fig02.jpg" /> 
Figure 2: View at an opened project workflow in the data management platform. The figure shows associated metadata and notes to the opened step in the workflow. The left bar with icons provides quick access to different options and menus of the data management platform. The left panel includes a list of saved Workflows (shown only one saved workflow &#34;Exp2_ApoF_EFTEM_Grid7&#34;) and a blue button to add a new Workflow. The central panel shows individual steps in the opened Workflow, as is shown for the SPA workflow here. The blue button on top right can add an additional step to the opened workflow. The right panel includes space for either recorded Metadata or user-input Notes of the workflow, which may include text, tables and images. Different formatting options for the text are available. <a href="https://www.jove.com/files/ftp_upload/63519/63519fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Cryo-EM grids produced with traditional plunge-freezing devices, such as Vitrobot will typically display a gradient of ice thickness over the grid surface. Some grids can be also damaged (bent) after manual handling and/or clipping into an autogrid ring carrier. Figure 3 shows examples of different grids as shown in the Atlas overview. The grids with thick ice or damage should be excluded from further investigation.
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/63519/63519fig03.jpg" /> 
Figure 3: Gallery of different grids as seen in the Atlas overview. (A) A bad grid with thick ice, (B) a bent grid with bad ice and ice contamination, (C) an acceptable grid with good ice gradient, (D) a typical grid with good thin ice and small ice gradient. <a href="https://www.jove.com/files/ftp_upload/63519/63519fig03large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
The selection of grid squares with no damage and optimal ice thickness is critical for collecting high-resolution datasets. Ice thickness may vary even at the level of individual grid squares, and it is, therefore, important to select only holes with optimally thin ice from each selected grid square. Figure 4 shows a suitable grid square with intact foil and thin ice in the center. The shown grid square is good for setting a filter for automated selection of holes with thin ice in all selected grid squares as it contains a range of different ice thickness as well as empty holes without ice, which is extremely useful for setting an appropriate range of intensity in the ice filter in the Hole Selection task.
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/63519/63519fig04.jpg" /> 
Figure 4: An example grid square with a gradient of ice thickness, from empty grid squares in the center and thick ice near the grid bars. The filter of ice quality can be used to select the range of intensities inside holes with the ideal ice thickness that are accordingly selected in the grid square (the holes with green overlay). <a href="https://www.jove.com/files/ftp_upload/63519/63519fig04large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Benchmark results using the described protocol were obtained using the sample of mouse apo-ferritin (apoF) from the Kikkawa group11. ApoF is a highly &#945;-helical protein that forms a very stable octahedral cage. The high stability and high symmetry make apoF an optimal sample for high-resolution cryo-EM imaging and image processing. ApoF has therefore become a standard sample for assessing the performance of cryo-EM instruments11,12,13. A frozen aliquot containing 15 mg/mL purified apoF sample was thawed on ice and clarified by centrifugation at 10,000 x g for 10 min. The supernatant was diluted to 5 mg/mL with 20 mM HEPES pH 7.5, 150 mM NaCl. 3 &#181;L of the diluted sample was applied onto a glow-discharged R-1.2/1.3, 300 mesh gold grids for 30 s. Then the grids were blotted for 5 s before plunge-freezing into liquid ethane cooled by liquid nitrogen. Plunge-freezing was performed using a fully automated vitrification system at 100% humidity and 4 &#176;C. All grids were clipped in autogrids and loaded into a 200 kV Cryo-TEM. About 3000 movies were collected at a throughput of 300 movies/h. Data were processed using the methods as described11 with the following modifications: i) Relion 4-beta version was used instead of Relion 3.1, ii) automated particle picking was done using 2D class averages of previous apoF reconstructions as references, and iii) the initial 3D model was generated from the previous apoF reconstruction low-passed to 15-&#197; resolution. Optics grouping was not done for this dataset as the used AFIS procedure34 has been proven to efficiently and reliably minimize beam-tilt induced phase shifts that do not limit data quality to reconstruct 3D maps at the reported resolutions. 3D refinement after Bayesian polishing and CTF refinement led to 1.68 &#197; resolution map. The resolution was further improved with Ewald sphere correction resulting in a 1.63 &#197; resolution map. The overview of data collection and processing parameters is shown in Table 2, and the final reconstructed density map is shown in Figure 5, with the Fourier Shell Correlation (FSC) curve shown in Supplementary Figure 8.
Table 2: Data collection and image processing parameters used for the 3D reconstruction of apo-ferritin. <a href="https://www.jove.com/files/ftp_upload/63519/Table 2-63519R2.xlsx" target="_blank">Please click here to download this Table.</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/63519/63519fig05.jpg" /> 
Figure 5: Cryo-EM reconstruction of apo-ferritin. (Left panel) 3D rendering of the reconstructed apoF cryo-EM map at 1.6 &#197; resolution. (Right panel) Detailed view of the reconstructed map at the level of individual amino acid side chains. The density of amino acid sidechains is well resolved, and the atomic model can be unambiguously built within this map. <a href="https://www.jove.com/files/ftp_upload/63519/63519fig05large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Effects and benefits of using an energy filter in the SPA reconstructions were evaluated using the prokaryotic 20S proteasome isolated from T. acidophilum. The prokaryotic 20S proteasome has been also used as a standard cryo-EM sample as it represents the stable catalytic core of the proteasome complex with the D7 symmetry. Grids were prepared by adding 4.5 &#181;L of the purified T. acidophilum 20S proteasome sample onto a glow-discharged 200-mesh R 2/1 copper grid. Samples were vitrified in a liquid ethane/propane mixture using a fully automated vitrification system set to 4 &#176;C and 100% humidity with blot force of 20 and blot time of 4.5 s.
Three different datasets were collected from the same cryo-EM grid with similar grid squares using a different slit width of the energy filter in the order: i) slit fully open (no slit inserted), ii) 20 eV slit and iii) 10 eV slit. The grid squares were selected using an ice quality filter within the analysis software. All other parameters for data collection and data processing were kept the same. Datasets were collected for 15 h with a total of 4000 movies and processed using the methods as described11 using Relion 3.1 with the modification that Laplacian-of-Gaussian particle picking algorithm was used to produce initial 2D class averages for reference-based particle picking from the full datasets. The same number (102,200) of randomly selected particles was chosen and used for the final iteration and 3D reconstruction of each dataset. Data processing variables are described in the table (Table 3) below to achieve the final reconstructed EM density map shown in Figure 6 with the FSC curve shown in Supplementary Figure 9. Optics grouping and Ewald sphere correction were not done for these datasets either.
Table 3: Data collection and image processing parameters used for the 3D reconstruction of the T. acidophilum 20S proteasome. <a href="https://www.jove.com/files/ftp_upload/63519/Table 3-63519R2.xlsx" target="_blank">Please click here to download this Table.</a>
Table 4: Summary of achieved resolution and B-factor for cryo-EM reconstructions of the T. acidophilum 20S proteasome using datasets with different energy slit width. <a href="https://www.jove.com/files/ftp_upload/63519/Table 4-63519R2.xlsx" target="_blank">Please click here to download this Table.</a>
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/63519/63519fig06.jpg" /> 
Figure 6: Effect of energy filtering on cryo-EM images. (A) Cryo-EM images at different defocus values collected with or without 10 eV slit. (B) Overview of the 20S proteasome cryo-EM map with segmented subunits. (C) Zoom view at the 20S proteasome map with a fitted atomic model. <a href="https://www.jove.com/files/ftp_upload/63519/63519fig06large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Supplementary Figure 1: Calibration of image shifts task (yellow ellipse) to align image shifts between different optical presets (red ellipses) in the analysis software, using a crystal of hexagonal ice that is visible in the full magnification range between 100x to 165,000x. (Top) Calibration between the Data Acquisition and Hole/Eucentric Height presets, (middle) calibration between the Hole/Eucentric Height and Grid Square presets, (bottom) calibration between the Grid Square and Atlas presets. <a href="https://www.jove.com/files/ftp_upload/63519/SuppFigure01_rev01_EPUalign-Final.pdf" target="_blank">Please click here to download this File.</a>
Supplementary Figure 2: Autostigmate function in analysis software (yellow ellipse). (Left image) Acquired image. (Right image) Fourier transfer of the acquired image showing concentric Thon rings and their CTF fit shown in radial beams. <a href="https://www.jove.com/files/ftp_upload/63519/SuppFigure02_rev01_EPUastigmatism-Final.pdf" target="_blank">Please click here to download this File.</a>
Supplementary Figure 3: User interface of the Autocoma function in analysis software (yellow ellipse) for coma alignment. The image panel shows Fourier transfer images acquired at different beam tilts and their CTF fits that are used for calculation of the coma. <a href="https://www.jove.com/files/ftp_upload/63519/SuppFigure03_rev01_EPUcoma-Final.pdf" target="_blank">Please click here to download this File.</a>
Supplementary Figure 4: User interface of Energy Filter tuning. Example of a good tunning report of the energy filter isochromacity with all parameters (shown in green text) within specifications. <a href="https://www.jove.com/files/ftp_upload/63519/SuppFigure04_rev01_Sherpa_isochromacity -final.pdf" target="_blank">Please click here to download this File.</a>
Supplementary Figure 5: User interface of Energy Filter tuning. Example of good tuning report of the energy filter magnification distortions with all parameters (shown in green text) within specifications. <a href="https://www.jove.com/files/ftp_upload/63519/SuppFigure05_rev01_Sherpa_mag-distortions-Final.pdf" target="_blank">Please click here to download this File.</a>
Supplementary Figure 6: User interface of Energy Filter tuning. Example of good tuning of the energy filter chromatic distortions with all parameters (shown in green text) within specifications. <a href="https://www.jove.com/files/ftp_upload/63519/SuppFigure06_rev01_Sherpa_chrom-distortions-Final.pdf" target="_blank">Please click here to download this File.</a>
Supplementary Figure 7: DataViz panel of the EPU Quality Monitor with an overview of data quality in a collected cryo-EM dataset. The graphs with aggregated data from all collected images/movies show values (dot plots) and distribution (bar plots) of selected critical quality indicators, such as the CTF fit confidence (blue), defocus (orange), and astigmatism (green). A subset of the collected images/movies can be selected by setting parameter filters in the top of the DataViz panel. After applying the filters, selected images/movies can be exported for further processing in another image processing package, such as Relion or CryoSpark. <a href="https://www.jove.com/files/ftp_upload/63519/SuppFigure07_rev01_DataViz.tif" target="_blank">Please click here to download this File.</a>
Supplementary Figure 8: FSC curve of the final reconstruction of apoF to 1.6 &#197; resolution, as reported by Relion 4-beta. The blue curve shows the FSC of masked 3D maps from two independently refined 3D reconstructions from two mutually exclusive half-datasets. According to the gold standard FSC at 0.143, the resolution of the final 3D map reconstructed from the full dataset corresponds to 1.6 &#197;. The orange curve shows the FSC of the masked 3D reconstructions with randomized phases. The rapid drop of the FSC curve indicates that the used mask did not contribute to the observed FSC of the original reconstructed maps (blue curve) beyond ~2 &#197; resolution. <a href="https://www.jove.com/files/ftp_upload/63519/SuppFigure08_rev01_FSC_apoF.tif" target="_blank">Please click here to download this File.</a>
Supplementary Figure 9: FSC curves of the final reconstruction of T. acidophilum 20S proteasome using different slit widths of the energy filter, as reported by Relion 3.1. The blue curves show the FSC of masked 3D maps from two independently refined reconstructions from two half-datasets of each dataset, respectively. The gold-standard FSC at 0.143 indicates the achieved resolutions of the final 3D maps reconstructed from the respective full datasets (2.3 &#197;, 2.2 &#197;, and 2.1 &#197; resolution, respectively). The red curves show the FSC of masked maps with randomized phases. The rapid drop of the red FSC curves indicates that the used mask did not contribute to the FSC of the original reconstructed maps beyond ~3 &#197; resolution. The green curves show the FSC of unmasked 3D maps, which are influenced by noise in the entire reconstructed 3D volume and therefore drop sooner than the FSC of the masked 3D maps. <a href="https://www.jove.com/files/ftp_upload/63519/SuppFigure09_rev02_T20S_FSCs.tif" target="_blank">Please click here to download this File.</a>
Availability of data: The cryo-EM density maps have been deposited in the EM Data Bank under accession numbers: apoferritin: EMD 14173, EMPIAR-10973. 20S proteasome: EMD 14467, EMPIAR-10976.

Watch this Scientific Journal Video about Routine Collection of High-Resolution cryo-EM Datasets Using 200 KV Transmission Electron Microscope at JoVE.com

Routine Collection of High-Resolution cryo-EM Datasets Using 200 KV Transmission Electron Microscope

High-resolution cryo-EM maps of macromolecules can be also achieved by using 200 kV TEM microscopes. This protocol shows the best practices for setting accurate optics alignments, data acquisition schemes, and selection of imaging areas that are all essential for the successful collection of high-resolution datasets using a 200 kV TEM.

Cryo-electron microscopy (cryo-EM) has been established as a routine method for protein structure determination during the past decade, taking an ...

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