We acknowledge Diamond Light Source for access to and support of the cryo-EM facilities at the UK&#39;s national Electron Bio-Imaging Centre (eBIC), funded by the Wellcome Trust, MRC, and BBRSC. We would also like to thank Andrew Howe for the acquisition of the Apoferritin tomogram (Movie 1), Ishika Kumar for the preparation and acquisition of the neuron tomogram (Movie 2), and Craig MacGregor-Chatwin for the Cyanobacteria lamella-tomogram (Movie 3).

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The authors have no conflicts of interest.

Tomo5 
The workflow description of the tomography software highlights one potential and most streamlined way for a (remote) batch tomography session setup. While the software is easy for beginners, some initial cryo-EM experience and basic tomography understanding can help with the setup. Critical steps are highlighted in the protocol and should help troubleshoot even if a different setup approach has been used. The advancement of the software will ease (remote) data collection and make cryo-ET more accessible to a wide user base.&#160;A few tips and tricks that can help troubleshoot commonly encountered problems are described below.
One important point to discuss is the choice of grids because, when tilting the specimen to &#177;60&#176;, grid bars at high tilts can obscure the view (Figure 8). On a TEM grid, the mesh size refers to the number of grid squares per unit length of the grid. Larger mesh numbers have more grid squares per unit length, a higher density of grid squares, and smaller grid squares, i.e., a 400-mesh grid has smaller squares than a 200-mesh grid. A good choice of grids for tomography is 200-mesh or 300-mesh grids. As shown in Figure 8, the available area to collect is reduced as the grid is tilted. At &#177;60&#176; tilt, a 300-mesh grid will have a small field of view on which a full tomogram can be acquired. The advantages of 200-mesh grids are that the larger grid squares make molecular tomography setup faster, and with the increased grid-square area, one square will likely be enough for an overnight collection. The disadvantage is that 200-mesh grids are more fragile, so handling and clipping require more finesse.
Moreover, if using holey support film (see Table of Materials) on EM grids, the hole spacing must be considered for the setup of the focus and tracking region in relation to the exposure region. Ideally, the beam diameter at the desired magnification should be small enough to cover the carbon area adjacent to the exposure area along the tilt axis for optimal and fast setup. This way, potential regions of interest in each hole can be acquired.
As the software&#39;s eucentric height routine is currently not as robust, such as the serialEM routine, the following tips can work around that problem. If the eucentric height determination fails using the eucentric height preset, one can use the overview preset instead and re-run &#34;Auto-eucentric by stage tilt&#34;; this can solve issues if the eucentric height is far away from 0. If this succeeds, one can re-run &#34;Auto-eucentric by stage tilt&#34; with &#34;Eucentric Height&#34; presets to improve precision. If it fails, one can run &#34;Auto-Eucentric by beam tilt&#34; with the eucentric height preset and then re-run &#34;Auto-Eucentric by stage tilt&#34; or manually set the z-height consolidated by &#34;Auto-Eucentric by beam tilt&#34; in the TEM User Interface under &#34;Stage&#34; settings. In case grids with a repeating pattern of holes are used, they may prevent the identification of a single cross-correlation peak. One can try altering the eucentric height preset to a lower defocus offset such as &#8722;25 &#181;m and/or a shorter exposure time to reduce cross-correlation from the hole patterns. On the other hand, using lacey grids/lamella may not provide sufficient signal for a strong cross-correlation peak. One can try altering the eucentric height preset to a greater defocus offset such as &#8722;75 &#181;m and/or an extended exposure time to enhance the cross-correlation peak. Another option is to adjust image filter settings; they can be found in the &#34;Preparation&#34; tab. Options to adjust the filter settings can be set for low (Overview/Gridsquare), medium (Eucentric Height), and high magnification (Tracking/Focus) to find the optimal cross-correlation peak for each preset. The required input is one image, i.e., at 0&#176; and one at 5&#176;, followed by clicking Compare to compare both images. Recommended starting value for the longest wavelength is one-quarter of the scale bar in the image and for the shortest wavelength is one-fortieth of the scale bar. If the peak is not robustly identified, one can optimize&#160;the settings until a convincing peak can be found. There is no need to re-acquire images every time; simply pressing &#34;Compare&#34; is enough. If TOMO still fails to automatically find the eucentric height, the manual eucentric height calibration can be used. One should center over a reasonably large ice crystal in overview magnification in the &#34;Preparation&#34; tab, then go to the the &#34;Stage control&#34; of the TEM User Interface, set alpha to &#8722;30&#176;, and adjust the stage z-value to re-center the crystal using the fluorescent screen image. Selecting &#34;High Resolution&#34; and &#34;High Contrast&#34; settings in the TEM User Interface will make this simple (buttons at the bottom of the fluorescent screen window). Optionally, if there is access to a camera with live mode, then this can be used to determine the eucentric height; it will be easier than on the fluorescent screen.
The biggest limitations in Tomo5 versions prior to 5.8 are the missing medium magnification montages, missing dose symmetric scheme, and problems related to eucentric height finding. These exist in serialEM, a freeware with rapid development and community support, a robust eucentric height routine, and the option to script, i.e., a custom-built dose symmetric scheme. From version 5.8 onwards in Tomo5, the most commonly encountered problem for finding the eucentric height, i.e., an unsuccessful looping around the target z-value, has been solved by implementing the option to set a eucentric height acceptance criterion. However, with different grid and sample types, it is highly recommended to adjust the image filter settings to reflect the unique imaging conditions of individual sessions and to give the best possible cross-correlation peak to find the eucentric height and for the focus and tracking region to work reliably during tomogram acquisition.

Overall, many facilities have rapidly adapted to remote operation during the pandemic. The Tomo5 software provides an easy access and user-friendly route to tomography which is well suited for remote operation. The advances made in the software will no doubt continue to make remote data collections and tomography collection in general more mainstream in the community.
emClarity 
As emClarity uses a template-based particle picking method, it needs a template for the object of interest. The particle picking (step 2.6) is very sensitive and key to the final structure. Before averaging and alignment (step 2.9), one must ensure to check carefully and manually remove the false positives. When a template is not available, emClarity might not be easy to use, but it is possible to use other software, for example, Dynamo37 and PEET48, to create an initial model.
For heterogeneous samples, emClarity is equipped with a classi&#64257;cation method that enables users to focus on specific features with different scales. It is helpful to run a few cycles of alignments before classification and run it at a higher binning (such as bin 4 or bin 3).
The up-to-date version of the software (V1.5.3.11) has significant updates compared with the first release (V1.0)17. These include, but are not limited to, a handedness check during CTF estimation (step 2.3); symmetry for alignments (CX, I, I2, O); calculation of per-particle 3D sampling functions (3DSF); a switch to MATLAB 2019a for compatibility and stability; and reconstruction using the raw projection images (cisTEM). The software will continue to improve for various samples, and the newest announcements can be found online (see Table of Materials).

Cryogenic electron microscopy (cryo-EM) is widely known to have experienced a renaissance period, accelerating it to become a core and centrally useful tool in structural biology. The development and utilization of direct electron detectors1,2,3, improved microscopes and electron sources3,4,5, improvements in automation/throughput6,7,8,9, and computational advances in single-particle analysis10,11,12,13,14 and tomography15,16,17 are all, in part, responsible for the recent success of the technique. These technological drivers have developed cryo-EM&#39;s capability to solve biological macromolecular structures under cryogenic and native conditions. The resolutions that are readily obtainable are sufficient for atomically accurate modeling and have brought the technique to the forefront of the structural biology arena. A reductionist approach to expressing and purifying a biological target of interest has long proven successful in macromolecular crystallography (MX) for basic biological research, drug discovery, and translational science. In the same approach, cryo-EM can now deliver results that parallel high-resolution MX studies. The current major success in the cryo-EM branch of structural biology is called single particle analysis (SPA), which acquires 2D projection images typically of a purified protein specimen18 to obtain thousands of views of a biological macromolecule19. These images (1) contain information from a range of views that fully represent the orientations of the target in 3D space and (2) capture the object conformational heterogeneity, which can later be separated and investigated.
An alternative approach to acquiring these 2D projection images of biological samples, even in situ and without purification, is cryo-electron tomography (cryo-ET). Cryo-ET takes a series of images of the same object at tilted angles by mechanically rotating the specimen. Thus, the 2D projections collected in SPA, representing the angular poses of the molecule of interest, are inherently collected as part of the cryo-ET imaging experiment20. Tomographic tilt series are then reconstructed into a tomogram that contains 3D representations of the imaged macromolecular complexes. The nature of tomographic data collection does, to a degree, decrease the reliance on averaging to achieve a full 3D representation of a molecule from a collection of 2D images. However, due to current stage designs, the specimen is typically tilted from &#8722;60&#176; to +60&#176;, leaving a missing wedge21 of information in the tomographic 3D reconstruction.
The 3D reconstructions in a single tomogram then have a missing wedge of information and low signal to noise. Individual macromolecules may be extracted as subtomograms and averaged together to tackle this. Where each macromolecule in a subtomogram is found at a different orientation, the missing wedge is oriented differently in each subtomogram of the target object, so averaging over many copies fills in information due to the missing wedge. Recent developments in image processing have also attempted to train artificial intelligence neural networks to fill in the missing wedge with meaningful data22. This averaging process also increases the signal to noise, akin to the goal of averaging in single particle analysis, so the reconstruction&#39;s quality and resolution improve. If the molecule of interest possesses symmetry, that too may be defined and employed during averaging, further improving reconstruction resolution. The extraction of 3D volumes of a macromolecule from a tomogram into a set of subtomograms and their subsequent processing is known as subtomogram averaging (STA)23. Where each subtomogram represents a unique copy of the molecule being studied, any structural heterogeneity may be interrogated using the STA workflow. As commonly utilized in the SPA workflow, classification techniques may be employed during STA to dissect the conformational states of the complex of interest. As well as STA enabling high-resolution reconstruction in cryo-ET, this approach makes the technique a powerful tool to interrogate the structural mechanisms of macromolecules in their native cellular environment or of targets often not amenable to SPA24,25,26.
Electron tomography has a long history of determining the 3D ultrastructure of cellular specimens at room temperature27. The acquisition of views by physical tilting of the specimen provides enough information for the 3D reconstruction of an object at cellular-length scales and is particularly important when cellular structures lack the regularity for averaging. Cells may also be frozen onto substrates for cryo-ET imaging at the cell edges where the specimen is thin enough to be electron transparent. Under these conditions, STA may be employed to determine macromolecular structures in a cellular environment, albeit when the specimen is thin enough to be electron transparent28. However, when combined with additional preparative techniques, including cryo-correlative light and electron microscopy (cryo-CLEM) and focused ion beam milling (cryo-FIB), cryo-ET can be used to image inside whole cells under cryogenic conditions29. This brings together the power of cryo-ET to study the cellular ultrastructure with the power of STA to determine the structures of macromolecular complexes in situ while identifying their cellular location30 and providing snapshots of complexes engaged in dynamic processes31. The ability of the technique to image cellular specimens and employ STA in several studies has highlighted the power of the technique to solve macromolecular structures in situ, even at resolutions comparable to SPA32. A further benefit is found in the knowledge of the original location of the macromolecule, represented by the final classified 3D reconstruction in the tomogram30. Therefore, the macromolecular structure can be correlated with the cellular ultrastructure. These observations across length scales will presumably lead to important findings where structural mechanisms may be correlated with cellular changes in the context of functional studies.
Cryo-ET and STA allow data collection in three major workflows: molecular, cellular, and lamella tomography. The structures of purified macromolecular complexes may be determined by cryo-ET by molecular tomography. Determining protein structures in their cellular environment where the cell is thin enough may be described as cellular tomography. More recently, with the development of cryogenic targeting and milling, these same techniques may be applied in lamella tomography workflows to determine the protein structures deep inside the cell in their native environment while revealing the cellular context in which those proteins are observed. Different data collection strategies can be used depending on the available software packages and, most importantly, depending on the requirement of the specimen. Molecular or non-adherent samples on a copper TEM grid of a purified protein typically require less handling and, thus, remain flat and undamaged in ideal cases. Electron tomograms can easily be set up in series across a holey-carbon grid to quickly acquire tens to hundreds of tomograms in a systematic manner. The simplest way for users to set up molecular tomography samples where proteins are abundantly present on the grid would be to use Tomo5 (software for 3D electron tomography used in the present study, see Table of Materials). Other tomography software such as Leginon9 and serialEM6 are also available; they offer more setup options for more personalized approaches for data collection but are more complex and consequently can be harder to navigate, particularly for users new to tomography and users accessing their session remotely. For a facility with a large and diverse user base, Tomo5 is easy to operate in a remote environment and to train users in. For adherent cells, grids typically require more handling steps, and the necessity to use fragile gold grids increases the need for improved care in handling and data collection strategies. To facilitate finding a cellular region of interest and avoid occlusion from the grid itself at high tilt angles, it is also beneficial to use larger mesh sizes, but at the cost that they are inherently more fragile. For lamella samples, the fragility of the sample is determined by the quality of the lamella, which can be variable. These factors increase the setup time and considerations, but the increased adaptability and robustness again make Tomo5 suitable for this type of data collection. However, specialized data collection scenarios exist for each workflow. BISECT and PACE-tomo (both run in&#160;SerialEM) introduces the possibility of scripted beam-image shifting during tomography acquisition to increase tomogram collection speed28, particularly in molecular tomography. Medium magnification montages (MMM) in SerialEM6,7,33 can better identify and precisely target molecular features in all workflows, although, at the time of writing, these features are beginning to be implemented in Tomo5.
Like SPA, cryo-ET and STA are becoming increasingly accessible through the improvements made to acquisition software and a wealth of available packages for subtomogram averaging16,17,32,34,35,36,37,38. In addition, during the pandemic, enabling remote access to cryo-EM instrumentation became essential to the continued operation of national facilities like the electron Bio-Imaging Centre (eBIC) at Diamond Light Source (DLS), UK. These developments have made cryo-ET more accessible and robust for researchers wishing to utilize the technique. Once data have been acquired, STA is an essential tool for analyzing recurrent objects to obtain maximum resolution reconstruction and allow the classification of macromolecular heterogeneity. The current protocol aims to provide a detailed walk-through of preparing a cryo-TEM microscope for cryo-ET data collection and how to perform subtomogram averaging using emClarity on a molecular tomography dataset of apoferritin as an example. The use of emClarity (software for high-resolution cryo-electron tomography and subtomogram averaging, see Table of Materials) requires running scripts from the command line, so a level of familiarity with Linux/UNIX systems is assumed.
The remote connection depends on the network environment in each institute/facility. At eBIC, the remote system uses programs&#160;that allow remote data collection on the specific network configuration used at Diamond. Remote connection to the microscope is facilitated by two platforms: NoMachine and TeamViewer (see Table of Materials). Using the program NoMachine, the user may log onto a remote Windows desktop. The remote Windows desktop provided by NoMachine resides on the same network as the microscope and, thus, acts as a virtual support PC to the microscope. From the virtual support PC, the user connects to the microscope via TeamViewer providing direct access and control to the microscope PC running TUI and Tomo.
The present protocol consists of two parts (step 1 and step 2). Step 1 focuses on remote cryo-ET data acquisition using Tomo5 (software for 3D electron tomography). The walk-through for a (remote) session captures images at increasingly higher magnifications to ultimately allow the user to direct the tomography software to target specimen areas for tomographic data collection. Figure 1 summarizes this process. Step 2 details cryo-ET STA data processing using emClarity (software for high-resolution cryo-electron tomography and subtomogram averaging). Figure 9 summarizes this process.
The protocol is intended for a remote audience. It assumes the person physically at the microscope and loading the samples has done the direct alignments and taken care of the camera tuning and gain reference acquisition. For this protocol, a three-condenser lens system with an autoloader is assumed. For further detailed guidelines on the tomography software, a detailed manual by the manufacturer is available in the Windows Start button where the software was loaded from.

<table><tbody><tr><td>&lt;strong&gt;Software&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>Tomography</td><td>Thermo Fisher Scientific</td><td>5.9.0</td><td>Internal terminology: Tomo5 in document</td></tr><tr><td>TEM server</td><td>Thermo Fisher Scientific</td><td>7.10.1</td><td></td></tr><tr><td>TIA</td><td>Thermo Fisher Scientific</td><td>5.10.1</td><td></td></tr><tr><td>DigitalMicrograph</td><td>Gatan</td><td>3.44</td><td></td></tr><tr><td>emClarity</td><td>Open-Source software</td><td>1.5.3.11</td><td>Software for high-resolution cryo-electron tomography and subtomogram averaging</td></tr><tr><td>IMOD</td><td>Open-Source software</td><td>4.11</td><td>Modeling, display and image processing programs used for 3D reconstruction and modeling of microscopy images with a special emphasis on electron microscopy data</td></tr><tr><td>MotionCor2</td><td>Free for academic use</td><td>1.1.0</td><td>A multi-GPU program that corrects beam-induced sample motion recorded&lt;br/&gt; on dose fractionated movie stacks</td></tr><tr><td>ETomo</td><td>Open-Source software</td><td>4.11</td><td>ETomo is an interface for running a subset of IMOD and PEET commands.&amp;nbsp;</td></tr><tr><td>NoMachine</td><td>NoMachine, freeware</td><td>7.9.2</td><td>Remote desktop software</td></tr><tr><td>TeamViewer</td><td>TeamViewer AG</td><td>-</td><td>Remote access and remote control computer software</td></tr><tr><td>&lt;strong&gt;Materials&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>Quantifoil (holey support film) EM grids</td><td>Quantifoil</td><td>-</td><td>A flat film of carbon with pre-defined hole size, shape and arrangement</td></tr><tr><td>&lt;strong&gt;Instrumentation&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>Titan Krios microscope</td><td>Thermo Fisher Scientific</td><td>Titan Krios G2</td><td></td></tr><tr><td>K3 camera and GIB energy filter</td><td>Gatan</td><td>-</td><td></td></tr><tr><td>Falcon 4 camera and Selectris X energy filter</td><td>Thermo Fisher Scientific</td><td>-</td><td></td></tr><tr><td>&lt;strong&gt;Website&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>Website 1: https://github.com/bHimes/emClarity/</td><td>-</td><td>-</td><td>Link to download the emClarity software package</td></tr><tr><td>Website 2: https://bio3d.colorado.edu/imod/</td><td>-</td><td>-</td><td>Link to download IMOD</td></tr><tr><td>Website 3: https://github.com/ffyr2w/emClarity-tutorial</td><td>-</td><td>-</td><td>Link to the emClarity online tutorial</td></tr><tr><td>Website 4: https://emcore.ucsf.edu/ucsf-software</td><td>-</td><td>-</td><td>Link to download MotionCor2</td></tr><tr><td>Website 5: https://github-wiki-see.page/m/bHimes/emClarity/wiki</td><td>-</td><td>-</td><td>Link to the newest announcements including updates and bug fixs for emClarity</td></tr></tbody></table>

cryo-electron tomography remote data collection and subtomogram averaging

Cryo-electron tomography (cryo-ET) has been gaining momentum in recent years, especially since the introduction of direct electron detectors, improved automated acquisition strategies, preparative techniques that expand the possibilities of what the electron microscope can image at high-resolution using cryo-ET and new subtomogram averaging software. Additionally, data acquisition has become increasingly streamlined, making it more accessible to many users. The SARS-CoV-2 pandemic has further accelerated remote cryo-electron microscopy (cryo-EM) data collection, especially for single-particle cryo-EM, in many facilities globally, providing uninterrupted user access to state-of-the-art instruments during the pandemic. With the recent advances in Tomo5 (software for 3D electron tomography), remote cryo-ET data collection has become robust and easy to handle from anywhere in the world. This article aims to provide a detailed walk-through, starting from the data collection setup in the tomography software for the process of a (remote) cryo-ET data collection session with detailed troubleshooting. The (remote) data collection protocol is further complemented with the workflow for structure determination at near-atomic resolution by subtomogram averaging with emClarity, using apoferritin as an example.

For cellular and lamella samples, the data collection strategy largely depends on the sample and the goal of the imaging study (Figure 1). The targeting approach depends on whether the molecular target is in situ or prepared from the purified macromolecular complex for high-resolution reconstruction specimens containing molecular targets. For purified complexes vitrified onto holey (carbon) grids, targeting can simply be based on imaging in the holes of the (carbon) support film. For in situ work, the targeting approach requires knowledge of the location of the molecular entity based on correlative data or known low-magnification cellular landmarks. Cellular landmarks would ideally be identifiable when taking overview images and, if sufficient to roughly localize regions of interest, can provide a quick way to confirm target identity with a search image. However, if the observable events are rare, then medium magnification search images may be needed to qualify the target is correct. Search maps are medium magnification montages of search images and can, thus, make target finding much easier, where they can be acquired at a magnification at which the feature of interest is visible. Search maps can then be screened to find and set up target batch positions. For specimens containing cellular features for ultrastructural reconstruction, the targeting approach is similar, though equally dependent on the visibility of the cellular event at various magnifications and its prevalence in the sample.
The data acquisition strategy must also be considered; in all cases, the study&#39;s goal largely determines how the data is collected. For reconstruction of the cellular ultrastructure, a low magnification (20-5 &#197;/px) and large field of view may be appropriate, but high magnifications are required to reconstruct molecular or high-resolution detail (5-1 &#197;/px). A dataset collected at 1.5 &#197;/px under ideal conditions will only physically be able to produce a reconstruction at the Nyquist frequency of 3.0 &#197;/px; however, in reality, many factors, including but not limited to specimen thickness, size, and heterogeneity, all affect the obtained reconstruction quality. The exact imaging parameters also balance the magnification based on the study&#39;s goal with the field of view to contain enough information. This article presents a subtomogram averaging case reaching 2.86 &#197;, but additional studies17,42,43,44,45 are presented in Table 1 to illustrate the collection parameters associated with studies targeting different outcomes17,42,45,46.
Once a targeting workflow and data acquisition regime for cryo-ET is established, data collection of many different sample types is possible. Representative tomograms of a variety of specimens are presented here: molecular samples such as apoferritin (Movie 1), thin cellular processes (Movie 2), and FIB milled lamella of the thick cellular specimen (Movie 3).
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/63923/63923fig01.jpg" /> 
Figure 1: Overview of the tomography workflow setup. The cryo-ET imaging workflow described in the protocol is shown as a flow chart. The images expected to be acquired are shown for the cellular and molecular workflow. The presented naming follows the Tomo5 convention, although most tomography acquisition software shares common principles for collecting these images. <a href="https://www.jove.com/files/ftp_upload/63923/63923fig01large.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/63923/63923fig02.jpg" /> 
Figure 2: Preparation tab and search preset conditions. (A) Overview image of the whole tab. Imaging condition presets are set in this tab, and the &#34;Calibrate Image Shift&#34; and &#34;Image Filter Settings&#34; can be found in the &#34;Tasks&#34; drop-down. (B) Zoom-in of the &#34;Presets&#34; drop-down where each preset can be selected for individual imaging conditions setup. (C) Image depicting an adequate magnification to fit both the exposure and the &#34;Focus and Tracking&#34; area into the field of view. <a href="https://www.jove.com/files/ftp_upload/63923/63923fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/63923/63923fig03.jpg" /> 
Figure 3: Dose calculations. Example dose calculations for possible tomogram acquisition schemes where the dose rate has been measured over vacuum. The two calculations determine the exposure time (s) for each tilt, whether targeting an optimal dose per tilt or an optimal total dose for the full tilt series. In subtomogram averaging, it is common practice to target an optimal dose per tilted image in the range of 3.0-3.5 e-/&#197;2. In both cases, the &#34;Fractions (Nr.)&#34; are set to 6 to achieve ~0.5 e-/&#197;2 of dose per movie frame of the tilt for sufficient signal to perform motion correction. <a href="https://www.jove.com/files/ftp_upload/63923/63923fig03large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/63923/63923fig04.jpg" /> 
Figure 4: Atlas tab. Overview image of the &#34;Atlas&#34; tab. The image has been cropped to avoid empty grid position spaces. The &#34;Tasks&#34; menu contains session setup preferences and grid selection spaces for individual selection of all grids inventoried and an option to acquire a single atlas after the cassette has been removed from the autoloader. A selected grid can be reset and then re-acquired. <a href="https://www.jove.com/files/ftp_upload/63923/63923fig04large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/63923/63923fig05.jpg" /> 
Figure 5: Calibrated image shifts. Image depicts an exposure and search image. The red cross in the search image is the shifted marker to correct the offset between exposure and search. One should redo image shift calibrations at the session start or after changing the imaging condition presets. <a href="https://www.jove.com/files/ftp_upload/63923/63923fig05large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/63923/63923fig06.jpg" /> 
Figure 6: Auto functions and apertures. (A) The &#34;Auto Functions&#34; tab depicts the &#34;Presets&#34; drop-down and the &#34;Task&#34; selection. Underlined in blue is the &#34;Thon Ring&#34; preset required for &#34;Autostigmate&#34; (also underlined in blue) and &#34;Autocoma&#34;. One should select respective presets for each task and then press the Start button. (B) Apertures are found in the TEM User Interface. Select desired &#34;Objective aperture&#34; after the auto functions have been performed and run &#34;Autostigmate&#34; with the aperture in. <a href="https://www.jove.com/files/ftp_upload/63923/63923fig06large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 7" class="xfigimg" src="/files/ftp_upload/63923/63923fig07..jpg" /> 
Figure 7: Tomography tab overview. Images show the Tomo 5.8 user interface. (A) Batch positions. The latest functions depicted in the image: the &#34;Acquire Search Map&#34; option; tomogram positions are shown in atlas view with the zoom-in option; highlighted at the top right is &#34;Select Positions&#34;; below all four positions have been selected to &#34;Update Defocus&#34; parameters. (B) Three positions are selected on the search map, labeled 1, 2, and 3. Position 1 will not pose a problem when &#34;Refine All&#34; is run. However, if in a high target density setting such as when lamellae positions are selected, as depicted for position 2 and position 3, then &#34;Refine All&#34; will run the &#34;Tracking&#34; and &#34;Focus&#34; routine on the &#34;Exposure&#34; region of position 2, exposing the target before the tomogram is acquired. The grid type is R1.2/1.3. Scale bar = 1.2 &#181;m. (C) Data acquisition. Selected positions setup in &#34;Batch Positions&#34; can now be individually acquired. <a href="https://www.jove.com/files/ftp_upload/63923/63923fig07large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 8" class="xfigimg" src="/files/ftp_upload/63923/63923fig8v2.jpg" /> 
Figure 8: Defining the maximum tilt angle. Figure depicts a step-by-step approach to determining the maximum tilt range for tomogram acquisitions. (A) 0&#176;&#160;overview and a search map in the center of the grid square. To test the tilt range, the angle can be set in the tomography software with &#34;Set Tilt (&#176;)&#34;&#160;by typing the desired value and then pressing Set. (B) The stage has been tilted to &#8722;60&#176;, showing that a corner of the search map would not be fully acquired at &#8722;60&#176;. (C) The stage has been tilted to 60&#176;. By counting the holes that have disappeared at &#177;60&#176;, one can get an idea about the tilt range of a grid square. Scale bars = 2.5 &#181;m. <a href="https://www.jove.com/files/ftp_upload/63923/63923fig08v2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 9" class="xfigimg" src="/files/ftp_upload/63923/63923fig09.jpg" /> 
Figure 9: emClarity flowchart. The flowchart described the various steps for cryo sub-tomogram averaging. Scale bars = 50 nm. <a href="https://www.jove.com/files/ftp_upload/63923/63923fig09large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 10" class="xfigimg" src="/files/ftp_upload/63923/63923fig10.jpg" /> 
Figure 10: Template matching using emClarity. (A) A typical micrograph of apoferritin on a graphene-coated grid. Defocus: &#8722;3.430 &#181;m. (B) A slice of the tomogram overlaid with model points after template searching. (C) A top and (D) a side projection views of model points, indicating a single flat layer of monodispersed apoferritin particles. Scale bars = 50 nm. <a href="https://www.jove.com/files/ftp_upload/63923/63923fig10large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 11" class="xfigimg" src="/files/ftp_upload/63923/63923fig11.jpg" /> 
Figure 11: Cryo-ET STA of apoferritin. (A) The final map after 21 cycles of sub-tomogram alignment. (B) The final map&#39;s Fourier shell correlation (FSC) plot with a reported resolution of 2.86 &#197;, containing 38 cone FSCs. (C) Representative density maps (fitted with PDB model 6s6147). <a href="https://www.jove.com/files/ftp_upload/63923/63923fig11large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<table border="1" fo:font-size="8px" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Sample</td>
			<td>Cryo-ET type</td>
			<td>&#197;/px</td>
			<td>Tilt range (+/-)</td>
			<td>Tilt step (&#176;)</td>
			<td>Defocus range (&#181;m)</td>
			<td>Total dose (e-/&#197;2)</td>
			<td>Resolution</td>
			<td>Raw data</td>
			<td>Reference</td>
		</tr>
		<tr>
			<td>Apoferritin</td>
			<td>Purified/Molecular (STA)</td>
			<td>1.34</td>
			<td>60</td>
			<td>3</td>
			<td>1.5 &#8211; 3.5</td>
			<td>102</td>
			<td>2.86</td>
			<td>EMPIAR-10787</td>
			<td>17 &#38; this paper</td>
		</tr>
		<tr>
			<td>HIV-1 Gag</td>
			<td>Purified/Molecular (STA)</td>
			<td>1.35</td>
			<td>60</td>
			<td>3</td>
			<td>1.5 &#8211; 3.96</td>
			<td>120</td>
			<td>3.1</td>
			<td>EMPIAR-10164</td>
			<td>17</td>
		</tr>
		<tr>
			<td>Ribosome</td>
			<td>Purified/Molecular (STA)</td>
			<td>2.1</td>
			<td>60</td>
			<td>3</td>
			<td>2.2 &#8211; 4.3</td>
			<td>120</td>
			<td>7</td>
			<td>EMPIAR-10304</td>
			<td>42</td>
		</tr>
		<tr>
			<td>SARS-CoV-2 spike</td>
			<td>Lamella/Molecular (STA)</td>
			<td>2.13</td>
			<td>54</td>
			<td>3</td>
			<td>2 &#8211; 7</td>
			<td>120</td>
			<td>16</td>
			<td>EMPIAR-10753</td>
			<td>45</td>
		</tr>
		<tr>
			<td>Neuron axon structure</td>
			<td>Cellular (ultrastructural)</td>
			<td>5.46</td>
			<td>60</td>
			<td>2</td>
			<td>3.5 &#8211; 5</td>
			<td>90</td>
			<td>N.D.</td>
			<td>EMPIAR-10922</td>
			<td>47</td>
		</tr>
	</tbody>
</table>
Table 1: Collection parameters for several cryo-ET studies. Studies targeting the reconstruction of molecular detail from purified or in situ proteins compared to a study aiming to resolve and segment the ultrastructural cellular features.
Movie 1: A tomogram of apoferritin samples on a normal EM grid and then imaged with a cryo-TEM equipped with an ultra-high-resolution camera with a compatible filter. Tilt series were acquired with a dose-symmetric scheme, with a tilt span of 54&#176; and a total dose of 134 e-/A2 in the electron tomography software. Scale bar = 50 nm. <a href="https://www.jove.com/files/ftp_upload/63923/movie1_ApoF_50nmscale.mp4" target="_blank">Please click here to download this Movie.</a>
Movie 2: A tomogram of a primary neuron grown on an EM grid and then directly imaged with a cryo-TEM equipped with an ultra-high-resolution camera with a compatible filter. The tilt series were acquired with a dose-symmetric scheme, with a tilt span of 60&#176; and a total dose of 120 e-/A2 in the electron tomography software. Scale bar = 100 nm. <a href="https://www.jove.com/files/ftp_upload/63923/Movie2_neuron_100nm.mp4" target="_blank">Please click here to download this Movie.</a>
Movie 3: A tomogram of a Cyanobacteria on an EM grid, subjected to FIB milling and then imaged with a cryo-TEM equipped with a high-speed camera and a compatible filter. The tilt series were acquired with a dose-symmetric scheme, with a tilt span of 50&#176; and a total dose of 120 e-/A2 in the electron tomography software. Scale bar = 87.2 nm. <a href="https://www.jove.com/files/ftp_upload/63923/Movie3_lamella_100pxScale.mp4" target="_blank">Please click here to download this Movie.</a>
Supplementary File 1: The parameter file template for estimating the defocus. <a href="https://www.jove.com/files/ftp_upload/63923/63923_Supplementary File 1.docx" target="_blank">Please click here to download this File.</a>
Supplementary Table 1: Data collection and microscope setup details. <a href="https://www.jove.com/files/ftp_upload/63923/63923_Supplementary Table 1.xlsx" target="_blank">Please click here to download this Table.</a>
Supplementary Table 2: List of commands in the order of execution. <a href="https://www.jove.com/files/ftp_upload/63923/63923_Supplementary Table 2.xlsx" target="_blank">Please click here to download this Table.</a>

Watch this Scientific Journal Video about Cryo-Electron Tomography Remote Data Collection and Subtomogram Averaging at JoVE.com

Cryo-Electron Tomography Remote Data Collection and Subtomogram Averaging

The present protocol describes high-resolution cryo-electron tomography remote data acquisition using Tomo5 and subsequent data processing and subtomogram averaging using emClarity. Apoferritin is used as an example to illustrate detailed step-by-step processes to achieve a cryo-ET structure at 2.86 &#197; resolution.

Cryo-electron tomography (cryo-ET) has been gaining momentum in recent years, especially since the introduction of direct electron detectors, improved ...

cryo-electron-tomography-remote-data-collection-subtomogram

Research

JoVE Journal

Biology

4.8K Views.  Diamond Light Source Ltd, Harwell Science & Innovation Campus. The present protocol describes high-resolution cryo-electron tomography remote data acquisition using Tomo5 and subsequent data processing and subtomogram averaging using emClarity. Apoferritin is used as an example to illustrate detailed step-by-step processes to achieve a cryo-ET structure at 2.86 Å resolution.

Video: Cryo-Electron Tomography Remote Data Collection and Subtomogram Averaging

Correlative Microscopy for 3D Structural Analysis of Dynamic Interactions

Cryo-electron tomography (cryoET) allows 3D visualization of cellular structures at molecular resolution in a close-to-physiological state1. However, direct visualization of individual viral complexes in their host cellular environment with cryoET is challenging2, due to the infrequent and dynamic nature of viral entry, particularly in the case of HIV-1. While time-lapse live-cell imaging has yielded a great deal of information about many aspects of the life cycle of HIV-13-7, the resolution afforded by live-cell microscopy is limited (~ 200 nm). Our work was aimed at developing a correlation method that permits direct visualization of early events of HIV-1 infection by combining live-cell fluorescent light microscopy, cryo-fluorescent microscopy, and cryoET. In this manner, live-cell and cryo-fluorescent signals can be used to accurately guide the sampling in cryoET. Furthermore, structural information obtained from cryoET can be complemented with the dynamic functional data gained through live-cell imaging of fluorescent labeled target.
In this video article, we provide detailed methods and protocols for structural investigation of HIV-1 and host-cell interactions using 3D correlative high-speed live-cell imaging and high-resolution cryoET structural analysis. HeLa cells infected with HIV-1 particles were characterized first by confocal live-cell microscopy, and the region containing the same viral particle was then analyzed by cryo-electron tomography for 3D structural details. The correlation between two sets of imaging data, optical imaging and electron imaging, was achieved using a home-built cryo-fluorescence light microscopy stage. The approach detailed here will be valuable, not only for study of virus-host cell interactions, but also for broader applications in cell biology, such as cell signaling, membrane receptor trafficking, and many other dynamic cellular processes.

We describe a correlative microscopy method that combines high-speed 3D live-cell fluorescent light microscopy and high-resolution cryo-electron tomography. We demonstrate the capability of the correlative method by imaging dynamic, small HIV-1 particles interacting with host HeLa cells.

Cryo-electron tomography (cryoET) allows 3D visualization of cellular structures at molecular resolution in a close-to-physiological state1. However, ...

Bioengineering

Averaging of Viral Envelope Glycoprotein Spikes from Electron Cryotomography Reconstructions using Jsubtomo

Enveloped viruses utilize membrane glycoproteins on their surface to mediate entry into host cells. Three-dimensional structural analysis of these glycoprotein &#8216;spikes&#8217; is often technically challenging but important for understanding viral pathogenesis and in drug design. Here, a protocol is presented for viral spike structure determination through computational averaging of electron cryo-tomography data. Electron cryo-tomography is a technique in electron microscopy used to derive three-dimensional tomographic volume reconstructions, or tomograms, of pleomorphic biological specimens such as membrane viruses in a near-native, frozen-hydrated state. These tomograms reveal structures of interest in three dimensions, albeit at low resolution. Computational averaging of sub-volumes, or sub-tomograms, is necessary to obtain higher resolution detail of repeating structural motifs, such as viral glycoprotein spikes. A detailed computational approach for aligning and averaging sub-tomograms using the Jsubtomo software package is outlined. This approach enables visualization of the structure of viral glycoprotein spikes to a resolution in the range of 20-40 &#197; and study of the study of higher order spike-to-spike interactions on the virion membrane. Typical results are presented for Bunyamwera virus, an enveloped virus from the family Bunyaviridae. This family is a structurally diverse group of pathogens posing a threat to human and animal health.

An approach is presented for determining structures of viral membrane glycoprotein complexes using a combination of electron cryo-tomography and sub-tomogram averaging with the computational package Jsubtomo.

Enveloped viruses utilize membrane glycoproteins on their surface to mediate entry into host cells. Three-dimensional structural analysis of these ...

Immunology and Infection

Using Tomoauto: A Protocol for High-throughput Automated Cryo-electron Tomography

Cryo-electron tomography (Cryo-ET) is a powerful three-dimensional (3-D) imaging technique for visualizing macromolecular complexes in their native context at a molecular level. The technique involves initially preserving the sample in its native state by rapidly freezing the specimen in vitreous ice, then collecting a series of micrographs from different angles at high magnification, and finally computationally reconstructing a 3-D density map. The frozen-hydrated specimen is extremely sensitive to the electron beam and so micrographs are collected at very low electron doses to limit the radiation damage. As a result, the raw cryo-tomogram has a very low signal to noise ratio characterized by an intrinsically noisy image. To better visualize subjects of interest, conventional imaging analysis and sub-tomogram averaging in which sub-tomograms of the subject are extracted from the initial tomogram and aligned and averaged are utilized to improve both contrast and resolution. Large datasets of tilt-series are essential to understanding and resolving the complexes at different states, conditions, or mutations as well as obtaining a large enough collection of sub-tomograms for averaging and classification. Collecting and processing this data can be a major obstacle preventing further analysis. Here we describe a high-throughput cryo-ET protocol based on a computer-controlled 300kV cryo-electron microscope, a direct detection device (DDD) camera and a highly effective, semi-automated image-processing pipeline software wrapper library tomoauto developed in-house. This protocol has been effectively utilized to visualize the intact type III secretion system (T3SS) in Shigella flexneri minicells. It can be applicable to any project suitable for cryo-ET.

We present a protocol on how to utilize high-throughput cryo-electron tomography to determine high resolution in situ structures of molecular machines. The protocol permits large amounts of data to be processed, avoids common bottlenecks and reduces resource downtime, allowing the user to focus on important biological questions.

Cryo-electron tomography (Cryo-ET) is a powerful three-dimensional (3-D) imaging technique for visualizing macromolecular complexes in their native ...

Electron Cryotomography of Bacterial Cells

While much is already known about the basic metabolism of bacterial cells, many fundamental questions are still surprisingly unanswered, including for instance how they generate and maintain specific cell shapes, establish polarity, segregate their genomes, and divide. In order to understand these phenomena, imaging technologies are needed that bridge the resolution gap between fluorescence light microscopy and higher-resolution methods such as X-ray crystallography and NMR spectroscopy.
Electron cryotomography (ECT) is an emerging technology that does just this, allowing the ultrastructure of cells to be visualized in a near-native state, in three dimensions (3D), with "macromolecular" resolution (~4nm).1, 2 In ECT, cells are imaged in a vitreous, "frozen-hydrated" state in a cryo transmission electron microscope (cryoTEM) at low temperature (&lt; -180&deg;C). For slender cells (up to ~500 nm in thickness3), intact cells are plunge-frozen within media across EM grids in cryogens such as ethane or ethane/propane mixtures. Thicker cells and biofilms can also be imaged in a vitreous state by first "high-pressure freezing" and then, "cryo-sectioning" them. A series of two-dimensional projection images are then collected through the sample as it is incrementally tilted along one or two axes. A three-dimensional reconstruction, or "tomogram" can then be calculated from the images. While ECT requires expensive instrumentation, in recent years, it has been used in a few labs to reveal the structures of various external appendages, the structures of different cell envelopes, the positions and structures of cytoskeletal filaments, and the locations and architectures of large macromolecular assemblies such as flagellar motors, internal compartments and chemoreceptor arrays.1, 2
In this video article we illustrate how to image cells with ECT, including the processes of sample preparation, data collection, tomogram reconstruction, and interpretation of the results through segmentation and in some cases correlation with light microscopy.

We illustrate here how to use electron cryotomography (ECT) to study the ultrastructure of bacterial cells in near-native states, to "macromolecular" (~4 nm) resolution.

While much is already known about the basic metabolism of bacterial cells, many fundamental questions are still surprisingly unanswered, including for ...

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.

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 ...

Biochemistry

Visualization of DNA Compaction in Cyanobacteria by High-voltage Cryo-electron Tomography

This protocol describes how to visualize the transient DNA compaction in cyanobacteria. DNA compaction is a dramatic cytoplasmic event recently found to occur in some cyanobacteria before cell division. However, due to the large cell size and the transient character, it is difficult to investigate the structure in detail. To overcome the difficulties, first, DNA compaction is reproducibly produced in the cyanobacterium Synechococcus elongatus PCC 7942 by synchronous culture using 12 h each light/dark cycle. Second, DNA compaction is monitored by fluorescence microscopy and captured by rapid freezing. Third, the detailed structure of DNA compacted cells is visualized in three dimensions (3D) by high voltage cryo-electron tomography. This set of methods is widely applicable to investigate transient structures in bacteria, e.g. cell division, chromosome segregation, phage infection etc., which are monitored by fluorescence microscopy and directly visualized by cryo-electron tomography at appropriate time points.

This protocol describes how to visualize the transient DNA compaction in cyanobacteria. Synchronous cultivation, monitoring by fluorescence microscopy, rapid freezing, and high voltage cryo-electron tomography are used. A protocol for these methodologies is presented, and future applications and developments are discussed.

This protocol describes how to visualize the transient DNA compaction in cyanobacteria. DNA compaction is a dramatic cytoplasmic event recently found to ...

A 3D Cartographic Description of the Cell by Cryo Soft X-ray Tomography

Imaging techniques are fundamental in order to understand cell organization and machinery in biological research and the related fields. Among these techniques, cryo soft X-ray tomography (SXT) allows imaging whole cryo-preserved cells in the water window X-ray energy range (284-543 eV), in which carbon structures have intrinsically higher absorption than water, allowing the 3D reconstruction of the linear absorption coefficient of the material contained in each voxel. Quantitative structural information at the level of whole cells up to 10 &#181;m thick is then achievable this way, with high throughput and spatial resolution down to 25-30 nm half-pitch. Cryo-SXT has proven itself relevant to current biomedical research, providing 3D information on cellular infection processes (virus, bacteria, or parasites), morphological changes due to diseases (such as recessive genetic diseases) and helping us understand drug action at the cellular level, or locating specific structures in the 3D cellular environment. In addition, by taking advantage of the tunable wavelength at synchrotron facilities, spectro-microscopy or its 3D counterpart, spectro-tomography, can also be used to image and quantify specific elements in the cell, such as calcium in biomineralization processes. Cryo-SXT provides complementary information to other biological imaging techniques such as electron microscopy, X-ray fluorescence or visible light fluorescence, and is generally used as a partner method for 2D or 3D correlative imaging at cryogenic conditions in order to link function, location, and morphology.

Here, a protocol describing the sample preparation and data collection steps required in cryo soft X-ray tomography (SXT) to image the ultrastructure of whole cryo-preserved cells at a resolution of 25 nm half pitch, is presented.

Imaging techniques are fundamental in order to understand cell organization and machinery in biological research and the related fields. Among these ...

Strategies for Optimization of Cryogenic Electron Tomography Data Acquisition

Cryogenic electron tomography (cryoET) is a powerful method to study the 3D structure of biological samples in a close-to-native state. Current state-of-the-art cryoET combined with subtomogram averaging analysis enables the high-resolution structural determination of macromolecular complexes that are present in multiple copies within tomographic reconstructions. Tomographic experiments usually require a vast amount of tilt series to be acquired by means of high-end transmission electron microscopes with important operational running-costs. Although the throughput and reliability of automated data acquisition routines have constantly improved over the recent years, the process of selecting regions of interest at which a tilt series will be acquired cannot be easily automated and it still relies on the user's manual input. Therefore, the set-up of a large-scale data collection session is a time-consuming procedure that can considerably reduce the remaining microscope time available for tilt series acquisition. Here, the protocol describes the recently developed implementations based on the SerialEM package and the PyEM software that significantly improve the time-efficiency of grid screening and large-scale tilt series data collection. The presented protocol illustrates how to use SerialEM scripting functionalities to fully automate grid mapping, grid square mapping, and tilt series acquisition. Furthermore, the protocol describes how to use PyEM to select additional acquisition targets in off-line mode after automated data collection is initiated. To illustrate this protocol, its application in the context of high-end data collection of Sars-Cov-2 tilt series is described. The presented pipeline is particularly suited to maximizing the time-efficiency of tomography experiments that require a careful selection of acquisition targets and at the same time a large amount of tilt series to be collected.

The increasing demand for large-scale data collection in cryogenic electron tomography requires high-throughput image acquisition routines. Described here is a protocol that implements the recent developments of advanced acquisition strategies aimed at maximizing the time-efficiency and throughput of tomographic data collection.

Cryogenic electron tomography (cryoET) is a powerful method to study the 3D structure of biological samples in a close-to-native state. Current ...

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Cryo-electron tomography (cryo-ET) allows researchers to image cells in their native, hydrated state at the highest resolution currently possible. The technique has several limitations, however, that make analyzing the data it generates time-intensive and difficult. Hand segmenting a single tomogram can take from hours to days, but a microscope can easily generate 50 or more tomograms a day. Current deep learning segmentation programs for cryo-ET do exist, but are limited to segmenting one structure at a time. Here, multi-slice U-Net convolutional neural networks are trained and applied to automatically segment multiple structures simultaneously within cryo-tomograms. With proper preprocessing, these networks can be robustly inferred to many tomograms without the need for training individual networks for each tomogram. This workflow dramatically improves the speed with which cryo-electron tomograms can be analyzed by cutting segmentation time down to under 30 min in most cases. Further, segmentations can be used to improve the accuracy of filament tracing within a cellular context and to rapidly extract coordinates for subtomogram averaging.

This is a method for training a multi-slice U-Net for multi-class segmentation of cryo-electron tomograms using a portion of one tomogram as a training input. We describe how to infer this network to other tomograms and how to extract segmentations for further analyses, such as subtomogram averaging and filament tracing.

Cryo-electron tomography (cryo-ET) allows researchers to image cells in their native, hydrated state at the highest resolution currently possible. The ...

Visualization of Organelles In Situ by Cryo-STEM Tomography

Cryogenic electron microscopy (cryo-EM) relies on the imaging of biological or organic specimens embedded in their native aqueous medium; water is solidified into a glass (i.e., vitrified) without crystallization. The cryo-EM method is widely used to determine the structure of biological macromolecules recently at a near-atomic resolution. The approach has been extended to the study of organelles and cells using tomography, but the conventional mode of wide-field transmission EM imaging suffers a severe limitation in the specimen thickness. This has led to a practice of milling thin lamellae using a focused ion beam; the high resolution is obtained by subtomogram averaging from the reconstructions, but three-dimensional relations outside the remaining layer are lost. The thickness limitation can be circumvented by scanned probe imaging, similar to the scanning EM or the confocal laser scanning microscope. While scanning transmission electron microscopy (STEM) in materials science provides atomic resolution in single images, the sensitivity of cryogenic biological specimens to electron irradiation requires special considerations. This protocol presents a setup for cryo-tomography using STEM. The basic topical configuration of the microscope is described for both two- and three-condenser systems, while automation is provided by the non-commercial SerialEM software. Enhancements for batch acquisition and correlative alignment to previously-acquired fluorescence maps are also described. As an example, we show the reconstruction of a mitochondrion, pointing out the inner and outer membrane and calcium phosphate granules, as well as surrounding microtubules, actin filaments, and ribosomes. Cryo-STEM tomography excels in revealing the theater of organelles in the cytoplasm and, in some cases, even the nuclear periphery of adherent cells in culture.

Visualization of Organelles In Situ by Cryo-STEM Tomography

Cryo-STEM tomography provides a means to visualize organelles of intact cells without embedding, sectioning, or other invasive preparations. The 3D resolution obtained is currently in the range of a few nanometers, with a field of view of several micrometers and an accessible thickness in the order of 1 &#181;m.

Cryo-Electron Tomography Remote Data Collection and Subtomogram Averaging

Summary

Explore More Videos

Correlative Microscopy for 3D Structural Analysis of Dynamic Interactions

Averaging of Viral Envelope Glycoprotein Spikes from Electron Cryotomography Reconstructions using Jsubtomo

Using Tomoauto: A Protocol for High-throughput Automated Cryo-electron Tomography

Electron Cryotomography of Bacterial Cells

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

Visualization of DNA Compaction in Cyanobacteria by High-voltage Cryo-electron Tomography

A 3D Cartographic Description of the Cell by Cryo Soft X-ray Tomography

Strategies for Optimization of Cryogenic Electron Tomography Data Acquisition

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Visualization of Organelles In Situ by Cryo-STEM Tomography