Some of this work was performed at the Simons Electron Microscopy Center at the New York Structural Biology Center, with support from the Simons Foundation (SF349247), NIH (U24 GM129539), and NY State Assembly.

Accelerating Macromolecular Structural Analysis Using Smart Leginon Autoscreen

<ol><li>Noble, A. J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Routine+single+particle+CryoEM+sample+and+grid+characterization+by+tomography%5BTitle%5D)+AND+%22eLife%22%5BJournal%5D)">Routine single particle CryoEM sample and grid characterization by tomography.</a> eLife. 7, e34257(2018).</li>
<li>Weissenberger, G., Henderikx, R. J. M., Peters, P. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Understanding+the+invisible+hands+of+sample+preparation+for+cryo-EM%5BTitle%5D)+AND+%22Nature+Methods%22%5BJournal%5D)">Understanding the invisible hands of sample preparation for cryo-EM.</a> Nature Methods. 18 (5), 463-471 (2021).</li>
<li>Carragher, B., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Current+outcomes+when+optimizing+%27standard%27+sample+preparation+for+single-particle+cryo-EM%5BTitle%5D)+AND+%22Journal+of+Microscopy%22%5BJournal%5D)">Current outcomes when optimizing 'standard' sample preparation for single-particle cryo-EM.</a> Journal of Microscopy. 276 (1), 39-45 (2019).</li>
<li>Kampjut, D., Steiner, J., Sazanov, L. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Cryo-EM+grid+optimization+for+membrane+proteins%5BTitle%5D)+AND+%22iScience%22%5BJournal%5D)">Cryo-EM grid optimization for membrane proteins.</a> iScience. 24 (3), 102139(2021).</li>
<li>Suloway, C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Automated+molecular+microscopy%3A+The+new+Leginon+system%5BTitle%5D)+AND+%22Journal+of+Structural+Biology%22%5BJournal%5D)">Automated molecular microscopy: The new Leginon system.</a> Journal of Structural Biology. 151 (1), 41-60 (2005).</li>
<li>Mastronarde, D. N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Automated+electron+microscope+tomography+using+robust+prediction+of+specimen+movements%5BTitle%5D)+AND+%22Journal+of+Structural+Biology%22%5BJournal%5D)">Automated electron microscope tomography using robust prediction of specimen movements.</a> Journal of Structural Biology. 152 (1), 36-51 (2005).</li>
<li>Koh, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Routine+collection+of+high-resolution+cryo-EM+datasets+using+200+KV+transmission+electron+microscope%5BTitle%5D)+AND+%22Journal+of+Visualized+Experiments%22%5BJournal%5D)">Routine collection of high-resolution cryo-EM datasets using 200 KV transmission electron microscope.</a> Journal of Visualized Experiments. 181, 63519(2022).</li>
<li>Cheng, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Fully+automated+multi-grid+cryoEM+screening+using+Smart+Leginon%5BTitle%5D)+AND+%22IUCrJ%22%5BJournal%5D)">Fully automated multi-grid cryoEM screening using Smart Leginon.</a> IUCrJ. 10 (1), 77-89 (2023).</li>
<li>Kim, P. T., Noble, A. J., Cheng, A., Bepler, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Learning+to+automate+cryo-electron+microscopy+data+collection+with+Ptolemy%5BTitle%5D)+AND+%22IUCrJ%22%5BJournal%5D)">Learning to automate cryo-electron microscopy data collection with Ptolemy.</a> IUCrJ. 10 (1), 90-102 (2023).</li>
<li>Bepler, T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Smart+data+collection+for+CryoEM%5BTitle%5D)+AND+%22Journal+of+Structural+Biology%22%5BJournal%5D)">Smart data collection for CryoEM.</a> Journal of Structural Biology. 214 (4), 107913(2022).</li>
<li>Bouvette, J., Huang, Q., Riccio, A. A., Copeland, W. C., Bartesaghi, A., Borgnia, M. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Automated+systematic+evaluation+of+cryo-EM+specimens+with+SmartScope%5BTitle%5D)+AND+%22eLife%22%5BJournal%5D)">Automated systematic evaluation of cryo-EM specimens with SmartScope.</a> eLife. 11, e80047(2022).</li>
<li>Deng, Y., Grollios, F., Kohr, H., van Knippenberg, B., Janus, M., Caglar, F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Smart+EPU%3A+SPA+Getting+Intelligent%5BTitle%5D)+AND+%22Microscopy+and+Microanalysis%22%5BJournal%5D)">Smart EPU: SPA Getting Intelligent.</a> Microscopy and Microanalysis. 27 (S1), 454-455 (2021).</li>
<li>Fan, Q., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((CryoRL%3A+Reinforcement+learning+enables+efficient+cryo-EM+data+collection%5BTitle%5D)+AND+%22arXiv%22%5BJournal%5D)">CryoRL: Reinforcement learning enables efficient cryo-EM data collection.</a> arXiv. , (2022).</li>
<li>Li, Y., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Optimized+path+planning+surpasses+human+efficiency+in+cryo-EM+imaging%5BTitle%5D)+AND+%22bioRxiv.+Biophysics%22%5BJournal%5D)">Optimized path planning surpasses human efficiency in cryo-EM imaging.</a> bioRxiv. Biophysics. , (2022).</li>
<li>Lander, G. C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Appion%3A+An+integrated%2C+database-driven+pipeline+to+facilitate+EM+image+processing%5BTitle%5D)+AND+%22Journal+of+Structural+Biology%22%5BJournal%5D)">Appion: An integrated, database-driven pipeline to facilitate EM image processing.</a> Journal of Structural Biology. 166 (1), 95-102 (2009).</li>
<li>Rice, W. J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Routine+determination+of+ice+thickness+for+cryo-EM+grids%5BTitle%5D)+AND+%22Journal+of+Structural+Biology%22%5BJournal%5D)">Routine determination of ice thickness for cryo-EM grids.</a> Journal of Structural Biology. 204 (1), 38-44 (2018).</li>
<li>Russo, C. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((The+potential+for+seeing+molecules+in+cells%2C+CZ+Imaging+Institute%27s+Hardware+Frontiers+for+CryoET+Workshop%5BTitle%5D)+AND+%22CZ+Imaging+Institute%27s+Hardware+Frontiers+for+CryoET+Workshop%22%5BJournal%5D)">The potential for seeing molecules in cells, CZ Imaging Institute's Hardware Frontiers for CryoET Workshop.</a> CZ Imaging Institute's Hardware Frontiers for CryoET Workshop. , https://chanzuckerberg.com/science/meetings/hardware-frontiers-cryoet-workshop/ (2023).</li>
<li>Punjani, A., Rubinstein, J. L., Fleet, D. J., Brubaker, M. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((cryoSPARC%3A+algorithms+for+rapid+unsupervised+cryo-EM+structure+determination%5BTitle%5D)+AND+%22Nature+Methods%22%5BJournal%5D)">cryoSPARC: algorithms for rapid unsupervised cryo-EM structure determination.</a> Nature Methods. 14 (3), 290-296 (2017).</li>
<li>Budell, W. C., Allegri, L., Dandey, V., Potter, C. S., Carragher, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Cryo-electron+microscopic+grid+preparation+for+time-resolved+studies+using+a+novel+robotic+system%2C+Spotiton%5BTitle%5D)+AND+%22Journal+of+Visualized+Experiments%22%5BJournal%5D)">Cryo-electron microscopic grid preparation for time-resolved studies using a novel robotic system, Spotiton.</a> Journal of Visualized Experiments. 168, 62271(2021).</li>
<li>Darrow, M. C., Booth, T., Moore, J. P., Doering, K., Thaw, P., King, R. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Enabling+a+paradigm+shift+in+CryoEM+sample+preparation+with+chameleon%5BTitle%5D)+AND+%22Microscopy+and+Microanalysis%22%5BJournal%5D)">Enabling a paradigm shift in CryoEM sample preparation with chameleon.</a> Microscopy and Microanalysis. 27 (S1), 524-525 (2021).</li>
</ol>

The authors declare that they have no competing financial interests.

In this protocol, we describe the pipeline for Smart Leginon Autoscreen, and additionally basic Leginon usage for those new to the collection software. Single particle cryoEM is poised to become the most productive three-dimensional (3D) protein structure resolving technique by the end of 202417. The single particle cryoEM pipeline consists of several steps that are constantly being optimized to increase data quality and throughput. Figure 2 shows the most common steps (sample preparation, grid preparation, screening time and effort, high-resolution collection time, live processing, and full post-processing) along with other components of the pipeline that can be improved (screening microscope access, stage speed and accuracy, camera speed, and high-resolution microscope access). Results from most steps become feedback loops into previous steps (blue arrows in Figure 2), making the entire pipeline highly interdependent. Each step in Figure 2 is colored to approximate how much of a bottleneck the step is relative to others. Smart Leginon Autoscreen significantly reduces operator time and effort for screening 12 grids from 6 h to less than 10 min, thus relieving that bottleneck and allowing for more rapid feedback to sample/grid preparation (Figure 3).
There are several critical steps in the Protocol, depicted in Figure 1. It is critical that the grid used for creating the template session is representative of the remaining grids to be screened. Importantly, Leginon remembers all settings in the entire setup process for creating a template session (blue steps in Figure 1), which allows for recurrent template sessions to be set up more quickly each time. When creating a template session, the most critical step is setting up targeting at all magnifications so that the parameters and thresholds reflect the expected variation across the grids to be screened. The various 'Test' buttons allow for efficiency in this setup process. During an Autoscreen session, it is critical to monitor the first few grids in Appion to quickly detect any issues and fix them inside of Leginon as soon as possible.
The typical workflow at SEMC is to feed Autoscreen data into CryoSPARC Live18 and use this additional information to inform the feedback loops into sample/grid preparation. During intensive researcher-operator cryoEM optimization days, information about the sample and grid conditions is fed back into sample and grid preparation while Autoscreen is still screening grids. This allows for several dozen grids to be frozen and screened per week8.
Smart Leginon Autoscreen works for the majority (80%-90%) of holey grids and conditions observed at SEMC. The remaining 10%-20% of grids include those that sometimes do not work well - grids with minimal contrast difference between holes and substrate; grids with smaller holes and spacing (e.g., 0.6/0.8) - and grids where targeting across multiple grids is often impractical - Spotiton/Chameleon19,20 grids that consist of stripes of sample across the grid; lacey grids. Note that the tilted grid collection with Autoscreen is in development but is not yet available. It may be possible to modify the protocol to work with Spotiton/Chameleon grids by first imaging areas of the stripe manually to determine narrow parameter thresholds, then attempting to group larger and smaller squares together, respectively, in step 2.1.7.4, and then selecting targets from the group with ice. The goal of this modification is to have Smart Leginon separate empty and non-empty squares into two groups. If parameters are found, they may not extend well to the remaining grids to be screened. It may also be possible to modify the protocol to work with lacey grids by removing the hl_finding.sh script in step 2.1.9.1 and configuring the parameters to target lighter/darker areas as desired. The success rate of this modification may vary from grid to grid based on ice thicknesses and grid material.
Troubleshooting during an Autoscreen session is possible and sometimes appropriate. Changes to preset (e.g., defocus) and targeting parameters (e.g., Hole Targeting thresholds) can be made during automated collection. While an Autoscreen session is collecting, a grid session cannot be canceled because it will terminate autoscreen.py. However, the Abort buttons in the Targeting nodes may be used to skip any part of a grid or an entire grid. Occasionally, autoscreen.py may use too much memory and freeze, offering two options: 'force quit' or 'wait'. If 'force quit' is selected, the entire script will terminate, requiring the user to rerun the script to be applied to the remaining grids for screening. If 'wait' is selected, the script will continue, and settings may be altered to prevent future freezing, e.g., turning off the image display in the Exposure node, decreasing pixel size in the atlas, or running a memory-clear script. If the program freezes without offering the two options, memory errors may not resolve on their own, causing a pause in acquisition. The 'force quit' option may be useful in this instance.
Smart Leginon Autoscreen is used regularly at SEMC. As bottlenecks in the single-particle cryoEM pipeline continue to be reduced, cryoEM adoption will continue to increase to help answer biological questions. This Protocol is a step in the direction of optimizing the entire pipeline by providing a clear path for significantly reducing feedback loops.

Single particle cryo-electron microscopy (cryoEM) allows for near-atomic resolution structure determination of purified macromolecular complexes. A single particle cryoEM experiment only requires one or two well-chosen grids selected from a much larger set of grids with varying sample and grid conditions. Microscope screening to examine these grids entails imaging each grid at several magnifications to determine which grid satisfies most key requirements for high-resolution data collection, including ice thickness, sufficient areas for full data collection, protein purity, protein concentration, protein stability, and minimal preferred orientation issues1. Optimizing for these key requirements often involves feedback between screening at the microscope and preparation conditions such as protein production, buffer selection, potential detergents, and grid type2,3,4 (Figure 1). Conventional grid screening is performed manually or semi-manually with software such as Leginon5, SerialEM6, and EPU7. Conventional screening requires the microscope operator to spend hours at the microscope to screen several grids, which creates a significant bottleneck in the high-resolution single particle workflow by occupying the operator with rote operations rather than sample/grid optimization.
Previously, Smart Leginon Autoscreen and the underlying machine learning software, Ptolemy, have been introduced, and their underlying methods and algorithms together with examples have been described8,9. Several other software packages are either capable of or working towards fully automated multi-grid screening10, including SmartScope11, Smart EPU12, and CryoRL13,14. To address the screening bottleneck, Smart Leginon allows the user to first set up screening parameters in a template microscope session, then use that template session&#39;s parameters as a template to screen the full cassette of grids in the microscope autoloader. All manual work during the cassette screening is eliminated, which allows for the optimization feedback loop to proceed significantly more efficiently.
In this protocol, the full Smart Leginon Autoscreen workflow is described so that the reader may perform fully automated multi-grid cryoEM screening independently. For those new to Leginon, the first section in the protocol describes conventional Leginon usage. This knowledge is composed of several years of experience across several autoloader microscopes, which is then built upon in the subsequent Smart Leginon section of the protocol. Additional tutorial videos may be found on https://memc.nysbc.org.

<table><tbody><tr><td>Glacios cryoTEM&amp;nbsp;</td><td>Thermo Fisher Scientific&amp;nbsp;</td><td>GLACIOSTEM</td><td>FEG, 200 keV, Falcon 3EC camera&amp;nbsp;</td></tr><tr><td>Krios cryoTEM&amp;nbsp;</td><td>Thermo Fisher Scientific&amp;nbsp;</td><td>KRIOSG4TEM</td><td>XFEG, 300 keV,&amp;nbsp;Gatan BioQuantum energy filter, Gatan K3 camera&amp;nbsp;</td></tr><tr><td>Leginon&amp;nbsp;</td><td>Simons Electron Microscopy Center&amp;nbsp;</td><td></td><td>http://leginon.org&amp;nbsp;</td></tr><tr><td>Ptolemy&amp;nbsp;</td><td>Simons Machine Learning Center&amp;nbsp;</td><td></td><td>https://github.com/SMLC-NYSBC/ptolemy&amp;nbsp;</td></tr></tbody></table>

cryo-electron microscopy screening automation across multiple grids using smart leginon

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

Following the protocol, cryoEM screening sessions may be run automatically and successfully for the majority (80%-90%) of holey grids and conditions. Several examples and experiments have been presented previously8,9 to demonstrate the expected outcomes of successful Smart Leginon Autoscreen sessions. A successful Autoscreen session begins with ~10 min of setup and commonly results in a full cassette of 12 grids screened automatically after about 6 h (30 min per grid) where 3-5 squares of different sizes and 3-5 holes per square are imaged at high magnification, allowing the user to quickly determine the characteristics of the sample on each grid and rapidly iterate through sample/grid conditions (Figure 3). Occasionally, sessions are unsuccessful, commonly due to Autoscreen targeting broken squares, not interpreting large ice thickness gradients across the grid or across squares properly, or failing to identify holes properly on carbon grids. Additionally, potential memory leaks may cause Leginon to crash due to excessive memory usage, which may be solved by freeing up RAM, or rebooting the computer, or ameliorated by adding more RAM to the computer.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/66007/66007fig01.jpg" /> 
Figure 1:&#160;Smart Leginon Autoscreen workflow.&#160;A high-level overview of the Smart Leginon Autoscreen workflow. First, a template session is created by selecting parameters for a representative grid in the batch of grids to be screened. Setting up Leginon and creating a template session takes less than 45 min. Second, Autoscreen is set up to use the template session parameters to screen all of the grids in the cassette. Setting up Autoscreen takes less than 10 min. Finally, Autoscreen ends the screening session.&#160;<a href="https://www.jove.com/files/ftp_upload/66007/66007fig01large.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/66007/66007fig02.jpg" /> 
Figure 2: Conventional single particle cryoEM pipeline prior to automated screening.&#160;The most common steps in the conventional single particle cryoEM pipeline prior to automated screening, together with components that can be improved. Each step is colored to approximate how much of a bottleneck the step is relative to others. The blue circular arrow represents several feedback loops between most steps. The throughput at several steps depends heavily on the sample, funding, and the researcher&#39;s location. <a href="https://www.jove.com/files/ftp_upload/66007/66007fig02large.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/66007/66007fig03.jpg" /> 
Figure 3: Representative Smart Leginon Autoscreen results. Representative multi-scale images following the Smart Leginon Autoscreen protocol collected on a TFS Krios cryoTEM with a BioQuantum energy filter and K3 camera. (A) A composite &#39;atlas&#39; image showing an overview of a cryoEM grid. (B-F) Multi-scale images from indicated locations in the grid atlas. Low magnification images in the first row, medium magnification images in the second row, and high magnification images in the third row were each selected automatically to obtain information about the sample from thin to thick ice squares. Ice thickness as estimated by Leginon is shown on the bottom. Scale bars are 500 &#181;m in (A) and 10&#8197;&#181;m for the first row, 5&#8197;&#181;m for the second row, and 100&#8197;nm for the third row for (B-F). This figure has been modified with permission from Cheng et al.8. <a href="https://www.jove.com/files/ftp_upload/66007/66007fig03large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td></td>
			<td>gr: Grid&#160;</td>
			<td>sq: Square&#160;</td>
			<td>hln: Hole&#160;</td>
			<td>fan: Auto- focus&#160;</td>
			<td>fcn: Central focus&#160;</td>
			<td>enn: Exposure&#160;</td>
		</tr>
		<tr>
			<td>Magnification&#160;</td>
			<td>210&#160;</td>
			<td>2600&#160;</td>
			<td>6700&#160;</td>
			<td>120000&#160;</td>
			<td>120000&#160;</td>
			<td>120000&#160;</td>
		</tr>
		<tr>
			<td>Defocus&#160;</td>
			<td>-0.0002&#160;</td>
			<td>-0.00015&#160;</td>
			<td>-0.00015&#160;</td>
			<td>-2 x 10-06&#160;</td>
			<td>-7 x 10-07&#160;</td>
			<td>-2.5 x 10-06&#160;</td>
		</tr>
		<tr>
			<td>Spot size&#160;</td>
			<td>5&#160;</td>
			<td>5&#160;</td>
			<td>4&#160;</td>
			<td>2&#160;</td>
			<td>2&#160;</td>
			<td>2&#160;</td>
		</tr>
		<tr>
			<td>Intensity&#160;</td>
			<td>1.1&#160;</td>
			<td>0.83&#160;</td>
			<td>0.65&#160;</td>
			<td>0.44&#160;</td>
			<td>0.44&#160;</td>
			<td>0.45&#160;</td>
		</tr>
		<tr>
			<td>Dimension&#160;</td>
			<td>1024 x 1024&#160;</td>
			<td>1024 x 1024&#160;</td>
			<td>1024 x 1024&#160;</td>
			<td>1024 x 1024&#160;</td>
			<td>1024 x 1024&#160;</td>
			<td>4096 x 4096&#160;</td>
		</tr>
		<tr>
			<td>Offset&#160;</td>
			<td>0, 0&#160;</td>
			<td>0, 0&#160;</td>
			<td>0, 0&#160;</td>
			<td>0, 0&#160;</td>
			<td>512, 512&#160;</td>
			<td>0, 0&#160;</td>
		</tr>
		<tr>
			<td>Binning&#160;</td>
			<td>4 x 4&#160;</td>
			<td>4 x 4&#160;</td>
			<td>4 x 4&#160;</td>
			<td>4 x 4&#160;</td>
			<td>2 x 2&#160;</td>
			<td>1 x 1&#160;</td>
		</tr>
		<tr>
			<td>Exposure time (ms)&#160;</td>
			<td>200&#160;</td>
			<td>500&#160;</td>
			<td>500&#160;</td>
			<td>500&#160;</td>
			<td>500&#160;</td>
			<td>1000&#160;</td>
		</tr>
		<tr>
			<td>Pre-Exposure (s)&#160;</td>
			<td>0&#160;</td>
			<td>0&#160;</td>
			<td>0&#160;</td>
			<td>0&#160;</td>
			<td>0&#160;</td>
			<td>0&#160;</td>
		</tr>
		<tr>
			<td>Dose (e/&#197;2)&#160;</td>
			<td>--&#160;</td>
			<td>--&#160;</td>
			<td>--&#160;</td>
			<td>36.5&#160;</td>
			<td>36.5&#160;</td>
			<td>64.7&#160;</td>
		</tr>
		<tr>
			<td>Save raw frames&#160;</td>
			<td>No&#160;</td>
			<td>No&#160;</td>
			<td>No&#160;</td>
			<td>No&#160;</td>
			<td>No&#160;</td>
			<td>Yes&#160;</td>
		</tr>
	</tbody>
</table>
Table 1: Preset parameters for cryoEM grid screening at Simons Electron Microscopy Center (SEMC) using a Glacios cryoTEM with a Falcon 3EC camera. Parameters for each preset commonly used on a Glacios cryoTEM with a Falcon 3EC camera at SEMC are shown. Different microscopes will have varying magnifications available and different experiments will use varying parameters such as defocus and exposure time.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td></td>
			<td>gr: Grid&#160;</td>
			<td>sq: Square&#160;</td>
			<td>hln: Hole&#160;</td>
			<td>fan: Auto- focus&#160;</td>
			<td>fcn: Central focus&#160;</td>
			<td>enn: Exposure&#160;</td>
		</tr>
		<tr>
			<td>Magnification&#160;</td>
			<td>64&#160;</td>
			<td>1700&#160;</td>
			<td>2850&#160;</td>
			<td>75000&#160;</td>
			<td>75000&#160;</td>
			<td>75000&#160;</td>
		</tr>
		<tr>
			<td>Defocus&#160;</td>
			<td>0&#160;</td>
			<td>-5 x 10-05&#160;</td>
			<td>-5 x 10-05&#160;</td>
			<td>-1 x 10-06&#160;</td>
			<td>-7 x 10-07&#160;</td>
			<td>-2 x 10-06&#160;</td>
		</tr>
		<tr>
			<td>Spot size&#160;</td>
			<td>6&#160;</td>
			<td>9&#160;</td>
			<td>9&#160;</td>
			<td>6&#160;</td>
			<td>6&#160;</td>
			<td>7&#160;</td>
		</tr>
		<tr>
			<td>Intensity&#160;</td>
			<td>0.001&#160;</td>
			<td>1.65 x 10-05&#160;</td>
			<td>1.5 x 10-05&#160;</td>
			<td>4.3 x 10-07&#160;</td>
			<td>4.3 x 10-07&#160;</td>
			<td>5.5 x 10-07&#160;</td>
		</tr>
		<tr>
			<td>Energy filter width&#160;</td>
			<td>--&#160;</td>
			<td>--&#160;</td>
			<td>--&#160;</td>
			<td>20&#160;</td>
			<td>20&#160;</td>
			<td>20&#160;</td>
		</tr>
		<tr>
			<td>Dimension&#160;</td>
			<td>1024 x 1024&#160;</td>
			<td>1024 x 1024&#160;</td>
			<td>1024 x 1024&#160;</td>
			<td>1024 x 1024&#160;</td>
			<td>2048 x 2048&#160;</td>
			<td>4096 x 4096&#160;</td>
		</tr>
		<tr>
			<td>Offset&#160;</td>
			<td>0, 0&#160;</td>
			<td>0, 0&#160;</td>
			<td>0, 0&#160;</td>
			<td>0, 0&#160;</td>
			<td>0, 0&#160;</td>
			<td>0, 0&#160;</td>
		</tr>
		<tr>
			<td>Binning&#160;</td>
			<td>4 x 4&#160;</td>
			<td>4 x 4&#160;</td>
			<td>4 x 4&#160;</td>
			<td>4 x 4&#160;</td>
			<td>2 x 2&#160;</td>
			<td>1 x 1&#160;</td>
		</tr>
		<tr>
			<td>Exposure time (ms)&#160;</td>
			<td>500&#160;</td>
			<td>2000&#160;</td>
			<td>1000&#160;</td>
			<td>500&#160;</td>
			<td>300&#160;</td>
			<td>8700&#160;</td>
		</tr>
		<tr>
			<td>Pre-Exposure (s)&#160;</td>
			<td>0&#160;</td>
			<td>0&#160;</td>
			<td>0&#160;</td>
			<td>0&#160;</td>
			<td>0&#160;</td>
			<td>0&#160;</td>
		</tr>
		<tr>
			<td>Dose (e/&#197;2)&#160;</td>
			<td>--&#160;</td>
			<td>--&#160;</td>
			<td>--&#160;</td>
			<td>--&#160;</td>
			<td>--&#160;</td>
			<td>47.4&#160;</td>
		</tr>
		<tr>
			<td>Save raw frames&#160;</td>
			<td>No&#160;</td>
			<td>No&#160;</td>
			<td>No&#160;</td>
			<td>No&#160;</td>
			<td>No&#160;</td>
			<td>Yes&#160;</td>
		</tr>
	</tbody>
</table>
Table 2: Preset parameters for cryoEM grid screening at SEMC using a Krios cryoTEM with a Selectris X and Falcon 4i camera. Parameters for each preset commonly used on a Krios with a Selectris X energy filter and Falcon 4i camera at SEMC are shown. Different microscopes will have varying magnifications available and different experiments will use varying parameters such as defocus and exposure time.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td></td>
			<td>gr: Grid&#160;</td>
			<td>sq: Square&#160;</td>
			<td>hln: Hole&#160;</td>
			<td>fan: Auto- focus&#160;</td>
			<td>fcn: Central focus&#160;</td>
			<td>enn: Exposure&#160;</td>
		</tr>
		<tr>
			<td>Magnification&#160;</td>
			<td>1550&#160;</td>
			<td>940&#160;</td>
			<td>2250&#160;</td>
			<td>81000&#160;</td>
			<td>81000&#160;</td>
			<td>81000&#160;</td>
		</tr>
		<tr>
			<td>Defocus&#160;</td>
			<td>0&#160;</td>
			<td>-5 x 10-05&#160;</td>
			<td>-5 x 10-05&#160;</td>
			<td>-1 x 10-06&#160;</td>
			<td>-7 x 10-07&#160;</td>
			<td>-2 x 10-06&#160;</td>
		</tr>
		<tr>
			<td>Spot size&#160;</td>
			<td>4&#160;</td>
			<td>8&#160;</td>
			<td>7&#160;</td>
			<td>6&#160;</td>
			<td>6&#160;</td>
			<td>6&#160;</td>
		</tr>
		<tr>
			<td>Intensity&#160;</td>
			<td>0.0015&#160;</td>
			<td>0.00017&#160;</td>
			<td>7.3 x 10-05&#160;</td>
			<td>1.3 x 10-06&#160;</td>
			<td>1.3 x 10-06&#160;</td>
			<td>9.2 x 10-07&#160;</td>
		</tr>
		<tr>
			<td>Energy filter width&#160;</td>
			<td>--&#160;</td>
			<td>--&#160;</td>
			<td>50&#160;</td>
			<td>20&#160;</td>
			<td>20&#160;</td>
			<td>20&#160;</td>
		</tr>
		<tr>
			<td>Dimension&#160;</td>
			<td>1024 x 1024&#160;</td>
			<td>1440 x 1024&#160;</td>
			<td>1440 x 1024&#160;</td>
			<td>1440 x 1024&#160;</td>
			<td>1008 x 1008&#160;</td>
			<td>5760 x 4092&#160;</td>
		</tr>
		<tr>
			<td>Offset&#160;</td>
			<td>0, 0&#160;</td>
			<td>0, 0&#160;</td>
			<td>0, 0&#160;</td>
			<td>0, 0&#160;</td>
			<td>936, 519&#160;</td>
			<td>0, 0&#160;</td>
		</tr>
		<tr>
			<td>Binning&#160;</td>
			<td>4 x 4&#160;</td>
			<td>8 x 8&#160;</td>
			<td>8 x 8&#160;</td>
			<td>8 x 8&#160;</td>
			<td>4 x 4&#160;</td>
			<td>2 x 2&#160;</td>
		</tr>
		<tr>
			<td>Exposure time (ms)&#160;</td>
			<td>250&#160;</td>
			<td>600&#160;</td>
			<td>600&#160;</td>
			<td>500&#160;</td>
			<td>500&#160;</td>
			<td>2100&#160;</td>
		</tr>
		<tr>
			<td>Pre-Exposure (s)&#160;</td>
			<td>0&#160;</td>
			<td>0&#160;</td>
			<td>0&#160;</td>
			<td>0&#160;</td>
			<td>0&#160;</td>
			<td>0&#160;</td>
		</tr>
		<tr>
			<td>Dose (e/&#197;2)&#160;</td>
			<td>--&#160;</td>
			<td>--&#160;</td>
			<td>--&#160;</td>
			<td>--&#160;</td>
			<td>--&#160;</td>
			<td>51&#160;</td>
		</tr>
		<tr>
			<td>Save raw frames&#160;</td>
			<td>No&#160;</td>
			<td>No&#160;</td>
			<td>No&#160;</td>
			<td>No&#160;</td>
			<td>No&#160;</td>
			<td>Yes&#160;</td>
		</tr>
	</tbody>
</table>
Table 3: Preset parameters for cryoEM grid screening at SEMC using a Krios cryoTEM with a BioQuantum and K3 camera. Parameters for each preset commonly used on a Krios with a BioQuantum energy filter and K3 camera at SEMC are shown. Different microscopes will have varying magnifications available and different experiments will use varying parameters such as defocus and exposure time.
Supplementary Figure 1: Square Targeting settings and Square settings for Smart Leginon. (A) Square Targeting settings. (B) Square settings. <a href="https://www.jove.com/files/ftp_upload/66007/Supplementary Figure (1).pdf" target="_blank">Please click here to download this File.</a>
Supplementary Figure 2: Hole Targeting settings and Hole settings for Smart Leginon. (A) Hole Targeting settings. (B) Hole settings. <a href="https://www.jove.com/files/ftp_upload/66007/Supplementary Figure (2).pdf" target="_blank">Please click here to download this File.</a>
Supplementary Figure 3: Exposure Targeting settings and Exposure settings for Smart Leginon. (A) Exposure Targeting settings. (B) Exposure settings. <a href="https://www.jove.com/files/ftp_upload/66007/Supplementary Figure (3).pdf" target="_blank">Please click here to download this File.</a>
Supplementary Figure 4: Focus settings and Focus Sequence settings for Smart Leginon. (A) Focus settings. (B) Focus Sequence settings (Defocus1). (C) Focus Sequence settings (Defocus2). <a href="https://www.jove.com/files/ftp_upload/66007/Supplementary Figure (4).pdf" target="_blank">Please click here to download this File.</a>
Supplementary Figure 5: Z_Focus settings and Z_Focus Sequence settings for Smart Leginon. (A) Z_Focus settings. (B) Z_Focus Sequence settings (Stage_Tilt_Rough). (C) Z_Focus Sequence settings (Stage_Tilt_Fine). <a href="https://www.jove.com/files/ftp_upload/66007/Supplementary Figure (5).pdf" target="_blank">Please click here to download this File.</a>
Supplementary Figure 6: An example atlas after setting up Smart Leginon Square_Targeting parameters. Blue circles are blobs, green plus signs are acquisition locations, and the brown &#39;x&#39; is the current stage location. <a href="https://www.jove.com/files/ftp_upload/66007/Supplementary Figure (6).pdf" target="_blank">Please click here to download this File.</a>
Supplementary Figure 7: An example atlas after setting up Smart Leginon Hole_Targeting parameters. Purple plus signs are lattice locations, green plus signs with boxes are acquisition locations, and the blue plus sign is the focus location. <a href="https://www.jove.com/files/ftp_upload/66007/Supplementary Figure (7).pdf" target="_blank">Please click here to download this File.</a>
Supplementary Figure 8: An example atlas after setting up Smart Leginon Exposure_Targeting parameters. Blue circles are blobs, green plus signs are acquisition locations, and the blue plus sign is the focus location. <a href="https://www.jove.com/files/ftp_upload/66007/Supplementary Figure (8).pdf" target="_blank">Please click here to download this File.</a>
Supplementary Figure 9: Smart Leginon Autoscreen terminal setup. <a href="https://www.jove.com/files/ftp_upload/66007/Supplementary Figure (9).pdf" target="_blank">Please click here to download this File.</a>
Supplementary Figure 10: Smart Leginon Autoscreen gui setup. <a href="https://www.jove.com/files/ftp_upload/66007/Supplementary Figure (10).pdf" target="_blank">Please click here to download this File.</a>

Watch this Scientific Journal Video about Author Spotlight: Enhancing Cryo-Electron Microscopy by Automated Data Collection and Analysis Techniques at JoVE.com

Author Spotlight: Enhancing Cryo-Electron Microscopy by Automated Data Collection and Analysis Techniques

Cryo-electron microscopy (cryoEM) multi-grid screening is often a tedious process that demands hours of attention. This protocol shows how to set up a standard Leginon collection and Smart Leginon Autoscreen to automate this process. This protocol can be applied to the majority of cryoEM holey foil grids.

Cryo-Electron Microscopy Screening Automation Across Multiple Grids Using Smart Leginon

Advancements in cryo-electron microscopy (cryoEM) techniques over the past decade have allowed structural biologists to routinely resolve macromolecular ...

cryo-electron-microscopy-screening-automation-across-multiple-grids

Research

JoVE Journal

Biochemistry

976 Views.  Simons Electron Microscopy Center, New York Structural Biology Center. In het Simons Electron Microscopy Center streven we ernaar om uitstekende elektronenmicroscopiediensten, expertise en training te bieden aan lokale en nationale onderzoekers, en om innovatieve methoden te ontwikkelen om workflows te optimaliseren. Ons werkterrein omvat service, ontwikkeling, machine learning en biomedisch onderzoek. We proberen een antwoord te geven: hoe kunnen we onze diensten beter optimaliseren?Machine learning verandert het vakgebied van de elektronenmicroscopie. H...

Video: Auteur in de schijnwerpers: Verbetering van cryo-elektronenmicroscopie door geautomatiseerde gegevensverzameling en analysetechnieken

Expression and Purification of the Human Lipid-sensitive Cation Channel TRPC3 for Structural Determination by Single-particle Cryo-electron Microscopy

Transient receptor potential channels (TRPCs) of the canonical TRP subfamily are nonselective cation channels that play an essential role in calcium homeostasis, particularly store-operated calcium entry, which is critical to maintaining proper function of synaptic vesicle release and intracellular signaling pathways. Accordingly, TRPC channels have been implicated in a variety of human diseases including cardiovascular disorders such as cardiac hypertrophy, neurodegenerative disorders such as Parkinson&#39;s disease, and neurologic disorders such as spinocerebellar ataxia. Therefore, TRPC channels represent a potential pharmacologic target in human diseases. However, the molecular mechanisms of gating in these channels are still unclear. The difficulty in obtaining large quantities of stable, homogeneous, and purified protein has been a limiting factor in structure determination studies, particularly for mammalian membrane proteins such as the TRPC ion channels. Here, we present a protocol for the large-scale expression of mammalian ion channel membrane proteins using a modified baculovirus gene transfer system and the purification of these proteins by affinity and size-exclusion chromatography. We further present a protocol to collect single-particle cryo-electron microscopy images from purified protein and to use these images to determine the protein structure. Structure determination is a powerful method for understanding the mechanisms of gating and function in ion channels.

This protocol describes techniques used to determine ion channel structures by cryo-electron microscopy, including a baculovirus system used to efficiently express genes in mammalian cells with minimum effort and toxicity, protein extraction, purification, and quality checking, sample grid preparation and screening, as well as data collection and processing.

Transient receptor potential channels (TRPCs) of the canonical TRP subfamily are nonselective cation channels that play an essential role in calcium ...

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 ...

Bioengineering

Preparing Lamellae from Vitreous Biological Samples Using a Dual-Beam Scanning Electron Microscope for Cryo-Electron Tomography

Presented here is a protocol for preparing cryo-lamellae from plunge-frozen grids of Plasmodium falciparum-infected&#160;human erythrocytes, which could easily be adapted for other biological samples. The basic principles for preparing samples, milling, and viewing lamellae are common to all instruments and the protocol can be followed as a general guide to on-grid cryo-lamella preparation for cryo-electron microscopy (cryoEM) and cryo-electron tomography (cryoET). Electron microscopy grids supporting the cells are plunge-frozen into liquid nitrogen-cooled liquid ethane using a manual or automated plunge freezer, then screened on a light microscope equipped with a cryo-stage. Frozen grids are transferred into a cryo-scanning electron microscope equipped with a focused ion beam (cryoFIB-SEM). Grids are routinely sputter coated prior to milling, which aids dispersal of charge build-up during milling. Alternatively, an e-beam rotary coater can be used to apply a layer of carbon-platinum to the grids, the exact thickness of which can be more precisely controlled. Once inside the cryoFIB-SEM an additional coating of an organoplatinum compound is applied to the surface of the grid via a gas injection system (GIS). This layer protects the front edge of the lamella as it is milled, the integrity of which is critical for achieving uniformly thin lamellae. Regions of interest are identified via SEM and milling is carried out in a step-wise fashion, reducing the current of the ion beam as the lamella reaches electron transparency, in order to avoid excessive heat generation. A grid with multiple lamellae is then transferred to a transmission electron microscope (TEM) under cryogenic conditions for tilt-series acquisition. A robust and contamination-free workflow for lamella preparation is an essential step for downstream techniques, including cellular cryoEM, cryoET, and sub-tomogram averaging. Development of these techniques, especially for lift-out and milling of high-pressure frozen samples, is of high-priority in the field.

Using focused ion beam milling to produce vitreous on-grid lamellae from plunge frozen biological samples for cryo-electron tomography.

Presented here is a protocol for preparing cryo-lamellae from plunge-frozen grids of Plasmodium falciparum-infected&#160;human erythrocytes, which could ...

Single Particle Cryo-Electron Microscopy: From Sample to Structure

Cryo-electron microscopy (cryoEM) is a powerful technique for structure determination of macromolecular complexes, via single particle analysis (SPA). The overall process involves i) vitrifying the specimen in a thin film supported on a cryoEM grid; ii) screening the specimen to assess particle distribution and ice quality; iii) if the grid is suitable, collecting a single particle dataset for analysis; and iv) image processing to yield an EM density map. In this protocol, an overview for each of these steps is provided, with a focus on the variables which a user can modify during the workflow and the troubleshooting of common issues. With remote microscope operation becoming standard in many facilities, variations on imaging protocols to assist users in efficient operation and imaging when physical access to the microscope is limited will be described.

Structure determination of macromolecular complexes using cryoEM has become routine for certain classes of proteins and complexes. Here, this pipeline is summarized (sample preparation, screening, data acquisition and processing) and readers are directed towards further detailed resources and variables that may be altered in the case of more challenging specimens.

Cryo-electron microscopy (cryoEM) is a powerful technique for structure determination of macromolecular complexes, via single particle analysis (SPA). The ...

Micropatterning Transmission Electron Microscopy Grids to Direct Cell Positioning within Whole-Cell Cryo-Electron Tomography Workflows

Whole-cell cryo-electron tomography (cryo-ET) is a powerful technology that is used to produce nanometer-level resolution structures of macromolecules present in the cellular context and preserved in a near-native frozen-hydrated state. However, there are challenges associated with culturing and/or adhering cells onto TEM grids in a manner that is suitable for tomography while retaining the cells in their physiological state. Here, a detailed step-by-step protocol is presented on the use of micropatterning to direct and promote eukaryotic cell growth on TEM grids. During micropatterning, cell growth is directed by depositing extra-cellular matrix (ECM) proteins within specified patterns and positions on the foil of the TEM grid while the other areas remain coated with an anti-fouling layer. Flexibility in the choice of surface coating and pattern design makes micropatterning broadly applicable for a wide range of cell types. Micropatterning is useful for studies of structures within individual cells as well as more complex experimental systems such as host-pathogen interactions or differentiated multi-cellular communities. Micropatterning may also be integrated into many downstream whole-cell cryo-ET workflows, including correlative light and electron microscopy (cryo-CLEM) and focused-ion beam milling (cryo-FIB).

The goal of this protocol is to direct cell adhesion and growth to targeted areas of grids for cryo-electron microscopy. This is achieved by applying an anti-fouling layer that is ablated in user-specified patterns followed by deposition of extra-cellular matrix proteins in the patterned areas prior to cell seeding.

Whole-cell cryo-electron tomography (cryo-ET) is a powerful technology that is used to produce nanometer-level resolution structures of macromolecules ...

A Robust Single-Particle Cryo-Electron Microscopy (cryo-EM) Processing Workflow with cryoSPARC, RELION, and Scipion

Recent advances in both instrumentation and image processing software have made single-particle cryo-electron microscopy (cryo-EM) the preferred method for structural biologists to determine high-resolution structures of a wide variety of macromolecules. Multiple software suites are available to new and expert users for image processing and structure calculation, which streamline the same basic workflow: movies acquired by the microscope detectors undergo correction for beam-induced motion and contrast transfer function (CTF) estimation. Next, particle images are selected and extracted from averaged movie frames for iterative 2D and 3D classification, followed by 3D reconstruction, refinement, and validation. Because various software packages employ different algorithms and require varying levels of expertise to operate, the 3D maps they generate often differ in quality and resolution. Thus, users regularly transfer data between a variety of programs for optimal results. This paper provides a guide for users to navigate a workflow across the popular software packages: cryoSPARC v3, RELION-3, and Scipion 3 to obtain a near-atomic resolution structure of the adeno-associated virus (AAV). We first detail an image processing pipeline with cryoSPARC v3, as its efficient algorithms and easy-to-use GUI allow users to quickly arrive at a 3D map. In the next step, we use PyEM and in-house scripts to convert and transfer particle coordinates from the best quality 3D reconstruction obtained in cryoSPARC v3 to RELION-3 and Scipion 3 and recalculate 3D maps. Finally, we outline steps for further refinement and validation of the resultant structures by integrating algorithms from RELION-3 and Scipion 3. In this article, we describe how to effectively utilize three processing platforms to create a single and robust workflow applicable to a variety of data sets for high-resolution structure determination.

A Robust Single-Particle Cryo-Electron Microscopy cryo-EM Processing Workflow with cryoSPARC, RELION, and Scipion

This article describes how to effectively utilize three cryo-EM processing platforms, i.e., cryoSPARC v3, RELION-3, and Scipion 3, to create a single and robust workflow applicable to a variety of single-particle data sets for high-resolution structure determination.

Recent advances in both instrumentation and image processing software have made single-particle cryo-electron microscopy (cryo-EM) the preferred method ...

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 ...

Preparation of Nucleosome Core Particles Complexed with DNA Repair Factors for Cryo-Electron Microscopy Structural Determination

DNA repair in the context of chromatin is poorly understood. Biochemical studies using nucleosome core particles, the fundamental repeating unit of chromatin, show most DNA repair enzymes remove DNA damage at reduced rates as compared to free DNA. The molecular details on how base excision repair (BER) enzymes recognize and remove DNA damage in nucleosomes have not been elucidated. However, biochemical BER data of nucleosomal substrates suggest the nucleosome presents different structural barriers dependent on the location of the DNA lesion and the enzyme. This indicates the mechanisms employed by these enzymes to remove DNA damage in free DNA may be different than those employed in nucleosomes. Given that the majority of genomic DNA is assembled into nucleosomes, structural information of these complexes is needed. To date, the scientific community lacks detailed protocols to perform technically feasible structural studies of these complexes. Here, we provide two methods to prepare a complex of two genetically fused BER enzymes (Polymerase &#946; and AP Endonuclease1) bound to a single-nucleotide gap near the entry-exit of the nucleosome for cryo-electron microscopy (cryo-EM) structural determination. Both methods of sample preparation are compatible for vitrifying quality grids via plunge freezing. This protocol can be used as a starting point to prepare other nucleosomal complexes with different BER factors, pioneer transcription factors, and chromatin-modifying enzymes.

This protocol outlines in detail the preparation of nucleosomal complexes using two methods of sample preparation for freezing TEM grids.

DNA repair in the context of chromatin is poorly understood. Biochemical studies using nucleosome core particles, the fundamental repeating unit of ...

Single-Particle Cryo-EM Data Collection with Stage Tilt using Leginon

Single-particle analysis (SPA) by cryo-electron microscopy (cryo-EM) is now a mainstream technique for high-resolution structural biology. Structure determination by SPA relies upon obtaining multiple distinct views of a macromolecular object vitrified within a thin layer of ice. Ideally, a collection of uniformly distributed random projection orientations would amount to all possible views of the object, giving rise to reconstructions characterized by isotropic directional resolution. However, in reality, many samples suffer from preferentially oriented particles adhering to the air-water interface. This leads to non-uniform angular orientation distributions in the dataset and inhomogeneous Fourier-space sampling in the reconstruction, translating into maps characterized by anisotropic resolution. Tilting the specimen stage provides a generalizable solution to overcoming resolution anisotropy by virtue of improving the uniformity of orientation distributions, and thus the isotropy of Fourier space sampling. The present protocol describes a tilted-stage automated data collection strategy using Leginon, a software for automated image acquisition. The procedure is simple to implement, does not require any additional equipment or software, and is compatible with most standard transmission electron microscopes (TEMs) used for imaging biological macromolecules.

The present protocol describes a generalized and easy-to-implement scheme for tilted single-particle data collection in cryo-EM experiments. Such a procedure is especially useful for obtaining a high-quality EM map for samples suffering from preferential orientation bias due to adherence to the air-water interface.

Single-particle analysis (SPA) by cryo-electron microscopy (cryo-EM) is now a mainstream technique for high-resolution structural biology. Structure ...

User-friendly, High-throughput, and Fully Automated Data Acquisition Software for Single-particle Cryo-electron Microscopy

In the past several years, technological and methodological advancements in single-particle cryo-electron microscopy (cryo-EM) have paved a new avenue for the high-resolution structure determination of biological macromolecules. Despite the remarkable advances in cryo-EM, there is still scope for improvement in various aspects of the single-particle analysis workflow. Single-particle analysis demands a suitable software package for high-throughput automatic data acquisition. Several automatic data acquisition software packages were developed for automatic imaging for single-particle cryo-EM in the last eight years. This paper presents an application of a fully automated image acquisition pipeline for vitrified biomolecules under low-dose conditions. 
It demonstrates a software package, which can collect cryo-EM data fully, automatically, and precisely. Additionally, various microscopic parameters are easily controlled by this software package. This protocol demonstrates the potential of this software package in automated imaging of the severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) spike protein with a 200 keV cryo-electron microscope equipped with a direct electron detector (DED). Around 3,000 cryo-EM movie images were acquired in a single session (48 h) of data collection, yielding an atomic-resolution structure of the spike protein of SARS-CoV-2. Furthermore, this structural study indicates that the spike protein adopts two major conformations, 1-RBD (receptor-binding domain) up open and all RBD down closed conformations.

Single-particle cryo-electron microscopy demands a suitable software package and user-friendly pipeline for high-throughput automatic data acquisition. Here, we present the application of a fully automated image acquisition software package, Latitude-S, and a practical pipeline for data collection of vitrified biomolecules under low-dose conditions.

In the past several years, technological and methodological advancements in single-particle cryo-electron microscopy (cryo-EM) have paved a new avenue for ...

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