Name: Author Spotlight: Enhancing Cryo-Electron Microscopy by Automated Data Collection and Analysis Techniques
Uploaded: 2023-12-01T14:45:00
Duration: 1 min 41 s
Description: Watch this Scientific Journal Video about Author Spotlight: Enhancing Cryo-Electron Microscopy by Automated Data Collection and Analysis Techniques at JoVE.com

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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Routine+single+particle+CryoEM+sample+and+grid+characterization+by+tomography.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Understanding+the+invisible+hands+of+sample+preparation+for+cryo-EM.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Current+outcomes+when+optimizing+'standard'+sample+preparation+for+single-particle+cryo-EM.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cryo-EM+grid+optimization+for+membrane+proteins.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Automated+molecular+microscopy:+The+new+Leginon+system.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Automated+electron+microscope+tomography+using+robust+prediction+of+specimen+movements.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Routine+collection+of+high-resolution+cryo-EM+datasets+using+200+KV+transmission+electron+microscope.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Fully+automated+multi-grid+cryoEM+screening+using+Smart+Leginon.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Learning+to+automate+cryo-electron+microscopy+data+collection+with+Ptolemy.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Smart+data+collection+for+CryoEM.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Automated+systematic+evaluation+of+cryo-EM+specimens+with+SmartScope.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Smart+EPU:+SPA+Getting+Intelligent.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=CryoRL:+Reinforcement+learning+enables+efficient+cryo-EM+data+collection.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Optimized+path+planning+surpasses+human+efficiency+in+cryo-EM+imaging.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Appion:+An+integrated,+database-driven+pipeline+to+facilitate+EM+image+processing.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Routine+determination+of+ice+thickness+for+cryo-EM+grids.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+potential+for+seeing+molecules+in+cells,+CZ+Imaging+Institute's+Hardware+Frontiers+for+CryoET+Workshop.">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. , (2023).</li><li>Punjani, A., Rubinstein, J. L., Fleet, D. J., Brubaker, M. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=cryoSPARC:+algorithms+for+rapid+unsupervised+cryo-EM+structure+determination.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cryo-electron+microscopic+grid+preparation+for+time-resolved+studies+using+a+novel+robotic+system,+Spotiton.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Enabling+a+paradigm+shift+in+CryoEM+sample+preparation+with+chameleon.">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 border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Glacios cryoTEM&#160;</td>
			<td>Thermo Fisher Scientific&#160;</td>
			<td>GLACIOSTEM</td>
			<td>FEG, 200 keV, Falcon 3EC camera&#160;</td>
		</tr>
		<tr>
			<td>Krios cryoTEM&#160;</td>
			<td>Thermo Fisher Scientific&#160;</td>
			<td>KRIOSG4TEM</td>
			<td>XFEG, 300 keV,&#160;Gatan BioQuantum energy filter, Gatan K3 camera&#160;</td>
		</tr>
		<tr>
			<td>Leginon&#160;</td>
			<td>Simons Electron Microscopy Center&#160;</td>
			<td></td>
			<td>http://leginon.org&#160;</td>
		</tr>
		<tr>
			<td>Ptolemy&#160;</td>
			<td>Simons Machine Learning Center&#160;</td>
			<td></td>
			<td>https://github.com/SMLC-NYSBC/ptolemy&#160;</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.

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

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.

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

At the Simons Electron Microscopy Center, we aim to provide superb electron microscopy services, expertise, and training to local and national researchers, and to develop innovative methods to optimize workflows. Our scope covers service, development, machine learning, and biomedical research. We are trying to answer, how can we better optimize our services?Machine learning is changing the field of electron microscopy. Data collection is becoming automated. Data analysis tools are rapidly becoming more efficient.This includes denoising images, particle picking, 3G flexibility analysis, and model building. We regularly update software to account for different situations, such as different grid holes, tilted collection, or bugs identified through beta testing software in our production environment. We also integrate feedback from our users, so we are continuously providing features that are useful for them.Smart Leginon addresses the labor-intensive and time-consuming tasks of screening one or more grids on a cryoTEM. It enables operators and researchers to more quickly screen and analyze grids, while freeing up time to optimize grid and sample conditions in parallel. This also reduces microscope time and cost.Smart Leginon autoscreen offers fully unattended multigrid screening, which takes about 10 minutes to set up screening for 12 grids. Compared to manual screening, our protocol saves several hours per day. Since Smart Leginon's introduction, other free and commercial software has been developed for similar automation.