We acknowledge support from the Structural and Computational Biology Unit and the Electron Microscopy Core Facility at the European Molecular Biology Laboratory in Heidelberg, Germany and from iNEXT-Discovery (project number 871037). We are extremely grateful for the excellent support from the author of the SerialEM software package, Professor David Mastronarde. We also thank Herman Fung for the critical reading of the manuscript.

The protocol described here is part of a more comprehensive document produced by the EMBL CryoEM Service Platform that includes thorough step-by-step instructions and screenshots illustrating the entire procedure of a typical cryoET session, including sample loading, grid mapping, microscope tuning, set up of acquisition points, and automated data collection. The full protocol can be downloaded at the following link: https://oc.embl.de/index.php/s/9OuTl8AazDkCNq0/download
1. Prerequisites
<ol>
	<li>Install SerialEM version 3.8 and set up to control the microscope and detector (http://bio3d.colorado.edu/SerialEM/betaHlp/html/setting_up_serialem.htm).</li>
	<li>Install a dummy instance of SerialEMversion 3.8 (https://sphinx-emdocs.readthedocs.io/en/latest/serialEM-note-setup-dummy.html).</li>
	<li>Install PyEM (https://git.embl.de/schorb/pyem).</li>
</ol>
2. Grid mapping
<ol>
	<li>Load a cassette with grids into the microscope autoloader.</li>
	<li>Set up imaging conditions suitable for mapping the entire grid. Do this at the lowest magnification possible taking into account the field of view on the detector used (EFTEM SA 2250x). Save the imaging conditions as a SerialEM Imaging State to make things convenient for later use.</li>
	<li>Set up full montage
	<ol>
		<li>Open the SerialEM Navigator menu.</li>
		<li>Select Montaging &#38; Grids.</li>
		<li>Select Set up Full Montage.</li>
	</ol>
	</li>
	<li>Start the script Grid_Mapping. Allow the script to wait for the autoloader to cool down; do an inventory and then map each grid that the inventory procedure found. Input an email via the Tilt Series Menu to allow the script to conveniently send an email when done.</li>
	<li>Save Navigator.</li>
	<li>Inspect all grid maps from the SerialEM Navigator and choose which grid to map further at higher magnification. 
	NOTE: Many TEMs equipped with an autoloader system will show a rotation of the grid when reloaded. It&#39;s best to remap any grid after reloading it. Alternative systems usually only suffer some shifts, which can be corrected in SerialEM with a Shift To Marker procedure.</li>
</ol>
3. Set up the SerialEM low dose
<ol>
	<li>Set the magnification of SerialEM low dose View and Preview modes (this is required for step 6).</li>
	<li>Acquire a View image, and save it as a map in the Navigator.</li>
	<li>Acquire a Preview image, and save it as a map in the Navigator.
	<ol>
		<li>Set both the modes to the same binning (binning 4 is suggested). This allows saving the two maps into one image stack.</li>
		<li>In the SerialEM Navigator window, change the label of the View map to View and the label of the Preview map to Preview.</li>
		<li>Save and close the navigator file. 
		NOTE: No target is needed for the initial View and Preview images, PyEM will merely use their imaging settings.</li>
	</ol>
	</li>
</ol>
4. Grid square mapping
<ol>
	<li>Set up the imaging conditions to map grid squares. It is of great importance to be able to see the sample of interest and the fiducial bead distribution. To ensure optimal contrast for grid square maps, perform the following steps.
	<ol>
		<li>Insert an objective aperture.</li>
		<li>If applicable, insert the energy filter slit.</li>
		<li>Use a defocus of -100 &#181;m.</li>
	</ol>
	</li>
	<li>Screen for good grid squares suitable for further mapping. After visual inspection of the squares from the grid map, take test images of the square with the imaging conditions to be used for grid square maps.</li>
	<li>When a good square is identified, mark its center in the grid map image using the Add Points feature of the SerialEM Navigator.</li>
	<li>Once all the points are added, in the SerialEM Navigator press Shift + A on the first point, then press Shift + A on the last point. 
	NOTE: All added points are now marked by an A, meaning they are all Acquisition Points.</li>
	<li>Press Shift + N (create a new file at item) on the first point and then again on the last point. In the dialog that comes up, select Montaged Images.</li>
	<li>When the montage dialog pops up, set up a montage size that covers one grid square. This depends on the magnification and the mesh size of the grids used and typically requires 2 x 2 to 4 x 4 montages. Give it a name with a number (e.g., squaremap_01.mrc) to allow the SerialEM to conveniently auto-number all the files per grid square.</li>
	<li>Start the grid-mapping by opening the SerialEM Navigator menu and click on Acquire At Items.</li>
	<li>In the pop-up menu, select the following options.
	<ol>
		<li>Select Rough Eucentricity.</li>
		<li>Select Acquire Map Image.</li>
		<li>Select Close Column Valves at End.</li>
		<li>Select Send Email at End.</li>
	</ol>
	</li>
</ol>
5. Selecting the targets
<ol>
	<li>Open a dummy SerialEM instance. This can be set up on the SerialEM PC that controls the microscope or on a different (support) PC if both computers share a network connection.</li>
	<li>Once the first grid square is mapped, use the SerialEM Navigator menu option Merge to see the montage in the dummy SerialEM instance.</li>
	<li>Double click on the Navigator window to open grid square map.</li>
	<li>Search the map and use the dummy SerialEM Navigator option Add Points to add image acquisition points on the target of interest.</li>
	<li>When done and after mapping the new squares, save the Navigator file and merge the Navigator again; continue until all grid squares are mapped.</li>
</ol>
6. Generate virtual maps
<ol>
	<li>Once again, merge the navigator file with the dummy SerialEM instance.</li>
	<li>Run PyEM Virtual maps script from dummy SerialEM menu, Tools and select Virtual Anchor Maps. This may take some time depending on the size and amount of the grid square maps and the binning of the View and Preview maps. 
	NOTE: PyEM starts a command window that automatically closes when done, then the new navigator file can be opened. Per montaged map that contains selected points, PyEM writes a single merged map and all its View and Preview maps. Finally, it writes a new Navigator file called &#60;navigatorfilename&#62;_automaps.nav.</li>
</ol>
7. Microscope tuning
<ol>
	<li>Check the microscope tuning. To assure proper microscope performance, use the same magnification and beam size setup for data acquisition, in the following order.
	<ol>
		<li>Run SeriaLEM Coma-free alignment by CTF (Zemlin tableau).</li>
		<li>Insert and center an objective aperture.</li>
		<li>Run SerialEM Correct Astigmatism by CTF (auto-stigmate).</li>
		<li>Run GIF Quick Tune (i.e., only slit focus). 
		&#8203;NOTE: As steps 7.1.1, 7.1.3, and 7.1.4 might require more dose rate, only the spot size should be changed; the beam size should not be changed as this causes beam tilt, which makes the tunings incorrect. Steps 7.1.1, 7.1.2, and 7.1.3&#160;are semi-automated in this publicly available script (https://serialemscripts.nexperion.net/script/47).</li>
	</ol>
	</li>
</ol>
8. Set up Navigator
<ol>
	<li>In SerialEM, open the new navigator file named &#60;navigatorfilename&#62;_automaps.nav. 
	NOTE: V_yyyy&#160;are the View maps and P_yyyy&#160;are the Preview maps. The preview maps are set as Acquisition Points.</li>
	<li>In the SerialEM Navigator window, deselect all A points, select the first V_yyyy&#160;map, select Collapse, click on A twice, and deselect Collapse.</li>
	<li>Select the first V_yyyy position, press Shift + T, then select the very last V_yyyy position, and press Shift + T again.</li>
	<li>Choose Single Frame Images in the properties of the file to open dialog.</li>
	<li>In the next File Properties dialog, select the desired parameters according to the imaging needs and the instrument setup.</li>
	<li>When prompted, give a name with a number (e.g., TS_001.mrc) and click on Save. 
	NOTE: SerialEM will auto-number the filenames for all tilt series.</li>
	<li>Set up the tilt series controller for the first TS position. When done, click on OK to set these parameters for all tilt series after this acquisition item. All Preview maps are now selected as TS (Tilt Series) with numbered file name TS_xxx.mrc. 
	NOTE: One can change the parameters manually later using the Navigator TSparams feature; changes will be applied for all items downwards on the list. The user has the option to run a custom script instead of the tilt series.</li>
</ol>
9. Set the focus/track positions
<ol>
	<li>Set the focus/track distance for each target if needed (make sure that SerialEM Low Dose is switched on).</li>
	<li>Double click on the View Map to load it.</li>
	<li>Select the Preview Map in the Navigator list.</li>
	<li>Select Edit Focus in the Navigator window.</li>
	<li>In the Low Dose Control panel, deselect Rotate inter-area axis to position Trial and Focus along the stage tilt axis.</li>
	<li>Click on the desired region in the loaded view map to set the focus/trial position for this tilt series.</li>
	<li>Ensure that the Navigator item has TSP set now; repeat the procedure for all the items. 
	&#8203;NOTE: Focus/track positions are automatically copied downwards in the Navigator. Therefore, if the focus/track position for the previous itemis on the correct side and of the correct distance, there is no need to change it for the current item.</li>
</ol>
10. Set up additional scripts
<ol>
	<li>Two scripts handle the Focus range: pretomo and duringtomo. The pretomo script runs before each tilt series and the duringtomo script during each tilt.</li>
	<li>Edit the focus range in the script pretomo.</li>
	<li>In the SerialEM Tilt Series menu, check Run Script in TS and select the script number of the duringtomo script.</li>
</ol>
11. Run
<ol>
	<li>Check the nitrogen tank level.</li>
	<li>Check whether the Autoloader turbo off is selected.</li>
	<li>Check the data storage free space.</li>
	<li>In the SerialEM file menu, deselect continuous saving for the log file and close any currently open logfile. Each tilt series will get its own log file.</li>
	<li>In the Navigator menu, click on Acquire at Items.</li>
	<li>Run the script PreTomo.</li>
	<li>Select Primary task Acquire tilt series.</li>
	<li>Select Run script after PostTomo.</li>
	<li>Select Close column valves at end.</li>
	<li>Select Send email at end.</li>
	<li>Click on GO. 
	NOTE: SerialEM will send an email per tilt series, either successful or error; error can, however, also mean the full tilt range was not reached.</li>
</ol>

<ol>
	<li>Adrian, M., Dubochet, J., Lepault, J., McDowall, A. W. <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+microscopy+of+viruses.">Cryo-electron microscopy of viruses.</a> Nature. 308 (5954), 32-36 (1984).</li><li>Cheng, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Single-particle+cryo-EM-How+did+it+get+here+and+where+will+it+go.">Single-particle cryo-EM-How did it get here and where will it go.</a> Science. 361 (6405), 876-880 (2018).</li><li>Lucic, V., Forster, F., Baumeister, W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Structural+studies+by+electron+tomography:+From+Cells+to+Molecules.">Structural studies by electron tomography: From Cells to Molecules.</a> Annual Review of Biochemistry. 74 (1), 833-865 (2005).</li><li>Pfeffer, S., Mahamid, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Unravelling+molecular+complexity+in+structural+cell+biology.">Unravelling molecular complexity in structural cell biology.</a> Current Opinion in Structural Biology. 52, 111-118 (2018).</li><li>Schur, F. K. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Toward+high-resolution+in+situ+structural+biology+with+cryo-electron+tomography+and+subtomogram+averaging.">Toward high-resolution in situ structural biology with cryo-electron tomography and subtomogram averaging.</a> Current Opinion in Structural Biology. 58, 1-9 (2019).</li><li>Bohning, J., Bharat, T. A. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Towards+high-throughput+in+situ+structural+biology+using+electron+cryotomography.">Towards high-throughput in situ structural biology using electron cryotomography.</a> Progress in Biophysics and Molecular Biology. , (2020).</li><li>Briggs, J. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Structural+biology+in+situ--the+potential+of+subtomogram+averaging.">Structural biology in situ--the potential of subtomogram averaging.</a> Current Opinion in Structural Biology. 23 (2), 261-267 (2013).</li><li>Wan, W., Briggs, J. A. G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Methods in Enzymology. 579, 329-367 (2016).</li><li>Tan, Y. Z., Cheng, A., 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=Automated+data+collection+in+single+particle+electron+microscopy.">Automated data collection in single particle electron microscopy.</a> Microscopy. 65 (1), 43-56 (2016).</li><li>Morado, D. R., Hu, B., Liu, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Using+Tomoauto:+A+protocol+for+high-throughput+automated+cryo-electron+tomography.">Using Tomoauto: A protocol for high-throughput automated cryo-electron tomography.</a> Journal of Visualized Experiments: JoVE. (107), e53608 (2016).</li><li>Resch, G. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Methods in Cell Biology. 152, 135-178 (2019).</li><li>Bharat, T. A., Kukulski, W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Correlative Imaging: Focusing on the Future. , 137-153 (2019).</li><li>Hagen, W. J. H., Wan, W., Briggs, J. A. G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Implementation+of+a+cryo-electron+tomography+tilt-scheme+optimized+for+high+resolution+subtomogram+averaging.">Implementation of a cryo-electron tomography tilt-scheme optimized for high resolution subtomogram averaging.</a> Journal of Structural Biology. 197 (2), 191-198 (2017).</li><li>O'Toole, E., vander Heide, P., McIntosh, R. J., Mastronarde, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Cellular Imaging: Electron Tomography and Related Techniques. , 95-116 (2018).</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>Schorb, M., Haberbosch, I., Hagen, W. J. H., Schwab, Y., 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=Software+tools+for+automated+transmission+electron+microscopy.">Software tools for automated transmission electron microscopy.</a> Nature Methods. 16, 471-477 (2019).</li><li>Turonova, 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=Benchmarking+tomographic+acquisition+schemes+for+high-resolution+structural+biology.">Benchmarking tomographic acquisition schemes for high-resolution structural biology.</a> Nature Communications. 11 (1), 876 (2020).</li><li>Chreifi, G., Chen, S., Metskas, L. A., Kaplan, M., Jensen, G. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Rapid+tilt-series+acquisition+for+electron+cryotomography.">Rapid tilt-series acquisition for electron cryotomography.</a> Journal of Structural Biology. 205 (2), 163-169 (2019).</li><li>Eisenstein, F., Danev, R., Pilhofer, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Improved+applicability+and+robustness+of+fast+cryo-electron+tomography+data+acquisition.">Improved applicability and robustness of fast cryo-electron tomography data acquisition.</a> Journal of Structural Biology. 208 (2), 107-114 (2019).</li><li>Turonova, 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=In+situ+structural+analysis+of+SARS-CoV-2+spike+reveals+flexibility+mediated+by+three+hinges.">In situ structural analysis of SARS-CoV-2 spike reveals flexibility mediated by three hinges.</a> Science. 370 (6513), 203-208 (2020).</li><li>O'Reilly, F. 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=In-cell+architecture+of+an+actively+transcribing-translating+expressome.">In-cell architecture of an actively transcribing-translating expressome.</a> Science. 369 (6503), 554-557 (2020).</li><li>Tegunov, D., Xue, L., Dienemann, C., Cramer, P., Mahamid, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Multi-particle+cryo-EM+refinement+with+M+visualizes+ribosome-antibiotic+complex+at+3.7+&#197;+inside+cells.">Multi-particle cryo-EM refinement with M visualizes ribosome-antibiotic complex at 3.7 &#197; inside cells.</a> Nature Methods. 18, 186-193 (2020).</li><li>Karuppasamy, M., Karimi Nejadasl, F., Vulovic, M., Koster, A. J., Ravelli, R. B. G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Radiation+damage+in+single-particle+cryo-electron+microscopy:+effects+of+dose+and+dose+rate.">Radiation damage in single-particle cryo-electron microscopy: effects of dose and dose rate.</a> Journal of Synchrotron Radiation. 18 (3), 398-412 (2011).</li><li>Liberta, F., 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=Cryo-EM+fibril+structures+from+systemic+AA+amyloidosis+reveal+the+species+complementarity+of+pathological+amyloids.">Cryo-EM fibril structures from systemic AA amyloidosis reveal the species complementarity of pathological amyloids.</a> Nature Communications. 10 (1), 1104 (2019).</li><li>Radamaker, L., 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=Cryo-EM+structure+of+a+light+chain-derived+amyloid+fibril+from+a+patient+with+systemic+AL+amyloidosis.">Cryo-EM structure of a light chain-derived amyloid fibril from a patient with systemic AL amyloidosis.</a> Nature Communications. 10 (1), 1103 (2019).</li></ol>

The authors declare no conflicts of interest.

From a niche technique, cryoET has now matured into a widespread method to perform structural studies at the cellular and molecular level with unprecedented reachable resolution21,22. The ever-increasing demand for cryoEM imaging has put a strain on the limited resources available to access this technology. Despite the opening of a number of national cryoEM facilities and the efforts of scientific institutes to increase their TEM capacity to support the needs of the community worldwide, access to cryoEM instruments is still limited and the time available for data collection must therefore be efficiently used by the users to maximize the yield of each microscopy session. The need to acquire hundreds of tilt series combined with the limited time available for data collection called for novel image acquisition routines to achieve better throughput without compromising data quality. Recent developments in hardware and imaging workflows have considerably increased the speed of tilt series acquisition18,19, thus resulting in a dramatic shift of the ratio between time spent to set up an acquisition point and time needed for the actual tilt series acquisition. Altogether, the procedure for setting up acquisition points is becoming one of the major bottlenecks for the achievable throughput of cryoET sessions.
The optimized protocol presented here enabled us to set up, in off-line mode, 171 positions for automated tomographic acquisition within the first day of a cryoET session while the microscope was actively engaged in other operations (e.g., square mapping, tuning, and automated tilt series acquisition), thus without affecting the microscope time available for data collection. In addition to maximizing the throughput of a cryoET session, this pipeline drastically reduces the amount of time invested by the user in the preparatory phase of an automated data collection session. In the described protocol, the user is asked to browse the mapped grid squares to identify suitable regions of interest and add them to the Serial EM Navigator as acquisition points. All targets will then be automatically processed in batch within SerialEM by the PyEM tool for the production of the virtual maps16. The presented computational approach is therefore considerably faster than acquiring real anchoring maps by eliminating the waiting periods associated with stage movement, image acquisition, change of imaging conditions between View and Preview, and by the eventual reiteration of these steps while centering at high magnification. Additionally, as each acquired image leads to accumulation of electron dose on the object of interest23, the use of virtual maps for the precise realignment of the targets reduces the radiation damage introduced in the preparatory phase of a cryoET session before the actual tilt series acquisition. The protocol described here makes use of both intermediate and high magnification virtual maps (Preview and View, respectively) for realignment of the target before tilt series acquisition. This procedure can easily be modified to only use the intermediate magnification View image when alignment accuracy is of less importance, for instance, in the case of large structures where ultimate target accuracy is of less concern10 or for single particle analysis samples that are poorly spread on the cryo-grid requiring the user's manual selection of each acquisition point24,25. Finally, an approach based on the off-line use of a dummy SerialEM instance also facilitates the setup of acquisition points through remote connection by minimizing the need of the physical presence of the user at the microscope, thus enabling more flexibility in terms of operational organization of the facility.
The recent advances in technology and methods for cryoET have drastically improved the speed and reliability of automated data collection sessions. However, further developments are required to address the remaining rate-limiting steps of this method. Most notably, the initial step of grid and square mapping is now becoming one of the major bottlenecks of the session setup, thus generating the need for hardware improvements aimed at increasing the speed of microscope stage movements and of image acquisition by the direct electron detectors. Additionally, the development of machine learning approaches to fully automate the process of target identification will be crucial to eliminate the need of the users' visual inspection for selecting the regions of interest, a time-consuming procedure that relies on the users' expertise.

Cryogenic electron microscopy (cryoEM) methods are based on the imaging of biological samples by means of a transmission electron microscope (TEM) after their rapid vitrification, a sample preparation process that preserves the molecular and cellular structures of specimens in a close-to-native and hydrated state1,2. In cryogenic electron tomography (cryoET) a 3D model of the vitrified sample is achieved by acquiring a number of images of the same region of interest from different orientations, the so-called tilt series, followed by the computational reconstruction of the tomographic volume3. This advanced imaging technique has matured into a powerful method for the structural investigation of biological processes in the context of their native cellular environments4,5,6.
In addition to the ultrastructural analysis of the vitrified sample, high-resolution reconstructions of macromolecular complexes that are present in multiple copies within the tomographic volume can be obtained by applying subtomogram averaging5. This reconstruction approach is based on the iterative alignment and averaging of sub-volumes containing the structure of interest and it is aimed at increasing the signal-to-noise ratio and the resolution of the final reconstruction7,8. Subtomogram averaging relies on the collection and processing of a large amount of data that often demands the acquisition of hundreds of tilt series by means of high-end TEMs with onerous operational running-costs.
Currently the setup of such automated cryoET sessions is a time-consuming process that usually relies on the user&#39;s manual input9,10,11. Typically, targets are identified by visual inspection of the mapped grid and subsequently set up for automated data collection. The user&#39;s efficiency in identifying acquisition points is often affected by the nature of the sample, becoming particularly challenging when analyzing purified macromolecules with suboptimal concentration or in the case of rare events within crowded cellular environments, implying the use of correlative approaches12. Furthermore, current workflows require the acquisition of images during set-up at various magnifications that will be later used for precise localization and centering of the target during automated acquisition11,13,14. These high-precision re-alignment steps are crucial for high-resolution applications, which demand the imaging to be performed at high magnification and require accurate centering steps for retaining the region of interest within the resulting small field of view. Altogether, several hours of each data collection session are committed for this time-consuming procedure during which the TEM is not engaged in tilt series acquisition. Therefore, depending on the amount of tilt series required, the identification and set-up of acquisition points can have a considerable impact on the microscope time available for data collection during a cryoET session.
Described here is an optimized protocol based on the SerialEM software package15 and the latest version the PyEM software16 to map grids, map grid squares, select targets, and set up automated data acquisition for large-scale tilt series collection. The key concept of this approach is to provide SerialEM with computationally generated images by PyEM for each acquisition item, termed virtual maps, for the accurate localization and centering of the target. To gain actual acquisition time, the selection of the targets as well as the creation of the virtual maps are performed off-line using a second dummy instance of SerialEM, decoupling the selection process of acquisition targets from TEM operations. While not addressing how to increase the data quality13,17 or the speed of tilt series acquisition18,19, this protocol is primarily focused on strategies to optimize the time-efficiency of large-scale automated cryoET sessions setup. Therefore, the implementation of the presented protocol is meant for those scientists establishing cryoET data collection workflows that desire to maximize the yield of automated data acquisition by increasing the microscope time available for tilt series acquisition.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Transmission Electron Microscope</td>
			<td></td>
			<td></td>
			<td>Our protocol is only based on computational workflows. The user will only need acess to a TEM of any kind</td>
		</tr>
	</tbody>
</table>

strategies for optimization of cryogenic electron tomography data acquisition

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

This procedure was used to acquire the Sars-Cov-2 tilt series described in Turonova et al. 202020; the whole dataset was produced using three distinct grids over three microscopy sessions at EMBL Heidelberg. The current study will focus on and describe the first 3-day (~72 h) session run with the first grid.
After the entire grid was mapped at low magnification (~10 min, see step 2), 71 suitable squares were selected on the grid map, and medium magnification maps (square maps) were acquired with settings (magnification, exposure, defocus) that allow for the direct visualization and identification of the sample of interest, coronaviruses in this case (see step 4) (Figure 1A). The acquisition time was ~3 min per square, 3 h 45 min in total.
As soon as the first square map was created, a dummy SerialEM instance (without any control on camera or microscope) was opened on a separate computer to visualize the square map and to add points on targets suitable for tilt series acquisition (see step 5) (Figure 1B). Newly acquired square maps were retrieved by merging the current dummy SerialEM navigator with the navigator from the acquiring SerialEM instance. After ~2 h of grid square acquisition and selection, 50 initial targets could be identified.
After the square map acquisitions had finished, SerialEM low-dose was set up and reference View and Preview images were taken and saved as maps (see step 3). The latter maps could then be used immediately on the dummy SerialEM instance to generate, from the corresponding square map images, the Virtual View (Figure 1C) and Virtual Preview (Figure 1D) maps of the 50 selected targets with the PyEM software suite, for a processing time of ~30 min (see step 6). This processing time on the dummy SerialEM session was used to perform final preparations of the microscope for acquisition: energy filter tuning, new camera gain reference image acquisition, astigmatism- and coma-free alignment of the objective lens.
Once microscope tuning was completed and virtual maps from the 50 initial targets generated, the actual SerialEM navigator to be used for acquisition was set up (see step 8), focus and track positions were set (step 9), and tilt series acquisition could be started (see steps 10 and 11). The Virtual View maps (Figure 1C) were used for an initial centering of the target (Figure 1E) followed by a final centering performed at the actual tilt series acquisition magnification (Figure 1F) using the Virtual Preview map (Figure 1D).
Starting with grid mapping at 9:30 in the morning, the acquisition of the tilt series for the 50 initial targets commenced at roughly 15:00. With the settings used for tomographic acquisition (see the reference for details), a tilt series took ~20 min to acquire, with enough targets then to run through the entire night. While the acquisition was running, the rest of the square maps could be inspected and more targets added, still off-line via the dummy SerialEM instance. 121 more targets were selected among the remaining square maps and added to the acquisition SerialEM navigator after virtual maps had been created for these new targets, enough to run until completion of the 72 h session.
This procedure (summarized in Figure 2) allowed, in a single working day, the setup of 171 targets for automated tomographic acquisitions for a 72 h (3 days) microscope session.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/62383/62383fig01.jpg" /> 
Figure 1: Example of square map with representative virtual maps and corresponding acquisitions after centering. (A) Representative square map of a Sars-Cov-2 cryoEM grid used in Turonova et al.20. Four regions of interest are marked with a red cross. Microscope magnification is 2,250x. (B) Crop out of the square map highlighting the areas that were used to generate the Virtual View (orange) and Virtual Preview (yellow) maps for the selected target (red cross) (C) Virtual View map. (D) Virtual Preview map. (E) Actual View acquisition after centering using the Virtual View map as a reference. Microscope magnification is 11,500x. (F) 0&#176; tilt acquisition from the tilt series after centering using the Virtual Preview map as a reference. Microscope magnification is 64,000x. <a href="https://jove.unicartagenaproxy.elogim.com/files/ftp_upload/62383/62383fig01large.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/62383/62383fig02.jpg" /> 
Figure 2: CryoET session workflow using SerialEM with PyEM tools. <a href="https://jove.unicartagenaproxy.elogim.com/files/ftp_upload/62383/62383fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Watch this Scientific Journal Video about Strategies for Optimization of Cryogenic Electron Tomography Data Acquisition at JoVE.com

Strategies for Optimization of Cryogenic Electron Tomography Data Acquisition

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

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

strategies-for-optimization-cryogenic-electron-tomography-data

Universidad de Cartagena UDEC

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JoVE Journal

Biology

4.3K Views.  European Molecular Biology Laboratory. The increasing demand for large-scale data collection in cryogenic electron tomography requires high-throughput image acquisition routines. Described here is a protocol that implements the recent developments of advanced acquisition strategies aimed at maximizing the time-efficiency and throughput of tomographic data collection.

Video: Strategies for Optimization of Cryogenic Electron Tomography Data Acquisition

Polymerase Chain Reaction: Basic Protocol Plus Troubleshooting and Optimization Strategies

In the biological sciences there have been technological advances that catapult the discipline into golden ages of discovery. For example, the field of microbiology was transformed with the advent of Anton van Leeuwenhoek&#39;s microscope, which allowed scientists to visualize prokaryotes for the first time. The development of the polymerase chain reaction (PCR) is one of those innovations that changed the course of molecular science with its impact spanning countless subdisciplines in biology. The theoretical process was outlined by Keppe and coworkers in 1971; however, it was another 14 years until the complete PCR procedure was described and experimentally applied by Kary Mullis while at Cetus Corporation in 1985. Automation and refinement of this technique progressed with the introduction of a thermal stable DNA polymerase from the bacterium Thermus aquaticus, consequently the name Taq DNA polymerase.
PCR is a powerful amplification technique that can generate an ample supply of a specific segment of DNA (i.e., an amplicon) from only a small amount of starting material (i.e., DNA template or target sequence). While straightforward and generally trouble-free, there are pitfalls that complicate the reaction producing spurious results. When PCR fails it can lead to many non-specific DNA products of varying sizes that appear as a ladder or smear of bands on agarose gels. Sometimes no products form at all. Another potential problem occurs when mutations are unintentionally introduced in the amplicons, resulting in a heterogeneous population of PCR products. PCR failures can become frustrating unless patience and careful troubleshooting are employed to sort out and solve the problem(s). This protocol outlines the basic principles of PCR, provides a methodology that will result in amplification of most target sequences, and presents strategies for optimizing a reaction. By following this PCR guide, students should be able to: 
 
&#9679; Set up reactions and thermal cycling conditions for a conventional PCR experiment 
&#9679; Understand the function of various reaction components and their overall effect on a PCR experiment 
&#9679; Design and optimize a PCR experiment for any DNA template 
&#9679; Troubleshoot failed PCR experiments

PCR has emerged as a common technique in many molecular biology laboratories. Provided here is a quick guide to several conventional PCR protocols. Because each reaction is a unique experiment, optimal conditions required to generate a product vary. Understanding the variables in a reaction will greatly enhance troubleshooting efficiency, thereby increasing the chance to obtain the desired result.

In the biological sciences there have been technological advances that catapult the discipline into golden ages of discovery. For example, the field of ...

Visualization of ATP Synthase Dimers in Mitochondria by Electron Cryo-tomography

Electron cryo-tomography is a powerful tool in structural biology, capable of visualizing the three-dimensional structure of biological samples, such as cells, organelles, membrane vesicles, or viruses at molecular detail. To achieve this, the aqueous sample is rapidly vitrified in liquid ethane, which preserves it in a close-to-native, frozen-hydrated state. In the electron microscope, tilt series are recorded at liquid nitrogen temperature, from which 3D tomograms are reconstructed. The signal-to-noise ratio of the tomographic volume is inherently low. Recognizable, recurring features are enhanced by subtomogram averaging, by which individual subvolumes are cut out, aligned and averaged to reduce noise. In this way, 3D maps with a resolution of 2 nm or better can be obtained. A fit of available high-resolution structures to the 3D volume then produces atomic models of protein complexes in their native environment. Here we show how we use electron cryo-tomography to study the in situ organization of large membrane protein complexes in mitochondria. We find that ATP synthases are organized in rows of dimers along highly curved apices of the inner membrane cristae, whereas complex I is randomly distributed in the membrane regions on either side of the rows. By subtomogram averaging we obtained a structure of the mitochondrial ATP synthase dimer within the cristae membrane.

We present a protocol of how to collect and process electron cryo-tomograms of whole mitochondria. The technique provides detailed insights into the structure, function, and organization of large membrane protein complexes in native biological membranes.

Electron cryo-tomography is a powerful tool in structural biology, capable of visualizing the three-dimensional structure of biological samples, such as ...

Preparation and Observation of Thick Biological Samples by Scanning Transmission Electron Tomography

This report describes a protocol for preparing thick biological specimens for further observation using a scanning transmission electron microscope. It also describes an imaging method for studying the 3D structure of thick biological specimens by scanning transmission electron tomography. The sample preparation protocol is based on conventional methods in which the sample is fixed using chemical agents, treated with a heavy atom salt contrasting agent, dehydrated in a series of ethanol baths, and embedded in resin. The specific imaging conditions for observing thick samples by scanning transmission electron microscopy are then described. Sections of the sample are observed using a through-focus method involving the collection of several images at various focal planes. This enables the recovery of in-focus information at various heights throughout the sample. This particular collection pattern is performed at each tilt angle during tomography data collection. A single image is then generated, merging the in-focus information from all the different focal planes. A classic tilt-series dataset is then generated. The advantage of the method is that the tilt-series alignment and reconstruction can be performed using standard tools. The collection of through-focal images allows the reconstruction of a 3D volume that contains all of the structural details of the sample in focus.

This report describes a sample preparation protocol and specific imaging conditions for performing scanning transmission electron tomography of thick biological specimens.

This report describes a protocol for preparing thick biological specimens for further observation using a scanning transmission electron microscope. It ...

Procedure and Key Optimization Strategies for an Automated Capillary Electrophoretic-based Immunoassay Method

New technologies that utilize capillary-based immunoassays promise faster and more quantitative protein assessment compared to traditional immunoassays. However, similar to other antibody-based protein assays, optimization of capillary-based immunoassay parameters, such as protein concentration, antibody dilution, and exposure time is an important prerequisite to the generation of meaningful and reliable data. Measurements must fall within the linear range of the assay where changes in signal are directly proportional to changes in lysate concentration. The process of choosing appropriate lysate concentrations, antibody dilutions, and exposure times in the human bronchial epithelial cell line, BEAS-2B, is demonstrated here. Assay linearity is shown over a range of whole cell extract protein concentrations with p53 and &#945;-tubulin antibodies. An example of signal burnout is seen at the highest concentrations with long exposure times, and an &#945;-tubulin antibody dilution curve is shown demonstrating saturation. In addition, example experimental results are reported for doxorubicin-treated cells using optimized parameters.

A capillary-based immunoassay using a commercial platform to measure target proteins from total protein preparations is demonstrated. In addition, assay parameters of exposure time, protein concentration, and antibody dilution are optimized for a cell culture model system.

New technologies that utilize capillary-based immunoassays promise faster and more quantitative protein assessment compared to traditional immunoassays. ...

3D Mitochondrial Ultrastructure of Drosophila Indirect Flight Muscle Revealed by Serial-section Electron Tomography

Mitochondria are cellular powerhouses that produce ATP, lipids, and metabolites, as well as regulate calcium homeostasis and cell death. The unique cristae-rich double membrane ultrastructure of this organelle is elegantly arranged to carry out multiple functions by partitioning biomolecules. Mitochondrial ultrastructure is intimately linked with various functions; however, the fine details of these structure-function relationships are only beginning to be described. Here, we demonstrate the application of serial-section electron tomography to elucidate mitochondrial structure in Drosophila indirect flight muscle. Serial-section electron tomography may be adapted to study any cellular structure in three-dimensions.

3D Mitochondrial Ultrastructure of Drosophila Indirect Flight Muscle Revealed by Serial-section Electron Tomography

In this protocol, we demonstrate the application of serial-section electron tomography to elucidate mitochondrial structure in Drosophila indirect flight muscle.

Mitochondria are cellular powerhouses that produce ATP, lipids, and metabolites, as well as regulate calcium homeostasis and cell death. The unique ...

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

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

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

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

Quantification of Site-specific Protein Lysine Acetylation and Succinylation Stoichiometry Using Data-independent Acquisition Mass Spectrometry

Post-translational modification (PTM) of protein lysine residues by N&#400;-acylation induces structural changes that can dynamically regulate protein functions, for example, by changing enzymatic activity or by mediating interactions. Precise quantification of site-specific protein acylation occupancy, or stoichiometry, is essential for understanding the functional consequences of both global low-level stoichiometry and individual high-level acylation stoichiometry of specific lysine residues. Other groups have reported measurement of lysine acetylation stoichiometry by comparing the ratio of peptide precursor isotopes from endogenous, natural abundance acylation and exogenous, heavy isotope-labeled acylation introduced after quantitative chemical acetylation of proteins using stable isotope-labeled acetic anhydride. This protocol describes an optimized approach featuring several improvements, including: (1) increased chemical acylation efficiency, (2) the ability to measure protein succinylation in addition to acetylation, and (3) improved quantitative accuracy due to reduced interferences using fragment ion quantification from data-independent acquisitions (DIA) instead of precursor ion signal from data-dependent acquisition (DDA). The use of extracted peak areas from fragment ions for quantification also uniquely enables differentiation of site-level acylation stoichiometry from proteolytic peptides containing more than one lysine residue, which is not possible using precursor ion signals for quantification. Data visualization in Skyline, an open source quantitative proteomics environment, allows for convenient data inspection and review. Together, this workflow offers unbiased, precise, and accurate quantification of site-specific lysine acetylation and succinylation occupancy of an entire proteome, which may reveal and prioritize biologically relevant acylation sites.

Here, we present unbiased quantification of site-specific protein acetylation and/or succinylation occupancy (stoichiometry) of an entire proteome through a ratiometric analysis of endogenous modifications to modifications introduced after quantitative chemical acylation using stable isotope-labeled anhydrides. In combination with sensitive data-independent acquisition mass spectrometry, accurate site occupancy measurements are obtained.

Post-translational modification (PTM) of protein lysine residues by N&#400;-acylation induces structural changes that can dynamically regulate protein ...

Optimization of Crystal Growth for Neutron Macromolecular Crystallography

The use of neutron macromolecular crystallography (NMX) is expanding rapidly with most structures determined in the last decade thanks to new NMX beamlines having been built and increased availability of structure refinement software. However, the neutron sources currently available for NMX are significantly weaker than equivalent sources for X-ray crystallography. Despite advances in this field, significantly larger crystals will always be required for neutron diffraction studies, particularly with the tendency to study ever-larger macromolecules and complexes. Further improvements in methods and instrumentation suited to growing larger crystals are therefore necessary for the use of NMX to expand.
In this work, we introduce rational strategies and a crystal growth bench (OptiCrys) developed in our laboratory that combines real-time observation through a microscope-mounted video camera with precise automated control of crystallization solutions (e.g., precipitant concentration, pH, additive, temperature). We then demonstrate how this control of temperature and chemical composition facilitates the search for optimal crystallization conditions using model soluble proteins. Thorough knowledge of the crystallization phase diagram is crucial for selecting the starting position and the kinetic path for any crystallization experiment. We show how a rational approach can control the size and number of crystals generated based on knowledge of multidimensional phase diagrams.

Structural studies of biomacromolecules by crystallography require high-quality crystals. Here we demonstrate a protocol that can be used by OptiCrys (a fully automated instrument developed in our lab) and/or microdialysis buttons for growing large high-quality crystals based on knowledge of the crystallization phase diagram.

The use of neutron macromolecular crystallography (NMX) is expanding rapidly with most structures determined in the last decade thanks to new NMX ...

Array Tomography Workflow for the Targeted Acquisition of Volume Information using Scanning Electron Microscopy

Electron microscopy is applied in biology and medicine for imaging of cellular and structural details at nanometer resolution. Historically, Transmission Electron Microscopy (TEM) provided insight into cell ultrastructure, but in the recent decade, the development of modern Scanning Electron Microscopes (SEM) has changed the way of looking inside the cells. Even though the resolution of TEM is superior when protein-level structural details are needed, SEM-resolution is sufficient for the majority of the organelle-level cell biology-related questions. The advancement in technology enabled automatic volume acquisition solutions such as Serial block-face imaging (SBF-SEM) and Focused ion beam SEM (FIB-SEM). Nevertheless, to this day, these methods remain inefficient when the identification and navigation to areas of interest are crucial. Without the means for precise localization of target areas before imaging, operators need to acquire much more data than they need (in SBF-SEM), or, even worse, prepare many grids and image them all (in TEM). We propose the strategy of "lateral screening" using Array Tomography in SEM, which facilitates the localization of areas of interest, followed by automated imaging of the relevant fraction of the total sample volume. Array tomography samples are conserved during imaging, and they can be arranged into section libraries ready for repeated imaging. Several examples are shown in which lateral screening enables us to analyze structural details that are incredibly challenging to access with any other method.

We describe the preparation of ribbons of serial sections and their collection on large transfer support for use as Array Tomography samples, along with automated imaging procedures in a scanning electron microscope. The protocol allows screening, retrieval, and targeted imaging of local, rare events, and the acquisition of large data volumes.

Electron microscopy is applied in biology and medicine for imaging of cellular and structural details at nanometer resolution. Historically, Transmission ...

Construction of a Realistic, Whole-Body, Three-Dimensional Equine Skeletal Model using Computed Tomography Data

Therapies based upon whole-body biomechanical assessments are successful for injury prevention and rehabilitation in human athletes. Similar approaches have rarely been used to study equine athletic injury. Degenerative osteoarthritis caused by mechanical stress can originate from chronic postural dysfunction, which, because the primary dysfunction is often distant from the site of tissue injury, is best identified through modeling whole-body biomechanics. To characterize whole-body equine kinematics, a realistic skeletal model of a horse was created from equine computed tomography (CT) data that can be used for functional anatomical and biomechanical modeling. Equine CT data were reconstructed into individual three-dimensional (3D) data sets (i.e., bones) using 3D visualization software and assembled into a complete 3D skeletal model. The model was then rigged and animated using 3D animation and modeling software. The resulting 3D skeletal model can be used to characterize equine postures associated with degenerative tissue changes as well as to identify postures that reduce mechanical stress at the sites of tissue injury. In addition, when animated into 4D, the model can be used to demonstrate unhealthy and healthy skeletal movements and can be used to develop preventative and rehabilitative individualized therapies for horses with degenerative lamenesses. Although the model will soon be available for download, it is currently in a format that requires access to the 3D animation and modeling software, which has quite a learning curve for new users. This protocol will guide users in (1) developing such a model for any organism of interest and (2) using this specific equine model for their own research questions.

The purpose of this protocol is to describe the method of creation of a realistic, whole-body, skeletal model of a horse that can be used for functional anatomical and biomechanical modeling to characterize whole-body mechanics.

Therapies based upon whole-body biomechanical assessments are successful for injury prevention and rehabilitation in human athletes. Similar approaches ...

Strategies for Optimization of Cryogenic Electron Tomography Data Acquisition

Summary

Explore More Videos

Chapters in this video

Polymerase Chain Reaction: Basic Protocol Plus Troubleshooting and Optimization Strategies

Visualization of ATP Synthase Dimers in Mitochondria by Electron Cryo-tomography

Preparation and Observation of Thick Biological Samples by Scanning Transmission Electron Tomography

Procedure and Key Optimization Strategies for an Automated Capillary Electrophoretic-based Immunoassay Method

3D Mitochondrial Ultrastructure of Drosophila Indirect Flight Muscle Revealed by Serial-section Electron Tomography

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

Quantification of Site-specific Protein Lysine Acetylation and Succinylation Stoichiometry Using Data-independent Acquisition Mass Spectrometry

Optimization of Crystal Growth for Neutron Macromolecular Crystallography

Array Tomography Workflow for the Targeted Acquisition of Volume Information using Scanning Electron Microscopy

Construction of a Realistic, Whole-Body, Three-Dimensional Equine Skeletal Model using Computed Tomography Data