Cryo-EM has become a standard technique for structure determination of proteins and their complexes. This protocol describes the best practices how to obtain high-resolution Cryo-EM datasets from the mid-range 200 kV TEM microscopes. Cryo-EM can determine protein structure in near native conditions in solution for multiple conformational states and functional states simultaneously, which is elusive for other structural techniques.

Obtained structural information can be used for elucidation of molecular mechanisms of protein function, as well as for structure-based drug design. For example, a recent publication in the structure of amyloid fibrils revealed multiple binding sites of a vital ligand. However, in this protocol, we use the standard samples of apoferritin and proteasome to demonstrate the critical steps in obtaining high-resolution Cryo-EM data.

Operation of a Cryo-TEM has become more easy over the last years, especially due to the introduction of advanced automation features. However, for the first session, we would advise you to have a training with more experienced users. From there, the progression in the technique is relatively fast.

Demonstrating the procedure will be Adrian Koh, who is a senior application scientist at Thermo Fisher Scientific. Insert auto grids into the auto loader cassette under liquid nitrogen conditions. Insert the cassette with auto grids into a liquid nitrogen cooled transfer capsule.

Further insert the capsule into the microscope and click on the Dock button in the microscope UI to load the cassette from the capsule into the auto loader of the microscope. Click on the Inventory button to check the presence of auto grids in the loaded cassette. Then click on the Load and Unload buttons to insert the auto grids into the column for TEM imaging.

Select the Atlas tab and click on the New Session button to open a new session. Fill in details such as session name and data storage location and click on the Apply button. Select the grids of interest by selecting a checkbox next to the corresponding grid number.

Click the Start button to start a fully automated collection of atlases of all selected grids. When the collection is completed, click on grid labels to review the acquired atlases. Select the EPU tab and go to New Session to create a new session in the left panel.

Select the New Session option to use the current set optical presets. Fill in the session name. Select the manual type of the session to have control over the selection of individual holes and grid squares selected for data collection later in the protocol.

Select the faster acquisition mode to use aberration-free image shifting for data collection. Then enter the location to save the metadata. Click on the Apply button to create a new session.

Select the square selection task in the left panel to show the collected atlas of the grid. Identify the grid squares with intact support foil without damage, thin vitreous ice and foil holes, negligible crystalline ice contamination in the grid square and minimal brightness gradient across the grid square and within individual foil holes. Select the grid squares for data collection, either in the full atlas or high-quality tile images.

Right-click on a grid square of interest and choose the whole selection task. Click on the Auto Eucentric button to automatically move to the first selected grid square. Adjust the eucentric height and acquire a grid square image for finding foil holes.

Click on the Find Holes button to find foil holes in the image. Click on the Remove Holes button to close the grid bar button to deselect holes near grid bars. Adjust limits in the brightness histogram of the ice filter to remove all holes with too thick ice and all empty holes.

Right-click on a hole in the grid square image and select move stage to location move stage here. Select the template definition task in the left panel. Click on the Acquire button to acquire a whole image.

Set values for delay after image shift to 0.5 seconds and delay after stage shift to five seconds. Click on the Find and Center Hole button to center a hole in the image. Select the Add Acquisition Area button and click on the image to select the location in the centered hole where high magnification image acquisition will be taken.

Select the Add Auto Focus Area button and click on the image to select the location on the support foil next to the centered hole where image auto focus will be performed. Click on the green Acquisition Area to set a sequence of defocus values in the defocus list in the top section of the software window. After setting the auto focus specific settings, choose the option After Centering to auto focus at the start of each AFIS cluster.

Choose the option Objective Lens for faster auto focusing and reduced stage drift. Click on the Prepare All Squares button in the hole selection task to automatically set data collection and all other selected grid squares according to the used settings in this first grid square. Select the template definition task, acquire a new image, move the stage to a clean area on carbon foil by right-click and select the menu option move stage here.

Select the Auto Functions tab. After setting the desired defocus and iterate, switch to the auto focus preset and click on the start Button to run the auto focus function. Select the Autostigmate task, switch to the Thon Ring preset and press the Start button.

Select the Autocoma task and press the Start button. Move stage to an area with a broken grid square. Confirm transparency of area by taking a single auto focus image.

Open Sherpa UI and select the Energy Filter application. Click on the Center button and the zero loss option to center the zero loss energy filter slit. Click on the Tune button in the isochromaticity option.

Click on the Tune Magnification and Tune Distortions option in the geometric and chromatic distortions. Go to the EPU tab, select the Automated Acquisition task and click on the Start Run button to begin fully automated data collection. The figure shows Cryo-EM grids displaying a gradient of ice thickness over the grid surface.

The grids excluded from the further investigation are bad grid with thick ice and a bent grid with bad ice and contamination, while acceptable grids are those with good ice gradient and a typical grid with good thin ice and small ice gradient. The figure shows the final 3D rendering of the reconstructed apoferritin Cryo-EM map. The high stability and symmetry make it an optimal benchmark sample for high-resolution Cryo-EM imaging and image processing.

Bayesian polishing, CTF refinement, and Ewald sphere correction resulted in a 1.63 angstrom resolution map. The figure shows a detailed view of the reconstructed apoferritin Cryo-EM map at the individual amino acid side chain level. The density of amino acid side chains is well-resolved and the atomic model can be unambiguously built within the map.

The image here shows two different datasets at different defocus values collected from the same Cryo-EM grid with similar grid squares with a slit fully open and a 10 electron volt slit which indicates that the 10 electron volt slit significantly improves image contrast. The figure herein shows an overview of the 20S proteasome Cryo-EM map with segmented subunits used as standard Cryo-EM sample. And its zoomed view with a fitted atomic model represents the stable catalytic core of the proteasome complex with the D7 symmetry.

The protocol contains two critical steps. One, looking for areas with thin vitreous ice containing homogenous particles. And two, setting up parallel illumination for data acquisition.

With high resolution reconstructions of proteins achieving two to three angstroms in resolution, you're better able to understand the molecular mechanisms of disease and improve drug leads. Previously, crystallization was necessary if you wanted to study the structural basis of biological phenomena. Today with Cryo-EM, that's no longer necessary.

And so with Cryo-EM, we enable scientists to study a wider range of biological phenomena at a structural level.

Summary

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Structure based Drug Design

Apoferritin

Proteasome