Automation in Single-Particle Electron Microscopy Jian Guan Hafenstein Lab Today I will give a presentation about the automation in Single-Particle Electron Microscopy

The definition of Automation “automation is a comprehensive and versatile strategy that can deliver biological information on an unprecedented scale beyond the scope available with classical manual approaches” So first of all, what is automation. Automation is a comprehensive and versatile strategy that can deliver biological information on an unprecedented scale beyond the scope available with classical manual approaches. Automation should strive to be more than a mere elimination of repetitive tasks. It should also serve as an aid to expert users, encouraging development of next generation algorithms and technical advances. Automation in Single-Particle Electron Microscopy: Connecting the Pieces, Lyumkis D, et al. Methods in Enzymology, Volume 483,2010

Huge number of Particles Demands When the automation meets the Cryo-EM, as shown in this graph, it composes of a series steps which requires increasing sophisticated techniques for performing different steps to obtain refined electron density map, we must maintain control over automated routinees in a balance way between manual control and automated efficiency. Most of the biologically active particles, are flexible and heterogeneous. In order to separate those particle images into different classes of homogeneous conformations prior to the image reconstruction, large numbers of particle images are necessary. large datasets have become the norm, regardless of the targeted resolution, for many single particle studies. The demand for such large datasets necessitates reliable and efficient automation of image data collection for single particle cryo-EM. Website of National Center for Macromolecular Imaging (NCMI) Automation

Software systems with various degrees of automation and robustness AutoEM semi-automated FEI Tecnai series EM Leginon advanced automation Philips CM series and FEI Tecnai Serial EM automation tomography FEI and JEOL TOM Toolbox FEI Batch Tomography for cryo-tomography FEI JADAS JEOL James Conway EPU (FEI version of Leginon) Researchers in both industry and academic institutions have developed several software systems with various degrees of automation and robustness. Here I give some examples. I will talk about the Leginon and JDAS in detail today. Leginon is the most advanced automation package designed to provide fully automated functions for the Phillips CM series as well as the FEI Tecnai series of transmission electron microscopes. And JADAS is developed for present and future models of JEOL instruments in order to translate our high resolution imaging experience into an industrial grade software product. We use JADAS for our advanced cryo-EM systems JEOL 2100, and in James Conway’s lab, they use EPU (the FEI version of Leginon) as their automation system. Susan Hafensein

Outline General Information Work flow Grid Searching Selective squares on the grid according to ice thickness Centering the holes So today, I will give both introduction and comparisons between the two automation systems in most the aspects of the Cryo-EM protocol. Focus and Astigmatism Correction Modularity and Flexibility of JADAS Examples Speed of data collection

General Information JADAS EPU JEOL Automated Data Acquisition System (JADAS) software was developed by JEOL collaboration with NCMI Baylor college of Medicine Developed for current generation of JEOL instruments. Written with C# programming language. Runs only on Windows OS with at least 1GB memory EPU Stands for “E pluribus Unum”, Latin phrase for “out of many, one” FEI version of Leginon Using Python programming language Compatible with both Linux and Windows OS JADAS is abbreviation of ----

Work flow JADAS Both of them are developed to emulate all of the decisions and actions of a highly trained microscopist in collecting data from a vitreous ice specimen. They are very similar, as shown in the workflow scheme here. Generally speaking, they include identifying suitable areas of vitreous ice at low magnification, determining the presence and location of specimen on the grid, automatically adjusting imaging parameters (defocus, astigmatism) under low-dose conditions, and acquiring images at high magnification to either film or a digital camera. Once there are no more holes which satisfy the intensity criterion in the current search step view, they move the stage to the next chosen grid square and repeats the local search again until it travels through all the grid squares selected in the global search step or until the user suspends the operation. Then I will introduce each step in detail.

Grid Searching JADAS 100-150X First of all, they use montage-assistant global search mode under low magnification to locate each grid square based on its periodicity[ˌpɪrɪr'dɪsətɪ]. Grid Square Selection GUI for montage-assisted global search. As shown here in JADAS, the montage image at low magnification is displayed on the upper left. Detected grid squares are marked in colors on the montage image. The brightness histogram of the squares is shown on the upper right and the user can move sliders to set the threshold for squares selection (light blue) or discard (light peach). User can also manually select squares (green) for data acquisition, mark squares (red) to not to take images and choose a square (bright yellow) for calibration.

Grid Searching Leginon 165X-320X 500-1000 squares In EPU, the global searching is called atlas acquisition. But it did it in the same way.

Selective squares on the grid according to ice thickness The thickness of a vitreous ice layer can be estimated as (Eusemann et al., 1982; Lepault et al., 1982) t < K ln(I0/I) I0 is the intensity of a bright-field image in the absence of ice and I is the intensity of the image in the presence of an ice layer of thickness t. K is a constant that is dependent on the geometry of the microscope. After global searching, there comes the selecting squares on the grid. The automated hole identification technique for both automation system rely on the intensity measurement. The relationship between ice thickness and intensity is shown here. They use this relationship to set parameters on the automated hole finder to find only those areas of the grid that contain ice of a specified thickness. The user then sets the image intensity thresholds corresponding to a suitable ice thickness according to the thickness of the specific specimen. These thresholds instruct JADAS to skip squares with average intensity levels that are too low or too high. The currently used ice thickness is in a range of from 500 to 1500 Å. The variance of the intensity within each hole is useful in selecting only those holes that contain ice of fairly uniform thickness. And once these parameters are established they can consistently be used between experiments to select ice of a predictable thickness. Thickness Range: 500 to 1500 Å

Intensity Based Hole selection GUI automatic local search for JADAS Here shows the Intensity Based Hole Selection GUI for automatic local search. Green circles mark those holes that meet the preset image intensity criteria. Light peach circles mark those holes that do not meet the criteria. Magenta[mə'dʒentə] cross indicates the hole that is being imaged at the moment. b. Manual Interactive Positioning GUI. User can manually select the positions (green) for focusing or the positions (magenta) for imaging. the selection of a desired range of ice thickness to be used when selecting target areas where high-magnification images will be recorded. After the initial calibration is completed the next step is to identify individual grid squares that are intact and uncontaminated and contain holes with suitable vitreous ice.

Ice filter for EPU (Leginon) The ice filter in EPU or Leginon for hole selection.

Centering the holes Jadas Once holes containing ice of suitable thickness have been identified, the hole must be located at the center of the field of view, and a decision must be made as to whether the hole is suitable for further analysis. Jadas and Leginon deal with it in a similar way. They took images with the centers of holes positioned at the center of the CCD frame. Under this magnification, each hole was expected to fall within the CCD frame. To measure the precision of the positioning of the recorded holes, they measured the deviation of the center of each hole from the center of the CCD frame. Analysis of the frames revealed that 93% of the hole centers fell within the distance less than 0.3 μm from the CCD frame centersa distance of 0.3 μm between the hole center and the CCD frame center will still guarantee most of the CCD image taken within the hole.

Focus and Astigmatism Correction Once a decision to acquire a high-magnification image has been made the specimen must be examined to ensure that it is not drifting and the focus and astigmatism must be adjusted. Drift measurement, focusing, and astigmatism correction procedures are all performed by the automated system under low-dose conditions. Both of them use the well-established methods based on beam tilt-induced image shifts. This method relies on the fact that the amount of image displacement resulting from an induced beam tilt is linearly related to the amount of defocus. For JADAS, besides of Beam shift, diffractogram based method can also be used.

Ligenon and JADAS can analyze a diffractogram for stigmatism correction also. When the specimen is on a holey carbon film, the software can automatically adjust the objective lens stigmator by measuring the ellipticity of the contrast transfer function rings to correct the astigmatism.

Modularity and Flexibility of JADAS Coupling with other tools developed elsewhere. EMEN2 integrate image processing function from EMAN to assess the data quality in real time Off-the-shelf remote logon software (e.g. WebEX: http://www.webex.com/) to monitor the data collection process and even operate JADAS remotely. The capability of JADAS can be enhanced further by coupling it with other tools developed elsewhere. For instance, by integrating with the newly developed database EMEN2 JADAS-acquired data, along with the imaging condition parameters, can readily be uploaded to the database while data collection is still in progress. Another possibility is to integrate image processing function from EMAN to assess the data quality in real time, thereby giving researchers immediate feedback during data acquisition. By combining the utility of off-the-shelf remote logon software (e.g. WebEX: http://www.webex.com/), researchers can also monitor the data collection process and even operate JADAS remotely.

Examples JADAS EPU

Speed of data collection JADAS Using JEM3200FSC, record 30-40 4k×4k CCD images per hour, if each image has 100 particles One day: 960 images 96000 particles EPU one hour to set up a run for days or weeks, one million particles in a four-day session on an FEI Titan Krios™ microscope. One day: 250000 particles