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Single-particle cryo-electron microscopy (cryoEM) is a swiftly growing method for understanding protein structure. With increasing demand for high-throughput, high-resolution cryoEM services comes greater demand for rapid and automated cryoEM grid and sample screening. During screening, optimal grids and sample conditions are identified for subsequent high-resolution data collection. Screening is a major bottleneck for new cryoEM projects because grids must be optimized for several factors, including grid type, grid hole size, sample concentration, buffer conditions, ice thickness and particle behavior. Even for mature projects, multiple grids are commonly screened to select a subset for high-resolution data collection. Here, machine learning and novel purpose-built image-processing and microscope-handling algorithms are incorporated into the automated data-collection software Leginon , to provide an open-source solution for fully automated high-throughput grid screening. This new version, broadly called Smart Leginon , emulates the actions of an operator in identifying areas on the grid to explore as potentially useful for data collection. Smart Leginon Autoscreen sequentially loads and examines grids from an automated specimen-exchange system to provide completely unattended grid screening across a set of grids. Comparisons between a multi-grid autoscreen session and conventional manual screening by 5 expert microscope operators are presented. On average, Autoscreen reduces operator time from ∼6 h to <10 min and provides a percentage of suitable images for evaluation comparable to the best operator. The ability of Smart Leginon to target holes that are particularly difficult to identify is analyzed. Finally, the utility of Smart Leginon is illustrated with three real-world multi-grid user screening/collection sessions, demonstrating the efficiency and flexibility of the software package. The fully automated functionality of Smart Leginon significantly reduces the burden on operator screening time, improves the throughput of screening and recovers idle microscope time, thereby improving availability of cryoEM services.

CryoEM democratization is hampered by access to costly plunge-freezing supplies. We introduce methods, called CryoCycle, for reliably blotting, vitrifying, and reusing clipped cryoEM grids. We demonstrate that vitreous ice may be produced by plunging clipped grids with purified proteins into liquid ethane and that clipped grids may be reused several times for different protein samples. Furthermore, we demonstrate the vitrification of thin areas of cells prepared on gold-coated, pre-clipped grids.

The most widely used sample preparation method for single particle cryo-electron microscopy (cryo-EM) today involves the application of 3-4 μl of sample onto a cryo-EM grid, removing most of the liquid by blotting with filter paper, then rapidly plunging into liquid ethane to vitrify the sample. To determine if the grid has appropriate ice thicknesses and sufficient area for cryo-EM imaging, the grid must be inserted into a transmission electron microscope (TEM) and screened. This process to evaluate grid quality is costly and time consuming. Here, we present our initial attempt to image the sample preparation process in one of the most commonly used plunge freezing devices, the Vitrobot. We do this by building the Vitrocam, a Raspberry Pi high-speed camera, that captures images of grids mid-plunge. Images from the Vitrocam can be correlated to TEM atlases and show promise for providing preliminary feedback on grid quality and ice thickness.

Single particle cryo-electron microscopy (cryoEM) is a swiftly growing method for understanding protein structure. With increasing demand for high-throughput, high-resolution cryoEM services comes greater demand for rapid and automated cryoEM grid and sample screening. During screening, optimal grids and sample conditions are identified for subsequent high-resolution data collection. Screening is a major bottleneck for new cryoEM projects because grids must be optimized over several factors, including grid type, grid hole size, sample concentration, buffer conditions, ice thickness, and particle behaviors. Even for mature projects, multiple grids are commonly screened to select a subset for high-resolution data collection. Here, machine learning and novel, purpose-built image processing and microscope-handling algorithms are incorporated into the automated data collection software, Leginon, to provide an open-source solution for fully automated, high-throughput grid screening. This new version, broadly called Smart Leginon, emulates the actions of an operator in identifying areas on the grid to explore as potentially useful for data collection. Smart Leginon Autoscreen sequentially loads and examines grids from an automated specimen exchange system to provide completely unattended grid screening across a set of grids. Comparisons between a multi-grid Autoscreen session and conventional manual screening by five expert microscope operators are presented. On average, Autoscreen reduces operator time from ~6 hours to <10 minutes and provides a comparable percentage of suitable images for evaluation as the best operator. Smart Leginon's ability to target holes that are particularly difficult to identify is analyzed. Finally, Smart Leginon's utility is illustrated with three real-world multi-grid user screening/collection sessions, demonstrating the efficiency and flexibility of the software package. Smart Leginon's fully automated functionality significantly reduces the burden on operator screening time, improves the throughput of screening, and recovers idle microscope time, thereby improving availability of cryoEM services. Competing Interest Statement The authors have declared no competing interest.