Fully Automated Multi-Grid Cryo-EM Screening using Smart Leginon
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 Leginons ability to target holes that are particularly difficult to identify is analyzed. Finally, Smart Leginons utility is illustrated with three real-world multi-grid user screening/collection sessions, demonstrating the efficiency and flexibility of the software package. Smart Leginons 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.