High-throughput cryo-EM enabled by user-free preprocessing routines

Abstract

The growth of single-particle cryo-EM into a mainstream structural biology tool has allowed for many important biological discoveries. Continued developments in data collection strategies alongside new sample preparation devices heralds a future where users will collect multiple datasets per microscope session. To make cryo-EM data processing more automatic and user-friendly, we have developed an automatic pipeline for cryo-EM data preprocessing and assessment using a combination of deep learning and image analysis tools. We have verified the performance of this pipeline on a number of datasets and extended its scope to include sample screening by the user-free assessment of the qualities of a series of datasets under different conditions. We propose that our workflow provides a decision-free solution for cryo-EM, making data preprocessing more generalized and robust in the high-throughput era as well as more convenient for users from a range of backgrounds.