Advances in automation for cryo-electron tomography data collection

Abstract

Cryo-electron microscopy has become the preferred method for determining structures of macromolecular complexes both in isolation, using single particle analysis, and in their cellular contexts, using cryo-electron tomography (Cryo-ET) combined with subvolume averaging (SVA). Collection of tilt series for Cryo-ET introduces challenges such as low signal-to-noise ratios, sample radiation sensitivity, and mechanical imprecision of the microscope stage – particularly at high magnifications. Strategies to improve throughput and resolution include continuous tilt and beam-image-shift parallel acquisition, real-time predictive adjustments, and machine learning-driven targeting. Additionally, montage tomography has increased the observable cellular area, while innovations like rectangular condenser apertures promise improved dose efficiency. Web-based and machine learning-enhanced solutions for automated and remote microscope operation are improving the user experience. Collectively, these advancements represent a critical step towards robust, high-resolution, and user-friendly Cryo-ET, facilitating the visualization of macromolecular assemblies within their authentic biological environments.