RESUMEN
In situ cryo-electron tomography enables investigation of macromolecules in their native cellular environment. Samples have become more readily available owing to recent software and hardware advancements. Data collection, however, still requires an experienced operator and appreciable microscope time to carefully select targets for high-throughput tilt series acquisition. Here, we developed smart parallel automated cryo-electron tomography (SPACEtomo), a workflow using machine learning approaches to fully automate the entire cryo-electron tomography process, including lamella detection, biological feature segmentation, target selection and parallel tilt series acquisition, all without the need for human intervention. This degree of automation will be essential for obtaining statistically relevant datasets and high-resolution structures of macromolecules in their native context.
RESUMEN
Imaging large fields of view at a high magnification requires tiling. Transmission electron microscopes typically have round beam profiles; therefore, tiling across a large area is either imperfect or results in uneven exposures, a problem for dose-sensitive samples. Here, we introduce a square electron beam that can easily be retrofitted in existing microscopes, and demonstrate its application, showing that it can tile nearly perfectly and deliver cryo-electron microscopy imaging with a resolution comparable to conventional set-ups.