  Over the past decade, cryogenic electron microscopy (cryo-EM) has emerged as
a primary method for determining near-native, near-atomic resolution 3D
structures of biological macromolecules. In order to meet increasing demand for
cryo-EM, automated methods to improve throughput and efficiency while lowering
costs are needed. Currently, all high-magnification cryo-EM data collection
softwares require human input and manual tuning of parameters. Expert operators
must navigate low- and medium-magnification images to find good
high-magnification collection locations. Automating this is non-trivial: the
images suffer from low signal-to-noise ratio and are affected by a range of
experimental parameters that can differ for each collection session. Here, we
use various computer vision algorithms, including mixture models, convolutional
neural networks, and U-Nets to develop the first pipeline to automate low- and
medium-magnification targeting. Learned models in this pipeline are trained on
a large internal dataset of images from real world cryo-EM data collection
sessions, labeled with locations that were selected by operators. Using these
models, we show that we can effectively detect and classify regions of interest
in low- and medium-magnification images, and can generalize to unseen sessions,
as well as to images captured using different microscopes from external
facilities. We expect our open-source pipeline, Ptolemy, will be both
immediately useful as a tool for automation of cryo-EM data collection, and
serve as a foundation for future advanced methods for efficient and automated
cryo-EM microscopy.
