1. Automated systematic evaluation of cryo-EM specimens with SmartScope
- Author
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Jonathan Bouvette, Qinwen Huang, Amanda A Riccio, William C Copeland, Alberto Bartesaghi, and Mario J Borgnia
- Subjects
cryo-electron microscopy ,automation ,machine learning ,deep learning ,object recognition ,software platform ,Medicine ,Science ,Biology (General) ,QH301-705.5 - Abstract
Finding the conditions to stabilize a macromolecular target for imaging remains the most critical barrier to determining its structure by cryo-electron microscopy (cryo-EM). While automation has significantly increased the speed of data collection, specimens are still screened manually, a laborious and subjective task that often determines the success of a project. Here, we present SmartScope, the first framework to streamline, standardize, and automate specimen evaluation in cryo-EM. SmartScope employs deep-learning-based object detection to identify and classify features suitable for imaging, allowing it to perform thorough specimen screening in a fully automated manner. A web interface provides remote control over the automated operation of the microscope in real time and access to images and annotation tools. Manual annotations can be used to re-train the feature recognition models, leading to improvements in performance. Our automated tool for systematic evaluation of specimens streamlines structure determination and lowers the barrier of adoption for cryo-EM.
- Published
- 2022
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