cryo-electron microscopy data collection and image analysis
Dr. Michael Cianfrocco uses cryo-electron microscopy (cryo-EM) to determine protein structures to understand how nanometer-sized molecular machines work. While a powerful technique, cryo-EM data collection and subsequent image analysis remain bespoke, clunky, and heuristic. Dr. Cianfrocco is coupling his 16+ years of experience with artificial intelligence to automate data collection and processing by capturing human expertise into AI-based algorithms. Recently, his laboratory implemented reinforcement learning to guide cryo-electron microscopes for data collection [1, 2]. This work combined real-world datasets and Dr. Cianfrocco’s expertise with AI-driven optimization algorithms to find the ‘best’ areas of cryo-EM samples for data collection.
Human users must curate and select areas for subsequent analysis after data collection. Subjective decisions guide how to process the single particles and determine what constitutes ‘good’ data. To automate subsequent preprocessing, Dr. Cianfrocco’s lab built the first AI-backed data preprocessing in cryo-EM by training CNNs to recognize ‘good’ and ‘bad’ cryo-EM data [3]. This work enabled fully-automated cryo-EM data preprocessing, the first step in the processing pipeline of cryo-EM data. In the future, Dr. Cianfrocco wants to continue improving cryo-EM workflows to make them robust and automated, eventually surpassing human experts in the ability of algorithms to collect and analyze cryo-EM data. 1. Fan Q*, Li Y*, et al. “CryoRL: Reinforcement Learning Enables Efficient Cryo-EM Data Collection.” arXiv preprint arXiv:2204.07543 (2022). 2. Li Y*, Fan Q*, Optimized path planning surpasses human efficiency in cryo-EM imaging. bioRxiv 2022.06.17.496614 (2022). 3. Li Y, High-Throughput Cryo-EM Enabled by User-Free Preprocessing Routines. Structure. 2020 Jul 7;28(7):858-869.e3.
