The goal of specimen screening is to learn as much as possible from each condition, often taking advantage of the heterogeneous landscape of each grid to extract valuable information about the behavior of the macromolecule of interest. Instead, an iterative search is performed in which a limited number of parameters are evaluated, and new conditions are selected based on the results. Testing all combinations is impractical because the number grows significantly with the inclusion of each parameter. Evaluating each combination of parameters involves comprehensive sampling of one or more grids using a cryo-EM. These artifacts can severely limit the quality of specimens and are typically addressed through an optimization process in which several parameters are varied to increase the stability and mono-dispersity of the target macromolecule ( Passmore and Russo, 2016). In addition, vitrification methods typically yield variations in ice thickness across the grid. During specimen preparation, interactions with the air-water interface facilitated by the confinement into a thin layer of buffer can destabilize protein complexes leading to denaturation and aggregation or force the molecules into a ‘preferred orientation’ ( Noble et al., 2018). The ideal specimen for solving a structure is a single layer of randomly oriented macromolecular complexes embedded into a thin slab of vitreous ice. However, optimizing specimens for high-resolution cryo-EM imaging remains a significant barrier ( Weissenberger et al., 2021).
Smartscope software#
Over the past decade, advances in hardware and software have improved the resolution and throughput of single particle analysis (SPA), establishing cryo-EM as a method of choice in structural biology.
Our automated tool for systematic evaluation of specimens streamlines structure determination and lowers the barrier of adoption for cryo-EM.
Smartscope manual#
Manual annotations can be used to re-train the feature recognition models, leading to improvements in performance. A web interface provides remote control over the automated operation of the microscope in real time and access to images and annotation tools. 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. Here, we present SmartScope, the first framework to streamline, standardize, and automate specimen evaluation in 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. 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).
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