ArticleIUCrJ2023
Fully automated multi-grid cryoEM screening using Smart Leginon.
Article in IUCrJ, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
22 citing papers in PubMed, 34 citations in OpenAlex.
- LowDoseWizard: rapid and standardized setup of low-dose cryo-TEM imaging in SerialEM.Acta crystallographica. Section D, Structural biology · 2026Article
- prismPYP: Power-spectrum and image domain learning for self-supervised micrograph evaluation.Structure (London, England : 1993) · 2026Article
- Pathogenic KIF1A R350 mutations disrupt a conserved and conformation-dependent kinesin-tubulin salt bridge.Nature communications · 2026Article
- MISO: microfluidic protein isolation enables single-particle cryo-EM structure determination from a single cell colony.Nature methods · 2025Article
- Distinct quaternary states, intermediates, and autoinhibition during loading of the DnaB-replicative helicase by the phage λP helicase loader.Nucleic acids research · 2025Article
- Few-Shot Classification of Cryo-EM Micrographs Using Triplet Loss Embeddings.bioRxiv : the preprint server for biology · 2025Article
- Advances in cryo-electron microscopy (cryoEM) for structure-based drug discovery.Expert opinion on drug discovery · 2025Review
- Rapid structural analysis of bacterial ribosomes in situ.Communications biology · 2025Article
- Smart parallel automated cryo-electron tomography.Nature methods · 2024Article
- CryoCycle your grids: Plunge vitrifying and reusing clipped grids to advance cryoEM democratization.Research square · 2024Article
- Miffi: Improving the accuracy of CNN-based cryo-EM micrograph filtering with fine-tuning and Fourier space information.Journal of structural biology · 2024Article
- CryoCycle your grids: Plunge vitrifying and reusing clipped grids to advance cryoEM democratization.bioRxiv : the preprint server for biology · 2024Article
- VitroJet: new features and case studies.Acta crystallographica. Section D, Structural biology · 2024Article
- Tomo Live: an on-the-fly reconstruction pipeline to judge data quality for cryo-electron tomography workflows.Acta crystallographica. Section D, Structural biology · 2024Article
- Rapid structural analysis of bacterial ribosomesbioRxiv : the preprint server for biology · 2024Article
- Miffi: Improving the accuracy of CNN-based cryo-EM micrograph filtering with fine-tuning and Fourier space information.bioRxiv : the preprint server for biology · 2024Article
- The advent of preventive high-resolution structural histopathology by artificial-intelligence-powered cryogenic electron tomography.Frontiers in molecular biosciences · 2024Review
- Scaling up cryo-EM for biology and chemistry: The journey from niche technology to mainstream method.Structure (London, England : 1993) · 2023Review
- Cryo-Electron Microscopy Screening Automation across Multiple Grids using Smart Leginon.Journal of visualized experiments : JoVE · 2023Article
- nextPYP: a comprehensive and scalable platform for characterizing protein variability in situ using single-particle cryo-electron tomography.Nature methods · 2023Article
Corrections and comments
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Authors and funding
16 authors at 1 institution in 1 country.
Funding
Abstract
Single-particle cryo-electron microscopy (cryoEM) is a swiftly growing method for understanding protein structure. With increasing demand for high-throughput, high-resolution cryoEM services comes greater demand for rapid and automated cryoEM grid and sample screening. During screening, optimal grids and sample conditions are identified for subsequent high-resolution data collection. Screening is a major bottleneck for new cryoEM projects because grids must be optimized for several factors, including grid type, grid hole size, sample concentration, buffer conditions, ice thickness and particle behavior. Even for mature projects, multiple grids are commonly screened to select a subset for high-resolution data collection. Here, machine learning and novel purpose-built image-processing and microscope-handling algorithms are incorporated into the automated data-collection software Leginon, to provide an open-source solution for fully automated high-throughput grid screening. This new version, broadly called Smart Leginon, emulates the actions of an operator in identifying areas on the grid to explore as potentially useful for data collection. Smart Leginon Autoscreen sequentially loads and examines grids from an automated specimen-exchange system to provide completely unattended grid screening across a set of grids. Comparisons between a multi-grid autoscreen session and conventional manual screening by 5 expert microscope operators are presented. On average, Autoscreen reduces operator time from ∼6 h to <10 min and provides a percentage of suitable images for evaluation comparable to the best operator. The ability of Smart Leginon to target holes that are particularly difficult to identify is analyzed. Finally, the utility of Smart Leginon is illustrated with three real-world multi-grid user screening/collection sessions, demonstrating the efficiency and flexibility of the software package. The fully automated functionality of Smart Leginon significantly reduces the burden on operator screening time, improves the throughput of screening and recovers idle microscope time, thereby improving availability of cryoEM services.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.