Evidence map›Paper›PMID 35997703›Full record

ArticleeLife2022

Automated systematic evaluation of cryo-EM specimens with SmartScope.

Jonathan Bouvette, Qinwen Huang, Amanda A Riccio, William C Copeland, Alberto Bartesaghi, Mario J Borgnia

Abstract read
In one paragraph

Article in eLife, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.

0numbers the graph read from it
0cells of the map it votes in
41citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

41 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. LowDoseWizard: rapid and standardized setup of low-dose cryo-TEM imaging in SerialEM.Acta crystallographica. Section D, Structural biology · 2026
    Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Review
  10. Article
  11. Article
  12. The Mechanism of Mineral Nucleation and Growth in a Mini-Ferritin.Journal of the American Chemical Society · 2025
    Article
  13. Article
  14. Article
  15. Finding antibodies in cryo-EM maps with CrAI.Bioinformatics (Oxford, England) · 2025
    Article
  16. Article
  17. Article
  18. Review
  19. Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Jonathan Bouvette *Genome Integrity and Structural Biology Laboratory, National Institute of Environmental Health Sciences, Research Triangle Park, United States.ORCID 0000-0003-3550-5319
Qinwen Huang *Department of Computer Science, Duke University, Durham, United States.ORCID 0000-0002-7082-5257
Amanda A RiccioGenome Integrity and Structural Biology Laboratory, National Institute of Environmental Health Sciences, Research Triangle Park, United States.ORCID 0000-0001-7782-0363
William C CopelandGenome Integrity and Structural Biology Laboratory, National Institute of Environmental Health Sciences, Research Triangle Park, United States.ORCID 0000-0002-0359-0953
Alberto BartesaghiDepartment of Computer Science, Duke University, Durham, United States.ORCID 0000-0002-7360-1523
Mario J BorgniaGenome Integrity and Structural Biology Laboratory, National Institute of Environmental Health Sciences, Research Triangle Park, United States.ORCID 0000-0001-9159-1413

Funding

NIEHS Cryo-EM Core FacilityZICES103326 · NIEHS · NATIONAL INSTITUTE OF ENVIRONMENTAL HEALTH SCIENCES · PI BORGNIA, MARIO · 2017 to 2025
$17.0M
Resource for Biocomputing Visualization and InformaticsP41GM103311 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI FERRIN, THOMAS E · 2012 to 2017
$8.2M
Epitope dynamics of SARS-CoV-2 S protein by cryo-electron tomography and single particle cryo-EMZIAES103341 · NIEHS · NATIONAL INSTITUTE OF ENVIRONMENTAL HEALTH SCIENCES · PI BORGNIA, MARIO · 2020 to 2025
$3.9M
Molecular Study Of The Mitochondrial DNA PolymeraseZ01ES065078 · NIEHS · NATIONAL INSTITUTE OF ENVIRONMENTAL HEALTH SCIENCES · PI COPELAND, WILLIAM · 1994 to 2008
$2.8M
Intramural NIH HHS Z01 ES065078Intramural NIH HHS ZIA ES103341Intramural NIH HHS ZIC ES103326NIGMS NIH HHS P41 GM103311
6 · The paper itself

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.

Indexed as

Cryoelectron MicroscopyAutomationMacromolecular SubstancesMacromolecular Substancesautomationcryo-electron microscopydeep learninghumanmachine learningmolecular biophysicsobject recognitionsoftware platformstructural biology

Identifiers

PMID35997703
PMCPMC9398423

What OpenQuestion holds

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Registered trials

None linked

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.