Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

Deep Learning Improves Macromolecules Localization and Identification in 3D Cellular Cryo-Electron Tomograms

Emmanuel Moebel, Antonio Martinez-Sanchez, Damien Larivière, Eric Fourmentin, Julio Ortiz, Wolfgang Baumeister, Charles Kervrann
doi: https://doi.org/10.1101/2020.04.15.042747
Emmanuel Moebel
1Serpico Project-Team, CNRS-UMR 144, Inria, CNRS, Institut Curie, PSL Research University, Campus Universitaire de Beaulieu, 35 042 Rennes Cedex, France
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Antonio Martinez-Sanchez
2Max Planck Institute of Biochemistry, Am Klopferspitz 18, 82152 Martinsried, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Damien Larivière
3Fourmentin-Guilbert Scientific Foundation, 2 Avenue du Pavé Neuf, 93 160 Noisy-le-Grand, France
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Eric Fourmentin
3Fourmentin-Guilbert Scientific Foundation, 2 Avenue du Pavé Neuf, 93 160 Noisy-le-Grand, France
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Julio Ortiz
4Ernst Ruska-Centre, Wilhelm-Johnen-Straße, 52425 Jülich, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Wolfgang Baumeister
2Max Planck Institute of Biochemistry, Am Klopferspitz 18, 82152 Martinsried, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Charles Kervrann
1Serpico Project-Team, CNRS-UMR 144, Inria, CNRS, Institut Curie, PSL Research University, Campus Universitaire de Beaulieu, 35 042 Rennes Cedex, France
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: charles.kervrann@inria.fr
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Preview PDF
Loading

abstract

Cryo-electron tomography (cryo-ET) allows one to visualize and study the 3D spatial distribution of macromolecules, in their native states and at nanometer resolution in single cells. While this label-free cryogenic imaging technology produces data containing rich structural information, automatic localization and identification of macromolecules are prone to noise and reconstruction artifacts, and to the presence of many molecular species in small areas. Hence, we present a computational procedure that uses artificial neural networks to accurately localize several macromolecular species in cellular cryo-electron tomograms. The DeepFinder algorithm leverages deep learning and outperforms the commonly-used template matching method on synthetic datasets. Meanwhile, DeepFinder is very fast when compared to template matching, and is better capable of localizing and identifying small macro-molecules than other competitive deep learning methods. On experimental cryo-ET data depicting ribosomes, the localization and structure resolution (determined through subtomogram averaging) results obtained with DeepFinder are consistent with those obtained by experts. The DeepFinder algorithm is able to imitate the analysis performed by experts, and is therefore a very promising algorithm to investigate efficiently the contents of cellular tomograms. Furthermore, we show that DeepFinder can be combined with a template matching procedure to localize the missing macromolecules not found by one or the other method. Application of this collaborative strategy allowed us to find additional 20.5% membrane-bound ribosomes that had been missed or discarded during manual template matching-assisted annotation.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
Back to top
PreviousNext
Posted April 16, 2020.
Download PDF
Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
Deep Learning Improves Macromolecules Localization and Identification in 3D Cellular Cryo-Electron Tomograms
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
Deep Learning Improves Macromolecules Localization and Identification in 3D Cellular Cryo-Electron Tomograms
Emmanuel Moebel, Antonio Martinez-Sanchez, Damien Larivière, Eric Fourmentin, Julio Ortiz, Wolfgang Baumeister, Charles Kervrann
bioRxiv 2020.04.15.042747; doi: https://doi.org/10.1101/2020.04.15.042747
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
Deep Learning Improves Macromolecules Localization and Identification in 3D Cellular Cryo-Electron Tomograms
Emmanuel Moebel, Antonio Martinez-Sanchez, Damien Larivière, Eric Fourmentin, Julio Ortiz, Wolfgang Baumeister, Charles Kervrann
bioRxiv 2020.04.15.042747; doi: https://doi.org/10.1101/2020.04.15.042747

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Bioinformatics
Subject Areas
All Articles
  • Animal Behavior and Cognition (3514)
  • Biochemistry (7365)
  • Bioengineering (5342)
  • Bioinformatics (20318)
  • Biophysics (10041)
  • Cancer Biology (7773)
  • Cell Biology (11348)
  • Clinical Trials (138)
  • Developmental Biology (6450)
  • Ecology (9979)
  • Epidemiology (2065)
  • Evolutionary Biology (13354)
  • Genetics (9370)
  • Genomics (12607)
  • Immunology (7724)
  • Microbiology (19087)
  • Molecular Biology (7459)
  • Neuroscience (41134)
  • Paleontology (300)
  • Pathology (1235)
  • Pharmacology and Toxicology (2142)
  • Physiology (3177)
  • Plant Biology (6878)
  • Scientific Communication and Education (1276)
  • Synthetic Biology (1900)
  • Systems Biology (5328)
  • Zoology (1091)