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Moebel, E.* ; Martinez-Sanchez, A.* ; Lamm, L. ; Righetto, R.D. ; Wietrzynski, W. ; Albert, S.* ; Larivière, D.* ; Fourmentin, E.* ; Pfeffer, S.* ; Ortiz, J.* ; Baumeister, W.* ; Peng, T. ; Engel, B.D. ; Kervrann, C.*

Deep learning improves macromolecule identification in 3D cellular cryo-electron tomograms.

Nat. Methods 18, 1386-1394 (2021)
Postprint Forschungsdaten DOI PMC
Open Access Green
Cryogenic electron tomography (cryo-ET) visualizes the 3D spatial distribution of macromolecules at nanometer resolution inside native cells. However, automated identification of macromolecules inside cellular tomograms is challenged by noise and reconstruction artifacts, as well as the presence of many molecular species in the crowded volumes. Here, we present DeepFinder, a computational procedure that uses artificial neural networks to simultaneously localize multiple classes of macromolecules. Once trained, the inference stage of DeepFinder is faster than template matching and performs better than other competitive deep learning methods at identifying macromolecules of various sizes in both synthetic and experimental datasets. On cellular cryo-ET data, DeepFinder localized membrane-bound and cytosolic ribosomes (roughly 3.2 MDa), ribulose 1,5-bisphosphate carboxylase–oxygenase (roughly 560 kDa soluble complex) and photosystem II (roughly 550 kDa membrane complex) with an accuracy comparable to expert-supervised ground truth annotations. DeepFinder is therefore a promising algorithm for the semiautomated analysis of a wide range of molecular targets in cellular tomograms.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter In-situ; Single; Classification; Visualization; Localization
Sprache englisch
Veröffentlichungsjahr 2021
HGF-Berichtsjahr 2021
ISSN (print) / ISBN 1548-7091
e-ISSN 1548-7105
Zeitschrift Nature Methods
Quellenangaben Band: 18, Heft: 11, Seiten: 1386-1394 Artikelnummer: , Supplement: ,
Verlag Nature Publishing Group
Verlagsort New York, NY
Begutachtungsstatus Peer reviewed
Institut(e) Helmholtz Pioneer Campus (HPC)
Helmholtz Artifical Intelligence Cooperation Unit (HAICU)
POF Topic(s) 30203 - Molecular Targets and Therapies
30205 - Bioengineering and Digital Health
Forschungsfeld(er) Pioneer Campus
Enabling and Novel Technologies
PSP-Element(e) G-510008-001
G-530006-001
Förderungen Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy
Helmholtz Association
Munich School for Data Science (MUDS)
DFG
France-BioImaging infrastructure (French National Research Agency)
Region Bretagne (Brittany Council)
Fourmentin-Guilbert Foundation
DOI 10.1038/s41592-021-01275-4
WOS ID WOS:000709668800001
Scopus ID 85117453449
PubMed ID 34675434
Erfassungsdatum 2021-11-03
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