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

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...

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Bibliographic Details
Published in:Nature methods Vol. 18; no. 11; pp. 1386 - 1394
Main Authors: Moebel, Emmanuel, Martinez-Sanchez, Antonio, Lamm, Lorenz, Righetto, Ricardo D, Wietrzynski, Wojciech, Albert, Sahradha, Larivière, Damien, Fourmentin, Eric, Pfeffer, Stefan, Ortiz, Julio, Baumeister, Wolfgang, Peng, Tingying, Engel, Benjamin D, Kervrann, Charles
Format: Journal Article
Language:English
Published: United States Nature Publishing Group 01.11.2021
Subjects:
Algorithms
Annotations
Artificial neural networks
Chlamydomonas reinhardtii - metabolism
Computer applications
Cryoelectron Microscopy - methods
Datasets
Deep Learning
Electron Microscope Tomography - methods
Electrons
Image Processing, Computer-Assisted - methods
Machine learning
Macromolecular Substances - chemistry
Macromolecules
Membranes
Neural networks
Neural Networks, Computer
Oxygenase
Photosystem II
Photosystem II Protein Complex - chemistry
Ribosomes
Ribosomes - chemistry
Ribulose-Bisphosphate Carboxylase - chemistry
Spatial distribution
Template matching
ISSN:1548-7091, 1548-7105, 1548-7105
Online Access:Get full text
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Summary: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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ISSN:1548-7091
1548-7105
1548-7105
DOI:10.1038/s41592-021-01275-4