ArticleNature methods2021
Deep learning improves macromolecule identification in 3D cellular cryo-electron tomograms.
Article in Nature methods, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 94 papers.
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Who cites it
94 citing papers in PubMed.
- MemBrain v2: an end-to-end tool for the analysis of membranes in cryo-electron tomography.Nature methods · 2026Article
- Graph identification of proteins in tomograms (GRIP-Tomo) 2.0: Topologically aware classification for proteins.Protein science : a publication of the Protein Society · 2026Article
- Lessons learned from a Kaggle challenge for particle picking in cryo-electron tomography.Nature methods · 2026Article
- Article
- CoCryoViS: Collaborative online cryo-electron tomography visualization system.Protein science : a publication of the Protein Society · 2026Article
- Applications and prospects of cryo-electron tomography in drug discovery and understanding disease.Current opinion in structural biology · 2026Review
- TomoSwin3D: a Swin3D Transformer for the Identification and Classification of Macromolecules in 3D Cryo-ET Tomograms.bioRxiv : the preprint server for biology · 2026Article
- Cryo-FIB-enabled 3D imaging of biological specimens.Applied microscopy · 2026Review
- rRNA expansion segments mediate ribosome dimerization as a conserved stress response.Nucleic acids research · 2026Article
- Structural remodeling of the mitochondrial protein biogenesis machinery under proteostatic stress.Science advances · 2026Article
- Leveraging AI for cell biology discovery.Biochemical Society transactions · 2026Review
- MVGFormer: Multi-view perspective with graph-guided transformer for cryo-ET segmentation.Knowledge-based systems · 2026Article
- Structural Analysis of Membrane Proteins in Cell-Derived Microvesicles.Methods in molecular biology (Clifton, N.J.) · 2026Article
- ExoSloNano: multimodal nanogold labels for identification of macromolecules in live cells and cryo-electron tomograms.Nature methods · 2026Article
- Multiscale structure of chromatin condensates explains phase separation and material properties.Science (New York, N.Y.) · 2025Article
- Mechanisms of COPII coat assembly and cargo recognition in the secretory pathway.Nature reviews. Molecular cell biology · 2025Review
- Localization of macromolecules in crowded cellular cryo-electron tomograms from extremely sparse labels.Briefings in bioinformatics · 2025Article
- PickET: An unsupervised method for localizing macromolecules in cryo-electron tomograms.Research square · 2025Article
- cryoTIGER: deep-learning based tilt interpolation generator for enhanced reconstruction in cryo electron tomography.Communications biology · 2025Article
- Template Learning: Deep learning with domain randomization for particle picking in cryo-electron tomography.Nature communications · 2025Article
34 more citing papers are in PubMed but not listed here.
Corrections and comments
- Erratum issued
Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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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