AIMC Topic: Electron Microscope Tomography

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Dimeric gold nanoparticles enable multiplexed labeling in cryoelectron tomography.

Proceedings of the National Academy of Sciences of the United States of America
Cryoelectron tomography (cryo-ET) enables three-dimensional visualization of molecular structures within tissue and intact cells, providing a powerful tool for studying the spatial organization of biological components at nanometer resolution. Realiz...

cryoTIGER: deep-learning based tilt interpolation generator for enhanced reconstruction in cryo electron tomography.

Communications biology
Cryo-electron tomography enables the visualization of macromolecular complexes within native cellular environments but is limited by incomplete angular sampling and the maximal electron dose that biological specimens can be exposed to. Here, we devel...

Template Learning: Deep learning with domain randomization for particle picking in cryo-electron tomography.

Nature communications
Cryo-electron tomography (cryo-ET) enables three-dimensional visualization of biomolecules and cellular components in their near-native state. A key challenge in cryo-ET data analysis is particle picking, often performed by template matching, which r...

FakET: Simulating cryo-electron tomograms with neural style transfer.

Structure (London, England : 1993)
In cryo-electron microscopy, accurate particle localization and classification are imperative. Recent deep learning solutions, though successful, require extensive training datasets. The protracted generation time of physics-based models, often emplo...

Smart parallel automated cryo-electron tomography.

Nature methods
In situ cryo-electron tomography enables investigation of macromolecules in their native cellular environment. Samples have become more readily available owing to recent software and hardware advancements. Data collection, however, still requires an ...

Missing Wedge Completion via Unsupervised Learning with Coordinate Networks.

International journal of molecular sciences
Cryogenic electron tomography (cryoET) is a powerful tool in structural biology, enabling detailed 3D imaging of biological specimens at a resolution of nanometers. Despite its potential, cryoET faces challenges such as the missing wedge problem, whi...

DeepETPicker: Fast and accurate 3D particle picking for cryo-electron tomography using weakly supervised deep learning.

Nature communications
To solve three-dimensional structures of biological macromolecules in situ, large numbers of particles often need to be picked from cryo-electron tomograms. However, adoption of automated particle-picking methods remains limited because of their tech...

A deep learning approach to the automatic detection of alignment errors in cryo-electron tomographic reconstructions.

Journal of structural biology
Electron tomography is an imaging technique that allows for the elucidation of three-dimensional structural information of biological specimens in a very general context, including cellular in situ observations. The approach starts by collecting a se...

Genetically encoded multimeric tags for subcellular protein localization in cryo-EM.

Nature methods
Cryo-electron tomography (cryo-ET) allows for label-free high-resolution imaging of macromolecular assemblies in their native cellular context. However, the localization of macromolecules of interest in tomographic volumes can be challenging. Here we...

PickYOLO: Fast deep learning particle detector for annotation of cryo electron tomograms.

Journal of structural biology
Particle localization (picking) in digital tomograms is a laborious and time-intensive step in cryogenic electron tomography (cryoET) analysis often requiring considerable user involvement, thus becoming a bottleneck for automated cryoET subtomogram ...