Advanced Electron Microscopy Techniques and Applications

DISCA uses deep learning to automatically cluster cryo-ET data into homogeneous structural subsets without templates or labels.

This paper introduces a permeabilization-free protocol for immunohistochemistry in thick tissue sections, enabling correlative microscopy with electron microscopy and two-photon imaging.

The paper introduces hardware and software improvements to enhance cryo-FIB milling for cryo-electron tomography.

This paper introduces DeepMM, a deep learning framework that automatically reconstructs full-atom protein structures from cryo-EM maps with high accuracy and coverage.

The paper demonstrates improved signal-to-noise and resolution in EFTEM elemental mapping by using lower energy-loss N4,5 edges instead of core-loss M4,5 edges for lanthanide detection.

This study evaluates 15 scoring functions to rank the quality of 3D subtomogram clusters from cryo-electron tomography, identifying two robust methods that work across varying signal-to-noise ratios.

DRPnet automatically detects particle centers in cryoEM micrographs using a deep learning approach, achieving comparable or better performance than manual methods with reduced user interaction.

This paper introduces DEFMap, a deep learning method that extracts dynamics information from cryo-EM maps, achieving results comparable to molecular dynamics simulations and experimental approaches.

This paper introduces SuperEM, a deep learning method that improves cryo-EM map resolution, enhancing protein structure modeling.

CryoDRGN uses deep neural networks to reconstruct heterogeneous protein complexes and their motion from cryo-EM data, revealing previously unseen structural states.

DeepTracer is a deep learning method for fast cryo-EM structure modeling of protein complexes, showing improved accuracy and efficiency compared to existing methods.

EM-stellar benchmarks deep learning methods for EM image segmentation, showing performance varies with image properties and no method consistently outperforms others.

This paper introduces a regularisation framework for cryo-EM structure determination using a convolutional neural network trained on known macromolecular structures, showing improved reconstructions on simulated data.

MorphOT interpolates between EM maps using optimal transport to generate morphing trajectories, avoiding blending by displacing densities.

This paper introduces a quantum-based refinement method for cryo-EM maps to improve atomic model accuracy at low resolutions using quantum-chemical restraints.

DeepCryoPicker automates single particle picking in cryo-EM using deep learning, outperforming semi-automated methods without human intervention.

Topaz-Denoise improves cryoEM and cryoET image quality by increasing SNR, enabling faster analysis and solving previously elusive structures.

The paper reports the high-resolution cryo-EM structure of urease from Yersinia enterocolitica, revealing the bimetal active site and enabling drug development insights.

The paper describes a high-throughput transmission electron microscopy pipeline for imaging neuronal circuits, achieving petascale data acquisition of mouse neocortex.

This paper optimizes mask diameter and box size for 200 keV cryo-EM to improve resolution for proteins under 200 kDa.

The paper develops a method to estimate membrane curvature from cryo-ET data, showing its validity on benchmark surfaces and application to biological data.

This study uses MicroED to visualize a sulfonamide inhibitor bound to human carbonic anhydrase II at the atomic level, demonstrating its potential for drug discovery.

The paper determines the sub-3 Å resolution cryo-EM structures of Achromobacter cycloclastes NiR at two pH values, identifies structural artifacts, and suggests local changes around the Cu site.

The paper introduces ATUM-FIB, a hybrid microscopy technique that combines serial sectioning with FIB-SEM to enable targeted ultrastructural analysis at isotropic resolution.

MicrographCleaner uses deep learning to improve cryo-EM micrograph preprocessing by identifying suitable regions for particle picking.

This study demonstrates that a multi-purpose TEM with a side-entry cryo-holder can achieve high-resolution cryo-EM single particle analysis, as shown by a 3 Å resolution map of apoferritin.

The paper introduces Haruspex, a convolutional neural network that automatically annotates Cryo-EM maps for protein secondary structures and RNA/DNA, achieving high recall and precision on test data.

This paper describes an automated method for preparing cryoET samples, reducing preparation time and user input while enabling high-quality lamellae for imaging and subtomogram averaging.

This paper introduces a method for rapid protein structure determination using serial electron diffraction in a scanning transmission electron microscope, achieving high-resolution results with minimal sample usage.

This study benchmarks tomographic acquisition schemes for cryo electron tomography to optimize resolution for subtomogram averaging using HIV-1 lattice.