Advanced Electron Microscopy Techniques and Applications

doubleHelix identifies and validates nucleic acid sequences in cryo-EM and crystal structures, improving accuracy at lower resolutions and detecting errors missed by existing methods.

This paper proposes a method to reconstruct 3D structures from cryo-EM micrographs without detecting individual particles, aiming to improve detection of small molecules.

The paper investigates how support grid design in cryo-TEM affects fluorescence microscopy illumination and demonstrates improvements in super-resolution image quality through optimal grid selection.

This study uses cryo-electron tomography to reveal the structure of Anabaena flos-aquae gas vesicles, showing how GvpA subunits form the shell and how GvpC provides structural reinforcement.

The paper identifies thermal magnetic field fluctuations as a resolution limit in transmission electron microscopy with phase plates and shows that redesigning the laser phase plate mitigates this issue.

The paper introduces an epoxidized graphene grid (EG-grid) that improves particle density and orientation in cryoEM, enabling high-resolution structural analysis with fewer micrographs.

DeePiCt is a deep-learning framework for segmenting and localizing macromolecular complexes in cryo-electron tomograms, showing effectiveness in identifying low-abundance structures across species.

The paper introduces DeepTracer-2.0, an AI pipeline that automatically models protein-DNA/RNA complexes from cryo-EM maps, improving accuracy and efficiency in macromolecular structure prediction.

This paper introduces DeepProLigand, a deep learning pipeline that improves protein-ligand interaction modeling using cryo-EM data and templates, achieving top performance in a benchmark challenge.

Okapi-EM is a Napari plugin that processes and analyses cryogenic serial FIB/SEM images by aligning slices, evaluating image quality, and mitigating charging artefacts.

The paper improves CNN-based cryo-EM micrograph filtering by using fine-tuning and Fourier space information, resulting in higher prediction accuracy.

Ot2Rec is a semi-automatic, extensible workflow for cryo-ET image processing that enables flexible, automated tomographic reconstruction through plugins.

This paper introduces a fast multi-position tomographic acquisition method for subtomogram averaging, achieving sub-nanometer resolution in situ.

This paper introduces HaPi, a method to automatically determine the handedness of CryoEM maps, achieving 89% accuracy on simulated data and identifying 11 incorrectly handed structures in a database.

This paper introduces a new cryogenic microscope that combines fluorescence, ion beam, and electron beam imaging to improve the targeting and fabrication of lamellae for high-resolution imaging.

EMPIAR provides a public archive for cryo-EM image data and 3D reconstructions, supporting data deposition, search, visualization, and programmatic access.

This paper introduces an in situ X-ray assisted staining method for electron microscopy, enabling real-time observation of staining kinetics and development of a diffusion-reaction-advection model for osmium accumulation in large biological samples.

This paper explores unsupervised classification of macromolecular complexes in cryo-ET data using transfer learning and deep learning, showing promising results on real data without manual annotation.

This paper introduces AlphaCryo4D, a deep learning framework for atomic-level cryo-EM reconstructions of conformational continua in biomolecular complexes, demonstrating its ability to visualize dynamic components and hidden conformational space.

This paper introduces native ES-IBD for preparing high-purity, ice-free cryo-EM samples, showing structural integrity but limitations in 2D classes and 3D EM density maps.

This paper introduces an in-silico method using Feret signatures to characterize nanodisc samples, enabling the identification of low-heterogeneity preparations for accurate structure determination of membrane proteins.

The paper presents a scalable pipeline for assembling large electron microscopy datasets into 3D volumes, achieving high throughput and modular design for efficient processing.

MemBrain detects membrane proteins in cryo-ET data using a deep learning pipeline, achieving high accuracy with minimal labels and generalizing across different cell types and imaging methods.

The paper demonstrates that dAPEX2/DAB labeling is compatible with 3D volume EM techniques to map long-range synaptic inputs in the mouse brain.

The paper introduces checkMySequence, an automated method to detect register-shift errors in cryo-EM protein models, improving accuracy in cases of low map resolution.

This paper introduces DEMO-EM, a method for assembling multi-domain protein structures from cryo-EM maps, achieving high accuracy and outperforming existing methods.

The paper describes a three-tiered validation system for cryo-EM structures in EMDB, focusing on data quality and model fitting, with tier 1 providing extensive metrics and tier 3 integrating with wwPDB.

The paper evaluates the performance of new cryo-EM hardware for cryo-ET and subtomogram averaging using EIAV virus-like particles, showing comparable resolution and improved reconstructions with advanced processing methods.

The paper presents a refinement method using HADDOCK2.4 to reduce atomic clashes in low-to-medium resolution cryo-EM complexes by optimizing protein-protein interfaces.