Advanced Electron Microscopy Techniques and Applications

The paper evaluates ultrastructural preservation quality in postmortem brain tissue using EM and light microscopy to identify artifacts and assess their impact on research applications.

This paper introduces a method using liposome encapsulation to improve cryo-EM structural determination by preserving macromolecules in a near-native environment, reducing disassembly and orientation artifacts.

The paper introduces MotorBench, a dataset of bacterial flagellar motors for testing automated detection algorithms in cryo-ET.

The paper introduces a MATLAB script package for unbiased analysis of protein polymerization from cryo-ET data, demonstrating its application to Rubisco in -carboxysomes.

Chronobot improves cryo-EM sample quality by 11-fold using deep learning for ice thickness quantification, enabling visualization of transient RecA-dsDNA intermediates during homology search.

This paper introduces EM-DINO and OmniEM, a foundation model and architecture for EM image analysis, improving restoration and segmentation across diverse datasets.

AutoLEI provides a GUI for automated real-time and offline batch processing of 3D ED/MicroED data, improving efficiency and reducing redundant work.

The paper introduces VP-CLEM-Kit, a cost-effective SR-vCLEM pipeline for visual proteomics, achieving 5-fold resolution improvement at 7-fold lower cost.

The paper warns that counting particle images in cryo-EM can lead to biased estimates of conformational state populations due to noise and orientation uncertainty, and suggests using full image datasets for more accurate modeling.

This study uses shotgun-EM to identify and characterize three protein complexes, including the first experimentally determined structure of Pks16 in D. discoideum.

This paper introduces SISTM to map 3D synaptic nanoarchitecture in brain tissue across species using cryo-ET and STA, revealing conserved structural features in human postmortem tissue.

The paper introduces cryoPARES, a deep learning method for efficient cryo-EM image alignment, enabling faster and automated structure determination with reduced manual intervention.

This study benchmarks deep learning methods for building atomic models from cryo-EM density maps, showing their superiority over traditional methods and the benefits of integrating AlphaFold.

The paper develops a correlative cryo-light/electron microscopy workflow to target and image synapses with cryo-ET, revealing excitatory synapses.

This paper introduces a deconvolution method called AR-Decon to improve the quality of cryo-EM maps and shows it is robust and broadly applicable.

This paper introduces a graphene-based affinity cryo-EM grid (GFD-A) for capturing endogenous protein complexes, achieving resolutions of 2.71 Å for tagged proteins and 3.0–3.9 Å for endogenous RNase MRP and RNase P.

This paper combines MicroED and native mass spectrometry to determine structures of enzyme-inhibitor complexes and identify high-affinity ligands from crude samples.

The paper uses volume electron microscopy techniques to study ultrastructural changes in germline intercellular bridges during Drosophila oogenesis, revealing new structural features and developmental patterns.

This paper describes optimized cryo-ET sample preparation workflows for plant tissues, achieving subnanometer resolution through targeted FIB milling and lamella trimming.

SLICK simplifies and increases throughput for in situ cryo-ET sample preparation.

This paper develops a method combining generative AI and molecular dynamics to improve ligand modeling in cryo-EM maps of protein-ligand complexes.

The paper presents a cryoEM structure of the FliPQR complex in closed form, revealing how FliE opens the periplasmic gate for flagellar rod assembly.

The paper improves modeling of cryo-EM maps for membrane proteins with multiple states using generative AI and density-guided simulations.

The paper uses cryoEM and machine learning to identify and classify novel oligomeric protein complexes from Azotobacter vinelandii, revealing new structural states of proteins like phosphoglucoisomerase and pyridine transhydrogenase.

The paper presents an in silico method to classify and identify co-purified protein complexes using cryoEM data, revealing new structures and multiple membrane scaffold protein assembly states.

The paper trains segmentation networks for cryo-ET data using synthetic and real data, showing that synthetic data with molecular diversity improves performance but real data is needed for generalization.

The paper introduces VitisC, a method to visualize small proteins using cryo-EM by attaching them to a filamentous scaffold formed by E. coli CTP synthase, achieving a resolution of 3.39 Å.

The paper presents a comprehensive segmentation strategy for cryoET data using convolutional neural networks, enabling efficient analysis of large datasets and improving particle picking and template matching.

CryoNeRF improves cryo-EM structure reconstruction by handling both homogeneous and heterogeneous data, achieving better performance than existing methods.

TARDIS is a deep learning framework that rapidly and accurately segments membranes and filaments in electron microscopy data, enabling large-scale biophysical analysis.