Haury, M. | bioRxiv Science

bioRxiv2026

Characterization Standard for In-situ Cryo-electron Tomography

Standardized biological specimens are essential for optimizing cryoEM workflows and benchmarking instrument performance. While apoferritin fulfills this role for single-particle analysis, no equivalent exists for cryo-electron tomography. Ribosomes are frequently used but require large datasets due to C1 symmetry and structural heterogeneity, limiting rapid optimization and standardized comparison of workflows. Here, we present PP7 virus-like particles (VLPs) overexpressed in E. coli as a scalable in situ benchmark. VLPs have high orders of symmetry enabling rapid, high-resolution validation of tomographic pipelines from minimal datasets, while their distinct structural features across low to high resolutions provide a practical resolution metric.

cell biology↗

bioRxiv2024

Annotating CryoET Volumes: A Machine Learning Challenge

Cryo-electron tomography (cryoET) has emerged as a powerful structural biology tool for understanding protein complexes in their native cellular environments. Presently, 3D volumes of cellular environments can be acquired in the thousands in a few days where each volume provides a rich and complex cellular landscape. Despite numerous innovations, localizing and identifying the vast majority of protein species in these volumes remains prohibitively difficult. Machine learning based methods provide an opportunity to automate the process of labeling and annotating cryoET volumes. Due to current bottlenecks in the annotation process, and a lack of large standardized datasets, training datasets for machine learning algorithms have been scarce. Here, we present a defined "phantom" sample, along with "ground truth" annotations, that will be the basis of a machine learning challenge to bring cryoET and ML experts together and spur creativity to address this annotation problem. We have also set up a cryoET data portal that provides additional diverse sets of annotated 3D volumes from cryoET experts across the world for the machine learning challenge.

cell biology↗