Beata Turoňová

It has long been known that poxviruses transcribe more than a hundred early mRNAs within their viral core, yet how these transcripts exit the core remains unknown. Here, we combine cryo-electron tomography with deep-learning-based particle picking, subtomogram averaging, cross-linking mass spectrometry, and AlphaFold3 modeling to resolve the molecular architecture of a long-sought pore within the poxvirus core. The pore is formed by a heteromeric assembly of the viral proteins E8, E6, and L3, which together create a hollow hexameric tube spanning both the core wall and the surface-exposed palisade layer. Within the pore channel, we observe a rod-like density consistent with the viral RNA helicase I8, which may contribute to the release of the newly synthesized viral mRNAs from the core. We further observe a state of the pore lacking the outer and basal rings under transcription conditions. Together, our findings identify the structure and dynamics of a candidate mRNA-release pore within the surface layer of a double-stranded DNA virus and suggest that it may form part of the poxvirus transcription machinery.

Cryo-electron tomography (cryo-ET) enables high-resolution, three-dimensional imaging of cellular structures in their native, frozen state. However, image quality is limited by a trade-off between angular sampling and radiation damage. Therefore, the choice of the angular increment during data collection is a critical parameter that affects tomogram quality and downstream analyses. Optimising this increment is challenging due to the high demands on microscope time, storage, and computation. In this study, we systematically evaluated tilt increments of 1°, 2°, 3°, 5°, and 10° using lamellae from Dictyostelium discoideum cells. We found that at a constant total electron dose, finer tilt increments (1–3°) produced better-aligned tomograms with higher signal-to-noise ratios and improved outcomes in template matching and subtomogram averaging. A 3° increment emerged as the optimal balance between data quality, alignment accuracy, dose per image, and processing efficiency. This practical recommendation supports both high-throughput and high-resolution structural studies and can guide future cryo-ET data acquisition strategies.

In eukaryotes, meters of DNA are packaged into micrometer scale nuclei. Nucleosomes, as the major organizational unit, have been extensively studied in vitro, yet the elaborate 3D structure of chromatin inside cells and its distinct oligo-nucleosome arrangements remain poorly resolved. Here, we combine cryo-electron tomography with template matching, subtomogram averaging and molecular simulations to visualize nucleosomes and chromatin structure inside human cells. We confidently assign individual nucleosomes and report their in-situ structure at secondary structure resolution. By predicting the paths of linker DNA, we identify oligo-nucleosome arrangements and uncover higher-order chromatin structures in situ, including a 37-nm wide, elongated but non-fibrous arrangement. In situ structural biology thus reveals the molecular chromatin organization inside cells and sets the stage for 3D genomics.

Cryo-electron tomography (cryo-ET) enables the visualization of cellular structures in near-native environments, but its potential for spatial analysis has been underutilized due to a lack of versatile tools accommodating biological sample diversity. Available solutions often rely on case-specific or hypothesis-driven approaches, while holistic analyses remain challenging. In this work, we introduce TANGO (Twist-Aware Neighborhoods for Geometric Organization), a framework leveraging point cloud descriptors to analyze spatial arrangements of particles, such as macromolecular complexes, in cryo-ET. By encoding relative positions and orientations of particles as twist vectors, TANGO enables rotationally invariant feature extraction, including structured neighborhood occupancy, lattice topology, or angular deviations. Its modular design and user-friendly interface allow for customization of features, facilitating exploratory analyses of spatial patterns in diverse experimental datasets. With its open-source Python implementation, TANGO advances the ability to decode complex cellular architectures and their functional relationships, offering a particle data analysis tool for the cryo-ET community.

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 developed cryoTIGER (Tilt Interpolation Generator for Enhanced Reconstruction), a computational workflow leveraging deep learning-based frame interpolation to generate intermediate tilt images. By interpolating between tilt series projections, cryoTIGER improves angular sampling, leading to enhanced 3D reconstructions, more refined particle localization, and improved segmentation of cellular structures. We evaluated our interpolation workflow on diverse datasets and compared its performance against non-interpolated data. Our results demonstrate that deep learning-based interpolation improves image quality and structural recovery. The presented cryoTIGER framework offers a computational alternative to denser sampling during tilt series acquisition, paving the way for enhanced cryo-ET workflows and advancing structural biology research.

Cryo-electron tomography (cryo-ET) enables high-resolution, three-dimensional imaging of cellular structures in their native, frozen state. However, image quality is limited by a trade-off between angular sampling and radiation damage. Therefore, the choice of the angular increment during data collection is a critical parameter that affects tomogram quality and downstream analyses. Optimising this increment is challenging due to the high demands on microscope time, storage, and computation. In this study, we systematically evaluated tilt increments of 1°, 2°, 3°, 5°, and 10° using lamellae from Dictyostelium discoideum cells. We found that at a constant total electron dose, finer tilt increments (1–3°) produced better-aligned tomograms with higher signal-to-noise ratios and improved outcomes in template matching and subtomogram averaging. A 3° increment emerged as the optimal balance between data quality, alignment accuracy, dose per image, and processing efficiency. This practical recommendation supports both high-throughput and high-resolution structural studies and can guide future cryo-ET data acquisition strategies.

Cryo-electron tomography (cryo-ET) enables the visualization of cellular structures in near-native environments, but its potential for spatial analysis has been underutilized due to a lack of versatile tools accommodating biological sample diversity. Available solutions often rely on case-specific or hypothesis-driven approaches, while holistic analyses remain challenging. In this work, we introduce TANGO (Twist-Aware Neighborhoods for Geometric Organization), a novel framework leveraging point cloud descriptors to analyze spatial arrangements of particles, such as macromolecular complexes, in cryo-ET. By encoding relative positions and orientations of particles as twist vectors, TANGO enables rotationally invariant feature extraction, including structured neighborhood occupancy, lattice topology, or angular deviations. Its modular design and user-friendly interface allow for customization of features, facilitating exploratory analyses of spatial patterns in diverse experimental datasets. With its open-source Python implementation, TANGO advances the ability to decode complex cellular architectures and their functional relationships, offering a valuable tool for the cryo-ET community.

In eukaryotes, meters of DNA are packaged into micrometer scale nuclei. Nucleosomes, as the major organizational unit, have been extensively studied in vitro, yet the elaborate 3D structure of chromatin inside cells and its distinct multi-nucleosome arrangements remain poorly resolved. Here, we combine cryo-electron tomography with template matching, subtomogram averaging and molecular simulations to visualize nucleosomes and chromatin structure inside human cells. We confidently assign individual nucleosomes and report their in-situ structure at secondary structure resolution. By tracing linker DNA, we identify multi-nucleosome arrangements and uncover higher-order chromatin structures in situ, including a 37-nm wide, elongated but non-fibrous arrangement. In situ structural biology thus reveals the molecular chromatin organization inside cells and sets the stage for 3D genomics.