Biology subjects

Majtner, T.

Publications and source records attributed to Majtner, T..

6 recordsLinked to original sources

bioRxiv2025

Optimal tilt-increment for cryo-ET

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{degrees}, 2{degrees}, 3{degrees}, 5{degrees}, and 10{degrees} using lamellae from Dictyostelium discoideum cells. We found that at a constant total electron dose, finer tilt increments (1-3{degrees}) produced better-aligned tomograms with higher signal-to-noise ratios and improved outcomes in template matching and subtomogram averaging. A 3{degrees} 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.

biophysics↗

bioRxiv2025

Molecular architecture of heterochromatin at the nuclear periphery of primary human cells

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.

biophysics↗

bioRxiv2025

Automated Removal of Corrupted Tilts in Cryo-Electron Tomography

Cryo-electron tomography (cryo-ET) enables the visualization of macromolecular structures in their near-native cellular environment. However, acquired tilt series are often compromised by image corruption due to drift, contamination, and ice reflections. Manually identifying and removing corrupted tilts is subjective and time-consuming, making an automated approach necessary. In this study, we present a deep learning-based method for automatically removing corrupted tilts. We evaluated 13 different neural network architectures, including convolutional neural networks (CNNs) and transformers. Using a dataset of 435 annotated tilt series, we trained models for both binary and multiclass classification of corrupted tilts. We demonstrate the high efficiency and reliability of these automated approaches for removing corrupted tilts in cryo-ET and provide a framework, including models trained on cryo-ET data, that allows users to apply these models directly to their tilt series, improving the quality and consistency of downstream cryo-ET data processing.

molecular biology↗

bioRxiv2024

cryoTIGER: Deep-Learning Based Tilt Interpolation Generator for Enhanced Reconstruction in Cryo Electron Tomography

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 specimen can be exposed to. Here, we developed cryoTIGER (Tilt Interpolation Generator for Enhanced Reconstruction), a computational workflow leveraging a 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.

molecular biology↗

bioRxiv2023

High-confidence 3D template matching for cryo-electron tomography

Cryo-electron tomography (CryoET) resolves individual macromolecules inside living cells. However, the complex composition and high density of cells challenge the faithful identification of features in tomograms. Here, we capitalize on recent advances in electron tomography and demonstrate that 3D template matching (TM) localizes a wide range of structures inside crowded eukaryotic cells with confidence 10 to 100-fold above the noise level. We establish a TM pipeline with systematically tuned parameters for automated, objective and comprehensive feature identification. High-fidelity and high-confidence localizations of nuclear pore complexes, vaults, ribosomes, proteasomes, lipid membranes and microtubules, and individual subunits, demonstrate that TM is generic. We resolve [~]100-kDa proteins, connect the functional states of complexes to their cellular localization, and capture vaults carrying ribosomal cargo in situ. By capturing individual molecular events inside living cells with defined statistical confidence, high-confidence TM greatly speeds up the CryoET workflow and sets the stage for visual proteomics.

molecular biology↗

bioRxiv2021

ENNGene: an Easy Neural Network model building tool for Genomics

BackgroundThe recent big data revolution in Genomics, coupled with the emergence of Deep Learning as a set of powerful machine learning methods, has shifted the standard practices of machine learning for Genomics. Even though Deep Learning methods such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are becoming widespread in Genomics, developing and training such models is outside the ability of most researchers in the field. ResultsHere we present ENNGene - Easy Neural Network model building tool for Genomics. This tool simplifies training of custom CNN or hybrid CNN-RNN models on genomic data via an easy-to-use Graphical User Interface. ENNGene allows multiple input branches, including sequence, evolutionary conservation, and secondary structure, and performs all the necessary preprocessing steps, allowing simple input such as genomic coordinates. The network architecture is selected and fully customized by the user, from the number and types of the layers to each layer's precise set-up. ENNGene then deals with all steps of training and evaluation of the model, exporting valuable metrics such as multi-class ROC and precision-recall curve plots or TensorBoard log files. To facilitate interpretation of the predicted results, we deploy Integrated Gradients, providing the user with a graphical representation of an attribution level of each input position. To showcase the usage of ENNGene, we train multiple models on the RBP24 dataset, quickly reaching the state of the art while improving the performance on more than half of the proteins by including the evolutionary conservation score and tuning the network per protein. ConclusionsAs the role of DL in big data analysis in the near future is indisputable, it is important to make it available for a broader range of researchers. We believe that an easy-to-use tool such as ENNGene can allow Genomics researchers without a background in Computational Sciences to harness the power of DL to gain better insights into and extract important information from the large amounts of data available in the field. Contactpanagiotis.alexiou@ceitec.muni.cz

bioinformatics↗