Poege, M. | Search bioRxiv

bioRxiv2023

Serial Lift-Out - Sampling the Molecular Anatomy of Whole Organisms

Cryo-focused ion beam milling of frozen-hydrated cells and subsequent cryo-electron tomography (cryo-ET) has enabled the structural elucidation of macromolecular complexes directly inside cells. Application of the technique to multicellular organisms and tissues, however, is still limited by sample preparation. While high-pressure freezing enables the vitrification of thicker samples, it prolongs subsequent preparation due to increased thinning times and the need for extraction procedures. Additionally, thinning removes large portions of the specimen, restricting the imageable volume to the thickness of the final lamella, typically < 300 nm. Here, we introduce Serial Lift-Out, an enhanced lift-out technique that increases throughput and obtainable contextual information by preparing multiple sections from single transfers. We apply Serial Lift-Out to C. elegans L1 larvae yielding a cryo-ET dataset sampling the worms anterior-posterior axis and resolve its ribosome structure to 7 [A], illustrating how Serial Lift-Out enables the study of multicellular molecular anatomy.

biophysics↗

bioRxiv2022

MemBrain: A Deep Learning-aided Pipeline for Automated Detection of Membrane Proteins in Cryo-electron Tomograms

Background and ObjectiveCryo-electron tomography (cryo-ET) is an imaging technique that enables 3D visualization of the native cellular environment at sub-nanometer resolution, providing unpreceded insights into the molecular organization of cells. However, cryo-electron tomograms suffer from low signal-to-noise ratios and anisotropic resolution, which makes subsequent image analysis challenging. In particular, the automated detection of membrane-embedded proteins is a problem still lacking satisfactory solutions. MethodsWe present MemBrain - a new deep learning-based pipeline that automatically detects membrane-bound protein complexes in cryo-electron tomograms. After subvolumes are sampled along a segmented membrane, each subvolume is assigned a score using a convolutional neural network (CNN), and protein positions are extracted by a clustering algorithm. Incorporating rotational subvolume normalization and using a tiny receptive field simplify the task of protein detection and thus facilitate the network training. ResultsMemBrain requires only a small quantity of training labels and achieves excellent performance with only a single annotated membrane (F1 score: 0.88). A detailed evaluation shows that our fully trained pipeline outperforms existing classical computer vision-based and CNN-based approaches by a large margin (F1 score: 0.92 vs. max. 0.63). Furthermore, in addition to protein center positions, MemBrain can determine protein orientations, which has not been implemented by any existing CNN-based method to date. We also show that a pre-trained MemBrain program generalizes to tomograms acquired using different cryo-ET methods and depicting different types of cells. ConclusionsMemBrain is a powerful and label-efficient tool for the detection of membrane protein complexes in cryo-ET data, with the potential to be used in a wide range of biological studies. It is generalizable to various kinds of tomograms, making it possible to use pretrained models for different tasks. Its efficiency in terms of required annotations also allows rapid training and fine-tuning of models. The corresponding code, pretrained models, and instructions for operating the MemBrain program can be found at: https://github.com/CellArchLab/MemBrain

bioinformatics↗