Biology subjects

Lamm, L.

Publications and source records attributed to Lamm, L..

5 recordsLinked to original sources

bioRxiv2024

Mean Shift Clustering as a Loss Function for Accurate and Segmentation-aware Localization of Macromolecules in Cryo-electron Tomography

Cryo-electron tomography allows us to visualize and analyze the native cellular environment on a molecular level in 3D. To reliably study structures and interactions of proteins, they need to be accurately localized. Recent detection methods train a segmentation network and use post-processing to determine protein locations, often leading to inaccurate and inconsistent locations. We present an end-to-end learning approach for more accurate protein center identification by introducing a differentiable, scoremap-guided Mean Shift clustering implementation. To make training computationally feasible, we sample random cluster points instead of processing the entire image. We show that our Mean Shift loss leads to more accurate cluster center positions compared to the classical Dice loss. When combining these loss functions, we can enhance 3D protein shape preservation and improve clustering with more accurate, localization-focused score maps. In addition to improved protein localization, our method provides more efficient training with sparse ground truth annotations, due to our point sampling strategy.

bioinformatics↗

bioRxiv2024

MemBrain v2: an end-to-end tool for the analysis of membranes in cryo-electron tomography

Cryo-electron tomography (cryo-ET) provides unique insights into macromolecular complexes in their native environments, yet membrane analysis remains a major bottleneck due to low signal-to-noise ratios, missing wedge artifacts, and the complexity of membrane-associated proteins. Existing tools often require extensive manual annotation, struggle with generalization across datasets, and lack integrated solutions for segmentation, protein localization, and quantitative analysis. We introduce MemBrain v2, a deep learning-enabled framework that unifies these tasks into a streamlined pipeline. MemBrain-seg leverages a diverse, collaboratively generated training dataset and specialized model training strategies to achieve generalizable membrane segmentation across variable tomographic conditions. MemBrain-pick enables data-efficient localization of membrane-bound proteins by integrating geometric constraints with deep learning, reducing the need for extensive manual annotation. MemBrain-stats provides quantitative insights into protein distributions, computing spatial metrics to analyze intra-membrane particle organization. MemBrain v2 integrates seamlessly into cryo-ET workflows, providing an accessible and structured approach to membrane analysis. The full package is available at https://github.com/CellArchLab/MemBrain-v2.

bioinformatics↗

bioRxiv2023

Diatom pyrenoids are encased in a protein shell that enables efficient CO2 fixation

Pyrenoids are subcompartments of algal chloroplasts that concentrate Rubisco enzymes and their CO2 substrate, thereby increasing the efficiency of carbon fixation. Diatoms perform up to 20% of global CO2 fixation, but their pyrenoids remain poorly characterized at a molecular level. Here, we used in vivo photo-crosslinking to catalogue components of diatom pyrenoids and identified a pyrenoid shell (PyShell) protein, which we localized to the pyrenoid periphery of both the pennate diatom, Pheaodactylum tricornutum, and the centric diatom, Thalassiosira pseudonana. In situ cryo-electron tomography (cryo-ET) revealed that the pyrenoids of both diatom species are encased in a lattice-like protein sheath. Disruption of PyShell expression in T. pseudonana resulted in the absence of this protein sheath, altered pyrenoid morphology, and a high-CO2 requiring phenotype, with impaired growth and reduced carbon fixation efficiency under standard atmospheric conditions. Pyrenoids in mutant cells were fragmented and lacked the thylakoid membranes that normally traverse the Rubisco matrix, demonstrating how the PyShell plays a guiding role in establishing pyrenoid architecture. Recombinant PyShell proteins self-assembled into helical tubes, enabling us to determine a 3.0 [A]-resolution PyShell structure. We then fit this in vitro structure into an in situ subtomogram average of the pyrenoids protein sheath, yielding a putative atomic model of the PyShell within diatom cells. The structure and function of the diatom PyShell provides a new molecular view of how CO2 is assimilated in the ocean, a crucial biome that is on the front lines of climate change.

cell biology↗

bioRxiv2023

Simulating the cellular context in synthetic datasets for cryo-electron tomography

Cryo-electron tomography (cryo-ET) allows to visualize the cellular context at macromolecular level. To date, the impossibility of obtaining a reliable ground truth is limiting the application of deep learning-based image processing algorithms in this field. As a consequence, there is a growing demand of realistic synthetic datasets for training deep learning algorithms. In addition, besides assisting the acquisition and interpretation of experimental data, synthetic tomograms are used as reference models for cellular organization analysis from cellular tomograms. Current simulators in cryo-ET focus on reproducing distortions from image acquisition and tomogram reconstruction, however, they can not generate many of the low order features present in cellular tomograms. Here we propose several geometric and organization models to simulate low order cellular structures imaged by cryo-ET. Specifically, clusters of any known cytosolic or membrane bound macromolecules, membranes with different geometries as well as different filamentous structures such as microtubules or actin-like networks. Moreover, we use parametrizable stochastic models to generate a high diversity of geometries and organizations to simulate representative and generalized datasets, including very crowded environments like those observed in native cells. These models have been implemented in a multiplatform open-source Python package, including scripts to generate cryo-tomograms with adjustable sizes and resolutions. In addition, these scripts provide also distortion-free density maps besides the ground truth in different file formats for efficient access and advanced visualization. We show that such a realistic synthetic dataset can be readily used to train generalizable deep learning algorithms.

bioinformatics↗

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↗