MemBrain v2: an end-to-end tool for the analysis of membranes in cryo-electron tomography
Cryo-electron tomography 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 particles. Existing tools often require extensive manual annotation, struggle with generalization across datasets and lack integrated solutions for segmentation, particle 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 particles by integrating geometric constraints with deep learning, reducing the need for extensive manual annotation. MemBrain-stats provides quantitative insights into particle distributions, computing spatial metrics to analyze intramembrane particle organization. MemBrain v2 integrates seamlessly into cryo-electron tomography workflows, providing an accessible and structured approach to membrane analysis.
Authors
- Sebastian Ziegler (ORCID: https://orcid.org/0000-0002-1693-5586)
- Julia A. Schnabel (ORCID: https://orcid.org/0000-0001-6107-3009)
- Wojciech Wietrzyñski (ORCID: https://orcid.org/0000-0001-8898-2392)
- Antonio Martínez-Sánchez (ORCID: https://orcid.org/0000-0002-5865-2138)
- Lorenz Lamm (ORCID: https://orcid.org/0000-0003-0698-7769)
- Benjamin D. Engel (ORCID: https://orcid.org/0000-0002-0941-4387)
- Hanyi Zhang (ORCID: https://orcid.org/0000-0003-0109-6481)
- Alister Burt (ORCID: https://orcid.org/0000-0002-9341-2295)
- Tingying Peng (ORCID: https://orcid.org/0000-0002-7881-1749)
- Kevin A. Yamauchi (ORCID: https://orcid.org/0000-0002-7818-1388)
- Fabian Isensee (ORCID: https://orcid.org/0000-0002-3519-5886)
- Ricardo D. Righetto (ORCID: https://orcid.org/0000-0003-4247-4303)
- Simon Zufferey
- Ye Liu (ORCID: https://orcid.org/0000-0002-1901-669X)
Institutions
- SIB Swiss Institute of Bioinformatics (CH)
- Helmholtz Association of German Research Centres (DE)
- MRC Laboratory of Molecular Biology (GB)
- German Cancer Research Center (DE)
- King's College London (GB)
- University of Basel (CH)
- Heidelberg University (DE)
- Center for Environmental Health (US)
- Helmholtz Zentrum München (DE)
- ETH Zurich (CH)
- Munich Center for Machine Learning
- Technical University of Munich (DE)
- Universidad de Murcia (ES)
Publication Details
- Journal
- Nature Methods
- Published
- 2026-09-08
- DOI
- https://doi.org/10.1038/s41592-026-03178-8
- Citations
- 178
- Primary Topic
- Advanced Electron Microscopy Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 188.83