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Peer-reviewedCC BYNature Methods

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

  • Lorenz Lamm1,2,3,4,5,6
  • Simon Zufferey4
  • Hanyi Zhang7,6,4
  • Ricardo Diogo Righetto4
  • Florent Waltz4,8,9
  • Wojciech Wietrzyñski4,10
  • Kevin A. Yamauchi8,9,5,6
  • Alister Burt11,12,5,6
  • Ye Liu7,6,13
  • Antonio Martínez-Sánchez13,14,15
  • Sebastian Ziegler14,15
  • Fabian Isensee14,15,5,16,6
3 more authors
  • Julia A. Schnabel7,17,16,6,4
  • Benjamin D. Engel18,5,6
  • Tingying Peng19,20
20 affiliations
  1. 1Biozentrum, University of Basel, Basel, Switzerland. [email protected]
  2. 2Helmholtz Munich, German Research Center for Environment and Health, Munich, Germany. [email protected]
  3. 3School of Computation, Information and Technology, Technical University of Munich, Munich, Germany. [email protected]
  4. 4Biozentrum, University of Basel, Basel, Switzerland
  5. 5Helmholtz Munich – German Research Center for Environment and Health, Munich, Germany
  6. 6School of Computation, Information and Technology, Technical University of Munich, Munich, Germany
  7. 7Helmholtz Munich, German Research Center for Environment and Health, Munich, Germany
  8. 8Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
  9. 9Swiss Institute of Bioinformatics, Basel, Switzerland
  10. 10MRC Laboratory of Molecular Biology, Cambridge CB2 0QH, UK
  11. 11Department of Structural Biology, Genentech, South San Francisco, CA, USA
  12. 12MRC Laboratory of Molecular Biology, Cambridge Biomedical Campus, Cambridge, UK
  13. 13Department of Information and Communications Engineering, Faculty of Computers Sciences, University of Murcia, Murcia, Spain
  14. 14German Cancer Research Center (DKFZ), Division of Medical Image Computing, Heidelberg, Germany
  15. 15Helmholtz Imaging, German Cancer Research Center (DKFZ), Heidelberg, Germany
  16. 16School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
  17. 17Munich Center for Machine Learning, Munich, Germany
  18. 18Biozentrum, University of Basel, Basel, Switzerland. [email protected]
  19. 19Helmholtz Munich, German Research Center for Environment and Health, Munich, Germany. [email protected]
  20. 20School of Computation, Information and Technology, Technical University of Munich, Munich, Germany. [email protected]
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Abstract

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.

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Cite

BibTeXRIS
BibTeX
@article{lamm2026membrain,
  title = {{MemBrain v2: an end-to-end tool for the analysis of membranes in cryo-electron tomography}},
  author = {Lorenz Lamm and Simon Zufferey and Hanyi Zhang and Ricardo Diogo Righetto and Florent Waltz and Wojciech Wietrzyñski and Kevin A. Yamauchi and Alister Burt and Ye Liu and Antonio Martínez-Sánchez and Sebastian Ziegler and Fabian Isensee and Julia A. Schnabel and Benjamin D. Engel and Tingying Peng},
  journal = {Nature Methods},
  year = {2026},
  month = sep,
  doi = {10.1038/s41592-026-03178-8},
  url = {https://doi.org/10.1038/s41592-026-03178-8},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - MemBrain v2: an end-to-end tool for the analysis of membranes in cryo-electron tomography
AU  - Lorenz Lamm
AU  - Simon Zufferey
AU  - Hanyi Zhang
AU  - Ricardo Diogo Righetto
AU  - Florent Waltz
AU  - Wojciech Wietrzyñski
AU  - Kevin A. Yamauchi
AU  - Alister Burt
AU  - Ye Liu
AU  - Antonio Martínez-Sánchez
AU  - Sebastian Ziegler
AU  - Fabian Isensee
AU  - Julia A. Schnabel
AU  - Benjamin D. Engel
AU  - Tingying Peng
PY  - 2026
DA  - 2026/09/08
JO  - Nature Methods
DO  - 10.1038/s41592-026-03178-8
UR  - https://doi.org/10.1038/s41592-026-03178-8
AB  - 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.
SN  - 1548-7091
SN  - 1548-7105
ER  -