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Lamm, L. ; Zufferey, S.* ; Zhang, H. ; Righetto, R.D.* ; Waltz, F.* ; Wietrzyñski, W.* ; Yamauchi, K.A.* ; Burt, A.* ; Liu, Y. ; Martínez-Sánchez, A.* ; Ziegler, S.* ; Isensee, F.* ; Schnabel, J.A. ; Engel, B.D. ; Peng, T.

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

Nat. Methods, DOI: 10.1038/s41592-026-03178-8 (2026)
Verlagsversion Forschungsdaten DOI PMC
Open Access Hybrid
Creative Commons Lizenzvertrag
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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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Computer Science ; Robustness (evolution) ; End-to-end Principle ; Artificial Intelligence ; Segmentation ; Membrane ; Data Mining ; Biology; Contact Sites; Augmentation
ISSN (print) / ISBN 1548-7091
e-ISSN 1548-7105
Zeitschrift Nature Methods
Verlag Nature Publishing Group
Verlagsort New York, NY
Begutachtungsstatus Peer reviewed
Institut(e) Helmholtz Artificial Intelligence Cooperation Unit (HAI)
Institute of Computational Biology (ICB)
Institute of AI for Health (AIH)
Helmholtz Pioneer Campus (HPC)
Förderungen Human Frontier Science Program (HFSP)
Fundación Séneca (Fundacion Seneca)