Evidence map›Paper›PMID 42711494›Full record

ArticleNature methods2026

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

Lorenz Lamm, Simon Zufferey, Hanyi Zhang, Ricardo D Righetto, Florent Waltz, Wojciech Wietrzynski, Kevin A Yamauchi, Alister Burt, Ye Liu, Antonio Martinez-Sanchez and 5 more

Abstract read
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In one paragraph

Article in Nature methods, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 94 papers.

0numbers the graph read from it
0cells of the map it votes in
94citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

94 citing papers in PubMed.

  1. Visualization of membrane-stabilized SorCS2Journal of structural biology: X · 2026
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  17. The molecular architecture of tunneling nanotubes.bioRxiv : the preprint server for biology · 2026
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  19. mBio · 2026
    Article
  20. Article

34 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors.

Lorenz LammHelmholtz Munich, German Research Center for Environment and Health, Munich, Germany. lorenz.lamm@tum.de.ORCID http://orcid.org/0000-0003-0698-7769
Simon ZuffereyBiozentrum, University of Basel, Basel, Switzerland.ORCID http://orcid.org/0009-0005-5483-4566
Hanyi ZhangHelmholtz Munich, German Research Center for Environment and Health, Munich, Germany.ORCID http://orcid.org/0009-0001-6314-9158
Ricardo D RighettoBiozentrum, University of Basel, Basel, Switzerland.ORCID http://orcid.org/0000-0003-4247-4303
Florent WaltzBiozentrum, University of Basel, Basel, Switzerland.
Wojciech WietrzynskiBiozentrum, University of Basel, Basel, Switzerland.
Kevin A YamauchiDepartment of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland.ORCID http://orcid.org/0000-0002-7818-1388
Alister BurtMRC Laboratory of Molecular Biology, Cambridge Biomedical Campus, Cambridge, UK.ORCID http://orcid.org/0000-0002-9341-2295
Ye LiuHelmholtz Munich, German Research Center for Environment and Health, Munich, Germany.
Antonio Martinez-SanchezDepartment of Information and Communications Engineering, Faculty of Computers Sciences, University of Murcia, Murcia, Spain.ORCID http://orcid.org/0000-0002-5865-2138
Sebastian ZieglerGerman Cancer Research Center (DKFZ), Division of Medical Image Computing, Heidelberg, Germany.
Fabian IsenseeGerman Cancer Research Center (DKFZ), Division of Medical Image Computing, Heidelberg, Germany.ORCID http://orcid.org/0000-0002-3519-5886
Julia A SchnabelHelmholtz Munich, German Research Center for Environment and Health, Munich, Germany.
Benjamin D EngelBiozentrum, University of Basel, Basel, Switzerland. ben.engel@unibas.ch.ORCID http://orcid.org/0000-0002-0941-4387
Tingying PengHelmholtz Munich, German Research Center for Environment and Health, Munich, Germany. tingying.peng@helmholtz-munich.de.ORCID http://orcid.org/0000-0002-7881-1749

Funding

Fundación Séneca (Fundacion Seneca) FSRM/10.13039/100007801(22686/PI/24)Human Frontier Science Program (HFSP) RGP0005/2021
6 · The paper itself

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.

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.