Evidence map›Paper›PMID 32776920›Full record

ArticlePLoS computational biology2020

Reliable estimation of membrane curvature for cryo-electron tomography.

Maria Salfer, Javier F Collado, Wolfgang Baumeister, Rubén Fernández-Busnadiego, Antonio Martínez-Sánchez

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Article in PLoS computational biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.

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0cells of the map it votes in
25citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

25 citing papers in PubMed.

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  15. In situ snapshots along a mammalian selective autophagy pathway.Proceedings of the National Academy of Sciences of the United States of America · 2023
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  19. In situ structural analysis reveals membrane shape transitions during autophagosome formation.Proceedings of the National Academy of Sciences of the United States of America · 2022
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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

5 authors.

Maria SalferDepartment of Molecular Structural Biology, Max Planck Institute of Biochemistry, Martinsried, Germany.ORCID 0000-0002-7604-6617
Javier F ColladoDepartment of Molecular Structural Biology, Max Planck Institute of Biochemistry, Martinsried, Germany.ORCID 0000-0002-8707-6619
Wolfgang BaumeisterDepartment of Molecular Structural Biology, Max Planck Institute of Biochemistry, Martinsried, Germany.
Rubén Fernández-BusnadiegoDepartment of Molecular Structural Biology, Max Planck Institute of Biochemistry, Martinsried, Germany.ORCID 0000-0002-8366-7622
Antonio Martínez-SánchezDepartment of Molecular Structural Biology, Max Planck Institute of Biochemistry, Martinsried, Germany.ORCID 0000-0002-5865-2138

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Curvature is a fundamental morphological descriptor of cellular membranes. Cryo-electron tomography (cryo-ET) is particularly well-suited to visualize and analyze membrane morphology in a close-to-native state and molecular resolution. However, current curvature estimation methods cannot be applied directly to membrane segmentations in cryo-ET, as these methods cannot cope with some of the artifacts introduced during image acquisition and membrane segmentation, such as quantization noise and open borders. Here, we developed and implemented a Python package for membrane curvature estimation from tomogram segmentations, which we named PyCurv. From a membrane segmentation, a signed surface (triangle mesh) is first extracted. The triangle mesh is then represented by a graph, which facilitates finding neighboring triangles and the calculation of geodesic distances necessary for local curvature estimation. PyCurv estimates curvature based on tensor voting. Beside curvatures, this algorithm also provides robust estimations of surface normals and principal directions. We tested PyCurv and three well-established methods on benchmark surfaces and biological data. This revealed the superior performance of PyCurv not only for cryo-ET, but also for data generated by other techniques such as light microscopy and magnetic resonance imaging. Altogether, PyCurv is a versatile open-source software to reliably estimate curvature of membranes and other surfaces in a wide variety of applications.

Indexed as

SoftwareAlgorithmsAnimalsCell MembraneCryoelectron MicroscopyHeLa CellsHumansImaging, Three-DimensionalMiceSaccharomyces cerevisiae

Identifiers

PMID32776920
PMCPMC7444595

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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.