Evidence map›Paper›PMID 41385553›Full record

ArticlePloS one2025

Tracing low-level structures in cryo-electron tomography.

Pelayo Alvarez Brecht, Francisco Aguilar-Martínez, Christian Biertümpfel, Irene Diaz, Naoko Mizuno, Antonio Martinez-Sanchez

Abstract read
In one paragraph

Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

6 authors.

Pelayo Alvarez BrechtNIH Graduate Partnership Program, Bethesda, Maryland, United States of America.
Francisco Aguilar-MartínezDepartment of Information and Communications Engineering, Universidad de Murcia, Murcia, Spain.
Christian BiertümpfelLaboratory of Structural Cell Biology, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland, United States of America.
Irene DiazUniversity of Oviedo, Department Computer Sciences, Oviedo, Spain.
Naoko MizunoLaboratory of Structural Cell Biology, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland, United States of America.
Antonio Martinez-SanchezDepartment of Information and Communications Engineering, Universidad de Murcia, Murcia, Spain.ORCID https://orcid.org/0000-0002-5865-2138

Funding

Molecular organization of pathways governing cell-shape formationZIAHL006264 · NHLBI · NATIONAL HEART, LUNG, AND BLOOD INSTITUTE · PI MIZUNO, NAOKO · 2021 to 2025
$11.6M
Intramural NIH HHS ZIA HL006264
6 · The paper itself

Abstract

Cryo-electron tomography is an imaging technique that provides 3D images (tomograms) in situ of cells with sub-nanometer resolution. Typically, the first step in the analysis is to classify the tomogram voxels into different structures, named semantic segmentation. However, the segmentation results are sets of voxels, hindering further quantitative analysis. In this paper, we define and implement algorithms to convert the semantic segmentation of the main structures in a cellular cryo-electron tomogram (membranes, filaments, cytosolic and membrane-bound macromolecules) into specific skeletons, preserving their topological and geometrical information. Additionally, we have defined a metric for comparing segmentations in cryo-ET coming from different methods more robust than the standard DICE. We also demonstrate how this approach can be used to trace cellular features by analyzing several in situ cellular cryo-electron tomograms.

Indexed as

Cryoelectron MicroscopyElectron Microscope TomographyAlgorithmsHumansImage Processing, Computer-AssistedImaging, Three-Dimensional

Identifiers

PMID41385553
PMCPMC12700388

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Registered trials

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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.