Evidence map›Paper›PMID 35179484›Full record

ArticleeLife2022

Quantitative mapping of keratin networks in 3D.

Reinhard Windoffer, Nicole Schwarz, Sungjun Yoon, Teodora Piskova, Michael Scholkemper, Johannes Stegmaier, Andrea Bönsch, Jacopo Di Russo, Rudolf E Leube

Abstract read
In one paragraph

Article in eLife, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing 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

13 citing papers in PubMed.

  1. Article
  2. Article
  3. Vimentin Intermediate Filaments: A Paradigm Shift From Static Structure to Dynamic Cytoplasmic Network.BioEssays : news and reviews in molecular, cellular and developmental biology · 2026
    Review
  4. Cytoplasmic crowding acts as a porous medium reducing macromolecule diffusion.Proceedings of the National Academy of Sciences of the United States of America · 2026
    Article
  5. Article
  6. Review
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Cortical tension regulates desmosomal morphogenesis.Frontiers in cell and developmental biology · 2022
    Article
  13. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors.

Reinhard WindofferInstitute of Molecular and Cellular Anatomy, RWTH Aachen University, Aachen, Germany.ORCID 0000-0003-1403-5880
Nicole SchwarzInstitute of Molecular and Cellular Anatomy, RWTH Aachen University, Aachen, Germany.
Sungjun YoonInstitute of Molecular and Cellular Anatomy, RWTH Aachen University, Aachen, Germany.ORCID 0000-0001-7363-3261
Teodora PiskovaInstitute of Molecular and Cellular Anatomy, RWTH Aachen University, Aachen, Germany.ORCID 0000-0002-5143-2634
Michael ScholkemperDepartment of Computer Science, RWTH Aachen University, Aachen, Germany.ORCID 0000-0002-3669-8119
Johannes StegmaierInstitute of Imaging and Computer Vision, RWTH Aachen University, Aachen, Germany.ORCID 0000-0003-4072-3759
Andrea BönschVisual Computing Institute, RWTH Aachen University, Aachen, Germany.ORCID 0000-0001-5077-3675
Jacopo Di RussoInstitute of Molecular and Cellular Anatomy, RWTH Aachen University, Aachen, Germany.ORCID 0000-0001-6731-9612
Rudolf E LeubeInstitute of Molecular and Cellular Anatomy, RWTH Aachen University, Aachen, Germany.ORCID 0000-0002-5519-7379

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Mechanobiology requires precise quantitative information on processes taking place in specific 3D microenvironments. Connecting the abundance of microscopical, molecular, biochemical, and cell mechanical data with defined topologies has turned out to be extremely difficult. Establishing such structural and functional 3D maps needed for biophysical modeling is a particular challenge for the cytoskeleton, which consists of long and interwoven filamentous polymers coordinating subcellular processes and interactions of cells with their environment. To date, useful tools are available for the segmentation and modeling of actin filaments and microtubules but comprehensive tools for the mapping of intermediate filament organization are still lacking. In this work, we describe a workflow to model and examine the complete 3D arrangement of the keratin intermediate filament cytoskeleton in canine, murine, and human epithelial cells both, in vitro and in vivo. Numerical models are derived from confocal airyscan high-resolution 3D imaging of fluorescence-tagged keratin filaments. They are interrogated and annotated at different length scales using different modes of visualization including immersive virtual reality. In this way, information is provided on network organization at the subcellular level including mesh arrangement, density and isotropic configuration as well as details on filament morphology such as bundling, curvature, and orientation. We show that the comparison of these parameters helps to identify, in quantitative terms, similarities and differences of keratin network organization in epithelial cell types defining subcellular domains, notably basal, apical, lateral, and perinuclear systems. The described approach and the presented data are pivotal for generating mechanobiological models that can be experimentally tested.

Indexed as

CytoskeletonKeratinsActin CytoskeletonAnimalsCytoskeletal ProteinsDogsHumansIntermediate FilamentsMiceCytoskeletal ProteinsKeratins3D networkcell biologycytoskeletonhumanintermediate filamentkeratinmouseretinal pigment epitheliumvirtual reality

Identifiers

PMID35179484
PMCPMC8979588

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.