Quantitative mapping of keratin networks in 3D
- RWTH Aachen University, Germany
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.
Data availability
Software, original and processed data are available athttp://kernet.rwth-aachen.de/andhttps://github.com/VRGroupRWTH/Zytoskelett
-
KerNet, 3D fluorescence and segmentation datasets of keratin networks from MDCK, HaCaT, and RPE cellsDryad Digital Repository, doi:10.5061/dryad.3xsj3txht.
Article and author information
Author details
Reinhard Windoffer
Johannes Stegmaier
Funding
Deutsche Forschungsgemeinschaft (WI173/8-2)
- Reinhard Windoffer
Deutsche Forschungsgemeinschaft (LE566/18-2)
- Rudolf Leube
Deutsche Forschungsgemeinschaft (GRK2415/363055819)
- Reinhard Windoffer
- Nicole Schwarz
- Sungjun Yoon
- Teodora Piskova
- Rudolf Leube
RWTH Aachen University (rwth0452)
- Reinhard Windoffer
Medizinische Fakultät, RWTH Aachen University (IZKF)
- Teodora Piskova
- Jacopo Di Russo
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Ethics
Animal experimentation: All animal experiments were conducted in accordance with the guidelines for the care and use of laboratory animals and were approved by the Landesamt für Natur, Umwelt und Verbraucherschutz Nordrhein-Westfalen (LANUV; reference number 84-02.04.2015.A190 and approvals according to {section sign}4 of the German Animal Welfare Act).
Copyright
© 2022, Windoffer et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
Metrics
-
- 1,799
- views
-
- 287
- downloads
-
- 8
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
A two-part list of links to download the article, or parts of the article, in various formats.
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Quantitative mapping of keratin networks in 3D
eLife 11:e75894.
https://doi.org/10.7554/eLife.75894
