Quantitative mapping of keratin networks in 3D

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.

Combining Image Restoration and Traction Force Microscopy to Study Extracellular Matrix-Dependent Keratin Filament Network Plasticity

“…The observations indicate that laminin-332 enhances the persistence of cell migration and mandate the investigation of KF network organization at subcellular resolution. The speed of image recording was, however, not fast enough to obtain image stacks, which would allow satisfactory resolution of single KFs or KF bundles in the motile cells and therefore prevented detailed network analyses using previously described image analysis techniques ( Windoffer et al, 2022 ).…”

“…The performance of the restoration model was further validated by comparing the results of network extraction from the raw and processed images using TSOAX and KerNet ( Figure 3 ; cf. ( Windoffer et al, 2022 )], which showed a significant improvement on the resemblance of the model with the original network structure. The trained CARE model ( Supplementary File S1 ) was henceforth used on all keratin network images prior to their segmentation analysis for the extraction of KF properties.…”

“…These segments, however, are represented individually, not as a connected network. In order to obtain the entire KF network as a single, connected structure, KerNet software was used (kernet.rwth-aachen.de; ( Windoffer et al, 2022 )). In short, KerNet connects vertices of different segments that are within a defined proximity to form an intersection, called node.…”

“…Recent studies have emphasized that KFs are highly dynamic structures with varying turnover and cell type-specific organization ( Iwatsuki and Suda, 2010 ; Windoffer et al, 2011 ; Quinlan et al, 2017 ; Tateishi et al, 2017 ; Pora et al, 2020 ). The highly intricate subcellular 3D network arrangement poses additional, difficult-to-address challenges, which necessitate high-resolution network analyses ( Nahidiazar et al, 2015 ; Quinlan et al, 2017 ; Moch et al, 2020 ; Moch and Leube, 2021 ; Prechova et al, 2022 ; Windoffer et al, 2022 ). Correlating the subcellular organization with specific functions and changes of the microenvironment has been difficult to accomplish.…”

Capturing intermediate filament networks

“…Recent advances in microscopy and image analysis have provided transformative insight into the three-dimensional (3D) architecture of cellular components in tissues, organs and entire organisms. Now, in eLife, Rudolf Leube and colleagues at RWTH Aachen University – including Reinhard Windoffer as first author – report on new insights into networks of intermediate filaments in three different types of epithelial cells ( Windoffer et al, 2022 ).…”