Combining Image Restoration and Traction Force Microscopy to Study Extracellular Matrix-Dependent Keratin Filament Network Plasticity - Yoon, Sungjun; Windoffer, Reinhard; Kozyrina, Aleksandra N.; Piskova, Teodora; Di Russo, Jacopo; Leube, Rudolf E. (Frontiers in Cell and Developmental Biology, 2022)
Keratin intermediate filaments are dynamic cytoskeletal components that are responsible for tuning the mechanical properties of epithelial tissues. Although it is known that keratin filaments (KFs) are able to sense and respond to changes in the physicochemical properties of the local niche, a direct correlation of the dynamic three-dimensional network structure at the single filament level with the microenvironment has not been possible because of the technical challenges of multimodal monitoring at high temporal and spatial resolution. Here, we report a novel method that combines a machine learning-based image restoration technique and traction force microscopy to decipher the fine details of KF network properties in living cells grown on defined extracellular matrices (ECMs). Our approach utilizes Content-Aware Image Restoration (CARE) to enhance the temporal resolution of confocal fluorescence microscopy by at least five fold while preserving the spatial resolution required for accurate extraction of KF network structure at the single KF/KF bundle level. The restored images are used to segment the KF network, allowing numerical analyses of its local properties. We show that these tools can be used to study the impact of ECM composition and local mechanical perturbations on KF network properties and corresponding traction force patterns in size-controlled keratinocyte assemblies. The combination of techniques thereby facilitates, for the first time, a direct comparison between the changes in KF network organization at the single filament level and local force distribution in defined paradigms.