Keratin intermediate filament networks are part of the cytoskeleton in epithelial cells.They were found to regulate viscoelastic properties and motility of cancer cells. Due to unique biochemical properties of keratin polymers, the knowledge of the mechanisms controlling keratin network formation is incomplete. A combination of deterministic and stochastic modeling techniques can be a valuable source of information since they can describe known mechanisms of network evolution while reflecting the uncertainty with respect to a variety of molecular events. We applied the concept of * These authors contributed equally. † corresponding author A c c e p t e d m a n u s c r i p tSimulating the formation of keratin filament networks by a PDMP piecewise-deterministic Markov processes to the modeling of keratin network formation in high spatiotemporal resolution. The deterministic component describes the diffusion-driven evolution of a pool of soluble keratin filament precursors fueling various network formation processes. Instants of network formation events are determined by a stochastic point process on the time axis. A probability distribution controlled by model parameters exercises control over the frequency of different mechanisms of network formation to be triggered. Locations of the network formation events are assigned dependent on the spatial distribution of the soluble pool of filament precursors.Based on this modeling approach, simulation studies revealed that the architecture of keratin networks mostly depends on the balance between filament elongation and branching processes. The spatial distribution of network mesh size, which strongly influences the mechanical characteristics of filament networks, mostly depends on lateral annealing processes. This mechanism which is a specific feature of intermediate filament networks appears to be a major and fast regulator of cell mechanics.

We identified tomographic reconstruction of a scanning electron microscopy tilt series recording the secondary electron signal as a well-suited method to generate high-contrast three-dimensional data of intermediate filament (IF) networks in pancreatic cancer cells. Although the tilt series does not strictly conform to the projection requirement of tomographic reconstruction, this approach is possible due to specific properties of the detergent-extracted samples. We introduce an algorithm to extract the graph structure of the IF networks from the tomograms based on image analysis tools. This allows a high-resolution analysis of network morphology, which is known to control the mechanical response of the cells to large-scale deformations. Statistical analysis of the extracted network graphs is used to investigate principles of structural network organization which can be linked to the regulation of cell elasticity.

A strategy to mitigate typical reconstruction artefacts in missing wedge computed tomography is presented. These artefacts appear as elongations of reconstructed details along the mean direction (i.e. the symmetry centre of the projections). Although absent in standard computed tomography applications, they are most prominent in advanced electron tomography and also in special topics of X-ray and neutron tomography under restricted geometric boundary conditions. We investigate the performance of the DIRECTT (Direct Iterative Reconstruction of Computed Tomography Trajectories) algorithm to reduce the directional artefacts in standard procedures. In order to be sensitive to the anisotropic nature of missing wedge artefacts, we investigate isotropic substructures of metal foam as well as circular disc models. Comparison is drawn to filtered backprojection and algebraic techniques. Reference is made to reconstructions of complete data sets. For the purpose of assessing the reconstruction quality, Fourier transforms are employed to visualize the missing wedge directly. Deficient reconstructions of disc models are evaluated by a length-weighted kernel density estimation, which yields the probabilities of boundary orientations. The DIRECTT results are assessed at different signal-to-noise ratios by means of local and integral evaluation parameters.

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The three-dimensional (3D) keratin filament network of pancreatic carcinoma cells was investigated with different electron microscopical approaches. Semithin sections of high-pressure frozen and freeze substituted cells were analyzed with scanning transmission electron microscope (STEM) tomography. Preservation of subcellular structures was excellent, and keratin filaments could be observed; however, it was impossible to three-dimensionally track the individual filaments. To obtain a better signal-to-noise ratio in transmission mode, we observed ultrathin sections of high-pressure frozen and freeze substituted samples with low-voltage (30 kV) STEM. Contrast was improved compared to 300 kV, and individual filaments could be observed. The filament network of samples prepared by detergent extraction was imaged by high-resolution scanning electron microscopy (SEM) with very good signal-to-noise ratio using the secondary electron signal and the 3D structure could be elucidated by SEM tomography. In freeze-dried samples it was possible to discern between keratin filaments and actin filaments because the helical arrangement of actin subunits in the F-actin could be resolved. When comparing the network structures of the differently prepared samples, we found no obvious differences in filament length and branching, indicating that the intermediate filament network is less susceptible to preparation artifacts than the actin network.