Knee osteoarthritis is a common complication and can lead to total loss of joint function in patients. Treatment by either partial or total knee replacement with appropriate UHMWPE based implantsis highly invasive, may cause complications and may show unsatisfying results. Alternatively, treatment may be done by insertion of an elastic interpositional knee spacer with optimized material characteristics.
We report the development of high performance polyurethane-based polymers modified with bioactive molecules for fabrication of such knee spacers. In order to tailor mechanical and tribological properties and to improve resist to enzymatic degradation we propose a core-shell model for the spacer with specifically adapted properties.
Cell-cell and cell-extracellular matrix (ECM) adhesion regulates fundamental cellular functions and is crucial for cell-material contact. Adhesion is influenced by many factors like affinity and specificity of the receptor-ligand interaction or overall ligand concentration and density. To investigate molecular details of cell ECM and cadherins (cell-cell) interaction in vascular cells functional nanostructured surfaces were used Ligand-functionalized gold nanoparticles (AuNPs) with 6-8 nm diameter, are precisely immobilized on a surface and separated by non-adhesive regions so that individual integrins or cadherins can specifically interact with the ligands on the AuNPs. Using 40 nm and 90 nm distances between the AuNPs and functionalized either with peptide motifs of the extracellular matrix (RGD or REDV) or vascular endothelial cadherins (VEC), the influence of distance and ligand specificity on spreading and adhesion of endothelial cells (ECs) and smooth muscle cells (SMCs) was investigated. We demonstrate that RGD-dependent adhesion of vascular cells is similar to other cell types and that the distance dependence for integrin binding to ECM-peptides is also valid for the REDV motif. VEC-ligands decrease adhesion significantly on the tested ligand distances. These results may be helpful for future improvements in vascular tissue engineering and for development of implant surfaces.
In vivo, cells encounter different physical and chemical signals in the extracellular matrix (ECM) which regulate their behavior. Examples of these signals are micro- and nanometer-sized features, the rigidity, and the chemical composition of the ECM. The study of cell responses to such cues is important to understand complex cell functions, some diseases, and is basis for the development of new biomaterials for applications in medical implants or regenerative medicine. Therefore, the development of new methods for surface modifications with controlled physical and chemical features is crucial. In this work, we report a new combination of micelle nanolithography (BCML) and soft micro-lithography, for the production of polyethylene glycol (PEG) hydrogels, with a micro-grooved surface and decoration with hexagonally precisely arranged gold nanoparticles (AU NPs). The Au-NPs are used for binding adhesive ligands in a well-defined density. First tests were performed by culturing human fibroblasts on the gels. Adhesion and alignment of the cells along the parallel grooves of the surface were investigated. The substrates could provide a new platform for studying cell contact guidance by micro structures, and may enable a more precise control of cell behavior by nanometrically controlled surface functionalization.
Surface topographies are often discussed as an important parameter influencing basic cell behavior. Whereas most in vitro studies deal with microstructures with sharp edges, smooth, curved microscale topographies might be more relevant concerning in-vivo situations. Addressing the lack of highly defined surfaces with varying curvature, we present a topography chip system with 3D curved features of varying spacing, curvature radii as well as varying overall dimensions of curved surfaces. The CurvChip is produced by low-cost photolithography with thermal reflow, subsequent (repetitive) PDMS molding and hot embossing. The platform facilitates the systematic in-vitro investigation of the impact of substrate curvature on cell types like epithelial, endothelial, smooth muscle cells, or stem cells. Such investigations will not only help to further understand the mechanism of curvature sensation but may also contribute to optimize cell-material interactions in the field of regenerative medicine.
Adapting characteristics of biomaterials specifically for in vitro and in vivo applications is becoming increasingly important in order to control interactions between material and biological systems. These complex interactions are influenced by surface properties like chemical composition, charge, mechanical and topographic attributes. In many cases it is not useful or even not possible to alter the base material but changing surface, to improve biocompatibility or to make surfaces bioactive, may be achieved by thin coatings. An already established method is the coating with polyelectrolyte multilayers (PEM). To adjust adhesion, proliferation and improve vitality of certain cell types, we modified the roughness of PEM coatings. We included different types nanoparticles (NP’s) in different concentrations into PEM coatings for controlling surface roughness. Surface properties were characterized and the reaction of 3 different cell types on these coatings was tested.
The physiology of vascular cells depends on stimulating mechanical forces caused by pulsatile flow. Thus, mechano-transduction processes and responses of primary human endothelial cells (ECs) and smooth muscle cells (SMCs) have been studied to reveal cell-type specific differences which may contribute to vascular tissue integrity. Here, we investigate the dynamic reorientation response of ECs and SMCs cultured on elastic membranes over a range of stretch frequencies from 0.01 to 1 Hz. ECs and SMCs show different cell shape adaptation responses (reorientation) dependent on the frequency. ECs reveal a specific threshold frequency (0.01 Hz) below which no responses is detectable while the threshold frequency for SMCs could not be determined and is speculated to be above 1 Hz. Interestingly, the reorganization of the actin cytoskeleton and focal adhesions system, as well as changes in the focal adhesion area, can be observed for both cell types and is dependent on the frequency. RhoA and Rac1 activities are increased for ECs but not for SMCs upon application of a uniaxial cyclic tensile strain. Analysis of membrane protrusions revealed that the spatial protrusion activity of ECs and SMCs is independent of the application of a uniaxial cyclic tensile strain of 1 Hz while the total number of protrusions is increased for ECs only. Our study indicates differences in the reorientation response and the reaction times of the two cell types in dependence of the stretching frequency, with matching data for actin cytoskeleton, focal adhesion realignment, RhoA/Rac1 activities, and membrane protrusion activity. These are promising results which may allow cell-type specific activation of vascular cells by frequency selective mechanical stretching. This specific activation of different vascular cell types might be helpful in improving strategies in regenerative medicine.
Poly(dimethylsiloxane) can be covalently coated with ultrathin NCO-sP(EO-stat-PO) hydrogel layers which permit covalent binding of cell adhesive moieties, while minimizing unspecific cell adhesion on non-functionalized areas. We applied long term uniaxial cyclic tensile strain (CTS) and revealed (a) the preservation of protein and cell-repellent properties of the NCO-sP(EO-stat-PO) coating and (b) the stability and bioactivity of a covalently bound fibronectin (FN) line pattern. We studied the adhesion of human dermal fibroblast (HDFs) on non-modified NCO-sP(EO-stat-PO) coatings and on the FN. HDFs adhered to FN and oriented their cell bodies and actin fibers along the FN lines independently of the direction of CTS. This mechanical long term stability of the bioactive, patterned surface allows unraveling biomechanical stimuli for cellular signaling and behavior to understand physiological and pathological cell phenomenon. Additionally, it allows for the application in wound healing assays, tissue engineering, and implant development demanding spatial control over specific cell adhesion.
The extracellular environment of vascular cells in vivo is complex in its chemical composition, physical properties, and architecture. Consequently, it has been a great challenge to study vascular cell responses in vitro, either to understand their interaction with their native environment or to investigate their interaction with artificial structures such as implant surfaces. New procedures and techniques from materials science to fabricate bio-scaffolds and surfaces have enabled novel studies of vascular cell responses under well-defined, controllable culture conditions. These advancements are paving the way for a deeper understanding of vascular cell biology and materials–cell interaction. Here, we review previous work focusing on the interaction of vascular smooth muscle cells (SMCs) and endothelial cells (ECs) with materials having micro- and nanostructured surfaces. We summarize fabrication techniques for surface topographies, materials, geometries, biochemical functionalization, and mechanical properties of such materials. Furthermore, various studies on vascular cell behavior and their biological responses to micro- and nanostructured surfaces are reviewed. Emphasis is given to studies of cell morphology and motility, cell proliferation, the cytoskeleton and cell-matrix adhesions, and signal transduction pathways of vascular cells. We finalize with a short outlook on potential interesting future studies.
Digital light microscopy techniques are among the most widely used methods in cell biology and medical research. Despite that, the automated classification of objects such as cells or specific parts of tissues in images is difficult. We present an approach to classify confluent cell layers in microscopy images by learned deep correlation features using deep neural networks. These deep correlation features are generated through the use of gram-based correlation features and are input to a neural network for learning the correlation between them. In this work we wanted to prove if a representation of cell data based on this is suitable for its classification as has been done for artworks with respect to their artistic period. The method generates images that contain recognizable characteristics of a specific cell type, for example, the average size and the ordered pattern.
We present an approach for segmenting individual cells and lamellipodia in epithelial cell clusters using fully convolutional neural networks. The method will set the basis for measuring cell cluster dynamics and expansion to improve the investigation of collective cell migration phenomena. The fully learning-based front-end avoids classical feature engineering, yet the network architecture needs to be designed carefully. Our network predicts how likely each pixel belongs to one of the classes and, thus, is able to segment the image. Besides characterizing segmentation performance, we discuss how the network will be further employed.