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In vitro, hydrogel-based ECMs for functionalizing surfaces of various material have played an essential role in mimicking native tissue matrix. Polydimethylsiloxane (PDMS) is widely used to build microfluidic or organ-on-chip devices compatible with cells due to its easy handling in cast replication. Despite such advantages, the limitation of PDMS is its hydrophobic surface property. To improve wettability of PDMS-based devices, alginate, a naturally derived polysaccharide, was covalently bound to the PDMS surface. This alginate then crosslinked further hydrogel onto the PDMS surface in desired layer thickness. Hydrogel-modified PDMS was used for coating a topography chip system and in vitro investigation of cell growth on the surfaces. Moreover, such hydrophilic hydrogel-coated PDMS is utilized in a microfluidic device to prevent unspecific absorption of organic solutions. Hence, in both exemplary studies, PDMS surface properties were modified leading to improved devices.
Analysis of multicellular patterns is required to understand tissue organizational processes. By using a multi-scale object oriented image processing method, the spatial information of cells can be extracted automatically. Instead of manual segmentation or indirect measurements, such as general distribution of contrast or flow, the orientation and distribution of individual cells is extracted for quantitative analysis. Relevant objects are identified by feature queries and no low-level knowledge of image processing is required.
Increasing number of studies are focused on how adherent cells respond, in vitro, to different properties of a material. Typical properties are the surface chemistry, topographical cues (at the nano- and micro-scale) of the surface, and the substrate stiffness. Cell Response studies are of importance for designing new biomaterials with applications in cell culture technologies, regenerative medicine, or for medical implants. However, only very few studies take the cell age factor, respectively the donor age, into account. In this work, we tested two types of human vascular cells (smooth muscle and endothelial cells) from old and young donors on (a) micro-structured surfaces made of pol (dimethylsiloxane) or on (b) flat polyacrylamide hydrogels with varying stiffnesses. These experiments reveal age-dependent and cell typedependent differences in the cell response to the topography and stiffness, and may establish the Basis for further studies focusing on cell age-dependent responses.
Focal adhesion clusters (FAC) are dynamic and complex structures that help cells to sense physicochemical properties of their environment. Research in biomaterials, cell adhesion or cell migration often involves the visualization of FAC by fluorescence staining and microscopy, which necessitates quantitative analysis of FAC and other cell features in microscopy images using image processing. Fluorescence microscopy images of human umbilical vein endothelial cells (HUVEC) obtained at 63x magnification were quantitatively analysed using ImageJ software. A generalised algorithm for selective segmentation and morphological analysis of FAC, nucleus and cell morphology is implemented. Further, a method for discrimination of FACnear the nucleus and around the periphery is implemented using masks. Our algorithm is able to effectively quantify different morphological characteristics of cell components and shows a high sensitivity and specificity while providing a modular software implementation.
Endogenous electrical fields play an important role in various physiological and pathological events. Yet the effects of electrical cues on processes such as wound healing, tumor development or metastasis are still rarely investigated, though it is known that direct current electrical fields can alter cell migration or proliferation in vitro. Several 2D experimental models for studying cell responses to direct current electrical fields have been presented and characterized but suitable experimental models for electrotaxis studies in 3D are rare. Here we present a novel, easy-to-produce, multi-well-based galvanotactic-chamber for the use in 2D and 3D cell experiments for investigations on the influence of electrical fields on tumor cell migration and tumor spheroid growth. Our presented system allows the simultaneous application of electrical field to cells in four chambers, either cultured on the bottom of the culture-plate (2D) or embedded in hydrogel filled channels(3D). The set-up is also suitable for, live-cell-imaging. Validation tests show stable electrical fields and high cell viabilities inside the channel. Tumor spheroids of various diameters can be exposed to direct current electrical fields up to one week.
Soft lithography, a tool widely applied in biology and life sciences with numerous applications, uses the soft molding of photolithography-generated master structures by polymers. The central part of a photolithography set-up is a mask-aligner mostly based on a high-pressure mercury lamp as an ultraviolet (UV) light source. This type of light source requires a high level of maintenance and shows a decreasing intensity over its lifetime, influencing the lithography outcome. In this paper, we present a low-cost, bench-top photolithography tool based on ninety-eight 375 nm light-emitting diodes (LEDs). With approx. 10 W, our presented lithography set-up requires only a fraction of the energy of a conventional lamp, the LEDs have a guaranteed lifetime of 1000 h, which becomes noticeable by at least 2.5 to 15 times more exposure cycles compared to a standard light source and with costs less than 850 C it is very affordable. Such a set-up is not only attractive to small academic and industrial fabrication facilities who want to enable work with the technology of photolithography and cannot afford a conventional set-up, but also microfluidic teaching laboratories and microfluidic research and development laboratories, in general, could benefit from this cost-effective alternative. With our self-built photolithography system, we were able to produce structures from 6 μm to 50 μm in height and 10 μm to 200 μm in width. As an optional feature, we present a scaled-down laminar flow hood to enable a dust-free working environment for the photolithography process.
A series of novel biomedical TPCUs with different percentages of hard segment and a silicone component in the soft segment were synthesized in a multi stage one-pot method. The kinetic profiles of the urethane formation in TPCU-based copolymer systems were monitored by rheological, in line FTIR spectroscopic (React IR) and real-time calorimetric (RC1) methods. This process-analytically monitored multi step synthesis was successfully used to optimize the production of medical-grade TPCU elastomers on preparative scale (in lots of several kg) with controlled molecular structure and mechanical properties. Various surface and bulk analytical methods as well as systematic studies of the mechanic response of the elastomer end-products towards compression and tensile loading were used to estimate the bio-stability of the prepared TPCUs in vitro after 3 months. The tests suggested that high bio-stability of all polyurethane formulations using accelerating in vitro test can be attributed to the synthetic design as well as to the specific techniques used for specimen preparation, namely: (1) the annealing for reducing residual polymer surface stress and preventing IES, (2) stabilization of the morphology by long time storage of the specimens after processing before being immersed in the test liquids, (3) purification by extraction to remove the shot chain oligomers which are the most susceptible to degradation. All mechanical tests were performed on cylindrical and circular disc specimens for modelling the thickness of the meniscus implants under application-relevant stress conditions.
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.
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.
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.