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Characterization of (bio)macromolecules and polymeric materials with modern scattering methods
(2018)
The analysis of polymers, biopolymers and polymeric materials is of great interest in biomaterials science. Here small-angle x-ray scattering (SAXS), static light scatterin (SLS) and dynamic light scattering (DLS) are described. Current efforts for digitalization of this methods are explaind with respect to modern data science in biomedical research.
During the last years, there has been a rapid rise in the use of nanomaterials in consumer products. Especially silver nanoparticles are frequently used because of their well-known optical and antimicrobial properties. However, the toxicological studies focusing on silver nanoparticles are controversial, either claiming or denying a specific nano-efffect. To contribute to localizing nanoparticles in toxicological studies and to investigate the interaction of particles with cells, a fluorescent marker is often used to monitor their transport and possible degradation. A major problem, in this context is the issue of binding stability of a fluorescent marker which is attached to the particle.
In order to overcome this problem we provide an investigation of the binding properties of fluorescence-labeled BSA to small silver nanoparticles. Therefore, we synthesized small silver nanoparticles which are stabilized by poly(acrylic acid). The particles are available as reference candidate material and were thoroughly characterized in an earlier study. The ligand was exchanged by fluorescence marked albumin (BSA-FITC). The adsorption of the ligands was monitored by dynamic light scattering (DLS). To verify that the observed effects on the hydrodynamic radius originate from the successful ligand exchange and not from agglomeration or aggregation we used small angle X-ray scattering (SAXS). The fluorescent particles were characterized by UV/Vis and fluorescence spectroscopy. Afterwards, desorption of the ligand BSA-FITC was monitored by fluorescence spectroscopy and the uptake of particles in different in vitro models was studied.
The particles are spherical and show no sign of aggregation after successful ligand exchange. The fluorescence intensity is quenched significantly by the presence of the silver cores as expected, but the remaining fluorescence intensity was high enough to use these particles in biological investigations. Half-life of fluorescence labeling on the particle was 21 d in a highly concentrated solution of non-labeled BSA. Thus, a very high dilution and long incubation times are needed to remove BSA-FITC from the particles. Finally, the fluorescence-labeled silver nanoparticles were used for uptake studies in human liver and intestinal cells, showing a high uptake for HepG2 liver cells and almost no uptake in differentiated intestinal Caco-2 cells. In conclusion, we showed production of fluorescence-marked silver nanoparticles. The fluorescence marker is strongly adsorbed to the silver surface which is crucial for future investigations in biological matrices. This is necessary for a successful investigation of the toxicological potential of silver nanoparticles.
Data analysis of SAS measurements has been dominated by the classical curve fitting approach. This method finds optimal parameters of a scattering model composed of analytical expressions. SASfit represents such a classical curve fitting toolbox: it is one of the mature programs for small-angle scattering data analysis and has been available and used for many years. The latest developments [1] will be extended by improving the interoperability of the extensive data base of models with third-party analysis software. An updated format of model definitions is also presented, which allows model function plug-ins to be used with the Python language.
To complement the classical curve fitting method, the user-friendly opensource Monte Carlo regression package McSAS was developed. Most importantly, the form-free Monte Carlo approach of McSAS means that it is not necessary to provide any further mathematical restrictions to the Parameter distribution. Future developments include separating the core optimization from the GUI (allowing 'headless' integration), as well as parallel computing which reduces the computing time proportional to the number of available computing cores. The headless mode is presented by an example of Operation within interactive programming environments such as a Jupyter notebook.
The promising results of Monte Carlo based data analysis for determining form-free Parameter distributions motivated the evaluation of the method with dynamic light scattering (DLS) data. For this purpose, the method was adapted for analyzing correlation curves such as those from multi-angle dynamic light scattering (DLS) data. The development of McDLS intends to overcome limitations of existing methods at reliably determining the modality of size distributions. An example of Monte Carlo based data analysis of multimodal DLS measurements will be presented.