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Organisationseinheit der BAM
Polymeric nanoparticles (NP) are of increasing importance for a wide range of applications in the material and life sciences, including their use as drug carriers, fluorescent sensors and multimodal reporters in a large variety of bioassays and bioimaging studies. Application-relevant properties of NP include their size (and size distribution), shape, optical properties, and ease of subsequent functionalization, e.g. with linkers, sensor molecules, and bioligands. In this respect, knowledge of the chemical nature, the total number of surface groups and the number of groups accessible for subsequent coupling reactions is mandatory.1 Commercially available polystyrene NP often contain different additives like stabilizers, radical starters and crosslinkers, which can influence the quantification of surface functionalities. Moreover, they often have unknown surface group densities that may vary from batch to batch, which complicates or even hampers their reliable use in many (bio)applications. To circumvent these issues, we synthesized amino- and carboxy-functionalized, monodisperse 100 nm polystyrene NP with three different, well-defined surface group densities. Using a recently developed approach for the quantification of functional groups on nano- and microparticles with cleavable reporters,2 we quantified the assessible functional groups on the self-synthesised PS particles via optical spectroscopy (photometry) and inductively coupled optical emission spectrometry (32S ICP-OES). In addition, we developed a fluorimetric approach for the quantification of surface functional groups on nanoparticles based on the labelling with luminescent lanthanide complexes (LLC). In contrast to common organic dyes, LLC are not prone to photo¬luminescence quenching arising from reabsorption or dye aggregation, and thus, enable a reliable fluorometric quantification of the assessible functional groups on NP surfaces. Moreover, lanthanide tags can be detected with high specificity and sensitivity with analytical techniques such as XPS and ICP-MS, which allow for the multimodal validation of the fluorometric quantification approach.
Extracellular vesicles (EV) are cell-derived particles in body fluids, which have excellent potential as next-generation biomarkers. The exploitation of EV requires reliable measurements, which is currently very difficult, as most EV are smaller than 200 nm. At present, flow cytometry (FCM) is the most appropriate technique for EV analysis in biological samples, as FCM is readily available in many clinical laboratories and allows to identify cell-specific EV at high throughput. However, due to technical variations between different FCM instruments, EV concentration measurements are currently not well comparable between most laboratories. Therefore, EV reference materials and standardized reference methods are urgently needed to calibrate flow rate, light scattering intensity, and fluorescence intensity of FCM in the sub-micrometer size range. This requires a better matching of the optical properties of calibration beads and EV as can be realized with current polystyrene calibration beads.
The EMPIR project 18HLT01 “MetVes II” aims to develop synthetic reference materials and traceable measurement methods to standardize EV measurements. The reference materials should resemble EV properties, so that calibrations are reliable and do not require a change of acquisition settings. Hence, the reference materials should contain particles with a traceable number concentration in the range of 109–1012 particles/mL to calibrate flow rate, a traceable size with discrete diameters between 50–1000 nm and a refractive index (RI) in the range of 1.37–1.42 to calibrate scattering intensity, and a traceable fluorescence intensity between 100–100,000 molecules of equivalent soluble fluorochromes (MESF). At BAM, various approaches to prepare such low-RI nanometer-sized reference materials will be studied, preliminary results of the primary characterization of these candidate reference particles will be presented, and possible applications besides FCM-based EV detection will be outlined.