Browsing by Subject "AF4"

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  • Puutio, Johanna (Helsingin yliopisto, 2020)
    Extracellular vesicles (EVs) are phospholipid bilayer-enclosed nanoparticles that are secreted by eukaryotic and prokaryotic cells. EVs carry macromolecules and signalling molecules to adjacent cells and play an important role in intercellular communication under both pathologic and homeostatic conditions. Therefore, they have become of significant interest for their therapeutic, diagnostic and prognostic potential. EVs are small and highly heterogeneous in size, shape, cargo and membrane composition, posing several challenges for establishing analytical and clinical guidelines. Therefore, EV research requires standardized and robust methods for their separation and characterization. In this study physical and immunochemical methods were employed to characterize human platelet-derived EVs (pEVs) generated from platelets activated with different external biochemical stimuli. The platelet-activating effect of the pro-inflammatory S100A8/A9 protein complex and a combination of thrombin and collagen were studied with nano flow cytometry. The size distribution of pEVs was studied with nanoparticle tracking analysis (NTA) and asymmetrical flow field-flow fractionation (AF4), which represents a newly emerging method on the EV field. Finally, fluorescent labelling and co-localization analysis were employed to characterize membrane marker composition of pEVs and assess its usefulness as an analytic tool for EV research. We succeeded in providing new hints towards meaningful discoveries in platelet biology by characterizing the way platelets respond to inflammatory and hemostatic signals by shedding pEVs. When platelet activation markers are characterized with flow cytometry, the S100A8/A9 protein appeared to cause a shift in membrane activation markers when compared to the thrombin- collagen mix and the baseline control. Increased TLT-1 translocation and decreased integrin αIIbβ3 expression on pEV surfaces suggests that S100A8/A9 induced pEV secretion through differently packed platelet α-granules, rather than from the plasma membrane. An increase in TLT-1 expression compared to decreased P-selectin and αIIbβ3 suggests that S100A8/A9 stimulation shifts platelet phenotype towards secretion rather than aggregation. A protocol for small pEV separation with AF4-MALS was set up. With this method, subtle differences between small pEV populations were seen that were not distinguishable with NTA or flow cytometry. When investigated with AF4-MALS, S100A8/A9 induced pEVs appeared larger than those produced with thrombin- collagen activation. The mean particle sizes of the pEV populations obtained from activated platelets were generally also larger than those produced without an activator. We tested novel methods to detect subtle differences in small EV population sizes that are easily missed with conventional methods due to their technical limitations. A well-optimised AF4 protocol can detect different pEV subpopulations and is a promising tool for EV. In the future, when AF4 is combined with a MALS detector and a fraction collector, nanoimaging of fluorescently labelled EVs could be combined with it as a downstream application to obtain information on their versatile biological functions.
  • Silva, Oscar S. (Helsingin yliopisto, 2020)
    Asymmetrical flow field-flow fractionation (AF4) is a separation and characterization technique for macromolecules and particles, which has been gaining popularity in a multitude of scientific and industrial applications. AF4 is considered a challenging experimental technique to optimize and relatively few tools exist for this purpose. One of the main aims of the work was to provide practitioners of AF4 techniques with software tools, which bridge the gap between domain knowledge and AF4 theory for a more fluid experimental design workflow. This is made possible by a feature of AF4, which makes it stand out from related separation methods by enabling theory-driven prediction of sample behavior over the course of the experiment. In the first part of the computer experiments carried out, an algorithm based on probability theory was developed for predicting the ideal separation of samples based on readily obtainable sample properties. Among the obtained results is a predicted fractogram, which is the end product of an AF4 experiment run. The ability to predict separation of samples finds use in AF4 method development as well as other applications relevant to experimental work. The algorithmic models were constructed to describe real life systems for which experimental data was available and against which performance could be tested. The real world systems modeled included two AF4 instrument channels with different geometries and both natural and synthetic polymer samples. Prediction by the algorithm was compared to previously published experimental data from other authors, after configuring the algorithm to the corresponding experimental setups. The results suggest that the algorithm can relatively closely approximate predictions made by the underlying ideal AF4 theory. For a disperse polymer sample in a separation program for which no simple theoretical result was available, the algorithm's predictions gave promising results for approximating the shape of fractogram curve. In the second part of the computer experiments, a theory based model was fitted to experimental data and performing statistical inference was explored as a technique. Bayesian data analysis was used therein to complete a routine task in AF4 operation and subsequent data processing. The data analysis provided an estimate congruent with theory and external estimates given for the same data by other researchers. Looking forward, possible enhancements to the presented models and their applicability more widely to AF4 work as well as possible developments of computational models in the field are discussed.