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Opponent: Associate Professor Shannon Stott,
Supervisor: Apurba Dev, Uppsala Universitet; Jan Linnros
Extracellular vesicles (EVs, ∼ 30 nm−5 μm ) are lipid bilayer-enclosed particles expressingvaluable biological information such as proteins, lipids, and nucleic acids that reflecttheir shedding cell. The discovery of their importance in cell-to-cell communicationsparked a boom in research. Their abundance, ability to freely surpass natural barriersin the body, and reflection to the original cell make them suitable players in fieldssuch as treatment monitoring and targeted drug delivery. By investigating how EVsubpopulations interact with cells, we may also gain further insights into theoreticalquestions such as how cells communicate and how cells respond to external stimuli.Virtually all cells in the body release EVs; each cell may contain multiple origin spots forbiogenesis, and EVs may have different intended purposes that are also reflected in theircomposition. Therefore, EVs are extremely heterogeneous in size, expression level ofbiomolecules, and nanomechanical properties such as elasticity. This heterogeneity andthe small size of the vesicle pose technical challenges for the characterization platformsexisting today. EVs may be studied in bulk with a single output for an entire particleensemble or individually, yielding a characterization of individual EVs in a sample.Bulk methods are often faster, offer higher throughput, and may be the only option foranalyzing some parts, such as RNA. However, for complete characterization, we need toretrieve information on single EVs. This thesis explores techniques to characterize EVson a single vesicle level with three different platforms: a fluorescence microscope, anatomic force microscope, and a combined fluorescence and atomic force microscope.First, a fluorescence microscope is used to study EVs released by cells in a cancercell line model study. The cells are either left untreated or treated with two drugs: onethat the cells should respond to and one that they should be immune to. Five relevantsurface proteins were stained, imaged, and analyzed. The study revealed the possibilityof monitoring drug responses through immunofluorescence. Next, the platform was usedto study lung cancer patients undergoing treatment with EVs retrieved through liquidbiopsy. Each patient generated two sets of EVs: one sample from before treatmentand one sample after treatment, but before the tumor stopped responding to the drug.While the study revealed changes in individual proteins when comparing the two sampleswithin each patient, it was difficult to distinguish a pattern regarding the length oftreatment before drug resistance. It was not until we studied the correlation of proteinsand combined all protein expressions in a sample into a joint probability distributionthat trends became clearer. Longer treatments, for example, were found to have astronger positive correlation among the proteins. This highlights the importance ofincluding sophisticated statistical methods to analyze clinical EV samples on a singleEV level. Next, a theoretical model taking into account the EV’s liquid properties was con-structed. The model agrees with force spectroscopy measurements performed with force microscopy. Three EV samples with different protein expression levels were comparedin terms of elasticity moduli. With the low throughput of EVs in the technique, astatistical framework to compare the distributions of stiffness values was developed. Theframework revealed a large variation in stiffness values extracted from a single vesicle,which is hypothetically attributed to thermal fluctuations and diffusion of membranemolecules.Finally, we combined the fluorescence microscope and atomic force microscope toinvestigate subpopulations and heterogeneity in single EVs with both protein expressionand precise mechanical measurements of size and Young’s modulus. The platformrevealed distinct subpopulations with unique properties in the analyzed parameters. These combined measurements are the first of their kind, and a combined platformcharacterizing EVs in multiple ways may offer great insights into EV biology.