Acoustic Trapping of Extracellular Vesicles
Bibliographic record
Abstract
Extracellular vesicles (EVs) are membrane enclosed biological nanoparticles released by cells. In the past two decades, EVs have gained a lot of attention due to their role in numerous biological processes. However, their small nature complicates isolating them from biological fluids with cumbersome techniques, such as ultracentrifugation. Acoustic trapping is a technique that uses ultrasonic standing waves to isolate particles from their surrounding fluid. In this way, acoustic trapping can be used to isolate EVs. This thesis describes the development of a new acoustic trapping platform, the Multinode Acoustic Trap (MAT). MAT is designed to have higher throughput and capacity than previously reported acoustic trapping platforms. The faster processing by MAT facilitates the study of EVs, and MAT-isolated EVs and their function was further explored in this thesis, mainly through mass spectrometry based proteomics. In Paper I, it was shown that MAT was capable of operating at 30 times higher throughput and displayed 40 times higher seed particle capacity than previous systems. We demonstrate that the platform can isolate extracellular vesicles from urine samples and generates sufficient amount of material for RNA and mass spectrometry analysis. In Paper II, MAT was used to isolate platelet derived EVs (PEVs) from plasma samples to investigate differences between pathogen-activated platelets and endogenous activation. Complement proteins and IgG3 were found to be enriched in pathogen-activated PEVs. We demonstrate that the MAT produces functionally intact EVs, and that PEVs have immunomodulatory effects. We also show that bacterial M1 protein from Streptococcus pyogenes binds to PEVs and can be transported with them, a previously unknown mechanism that could contribute to systemic responses during diseases such as sepsis. In Paper III, MAT was used to isolate EVs from small volumes of septic mouse plasma. We demonstrate that EVs during sepsis exhibit changes in their proteomes, with an emphasis on leukocyte migration. We show that the MAT gives fast and easy access to the EV proteome and provides information that cannot be obtained from plasma alone. The small volumes required also means that MAT is suitable for handling biobanked samples and can therefore be used to provide additional information from already collected samples. Finally, Paper IV explores the feasibility of combining acoustic trapping with immunoaffinity-based separation, in order to access different subpopulations of EVs. CD9-positive EVs were separated from other EVs by means of functionalized silica beads. Compared to just incubating the beads in plasma, utilizing an acoustic trap gave access to two populations of EVs rather than just the CD9-positive ones. Processing with the acoustic trap was also faster, and the enriched proteins derived from seed particle-bound EVs were more related to CD9, indicating an enrichment of EVs defined by the chosen antibody. As a whole, this thesis broadens the applicability of acoustic trapping as a tool for isolating EVs, and demonstrates the usefulness of the technique through the subsequent analysis of the isolated EVs to reveal new aspects of their biological function.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".