Acoustic Trapping of Extracellular Vesicles
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».