Abstract B031: Isolation and characterization of extracellular vesicles and nanoparticles from osteosarcoma cell lines: Unveiling supermeres and exomeres for therapeutic insights
Notice bibliographique
Résumé
Abstract Objective: Osteosarcoma, a malignant bone tumor derived from osteoblasts, the cells responsible for bone formation, commonly affects adolescents but can also arise in older individuals. Treatment typically involves a combination of surgical procedures and chemotherapy which doesn’t have favorable outcomes. However, Extracellular vesicles (EVs), involved in cell-to-cell communication, are emerging as biomarkers for diagnosis and therapy. Recent studies on nanoparticles have unveiled novel types such as "Exomeres and supermeres," demonstrating specific functionalities abundant with potential circulating biomarkers and therapeutic targets across various human diseases. These discoveries pave the way for innovative approaches to diagnosis and treatment, distinct from traditional methods. Methods: High-grade KHOS, MG63, and MG63.3 (Isogenic cell lines) osteosarcoma cell lines, along with human fetal osteoblasts (hFOB1.19), were cultured in serum-free conditioned media for 48 hours. Following this, EVs and nanoparticles were isolated from both osteosarcoma and osteoblast cell lines using a multi-step ultracentrifugation method. Nanoparticle tracking analysis (NTA) was employed to assess particle sizes; however, its ability to detect nanoparticles smaller than 30nm, such as exomeres and supermeres, is limited. Transmission electron microscopy (TEM) was utilized to validate particle sizes, providing visualization and characterization of EVs and nanoparticles based on their diameters. To obtain the proteomic profile of each particle type, we employed an untargeted mass spectrometry approach. Results: Osteosarcoma cell lines, as well as normal osteoblasts, were found to produce EVs and nanoparticles within the expected size range. Transmission electron microscopy (TEM) analysis confirmed the presence of both large and small EVs in their respective fractions. Notably, TEM images of exomeres and supermeres fractions provided novel evidence of particle production by both osteoblasts and osteosarcoma cells. The mass spectrometry findings revealed proteins that could serve as promising targets for extracellular nanoparticles. Additionally, certain proteins exhibited a higher specificity within the superemes. Conclusion: This study shows the isolation and characterization of distinctive extracellular nanoparticles named exomeres and supermeres. These nanoparticles exhibit clear variations in size, morphology, and composition, suggesting their potential as biomarkers for osteosarcoma and their involvement in the tumor microenvironment and metastasis. Citation Format: Marjan Khatami, Geoffroy Danieau, Lata Adnani, Laura Montermini, Nadim Tawil, Brian Meehan, Janusz Rak, Livia Garzia. Isolation and characterization of extracellular vesicles and nanoparticles from osteosarcoma cell lines: Unveiling supermeres and exomeres for therapeutic insights [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pediatric Cancer Research; 2024 Sep 5-8; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl):Abstract nr B031.
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 | 0,000 |
| 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 ».