Abstract B031: Isolation and characterization of extracellular vesicles and nanoparticles from osteosarcoma cell lines: Unveiling supermeres and exomeres for therapeutic insights
Bibliographic record
Abstract
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.
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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 | 0.000 |
| 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".