Abstract 1741: Cancer cell-derived extracellular vesicle mimicking: A parametric study of the physicochemical characteristics of EVs and their influence on cellular uptake in metastasis
Bibliographic record
Abstract
Abstract Introduction: Extracellular vesicles (EVs) play a crucial role in disseminating cancer to distant organs, communicating with the tumor microenvironment to prepare the metastatic niche, and through horizontal transfer of oncogenic traits to recipient cells. EV parameters influencing cellular uptake include surface proteins, lipid profile, and physicochemical properties such as size and zeta potential. EVs isolated from cells are heterogeneous populations, which hinder the study of single EV variables and their influence on cellular uptake. Moreover, EV isolation is a lengthy and laborious process with an extremely low yield. Liposomes are synthetic vesicles that share properties with EVs, such as the lipid bilayer and the capability to encapsulate relevant biomolecules. In this study, we used a uveal melanoma (UM) model, a highly liver metastatic tumor, to characterize naturally occurring EVs and model them using liposomes. Methods: We utilized liposomes as a model of EVs to study how the size and zeta potential influence cellular internalization. EVs were isolated from primary UM cells (MP41) using ultracentrifugation (UC) and characterized using dynamic light scattering, nanoparticle tracking analysis, as well as electrophoretic mobility for hydrodynamic size, concentration, and zeta potential, respectively. Liposomes mimicking EVs were produced using the periodic disturbance mixer (nanoprecipitation). The lipid composition was DMPC: CHOL: DHP at different ratios and 0.2% molar concentration of SP-DiIC18(3) as labeling reagent. Liposomes were then dialyzed overnight using a membrane size of 12kDa. Cellular uptake was assessed in immortalized human hepatocytes, representing the liver microenvironment. Results: Using surface response methodology (SRM), we created a model to predict size and zeta potential depending on liposome manufacturing conditions. We produced anionic and neutral liposomes with zeta potential ~-30 mV and 0mV, respectively, and with a size of ~150 nm, similar to EVs derived from MP41 cells in order to evaluate the influence of zeta potential in cellular uptake. Our results showed no significant differences at 0 and 6 hours between anionic and neutral EV-like liposomes. By contrast, the uptake of the anionic liposomes was significantly higher at 24 h compared to neutral ones. Moreover, EV-like liposomes were produced 1000x more concentrated than naturally occurring EVs isolated using UC. Conclusion: In this study, we describe a novel approach using synthetic biology to produce EV-like liposomes as a model to study EV cellular uptake and its role in metastasis. Our data demonstrate that liposomes are a feasible tool to study EV variables individually, thereby addressing the high heterogeneity of biological samples, and producing high liposome yield, thereby helping to accelerate research Citation Format: Rubén Rodrigo López Salazar, Chaymaa Zouggari, Thupten Tsering, Prisca Bustamante Alvarez, Ion Stiharu, Catherine Mounier, Vahe Nerguizian, Julia V. Burnier. Cancer cell-derived extracellular vesicle mimicking: A parametric study of the physicochemical characteristics of EVs and their influence on cellular uptake in metastasis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1741.
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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 | 0.000 |
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".