Abstract 483: Unraveling the impact of cancer cell derived membrane and cytosolic proteins using synthetic small extracellular vesicles
Bibliographic record
Abstract
Abstract Background: Extracellular vesicles (EVs) are couriers in cell-cell communication. Evidence suggests that EV biology, from cargo packing, biodistribution, to cell uptake is a non-random and well-orchestrated process. Substantial efforts have been devoted to studying the pathological roles and therapeutic potential of EVs. It is speculated that surface properties, such as membrane proteins, mediate the interaction between EVs and recipient cells, while the enclosed “messages” exert downstream effects. This concept is exemplified in cancer metastasis, where cancer cell-derived EVs were shown to establish pre-metastatic niches and guide cancer organotropism. We propose selectively loading cancer cell membrane and cytosolic proteins into synthetic vesicles (SVs) like liposomes. This approach allows for the isolated study of individual factors, in contrast to the complexity of naturally occurring EVs. Methods: Cellular membrane and cytosolic proteins were extracted separately from a cancer cell line (MP41) (Mem-PERTM Plus kit). Empty-SV, Mem-SV (with membrane proteins), Cyto-SV (with cytosolic proteins), and Complete-SV (with both) were synthesized in a 3D-printed microfluidic chip and subsequently dialyzed (1000 KDa) overnight. The volume of protein buffer was controlled in all SV syntheses. EVs were isolated from the conditioned medium by ultrafiltration (Amicon) followed by ultracentrifugation. The size (Nanosight), zeta potential (ZetaView), and morphology (Transmission Electron Microscopy) of SVs and EVs were analyzed. Proteins incorporated into SVs were validated by stain-free gel, western blot, and CytoFLEX. SVs and EVs were stained by sp-DiIC18 fluorescent dye. The number of fluorescent particles was quantified (CytoFLEX) and controlled in cell uptake analysis on hepatocytes (IHH) and fibroblasts (BJ) (Incucyte). Results: All SVs were successfully synthesized with a mean size of ~100 nm. The zeta potential of both Mem-SV (-27.03 mV) and Complete-SV (-24.78 mV) is close to MP41 EVs (-26.01 mV) (P > 0.05) and are more negative than Empty-SV (-11.30 mV) and Cargo-SV (-13.38 mV) (P < 0.05). Total protein incorporation efficiency was estimated to be ~30%. The relative level of specific proteins (e.g. integrin av, TSG101) in Mem-SV and Cyto-SV matched the level in the raw membrane and cytosolic proteins by western blot. Integrin avb5 was detected on the surface of Mem-SV. Membrane and cytosolic protein incorporation increased the uptake efficiency of SVs compared to Empty-SVs, and the efficiency varied with cell type and protein content. Conclusion: In this study, we demonstrate that membrane and cytosolic proteins differentially impact the physical (zeta potential) and functional (cellular uptake) properties of SVs. Investigating these distinct roles could further our understanding of cancer EV biology, as well as advance drug delivery systems. Citation Format: Yunxi Chen, Rubén R. López, Thupten Tsering, Chaymaa Zouggari Ben El Khyat, Vahé Nerguizian, Julia V. Burnier. Unraveling the impact of cancer cell derived membrane and cytosolic proteins using synthetic small extracellular vesicles [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 483.
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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".