Abstract 5311: Lipidomic profiling of extracellular vesicles derived from cancer cell lines: Lipid species as potential biomarkers and cellular uptake enhancers
Notice bibliographique
Résumé
Abstract Introduction: Extracellular vesicles (EVs) are lipid bilayer-made particles shed by cells to the extracellular space. They carry different cargo proteins, nucleic acids, and other metabolites. EVs play a role in disseminating cancer to distant organs by communicating with the tumor microenvironment to prepare the metastatic niche and also through horizontal transfer of oncogenic traits to recipient cells. The EV surface, which includes proteins and lipids, plays a role in organotropism and cellular uptake. While proteins have been extensively characterized, lipids have not been explored sufficiently. This work aims to evaluate EV lipids as potential biomarkers and their role in enhancing cellular uptake. Methods: To detect which lipid species (LS) were differentially expressed, we used two cell models of liver metastatic cells: colorectal cancer (HT29) and uveal melanoma (MP41, MP46, MEL 270, OMM 2.5) cell lines. Colon (CCD18-Co) and fibroblast (BJ) immortalized non-cancerous cells were used as controls. EVs were isolated from culture media by ultrafiltration using 100 kDa units filters. Lipids were extracted by methyl-tert-butyl ether for high-throughput lipidomics. High-resolution ‘shotgun’ mass spectrometry was performed. Data was analyzed using LipidView software (SCIEX), and the lipid % normalized was reported. MarkerView (SCIEX) was used to perform Principal Component Analysis. The LS segregating cancerous vs. non-cancerous cells were identified. To evaluate the influence on cellular uptake, we used liposomes as EV models with lipid formulations containing the segregating LS to compare them with naturally occurring lipids and EVs using Incucyte live cell imaging. Results: We identified four LS that segregated EV subpopulations. PE 34:1 and PS 36:1 divided cancerous vs non-cancerous cells, uveal melanoma cells were segregated by PE 36:2, and normal colon cells were segregated by LPC 18:0. We validated the effect of PS 36:1 in cellular uptake by producing liposomes with a lipid formulation resembling the lipid profile of naturally occurring EVs lipid profile with an artificially high DOPS concentration (17% of the total molar ratio). We determined that human hepatocytes preferentially internalized liposomes made of naturally occurring EVs, followed by the ones with a high concentration of DOPS and lastly by a control EV lipid formulation. Conclusion: This study identified EV LS that segregated cancerous, normal, and melanoma cell lines. We showed that LS could be used to distinguish cell populations. Moreover, we demonstrated that LS alone influences cellular uptake and that adding the segregating LS to lipid formulations in excess effects cellular uptake. These results pave the way to identify EV lipid biomarkers and better understand EV based cancer dissemination. Citation Format: Ruben R. Lopez Salazar, Prisca Bustamante, Chaymaa Zouggari, Yunxi Chen, Thupten Tsering, Ion Stiharu, Catherine Mounier, Vahe Nerguizian, Julia Burnier. Lipidomic profiling of extracellular vesicles derived from cancer cell lines: Lipid species as potential biomarkers and cellular uptake enhancers. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5311.
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 | 0,001 | 0,001 |
| 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 ».