Abstract 2988: Quantifying the Uptake of Metal Based Cancer Therapy Drugs Using Single Cell ICP-MS
Notice bibliographique
Résumé
Metallic based cancer therapy drugs have been around for several years, the most widely used being platinum-based drugs, however these come with severe side effects due to the non-specific targeting of these drugs. Recently a number of nanoparticle-based cancer therapeutics have been approved for clinical use or are currently under development. Advantages that engineered nanoparticles may offer over conventional small molecule drugs include: (i) prolonged circulation time in the body; (ii) reduction of nonspecific cellular uptake along with undesirable off-target and other side effects; and (iii) improvement in cellular interactions through specific cancer cell targeting moieties.The therapeutic effect of cancer treatment is related to the amount of drug that interacts with each individual cancer cell. Traditional drug research techniques, such as conventional inductively coupled plasma mass spectrometry (ICP-MS), have been limited to cell ensemble measurements, which require homogenization of a given cell population for quantitative analysis. All cells within this population are assumed to be similar and therefore assumed to interact with the same amount of drug, however, recent studies demonstrate that cell populations are heterogeneous, and differences exist even for cells from the same cell population and cell line. For example, gene expression measurements based on homogenized cell populations are misleading as they only provide averaged results and do not account for the small but critical changes occurring in individual cells such as size, protein levels, and expressed RNA transcripts. These variations are key aspects when answering previously unsolvable questions in cancer research, stem cell biology, immunology, developmental biology, and neurology.To overcome these limitations, PerkinElmer developed Single-Cell (SC) ICP-MS, which allows the rapid analysis of a large number of individual cells rather than a cell population as a whole or only a few cells. This allows for the quantification of the metal mass in individual cells, resulting in a histogram of the population showing not only the most frequent and mean masses of drug in the population but also the distribution throughout the population. Here we will show results for both cisplatin uptake and surface modified gold nanoparticle uptake into cancer cells. The first resulting in a wide distribution in the amount of platinum measured per cell over time with differences in resistant and non-resistant cancer cells. While the latter shows the ability of this technique to quantify the number for modified Au nanoparticles per cell as well as the number of cells containing the drug.Note: This abstract was not presented at the meeting.Citation Format: Chady Stephan, Ruth Merrifield, Stefan Wilhelm. Quantifying the Uptake of Metal Based Cancer Therapy Drugs Using Single Cell ICP-MS [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2988.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| 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 | 0,000 |
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 tête enseignante, 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 ».