Abstract 5015: Drug screening and phenotypic analysis in a microwell-based 3D cell culture system
Notice bibliographique
Résumé
Abstract Three-dimensional (3D) cell culture models provide more physiologically relevant drug and toxicity screening platforms than traditional 2D platforms (1). Phenotypic analysis is an informative endpoint for high-throughput screens; however, variability in 3D culture systems can make statistically sound identification of good candidate compounds from a small number of replicates difficult. To minimize this variability we tested AggreWell™, a microwell-based 3D cell culture device that is a prototype system for 3D culture including drug and toxicity screening. The geometry of this system provides precise localization, segmentation and uniformity of spheroids derived from various cell types, and is predicted to minimize experimental variability. To test this directly, estrogen receptor-expressing MCF-7 breast cancer cells were seeded into AggreWell™400 plates in MammoCult™ medium in triplicate, and 24 hours after seeding, the resulting spheroids were treated with a single drug or combination of tamoxifen (TMX), the HER2 and EGFR inhibitor lapatinib (LTB), and the γ-secretase and Notch inhibitor DAPT. Three days later, the spheroids were imaged for the following parameters: size, brightness, shape, and specific morphologic features. Spheroids were also dissociated for viable cell counts. The IC50 values for drug effects for TMX and LTB are 21.6 and 6.3 uM, respectively, whereas DAPT had no effect on the spheroids at doses up to 100 uM. These results are consistent with previous published results and allow for correlation of observed changes in morphologic and cell viability post-treatment. For combinatorial drug treatments, concentrations of 20 uM TMX, 5 uM LTB, and 10 uM DAPT were used. TMX treatment exhibited the greatest reduction of cell reduction (52%). Combination of TMX with DAPT, LTB, or both, resulted in synergistic reductions of viability of 64%, 74% and 83%, respectively. Morphologic features of these cultures analyzed by principal component analysis reveal that parameters including circularity and gray values are associated with untreated cultures, whereas spheroid perimeter and area are associated with treated cultures. Graphing combinations of these parameters, for example, area vs. integrated density, delineates a clear decision plane for classifying treated versus untreated conditions. Analysis of variability in aggregate morphology demonstrates that measurement of multiple spheroids per well in AggreWell™ increased experimental power (e.g., power of 0.8 for 16 spheroids versus 0.3 for 4 spheroids, respectively; α = 0.05) and reduced experimental variance. These results confirm that morphologic analysis of spheroids grown on AggreWell™ plates is highly suited for high-throughput 3D drug screening.Reference: 1. Lee GY et al. Nat. Methods 2007. Citation Format: Michael Hiatt, Marta Mroczek, Eric Jervis, Terry E. Thomas, Allen C. Eaves, Sharon Louis. Drug screening and phenotypic analysis in a microwell-based 3D cell culture system [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 5015.
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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,004 |
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 ».