Improved drug activity in high-content screening of the microtubule network
Notice bibliographique
Résumé
High-Content Screening (HCS) is a technology based on the automation of fluorescence microscopy, to screen and analyze the spatial and morphological properties of individual cells. This automation has made it possible to acquire, process, and archive tens of millions of cell images, and hundreds of compounds at a time, facilitating drug discovery. Although the large number of test compounds and cells has advantages, it removes the possibility of human inspection and so relies on quantitative analytical approaches. Much of current practice, however, does not take full advantage of the information-rich content of HCS screens and instead relies on measurement and analysis methods developed for High-Throughput Screens (HTS) which generate only one output per well (brightness) and which are unable to detect treatments which affect only a subpopulation of cells. Interest in cell subpopulations (heterogeneity) has been gaining interest lately especially in cancer cells and with the advent of single cell sequencing. This has put forward a need for evaluating a summary statistic that is sensitive to subpopulations and hence could possibly classify drug effects more accurately.Another issue in HCS is the lack of metrics for characterizing biologically-relevant phenotypes beyond changes in brightness. This is especially true in the case of the microtubule structure where texture measures are abundant but hard to interpret. Here we evaluate a family of metrics that quantify measures that are directly related to microtubule structures, such as number of branch points of fibers, potentially offering insights concerning biological mechanisms. In this study, the aim is to examine the morphology of cells treated with compounds with well-known effects on cells and in particular, on the fibrous microtubule structure, in a data set of thousands of confocal microscope images of HeLa cells. First, the performances of 14 fiber-specific metrics will be assessed in distinguishing between active and inactive compounds in both lysed and non-lysed cells. Second we present Receiver Operator Characteristic (ROC) curves as an alternative estimate of treatment effect that is potentially more sensitive to cell subpopulations than standard summary statistics. Our results show that both ROC curves and the tested fiber morphology metrics are interpretable, and provide a basis for determining active compounds under different conditions. They also outperform the standard mean fluorescence of cells in distinguishing between drug-treated and control cells, providing a relevant biological framework in which hypotheses may be developed.
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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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 ».