Label-free detection of cellular drug responses by high-throughput bright-field imaging and machine learning.
Sci Rep
; 7(1): 12454, 2017 09 29.
Article
en En
| MEDLINE
| ID: mdl-28963483
ABSTRACT
In the last decade, high-content screening based on multivariate single-cell imaging has been proven effective in drug discovery to evaluate drug-induced phenotypic variations. Unfortunately, this method inherently requires fluorescent labeling which has several drawbacks. Here we present a label-free method for evaluating cellular drug responses only by high-throughput bright-field imaging with the aid of machine learning algorithms. Specifically, we performed high-throughput bright-field imaging of numerous drug-treated and -untreated cells (N = ~240,000) by optofluidic time-stretch microscopy with high throughput up to 10,000 cells/s and applied machine learning to the cell images to identify their morphological variations which are too subtle for human eyes to detect. Consequently, we achieved a high accuracy of 92% in distinguishing drug-treated and -untreated cells without the need for labeling. Furthermore, we also demonstrated that dose-dependent, drug-induced morphological change from different experiments can be inferred from the classification accuracy of a single classification model. Our work lays the groundwork for label-free drug screening in pharmaceutical science and industry.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Procesamiento de Imagen Asistido por Computador
/
Paclitaxel
/
Aprendizaje Automático
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Microscopía de Interferencia
/
Antineoplásicos Fitogénicos
Tipo de estudio:
Diagnostic_studies
Límite:
Humans
Idioma:
En
Revista:
Sci Rep
Año:
2017
Tipo del documento:
Article
País de afiliación:
Japón