|Title||Machine Learning Enables Live Label-Free Phenotypic Screening in Three Dimensions.|
|Publication Type||Journal Article|
|Year of Publication||2018|
|Authors||O'Duibhir E, Paris J, Lawson H, Sepulveda C, Shenton DDoughty, Carragher NO, Kranc KR|
|Journal||Assay Drug Dev Technol|
|Date Published||2018 Jan|
There is a large amount of information in brightfield images that was previously inaccessible by using traditional microscopy techniques. This information can now be exploited by using machine-learning approaches for both image segmentation and the classification of objects. We have combined these approaches with a label-free assay for growth and differentiation of leukemic colonies, to generate a novel platform for phenotypic drug discovery. Initially, a supervised machine-learning algorithm was used to identify in-focus colonies growing in a three-dimensional (3D) methylcellulose gel. Once identified, unsupervised clustering and principle component analysis of texture-based phenotypic profiles were applied to group similar phenotypes. In a proof-of-concept study, we successfully identified a novel phenotype induced by a compound that is currently in clinical trials for the treatment of leukemia. We believe that our platform will be of great benefit for the utilization of patient-derived 3D cell culture systems for both drug discovery and diagnostic applications.
|Alternate Journal||Assay Drug Dev Technol|