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Machine Learning Enables Live Label-Free Phenotypic Screening in Three Dimensions.

TitleMachine Learning Enables Live Label-Free Phenotypic Screening in Three Dimensions.
Publication TypeJournal Article
Year of Publication2018
AuthorsO'Duibhir E, Paris J, Lawson H, Sepulveda C, Shenton DDoughty, Carragher NO, Kranc KR
JournalAssay Drug Dev Technol
Volume16
Issue1
Pagination51-63
Date Published2018 Jan
ISSN1557-8127
Abstract

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.

DOI10.1089/adt.2017.819
Alternate JournalAssay Drug Dev Technol
PubMed ID29345979
Publication institute
CRM