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

Eoghan O'Duibhir, Jasmin Paris, Hannah Lawson, Catarina Pires Sepulveda, Dahlia Doughty Shenton, Neil Carragher, Kamil Kranc

Research output: Contribution to journalArticlepeer-review

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.
Original languageEnglish
Pages (from-to)51-63
Number of pages12
JournalAssay and Drug Development Technologies
Volume16
Issue number1
Early online date1 Jan 2018
DOIs
Publication statusE-pub ahead of print - 1 Jan 2018

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