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 language | English |
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Pages (from-to) | 51-63 |
Number of pages | 12 |
Journal | Assay and Drug Development Technologies |
Volume | 16 |
Issue number | 1 |
Early online date | 1 Jan 2018 |
DOIs | |
Publication status | E-pub ahead of print - 1 Jan 2018 |
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Jasmin Paris
- Royal (Dick) School of Veterinary Studies - ECAT Linked CRUK-Funded Clinical Lecturer
Person: Academic: Research Active