CancerCellTracker: a brightfield time-lapse microscopy framework for cancer drug sensitivity estimation
Author:
Jiang Qibing1,
Sudalagunta Praneeth2,
Silva Maria C2,
Canevarolo Rafael R2,
Zhao Xiaohong2,
Ahmed Khandakar Tanvir1,
Alugubelli Raghunandan Reddy2,
DeAvila Gabriel2,
Tungesvik Alexandre2,
Perez Lia2,
Gatenby Robert A2,
Gillies Robert J2,
Baz Rachid2,
Meads Mark B2,
Shain Kenneth H2,
Silva Ariosto S2,
Zhang Wei1ORCID
Affiliation:
1. Department of Computer Science, University of Central Florida , Orlando, FL 32816, USA
2. Departments of Malignant Hematology and Chemical Biology and Molecular Medicine, H. Lee Moffitt Cancer Center and Research Institute , Tampa, FL 33612, USA
Abstract
Abstract
Motivation
Time-lapse microscopy is a powerful technique that relies on images of live cells cultured ex vivo that are captured at regular intervals of time to describe and quantify their behavior under certain experimental conditions. This imaging method has great potential in advancing the field of precision oncology by quantifying the response of cancer cells to various therapies and identifying the most efficacious treatment for a given patient. Digital image processing algorithms developed so far require high-resolution images involving very few cells originating from homogeneous cell line populations. We propose a novel framework that tracks cancer cells to capture their behavior and quantify cell viability to inform clinical decisions in a high-throughput manner.
Results
The brightfield microscopy images a large number of patient-derived cells in an ex vivo reconstruction of the tumor microenvironment treated with 31 drugs for up to 6 days. We developed a robust and user-friendly pipeline CancerCellTracker that detects cells in co-culture, tracks these cells across time and identifies cell death events using changes in cell attributes. We validated our computational pipeline by comparing the timing of cell death estimates by CancerCellTracker from brightfield images and a fluorescent channel featuring ethidium homodimer. We benchmarked our results using a state-of-the-art algorithm implemented in ImageJ and previously published in the literature. We highlighted CancerCellTracker’s efficiency in estimating the percentage of live cells in the presence of bone marrow stromal cells.
Availability and implementation
https://github.com/compbiolabucf/CancerCellTracker.
Supplementary information
Supplementary data are available at Bioinformatics online.
Funder
Pentecost Family Foundation and Pentecost Family Myeloma Research Center (PMRC) at Moffitt Cancer Center
H. Lee Moffitt Cancer Center Physical Sciences in Oncology
Lee Moffitt Cancer Center’s Team Science Grant
Miles for Moffitt Foundation
Cancer Center Support
Moffitt Cancer Center. Access to primary cells was made possible through the Total Cancer Care Protocol at the Moffitt Cancer Center
Publisher
Oxford University Press (OUP)
Subject
Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability
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