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Towards Image-based Cancer Cell Lines Authentication Using Deep Neural Networks

Mzurikwao, Deogratias, Khan, Muhammad Usman, Williams Samuel, Oluwarotimi, Cinatl, Jindrich, Wass, Mark N., Michaelis, Martin, Marcelli, Gianluca, Ang, Chee Siang (2020) Towards Image-based Cancer Cell Lines Authentication Using Deep Neural Networks. Scientific Reports, 10 . Article Number 19857. ISSN 2045-2322. (doi:10.1038/s41598-020-76670-6) (KAR id:83756)

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Abstract

Although short tandem repeat (STR) analysis is available as a reliable method for the determination of the genetic origin of cell lines, the occurrence of misauthenticated cell lines remains an important issue. Reasons include the cost, effort and time associated with STR analysis. Moreover, there are currently no methods for the discrimination between isogenic cell lines (cell lines of the same genetic origin, e.g. different cell lines derived from the same organism, clonal sublines, sublines adapted to grow under certain conditions). Hence, additional complementary, ideally low-cost and low-effort methods are required that enable 1) the monitoring of cell line identity as part of the daily laboratory routine and 2) the authentication of isogenic cell lines. In this research, we automate the process of cell line identification by image-based analysis using deep convolutional neural networks. Two different convolutional neural networks models (MobileNet and InceptionResNet V2) were trained to automatically identify four parental cancer cell line (COLO 704, EFO-21, EFO-27 and UKF-NB-3) and their sublines adapted to the anti-cancer drugs cisplatin (COLO-704rCDDP1000, EFO-21rCDDP2000, EFO-27rCDDP2000) or oxaliplatin (UKF-NB-3rOXALI2000), hence resulting in an eight-class problem. Our best performing model, InceptionResNet V2, achieved an average of 0.91 F1-score on 10-fold cross validation with an average area under the curve (AUC) of 0.95, for the 8-class problem. Our best model also achieved an average F1-score of 0.94 and 0.96 on the authentication through a classification process of the four parental cell lines and the respective drug-adapted cells, respectively, on a four-class problem separately. These findings provide the basis for further development of the application of deep learning for the automation of cell line authentication into a readily available easy-to-use methodology that enables routine monitoring of the identity of cell lines including isogenic cell lines. It should be noted that, this is just a proof of principal that, images can also be used as a method for authentication of cancer cell lines and not a replacement for the STR method.

Item Type: Article
DOI/Identification number: 10.1038/s41598-020-76670-6
Subjects: Q Science
R Medicine
T Technology
Divisions: Faculties > Sciences > School of Biosciences
Faculties > Sciences > School of Engineering and Digital Arts
Depositing User: Deogratias Mzurikwao
Date Deposited: 27 Oct 2020 10:55 UTC
Last Modified: 17 Nov 2020 12:12 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/83756 (The current URI for this page, for reference purposes)
Wass, Mark N.: https://orcid.org/0000-0001-5428-6479
Michaelis, Martin: https://orcid.org/0000-0002-5710-5888
Marcelli, Gianluca: https://orcid.org/0000-0002-7475-7327
Ang, Chee Siang: https://orcid.org/0000-0002-1109-9689
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