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An automated cell line authentication method for AstraZeneca global cell bank using deep neural networks on brightfield images

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posted on 2022-06-01, 16:30 authored by L Tong, A Corrigan, NR Kumar, K Hallbrook, J Orme, Y Wang, Huiyu Zhou

Cell line authentication is important in the biomedical field to ensure that researchers are not working with misidentified cells. Short tandem repeat is the gold standard method, but has its own limitations, including being expensive and time-consuming. Deep neural networks achieve great success in the analysis of cellular images in a cost-effective way. However, because of the lack of centralized available datasets, whether or not cell line authentication can be replaced or supported by cell image classification is still a question. Moreover, the relationship between the incubation times and cellular images has not been explored in previous studies. In this study, we automated the process of the cell line authentication by using deep learning analysis of brightfield cell line images. We proposed a novel multi-task framework to identify cell lines from cell images and predict the duration of how long cell lines have been incubated simultaneously. Using thirty cell lines’ data from the AstraZeneca Cell Bank, we demonstrated that our proposed method can accurately identify cell lines from brightfield images with a 99.8% accuracy and predicts the incubation durations for cell images with the coefficient of determination score of 0.927. Considering that new cell lines are continually added to the AstraZeneca Cell Bank, we integrated the transfer learning technique with the proposed system to deal with data from new cell lines not included in the pre-trained model. Our method achieved excellent performance with a precision of 97.7% and recall of 95.8% in the detection of 14 new cell lines. These results demonstrated that our proposed framework can effectively identify cell lines using brightfield images. 

Funding

University of Leicester GTA studentship (GTA 2020), China Scholarship Council and AstraZeneca—University Leicester collaboration agreement (CR-019972)

AstraZeneca

History

Citation

Sci Rep 12, 7894 (2022). https://doi.org/10.1038/s41598-022-12099-3

Author affiliation

School of Computing and Mathematical Sciences, University of Leicester

Version

  • VoR (Version of Record)

Published in

Scientific Reports

Volume

12

Pagination

7894

Publisher

Nature Publishing Group

issn

2045-2322

Acceptance date

2022-05-05

Copyright date

2022

Available date

2022-06-01

Language

en

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