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Investigating the use of Deep Learning Algorithms to Automatically Score Micronuclei in Human Cell Lines / RACHEL BARNES

Swansea University Author: RACHEL BARNES

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Abstract

The in vitro micronucleus (MN) assay is a globally used test to quantify DNA damage induced by test chemicals from various industries such as pharmaceuticals, cosmetics and agriculture. Currently, manual scoring is used which is extremely time-consuming and scorer subjective so causes a significant...

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Published: Swansea, Wales, UK 2023
Institution: Swansea University
Degree level: Master of Research
Degree name: MSc by Research
Supervisor: Johnson, George E. and Rees, Paul.
URI: https://cronfa.swan.ac.uk/Record/cronfa63717
first_indexed 2023-06-27T09:24:42Z
last_indexed 2024-11-25T14:12:47Z
id cronfa63717
recordtype RisThesis
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spelling 2023-06-27T10:29:00.2724135 v2 63717 2023-06-27 Investigating the use of Deep Learning Algorithms to Automatically Score Micronuclei in Human Cell Lines 6f5583e95482e1356e510c730ce505f8 RACHEL BARNES RACHEL BARNES true false 2023-06-27 The in vitro micronucleus (MN) assay is a globally used test to quantify DNA damage induced by test chemicals from various industries such as pharmaceuticals, cosmetics and agriculture. Currently, manual scoring is used which is extremely time-consuming and scorer subjective so causes a significant bottleneck in the use of the MN assay. This project shows that imaging flow cytometry coupled with deep learning neural networks can be reliably and accurately used with inter-laboratory function, to automatically score micronucleus events in chemically exposed human B lymphoblastoid cells called TK6 cells. Images were taken from both the cytokinesis-block micronucleus (CBMN) assay and the mononucleate MN assay at Newcastle University. Six different chemicals were tested in this study which are known genotoxic agents and known non-genotoxic agents: aroclor, carbendazim, methyl methanosulphate (MMS), vinblastine, benzo(a)pyrene, D-mannitol. These images were then inputted into a “Deep Flow” neural network, coded in the MATLAB platform which was previously trained on human-scored images assembled from the CBMN assay conducted by Cardiff and Cambridge universities, using MMS and carbendazim treated TK6 cells. Using image data from multiple laboratories in this study provides evidence that the neural network can be used to score unseen data from any laboratory. The neural network correctly scores micronucleus events for both the CBMN and mononucleate MN assays at a percentage confidence of 70% and above. Dose response data for each chemical is parallel to ECVAM guidelines. The aneugen, carbendazim, was shown by the deep learning algorithm to increase the mean dose response by 3.4-fold which shows that as the dose of carbendazim increases, the abundance of micronuclei increases. Further optimisation of the ground truth will prevent underscoring of micronuclei in binucleated cells. It can be concluded that with further optimisation and development of the neural network, this automated platform offers a great potential for the use of the in vitro MN assay to be widened. This method has a higher throughput and has the capability to test greater numbers of compounds and chemicals, therefore, this method will be able to keep up with the increasing demand for genotoxicity testing in industrial and pharmaceutical settings. E-Thesis Swansea, Wales, UK Genetic Toxicology, Artificial Intelligence, Machine Learning 1 6 2023 2023-06-01 COLLEGE NANME COLLEGE CODE Swansea University Johnson, George E. and Rees, Paul. Master of Research MSc by Research 2023-06-27T10:29:00.2724135 2023-06-27T10:22:00.1340239 Faculty of Medicine, Health and Life Sciences Swansea University Medical School - Biomedical Science RACHEL BARNES 1 63717__27978__7f077fcb70ed4b06a324ab0d5a1552d5.pdf 2023_Barnes_R.final.63717.pdf 2023-06-27T10:25:08.0412500 Output 2618857 application/pdf E-Thesis – open access true Copyright: The Author, Rachel Barnes, 2023. true eng
title Investigating the use of Deep Learning Algorithms to Automatically Score Micronuclei in Human Cell Lines
spellingShingle Investigating the use of Deep Learning Algorithms to Automatically Score Micronuclei in Human Cell Lines
RACHEL BARNES
title_short Investigating the use of Deep Learning Algorithms to Automatically Score Micronuclei in Human Cell Lines
title_full Investigating the use of Deep Learning Algorithms to Automatically Score Micronuclei in Human Cell Lines
title_fullStr Investigating the use of Deep Learning Algorithms to Automatically Score Micronuclei in Human Cell Lines
title_full_unstemmed Investigating the use of Deep Learning Algorithms to Automatically Score Micronuclei in Human Cell Lines
title_sort Investigating the use of Deep Learning Algorithms to Automatically Score Micronuclei in Human Cell Lines
author_id_str_mv 6f5583e95482e1356e510c730ce505f8
author_id_fullname_str_mv 6f5583e95482e1356e510c730ce505f8_***_RACHEL BARNES
author RACHEL BARNES
author2 RACHEL BARNES
format E-Thesis
publishDate 2023
institution Swansea University
college_str Faculty of Medicine, Health and Life Sciences
hierarchytype
hierarchy_top_id facultyofmedicinehealthandlifesciences
hierarchy_top_title Faculty of Medicine, Health and Life Sciences
hierarchy_parent_id facultyofmedicinehealthandlifesciences
hierarchy_parent_title Faculty of Medicine, Health and Life Sciences
department_str Swansea University Medical School - Biomedical Science{{{_:::_}}}Faculty of Medicine, Health and Life Sciences{{{_:::_}}}Swansea University Medical School - Biomedical Science
document_store_str 1
active_str 0
description The in vitro micronucleus (MN) assay is a globally used test to quantify DNA damage induced by test chemicals from various industries such as pharmaceuticals, cosmetics and agriculture. Currently, manual scoring is used which is extremely time-consuming and scorer subjective so causes a significant bottleneck in the use of the MN assay. This project shows that imaging flow cytometry coupled with deep learning neural networks can be reliably and accurately used with inter-laboratory function, to automatically score micronucleus events in chemically exposed human B lymphoblastoid cells called TK6 cells. Images were taken from both the cytokinesis-block micronucleus (CBMN) assay and the mononucleate MN assay at Newcastle University. Six different chemicals were tested in this study which are known genotoxic agents and known non-genotoxic agents: aroclor, carbendazim, methyl methanosulphate (MMS), vinblastine, benzo(a)pyrene, D-mannitol. These images were then inputted into a “Deep Flow” neural network, coded in the MATLAB platform which was previously trained on human-scored images assembled from the CBMN assay conducted by Cardiff and Cambridge universities, using MMS and carbendazim treated TK6 cells. Using image data from multiple laboratories in this study provides evidence that the neural network can be used to score unseen data from any laboratory. The neural network correctly scores micronucleus events for both the CBMN and mononucleate MN assays at a percentage confidence of 70% and above. Dose response data for each chemical is parallel to ECVAM guidelines. The aneugen, carbendazim, was shown by the deep learning algorithm to increase the mean dose response by 3.4-fold which shows that as the dose of carbendazim increases, the abundance of micronuclei increases. Further optimisation of the ground truth will prevent underscoring of micronuclei in binucleated cells. It can be concluded that with further optimisation and development of the neural network, this automated platform offers a great potential for the use of the in vitro MN assay to be widened. This method has a higher throughput and has the capability to test greater numbers of compounds and chemicals, therefore, this method will be able to keep up with the increasing demand for genotoxicity testing in industrial and pharmaceutical settings.
published_date 2023-06-01T05:09:12Z
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score 11.089718

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