Label‐Free Identification of White Blood Cells Using Machine Learning

Nassar, Mariam; Doan, Minh; Filby, Andrew; Wolkenhauer, Olaf; Fogg, Darin K.; Piasecka, Justyna; Thornton, Cathy; Carpenter, Anne E.; Summers, Huw; Rees, Paul; Hennig, Holger

doi:10.1002/cyto.a.23794

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Label‐Free Identification of White Blood Cells Using Machine Learning

Mariam Nassar, Minh Doan, Andrew Filby, Olaf Wolkenhauer, Darin K. Fogg, Justyna Piasecka, Cathy Thornton

, Anne E. Carpenter, Huw Summers

, Paul Rees

, Holger Hennig

Cytometry Part A

Swansea University Authors: Cathy Thornton , Huw Summers , Paul Rees

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DOI (Published version): 10.1002/cyto.a.23794

Abstract

White blood cell (WBC) differential counting is an established clinical routine to assess patient immune system status. Fluorescent markers and a flow cytometer are required for the current state-of-the-art method for determining WBC differential counts. However, this process requires several sample...

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Published in:	Cytometry Part A
ISSN:	1552-4922 1552-4930
Published:	2019
Online Access:	Check full text
URI:	https://cronfa.swan.ac.uk/Record/cronfa50355
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first_indexed	2019-05-14T14:00:20Z
last_indexed	2019-06-05T11:07:12Z
id	cronfa50355
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spelling	2019-05-29T12:06:42.1838656 v2 50355 2019-05-14 Label‐Free Identification of White Blood Cells Using Machine Learning c71a7a4be7361094d046d312202bce0c 0000-0002-5153-573X Cathy Thornton Cathy Thornton true false a61c15e220837ebfa52648c143769427 0000-0002-0898-5612 Huw Summers Huw Summers true false 537a2fe031a796a3bde99679ee8c24f5 0000-0002-7715-6914 Paul Rees Paul Rees true false 2019-05-14 BMS White blood cell (WBC) differential counting is an established clinical routine to assess patient immune system status. Fluorescent markers and a flow cytometer are required for the current state-of-the-art method for determining WBC differential counts. However, this process requires several sample preparation steps and may adversely disturb the cells. We present a novel label-free approach using an imaging flow cytometer and machine learning algorithms, where live, unstained WBCs were classified. It achieved an average F1-score of 97% and two subtypes of WBCs, B and T lymphocytes, were distinguished from each other with an average F1-score of 78%, a task previously considered impossible for unlabeled samples. We provide an open-source workflow to carry out the procedure. We validated the WBC analysis with unstained samples from 85 donors. The presented method enables robust and highly accurate identification of WBCs, minimizing the disturbance to the cells and leaving marker channels free to answer other biological questions. It also opens the door to employing machine learning for liquid biopsy, here, using the rich information in cell morphology for a wide range of diagnostics of primary blood. Journal Article Cytometry Part A 1552-4922 1552-4930 high-content analysis; imaging flow cytometry; label-free classification; liquid biopsy; lymphocytes; machine learning; personalized medicine; white blood cell count; white blood cells 31 12 2019 2019-12-31 10.1002/cyto.a.23794 COLLEGE NANME Biomedical Sciences COLLEGE CODE BMS Swansea University 2019-05-29T12:06:42.1838656 2019-05-14T11:04:23.4615650 Faculty of Medicine, Health and Life Sciences Swansea University Medical School - Medicine Mariam Nassar 1 Minh Doan 2 Andrew Filby 3 Olaf Wolkenhauer 4 Darin K. Fogg 5 Justyna Piasecka 6 Cathy Thornton 0000-0002-5153-573X 7 Anne E. Carpenter 8 Huw Summers 0000-0002-0898-5612 9 Paul Rees 0000-0002-7715-6914 10 Holger Hennig 11 0050355-29052019120538.pdf 50355.pdf 2019-05-29T12:05:38.3800000 Output 2098974 application/pdf Version of Record true 2019-05-28T00:00:00.0000000 Released under the terms of a Creative Commons Attribution License (CC-BY). true eng
title	Label‐Free Identification of White Blood Cells Using Machine Learning
spellingShingle	Label‐Free Identification of White Blood Cells Using Machine Learning Cathy Thornton Huw Summers Paul Rees
title_short	Label‐Free Identification of White Blood Cells Using Machine Learning
title_full	Label‐Free Identification of White Blood Cells Using Machine Learning
title_fullStr	Label‐Free Identification of White Blood Cells Using Machine Learning
title_full_unstemmed	Label‐Free Identification of White Blood Cells Using Machine Learning
title_sort	Label‐Free Identification of White Blood Cells Using Machine Learning
author_id_str_mv	c71a7a4be7361094d046d312202bce0c a61c15e220837ebfa52648c143769427 537a2fe031a796a3bde99679ee8c24f5
author_id_fullname_str_mv	c71a7a4be7361094d046d312202bce0c_*_Cathy Thornton a61c15e220837ebfa52648c143769427__Huw Summers 537a2fe031a796a3bde99679ee8c24f5_**_Paul Rees
author	Cathy Thornton Huw Summers Paul Rees
author2	Mariam Nassar Minh Doan Andrew Filby Olaf Wolkenhauer Darin K. Fogg Justyna Piasecka Cathy Thornton Anne E. Carpenter Huw Summers Paul Rees Holger Hennig
format	Journal article
container_title	Cytometry Part A
publishDate	2019
institution	Swansea University
issn	1552-4922 1552-4930
doi_str_mv	10.1002/cyto.a.23794
college_str	Faculty of Medicine, Health and Life Sciences
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hierarchy_top_title	Faculty of Medicine, Health and Life Sciences
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hierarchy_parent_title	Faculty of Medicine, Health and Life Sciences
department_str	Swansea University Medical School - Medicine{{{_:::_}}}Faculty of Medicine, Health and Life Sciences{{{_:::_}}}Swansea University Medical School - Medicine
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description	White blood cell (WBC) differential counting is an established clinical routine to assess patient immune system status. Fluorescent markers and a flow cytometer are required for the current state-of-the-art method for determining WBC differential counts. However, this process requires several sample preparation steps and may adversely disturb the cells. We present a novel label-free approach using an imaging flow cytometer and machine learning algorithms, where live, unstained WBCs were classified. It achieved an average F1-score of 97% and two subtypes of WBCs, B and T lymphocytes, were distinguished from each other with an average F1-score of 78%, a task previously considered impossible for unlabeled samples. We provide an open-source workflow to carry out the procedure. We validated the WBC analysis with unstained samples from 85 donors. The presented method enables robust and highly accurate identification of WBCs, minimizing the disturbance to the cells and leaving marker channels free to answer other biological questions. It also opens the door to employing machine learning for liquid biopsy, here, using the rich information in cell morphology for a wide range of diagnostics of primary blood.
published_date	2019-12-31T04:01:47Z
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score	11.014358

Label‐Free Identification of White Blood Cells Using Machine Learning

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