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Label-Free Identification of White Blood Cells Using Machine Learning

Lookup NU author(s): Dr Andrew Filby

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


Abstract

© 2019 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry. 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. © 2019 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.


Publication metadata

Author(s): Nassar M, Doan M, Filby A, Wolkenhauer O, Fogg DK, Piasecka J, Thornton CA, Carpenter AE, Summers HD, Rees P, Hennig H

Publication type: Article

Publication status: Published

Journal: Cytometry Part A

Year: 2019

Volume: 95

Issue: 8

Pages: 836-842

Print publication date: 14/08/2019

Online publication date: 13/05/2019

Acceptance date: 25/04/2019

Date deposited: 29/05/2019

ISSN (print): 1552-4922

ISSN (electronic): 1552-4930

Publisher: John Wiley & Sons, Inc.

URL: https://doi.org/10.1002/cyto.a.23794

DOI: 10.1002/cyto.a.23794


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Funding

Funder referenceFunder name
BB/N005163/1
BB/P026818/1
GM122547
NSFDBI 1458626

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