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Nassar M, Doan M, Filby A, Wolkenhauer O, Fogg DK, Piasecka J, Thornton CA, Carpenter AE, Summers HD, Rees P, Hennig H. Label-Free Identification of White Blood Cells Using Machine Learning. Cytometry A 2019; 95:836-842. [PMID: 31081599 PMCID: PMC6767740 DOI: 10.1002/cyto.a.23794] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2018] [Revised: 03/27/2019] [Accepted: 04/25/2019] [Indexed: 11/07/2022]
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 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.
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Affiliation(s)
- Mariam Nassar
- Department of Systems Biology & Bioinformatics, University of Rostock, 18051, Rostock, Germany
| | - Minh Doan
- Imaging Platform at the Broad Institute of Harvard and MIT, 415 Main St, Cambridge, Massachusetts, 02142
| | - Andrew Filby
- Flow Cytometry Core Facility, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, NE1 7RU, UK
| | - Olaf Wolkenhauer
- Department of Systems Biology & Bioinformatics, University of Rostock, 18051, Rostock, Germany.,Stellenbosch Institute for Advanced Study (STIAS), Stellenbosch, South Africa
| | - Darin K Fogg
- Autograph Biosciences, Inc., Montreal, Quebec, Canada
| | - Justyna Piasecka
- Centre for Nanohealth, Swansea University, Singleton Park, Swansea, SA2 8PP, UK
| | | | - Anne E Carpenter
- Imaging Platform at the Broad Institute of Harvard and MIT, 415 Main St, Cambridge, Massachusetts, 02142
| | - Huw D Summers
- Centre for Nanohealth, Swansea University, Singleton Park, Swansea, SA2 8PP, UK
| | - Paul Rees
- Centre for Nanohealth, Swansea University, Singleton Park, Swansea, SA2 8PP, UK
| | - Holger Hennig
- Department of Systems Biology & Bioinformatics, University of Rostock, 18051, Rostock, Germany.,Imaging Platform at the Broad Institute of Harvard and MIT, 415 Main St, Cambridge, Massachusetts, 02142
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