Label-Free Identification of White Blood Cells Using Machine Learning.

Journal: Cytometry. Part A : the journal of the International Society for Analytical Cytology
PMID: 31081599

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.

Authors

  • 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. Electronic address: Andrew.Filby@newcastle.ac.uk.
  • Olaf Wolkenhauer
    Department of Systems Biology and Bioinformatics, Institute of Computer Science, University of Rostock, Rostock, Germany.
  • Darin K Fogg
    Autograph Biosciences, Inc., Montreal, Quebec, Canada.
  • Justyna Piasecka
    Centre for Nanohealth, Swansea University, Singleton Park, Swansea, SA2 8PP, UK.
  • Catherine A Thornton
    Centre for Nanohealth, Swansea University, Singleton Park, Swansea, SA2 8PP, UK.
  • Anne E Carpenter
    The Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA 02142, United States. Electronic address: anne@broadinstitute.org.
  • Huw D Summers
    Department of Biomedical Engineering, Swansea University, Swansea, UK.
  • Paul Rees
    Imaging Platform at the Broad Institute of Harvard and MIT, 415 Main St, Cambridge, MA 02142, USA; College of Engineering, Swansea University, Singleton Park, Swansea SA2 8PP, UK.
  • Holger Hennig
    Imaging Platform at the Broad Institute of Harvard and MIT, 415 Main St, Cambridge, MA 02142, USA; Dept. of Systems Biology & Bioinformatics, University of Rostock, 18051 Rostock, Germany; College of Engineering, Swansea University, Singleton Park, Swansea SA2 8PP, UK.

Keywords

External Resources

Popular Topics
Recent Journals