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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; Catherine Thornton; Anne E. Carpenter; Huw Summers; Paul Rees; Holger Hennig
Cytometry Part A
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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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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.
high-content analysis; imaging flow cytometry; label-free classification; liquid biopsy; lymphocytes; machine learning; personalized medicine; white blood cell count; white blood cells
Swansea University Medical School