Skip to content
Home
About Us
Resources
Profiles Metrics
Authors Directory
Institutions Directory
Top Authors
Top Institutions
Top Sponsors
AI Digest
Contact Us
Menu
Home
About Us
Resources
Profiles Metrics
Authors Directory
Institutions Directory
Top Authors
Top Institutions
Top Sponsors
AI Digest
Contact Us
Home
About Us
Resources
Profiles Metrics
Authors Directory
Institutions Directory
Top Authors
Top Institutions
Top Sponsors
AI Digest
Contact Us
Menu
Home
About Us
Resources
Profiles Metrics
Authors Directory
Institutions Directory
Top Authors
Top Institutions
Top Sponsors
AI Digest
Contact Us
Publication Details
AFRICAN RESEARCH NEXUS
SHINING A SPOTLIGHT ON AFRICAN RESEARCH
biochemistry, genetics and molecular biology
Label-Free Identification of White Blood Cells Using Machine Learning
Cytometry Part A, Volume 95, No. 8, Year 2019
Notification
URL copied to clipboard!
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. © 2019 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.
Available Materials
https://efashare.b-cdn.net/share/pmc/articles/PMC6767740/bin/CYTO-95-836-s001.docx
https://efashare.b-cdn.net/share/pmc/articles/PMC6767740/bin/CYTO-95-836-s002.cppipe
Authors & Co-Authors
Nassar, Mariam
Germany, Rostock
Universität Rostock
Doan, Minh
United States, Cambridge
Broad Institute
Filby, Andrew J.
United Kingdom, Newcastle
University of Newcastle Upon Tyne, Faculty of Medical Sciences
Wolkenhauer, Olaf
Germany, Rostock
Universität Rostock
South Africa, Stellenbosch
Stellenbosch Institute for Advanced Study
Fogg, Darin K.
Canada, Montreal
Institut de Recherche en Immunologie et en Cancérologie de L’université de Montréal
Piasecka, Justyna
United Kingdom, Swansea
Swansea University
Thornton, Catherine A.
United Kingdom, Swansea
Swansea University
Carpenter, Anne Elizabeth
United States, Cambridge
Broad Institute
Summers, Huw D.
United Kingdom, Swansea
Swansea University
Rees, Paul E.T.
United Kingdom, Swansea
Swansea University
Hennig, Holger
Germany, Rostock
Universität Rostock
United States, Cambridge
Broad Institute
Statistics
Citations: 63
Authors: 11
Affiliations: 6
Identifiers
Doi:
10.1002/cyto.a.23794
ISSN:
15524922
Research Areas
Health System And Policy