Unsupervised flow cytometry analysis in hematological malignancies : A new paradigm
(2021) In International Journal of Laboratory Hematology 43(S1). p.54-64- Abstract
Ever since hematopoietic cells became “events” enumerated and characterized in suspension by cell counters or flow cytometers, researchers and engineers have strived to refine the acquisition and display of the electronic signals generated. A large array of solutions was then developed to identify at best the numerous cell subsets that can be delineated, notably among hematopoietic cells. As instruments became more and more stable and robust, the focus moved to analytic software. Almost concomitantly, the capacity increased to use large panels (both with mass and classical cytometry) and to apply artificial intelligence/machine learning for their analysis. The combination of these concepts raised new analytical possibilities, opening an... (More)
Ever since hematopoietic cells became “events” enumerated and characterized in suspension by cell counters or flow cytometers, researchers and engineers have strived to refine the acquisition and display of the electronic signals generated. A large array of solutions was then developed to identify at best the numerous cell subsets that can be delineated, notably among hematopoietic cells. As instruments became more and more stable and robust, the focus moved to analytic software. Almost concomitantly, the capacity increased to use large panels (both with mass and classical cytometry) and to apply artificial intelligence/machine learning for their analysis. The combination of these concepts raised new analytical possibilities, opening an unprecedented field of subtle exploration for many conditions, including hematopoiesis and hematological disorders. In this review, the general concepts and progress achieved in the development of new analytical approaches for exploring high-dimensional data sets at the single-cell level will be described as they appeared over the past few years. A larger and more practical part will detail the various steps that need to be mastered, both in data acquisition and in the preanalytical check of data files. Finally, a step-by-step explanation of the solution in development to combine the Bioconductor clustering algorithm FlowSOM and the popular and widely used software Kaluza® (Beckman Coulter) will be presented. The aim of this review was to point out that the day when these progresses will reach routine hematology laboratories does not seem so far away.
(Less)
- author
- Béné, Marie C. ; Lacombe, Francis and Porwit, Anna LU
- organization
- publishing date
- 2021
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- artificial intelligence, flow cytometry, machine learning, unsupervised analysis
- in
- International Journal of Laboratory Hematology
- volume
- 43
- issue
- S1
- pages
- 11 pages
- publisher
- Wiley-Blackwell
- external identifiers
-
- pmid:34288436
- scopus:85110917064
- ISSN
- 1751-5521
- DOI
- 10.1111/ijlh.13548
- language
- English
- LU publication?
- yes
- id
- 4d476eab-66ef-4d0b-b502-bffe6b904dda
- date added to LUP
- 2021-08-20 13:48:26
- date last changed
- 2024-09-07 22:54:37
@article{4d476eab-66ef-4d0b-b502-bffe6b904dda,
abstract = {{<p>Ever since hematopoietic cells became “events” enumerated and characterized in suspension by cell counters or flow cytometers, researchers and engineers have strived to refine the acquisition and display of the electronic signals generated. A large array of solutions was then developed to identify at best the numerous cell subsets that can be delineated, notably among hematopoietic cells. As instruments became more and more stable and robust, the focus moved to analytic software. Almost concomitantly, the capacity increased to use large panels (both with mass and classical cytometry) and to apply artificial intelligence/machine learning for their analysis. The combination of these concepts raised new analytical possibilities, opening an unprecedented field of subtle exploration for many conditions, including hematopoiesis and hematological disorders. In this review, the general concepts and progress achieved in the development of new analytical approaches for exploring high-dimensional data sets at the single-cell level will be described as they appeared over the past few years. A larger and more practical part will detail the various steps that need to be mastered, both in data acquisition and in the preanalytical check of data files. Finally, a step-by-step explanation of the solution in development to combine the Bioconductor clustering algorithm FlowSOM and the popular and widely used software Kaluza® (Beckman Coulter) will be presented. The aim of this review was to point out that the day when these progresses will reach routine hematology laboratories does not seem so far away.</p>}},
author = {{Béné, Marie C. and Lacombe, Francis and Porwit, Anna}},
issn = {{1751-5521}},
keywords = {{artificial intelligence; flow cytometry; machine learning; unsupervised analysis}},
language = {{eng}},
number = {{S1}},
pages = {{54--64}},
publisher = {{Wiley-Blackwell}},
series = {{International Journal of Laboratory Hematology}},
title = {{Unsupervised flow cytometry analysis in hematological malignancies : A new paradigm}},
url = {{http://dx.doi.org/10.1111/ijlh.13548}},
doi = {{10.1111/ijlh.13548}},
volume = {{43}},
year = {{2021}},
}