Unsupervised Analysis of Flow Cytometry Data in a Clinical Setting Captures Cell Diversity and Allows Population Discovery.

Details

Serval ID
serval:BIB_E707FFE34C52
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Unsupervised Analysis of Flow Cytometry Data in a Clinical Setting Captures Cell Diversity and Allows Population Discovery.
Journal
Frontiers in immunology
Author(s)
Baumgaertner P., Sankar M., Herrera F., Benedetti F., Barras D., Thierry A.C., Dangaj D., Kandalaft L.E., Coukos G., Xenarios I., Guex N., Harari A.
ISSN
1664-3224 (Electronic)
ISSN-L
1664-3224
Publication state
Published
Issued date
2021
Peer-reviewed
Oui
Volume
12
Pages
633910
Language
english
Notes
Publication types: Journal Article
Publication Status: epublish
Abstract
Data obtained with cytometry are increasingly complex and their interrogation impacts the type and quality of knowledge gained. Conventional supervised analyses are limited to pre-defined cell populations and do not exploit the full potential of data. Here, in the context of a clinical trial of cancer patients treated with radiotherapy, we performed longitudinal flow cytometry analyses to identify multiple distinct cell populations in circulating whole blood. We cross-compared the results from state-of-the-art recommended supervised analyses with results from MegaClust, a high-performance data-driven clustering algorithm allowing fast and robust identification of cell-type populations. Ten distinct cell populations were accurately identified by supervised analyses, including main T, B, dendritic cell (DC), natural killer (NK) and monocytes subsets. While all ten subsets were also identified with MegaClust, additional cell populations were revealed (e.g. CD4 <sup>+</sup> HLA-DR <sup>+</sup> and NKT-like subsets), and DC profiling was enriched by the assignment of additional subset-specific markers. Comparison between transcriptomic profiles of purified DC populations and publicly available datasets confirmed the accuracy of the unsupervised clustering algorithm and demonstrated its potential to identify rare and scarcely described cell subsets. Our observations show that data-driven analyses of cytometry data significantly enrich the amount and quality of knowledge gained, representing an important step in refining the characterization of immune responses.
Keywords
analytical immunology, data-driven analysis, flow cytometry, immune monitoring, unsupervised clustering
Pubmed
Web of science
Open Access
Yes
Funding(s)
Swiss National Science Foundation
Create date
21/05/2021 16:09
Last modification date
29/05/2021 6:31
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