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

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 canc...

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Autores principales: Baumgaertner, Petra, Sankar, Martial, Herrera, Fernanda, Benedetti, Fabrizio, Barras, David, Thierry, Anne-Christine, Dangaj, Denarda, Kandalaft, Lana E., Coukos, George, Xenarios, Ioannis, Guex, Nicolas, Harari, Alexandre
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8119773/
https://www.ncbi.nlm.nih.gov/pubmed/33995353
http://dx.doi.org/10.3389/fimmu.2021.633910
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author Baumgaertner, Petra
Sankar, Martial
Herrera, Fernanda
Benedetti, Fabrizio
Barras, David
Thierry, Anne-Christine
Dangaj, Denarda
Kandalaft, Lana E.
Coukos, George
Xenarios, Ioannis
Guex, Nicolas
Harari, Alexandre
author_facet Baumgaertner, Petra
Sankar, Martial
Herrera, Fernanda
Benedetti, Fabrizio
Barras, David
Thierry, Anne-Christine
Dangaj, Denarda
Kandalaft, Lana E.
Coukos, George
Xenarios, Ioannis
Guex, Nicolas
Harari, Alexandre
author_sort Baumgaertner, Petra
collection PubMed
description 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(+)HLA-DR(+) 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.
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spelling pubmed-81197732021-05-15 Unsupervised Analysis of Flow Cytometry Data in a Clinical Setting Captures Cell Diversity and Allows Population Discovery Baumgaertner, Petra Sankar, Martial Herrera, Fernanda Benedetti, Fabrizio Barras, David Thierry, Anne-Christine Dangaj, Denarda Kandalaft, Lana E. Coukos, George Xenarios, Ioannis Guex, Nicolas Harari, Alexandre Front Immunol Immunology 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(+)HLA-DR(+) 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. Frontiers Media S.A. 2021-04-30 /pmc/articles/PMC8119773/ /pubmed/33995353 http://dx.doi.org/10.3389/fimmu.2021.633910 Text en Copyright © 2021 Baumgaertner, Sankar, Herrera, Benedetti, Barras, Thierry, Dangaj, Kandalaft, Coukos, Xenarios, Guex and Harari https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Immunology
Baumgaertner, Petra
Sankar, Martial
Herrera, Fernanda
Benedetti, Fabrizio
Barras, David
Thierry, Anne-Christine
Dangaj, Denarda
Kandalaft, Lana E.
Coukos, George
Xenarios, Ioannis
Guex, Nicolas
Harari, Alexandre
Unsupervised Analysis of Flow Cytometry Data in a Clinical Setting Captures Cell Diversity and Allows Population Discovery
title Unsupervised Analysis of Flow Cytometry Data in a Clinical Setting Captures Cell Diversity and Allows Population Discovery
title_full Unsupervised Analysis of Flow Cytometry Data in a Clinical Setting Captures Cell Diversity and Allows Population Discovery
title_fullStr Unsupervised Analysis of Flow Cytometry Data in a Clinical Setting Captures Cell Diversity and Allows Population Discovery
title_full_unstemmed Unsupervised Analysis of Flow Cytometry Data in a Clinical Setting Captures Cell Diversity and Allows Population Discovery
title_short Unsupervised Analysis of Flow Cytometry Data in a Clinical Setting Captures Cell Diversity and Allows Population Discovery
title_sort unsupervised analysis of flow cytometry data in a clinical setting captures cell diversity and allows population discovery
topic Immunology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8119773/
https://www.ncbi.nlm.nih.gov/pubmed/33995353
http://dx.doi.org/10.3389/fimmu.2021.633910
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