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Automated flow cytometric identification of disease-specific cells by the ECLIPSE algorithm

Multicolor Flow Cytometry (MFC)-based gating allows the selection of cellular (pheno)types based on their unique marker expression. Current manual gating practice is highly subjective and may remove relevant information to preclude discovery of cell populations with specific co-expression of multipl...

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Autores principales: Folcarelli, Rita, van Staveren, Selma, Bouman, Roel, Hilvering, Bart, Tinnevelt, Gerjen H., Postma, Geert, van den Brink, Oscar F., Buydens, Lutgarde M. C., Vrisekoop, Nienke, Koenderman, Leo, Jansen, Jeroen J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6053450/
https://www.ncbi.nlm.nih.gov/pubmed/30026601
http://dx.doi.org/10.1038/s41598-018-29367-w
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author Folcarelli, Rita
van Staveren, Selma
Bouman, Roel
Hilvering, Bart
Tinnevelt, Gerjen H.
Postma, Geert
van den Brink, Oscar F.
Buydens, Lutgarde M. C.
Vrisekoop, Nienke
Koenderman, Leo
Jansen, Jeroen J.
author_facet Folcarelli, Rita
van Staveren, Selma
Bouman, Roel
Hilvering, Bart
Tinnevelt, Gerjen H.
Postma, Geert
van den Brink, Oscar F.
Buydens, Lutgarde M. C.
Vrisekoop, Nienke
Koenderman, Leo
Jansen, Jeroen J.
author_sort Folcarelli, Rita
collection PubMed
description Multicolor Flow Cytometry (MFC)-based gating allows the selection of cellular (pheno)types based on their unique marker expression. Current manual gating practice is highly subjective and may remove relevant information to preclude discovery of cell populations with specific co-expression of multiple markers. Only multivariate approaches can extract such aspects of cell variability from multi-dimensional MFC data. We describe the novel method ECLIPSE (Elimination of Cells Lying in Patterns Similar to Endogeneity) to identify and characterize aberrant cells present in individuals out of homeostasis. ECLIPSE combines dimensionality reduction by Simultaneous Component Analysis with Kernel Density Estimates. A Difference between Densities (DbD) is used to eliminate cells in responder samples that overlap in marker expression with cells of controls. Thereby, subsequent data analyses focus on the immune response-specific cells, leading to more informative and focused models. To prove the power of ECLIPSE, we applied the method to study two distinct datasets: the in vivo neutrophil response induced by systemic endotoxin challenge and in studying the heterogeneous immune-response of asthmatics. ECLIPSE described the well-characterized common response in the LPS challenge insightfully, while identifying slight differences between responders. Also, ECLIPSE enabled characterization of the immune response associated to asthma, where the co-expressions between all markers were used to stratify patients according to disease-specific cell profiles.
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spelling pubmed-60534502018-07-23 Automated flow cytometric identification of disease-specific cells by the ECLIPSE algorithm Folcarelli, Rita van Staveren, Selma Bouman, Roel Hilvering, Bart Tinnevelt, Gerjen H. Postma, Geert van den Brink, Oscar F. Buydens, Lutgarde M. C. Vrisekoop, Nienke Koenderman, Leo Jansen, Jeroen J. Sci Rep Article Multicolor Flow Cytometry (MFC)-based gating allows the selection of cellular (pheno)types based on their unique marker expression. Current manual gating practice is highly subjective and may remove relevant information to preclude discovery of cell populations with specific co-expression of multiple markers. Only multivariate approaches can extract such aspects of cell variability from multi-dimensional MFC data. We describe the novel method ECLIPSE (Elimination of Cells Lying in Patterns Similar to Endogeneity) to identify and characterize aberrant cells present in individuals out of homeostasis. ECLIPSE combines dimensionality reduction by Simultaneous Component Analysis with Kernel Density Estimates. A Difference between Densities (DbD) is used to eliminate cells in responder samples that overlap in marker expression with cells of controls. Thereby, subsequent data analyses focus on the immune response-specific cells, leading to more informative and focused models. To prove the power of ECLIPSE, we applied the method to study two distinct datasets: the in vivo neutrophil response induced by systemic endotoxin challenge and in studying the heterogeneous immune-response of asthmatics. ECLIPSE described the well-characterized common response in the LPS challenge insightfully, while identifying slight differences between responders. Also, ECLIPSE enabled characterization of the immune response associated to asthma, where the co-expressions between all markers were used to stratify patients according to disease-specific cell profiles. Nature Publishing Group UK 2018-07-19 /pmc/articles/PMC6053450/ /pubmed/30026601 http://dx.doi.org/10.1038/s41598-018-29367-w Text en © The Author(s) 2018 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Folcarelli, Rita
van Staveren, Selma
Bouman, Roel
Hilvering, Bart
Tinnevelt, Gerjen H.
Postma, Geert
van den Brink, Oscar F.
Buydens, Lutgarde M. C.
Vrisekoop, Nienke
Koenderman, Leo
Jansen, Jeroen J.
Automated flow cytometric identification of disease-specific cells by the ECLIPSE algorithm
title Automated flow cytometric identification of disease-specific cells by the ECLIPSE algorithm
title_full Automated flow cytometric identification of disease-specific cells by the ECLIPSE algorithm
title_fullStr Automated flow cytometric identification of disease-specific cells by the ECLIPSE algorithm
title_full_unstemmed Automated flow cytometric identification of disease-specific cells by the ECLIPSE algorithm
title_short Automated flow cytometric identification of disease-specific cells by the ECLIPSE algorithm
title_sort automated flow cytometric identification of disease-specific cells by the eclipse algorithm
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6053450/
https://www.ncbi.nlm.nih.gov/pubmed/30026601
http://dx.doi.org/10.1038/s41598-018-29367-w
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