CellCycleTRACER accounts for cell cycle and volume in mass cytometry data

Recent studies have shown that cell cycle and cell volume are confounding factors when studying biological phenomena in single cells. Here we present a combined experimental and computational method, CellCycleTRACER, to account for these factors in mass cytometry data. CellCycleTRACER is applied to...

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Autores principales: Rapsomaniki, Maria Anna, Lun, Xiao-Kang, Woerner, Stefan, Laumanns, Marco, Bodenmiller, Bernd, Martínez, María Rodríguez
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2018
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5809393/
https://www.ncbi.nlm.nih.gov/pubmed/29434325
http://dx.doi.org/10.1038/s41467-018-03005-5
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author Rapsomaniki, Maria Anna
Lun, Xiao-Kang
Woerner, Stefan
Laumanns, Marco
Bodenmiller, Bernd
Martínez, María Rodríguez
author_facet Rapsomaniki, Maria Anna
Lun, Xiao-Kang
Woerner, Stefan
Laumanns, Marco
Bodenmiller, Bernd
Martínez, María Rodríguez
author_sort Rapsomaniki, Maria Anna
collection PubMed
description Recent studies have shown that cell cycle and cell volume are confounding factors when studying biological phenomena in single cells. Here we present a combined experimental and computational method, CellCycleTRACER, to account for these factors in mass cytometry data. CellCycleTRACER is applied to mass cytometry data collected on three different cell types during a TNFα stimulation time-course. CellCycleTRACER reveals signaling relationships and cell heterogeneity that were otherwise masked.
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spelling pubmed-58093932018-02-14 CellCycleTRACER accounts for cell cycle and volume in mass cytometry data Rapsomaniki, Maria Anna Lun, Xiao-Kang Woerner, Stefan Laumanns, Marco Bodenmiller, Bernd Martínez, María Rodríguez Nat Commun Article Recent studies have shown that cell cycle and cell volume are confounding factors when studying biological phenomena in single cells. Here we present a combined experimental and computational method, CellCycleTRACER, to account for these factors in mass cytometry data. CellCycleTRACER is applied to mass cytometry data collected on three different cell types during a TNFα stimulation time-course. CellCycleTRACER reveals signaling relationships and cell heterogeneity that were otherwise masked. Nature Publishing Group UK 2018-02-12 /pmc/articles/PMC5809393/ /pubmed/29434325 http://dx.doi.org/10.1038/s41467-018-03005-5 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
Rapsomaniki, Maria Anna
Lun, Xiao-Kang
Woerner, Stefan
Laumanns, Marco
Bodenmiller, Bernd
Martínez, María Rodríguez
CellCycleTRACER accounts for cell cycle and volume in mass cytometry data
title CellCycleTRACER accounts for cell cycle and volume in mass cytometry data
title_full CellCycleTRACER accounts for cell cycle and volume in mass cytometry data
title_fullStr CellCycleTRACER accounts for cell cycle and volume in mass cytometry data
title_full_unstemmed CellCycleTRACER accounts for cell cycle and volume in mass cytometry data
title_short CellCycleTRACER accounts for cell cycle and volume in mass cytometry data
title_sort cellcycletracer accounts for cell cycle and volume in mass cytometry data
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5809393/
https://www.ncbi.nlm.nih.gov/pubmed/29434325
http://dx.doi.org/10.1038/s41467-018-03005-5
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