Cell‐to‐cell and type‐to‐type heterogeneity of signaling networks: insights from the crowd

Abstract Recent technological developments allow us to measure the status of dozens of proteins in individual cells. This opens the way to understand the heterogeneity of complex multi‐signaling networks across cells and cell types, with important implications to understand and treat diseases such a...

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Autores principales:	Attila Gabor, Marco Tognetti, Alice Driessen, Jovan Tanevski, Baosen Guo, Wencai Cao, He Shen, Thomas Yu, Verena Chung, Single Cell Signaling in Breast Cancer DREAM Consortium members, Bernd Bodenmiller, Julio Saez‐Rodriguez
Formato:	article
Lenguaje:	EN
Publicado:	Wiley 2021
Materias:	cell signaling crowdsourcing mass cytometry predictive modeling single cell Biology (General) QH301-705.5 Medicine (General) R5-920
Acceso en línea:	https://doaj.org/article/bcd5b154a6e240949fe3c514a36f69b2
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spelling	oai:doaj.org-article:bcd5b154a6e240949fe3c514a36f69b22021-11-11T11:30:47ZCell‐to‐cell and type‐to‐type heterogeneity of signaling networks: insights from the crowd1744-429210.15252/msb.202110402https://doaj.org/article/bcd5b154a6e240949fe3c514a36f69b22021-10-01T00:00:00Zhttps://doi.org/10.15252/msb.202110402https://doaj.org/toc/1744-4292Abstract Recent technological developments allow us to measure the status of dozens of proteins in individual cells. This opens the way to understand the heterogeneity of complex multi‐signaling networks across cells and cell types, with important implications to understand and treat diseases such as cancer. These technologies are, however, limited to proteins for which antibodies are available and are fairly costly, making predictions of new markers and of existing markers under new conditions a valuable alternative. To assess our capacity to make such predictions and boost further methodological development, we organized the Single Cell Signaling in Breast Cancer DREAM challenge. We used a mass cytometry dataset, covering 36 markers in over 4,000 conditions totaling 80 million single cells across 67 breast cancer cell lines. Through four increasingly difficult subchallenges, the participants predicted missing markers, new conditions, and the time‐course response of single cells to stimuli in the presence and absence of kinase inhibitors. The challenge results show that despite the stochastic nature of signal transduction in single cells, the signaling events are tightly controlled and machine learning methods can accurately predict new experimental data.Attila GaborMarco TognettiAlice DriessenJovan TanevskiBaosen GuoWencai CaoHe ShenThomas YuVerena ChungSingle Cell Signaling in Breast Cancer DREAM Consortium membersBernd BodenmillerJulio Saez‐RodriguezWileyarticlecell signalingcrowdsourcingmass cytometrypredictive modelingsingle cellBiology (General)QH301-705.5Medicine (General)R5-920ENMolecular Systems Biology, Vol 17, Iss 10, Pp n/a-n/a (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	cell signaling crowdsourcing mass cytometry predictive modeling single cell Biology (General) QH301-705.5 Medicine (General) R5-920
spellingShingle	cell signaling crowdsourcing mass cytometry predictive modeling single cell Biology (General) QH301-705.5 Medicine (General) R5-920 Attila Gabor Marco Tognetti Alice Driessen Jovan Tanevski Baosen Guo Wencai Cao He Shen Thomas Yu Verena Chung Single Cell Signaling in Breast Cancer DREAM Consortium members Bernd Bodenmiller Julio Saez‐Rodriguez Cell‐to‐cell and type‐to‐type heterogeneity of signaling networks: insights from the crowd
description	Abstract Recent technological developments allow us to measure the status of dozens of proteins in individual cells. This opens the way to understand the heterogeneity of complex multi‐signaling networks across cells and cell types, with important implications to understand and treat diseases such as cancer. These technologies are, however, limited to proteins for which antibodies are available and are fairly costly, making predictions of new markers and of existing markers under new conditions a valuable alternative. To assess our capacity to make such predictions and boost further methodological development, we organized the Single Cell Signaling in Breast Cancer DREAM challenge. We used a mass cytometry dataset, covering 36 markers in over 4,000 conditions totaling 80 million single cells across 67 breast cancer cell lines. Through four increasingly difficult subchallenges, the participants predicted missing markers, new conditions, and the time‐course response of single cells to stimuli in the presence and absence of kinase inhibitors. The challenge results show that despite the stochastic nature of signal transduction in single cells, the signaling events are tightly controlled and machine learning methods can accurately predict new experimental data.
format	article
author	Attila Gabor Marco Tognetti Alice Driessen Jovan Tanevski Baosen Guo Wencai Cao He Shen Thomas Yu Verena Chung Single Cell Signaling in Breast Cancer DREAM Consortium members Bernd Bodenmiller Julio Saez‐Rodriguez
author_facet	Attila Gabor Marco Tognetti Alice Driessen Jovan Tanevski Baosen Guo Wencai Cao He Shen Thomas Yu Verena Chung Single Cell Signaling in Breast Cancer DREAM Consortium members Bernd Bodenmiller Julio Saez‐Rodriguez
author_sort	Attila Gabor
title	Cell‐to‐cell and type‐to‐type heterogeneity of signaling networks: insights from the crowd
title_short	Cell‐to‐cell and type‐to‐type heterogeneity of signaling networks: insights from the crowd
title_full	Cell‐to‐cell and type‐to‐type heterogeneity of signaling networks: insights from the crowd
title_fullStr	Cell‐to‐cell and type‐to‐type heterogeneity of signaling networks: insights from the crowd
title_full_unstemmed	Cell‐to‐cell and type‐to‐type heterogeneity of signaling networks: insights from the crowd
title_sort	cell‐to‐cell and type‐to‐type heterogeneity of signaling networks: insights from the crowd
publisher	Wiley
publishDate	2021
url	https://doaj.org/article/bcd5b154a6e240949fe3c514a36f69b2
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Cell‐to‐cell and type‐to‐type heterogeneity of signaling networks: insights from the crowd

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