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Cell-to-cell and type-to-type heterogeneity of signaling networks: Insights from the crowd

View ORCID ProfileAttila Gabor, View ORCID ProfileMarco Tognetti, Alice Driessen, View ORCID ProfileJovan Tanevski, View ORCID ProfileBaosen Guo, Wencai Cao, He Shen, Thomas Yu, Verena Chung, Single Cell Signaling in Breast Cancer DREAM Consortium members, View ORCID ProfileBernd Bodenmiller, View ORCID ProfileJulio Saez-Rodriguez
doi: https://doi.org/10.1101/2021.03.23.436603
Attila Gabor
1Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, Bioquant, Heidelberg, Germany
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  • ORCID record for Attila Gabor
Marco Tognetti
2Department of Quantitative Biomedicine & Institute of Molecular Life Sciences, University of Zurich, Zurich, Switzerland
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Alice Driessen
1Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, Bioquant, Heidelberg, Germany
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Jovan Tanevski
1Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, Bioquant, Heidelberg, Germany
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Baosen Guo
3Division of AI & Bioinformatics, Shenzhen Digital Life Institute, Shenzhen, China
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Wencai Cao
3Division of AI & Bioinformatics, Shenzhen Digital Life Institute, Shenzhen, China
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He Shen
3Division of AI & Bioinformatics, Shenzhen Digital Life Institute, Shenzhen, China
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Thomas Yu
4Sage Bionetworks, Seattle, USA
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Verena Chung
4Sage Bionetworks, Seattle, USA
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Bernd Bodenmiller
2Department of Quantitative Biomedicine & Institute of Molecular Life Sciences, University of Zurich, Zurich, Switzerland
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  • For correspondence: julio.saez@uni-heidelberg.de bernd.bodenmiller@uzh.ch
Julio Saez-Rodriguez
1Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, Bioquant, Heidelberg, Germany
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  • For correspondence: julio.saez@uni-heidelberg.de bernd.bodenmiller@uzh.ch
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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 organised the Single Cell Signaling in Breast Cancer DREAM challenge. We used a mass cytometry data set, covering 36 markers in over 4,000 conditions totalling 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.

Key points

  • Over 80 million single-cell multiplexed measurements across 67 cell lines, 54 conditions and 10 time points to benchmark predictive models of single cell signaling

  • 73 approaches from 27 teams for predicting response to kinase inhibitors on single cell level, and dynamic response from unperturbed basal omics data

  • Predictions of single marker models correlate with measurements with a correlation coefficient of 0.76

  • Top models of whole signaling response models perform almost as well as a biological replicate

  • Cell-line specific variation in dynamics can be predicted from basal omics

Figure1

Competing Interest Statement

JSR has received funding from GSK and Sanofi and expects consultant fees from Travere Therapeutics.

Footnotes

  • https://www.synapse.org/singlecellproteomics

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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Posted March 23, 2021.
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Cell-to-cell and type-to-type heterogeneity of signaling networks: Insights from the crowd
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
bioRxiv 2021.03.23.436603; doi: https://doi.org/10.1101/2021.03.23.436603
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Cell-to-cell and type-to-type heterogeneity of signaling networks: Insights from the crowd
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
bioRxiv 2021.03.23.436603; doi: https://doi.org/10.1101/2021.03.23.436603

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