Cell-to-cell and type-to-type heterogeneity of signaling networks: insights from the crowd.
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Authors
Gabor, Attila
Tognetti, Marco
Driessen, Alice
Tanevski, Jovan
Guo, Baosen
Cao, Wencai
Shen, He
Yu, Thomas
Chung, Verena
Single Cell Signaling in Breast Cancer DREAM Consortium members
Bodenmiller, Bernd
Saez-Rodriguez, Julio
Publication Date
2021-10Journal Title
Molecular Systems Biology
ISSN
1744-4292
Publisher
European Molecular Biology Organization
Volume
17
Issue
10
Pages
e10402-e10402
Language
eng
Type
Article
This Version
VoR
Metadata
Show full item recordCitation
Gabor, A., Tognetti, M., Driessen, A., Tanevski, J., Guo, B., Cao, W., Shen, H., et al. (2021). Cell-to-cell and type-to-type heterogeneity of signaling networks: insights from the crowd.. Molecular Systems Biology, 17 (10), e10402-e10402. https://doi.org/10.15252/msb.202110402
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.
Keywords
cell signaling, crowdsourcing, mass cytometry, predictive modeling, single cell
Identifiers
External DOI: https://doi.org/10.15252/msb.202110402
This record's URL: https://www.repository.cam.ac.uk/handle/1810/330761
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