Cell-to-cell and type-to-type heterogeneity of signaling networks : insights from the crowd
© 2021 The Authors. Published under the terms of the CC BY 4.0 license..
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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2021 |
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| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:17 |
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| Enthalten in: |
Molecular systems biology - 17(2021), 10 vom: 04. Okt., Seite e10402 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Gabor, Attila [VerfasserIn] |
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| Links: |
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| Themen: |
Cell signaling |
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| Anmerkungen: |
Date Completed 26.01.2022 Date Revised 07.11.2023 published: Print Citation Status MEDLINE |
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| doi: |
10.15252/msb.202110402 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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NLM332023680 |
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| 245 | 1 | 0 | |a Cell-to-cell and type-to-type heterogeneity of signaling networks |b insights from the crowd |
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| 500 | |a Date Completed 26.01.2022 | ||
| 500 | |a Date Revised 07.11.2023 | ||
| 500 | |a published: Print | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a © 2021 The Authors. Published under the terms of the CC BY 4.0 license. | ||
| 520 | |a 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 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, N.I.H., Extramural | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a cell signaling | |
| 650 | 4 | |a crowdsourcing | |
| 650 | 4 | |a mass cytometry | |
| 650 | 4 | |a predictive modeling | |
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| 700 | 1 | |a Tognetti, Marco |e verfasserin |4 aut | |
| 700 | 1 | |a Driessen, Alice |e verfasserin |4 aut | |
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| 700 | 1 | |a Guo, Baosen |e verfasserin |4 aut | |
| 700 | 1 | |a Cao, Wencai |e verfasserin |4 aut | |
| 700 | 1 | |a Shen, He |e verfasserin |4 aut | |
| 700 | 1 | |a Yu, Thomas |e verfasserin |4 aut | |
| 700 | 1 | |a Chung, Verena |e verfasserin |4 aut | |
| 700 | 0 | |a Single Cell Signaling in Breast Cancer DREAM Consortium members |e verfasserin |4 aut | |
| 700 | 1 | |a Bodenmiller, Bernd |e verfasserin |4 aut | |
| 700 | 1 | |a Saez-Rodriguez, Julio |e verfasserin |4 aut | |
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| 700 | 1 | |a Shih, Edward S C |e investigator |4 oth | |
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| 700 | 1 | |a Hwang, Jinseub |e investigator |4 oth | |
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