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...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Springer Nature
2021-10-01
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| Series: | Molecular Systems Biology |
| Subjects: | |
| Online Access: | https://doi.org/10.15252/msb.202110402 |
| _version_ | 1797282717212082176 |
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| 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 |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-07T17:17:27Z |
| format | Article |
| id | doaj.art-bcd5b154a6e240949fe3c514a36f69b2 |
| institution | Directory Open Access Journal |
| issn | 1744-4292 |
| language | English |
| last_indexed | 2024-03-07T17:17:27Z |
| publishDate | 2021-10-01 |
| publisher | Springer Nature |
| record_format | Article |
| series | Molecular Systems Biology |
| spelling | doaj.art-bcd5b154a6e240949fe3c514a36f69b22024-03-02T22:00:33ZengSpringer NatureMolecular Systems Biology1744-42922021-10-011710n/an/a10.15252/msb.202110402Cell‐to‐cell and type‐to‐type heterogeneity of signaling networks: insights from the crowdAttila Gabor0Marco Tognetti1Alice Driessen2Jovan Tanevski3Baosen Guo4Wencai Cao5He Shen6Thomas Yu7Verena Chung8Single Cell Signaling in Breast Cancer DREAM Consortium membersBernd Bodenmiller9Julio Saez‐Rodriguez10Institute for Computational Biomedicine Heidelberg University and Heidelberg University Hospital Faculty of Medicine Bioquant Heidelberg GermanyDepartment of Quantitative Biomedicine & Institute of Molecular Life Sciences University of Zurich Zurich SwitzerlandInstitute for Computational Biomedicine Heidelberg University and Heidelberg University Hospital Faculty of Medicine Bioquant Heidelberg GermanyInstitute for Computational Biomedicine Heidelberg University and Heidelberg University Hospital Faculty of Medicine Bioquant Heidelberg GermanyDivision of AI & Bioinformatics Shenzhen Digital Life Institute Shenzhen ChinaDivision of AI & Bioinformatics Shenzhen Digital Life Institute Shenzhen ChinaDivision of AI & Bioinformatics Shenzhen Digital Life Institute Shenzhen ChinaSage Bionetworks Seattle WA USASage Bionetworks Seattle WA USADepartment of Quantitative Biomedicine & Institute of Molecular Life Sciences University of Zurich Zurich SwitzerlandInstitute for Computational Biomedicine Heidelberg University and Heidelberg University Hospital Faculty of Medicine Bioquant Heidelberg GermanyAbstract 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.https://doi.org/10.15252/msb.202110402cell signalingcrowdsourcingmass cytometrypredictive modelingsingle cell |
| spellingShingle | 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 Molecular Systems Biology cell signaling crowdsourcing mass cytometry predictive modeling single cell |
| title | 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_short | 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 |
| topic | cell signaling crowdsourcing mass cytometry predictive modeling single cell |
| url | https://doi.org/10.15252/msb.202110402 |
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