Academic Journal
Cell‐to‐cell and type‐to‐type heterogeneity of signaling networks: insights from the crowd
| العنوان: | Cell‐to‐cell and type‐to‐type heterogeneity of signaling networks: insights from the crowd |
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| المؤلفون: | 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, Augustinas Prusokas, Alidivinas Prusokas, Renata Retkute, Anand Rajasekar, Karthik Raman, Malvika Sudhakar, Raghunathan Rengaswamy, Edward S.C. Shih, Min‐jeong Kim, Changje Cho, Dohyang Kim, Hyeju Oh, Jinseub Hwang, Kim Jongtae, Yeongeun Nam, Sanghoo Yoon, Taeyong Kwon, Kyeongjun Lee, Sarika Chaudhary, Nehal Sharma, Shreya Bande, Gao Gao fan zhu Cankut Cubuk, Pelin Gundogdu, Joaquin Dopazo, Kinza Rian, Carlos Loucera, Matias M Falco, Martin Garrido‐Rodriguez, Maria Peña‐Chilet, Huiyuan Chen, Gabor Turu, Laszlo Hunyadi, Adam Misak, Lisheng Zhou, Xiaoqing Jiang, Pieta Zhang, Aakansha Rai, Rintu Kutum, Sadhna Rana, Rajgopal Srinivasan, Swatantra Pradhan, James Li, Vladimir Bajic, Christophe Van Neste, Didier Barradas‐bautista, Somayah Abdullah Albarade, Igor Nikolskiy, Musalula Sinkala, Duc Tran, Hung Nguyen, Tin Nguyen, Alexander Wu, Benjamin DeMeo, Brian Hie, Rohit Singh, Jiwei Liu, Xueer Chen, Leonor Saiz, Jose M. G Vilar, Peng Qiu, Akash Gosain, Anjali Dhall, Dinesh Bajaj, Harpreet Kaur, Krishna Bagaria, Mayank Chauhan, Neelam Sharma, Gajendra Raghava, Sumeet Patiyal, Jianye Hao, Jiajie Peng, Shangyi Ning, Yi Ma, Zhongyu Wei, Atte Aalto, Jorge Goncalves, Laurent Mombaerts, Xinnan Dai, Jie Zheng, Piyushkumar Mundra, Fan Xu, Jie Wang, Krishna Kant Singh, Mingyu Lee |
| المصدر: | Molecular Systems Biology, Vol 17, Iss 10, Pp 1-16 (2021) |
| بيانات النشر: | Springer Nature, 2021. |
| سنة النشر: | 2021 |
| المجموعة: | LCC:Biology (General) LCC:Medicine (General) |
| مصطلحات موضوعية: | cell signaling, crowdsourcing, mass cytometry, predictive modeling, single cell, Biology (General), QH301-705.5, Medicine (General), R5-920 |
| الوصف: | 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. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 1744-4292 |
| Relation: | https://doaj.org/toc/1744-4292 |
| DOI: | 10.15252/msb.202110402 |
| URL الوصول: | https://doaj.org/article/bcd5b154a6e240949fe3c514a36f69b2 |
| رقم الانضمام: | edsdoj.bcd5b154a6e240949fe3c514a36f69b2 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 17444292 |
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| DOI: | 10.15252/msb.202110402 |