Cell‐to‐cell and type‐to‐type heterogeneity of signaling networks: insights from the crowd. Issue 10 (18th October 2021)

• Record Type: Journal Article
• Authors: Gabor, Attila; Tognetti, Marco; Driessen, Alice; Tanevski, Jovan; Guo, Baosen; Cao, Wencai; Shen, He; Yu, Thomas; Chung, Verena; Bodenmiller, Bernd; Saez‐Rodriguez, Julio
• Is Part Of: Molecular systems biology. Volume 17:Issue 10(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-10-18
• Subjects: cell signaling -- crowdsourcing -- mass cytometry -- predictive modeling -- single cell; Molecular biology -- Periodicals; Systems biology -- Periodicals; 572.8
• DOI: 10.15252/msb.202110402
• Languages: English
• ISSNs: 1744-4292

Abstract

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. SYNOPSIS: This study presents the results and conclusions of the 'Single Cell Signaling in Breast Cancer DREAM challenge', where teams were challenged to use state‐of‐the‐art methods for predicting single‐cell signaling from single‐cell and bulk proteomics, transcriptomics and genomics data. Over 80 million single‐cell multiplexed measurements across 67 cell lines, 54 conditions and 10 time points are used to benchmark predictive models of single‐cell signaling. 73 approaches are used by 27 teams for predicting responses to kinase inhibitors on single cell level, and dynamic responses from unperturbed basal omics data. Top models of whole signaling response models perform almost as well as a biological replicate. Cell‐line specific variation in dynamics can be partially predicted from basal omics. Abstract : This study presents the results and conclusions of the 'Single Cell Signaling in Breast Cancer DREAM challenge', where teams were challenged to use state‐of‐the‐art methods for predicting single‐cell signaling from single‐cell and bulk proteomics, transcriptomics and genomics data.