Institute of Molecular Life Sciences, University of Zürich, 8057 Zürich, Switzerland; Molecular Life Sciences PhD Program, Life Science Zürich Graduate School, ETH Zürich and University of Zürich, 8057 Zürich, Switzerland.
Institute of Molecular Life Sciences, University of Zürich, 8057 Zürich, Switzerland.
Mol Cell Proteomics. 2020 May;19(5):744-756. doi: 10.1074/mcp.R119.001790. Epub 2020 Mar 4.
Signaling networks process intra- and extracellular information to modulate the functions of a cell. Deregulation of signaling networks results in abnormal cellular physiological states and often drives diseases. Network responses to a stimulus or a drug treatment can be highly heterogeneous across cells in a tissue because of many sources of cellular genetic and non-genetic variance. Signaling network heterogeneity is the key to many biological processes, such as cell differentiation and drug resistance. Only recently, the emergence of multiplexed single-cell measurement technologies has made it possible to evaluate this heterogeneity. In this review, we categorize currently established single-cell signaling network profiling approaches by their methodology, coverage, and application, and we discuss the advantages and limitations of each type of technology. We also describe the available computational tools for network characterization using single-cell data and discuss potential confounding factors that need to be considered in single-cell signaling network analyses.
信号网络处理细胞内和细胞外信息,以调节细胞的功能。信号网络的失调导致细胞生理状态异常,并且常常导致疾病。由于细胞遗传和非遗传变异的许多来源,组织中细胞对刺激或药物处理的网络反应在很大程度上是异质的。信号网络异质性是许多生物学过程的关键,例如细胞分化和耐药性。直到最近,高通量单细胞测量技术的出现才使其有可能评估这种异质性。在这篇综述中,我们根据其方法、覆盖范围和应用对现有的单细胞信号网络分析方法进行了分类,并讨论了每种技术的优缺点。我们还描述了使用单细胞数据进行网络特征描述的可用计算工具,并讨论了单细胞信号网络分析中需要考虑的潜在混杂因素。