Department of Tumor Immunology and CMBI at the Nijmegen Centre for Molecular Life Sciences, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands.
J Proteome Res. 2010 Apr 5;9(4):1727-37. doi: 10.1021/pr9008546.
Gene expression is commonly used to study the activation of dendritic cells (DCs) to identify proteins that determine whether these cells induce an immunostimulatory or tolerogenic immune response. RNA expression, however, does not necessarily predict protein abundance and often requires large numbers of experiments for statistical significance. Proteomics provides a direct view on protein expression but is costly and time consuming. Here, we combined a comprehensive quantitative proteome and transcriptome analysis on a single batch of immature and cytokine cocktail matured human DCs and integrated resulting data sets at the pathway level. Although overall correlation between differential mRNA and protein expression was low, correlation between components of DC relevant pathways was significantly higher. Differentially expressed proteins and genes partly mapped to identical but also to different pathway components demonstrating that RNA and protein data not only supported but also complemented each other. We identified 5 dominant pathways, which confirmed the importance of cytokines, cell adhesion, and migration in DC maturation and also indicated a fundamental role for lipid metabolism. From these pathways we extracted novel maturation markers that might improve DC vaccine design. For several of the candidate markers we confirmed widespread significance examining DCs from multiple individuals, underscoring the validity of our approach. We conclude that integration of different but related data sets at the pathway level can significantly increase the predictive power of multi "omics" analyses.
基因表达通常用于研究树突状细胞 (DC) 的激活，以鉴定决定这些细胞诱导免疫刺激或免疫耐受反应的蛋白质。然而，RNA 表达并不一定能预测蛋白质丰度，并且通常需要大量实验才能达到统计学意义。蛋白质组学提供了一种直接观察蛋白质表达的方法，但成本高且耗时。在这里，我们对一批未成熟和细胞因子鸡尾酒成熟的人类 DC 进行了全面的定量蛋白质组学和转录组学分析，并在通路水平上整合了结果数据集。尽管差异 mRNA 和蛋白质表达之间的整体相关性较低，但 DC 相关通路成分之间的相关性显著更高。差异表达的蛋白质和基因部分映射到相同的通路成分，但也映射到不同的通路成分，表明 RNA 和蛋白质数据不仅相互支持，而且相互补充。我们确定了 5 个主要通路，这些通路证实了细胞因子、细胞黏附和迁移在 DC 成熟中的重要性，也表明了脂质代谢的基本作用。从这些通路中，我们提取了新的成熟标志物，这些标志物可能有助于改进 DC 疫苗设计。对于几个候选标志物，我们通过检查多个个体的 DC 来确认其广泛的意义，这突出了我们方法的有效性。我们得出结论，在通路水平上整合不同但相关的数据集可以显著提高多“组学”分析的预测能力。
