Dominant Processes during Human Dendritic Cell Maturation Revealed by Integration of Proteome and Transcriptome at the Pathway Level
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- 作者：Sonja I. Buschow ; Edwin Lasonder ; Hanneke W. M. van Deutekom ; Machteld M. Oud ; Luca Beltrame ; Martijn A. Huynen ; I. Jolanda M. de Vries ; Carl G. Figdor ; Duccio Cavalieri
- 刊名：Journal of Proteome Research
- 出版年：2010
- 出版时间：April 5, 2010
- 年：2010
- 卷：9
- 期：4
- 页码：1727-1737
- 全文大小：226K
- 年卷期：v.9,no.4(April 5, 2010)
- ISSN：1535-3907
文摘
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.