Friso Giulia, Olinares Paul Dominic B, van Wijk Klaas J
Department of Plant Biology, Cornell University, Ithaca, NY, USA.
Methods Mol Biol. 2011;775:265-82. doi: 10.1007/978-1-61779-237-3_14.
This chapter outlines a quantitative proteomics workflow using a label-free spectral counting technique. The workflow has been tested on different aspects of chloroplast biology in maize and Arabidopsis, including chloroplast mutant analysis, cell-type specific chloroplast differentiation, and the proplastid-to-chloroplast transition. The workflow involves one-dimensional SDS-PAGE of the proteomes of leaves or chloroplast subfractions, tryptic digestions, online LC-MS/MS using a mass spectrometer with high mass accuracy and duty cycle, followed by semiautomatic data processing. The bioinformatics analysis can effectively select best gene models and deals with quantification of closely related proteins; the workflow avoids overidentification of proteins and results in more accurate protein quantification. The final output includes pairwise comparative quantitative analysis, as well as hierarchical clustering for discovery of temporal and spatial patterns of protein accumulation. A brief discussion about potential pitfalls, as well as the advantages and disadvantages of spectral counting, is provided.
本章概述了一种使用无标记光谱计数技术的定量蛋白质组学工作流程。该工作流程已在玉米和拟南芥叶绿体生物学的不同方面进行了测试，包括叶绿体突变体分析、细胞类型特异性叶绿体分化以及前质体到叶绿体的转变。该工作流程包括对叶片或叶绿体亚组分的蛋白质组进行一维SDS-PAGE、胰蛋白酶消化、使用具有高质量精度和占空比的质谱仪进行在线LC-MS/MS，随后进行半自动数据处理。生物信息学分析可以有效地选择最佳基因模型并处理密切相关蛋白质的定量；该工作流程避免了蛋白质的过度鉴定，并导致更准确的蛋白质定量。最终输出包括成对比较定量分析，以及用于发现蛋白质积累的时间和空间模式的层次聚类。还提供了关于潜在陷阱以及光谱计数优缺点的简要讨论。
