Kislinger Thomas, Gramolini Anthony O, MacLennan David H, Emili Andrew
Banting and Best Department of Medical Research, University of Toronto, Toronto, Ontario, Canada.
J Am Soc Mass Spectrom. 2005 Aug;16(8):1207-20. doi: 10.1016/j.jasms.2005.02.015.
An optimized analytical expression profiling strategy based on gel-free multidimensional protein identification technology (MudPIT) is reported for the systematic investigation of biochemical (mal)-adaptations associated with healthy and diseased heart tissue. Enhanced shotgun proteomic detection coverage and improved biological inference is achieved by pre-fractionation of excised mouse cardiac muscle into subcellular components, with each organellar fraction investigated exhaustively using multiple repeat MudPIT analyses. Functional-enrichment, high-confidence identification, and relative quantification of hundreds of organelle- and tissue-specific proteins are achieved readily, including detection of low abundance transcriptional regulators, signaling factors, and proteins linked to cardiac disease. Important technical issues relating to data validation, including minimization of artifacts stemming from biased under-sampling and spurious false discovery, together with suggestions for further fine-tuning of sample preparation, are discussed. A framework for follow-up bioinformatic examination, pattern recognition, and data mining is also presented in the context of a stringent application of MudPIT for probing fundamental aspects of heart muscle physiology as well as the discovery of perturbations associated with heart failure.
报道了一种基于无凝胶多维蛋白质鉴定技术（MudPIT）的优化分析表达谱策略，用于系统研究与健康和患病心脏组织相关的生化（不）适应。通过将切除的小鼠心肌预分级为亚细胞成分，实现了增强的鸟枪法蛋白质组检测覆盖范围和改进的生物学推断，对每个细胞器级分使用多次重复的MudPIT分析进行详尽研究。轻松实现了数百种细胞器和组织特异性蛋白质的功能富集、高可信度鉴定和相对定量，包括检测低丰度转录调节因子、信号因子以及与心脏病相关的蛋白质。讨论了与数据验证相关的重要技术问题，包括最小化因偏差欠采样和虚假错误发现产生的伪像，以及进一步微调样品制备的建议。在严格应用MudPIT以探究心肌生理学基本方面以及发现与心力衰竭相关的扰动的背景下，还提出了后续生物信息学检查、模式识别和数据挖掘的框架。
