PEPPeR, a platform for experimental proteomic pattern recognition
PEPPeR, a platform for experimental proteomic pattern recognition
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DOI：
10.1074/mcp.m600222-mcp200
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发表时间：
2006-10-01
影响因子：
7
通讯作者：
Carr, Steven A.
中科院分区：
文献类型：
--
作者：
Jaffe, Jacob D.;Mani, D. R.;Carr, Steven A.
定量蛋白质组学在基础生物学的阐明和临床生物标志物的发现方面具有相当大的前景。然而，由于过度依赖基于鉴定的定量方法和与色谱分离重复性相关的问题，很难实现这一承诺。在这里，我们描述了被称为“地标匹配”和“峰值匹配”的新算法，它们大大减少了这些问题。Landmark Matching以一种利用来自不同数据采集策略的历史数据的方式，将肽身份独立于时间基础的传播到准确的质谱LC-MS特征上。峰值匹配建立在里程碑匹配的基础上，通过聚类在多个LC-MS实验中以独立于身份的方式识别相同的分子物种。我们将这些算法与其他算法、数据采集策略和实验设计捆绑在一起，创建了一个实验蛋白质组模式识别平台（PEPPeR）。这些发展使以前仅限于微阵列分析的已有统计工具能够用于蛋白质组学数据的处理。我们证明了所提出的平台可以在2.5个数量级上进行校准，并且可以在多个样品制备中具有良好的精度和误差特性的简单和复杂混合物中进行稳健的比例量化。我们还展示了基于两种混合物之间未确定的精确质量成分变化的统计显著性的从头标记发现。这些标记随后通过精确的质量驱动质谱/质谱采集鉴定，并证明是与已知蛋白质相关的污染物蛋白质，其浓度在两种混合物之间变化。这些结果为标记物发现提供了一个真实世界的验证平台。
Quantitative proteomics holds considerable promise for elucidation of basic biology and for clinical biomarker discovery. However, it has been difficult to fulfill this promise due to over-reliance on identification-based quantitative methods and problems associated with chromatographic separation reproducibility. Here we describe new algorithms termed "Landmark Matching" and "Peak Matching" that greatly reduce these problems. Landmark Matching performs time base-independent propagation of peptide identities onto accurate mass LC-MS features in a way that leverages historical data derived from disparate data acquisition strategies. Peak Matching builds upon Landmark Matching by recognizing identical molecular species across multiple LC-MS experiments in an identity-independent fashion by clustering. We have bundled these algorithms together with other algorithms, data acquisition strategies, and experimental designs to create a Platform for Experimental Proteomic Pattern Recognition (PEPPeR). These developments enable use of established statistical tools previously limited to microarray analysis for treatment of proteomics data. We demonstrate that the proposed platform can be calibrated across 2.5 orders of magnitude and can perform robust quantification of ratios in both simple and complex mixtures with good precision and error characteristics across multiple sample preparations. We also demonstrate de novo marker discovery based on statistical significance of unidentified accurate mass components that changed between two mixtures. These markers were subsequently identified by accurate mass-driven MS/MS acquisition and demonstrated to be contaminant proteins associated with known proteins whose concentrations were designed to change between the two mixtures. These results have provided a real world validation of the platform for marker discovery.