Evaluation of Normalization Methods on GeLC-MS/MS Label-Free Spectral Counting Data to Correct for Variation during Proteomic Workflows
Evaluation of Normalization Methods on GeLC-MS/MS Label-Free Spectral Counting Data to Correct for Variation during Proteomic Workflows
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DOI：
10.1007/s13361-011-0237-2
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发表时间：
2011-12-01
影响因子：
3.2
通讯作者：
Muddiman, David C.
中科院分区：
文献类型：
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作者：
Gokce, Emine;Shuford, Christopher M.;Muddiman, David C.
在无标记鸟枪蛋白质组学方法中，光谱计数（SpCs）的归一化对于实现可靠的相对定量是重要的。根据三种不同的SpC归一化方法（总光谱计数（TSpC）归一化、归一化光谱丰度因子（NSAF）归一化和归一化至选定蛋白质（NSP））校正凝胶样品制备和色谱性能之间日间变化的能力，对它们进行了评价。采用一维凝胶电泳和液相色谱-串联质谱（GeLC-MS/MS）技术，对稻瘟病菌生物分生孢子的3组光谱计数数据进行了分析。在1D-SDS-PAGE之前，将马肌红蛋白和鸡卵清蛋白掺入蛋白质提取物中作为NSP的内部蛋白质标准品。研究了不同数据集中相同蛋白质的SpC之间的相关性。我们报告说，TSpC归一化和NSAF归一化产生了几乎理想的斜率统一的归一化SpC与平均归一化SpC图，而NSP没有提供有效的校正的非归一化数据。此外，当在相对蛋白质定量之前利用TSpC标准化时，t检验和倍数变化揭示了确定真实的生物变化的截止限是SpC绝对数量的函数。例如，我们观察到方差随着SPC数量的增加而减少，这导致检测高度丰富的蛋白质的统计学显著但人为的变化的倾向更高。因此，我们建议对具有较高SPC的蛋白质应用较高的置信水平和较低的倍数变化截止值，而不是对整个数据集使用单一标准。通过选择适当的截止值以在不同的蛋白质水平上维持恒定的假阳性率（即，SpC水平），预期这将降低总体假阴性率，特别是对于具有较高SpC的蛋白质。
Normalization of spectral counts (SpCs) in label-free shotgun proteomic approaches is important to achieve reliable relative quantification. Three different SpC normalization methods, total spectral count (TSpC) normalization, normalized spectral abundance factor (NSAF) normalization, and normalization to selected proteins (NSP) were evaluated based on their ability to correct for day-today variation between gel-based sample preparation and chromatographic performance. Three spectral counting data sets obtained from the same biological conidia sample of the rice blast fungus Magnaporthe oryzae were analyzed by 1D gel and liquid chromatography-tandem mass spectrometry (GeLC-MS/MS). Equine myoglobin and chicken ovalbumin were spiked into the protein extracts prior to 1D-SDS-PAGE as internal protein standards for NSP. The correlation between SpCs of the same proteins across the different data sets was investigated. We report that TSpC normalization and NSAF normalization yielded almost ideal slopes of unity for normalized SpC versus average normalized SpC plots, while NSP did not afford effective corrections of the unnormalized data. Furthermore, when utilizing TSpC normalization prior to relative protein quantification, t-testing and fold-change revealed the cutoff limits for determining real biological change to be a function of the absolute number of SpCs. For instance, we observed the variance decreased as the number of SpCs increased, which resulted in a higher propensity for detecting statistically significant, yet artificial, change for highly abundant proteins. Thus, we suggest applying higher confidence level and lower fold-change cutoffs for proteins with higher SpCs, rather than using a single criterion for the entire data set. By choosing appropriate cutoff values to maintain a constant false positive rate across different protein levels (i.e., SpC levels), it is expected this will reduce the overall false negative rate, particularly for proteins with higher SpCs.