Institut Curie, Paris, France.
PLoS One. 2012;7(6):e38686. doi: 10.1371/journal.pone.0038686. Epub 2012 Jun 28.
Reverse phase protein array (RPPA) is a powerful dot-blot technology that allows studying protein expression levels as well as post-translational modifications in a large number of samples simultaneously. Yet, correct interpretation of RPPA data has remained a major challenge for its broad-scale application and its translation into clinical research. Satisfying quantification tools are available to assess a relative protein expression level from a serial dilution curve. However, appropriate tools allowing the normalization of the data for external sources of variation are currently missing.
Here we propose a new method, called NormaCurve, that allows simultaneous quantification and normalization of RPPA data. For this, we modified the quantification method SuperCurve in order to include normalization for (i) background fluorescence, (ii) variation in the total amount of spotted protein and (iii) spatial bias on the arrays. Using a spike-in design with a purified protein, we test the capacity of different models to properly estimate normalized relative expression levels. The best performing model, NormaCurve, takes into account a negative control array without primary antibody, an array stained with a total protein stain and spatial covariates. We show that this normalization is reproducible and we discuss the number of serial dilutions and the number of replicates that are required to obtain robust data. We thus provide a ready-to-use method for reliable and reproducible normalization of RPPA data, which should facilitate the interpretation and the development of this promising technology.
The raw data, the scripts and the normacurve package are available at the following web site: http://microarrays.curie.fr.
反相蛋白阵列（RPPA）是一种强大的斑点印迹技术，可同时研究大量样本中的蛋白质表达水平和翻译后修饰。然而，正确解释 RPPA 数据仍然是其广泛应用和转化为临床研究的主要挑战。目前已有令人满意的定量工具可用于根据一系列稀释曲线评估相对蛋白质表达水平。然而，目前缺乏适当的工具来对数据进行归一化以消除外部变异源的影响。
在这里，我们提出了一种新方法，称为 NormaCurve，它允许同时对 RPPA 数据进行定量和归一化。为此，我们修改了 SuperCurve 定量方法，以包括对（i）背景荧光、（ii）斑点总蛋白量的变化和（iii）阵列上的空间偏差的归一化。使用纯化蛋白的 Spike-in 设计，我们测试了不同模型正确估计归一化相对表达水平的能力。表现最佳的模型 NormaCurve 考虑了不使用一抗的阴性对照阵列、用总蛋白染色剂染色的阵列和空间协变量。我们表明这种归一化是可重复的，并讨论了获得稳健数据所需的连续稀释次数和重复次数。因此，我们提供了一种可靠且可重复的 RPPA 数据归一化的即用型方法，这应该有助于解释和开发这项有前途的技术。
原始数据、脚本和 normacurve 包可在以下网站获得：http://microarrays.curie.fr。
