NormaCurve: A SuperCurve-Based Method That Simultaneously Quantifies and Normalizes Reverse Phase Protein Array Data

Abstract

MotivationReverse 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.ResultsHere 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.AvailabilityThe raw data, the scripts and the NormaCurve package are available at the following web site: http://microarrays.curie.fr.

Realizing the Promise of Reverse Phase Protein Arrays for Clinical, Translational, and Basic Research: A Workshop Report

“…Normalization toward (a set of) housekeeping proteins should be used with caution, as the stability of these proteins in the studied lysates is difficult to assess. Currently, methods for staining total protein are widely used, such as Sypro Ruby, FAST green, and colloidal gold (31). Samples can also be normalized to DNA content, particularly when the sample is contaminated with blood, fat, or high-abundance proteins (69).…”

“…Accuracy is codified in the cv. For RPPAs performed in the most experienced academic laboratories, between-array cv values of 5% to 6% are frequently observed (31,71,91). Clinical assays generally require cv values of 15% or less.…”

“…2 RPPA technology has also been successfully applied to serum/plasma (26 -29). The technology is uniquely suited for profiling the state of in vivo signaling networks because of its minimal total cellular volume requirements, high sensitivity (picomole-to-femtomole range), and excellent precision (Ͻ15% cv) (3,6,13,30,31). Reverse phase protein arrays allow quantitative analysis of phosphorylated, glycosylated, acetylated, cleaved, or total cellular proteins from multiple samples as long as specific detection reagents of high quality are available (32).…”

“…Further marked improvements have been made in informatics approaches to deal with sample handling, regional staining correction, quality control, and the identification of high-quality samples and reagents. In many cases these have been integrated into publicly available algorithms such as "Supercurve" (46), "Normacurve" (31), and the RPPanalyzer that is available as an R-Package on the CRAN platform (47).…”

RPPanalyzer Toolbox: An Improved R Package for Analysis of Reverse Phase Protein Array Data

“…A major drawback of open-source RPPA tools such NormaCurve (12), Reno (13), and others (14) is their limitation in data preprocessing because they are restricted to data normalization steps without offering additional functions to plot the resulting data or to carry out a statistical analysis. In contrast, the R (http://www.R-project.org) package RPPanalyzer (15) offers data preprocessing in combination with graphical and statistical analysis options.…”