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Related Experiment Video

Updated: May 19, 2026

The Use of Reverse Phase Protein Arrays (RPPA) to Explore Protein Expression Variation within Individual Renal Cell Cancers

The Use of Reverse Phase Protein Arrays (RPPA) to Explore Protein Expression Variation within Individual Renal Cell Cancers

Published on: January 22, 2013

Improved data normalization methods for reverse phase protein microarray analysis of complex biological samples.

Antonella Chiechi¹, Claudius Mueller, Kevin M Boehm

¹Laboratory of Experimental Oncology, Istituto Ortopedico Rizzoli, Bologna, Italy; Center for Applied Proteomics and Molecular Medicine, George Mason University, Manassas, VA, USA.

|September 6, 2012

Summary

This summary is machine-generated.

This study introduces a new normalization method for reverse phase protein microarrays (RPMA) using gene microarray algorithms. Single-stranded DNA (ssDNA) is identified as a reliable normalization parameter for accurate proteomic analysis.

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Last Updated: May 19, 2026

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Published on: July 6, 2022

Area of Science:

Proteomics
Biotechnology
Molecular Biology

Background:

Reverse phase protein microarrays (RPMA) enable quantitative, multiplexed protein analysis from limited samples.
Accurate normalization is crucial to correct for sample variability caused by cell number, extracellular proteins, or red blood cells.

Purpose of the Study:

To develop and validate a robust normalization method for RPMA data.
To identify a normalization parameter independent of confounding factors like cell count and red blood cell contamination.
To optimize the proteomic data processing and analysis workflow for RPMA.

Main Methods:

Adapted gene microarray algorithms (geNorm, NormFinder) for RPMA data normalization.
Evaluated seven potential normalization analytes across diverse sample types (cell lines, laser capture microdissected tissues, blood-contaminated tissues).
Assessed normalization parameters for their ability to correct for red blood cell content.

Main Results:

Identified single-stranded DNA (ssDNA) as a normalization parameter proportional to total non-red blood cell content.
Demonstrated the suitability of ssDNA for RPMA normalization, particularly in blood-contaminated samples.
Showcased the effectiveness of geNorm and NormFinder in selecting stable normalization analytes.

Conclusions:

Single-stranded DNA (ssDNA) is a suitable and reliable normalization parameter for RPMA.
Gene microarray normalization algorithms can be effectively applied to optimize proteomic data analysis.
Modified RPMA processing allows flexible use of ssDNA or protein-based normalization.