Normalization of reverse phase protein microarray data: Choosing the best normalization analyte

Abstract

Reverse phase protein microarray (RPMA) are a relatively recent but widely used approach to measure a large number of proteins, in their original and posttranslational modified forms, in a small clinical sample. Data normalization is fundamental for this technology, to correct for the sample-to-sample variability in the many possible confounding factors: extracellular proteins, red blood cells, different number of cells in the sample. To address this need, we adopted gene microarray algorithms to tailor the RPMA processing and analysis to the specific study set. Using geNorm and NormFinder algorithms, we screened seven normalization analytes (ssDNA, glyceraldehyde 3-phosphate dehydrogenase (GAPDH), α/β-tubulin, mitochondrial ribosomal protein L11 (MRPL11), ribosomal protein L13a (RPL13a), β-actin, and total protein) across different sample sets, including cell lines, blood contaminated tissues, and tissues subjected to laser capture microdissection (LCM), to identify the analyte with the lowest variability. Specific normalization analytes were found to be advantageous for different classes of samples, with ssDNA being the optimal analyte to normalize blood contaminated samples.