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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Paweletz CP, Charboneau L, Bichsel VE et al (2001) Reverse phase protein microarrays which capture disease progression show activation of pro-survival pathways at the cancer invasion front. Oncogene 20:1981–1989
Liotta LA, Espina V, Mehta AI et al (2003) Protein microarrays: meeting analytical challenges for clinical applications. Cancer Cell 3:317–325
Mueller C, Liotta LA, Espina V (2010) Reverse phase protein microarrays advance to use in clinical trials. Mol Oncol 4:461–481
Espina V, Wulfkuhle JD, Calvert VS et al (2007) Reverse phase protein microarrays for monitoring biological responses. Methods Mol Biol 383:321–336
VanMeter AJ, Rodriguez AS, Bowman ED et al (2008) Laser capture microdissection and protein microarray analysis of human non-small cell lung cancer: differential epidermal growth factor receptor (EGPR) phosphorylation events associated with mutated EGFR compared with wild type. Mol Cell Proteomics 7:1902–1924
Emmert-Buck MR, Bonner RF, Smith PD et al (1996) Laser capture microdissection. Science 274:998–1001
Chiechi A, Novello C, Magagnoli G et al (2013) Elevated TNFR1 and serotonin in bone metastasis are correlated with poor survival following bone metastasis diagnosis for both carcinoma and sarcoma primary tumors. Clin Cancer Res 19:2473–2485
Chiechi A, Mueller C, Boehm KM et al (2012) Improved data normalization methods for reverse phase protein microarray analysis of complex biological samples. BioTechniques 0:1–7
Vandesompele J, De Preter K, Pattyn F et al (2002) Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biol 3:RESEARCH0034
Andersen CL, Jensen JL, Orntoft TF (2004) Normalization of real-time quantitative reverse transcription-PCR data: a model-based variance estimation approach to identify genes suited for normalization, applied to bladder and colon cancer data sets. Cancer Res 64:5245–5250
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer Science+Business Media New York
About this protocol
Cite this protocol
Chiechi, A. (2016). Normalization of Reverse Phase Protein Microarray Data: Choosing the Best Normalization Analyte. In: Jung, K. (eds) Statistical Analysis in Proteomics. Methods in Molecular Biology, vol 1362. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-3106-4_4
Download citation
DOI: https://doi.org/10.1007/978-1-4939-3106-4_4
Publisher Name: Humana Press, New York, NY
Print ISBN: 978-1-4939-3105-7
Online ISBN: 978-1-4939-3106-4
eBook Packages: Springer Protocols