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Improved data normalization methods for reverse phase protein microarray analysis of complex biological samples
 
Antonella Chiechi1,2, Claudius Mueller2, Kevin M. Boehm2, Alessandra Romano2, Maria Serena Benassi1, Piero Picci1, Lance A. Liotta2, and Virginia Espina2
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Supplementary Material
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We applied this concept of determining normalization molecules ex post facto in clinical tissue samples. The expression level of total protein, β-actin, ssDNA, α/β -tubulin, MRPL11, RPL13a, and GAPDH was determined by RPMA in bone metastasis samples contaminated by RBCs. The stability of the normalization analytes was calculated using both geNorm and NormFinder algorithms (Figure 3A-B). ssDNA and MRPL11 showed the least variability in the RBC-contaminated bone metastasis samples. Similar normalization comparisons were performed with a variety of tissue types (Supplementary Table 1).




Figure 3. geNorm and NormFinder algorithms are suitable for finding a normalization analyte for RPMAs. (Click to enlarge)


Fifteen bone marrow aspirate core biopsies from patients diagnosed with multiple myeloma with or without bone disease were analyzed for β-arrestin and IL1β protein expression. Data were normalized alternatively by ssDNA, β-actin, or total protein. ssDNA normalization demonstrated a higher resolution in uncovering differences between groups that would otherwise be obscured by the effect of blood contamination. (Figure 3C)

This study highlights the utility of data normalization for RPMA. We demonstrated the advantages of data set normalization using ssDNA for blood-contaminated samples and the application of functional genomic algorithms for determining the geometric mean of the most stable molecules in a data set. RPMA technology has advanced for use in clinical trials for predicting and monitoring individual patients’ response to treatments (3). The normalization approach we describe individualizes the choice of normalization parameters for a given data set, thereby reducing potential bias caused by sample-to-sample variability.

Acknowledgments

This work was supported by: George Mason University; the Italian Istituto Superiore di Sanita’ in the framework of the Italy/ USA cooperation agreement between the U.S. Department of Health and Human Services, George Mason University, and the Italian Ministry of Public Health; and Grant R21CA125698-01A1 to LAL from the National Cancer Institute program “Innovations in cancer sample preparation.”

Competing interests

The authors declare no competing interests.

Correspondence
Address correspondence to Antonella Chiechi, George Mason University, Center for Applied Proteomics and Molecular Medicine, 10900 University Blvd, Manassas, VA, USA. Email: [email protected]


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