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Molecular & Cellular Proteomics 4:773-784, 2005.
© 2005 by The American Society for Biochemistry and Molecular Biology, Inc.

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Research

Optimized Normalization for Antibody Microarrays and Application to Serum-Protein Profiling^*,S

Darren Hamelinck^,, Heping Zhou^,,¶, Lin Li^||, Cornelius Verweij^**, Deborah Dillon, Ziding Feng^||, Jose Costa and Brian B. Haab^,¶¶

From the The Van Andel Research Institute, Grand Rapids, Michigan 49503, ^|| Public Health Science Division, Fred Hutchinson Cancer Research Center, Seattle, Washington 98109, ^** Dept. of Molecular and Cell Biology, The University of Amsterdam, 1081 BT Amsterdam, The Netherlands, Department of Pathology, Brigham and Women’s Hospital, Boston, Massachusetts 02115, and Dept. of Pathology, Yale University School of Medicine, New Haven, Connecticut 06510

The measurements of coordinated patterns of protein abundance using antibody microarrays could be used to gain insight into disease biology and to probe the use of combinations of proteins for disease classification. The correct use and interpretation of antibody microarray data requires proper normalization of the data, which has not yet been systematically studied. Therefore we undertook a study to determine the optimal normalization of data from antibody microarray profiling of proteins in human serum specimens. Forty-three serum samples collected from patients with pancreatic cancer and from control subjects were probed in triplicate on microarrays containing 48 different antibodies, using a direct labeling, two-color comparative fluorescence detection format. Seven different normalization methods representing major classes of normalization for antibody microarray data were compared by their effects on reproducibility, accuracy, and trends in the data set. Normalization with ELISA-determined concentrations of IgM resulted in the most accurate, reproducible, and reliable data. The other normalization methods were deficient in at least one of the criteria. Multiparametric classification of the samples based on the combined measurement of seven of the proteins demonstrated the potential for increased classification accuracy compared with the use of individual measurements. This study establishes reliable normalization for antibody microarray data, criteria for assessing normalization performance, and the capability of antibody microarrays for serum-protein profiling and multiparametric sample classification.

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