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Mol Cell Proteomics. 2008 Dec;7(12):2464-74. doi: 10.1074/mcp.M800121-MCP200. Epub 2008 Aug 3.

Quantitative serum proteomics from surface plasmon resonance imaging.

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1
Institute for Systems Biology, Seattle, Washington 98103, USA. clausted@systemsbiology.org

Abstract

The detection and quantification of specific proteins in complex mixtures is a major challenge for proteomics. For example, the development of disease-related biomarker panels will require fast and efficient methods for obtaining multiparameter protein profiles. We established a high throughput, label-free method for analyzing serum using surface plasmon resonance imaging of antibody microarrays. Microarrays were fabricated using standard pin spotting on bare gold substrates, and samples were applied for binding analysis using a camera-based surface plasmon resonance system. We validated the system by measuring the concentrations of four serum proteins using part of a 792-feature microarray. Transferrin concentrations were measured to be 2.1 mg/ml in human serum and 1.2 mg/ml in murine serum, which closely matched ELISA determinations of 2.6 and 1.2 mg/ml, respectively. In agreement with expected values, human and mouse albumin levels were measured to be 24.3 and 23.6 mg/ml, respectively. The lower limits of detection for the four measurements ranged from 14 to 58 ng/ml or 175 to 755 pm. Where purified target proteins are not available for calibration, the microarrays can be used for relative protein quantification. We used the antibody microarray to compare the serum protein profiles from three liver cancer patients and three non-liver cancer patients. Hierarchical clustering of the serum protein levels clearly distinguished two distinct profiles. Thirty-nine significant protein changes were detected (p < 0.05), 10 of which have been observed previously in serum. alpha-Fetoprotein, a known liver cancer marker, was observed to increase. These results demonstrate the feasibility of this high throughput approach for both absolute and relative protein expression profiling.

PMID:
18678562
DOI:
10.1074/mcp.M800121-MCP200
[Indexed for MEDLINE]
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