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Protein Microarray

Researchers at PNNL are developing high-throughput, highly sensitive protein microarray detection technologies as well as analysis techniques, including ProMAT.

Every biological system has a characteristic and dynamic pattern of protein expression that reflects the state of the system. For example, the protein expression pattern in a cell changes as the cell progresses through the cell cycle as well as when the cell is exposed to an insult, such as radiation. Characteristic protein expression patterns are also observable in cell communities, organs, and whole organisms. Importantly, there are detectable differences in the patterns of protein expression in healthy versus compromised systems. Using proteomic technologies, it is possible to identify these patterns of expression and to use this knowledge to build tools to detect and quantify these patterns. With such tools, we can conduct protein expression analyses for systems biology and environmental biomarker studies. Also, protein expression patterns can be used to detect exposure to chemicals, infection with pathogenic agents, and the presence of diseases such as cancer.

Protein expression pattern analysis at Pacific Northwest National Laboratory (PNNL) combines our scientists' expertise in mass spectrometry, yeast surface display-based antibody selection, antibody microarray development, and biology. The first stage in protein expression pattern analysis is to define potential protein patterns using global proteomic methods, such as mass spectrometry. Once candidate biomarkers are identified, the next step is to validate the proteomic data. To this end, we are developing sophisticated, highly sensitive micro-enzyme-linked immunosorbent assays (micro-ELISAs) capable of rapidly detecting and quantifying dozens of proteins simultaneously using an automated protein analysis system. Our goal is to detect target proteins even at picogram per milliliter levels in complex biological samples. Also, we are developing a chemically robust, protein-based affinity reagent, which has applications to biodetection technologies such as protein microarrays.

Statistical validation using a large number of samples must be done to support the choice of biomarker panel for detecting a desired endpoint. In human and animal populations, levels of biomarkers that indicate a given condition may be affected by many factors, including age and other epidemiological factors. The accuracy of a panel of biomarkers can be improved by accounting for predictable effects of such factors. The ELISA microarray system offers a highly efficient method for analyzing multiple proteins in the large numbers of samples necessary to determine the clinical relevance of a biomarker profile and to characterize the factors other than disease that may alter biomarker levels.

Importantly, abnormalities in protein expression in humans can be detected in patients using samples obtained noninvasively. Currently, disease detection relies on symptoms, which are expressed well into the course of infection, and cancer detection relies primarily on physical examination. Disease states could be detected earlier and more accurately by supplementing current observation-based methods with a molecular assessment like protein microarray.