The study of protein expression profiles is becoming increasingly attractive in both the areas of functional and clinical proteomics, since the protein expression profiles can be used for identifying novel therapeutic targets and diagnostic biomarkers in a variety of human diseases. In practice, biomarkers may be expressed as either single marker or multiple markers whose patterns of up- and down-regulation may signal disease susceptibility, onset, progression, therapeutic response, drug efficacy or toxicity.

Efficient screening of biological samples for disease markers using modern analytical technologies including mass spectrometry is one of the challenges. This is because biological samples exhibit extreme complexity and there is a tremendously wide dynamic range in concentrations for endogenous biomarkers. Effective strategies to deal with these issues provide significant analytical challenges.

Key to an effective screening strategy is minimized sample preparation which, among other things, can discriminate against unknown, potentially important markers in the biological sample. Recently, several groups have described novel strategy by combining SELDI-TOF serum profiling with bioinformatics algorithm analysis to move beyond single-marker discovery to proteomic pattern analysis. The serum biomarker patterns appear to correlate well with several disease states such as ovarian cancer, prostate cancer and breast cancer. A potential drawback of the SELDI approach appears to be reproducibility as well as tedious, time-consuming procedures for sample preparation. The currently preferred strategy for some researchers is the direct injection of untreated biological samples albeit with a long, slow gradient LC/MS or LC/MS/MS analysis of the sample.

We have applied a new technology, chip-based nanoelectrospray infusion mass spectrometry analysis, for serum analysis followed by pattern recognition and discovery software analysis for ovarian cancer samples. This presentation will describe the strategy which includes a simple sample preparation procedure based upon a 250-fold dilution of the biological sample (no extraction), centrifugation, and then direct infusion of this diluted sample mixture to a QSTAR quadrupole-time-of-flight mass spectrometer coupled with an automated chip-based nanoelectrospray system. Specifically, automated nanoelectrospray techniques are employed using a microfabricated chip containing 400 microfabricated nanoelectrospray emitters on a silicon substrate chip. The chip is held and controlled by a robot (NanoMate HD). The robot sequentially and automatically aspirates individual samples from as many as 384 wells in a 384-well plate, and delivers 1-10 microliters of each diluted biological sample to the ESI chip to produce full-scan nanoelectrospray mass spectra via the QSTAR. The QSTAR is operated at a resolution of 7,000-10,000 in the MCA scan mode. The average acquisition time for each sample is approximately 60 sec (60 MCA scans) such that the analysis of a complete 384-well plate of samples takes only about seven hours. The extremely complex, mixed electrospray mass spectra acquired are analyzed by bioinformatics software that endeavors to differentiate control versus disease samples by subtle changes in the mass spectral patterns.

Examples will be shown for automated nanoelectrospray screening of biological samples originating from control and diseased patients. The ability of bioinformatics software to distinguish hidden patterns characteristic of certain diseases will be presented. The potential for using the new automated chip-based nanoESI/MS approach for biomarker screening in complex biological samples will be discussed.