Technology development for proteome analysis incorporating separations and mass spectrometry

Many significant advancements in biological science have followed technological advancements in analytical chemistry, which afford new opportunities for sample analysis. Proteomics is a perfect example of this, where advances in mass spectrometry, separation science, and computational analysis have permitted the comprehensive characterization of proteins. In doing so, one can essentially obtain a 'molecular' description of an organism, and therefore provide an improved understanding of biological pathways, assist in drug design, and also provide molecular fingerprints which identify a particular state of an organism (a.k.a. disease biomarkers). Current challenges in proteome analysis continue to evolve, as new technologies are created. What was once a problem of ionization and interpretation of mass spectrometry patterns, proteomics now focuses (among other aspects) on further increasing sample throughput to enable the analysis of clinically significant sample sizes. The characterization of protein modifications, quantitation of changing protein expression, and the validation of mass spectral data also continue to be topics of continued research in this field. The current application focuses on the development of innovative technologies incorporating separations and mass spectrometry for improved analysis of proteome samples. Proposed is the development of a unique platform for comprehensive and comparative profiling of proteome samples. This analytical strategy will therefore be applicable to clinical biomarker discovery or for fundamental investigations of protein samples. The approach is based on extensive proteome prefractionation, which will be accomplished using uniquely designed gel-free separation systems. Integration with mass spectrometry is afforded by a novel approach to protein digestion. The techniques also provide new opportunity for targeted analysis of enriched protein samples. These technologies will provide greater throughput, sensitivity, and selectivity of the detection platform, and also increase the validity of the data interpretation and analysis.