Evolution of Proteomic Methods for Analysis of Complex Biological Samples – Implications for Personalized Medicine

Abstract

We are on the threshold of a paradigm shift for proteomics, moving from largely a qualitative discipline, to now being capable of quantification of a protein within a complex sample at great sensitivity. The potential application of such advanced proteomic technology is enormous as we will be able to detect and quantify low levels of expressed proteins in complex samples, and so move comparative proteomics to a new level. The evolving practice of personalized medicine will be dependent on devising new techniques and methodologies that will allow the detection and quantification of proteins that are implicated in contributing to the diseased state. There is perhaps somewhere over 5000 genes that are linked to disease states and complex networks of interactions of these expressed genes ultimately lead to these disease states. The myriad of single nucleotide polymorphisms (SNPs) contribute to such phenomena, as an individual’s SNP profile play a major role in susceptibility to disease and in adverse reactions to drugs, for example. Coupled with mutations that occur throughout life, the complex “disease state” proteome will contain mutant proteins at low levels that need to be identified and quantified, so that therapeutic intervention based on rational scientific hypotheses can be investigated. Plasma and serum contain an unknown number of proteins with amounts ranging from pgg/L levels (i.e. very high dynamic range). As we know one of the major problems faced by proteomic studies of plasma or serum, or indeed any complex protein sample, is that a relatively small number of abundant proteins accounts for the great majority of protein content of the sample. The upshot is that the proteins of interest, which may have regulatory function, are masked by these abundant proteins, and non-targeted methods of proteomic analysis bias at the top end of the abundance scale. The development of new methods for quantifying low abundance proteins has evolved rapidly, concomitant with the evolution of powerful mass spectrometers of increasing sensitivity. The use of antibodies for targeting peptides prior to mass spectrometry analysis is becoming prominent, as a means of partitioning low abundance peptides away from peptides in the bulk sample. This review will provide a broad overview of the evolution of proteomic methods to analyse biological samples, including Differential In-Gel Electrophoresis (DIGE), Isotope-Coded Affinity Tag (ICAT), Isobaric tags for relative and absolute quantification (iTRAQ), Stable isotope labeling with amino acids in cell culture (SILAC), Unique ion signature Mass