Using Antibody-Assisted Differential Display of Proteome to Identify Potential Protein Markers

 

            The current technologies in proteomics studies include as 2-D gel, SELTI-TOF, MALTI-TOF, MS/MS, Yeast-two hybrid, etc.  These technologies have made valuable contribution to our understanding of proteome.  The sensitivity of these technologies ranges from sub-microgram to nanogram levels, which is commonly affected by many high abundant proteins in the samples such as albumin, globulins in serum and structural proteins in cancer cells and tissues, etc. 

 

Antibodies (Abs) have been used extensively as research reagents and diagnostic tools in many different formats (so-called immunoassay).  Abs are frequently used in studying proteins, steroid hormone and many other biological molecules for their expression, functions, localization because of their high specificity and high affinity and their detection can be up to pictogram levels.  The current antibody-based assays are rather limited to analyzing the presence of known analytes such as proteins, becoming a serious bias in proteome analysis, where a significant part consists of unknown gene products.

 

To identify potential cancer protein markers, we have developed a feasible method for the utilization of global chicken polyclonal antibodies in the analysis of differential expression of proteomes between different tissues, cell types and/or disease conditions in human.  We have chosen chickens as hosts for generating the global polyclonal antibodies because of their less homology than the mammalian species with the human genomes.  Using this technique we can study the differential protein expression via subtractive/absorptive approaches and to identify cancer protein markers expressed in different cancer cell types or tissues vs normal cells/tissues.  By subtracting out the common proteins using the global antibodies, one can fish out the targeted proteins and to investigate the correlation of these proteins with certain diseases as well as cellular functions.