Very few biomarkers developed by mass spectrometry have been successfully introduced into preclinical experimentation or clinical practice. An expert panel at this year’s “HUPO” meeting discussed proteomic’s lack of success at clinical biomarker implementation. Patrick Brown, M.D., Ph.D., professor in the department of biochemistry at the Stanford University School of Medicine, stated that the “biggest shortcoming” of proteomics has been biomarker discovery, in particular the way in which hype from a few years ago and the consequent failure to live up to it “unfairly damaged the proteomics community at large.”
He added that the field, unlike genomics, has had an “extreme neglect” of variations: whereas, genomics has focused on genetic differences and their possible meanings, the proteomics field has not focused on those areas, which has contributed to the field’s comparatively smaller role in the clinic. Lack of standardization, reliability, and reproducibility has hampered the ability of the proteomics community to move biomarkers into the clinical arena.
The investment in complex mass spectrometers has been made. Analysis of complex proteomes remains a daunting task, however. Modern proteomic mass spectrometry (MS) of complex samples involves a workflow that culminates in proteolytic digestion of proteins followed by liquid chromatography and MS. Spectra are matched with peptide hits using a searching algorithm such as Sequest, Mascot, or X-tandem. Two overarching hurdles are the inability to adequately sample all proteins in a complex matrix due to the high dynamic range of protein concentrations (1011 for plasma) and to identify the same proteins reproducibly in different laboratories using different MS platforms.
The former problem has led to multidimensional prefractionation techniques prior to analysis on the mass spectrometer. Invariably, investigators separate their proteins with SDS-PAGE, 2-D electrophoresis, MUDPIT, or isoelectric focusing methods prior to liquid chromatography and MS. However, each of these prefractionation methods suffers from lack of reproducibility.
Some of these methods, such as MUDPIT, cause information loss as aspects of protein heterogeneity, based on post-translational modifications, are thrown away. The latter problem can only be addressed when MS instrument manufacturers and software developers can unify MS/MS data searching in a way that is comprehensive and platform-independent.
Protein Forest has attacked the MS sample-prep reproducibility issue by developing a digitally formated chip (digital ProteomeChip—dPC™) that separates and concentrates proteins by their net charge. The value of the digital format is that the chips can be manufactured with a high degree of precision such that the pH of each component within the chips has little variability. This leads to a method where many of the uncontrolled variables apparent in isoelectric focusing strips and SDS-PAGE are eliminated.
Accordingly, total cell lysate data from dPC showed that the overlap in proteins identified by LC/MS/MS after multiple chips was 85% with a CV of 8.2% (n=6 pairwise comparisons; Figure 1). This high degree of reproducibility has also been observed with other sample types, including plasma, CSF, urine, and semen samples. This underscores the utility of the dPC for biomarker discovery and validation, and in diagnostics and forensics, where reproducibility is critical.