Mass spectrometry-based clinical proteomics

• Published in: Pharmacogenomics Vol. 4; no. 4; pp. 463 - 476
• Main Authors: Pusch, Wolfgang, Flocco, Mark T, Leung, Sau-Mei, Thiele, Herbert, Kostrzewa, Markus
• Format: Journal Article
• Language: English
• Published: England Ashley Publications Ltd 01.07.2003; Future Medicine Ltd
• Subjects: Algorithms; Bioinformatics; biomarker; biomarkers; Biomarkers - analysis; Body fluids; Data processing; Eye; Genotyping; Humans; MALDI; mass spectrometry; Mass spectroscopy; nucleic acids; pattern matching; proteomics; Proteomics - instrumentation; Proteomics - methods; Reviews; Saliva; Single-nucleotide polymorphism; Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization - instrumentation; Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization - methods; Urine
• ISSN: 1462-2416; 1744-8042
• DOI: 10.1517/phgs.4.4.463.22753

In recent years, mass spectrometry (MS) has been recognized as a 'Gold Standard' tool for the identification and analysis of individual proteins in expression proteomics studies. Moreover, MS has proven useful for the analysis of nucleic acids for single nucleotide polymorphism (SNP) genotyping purposes. With the increased usage of MS as a standard tool for life science applications and the advancement of MS instrumentation, sample preparation and bioinformatics, MS technology has entered novel screening and discovery application areas that are beyond the traditional protein identification and characterization applications. The areas of clinical diagnostics and predictive medicine are just two prime examples of these fields. Predictive markers or biomarkers for early diagnosis of diseases are of growing importance for the human healthcare community. The goal of using MS in clinical proteomics is to generate protein profiles (mass to charge [m/z] ratio versus signal intensity) from readily available body fluids like serum, saliva and urine to detect changes in protein levels that reflect changes in the disease states. Whereas the results originating from individual protein markers may be intriguing, data resulting from the analysis of complex, multiple biomarker patterns may be unequivocal. These biomarker patterns are hidden in complex mass spectra and are not always obvious to the human eye. Sophisticated bioinformatics algorithms have to be applied to determine these unique biomarker patterns. Here, we review the latest developments concerning the use of MS for the discovery of biomarker patterns and for the identification of individual biomarkers in the field of clinical proteomics applications.