Recently, applications of mass spectrometry in the field of clinical proteomics have gained tremendous visibility in the scientific and clinical community. One major objective is the search for potential biomarkers in complex body fluids like serum, plasma, urine, saliva, or cerebral spinal fluid. For this purpose, efficient visualization of large data sets derived from patient cohorts is crucial to provide clinical experts an interactive impression of the data quality. Additionally, it is necessary to apply statistical analysis and pattern matching algorithms to attain validated signal patterns that may allow for later applications in sample classification. We introduce the new ClinProTools bioinformatics software, which performs all major steps of profiling, screening, and monitoring applications in clinical proteomics. ClinProTools is the data interpretation software of the mass spectrometry-based ClinProt solutions for biomarker analysis. ClinProTools performs data pretreatment, visualization, statistics, pattern determination, pattern evaluation, and classification of spectra. This article will focus on ClinProTool's powerful and intuitive visualization options for clinical proteomics applications.
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.
In recent years a growing demand for simple and robust SNP genotyping platforms has arisen from the widespread use of SNPs in industrial and public research. The resulting knowledge about genotype/phenotype correlations is of special interest for the identification of potential new drug targets and in the field of pharmacogenomics. However, full exploitation of the available genomic information requires vast numbers of SNP analyses, as large cohorts of patients have to be screened for a large number of markers. Only very few of the current SNP genotyping techniques can cope with the resulting demands concerning sample throughput, automation, accuracy and cost-effectiveness. MALDI-TOF mass spectrometry has the potential to develop into a 'Gold Standard' for high-throughput SNP genotyping - if it has not already done so. This review will focus on the latest developments of this technology.
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