Abstract
Single biomarker detection is common in clinical laboratories due to the currently available method spectrum. For various diseases, however, no specific single biomarker could be identified. A strategy to overcome this diagnostic void is to shift from single analyte detection to multiplexed biomarker profiling. Mass spectrometric methods were employed for biomarker discovery in body fluids. The enormous complexity of biofluidic proteome compartments implies upstream fractionation. For this reason, mass spectrometry (MS) was coupled to two-dimensional gel electrophoresis, liquid chromatography, surface-enhanced laser desorption/ionization, or capillary electrophoresis (CE). Differences in performance and operating characteristics make them differentially suited for routine laboratory applications. Progress in the field of clinical proteomics relies not only on the use of an adequate technological platform, but also on a fast and efficient proteomic workflow including standardized sample preparation, proteomic data processing, statistical validation of biomarker selection, and sample classification. Based on CE-MS analysis, we describe how proteomic technology can be implemented in a clinical laboratory environment. In the last part of this review, we give an overview of CE-MS-based clinical studies and present information on identity and biological significance of the identified peptide biomarkers providing evidence of disease-induced changes in proteolytic processing and posttranslational modification.
| Original language | English |
|---|---|
| Pages (from-to) | 129-152 |
| Number of pages | 24 |
| Journal | Critical Reviews in Clinical Laboratory Sciences |
| Volume | 46 |
| Issue number | 3 |
| DOIs | |
| State | Published - 2009 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
Keywords
- Biomarker pattern
- Bladder cancer
- Capillary electrophoresis
- Chronic kidney disease
- Coronary artery disease
- Graft-versus-host disease
- Mass spectrometry
- Multivariate statistical analysis
- Prostate cancer
- Proteome database
- Proteomics/peptidomics
- Urine
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