Adapting mass spectrometry-based platforms for clinical proteomics applications: The capillary electrophoresis coupled mass spectrometry paradigm

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.

Figure 1

Schematic representation of on-line coupling of CE for peptide m/z separation and MS for mass detection, which is accomplished by a coaxial sheath-flow system. In the established setting, the nebulizer gas can be turned off during acquisition without disturbing ion spray stability.

Figure 2

CE-MS data from an individual urine sample. In A, all relevant peptides in the sample are shown. In B, peptides are presented that form a coronary artery disease (CAD)-specific marker pattern. Based on the CE-MS analysis, the patient presented here scored positive for CAD. X axis: CE migration time [min], y axis: log molecular mass [kDa], and z axis: relative ion signal.

Figure 3

Contour plot showing the organization of peptides in the CE-MS spectrogram in distinct charge-specific lines. The membership to a certain charge line allows reliable prediction of the number of basic amino acids and therefore has considerable predictive value for peptide identification in other MS systems.

Figure 4

List of all currently sequence-identified native human urinary peptides defined by their protein precursor. Given are the numbers of sequenced peptides for a particular protein, the SwissProt protein name for Homo sapiens, and the gene symbol. Additional information, like all peptides amino acid sequence, is accessible from the Mosaiques homepage, located at http://mosaiques-diagnostics.de/diapatpcms/mosaiquescms/front_content.php?idcat=257/.

Figure 5

Establishment of a database as a core information system of patient proteome and clinical data. Storage and retrieval of peptide profiles, peptide sequences, and patient clinical records allowing sample selection and differential proteomic profiling for the purpose of biomarker discovery and patient classification.

Figure 6

Performance characteristics of urinary peptide patterns for the diagnosis of chronic kidney disease (CKD), diabetic (DN), and IgA nephropathy (IgA-N). The compiled peptide patterns of healthy controls (NC) and patients consist of 20 to 100 single measurements. Besides biomarkers indicative for CKD present in both DN and IgA-N patients, other markers could be identified that are either specific for DN or IgA-N, allowing their differentiation. X axis: CE migration time [min], y axis: log molecular mass [kDa], and z axis: relative ion signal.