The data indicate that capillary electrophoresis with mass spectrometry coupling provides a promising tool that permits fast and accurate identification and differentiation of protein patterns in body fluids of healthy and diseased individuals, thus enabling diagnosis based on these patterns.

No abstract

The discovery of biomarkers is currently attracting much interest as it harbors great potential for the diagnosis and monitoring of human diseases. Here we have used two advanced mass spectroscopy based technologies, surface enhanced laser desorption ionization (SELDI-MS) and capillary electrophoresis/mass spectrometry (CE/MS), to obtain proteomic patterns of urine samples from patients suffering from membranous glomerulonephritis (MGN) and healthy volunteers. The results indicate that CE/MS analysis is able to display a rich and complex pattern of polypeptides with high resolution and high mass accuracy. In order to analyze these patterns, the MosaiqueVisu software was developed for peak identification, deconvolution and the display of refined maps in a three-dimensional format. The polypeptide profiles obtained with SELDI-MS from the same samples are much sparser and show lower resolution and mass accuracy. The SELDI-MS profiles are further heavily dependent on analyte concentration. SELDI-MS analysis identified three differentially expressed polypeptides, which are potential biomarkers that can distinguish healthy donors from patients with MGN. In contrast, approximately 200 potential biomarkers could be identified by CE/MS. Thus, while SELDI-MS is easy to use and requires very little sample, CE/MS generates much richer data sets that enable an in-depth analysis.

Renal disease in patients with Type II diabetes is the leading cause of terminal renal failure and a major healthcare problem. Hence early identification of patients prone to develop this complication is important. Diabetic renal damage should be reflected by a change in urinary polypeptide excretion at a very early stage. To analyse these changes, we used an online combination of CE/MS (capillary electrophoresis coupled with MS), allowing fast and accurate evaluation of up to 2000 polypeptides in urine. Employing this technology, we have examined urine samples from 39 healthy individuals and from 112 patients with Type II diabetes mellitus and different degrees of albumin excretion rate. We established a 'normal' polypeptide pattern in the urine of healthy subjects. In patients with Type II diabetes and normal albumin excretion rate, the polypeptide pattern in urine differed significantly from normal, indicating a specific 'diabetic' pattern of polypeptide excretion. In patients with higher grade albuminuria, we were able to detect a polypeptide pattern indicative of 'diabetic renal damage'. We also found this pattern in 35% of those patients who had low-grade albuminuria and in 4% of patients with normal albumin excretion. Moreover, we could identify several of the indicative polypeptides using MS/MS sequencing. We conclude that proteomic analysis with CE/MS permits fast and accurate identification and differentiation of polypeptide patterns in urine. Longitudinal studies should explore the potential of this powerful diagnostic tool for early detection of diabetic renal damage.

A phase 1 diagnostic study was performed to evaluate a novel technology for clinical proteom research based on capillary electrophoresis and mass spectrometry. Urine from 40 patients after hematopoietic stem cell transplantation (HSCT; 35 allogeneic, 5 autologous) and 5 patients with sepsis was collected for a period of 100 days and analyzed. More than 1000 different polypeptides could be detected in individual samples. Polypeptide patterns excreted in the urine of patients were significantly different from those of healthy volunteers. No significant differences were detected comparing different conditioning regimens. The aim of this study was to identify polypeptide patterns functioning as early indicators of graft-versus-host disease (GVHD). Eighteen patients developed GVHD after allogeneic HSCT. Sixteen differentially excreted polypeptides formed a pattern of early GVHD markers, allowing discrimination of GVHD from patients without complications with 82% specificity and 100% sensitivity, cross-validated. Inclusion of 13 sepsis-specific polypeptides allowed us to distinguish sepsis from GVHD with a specificity of 97% and a sensitivity of 100%. Sequencing 2 prominent GVHD-indicative polypeptides led to the identification of a peptide from leukotriene A4 hydrolase and a peptide from serum albumin. The data reveal that capillary electrophoresis and mass spectrom-