The Use of Urine Proteomic and Metabonomic Patterns for the Diagnosis of Interstitial Cystitis and Bacterial Cystitis

The advent of systems biology approaches that have stemmed from the sequencing of the human genome has led to the search for new methods to diagnose diseases. While much effort has been focused on the identification of disease-specific biomarkers, recent efforts are underway toward the use of proteomic and metabonomic patterns to indicate disease. We have developed and contrasted the use of both proteomic and metabonomic patterns in urine for the detection of interstitial cystitis (IC). The methodology relies on advanced bioinformatics to scrutinize information contained within mass spectrometry (MS) and high-resolution proton nuclear magnetic resonance (1H-NMR) spectral patterns to distinguish IC-affected from non-affected individuals as well as those suffering from bacterial cystitis (BC). We have applied a novel pattern recognition tool that employs an unsupervised system (self-organizing-type cluster mapping) as a fitness test for a supervised system (a genetic algorithm). With this approach, a training set comprised of mass spectra and 1H-NMR spectra from urine derived from either unaffected individuals or patients with IC is employed so that the most fit combination of relative, normalized intensity features defined at precise m/z or chemical shift values plotted in n-space can reliably distinguish the cohorts used in training. Using this bioinformatic approach, we were able to discriminate spectral patterns associated with IC-affected, BC-affected, and unaffected patients with a success rate of approximately 84%

Is Sarcosine a Biomarker for Prostate Cancer?

Sarcosine was suggested in a letter to Nature in 2009 as a biomarker for prostate cancer. This communication reviews what has been accomplished to date to determine whether sarcosine is or is not a biomarker for prostate cancer that can replace prostate-specific antigen tests

The Role of Electrophoresis in Disease Biomarker Discovery

There has been increased activity in the last few years in the search for disease markers using fractionation of complex biological fluids combined with MS. While electrophoretic and chromatographic separations have played a major role in this endeavor, this manuscript is limited to a review of electrophoretic methods that have been established for disease biomarker discovery. These methods include 2-DE, difference gel electrophoresis (DIGE), and CE. We define what constitutes a biomarker, identify the steps required for establishing a biomarker, and describe the parameters needed in the design of an ideal diagnostic test. The application, advantages, and limitations of CE, DIGE, and 2-DE in meeting the goal of discovering novel biomarkers is discussed in detail, along with a few selected examples that illustrate the search for biomarkers for cancer and neurological diseases

Clinical and Pharmaceutical Applications of Packed-Column Supercritical Fluid Chromatography

Packed-column supercritical fluid chromatography (pSFC) is a fast separation technique that combines the properties of HPLC and GC. pSFC with carbon dioxide as the mobile phase and packed silica column as the stationary phase possesses the properties of normal phase mechanism; however, the addition of modifiers to the mobile phase allows the separation of relatively polar compounds. In spite of its many positive attributes, pSFC has not been widely used in areas such as proteomics, where methods such as HPLC dominate. Packed column SFC has been extensively used in clinical and pharmaceutical laboratories, especially for separation of nonpolar and chiral drugs. This review will discuss recently published applications of pSFC, with a specific focus on its advantages and limitations for the analysis of pharmaceuticals with varying chemical properties