Proteomic profiling of human urine using multi-dimensional protein identification technology.

Human urine samples are ideal for proteomic profiling and have tremendous potential as sources of biomarkers. Multi-dimensional protein identification technology (MudPIT) is an effective approach to analyzing human urine or other fluids dominated by diverse metabolites. MudPIT analysis was used to identify 87 proteins in just 15 ml of human urine. A high throughput, reproducible, and sensitive technology, MudPIT may soon be used for more proteomic analyses of metabolites.

Exploring human plasma proteome strategies: high efficiency in-solution digestion protocol for multi-dimensional protein identification technology.

Multi-dimensional protein identification technology (MudPIT) is becoming a prevalent proteomic approach due to its high-throughput separations and accurate mass detection. Prior to MudPIT analysis, complicated samples required in-solution digestion. Unlike in-gel digestion, in which enzymes work on just a few proteins, in-solution digestion involves simultaneous digestion of hundreds or thousands of proteins. In-solution digestion protocols must therefore be very efficient. Few investigations have evaluated the efficiency of in-solution digestion protocols. The present research compared three such protocols. Results suggest that a protocol utilizing trifluoroethanol (TFE) as denaturant is most efficient.

Label-free semiquantitative peptide feature profiling of human breast cancer and breast disease sera via two-dimensional liquid chromatography-mass spectrometry.

A label-free semiquantitative peptide feature profiling method was developed in response to challenges associated with analysis of two-dimensional liquid chromatography-tandem mass spectrometry data. One hundred twenty human sera (49 from invasive breast carcinoma patients, 26 from non-invasive breast carcinoma patients, 35 from benign breast disease patients, and 10 from normal controls) were repeatedly analyzed using a standardized two-dimensional liquid chromatography-mass spectrometry method. Data were extracted using the novel semiquantitative peptide feature profiling method, which is based on comparisons of normalized relative ion intensities. Hierarchical cluster analyses and principle component analyses were used to evaluate the predicative capability of the extracted data, and results were promising. Extracted data were also randomly assigned to either a training group (65%) or to a test group (35%) for artificial neural network modeling. Models best identified invasive breast carcinomas (212 predictions, 94% accurate) and benign non-neoplastic breast disease (96 predictions, 81.3% accurate). These results suggest that, after further development, the novel method may be useful for large scale clinical proteomic profiling.