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.

Biomedical informatics: development of a comprehensive data warehouse for clinical and genomic breast cancer research.

The Windber Research Institute is an integrated high-throughput research center employing clinical, genomic and proteomic platforms to produce terabyte levels of data. We use biomedical informatics technologies to integrate all of these operations. This report includes information on a multi-year, multi-phase hybrid data warehouse project currently under development in the Institute. The purpose of the warehouse is to host the terabyte-level of internal experimentally generated data as well as data from public sources. We have previously reported on the phase I development, which integrated limited internal data sources and selected public databases. Currently, we are completing phase II development, which integrates our internal automated data sources and develops visualization tools to query across these data types. This paper summarizes our clinical and experimental operations, the data warehouse development, and the challenges we have faced. In phase III we plan to federate additional manual internal and public data sources and then to develop and adapt more data analysis and mining tools. We expect that the final implementation of the data warehouse will greatly facilitate biomedical informatics research.

High-throughput proteomic analysis of human infiltrating ductal carcinoma of the breast.

Large-scale proteomics will play a critical role in the rapid display, identification and validation of new protein targets, and elucidation of the underlying molecular events that are associated with disease development, progression and severity. However, because the proteome of most organisms are significantly more complex than the genome, the comprehensive analysis of protein expression changes will require an analytical effort beyond the capacity of standard laboratory equipment. We describe the first high-throughput proteomic analysis of human breast infiltrating ductal carcinoma (IDCA) using OCT (optimal cutting temperature) embedded biopsies, two-dimensional difference gel electrophoresis (2-D DIGE) technology and a fully automated spot handling workstation. Total proteins from four breast IDCAs (Stage I, IIA, IIB and IIIA) were individually compared to protein from non-neoplastic tissue obtained from a female donor with no personal or family history of breast cancer. We detected differences in protein abundance that ranged from 14.8% in stage I IDCA versus normal, to 30.6% in stage IIB IDCA versus normal. A total of 524 proteins that showed > or = three-fold difference in abundance between IDCA and normal tissue were picked, processed and identified by mass spectrometry. Out of the proteins picked, approximately 80% were unambiguously assigned identities by matrix-assisted laser desorbtion/ionization-time of flight mass spectrometry or liquid chromatography-tandem mass spectrometry in the first pass. Bioinformatics tools were also used to mine databases to determine if the identified proteins are involved in important pathways and/or interact with other proteins. Gelsolin, vinculin, lumican, alpha-1-antitrypsin, heat shock protein-60, cytokeratin-18, transferrin, enolase-1 and beta-actin, showed differential abundance between IDCA and normal tissue, but the trend was not consistent in all samples. Out of the proteins with database hits, only heat shock protein-70 (more abundant) and peroxiredoxin-2 (less abundant) displayed the same trend in all the IDCAs examined. This preliminary study demonstrates quantitative and qualitative differences in protein abundance between breast IDCAs and reveals 2-D DIGE portraits that may be a reflection of the histological and pathological status of breast IDCA.