OCT 24, 2017 07:00 AM PDT
WEBINAR: Biomarker Discovery: Metabolomics Differentiates Known Disease Classifications of Prostate Cancer
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  • Associate Professor & Director, University of Florida
      Dr. Garrett earned his PhD in Analytical Chemistry from the University of Florida in 2006 where he worked under the direction of Professor Richard Yost. He then joined the faculty in the Department of Medicine at the University of Florida as a Research Assistant Professor and Core Laboratory Director for the General Clinical Research Center. In 2014, he joined the Department of Pathology, Immunology and Laboratory Medicine as an Assistant professor. He is currently an Associate Professor and Director of high-throughput metabolomics for the Southeast Center for Integrated Metabolomics (SECIM).

      Dr. Garrett's research is focused on the application and development of mass spectrometry techniques and instruments for clinical research and translation to clinical diagnostics. He has a specific interest in developing approaches to small molecule quantitation or characterization using mass spectrometry, which would better understanding diseases, drug treatments, and therefore health. This interest involves the use and development of imaging mass spectrometry (IMS) to directly probe excised tissue sections for chemical distributions, traditional liquid chromatography tandem mass spectrometry of (UPLC/MS/MS) homogenized tissue and plasma or serum and the high resolution mass spectrometry for profiling the human metabolome. He has been at the fore-front of developing instrumentation and applications for IMS studies and plans to continue developing this technique to provide high-throughput analyses and better identification of unknowns.


    DATE: October, 24, 2017
    TIME:  7:00 AM PDT


    Metabolomics focuses on the chemical processes central to cellular metabolism. A robust mass spectrometry solution for screening metabolites is of increased interest allowing for a more integrated and routine analysis. A new QTOF System was developed for routine, robust workflows which require minimal MS expertise. The system integrates all data acquisition, processing and review in a single software. A prostate cancer study was used to determine whether the untargeted metabolomics workflow using the X500R System could find key differences between the samples.

    Urine samples were obtained with disease classifications, previously determined using accepted clinical techniques. Samples were extracted, dried and reconstituted in 50 µL of 0.1% formic acid in water. A standard reverse phase gradient was used employing mobile phase A as 0.1% formic acid in water and mobile phase B as acetonitrile. The data were collected using information dependent acquisition (IDA) on the X500R QTOF System (SCIEX). Data were processed in MarkerView™ Software 1.3 and PCA analysis was performed. Ions of interest were saved as an Interest List and copied into SCIEX OS Software where a formula was generated for each mz - RT ion pairs, these formulae were scored using MS and MS/MS data, and searched using databases.

    In this study, samples from a pilot prostate cancer study were analyzed and a clear difference between healthy and disease urine samples was detected using this untargeted metabolomics approach, confirming the original disease classifications. MarkerView Software was used to determine a list of the statistically significant analytes that distinguished the samples, and then the SCIEX OS compound searching provided formulae finding as well as structural matching through ChemSpider database. Most changes were in the small molecule amino acids. This pilot study provided confidence in the approach, and the next larger phase of the study analyzing a much larger set of samples is underway.


    Learning objectives:

    • Learn about the untargeted metabolomics workflow using the new X500R QTOF system
    • Learn how the X500R QTOF system allows the distinction between healthy and diseased cells
    • Learn about the streamlined data processing workflow for identifying and confirming biomarkers using database searches

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