OCT 24, 2017 07:00 AM PDT

WEBINAR: Biomarker Discovery: Metabolomics Differentiates Known Disease Classifications of Prostate Cancer

SPONSORED BY: SCIEX
C.E. CREDITS: P.A.C.E. CE | Florida CE
Speakers
  • Associate Professor, University of Florida
    Biography
      Dr. Garrett received his undergraduate degree from the University of Georgia in Chemistry graduating Summa Cum Laude with Highest Honors (1999). As an undergraduate, he worked in the lab of Dr. I. Jonathan Amster on the characterization of bacterial proteins using MALDI-TOF completing an undergraduate honors thesis entitle "Improved methods for on-probe cleanup of unpurified protein samples for MALDI time-of-flight mass spectrometry". He received his PhD in 2006 from the University of Florida working under the direction of Dr. Richard A. Yost. As a graduate student, he developed the first imaging mass spectrometry based on ion trap instrumentation through a partnership with Thermo and studied the disposition of phospholipids in brain tissue. His work involved the characterization of phospholipids using tandem mass spectrometry and described the need for tandem mass spectrometry to both separate and identify this complex lipids species directly from tissue. As a graduate student, he received a Grinter Fellowship, a Proctor & Gamble award for excellence in graduate research, the Baites-Laitnen award, and the Crow-Stasch award for excellence in publication. Finally, his publication entitled "Imaging mass spectrometry with a new intermediate-pressure MALDI linear ion trap mass spectrometer" received the best applied graduate student paper for the Internal Journal of Mass Spectrometry. He is currently an Associate Professor in the Department of Pathology, where he is also the Director of high throughput metabolomics, for the Southeast Center for Integrated Metabolomics and conducts research in the application of metabolomics to disease diagnosis and clinical diagnostics. Prof Garrett is the author of over 60 publications and is an Editorial Board for Clinical Mass Spectrometry. His current interest are in the application of direct tissue analysis approaches as well as the use of high resolution mass spectrometry in metabolomics and routine diagnostics.

    Abstract:

    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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