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GeneSpring MS GeneSpring for Metabolite BioMarker Analysis using Mass Spectrometry data

GeneSpring MS GeneSpring for Metabolite BioMarker Analysis using Mass Spectrometry data. Agilent Q-TOF VIP Visit Jan 16-17, 2007 Santa Clara, CA Thon de Boer GeneSpring MS Product Manager. Definition of a Biomarkers.

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GeneSpring MS GeneSpring for Metabolite BioMarker Analysis using Mass Spectrometry data

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  1. GeneSpring MSGeneSpring for Metabolite BioMarker Analysis using Mass Spectrometry data Agilent Q-TOF VIP Visit Jan 16-17, 2007 Santa Clara, CA Thon de Boer GeneSpring MS Product Manager

  2. Definition of a Biomarkers • Used to indicate or measure a biological process (for instance, levels of a specific protein in blood or spinal fluid, genetic mutations, or brain abnormalities observed in a PET scan or other imaging test). Detecting biomarkers specific to a disease can aid in the identification, diagnosis, and treatment of affected individuals and people who may be at risk but do not yet exhibit symptoms.www.alz.org/Resources/Glossary.asp • Any molecular species found to provide correlation to a particular phenotype or perturbation of a biological system. Co-variant analysis of multiple biomarkers or patterns usually results in higher correlation confidence.www.inproteomics.com/nwglosbc.html • A molecular marker associated with a biological function.www.genpromag.com/Glossary~LETTER~B.html

  3. GeneSpring MS – GeneSpring for Mass Spectrometry and Biomarker analysis • Allows the easy import, storage and visualization of LC/MS(/MS) and GC/MS data for many samples and exp. Designs • Identify significantly different metabolite abundances across experimental conditions without the need to be a statistical expert

  4. GeneSpring MS for Biomarker discoveryBuilt on the Agilent GeneSpring Platform

  5. Applications of GeneSpring MS • Find and monitor metabolite biomarkers for drug toxicity screening • Monitor the drug production plant’s efficiency • Classify unknown patient samples for disease outcome prediction based on their metabolite profiles

  6. Case study – Mayo Clinic – Poster at ASMS

  7. TP309 TP309-Xa21 PCA Analysis of 347 Features from 1-way ANOVA of Pxo99 and Mock classes only in resistant & susceptible Rice Lines PCA before 1-way ANOVA PCA after 1-way ANOVA TP309-NT TP309-Mock TP309-Pxo99 Xa21-NT Xa21-Mock Xa21-Pxo99 Xa21_Pxo99/RaxST- TP309-NT TP309-Mock TP309-Pxo99 Xa21-NT Xa21-Mock Xa21-Pxo99 Xa21_Pxo99/RaxST- Xa21 Line TP309 Line TP309-Pxo99 & Xa21-RaxST (Infected) TP309-NT & Mock Xa21-NT Xa21- Mock & Xa21-Pxo99 (Resistant) 347 Significant QC Features ALL 1822 QC Features

  8. GeneSpring MS • GeneSpring MS is designed to serve the Metabolomics needs of our customers • Metabolomics pillar of the GeneSpring Platform

  9. Sample Preparation GC/MS Analysis LC/MS Analysis Peak Finding Data normalization Statistical Analysis System Biology Analysis Metabolomics Workflow - Overview Metabolite Identification Needed for Understanding • Purpose: • extract and sub-fractionate metabolites from sample • Typical samples: • Body fluids • Tissue • Plants Purpose: Separate and detect metabolites by the appropriate tool Purpose: Find and quantitate all metabolites Purpose: Correct data for retention time and response drift Purpose: Find meaningful differences in sample sets Purpose: Understand the biological meaning of the data

  10. Feature Extraction Results in Reduced Set of Mass Features

  11. (Q)TOF LC/MS Workflow #1: Load Agilent MHD files .WIFF files MHD Files GeneSpring MS Mass Hunter Mol Feat Extr .D files Identify diff. expressed Biomarkers Using statistics Link to Metlin Etc. for Structure search List of interesting mass values 510.546, pI=4 786.562, pI=8 1575.897, pI=3 1945.78, pI=4 .

  12. TOF LC/MS Workflow #2: TOF-QTOF, ID before loaded in GSMS .WIFF files MHD Files GeneSpring MS Mass Hunter Mol Feat Extr Identify diff. expressed Biomarkers Using statistics Annotate peaks In GeneSpring MS MS/MS data files Spectrum Mill MS/MS Search MS/MS run (QTOF w same LC setup) List of interesting mass values (+ RT) Create Inclusion list file 510.546 786.562 1575.897 1945.78 . Re-run Samples on MS/MS (Ion Trap?) Picking just the Interesting peaks for MS/MS

  13. Workflow # 4: GC/MS analysis of metabolites AMDIS DB search (NIST) ELU/FIN files Unid/Identified masses GC/MS GeneSpring MS Identify diff. expressed Biomarkers Using statistics List of interesting mass values 510.546 786.562 1575.897 1945.78 .

  14. GeneSpring MS allows you to…Load data from a variety of MS systems • Load data from Agilent LC/MS (Q)TOF system (MHD files) • Load data from Agilent GC/MS system (ELU/FIN files from AMDIS • Load data from Agilent QQQ (Quant Result Files) (In 1.1) • Load data in mzData/mzXML format from LC/MS(/MS) systems (in 1.1)

  15. GeneSpring MS allows you to…Compare mass features across different runs • Each mass feature is represented as an entity with the following properties • RT; Retention time, or RI, retention index (normalized RT) • Mass; neutral mass

  16. Matrix Interference Target GeneSpring MS allows you to…Compare mass features across different runs • Mass features are “aligned” from one run to the next based on the similarity between the RT/mass values • For GC/MS data fragmentation spectra are used for alignment

  17. GeneSpring MS allows you to…Find statistically significant differences in abundance • Abundance of the metabolites or peptides can be compared across different runs using advanced statistics • ANOVA • T-test • Volcano plots • Correlation with meta data

  18. GeneSpring MS allows you to…Use PCA for QC and Biomarker discovery

  19. GeneSpring MS allows you to…Performs a variety of statistical analyses • Class prediction • K-nearest neighbors • SVM • Clustering • K-means • SOM • QT clustering • Hierarchical trees • Complete, average and single linkage (w. bootstrapping)

  20. GeneSpring MS allows you to…Visualize the MS data and analysis results in a variety of ways • Variety of plots to visualize • RT vs Mass plots • RT vs Ratio • Mass vs Ratio • Graphs of abundance across conditions • Scatter plots • Chromatograms

  21. Continue the workflow by creating inclusion lists for LC/MS/MS run on the QTOF or the QQQ • After finding potential biomarkers using the statistical techniques in GeneSpring MS, you can create Inclusion List files for the QTOF (or Ion Trap) and QQQ to determine identity of the biomarkers using the QTOF or to run large scale validation experiments with the QQQ

  22. Thon de BoerGeneSpring MS Product Manager Thank you for your attention

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