2007

GeneSpring MSGeneSpring for

Metabolite BioMarker Analysis using Mass

Spectrometry data

Agilent Q-TOF VIP VisitJan 16-17, 2007Santa Clara, CA

Thon de BoerGeneSpring MS Product Manager

2007

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

2007

GeneSpring MS – GeneSpring for Mass Spectrometry and Biomarker analysis

1. Allows the easy import, storage and visualization of LC/MS(/MS) and GC/MS data for many samples and exp. Designs

2. Identify significantly different metabolite abundances across experimental conditions without the need to be a statistical expert

2007

GeneSpring MS for Biomarker discoveryBuilt on the Agilent GeneSpring Platform

2007

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

2007

Case study – Mayo Clinic – Poster at ASMS

2007

PCA Analysis of 347 Features from 1-way ANOVA of Pxo99 and Mock classes only in resistant & susceptible Rice Lines

TP309-NTTP309-MockTP309-Pxo99

Xa21-NTXa21-MockXa21-Pxo99Xa21_Pxo99/RaxST-

Xa21 LineTP309 Line

PCA before 1-way ANOVA

TP309-NT & Mock

TP309-NTTP309-MockTP309-Pxo99

Xa21-NTXa21-MockXa21-Pxo99Xa21_Pxo99/RaxST-

Xa21-NT Xa21- Mock & Xa21-Pxo99 (Resistant)

TP309-Pxo99 & Xa21-RaxST(Infected)

PCA after 1-way ANOVA

ALL 1822 QC Features 347 Significant QC FeaturesTP309 TP309-Xa21

2007

GeneSpring MS

•GeneSpring MS is designed to serve the Metabolomics needs of our customers

•Metabolomics pillar of the GeneSpring Platform

2007

LC/MS Analysis

Metabolomics Workflow - Overview

Sample Preparation

Purpose: extract and sub-fractionate metabolites from sample

Typical samples: • Body fluids• Tissue• Plants

GC/MS Analysis

Purpose: Separate and detect metabolites by the appropriate tool

Purpose: Find meaningful differences in sample sets

Statistical Analysis

Purpose: Find and quantitate all metabolites

Peak Finding

Purpose: Correct data for retention time and response drift

Data normalization

Purpose: Understand the biological meaning of the data

System Biology Analysis

Metabolite Identification Needed for Understanding

2007

Feature Extraction Results in Reduced Set of Mass Features

2007

LC/MS Workflow #1: Load Agilent MHD filesGeneSpring MS

Identify diff. expressedBiomarkers Using statistics

List of interestingmass values

510.546, pI=4786.562, pI=81575.897, pI=31945.78, pI=4

.

.WIFF files

(Q)TOF Mass HunterMol Feat Extr

MHD Files

Link to MetlinEtc. forStructure search

.D files

2007

LC/MS Workflow #2: TOF-QTOF, ID before loaded in GSMS

List of interestingmass values (+ RT)Create Inclusion list file

510.546786.5621575.8971945.78

.

Re-run Sampleson MS/MS (Ion Trap?)Picking just theInteresting peaks forMS/MS

MS/MS datafilesSpectrum Mill

MS/MS SearchMS/MS run(QTOF w same LC setup)

Annotate peaksIn GeneSpring MS

GeneSpring MS

Identify diff. expressedBiomarkers Using statistics

.WIFF files

TOF

MHD Files

Mass HunterMol Feat Extr

2007

Workflow # 4: GC/MS analysis of metabolites

GeneSpring MS

Identify diff. expressedBiomarkers Using statistics

List of interestingmass values

510.546786.562

1575.8971945.78

.

GC/MSAMDISDB search (NIST)

ELU/FIN filesUnid/Identified masses

2007

GeneSpring MS allows you to…Load data from a variety of MS systems

1. Load data from Agilent LC/MS (Q)TOF system (MHD files)

2. Load data from Agilent GC/MS system (ELU/FIN files from AMDIS

3. Load data from Agilent QQQ (Quant Result Files) (In 1.1)

4. Load data in mzData/mzXML format from LC/MS(/MS) systems (in 1.1)

2007

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

2007

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

Interference

Matrix

Target

2007

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

2007

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

2007

GeneSpring MS allows you to…Performs a variety of statistical analyses

1. Class prediction

– K-nearest neighbors

– SVM

2. Clustering

– K-means

– SOM

– QT clustering

3. Hierarchical trees

– Complete, average and single linkage

(w. bootstrapping)

2007

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

2007

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

2007

Thon de BoerGeneSpring MS Product

Manager

Thank you for your attention