discovery metabolomics - quantitative profiling of the ... · pdf filediscovery metabolomics -...

ANSWERS FOR SCIENCE. KNOWLEDGE FOR LIFE.

Baljit Ubhi Ph.D

ASMS Baltimore, June 2014

Discovery Metabolomics - Quantitative Profiling of the Metabolome using TripleTOF® Technology

2 © 2014 AB SCIEX

What is Metabolomics?

• Also sometimes referred to as metabonomics or metabolic profiling

• Measurement of low molecular weight metabolites (molecular weight

2000 Daltons) in biological fluids or tissue extracts.

• Univariate and multivariate statistical data analyses

• Functional end-point of physiology and pathophysiology

‒ Metabolomics is an attractive tool as it reveals information closest to

the phenotypic level

• Direct mirror of environmental influences

‒ Nutrition

‒ Disease monitoring

‒ Exercise

‒ Drug Metabolism

DNA Genomics

RNA Transcriptomics PROTEIN

Proteomics

METABOLITE Metabolomics

3 © 2014 AB SCIEX

Importance of Metabolomics

• Drug Discovery

‒ Metabolomics not only plays a role in detection and diagnosis of

disease and also as both a predictive and pharmacodynamic

marker of drug efficacy.

• Toxicology and Safety Assessment

‒ Metabolic profiling can identify physiological transformation due

to toxic abuse or exposure

• Basic Research and Biomarker Discovery

‒ Metabolites are key analytes for effecting phenotype changes

resulting from genetic manipulations or system perturbations

• Nutrigenomics

‒ Connects the omics concepts of proteomics, genomics,

metabolomics and transcriptomics to human nutrition

• Other applications include the food industry, agrochemical industry,

environmental toxicology and biofuels.

4 © 2014 AB SCIEX

Workflow Strategies for Metabolomics

Targeted vs. Non-Targeted Analysis

Non-Targeted Analysis

MS → MS/MS

Advantages:

Detects Any Species

Disadvantages:

Lower Dynamic Range and

Sensitivity

ID Step Required

Sophisticated Informatics

Targeted Analysis

MRM → MS/MS

Advantages:

High Sensitivity, Throughput

and Dynamic Range

High Quant Quality

Simplified Informatics

Disadvantages:

Detects Specific Species

Upfront Method Development

5 © 2014 AB SCIEX

AB SCIEX TripleTOF® 5600+ System

• Sensitivity ‒ In MS and MS/MS modes

• Speed ‒ 10 ms accumulation times

‒ 100 MS/MS scans per second in IDA

• Resolution ‒ High resolution ~30,000

‒ High sensitivity ~15,000

• Mass Accuracy ‒ 1-2 ppm RMS (external calibration)

• Dynamic Range ‒ ~ 3-4 orders

6 © 2014 AB SCIEX

AB SCIEX TripleTOF® 5600+ System

Q0 High Pressure Cell

LINAC® collision cell

Accelerator TOF™

Analyzer

40 GHz Multichannel TDC Detector

Two-stage reflectron

30kHz Accelerator

15 kV Acceleration voltage

Ion compression optics

QJet® Ion Guide

Industry-Leading

Ion Sources

High Frequency Q1

Front end based on the

AB SCIEX Triple Quad™ 4500

and 5500 system

7 © 2014 AB SCIEX

Metabolomics Strategies – TripleTOF® 5600+ System

Single injection acquisition

MS and MS/MS data

Targeted data acquisition Target specific species ‘MRM-like’

Targeted processing

Non-targeted data acquisition Generic MS setup

Targeted processing Hypothesis Driven Biochemical

Pathway Approach

Non-targeted processing

Non Targeted Global Metabolite Profile

Scheduled MRMHR Workflow Data Dependent Acquisition

8 © 2014 AB SCIEX

Metabolomics Strategies – TripleTOF® 5600+ System

Single injection acquisition

MS and MS/MS data

Targeted data acquisition Target specific species ‘MRM-like’

Targeted processing

Non-targeted data acquisition Generic MS setup

Targeted processing Hypothesis Driven Biochemical

Pathway Approach

Non-targeted processing

Non Targeted Global Metabolite Profile

Scheduled MRMHR Workflow Data Dependent Acquisition


GLOBAL METABOLOMICS Non-Targeted Data Acquisition Non-targeted Data Processing

10 © 2014 AB SCIEX

Untargeted Metabolomics Workflow

LipidView® Software

MS and MS/MS

Mulitvariate Data Analysis Identify and confirm the metabolites/lipids

FormulaFinder™

predicts elemental composition

And assigns MS/MS fragmentation

MasterView™ Software

allows automated link to

ChemSpider for structural

information

Find differential features

Simplified statistics

11 © 2014 AB SCIEX

High Data Acquisition Speed

Single Injection Workflow

IDA Criteria Selection Criteria

(Intensity, DBS)

Survey Scan

(TOF MS)

Identify potential metabolites

based on accurate mass

m/z

Inte

nsity

(cp

s)

TOF MS scan

* * * *

Extracted Ion Chromatogram

(XIC)

Quantitative

Confirm metabolites based

on spectral match or

common NL or PI m/z

Inte

nsity

(cps)

NH

NH

CH3

O

CH3H

CH3

O

OOCH3CH3

H

H

TOF MS/MS scan Qualitative

Dependent Scan

(TOF MS/MS)

Top 15 precursors selected for

MS/MS

Total

cycle time

< 0.9 s

12 © 2014 AB SCIEX

MarkerView™ Software

Data Visualization Software

• Metabolomic and protein profiling workflows

• Peak extraction, alignment and filtered data

processing

• Multivariate data analysis (PCA/PCA-DA

scores and loadings plots)

• Univariate data analysis

• Principal Component Variable Grouping

(PCVG)

• Profile plots of differential ions including RT

information

• Link to raw data in the same software

• Post-analysis reports allow you to easily track

your work and record potential biomarkers.

13 © 2014 AB SCIEX

Principal Component Analysis (PCA-DA)

Scores Plot – Find the Differences

• Zucker rat model to study effects

of obesity, diabetes and

cardiovascular diseases

• Three Phenotypes

Lean – lacks mutation to

induce diabetic state and

obesity

Fatty – Diabetic and shows

mild weight gain on normal diet

Obese – Mutation creating

early and substantial weight

gain and obesity when on High

Fat diet

• Tight clustering of QC samples

highlights data reproducibility

(remove before final processing)

Pooled QCs

Lean

Obese

Fatty

14 © 2014 AB SCIEX

PCA-DA - Loadings Plot

Variables Responsible for Differences in Scores

m/z / retention time pairs

are the variables from the

TOF MS data

Higher

concentration

in Obese

Higher

concentration

In Fatty

15 © 2014 AB SCIEX

Principle Component Variable Grouping (PCVG)

Find Related Variables – by Degree of Difference

Only removing

discrete common

variables

Removing a larger

proportion of common

variables leaving the

ions with largest

difference

16 © 2014 AB SCIEX

MasterView™ Software for Comparative Screening

Compare a Sample vs Control

Sample Control

17 © 2014 AB SCIEX

Confident Identification of Features of Interest

18 © 2014 AB SCIEX

MasterView™ Software – Review Formula Finder Results

Sample

MS

Sample

MSMS

Control

MS

Control

MSMS

Click Formula for automatic link to Chemspider

19 © 2014 AB SCIEX

MasterView™ Software – Review ChemSpider Results

20 © 2014 AB SCIEX

IDA Explorer – 7435 MSMS Spectra

21 © 2014 AB SCIEX

FormulaFinder™ Tool in PeakView® Software

Predict Possible Formula – Rank #4 based on MS

XIC of

LPC 18:1

Isotope

Pattern

of LPC 18:1

Isotopic Pattern

22 © 2014 AB SCIEX

PeakView® Software - Confirmation of Potential Structure Differential Feature m/z 522.3554 – Rank #1 based on MS/MS

MSMS of

LPC 18:1

Structure from ChemSpider

IDA Explorer


GLOBAL LIPIDOMICS Non-Targeted Data Acquisition Non-targeted Data Processing

24 © 2014 AB SCIEX

Approaches to Statistical Data Analysis

Two analysis paths from the same dataset

Acquired Dataset

Process in LipidView™ Software

Import into MarkerView™ Software with lipid IDs

Process directly in MarkerView™ Software

Import Interest List into MasterView™ Software

to identify

Global Metabolomics

Approach

Global Lipidomics

Approach

25 © 2014 AB SCIEX

LipidView™ Software

• 58 lipid classes, ~28000 lipid species

represented in a lipid fragments database

• >600 characteristic lipid fragments lists

• Lipid catalogue, lipid calculator utilities

• Identification, qualitative, relative

quantitation and targeted analysis of lipids

• Support of lipid bioinformatics in medium

sample throughput <96 samples in a set

processed

• Seamless link to MarkerView™ Software

• Capabilities to process AB SCIEX

TripleTOF® system, AB SCIEX Triple

Quad™ system, QTRAP® system and

QSTAR® system data

26 © 2014 AB SCIEX

LipidView™ Software Lipid Class Profile (All)

30 © 2014 AB SCIEX

LipidView™ Software Results Table

Identify and Quantify a Large Number of Lipids

Export Results to MarkerView™ Software

for Statistical Data Analysis

Lipid ID‘s

31 © 2014 AB SCIEX

PCA Analysis in MarkerView™ Software

Find Differences between Lipid Species

Each m/z / RT pair now has a

lipid ID as opposed to a m/z. Scores plot

Loadings plot

32 © 2014 AB SCIEX

Other Statistical Tools

T-tests and Volcano Plots

Highly significant ions based on p-values from t tests

35 © 2014 AB SCIEX

LC-MS Solutions for Discovery Metabolomics

• TripleTOF® System has faster acquisition with no loss of

sensitivity or resolution:

o High resolution MS for accurate mass MS-based identification

o High speed, high resolution MS/MS for definitive confirmation

of metabolites acquired simultaneously, without a need to re-

sample

o Single injection workflow for higher productivity

• MarkerView™ Software MS feature extraction and

alignment across large sample sets for multivariate data

analysis.

• MasterView™ Software for known and unknown metabolite

features and comparative profiling of samples

o Powerful tools to ID confirmation, FormulaFinder™, Structural

Interpretation tool and links to ChemSpider searches

o Confirm identity by searching MS/MS against metabolite

libraries such as MassBank

36 © 2014 AB SCIEX

Acknowledgments

Jeff Patrick David Alonso Lorne Fell Joe Binkley Christina Nieh

Joe Shambaugh Anthony Taylor Tamas Rujan Jens Heofkens Alessio Ceroni Peter Haberl

Christie Hunter Fadi Abdi Aaron Hudson David Cox Lyle Burton Jean Baptiste Vincendet Brigitte Simons Jeremiah Tipton

37 © 2014 AB SCIEX

Trademarks/Licensing

For Research Use Only. Not for use in diagnostic procedures.

The trademarks mentioned herein are the property of AB Sciex Pte. Ltd.

or their respective owners. AB SCIEX™ is being used under license. All

rights reserved. Information subject to change without notice.

© 2014 AB SCIEX.

ANSWERS FOR SCIENCE. KNOWLEDGE FOR LIFE. ANSWERS FOR SCIENCE. KNOWLEDGE FOR LIFE.

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