swath™ acquisition; accelerating quantitative metabolomics · – 100% ms/ms coverage – “the...
Post on 21-Aug-2020
3 Views
Preview:
TRANSCRIPT
08/07/2013
1
SWATH™ Acquisition; Accelerating Quantitative Metabolomics
Brigitte Simons, Academic Business Market Development Manager
AB SCIEX, Canada
2 © 2013 AB SCIEX
Metabolomics Workflow Strategies
MS Accelerating the Future of Metabolomics = Optimizing and combining the best of targeted and non-targeted strategies for comprehensive data collection with
scalability and throughput
08/07/2013
2
3 © 2013 AB SCIEX
Pave the Way Forward… Reducing Data Interpretation Bottlenecks…
Data Acquisition
Metabolite ID
Processing & Analysis
https://xcmsonline.scripps.edu/
4 © 2013 AB SCIEX
Pave the Way Forward…Reducing Data Interpretation Bottlenecks…
Quehenberger et al, J Lipid Res. 2010 Nov;51(11):3299-305
Design and Implementation of Quantitative Lipid Internal Standards
SWATH MS for Lipidomics
08/07/2013
3
5 © 2013 AB SCIEX
Pave the Way Forward… Reducing Data Interpretation Bottlenecks…
N
NN
N
N
O
O
122 (+O)
SWATH MS for Metabolite Fishing
Buspirone
6 © 2013 AB SCIEX
1 amu 1 amu
1amu 0.01 amu 0.01 amu0.01 amu
MRM/SRM
MRMHR
Precursors FragmentsCID
Triple quadrupole
QTOFs
Transition
X amu
TripleTOF™5600+
SWATH-MS
m/z m/z
Quantitative LC-MS Techniques
08/07/2013
4
7 © 2013 AB SCIEX
Cycle Time
< 1 s
Retention Time
Q1 width is user defined!
e.g 25 Da
SWATH = Sequential Windowed Acquisition of All Theoretical MS
Q1 Isolation Strategy
m/z
8 © 2013 AB SCIEX
400-425
Easy Method Development and Data Mining is Carried Out Retrospectively
424-450
450-475
475-500
500-525525-550
Time, min
Fra
gmen
t Ion
m/z
, Da
24 LCMS maps of fragment ions
Time, min
08/07/2013
5
9 © 2013 AB SCIEX
Why SWATH Analysis for Metabolomics? Data Independent Acquisition – no prioritization or bias in precursor data
collection given LC separations are not always ideal
Best Quant and Best Qual metabolite ID all in one run– Quant is MS/MS for best selectivity and signal to noise
– No pre-designed inclusion lists
– No pre-set compound specific information
All the information is extracted post LC-MS acquisition
Designing a balance between Specificity vs Sensitivity– Dependence on separation of isomers
– Association of Precursor and putative Products signals is critical
– Managing LC eluted m/z in dense regions of the chromatography
A Permanent Digital Record of MS/MS fragment of your samples– Come back to it for hypothesis-driven discovery
– Build an MRM method to transfer for targeted analysis easily.
10 © 2013 AB SCIEX
Theoretical Human MetabolomesHow Important is Relative Quantitation?
M mM M nM pM fM
Secondary endogenous metabolites
Secondary food metabolites
Secondary drug metabolites
Lipids/Lipid derivatives200,000 (Lipidome)
10,000 (Drug metabolome)
100,000 (Food metabolome)
10,000 (Secondome)
Wishart Bioinformatics Workshop; www.bioinformatics.ca
08/07/2013
6
11 © 2013 AB SCIEX
The Importance of MS/MS for Metabolite ID
11
25%
90%
Slide courtesy of Jiang Zhu; Analytical Chemistry, 2013, 85, 798-804
12 © 2013 AB SCIEX
SWATH MS and Data Independent Acquisitions
SWATH™ Acquisition
Quantitative
Technical repsBiological reps
IDA for General Unknown Screening
Qualitative
Metabolite Database
SWATH™ XIC Manager
Fast targeted or untargeted XIC
generation
08/07/2013
7
13 © 2013 AB SCIEX
AB SCIEX SWATH Quant/Qual Experimental Workflow
LipidView® Software
MS and SWATH MS/MS
Multivariate Statistical Analysis – Find the differential features– Link to raw data
Identify and confirm metabolite/lipid formula or structure
FormulaFinderTM predicts elemental composition and MS/MS fragment assignment
ChemSpider for Structural information if not in your in‐
house library
toolsetstoolsets
14 © 2013 AB SCIEX
Metabolomics Study Design
• 12 plasma samples were consensually collected.
• 6 male and 6 female samples.
• Female samples were split into 2 groups, pre-menopausal (4) and post menopausal (2).
• Urine was diluted 1:4 with distilled water and centrifuged.
• A pooled sample was prepared using an aliquot from all samples.
08/07/2013
8
15 © 2013 AB SCIEX
Multivariate Statistical Analysis of MS Data
The PCA Scores (top) and Loadings (bottom) plots with Pareto scaling highlight the differences between the urine samples.
Principle Component Variable Grouping (PCVG) is used in the loadings plot to cluster ions with similar profiles (represented here in the same colour), accounting for 70 % of the variation.
m/z 655 is one of the peaks contributing to the difference in one of the urine samples
Differential sample
Component contributing to difference
16 © 2013 AB SCIEX
7-HYDROXYETHYLTHEOPHYLLINEC9H12N4O3
N-linolenoyl-glutamineC23H38N2O4
L-PhenylalanineC9H11NO2
N-Palmitoyl tryptophanFormula: C27H42N2O3
LC-MS Peak Alignment with Metabolite ID
08/07/2013
9
17 © 2013 AB SCIEX
Known Compound IdentificationScreen of SWATH MS/MS against in-house Libraries
Library Match Strategy for Known Compound Identification
• Identification criteria
1. Accurate mass TOF MS (ppm error)
2. Retention time (min)
Metabolite ID Confirmation criteria
3. TOF MS/MS spectral matching (purity score)
– Library MS/MS spectrum with experimental
4. Isotope pattern match
– theoretical vs. experimental
18 © 2013 AB SCIEX
Table showing name, formula, mass accuracy, RT, intensity, library hit & purity score
Arginine TOF MS spectrum
Mass accuracy: 0.1 ppm
Arginine TOF MS/MS spectrum
Purity score 85.3
Known Compound Identification- Library Match
08/07/2013
10
19 © 2013 AB SCIEX
Unknown Compound Identification
Steps for unknown compound ID using chemical logic
1. Elemental composition assignment using TOF MS
2. Elemental composition of fragment ion (TOF MS/MS)
3. Structure search for elemental composition in public database
( PubChem, ChemSpider, HMDB, MassBank, Metlin etc.)
4. Structural elucidation and fragmentation correlation
* All from a single injection
20 © 2013 AB SCIEX
162.0 162.5 163.0 163.5 164.0Mass/Charge, Da
0.0e0
5.0e4
1.0e5
1.5e5
2.0e5
2.5e5
Inte
nsity
163.1175 (1)163.1154 (1)
*162.1125 (1)
TOF MS Isotope pattern match
163.1154
1.2 ppm 163.1175
-0.6 ppm
Elemental Composition Assignment using TOF MS
08/07/2013
11
21 © 2013 AB SCIEX
60 70 80 90 100 110 120 130 140 150 160 170
Mass/Charge, Da
0
500
1000
1500In
ten
sit
y*162.1121*103.0388
*60.0806
*85.0283
*102.0914
*57.0335
*59.0725
Elemental Composition Assignment of Fragment Ions
TOF MS/MS Spectrum
Elemental composition
22 © 2013 AB SCIEX
Elemental Formula & Structure Search using Metabolite Databases including METLIN
08/07/2013
12
23 © 2013 AB SCIEX
Structural Elucidation in PeakView™ SoftwareMatch N-Acetylglutamic Acid Fragments from Structure with MS/MS Data
TOF MS spectra TOF MS/MS spectra
N-acetyl glutamic acidFragment assignment
24 © 2013 AB SCIEX
Hypothesis Driven Targeted Biochemical Pathway Workflow1. Targeted data processing using a list of known metabolites in any
biochemical pathway
2. Single injection workflow for quantitative and qualitative data
3. Validate hypothesis whether a specific pathway is effecting as a result of disease state or treatment or phenotype or genotype
4. Quick answers to biologists and fast turn around time
5. Ready to use list of metabolites in a given biochemical pathway like
– Glycolysis
– TCA cycle
– Acyl carnitines
– Phosphate pentose pathway
– Oxidative stress markers
– Eicosanoid pathway
– Leukotriene's
– Amino acids etc.
08/07/2013
13
25 © 2013 AB SCIEX
SWATH MS Quantitation of Acyl CarnitinesXIC Manager for Targeted Extraction of MS or MS/MS
Acyl carnitine C18C25H49NO4
MS/MS for confirmation
26 © 2013 AB SCIEX
Plasma
CSF
MarkerView™ SoftwareProfiling of Acyl Carnitines in CSF & Plasma Extracts
08/07/2013
14
27 © 2013 AB SCIEX
SWATH: 1 nM- 0.14 ng/mL SpermidineTOF MS: 1 nM- 0.14 ng/mL Spermidine
Quantitative Performance TOF MS Quant vs. SWATH Quant
28 © 2013 AB SCIEX
SWATH Acquisition: 1 nM- Amine-1 MW:153.05
High Selectivity with 5X less background noise
TOF MS: 1 nM- Amine-1 MW: 153.05
High background noise
* 8 scanned points across the peak * 8 scanned points across the peak
Quantitative Performance TOF MS Quant vs. SWATH Quant
08/07/2013
15
29 © 2013 AB SCIEX
TOF MS vs. SWATH™ Acquisition Quantification
SWATH Acquisition: 1 nM- Amine-2 MW : 177.1
High Selectivity with 10X less background noise
TOF MS: 1 nM- Amine-2 MW : 177.1
High background noise
* 8 scanned points across the peak * 8 scanned points across the peak
30 © 2013 AB SCIEX
cycle time~ 3.3 min
acquisition time
Q1 mass filter width = 0.7 Da
SWATH Acquisition Method Set-up for Lipidomics
200 ‐1200 (m/z)
700.550
701.551
702.552
703.553
704.554
705.555
1200.051
200.0550
08/07/2013
16
31 © 2013 AB SCIEX
Time (one cycle)
Pre
curs
or
mas
s m
/z
TOF MS Full Scan200 ms
1 ms
Total Cycle3.5 min
MS/MSALL covering 1000 MS/MS (200 ms each)
Q1 selection0.7 amu
Lipidomics by Flow Injection SWATHMethod Set-up
How to calculate cycle time?– Mass range
– Step size to cover mass range
– MS/MS accumulation time
– Looping in a TOF MS scan (~ 200 ms)
32 © 2013 AB SCIEX
0
100
200
conc
entr
atio
n, µ
M
TAG Lipid Profiling in Plasma
+MS TAG 52:3
Group A Group B
+MS/MS of TAG 52:3 NL 18:0
Group BGroup A
08/07/2013
17
33 © 2013 AB SCIEX
Conclusions
SWATH adopting the concept of Data Independent Acquisition is the future of Quant/Qual metabolomics workflows– MRM quantitative data mining retrospectively– 100% MS/MS coverage – “The Ultimate Safety Net”
Design a balance of sensitivity and selectivity– Lipidomics focuses data collection in MS/MS with narrow windows– Metabolomics by LC-SWATH is optimized for UPLC separations with wider
windows stepped quickly across a wider mass range– A totally generic method – no need to change the methods, CE is ramped
Follow up with a targeted MRMHR method, + CE optimized, and targeted data processing for quantitative reporting of the important metabolites
34 © 2013 AB SCIEX
Acknowledgments
University of Toronto & Toronto General Hospital
– Johanne Allard– Bianca Arendt– Elaheh Aghdassi– David Ma
VTT Technical Research Institute of Finland
– Kari Raino
AB SCIEX, USA– Fadi Abdi– Mark Cafazzo– Christie Hunter
AB SCIEX, CAN– Suma Ramagiri– Paul Baker– Eva Duchoslav– Dave Cox– Lyle Burton
AB SCIEX, UK– Vicki Gallant, PhD
– Baljit Ubhi, PhD
08/07/2013
18
35 © 2013 AB SCIEX
Thank You for Your Attention
36 © 2013 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.
© 2013 AB SCIEX. All rights reserved. Information subject to change without notice.
top related