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The world leader in serving science
Facilitate Metabolite Biomarker Discovery Using HRAM LC-MS-MS Approach on an Orbitrap Mass Spectrometer
Osama Abu-Nimreh
CMD Sales Support Specialist
MECEC , Dubai
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Metabolomics- Insights to Biology
Environment
Microbiome
DNA Genomics – 22,000 genes
Biological potential
RNA Transcriptomics – 100,000 transcripts
Response to conditions
Proteins Proteomics – 1,000,000 proteoforms
Biological Function
Metabolites
Lipids
Metabolomics >5,000 compounds
Lipidomics >30,000 species
Physiological state/phenotype
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Diverse Applications in Human Health and Disease Research
Cancer
Aging
Biomarker Discovery
Infectious Diseases
Inflammation/ Immunology
Cardiovascular
Personalized Medicine
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Biomarker Discovery Using Untargeted Metabolomics Profiling
Sample A1 Sample A2 Sample A3 Sample B1 Sample B2 Sample B3
Group A
Group B
• Study Design - ex: Normal vs. disease,
Different time course
• Sample Preparation and LC/MS Analysis - Extracting metabolites from samples
LC/MS/MS analysis
• Identify Unknown Metabolites - Propose name, chemical composition and
structure of unknown compounds
• Differential Analysis and Statistical
Analysis
- Detect metabolites of interests
(putative biomarkers)
Require High Coverage of a metabolome
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• Diversity in structures and physical chemical properties
- Require multiple technologies to capture a
metabolome
• Many isomeric and isobaric species
- Require high resolving power for correct ID
• Very low to very high concentrations
- Require High sensitivity and wide dynamic
range
• No single database to identify all unknown metabolites
- Require extensive library or fragment ion
prediction based on compound structure
Challenges for Untargeted Metabolomics
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Ultra high resolution, fast scan speed and good sensitivity
Excellent mass measurement accuracy and precision
High quality MS/MS spectrum (MSn capability)
Thermo Scientific™ Orbitrap™ Mass Spectrometer Portfolio
Thermo Scientific™ Q Exactive™ hybrid
quadrupole-Orbitrap mass spectrometer
Q Exactive HF
Thermo Scientific™ Orbitrap
Fusion™ Tribrid™ mass spectrometer
Orbitrap Fusion
Thermo Scientific™ Orbitrap™ Elite High-Field
Orbitrap Hybrid Mass Spectrometer
Thermo Scientific™ Orbitrap Fusion™
Lumos™ Tribrid™ Mass Spectrometer
Orbitrap Fusion Lumos
140,000@m/z 200
12 Hz @15,000 240,000@m/z 200
18 Hz @17,500
500,000@m/z 200
18 Hz @15,000
500,000@m/z 200
20 Hz @15,000
Q Exactive
Q Exactive Plus
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Orbitrap: Unmatched Resolution vs. QTOFs
0
50000
100000
150000
200000
250000
300000
350000
100 200 300 400 500 600 700 800 900 1000
Resolu
tion (
FW
HM
)
m/z
Q Exactive MS
Q Exactive HF MS
QTOFs
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Resolving Isobaric Metabolites with Orbitrap High Resolution
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PositiveMode20130518092831 #539 RT: 1.44 AV: 1 NL:T: FTMS + p ESI Full ms [100.00-212.08]
151.05 151.06
m/z
0
20
40
60
80
100
Rel
ativ
e A
bund
ance
R=120k
PositiveMode20130518092831 #511 RT: 1.33 AV: 1 NL:T: FTMS + p ESI Full ms [100.00-212.08]
151.05 151.06
m/z
0
20
40
60
80
100
Rel
ativ
e A
bund
ance
R=60k
PositiveMode20130518092831 #615 RT: 1.97 AV: 1 NL:T: FTMS + p ESI Full ms [100.00-212.08]
151.05 151.06
m/z
0
20
40
60
80
100
Rel
ativ
e A
bund
ance
R=240k
PositiveMode20130518092831 #237 RT: 0.51 AV: 1 NL:T: FTMS + p ESI Full ms [100.00-212.08]
151.05 151.06
m/z
0
20
40
60
80
100
Rel
ativ
e A
bund
ance
R=30k
High Resolution is Essential for Fine Isotopic Pattern Determination & Isotopic Labeling Experiments
• L-Methionine C5H11NO2S (+ mode)
PositiveMode20130518092831 #656 RT: 2.64 AV: 1 NL: 1.48E8T: FTMS + p ESI Full ms [100.00-212.08]
150.0 150.5 151.0 151.5 152.0
m/z
0
10
20
30
40
50
60
70
80
90
100
Rel
ativ
e A
bund
ance
150.0584R=627006
151.0618R=621502
152.0542R=601802
PositiveMode20130518092831 #654 RT: 2.60 AV: 1 NL:T: FTMS + p ESI Full ms [100.00-212.08]
151.05 151.06
m/z
0
20
40
60
80
100
Rel
ativ
e A
bund
ance
R=500k
0.7ppm
C5H11NO2S +H: C5 H12 N1 O2 S1 p(gss, s/p:40) Chrg 1 ...
150.0 150.5 151.0 151.5 152.0
m/z
0
10
20
30
40
50
60
70
80
90
100
Rel
ativ
e A
bund
ance
150.0583R=499553
151.0617R=499368
152.0541R=499443
R=500,000
0.0ppm
C5H11NO2S +H: C5 H12 N1 O2 S1 p(gss, s/p:40) Chrg 1 ...
151.050 151.055 151.060 151.065
m/z
0
20
40
60
80
100R
elat
ive
Abu
ndan
ce
151.0617
C413CH12O2NS
151.0625
C5H12O17ONS
151.0646
C5H112HO2NS
151.0577
C5H12O2N33S
151.0554
C5H12O215NS
Observed
Simulated
A0
A1 A2
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Resolving Isotopically Labeled Peaks with Orbitrap High Resolution
ASMS poster, M490
Anastasia Kalli et. al.
Only Orbitrap MS Can
Confidently Track
Isotopologues of
Metabolites in Complex
Samples for Quantitative
Flux Analysis
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Orbitrap: Unmatched Mass Accuracy vs. QTOFs
-10
-5
0
5
10
-10
-5
0
5
10
Scan
Err
or
(pp
m)
Scan
Err
or
(pp
m)
Q ToF
Error Range = 17.65 ppm !
Q Exactive
Error Range = 1.55 ppm
12 scans 12 scans
Data From Bristol-Myers Squibb Company
Accuracy and stability of mass measurement are crucial for metabolite identification
QTOF MS: Company X
Orbitrap MS: Q Exactive LC-MS
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Orbitrap: Unmatched Sensitivity vs. QTOFs
Amino Acid Full Scan LOQ
Data courtesy from Stanford University
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Orbitrap: Triple Quadrupole MS Comparable Sensitivity and Linear Dynamic Range
R2 = 0.9962
0.2–20000 pg spiked in plasma
(1 fmol–100 pmol on column)
QE HF
Quantitation of Citric Acid achieved at low fmol sensitivity using full scan MS
Five Orders of Magnitude Linear Dynamic
Range
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Quantitation of 17:0-14:1 PC achieved at attomole sensitivity using targeted MS/MS method
Orbitrap: Triple Quadrupole MS Comparable Sensitivity and Linear Dynamic Range
17:0-14:1 PC spiked into 500 ng Bovine
Heart Lipid Extract
0.02 pg – 2000 pg on column
QE HF
Log (Area)
Amount, pg
Five Orders of Magnitude Linear
Dynamic Range
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Thermo Scientific™ Compound Discoverer 2.0™
Complete small molecule structure identification in a Next Generation platform.
Compound Discoverer 2.0™ focuses on workflows for Discovery Metabolomics: Fundamental Research, Biomarker Discovery, Pharma, Environmental Research, Forensics, Foodomics, etc.
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Compound Discoverer 2.0: Destination Unknowns
Unknown Analysis
Identification
Statistics
mzCloud
ChemSpider
User Database{
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What is mzCloud ? www.mzCloud.org
• Cloud-based HRAM MSn
spectral libraries of small
molecules
• Base on Mass Frontier
technology
• Search, Compare, Annotate
Growing Every Day!
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Streamlined Untargeted Metabolomics Workflow Using Orbitrap MS and Compound Disocverer 2.0
Collect Urine, Plasma
Cell, Tissue etc…
Hypothesis,
Candidate
Biomarkers
Statistical analysis Trends Pathways
Collect HR LC/MS
profiles
Lists of
components Data processing
Metabolites
t-MS/MS
Ready for injection
samples
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Case Study: Discovery Diabetes Marker of Zucker Rat Plasma Using LC-HRAM Orbitrap MS
Obese (Zucker) vs. Lean
Rat
Lean Obese (Zucker)
Sample 1 Sample 2 Sample 3 Sample 1 Sample 2 Sample 3
Pos/Neg ion ESI
Vaporizer temp = 450°C
MS Resolution = 140K
Mass range: 67-1000 Da
Run Time = 15 min
Top5 dd-MS2 at 17.5K Resolution
Hypersil GOLD™ HPLC columns
150 x 2.1 mm, 1.9 μm
Column temp.: 55 oC
Injection vol: 5 µL
Mobile phase:
A = 0.1% formic acid in H2O,
B = 0.1% formic acid in MeOH
Flow rate: 0.45 mL/min
UltiMate™ 3000
Q Exactive™ Plus
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Identifying Unknowns: mzCloud
Automatically assign best MS and MS/MS spectra
in your dataset based on RT, intensity, adduct
Identifying Unknowns: mzCloud
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Compound name and structure
Spectrum match
Raw file
Reference (mzCloud)
Identifying Unknowns: mzCloud
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Reproducible High Quality LC-MS Data For Label Free Differential Analysis
[M+H]+
0.54 ppm
[M+Na]+
0.43 ppm (A)
(B)
(C)
QC with IS = d5-hippuric acid C9H4D5NO3
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Compound Discoverer Results Review of Interested Metabolite
(A) Compound of interest
(B) XIC (E) isotope match
(C) Predicted formula
(F) Trend
(D) MS spectra
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Advanced Features and Functionalities
Checked (checked compounds will be
carried through all analysis)
(G) Filter panel (H) Volcano plot
178.07244 232.08295
137.04582
119.03501
212.01514 225.0546656.96513 114.11062
243.06592137.04587
250.09351
287.05444
304.02682
348.02307
331.04495
97.02841
97.02835348.07001
348.07036
136.06183
136.06177
50 100 150 200 250 300 350
m/z
-6
-4
-2
0
2
4
6
Inte
nsity
[co
un
ts] (1
0^6
)
RAWFILE(top): pooled_top2_Ex6s_100ms_1E4, #701, RT=1.111 min, FTMS (+), MS2 (HCD, DDF, 348.07@31.67, z=+1) REFERENCE(bottom): mzCloud library C10 H14 N5 O7 P Adenosine 5'-monophosphate FTMS (+) MS2 (HCD 348.07@20.00)
mzCloud Library entry
query entry
(I) (J) (K) Automated
identification pathways mapping
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Finding Potential Markers With Less Effort
(A) Fatty Lean Ratio
(B) p-value
150 compounds were automatically assigned with
mzCloud library through MS/MS match
16 metabolites show significant changes (p-
value<0.05, fold change >1.5)
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Examples – Adenosine 5’-monophosphate (AMP)
178.07244 232.08295
137.04582
119.03501
212.01514 225.0546656.96513 114.11062
243.06592137.04587
250.09351
287.05444
304.02682
348.02307
331.04495
97.02841
97.02835348.07001
348.07036
136.06183
136.06177
50 100 150 200 250 300 350
m/z
-6
-4
-2
0
2
4
6
Inte
nsity
[co
un
ts] (1
0^6
) RAWFILE(top): pooled_top2_Ex6s_100ms_1E4, #701, RT=1.111 min, FTMS (+), MS2 (HCD, DDF, 348.07@31.67, z=+1) REFERENCE(bottom): mzCloud library C10 H14 N5 O7 P Adenosine 5'-monophosphate FTMS (+) MS2 (HCD 348.07@20.00)
Fatty Lean
Box-Plot
mzCloud Reference
Query (Raw data)
AMP
Fold change 7.5
P value 5.8e-6
MS/MS spectra match of raw data vs. mzCloud
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An Overview Of All Detected Metabolites With Biological Relevance
AMP related metabolites
mapping in the pathway
All Metabolic pathways that had
been hit in the analysis
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Fa
tty
Le
an
Groups
3.5
4.0
4.5
5.0
5.5
Are
a (
10
^6)
Acylcarnitine Subclass Were Upregulated In Obese Zucker Rats
Fa
tty
Le
an
Groups
12
14
16
18
20
22
Are
a (
10
^6)
Propionylcarnitine Palmitoylcarnitine
Fa
tty
Le
an
Groups
1.6
1.8
2.0
2.2
2.4
2.6
2.8
3.0
Are
a (
10
^6)
Hexanoylcarnitine
Fa
tty
Le
an
Groups
300
400
500
600
700
800
900
Are
a (
10
^3)
DL-Carnitine Acetyl-L-carnitine
Fa
tty
Le
an
Groups
6
7
8
9
10
11
12
13
14
15
Are
a (
10
^6)
Mihalik S.J.; Obesity (Silver Spring). 2010 Sep;18(9):1695-700
mzCloud Results
Elevated > 2-fold
In Fatty rat plasma
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Summary: The Exciting Scientific Metabolomics Solutions We Offer
Metabolomics
challenges
Compound Discoverer 2.0 mzCloudTM
GC/Q Exactive MS
Ion Chromatography/QE
OrbitrapTM Technology
Multi-omics Integration and Pathway partner
Wang, J. Anal. Chem. 2015, 87, 6371
Wang, J. Anal. Chem. 2014, 86, 5116
Peterson, A. Anal. Chem., 2014, 86, 10036
Peterson, A. Anal. Chem., 2014, 86, 10044 GC IC
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Acknowledgements
Junhua Wang
Ralf Tautenhahn
Tim J. Stratton
Caroline Ding
David A. Peake
Reiko Kiyonami
Mark Sanders
Julian Saba
Andreas Huhmer
Ken Miller
Dr. Robert Mistrik
The mzCloud Team
Dr. Kévin Contrepois
School of Medicine -
Department of Genetics
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The world leader in serving science