Figure 5. A streamlined workflow for profiling of the diabetic metabolome generated
by LC-Q Exactive Focus HRAM MS.
Finding the diabetes metabolite markers quickly by Compound Discoverer
software, the streamlined workflow
1) Based on the filtering on p-value and fold change, a list of compounds with
statistically significant changes are obtained (Figure 5 A-C).
2) Filtering on the mzCloud match score (e.g., >80) the compounds with high
confidence identification can be obtained (Figure 5 E).
3) Further filtering can be applied, e.g., number of KEGG pathways, which further
refines the results and links the compounds to biological pathways. AMP involves
extensively in 19 different pathways. Purine metabolism is shown here, related
compounds in the same pathway are highlighted (Figure 5 F,G).
4) Reviewing the interesting metabolite XIC, trend, isotopes, e.g., Adenosine 5'-
monophosphate (AMP) here, is highly expressed in Fatty rats (Figure 5 H-K).
ABSTRACT Metabolomics is a rapidly growing field of post-genomic biology, aiming to comprehensively
characterize the small molecules in biological systems. Here we present a workflow using a
RP-UHPLC/benchtop Quadrupole Orbitrap MS (Thermo Scientific™ Q Exactive™ Focus MS)
and a new software suite for data processing, results visualization and automated metabolite
identification for untargeted metabolomic profiling of plasma for discovery of metabolite
markers from Zucker Diabetic Fatty (ZDF) rats.
We explored different settings for acquiring tandem MS based on the top 2 experiment for the
Q Exactive Focus. The combined coverage of the metabolite identification based on MS/MS
spectral match to mzCloud™ (www.mzCloud.org) was comparable to a single top 10 or 5
experiment result on a standard Thermo Scientific™ Q Exactive™ MS. Significantly changing
metabolites were demonstrated and visualized based on statistical analysis; metabolites were
mapped to pathway and automatically identified by mzCloud using Thermo Scientific™
Compound Discoverer ™ 2.0 software. Two phenotypes of the rat (ZDF vs. lean wild type)
showed significant difference according to principal component analysis (PCA) and potential
metabolite markers were reported.
INTRODUCTION Here we present a workflow using a RP-UHPLC / benchtop Quadrupole Orbitrap MS (Q
Exactive Focus MS) and a new software suite for data processing, results visualization and
automated metabolite identification for untargeted metabolomics profiling of plasma samples.
We explored different settings for acquiring tandem MS2 based on top 2 experiment of the Q
Exactive Focus instrument. The combined coverage of metabolite identification based on
MS/MS spectral match to mzCloud was comparable to a single top 10 or 5 experiment from a
classic Q Exactive MS. Metabolites were reviewed by statistical analysis and mapped to
pathway and identified via mzCloud using Compound Discoverer 2.0 software. The potential
metabolite markers are reported for the two phenotypes of rat (ZDF vs. lean).
MATERIALS AND METHODS Zucker Rat plasma and Sample Preparation
Rat plasma was purchased from Bioreclamationivt (Westbury, NY). It was recovered from
whole blood of Zucker Lean (3 lots) and Zucker Fatty (3 lots) using EDTA as anti-coagulant by
Bioreclamationivt.
Plasma samples were deproteinized with 3-fold of organic solvent methanol (MeOH).
Endogenous metabolites were reconstituted in methanol/water (1:9) containing isotopically
labeled internal standard (IS), d5-hippuric acid at 5 µM for LC-MS analysis. Solvent blank,
pooled QC, and biological samples were injected in arranged order.
Liquid Chromatography
UHPLC separation was conducted on a Thermo Scientific™ Dionex™ UltiMate™ 3000 HPG
(high-pressure gradient) pump using Thermo Scientific™ Hypersil GOLD™ C18 column
1.9µm,150 x 2.1mm (P/N 25002-152130) at 450 μL/min, column temperature at 55 °C.
Applied linear gradient from 0.5–50% B for 5.5 min, followed by increasing to 98% at 6 min,
hold 98% B for 6 min, then decrease to 0.5% at 13 min, then equilibrate for another 2 min.
Mass Spectrometry
The Q Exactive Focus mass spectrometer was operated under electrospray ionization (H-ESI
II) positive mode. Full scan (m/z 67–1000) used resolution 70,000 (FWHM) at m/z 200, with
automatic gain control (AGC) target of 1×106 ions and a maximum ion injection time (IT) of
50 ms. Data-dependent MS/MS were acquired on a “Top2” data-dependent mode using the
following parameters: resolution 17,500; AGC 1×105 ions; maximum IT 50 ms; 1.5 amu
isolation window; combined NCE 15%, 35% and 50%; underfill ratio 1.0% (3e4); different
dynamic exclusion time 2, 3, 4, 5, and 6 s were explored.
CONCLUSIONS I.A powerful and affordable HRAM MS (Q Exactive Focus mass spectrometer) was applied for the pilot
study of diabetic metabolome. Excellent mass spectra data were produced, accommodating a wide
concentration dynamic range.
II.Highly reproducible LC-MS data were generated using Ultimate 3000 UHPLC and the Q Exactive
Focus MS, enabling high confidence metabolomics study.
III.Compounder Discoverer 2.0 software provides a streamlined workflow for metabolite markers
discovery and identification in an automated and confident fashion.
IV.mzCloud (www.mzcloud.com) provides a new powerful way for metabolite identification
V.The combination of multiple Top 2 ddMS2 runs generated MS2 spectra and IDs comparable to a
typical Top5 or Top10 ddMS2 using a classic Q Exactive MS
VI.This work was conducted using Top 2 ddMS2 mode, which was before the Top3 MS/MS capability
was enabled on a Q Exactive Focus instrument. So we would expect the Top3 will add more value to Q
Exactive Focus MS and make it an affordable HRAM Orbitrap for metabolomics researchers.
REFERENCES 1. Obesity (Silver Spring). 2010 Sep;18(9):1695-700
TRADEMARKS/LICENSING © 2016 Thermo Fisher Scientific Inc. All rights reserved. All trademarks are the property of Thermo
Fisher Scientific and its subsidiaries. This information is not intended to encourage use of these
products in any manner that might infringe the intellectual property rights of others.
Diabetes Markers: Using Orbitrap HRAM and a New Workflow for Differential Analysis of Zucker Rat Plasma Metabolome
Key potential diabetes metabolite markers
Figure 6A shows a table of the assigned metabolites. 160 compounds were assigned by
matching to mzCloud using ddMS2. Among the high scores (90) list, 16 metabolites show
significant changes (p-value<0.05, FC >1.5 (6B)). Among those assigned metabolites,
the acyl carnitine subclass including propionylcarnitine, palmitoylcarnitine,
hexanoylcarnitine, L-carnitine, acetyl-L-carnitine were all observed to be elevated in Fatty
rat plasma by > 2-fold. This agrees perfectly with report that plasma acylcarnitines levels
increased in obesity and type 2 diabetes [1]. They also show the same trend as AMP, but
further explanation is needed.
Junhua Wang1, Maciej Bromirski2, Ralf Tautenhahn1, David Peake1, Reiko Kiyonami1, Tina Settineri1, Ken Miller1
Thermo Fisher Scientific, San Jose, CA, USA; 2Thermo Fisher Scientific, Bremen, Germany
Column Thermo Scientific TM Hypersil Gold TM C18, 150 x 2.1
mm, 1.9 µm
Mobile Phase A = 0.1% formic acid in H2O
B = 0.1% formic acid in MeOH
Flow 0.45 mL/min
Temp 55 C
Inj. Vol 5 µL
Source ionization parameters were: spray voltage, 3.8 kV; capillary temperature, 325 °C;
heater temperature 400 °C and S-Lens level, 55. A Q Exactive MS was used to collect top 5
and top 10 ddMS2 data for comparison.
Study Design, Workflow and Data Processing
As a pilot study, two different phenotypes with 3 different biological lots of rat plasma from Zucker
Lean (n=3) and Zucker Fatty (n=3) were used for differential analysis of the diabetes metabolome
(Figure 1 A).
HRAM MS FS data (Figure 1A) were collected using RP-UHPLC-QE Focus MS at 70k resolution at
scan speed 3.5 Hz. The typical peak width is 6s at baseline, warranting 12-20 scans across the peak.
Top 2 data dependent (dd)MS2 were collected using different dynamic exclusion settings (3s and 6s).
Compound Discoverer 2.0 software was used for differential analysis and automated compound
identification via mzCloud (www.mzcloud.org) all within one single workflow (Figure 1B). The putative
metabolites were automatically searched and mapped in KEGG pathway (http://www.kegg.jp/) within
the same workflow.
Results and Discussion
MS/MS data acquisition and global metabolites identification
A comparison of the top 10 and top 5 ddMS2 on Q Exactive MS with top 2 ddMS2 experiment on Q
Exactive Focus MS (Figure 2). All ddMS2 were acquired with 3s (one half peak width) dynamic
exclusion time. For the Q Exactive Focus instrument, an additional top 2 ddMS2 using 6s exclusion
and lower intensity threshold was conducted (Top2 x2 column). It shows that a single “Top 2” ddMS2
did partly miss the precursors compared to Top 10 and 5, however a combination of the two different
“Top 2” ddMS2 triggered as many precursors (2A). The numbers of identified metabolites using CD
2.0 and mzCloud are depicted in 2B, showing that the combined “Top2” ddMS2 provided the largest
number of IDs. These results demonstrate the great usability of Q Exactive Focus MS for global
metabolite profiling and identification if multiple runs were combined.
Resolving power 70,000 @ m/z 200
Mass range 50 to 2000 m/z
Scan rate Up to 12 Hz at resolution setting of 17,500 @ m/z 200
Mass accuracy Internal: <1 ppm RMS, External: <3 ppm RMS
Sensitivity Full MS: 500 fg buspirone on column S/N 100:1
SIM: 50 fg buspirone on column S/N 100:1
Linear Dynamic range >1,000,000
Polarity switching One full cycle in <1 sec (one full positive mode scan and one full negative mode
scan at a resolution setting of 35,000)
data-dependent MS/MS
acquisition Top 2 ions
Figure 1. Discovery Metabolomics workflow.
824 805
572
791
Top10 Top5 Top2 Top2 x2
132 122
102
137
Top10 Top5 Top2 Top2 x2
Figure 2. (A ) Number of triggered MS2 from unique precursor ions and (B) Number of
identified metabolites from mzCloud on the Q Exactive MS and Q Exactive Focus MS.
High Quality Mass Spectrometry Raw Data
Wide dynamic range: Illustrated by chromatographic peaks (Figure 3 A) and mass spectra
(3B), LC-MS analysis of plasma could often suffer from huge interfering ion peaks. In this
case, a putative tripeptide metabolite peak is hidden under 4000-fold higher EDTA (anti-
coagulant) peak. Figure 3C shows the MS intensity magnified by 2500x, indicating the ability
of detecting low abundance species in a complex sample. Thus, the Q Exactive Focus MS
provides a wide intra-spectra dynamic range.
HRAM data finds the real difference: The mass range around the tripeptide in Figure 3C,
magnified in 3D, showing the ion of interest is well resolved from interfering ions, highlighting
the power of HRAM to find the real components.
E:\CD\QE-Focus_ZDF\pooled 11/02/15 17:59:58
RT: 0.81 - 0.96 SM: 5G
0.81 0.82 0.83 0.84 0.85 0.86 0.87 0.88 0.89 0.90 0.91 0.92 0.93 0.94 0.95
Time (min)
0
20
40
60
80
100
Rela
tive A
bundance
0
20
40
60
80
100
Rela
tive A
bundance
0.89293.09720
0.92293.09705
0.88368.16400
NL:8.13E9
m/z= 293.09467-293.10053 F: FTMS + p ESI Full ms MS pooled
NL:2.20E6
m/z= 368.15934-368.16670 F: FTMS + p ESI Full ms MS pooled
pooled #169 RT: 0.88 AV: 1 NL: 7.43E9T: FTMS + p ESI Full ms [67.00-1000.00]
140 160 180 200 220 240 260 280 300 320 340 360 380 400 420 440 460 480
m/z
0
10
20
30
40
50
60
70
80
90
100
Rela
tive A
bundance
293.0972
315.0787
235.0919160.0602
138.0547
331.0437193.1544 258.1098 424.1662371.0747 391.0760276.1172
297.1073
pooled #169 RT: 0.88 AV: 1 NL: 7.43E9T: FTMS + p ESI Full ms [67.00-1000.00]
150 200 250 300 350 400 450
m/z
0.00
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
0.10
0.11
0.12
0.13
0.14
0.15
0.16
0.17
0.18
0.19
0.20
0.21
0.22
0.23
0.24
0.25
0.26
0.27
0.28
0.29
0.30
0.31
0.32
0.33
0.34
0.35
0.36
0.37
0.38
0.39
Re
lative
Ab
un
da
nce
331.0437
247.0918
193.1544
258.1098
424.1662
203.0523371.0747
346.0085
pooled #167 RT: 0.87 AV: 1 NL: 3.90E6T: FTMS + p ESI Full ms [67.00-1000.00]
364 366 368 370 372 374 376 378 380
m/z
0
5
10
15
20
25
30
35
40
45
50
55
60
65
70
75
80
85
90
95
100
105
110
115
120
125
130
135
140
145
150
155
160
165
170
175
Rela
tive
Ab
un
da
nce
367.9897R=54002
371.0748R=52102
368.9951R=66000
377.0509R=62500
365.1055R=44600
372.1013R=50600
Dynamic
~4000:1
Int. ~4000:1
8E9
2E6
x2500
EDTA
Ala-Met-Phe
367.9
897
368.1
640
371.0
747
edetate
Ala-Met-Phe
368
.16
40
293.0
972
Zoom-in
Zoom-in
Full Scan
Res= 70,000
XIC
MS1
(A)
(C) (D)
(B)
Figure 3. High resolution and accurate mass resolves the buried peaks.
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, [email protected], z=+1) REFERENCE(bottom): mzCloud library C10 H14 N5 O7 P Adenosine 5'-monophosphate FTMS (+) MS2 (HCD [email protected])
Checked (checked compounds will be
carried through all analysis)
mzCloud Library entry
query entry
(A) Filter panel (B) Volcano plot
(H) XIC (I) MS spectra (J) isotope match
(C) Compounds list
(D) formula
(K) Box Plot
(E) Automated ID (F) Pathways (G) Mapping
Figure 6. Assigned metabolites and interactive view in Volcano plot.
IS = d5-hippuric acid
C9H4D5NO3
[M+H]+
0.54 ppm
[M+Na]+
0.43 ppm
(A)
(B)
(C)
Reproducible LC-MS data
Quality control (QC) of the LC-MS runs is very important in label-free differential metabolic
analysis. In this study, we spiked d5-hippuric acid as internal standard to monitor the
variations of both LC-MS intensity and the RT shift.
Figure 4A shows the internal standard (d5-hippuric acid) peak alignment result returned
from Compound Discoverer 2.0 for >20 repeating injections of pooled QCs and individual rat
plasma samples. The CVs for different biological batches were less than 5%, and the RT
shifts were within 2 seconds (4B), The mass measurement is excellent (< 0.5 ppm) for both
protonated and sodiated ions grouped by Compound Discoverer 2.0 (4C).
Figure 4. Reproducible LC-MS data for label free differential analysis.
(A)
(B)
(A) (B)
Scan this QR for the MS2 of AMP
using the mzCloud app
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