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

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tive A

bundance

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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

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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

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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

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Ab

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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

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4

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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)

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