Metabolomics Batch Data Analysis
Workﬂow to Characterize Differential
Metabolites in Bacteria

Application NoteAuthors

Abstract

Yuqin Dai and Steven M. Fischer

An accurate mass Q-TOF LC/MS workﬂow for discovery metabolomics was used

Agilent Technologies, Inc.

to study a bacterium in early and late stationary phases. The use of software

Santa Clara, CA, USA

that can process data in batch-mode made the data analysis more efﬁcient andautomated. A total of 488 features from the positive ion data and 623 featuresfrom the negative ion data were found using Agilent MassHunter Proﬁnder (batchfeature extraction software). Statistical analyses using Mass Proﬁler Professional(MPP) revealed features whose abundance differed signiﬁcantly in early stationary
phase versus late stationary phase. From the positive ion data, 57 features were
found to have higher abundances in the early stationary phase than in the latestationary phase. While in the negative ion data, 52 features exhibited higher
abundances in the early stationary phase compared to the late stationary phase.
To understand the biological and biochemical context of the metabolomics
data, we annotated and identiﬁed over 100 differential features using databasesearching, accurate mass MS/MS library matching, and MS/MS molecular
structure correlation.

Introduction
The growth of bacteria has been studied
for more than a century, including the
now classical deﬁnition of the phasesof growth identiﬁed by Jacob Monod1.
It was found that cells remain alive
and metabolically active during the latestationary phase, but often responddifferently to stimuli or stresses during
the early part of stationary phasecompared to the late stationary phase.
Researchers hypothesize that one ormore metabolites produced during thestationary phase may render a bacterium
less viable in late stationary phase or,alternatively, that a protective metabolite
may be lost during the progression ofstationary phase. To our knowledge, there
is no information available about thecharacteristics of those metabolites.
Here we present a Q-TOF LC/MS
metabolomics approach to investigate
differential metabolites of a bacteriumin the early stationary versus latestationary phase. The Agilent suiteof data processing software makes
feature ﬁnding, statistical analysis, andidentiﬁcation easier. This enables rapidtransformation of complex raw data
into biologically relevant metabolite
information.

Experimental

For LC/MS analysis, the sample
was resuspended in 500 µL ofwater/acetonitrile (3:7 v/v) solvent,then vortexed and centrifuged. The
supernatant was transferred from an
Eppendorf tube to a clean LC/MS vial.

Dynamic mass axis calibration wasachieved by continuous infusion ofa reference mass solution using theisocratic pump. Tables 1 and 2 summarize
the optimized LC and MS conditions.

Data analysis software
Instrumentation•LC/MS Analysis was performed using an
Agilent 1290 Inﬁnity LC system coupled
to either an Agilent 6230 TOF system oran Agilent 6550 iFunnel Q-TOF system.
The LC system consisted of the following
modules:

Agilent MassHunter Qualitative
Analysis (Qual) B.07.00•

Agilent MassHunter Proﬁnder
B.06.00, service pack 1•

Agilent 1290 Inﬁnity Binary Pump
(G4220A)

Agilent Mass Proﬁler Professional(MPP) B.13.0•

Agilent 1290 Inﬁnity Autosampler
with a thermostat (G4226A)

Agilent MassHunter Molecular
Structure Correlator (MSC) B.07.00•Pathway to PCDL B.07.00•••

Agilent 1290 Inﬁnity ThermostattedColumn Compartment (G1316C)•

Agilent 1100 Series Isocratic
Pump (G1310) with a 100:1 splitter(p/n G1607-60000)

Table 1. LC parameters.
Parameter

Agilent 1290 Inﬁnity LC system

Analytical column

Cogent Diamond Hydride HPLC Column, 2.1 × 150 mm, 4 µm, 100 A(p/n 70000-15P-2), Microsolv Technology Corporation

Guard column

Agilent ZORBAX SB-C8, 2.1 × 30 mm, 3.5 µm (p/n 827700-936)

Sample preparationColumn temperature

60 °C

Cell cultures were harvested either inearly or late stationary phase, rinsed
twice with phosphate-buffered saline toremove any residual extracellular media,
and then rinsed with water to remove
the buffer. The suspended cells werethen centrifuged to form a pellet and thewater was decanted. To the cell pellets
was added 0.5 mL of MeOH at –80 °C
and then 1.5 mL of 50 % MeOH/H2O
at 4 °C. The cells were mechanicallylysed, and the crude extract was spun at
48,000 g for 1 hour, then the supernatant
was collected. An aliquot of 500 µLof supernatant was added to a 3 kDaNanosep ﬁlter and spun at 14,000 g, theﬁltrate was collected, vacuum dried, andheld at ≤ –60 °C until analysis.

Injection volume

5 µL for proﬁling experiment on the Agilent 6230 TOF
10 µL for identiﬁcation experiment on the Agilent 6550 Q-TOF

Autosampler temperature 4 °C
Mobile phase forpositive ion modeA) 50 % water/50 % isopropanol containing 0.1 % formic acidB) 3 % water/97 % acetonitrile containing 0.1 % formic acid

Mobile phase fornegative ion modeA) 50 % water/50 % isopropanol/0.025 % formic acid and 5 µM EDTAB) 10 % water/90 % acetonitrile/5 mM ammonium formate/5 µM EDTA,
pH=7Flow rate

0.6 mL/min

Gradient for
positive ion mode

0–1 minutes 97 %B
1–15 minutes 97 %B to 10 %B

Gradient for
negative ion mode

0–1 minutes 99 %B
1–15 minutes 99 %B to 20 %B

Stop time

15 minutes

Post time

5 minutes

2

LC/MS metabolomics workﬂow
A high-resolution Q-TOF LC/MS
metabolomics workﬂow (Figure 1) was
developed for unbiased metabolomics
study of a bacterium in the earlystationary and late stationary phase.
The workﬂow was as follows:
1.

2.

3.
4.

5.

Acquire data from extracts in TOF
mode in positive and negative ion
modes, respectively.

Table 2. MS parameters.
Parameter

Agilent 6230 TOF system

Ion mode

Positive and NegativeSource

Agilent Dual ESI

Capillary voltage

3,500 V (±)

Dry gas temperature

350 °CDry gas ﬂow

10 L/min

Nebulizer pressure

45 psi

Fragmentor

100 V

MS range

25–1,600 m/z

Find features from raw data using
the Agilent MassHunter Proﬁnder2
Batch Recursive Feature Extraction
algorithm.

MS acquisition rate

2 spectra/sec

Reference massPositive ion mode: 64.01577 and 922.009798
Negative ion mode: 68.9957 and 966.0007

Reference pump ﬂow

0.5 mL/min (positive and negative ion modes)

Perform differential analysis andannotation using MPP.

Reference delivery

Agilent 1100 isocratic pump with 100:1 splitter (p/n G1607-60000)

Instrument mode

Extended dynamic range (2 GHz)

Parameter

Agilent 6550 Q-TOF system

Ion mode

Positive and NegativeSource

Agilent Dual ESI

Conduct targeted MS/MS analysisof the differential features of interest
using Q-TOF LC/MS.
Use Qualitative Analysis formetabolite identiﬁcation based on
MS/MS spectra matches or use
Molecular Structure Correlator(MSC) software to aid structural
elucidation of putative metabolites.

Capillary voltage

3,500 V (±)

Dry gas temperature

200 °CDry gas ﬂow

15 L/min

Nebulizer pressure

45 psiMS range

50–1,700 m/z

MS acquisition rate

3 spectra/sec

MS/MS range

25–1,700 m/z

MS/MS
acquisition rate

2 spectra/sec, Targeted MS/MS

Isolation width

Medium (~4 m/z)

Collision energy

10–40 ev

Reference mass

Positive ion mode: 64.01577 and 922.009798Negative ion mode: 68.9957 and 1033.9881

Reference pump ﬂow

0.6 mL/min (positive ion mode)
0.5 mL/min (negative ion mode)

Reference delivery

Agilent 1100 Isocratic Pump with 100:1 splitter (p/n G1607-60000)

Instrument mode

Extended dynamic range (2 GHz)

Differential
analysis (MPP)MS dataacquisition for
proﬁling (TOF)

1

2

2

3Feature
extraction(Proﬁnder)ID(Qual and MSC)

45MS/MS dataacquisition for
ID (Q-TOF)

Figure 1. LC/MS metabolomics workﬂow for proﬁling and identiﬁcation of bacterium metabolites.

3

Metabolite proﬁling by TOF LC/MSIn principle, either a TOF or a Q-TOF
can be used for metabolite proﬁling. Ifa laboratory only has one instrument, a
Q-TOF is the preferred instrument due
to its capability to perform both proﬁling
and MS/MS analysis for identiﬁcation.
However, if laboratory sample volume
requires multiple instruments, acombination of TOF and Q-TOF
instrumentation is more cost-effective.
For this study, we demonstrate the use of6230 TOF for proﬁling and 6550 Q-TOF foridentiﬁcation.
The difference in the metabolome ofa bacterium in the early versus latestationary phase was assessed by
performing metabolite proﬁling on cell
extracts of 10 separate cultures from theearly stationary phase and 9 cultures fromthe late stationary phase. A 6230 TOF
LC/MS was used to proﬁle these samples
in positive and negative ion modes.
Chromatographic separation used anaqueous normal phase (ANP) column
to maximize polar metabolite coverage.
Figure 2 shows the metabolite proﬁling
results of the cell extracts obtained in thenegative ion mode. An overlay of 38 totalion chromatograms (TICs) from 19 cell
extracts shows minimal retention time
variation across the chromatograms. The
reproducibility of retention time is crucial
in discovery metabolomics studies,because it is used for peak matchingacross different samples.

Batch data feature extraction using
Agilent MassHunter Proﬁnder
One of the bottlenecks in metabolomics
data analysis is the time-consuming
feature extraction from large amounts ofcomplex raw data. MassHunter Proﬁnder
Software was designed to increase
throughput using automated batch dataprocessing. In this study, Proﬁnder found488 features from the positive ion data,and 623 features from the negative ion
data (Figure 3) based on the user-deﬁned
compound ﬁltering criteria and manualcuration. The resulting features weresubsequently imported into MPP (B.13.0)
for differential analysis.

×10 7
1.7
1.6
1.5
1.4
1.3
1.2
CountsResults and Discussion

1.1
1.0
0.9
0.8
0.7
0.6
0.5
0.4
0.3
12

3

45

7
68
9
Acquisition time (min)1011

121314

Figure 2. Thirty-eight overlaid total ion chromatograms (TICs) from 19 cell extracts in ESI negative ion
mode. Each sample was injected twice.

Total 623 features found
Early
Stationary

Late
StationaryFigure 3. Untargeted feature extraction result of the negative ion MS data from the cell extracts in theearly and late stationary phase using the Proﬁnder Batch Recursive Feature Extraction algorithm.

4Differential analysis andannotation using MPP

To understand the biological andbiochemical context of the metabolomics
data, we performed annotation for thosedifferential features using the ID browser
tool in MPP and accurate mass databasematches with 10 ppm mass tolerance.
The three databases that were queried
were Agilent-Metlin, KEGG, and BioCyc.
Both the KEGG and BioCyc databases
were created using Agilent Pathway toPCDL software. Figure 5 shows the IDbrowser result for a set of differential
features acquired in the negative ion
mode. By clicking a compound, in thiscase L-Leucine, in the compound list
table, the corresponding MS spectrum
results, detailed information of isotopic
peaks, and chemical structure aredisplayed. Users can quickly review andrank hits based on parameters such as
accurate mass, isotopic pattern, retention
time, and match scores to select the best
hit for annotation.

3-Log10(corrected Pvalue)

To determine the differential featuresof a bacterium in the early versus latestationary phase, the resulting features
were evaluated by MPP using the Filter
on Volcano Plot algorithm with a cutoff of
P < 0.005 and fold change (FC) > 2. The
statistical results yielded features whose
abundance differed signiﬁcantly in theearly stationary versus late stationary
phase. For the positive ion data, 98 of
the 488 features displayed statisticalsigniﬁcance, and 57 of them were foundto have higher abundances in the earlystationary phase compared to the latestationary phase. Likewise, we detected
152 of 623 features that were signiﬁcantly
differential in the negative ion data(Figure 4), and 52 of them had higher
abundance in the early stationary thanin the late stationary phase (blue solid
square dots).

42

1-4-20Log2(fold change)

24

Figure 4. Volcano plot of the features in the early versus the late stationary phase (negative ion data).
Red dots represents those features whose abundances are lower in the early stationary phase than inthe late stationary phase (P < 0.005, FC > 2). Blue dots represents those features whose abundances arehigher in the early stationary phase than in the late stationary phase (P < 0.005, FC > 2).

12

3

4Figure 5. ID browser result for a set of differential features detected in the negative ion mode. (1) MS
spectrum result, (2) detail information of isotopic peak, (3) structure of the annotated compound,and (4) compound list.

5Compound identiﬁcation andsoftware-assisted structure
elucidation
To validate the initial annotations,targeted MS/MS experiments wereperformed for those differential featuresof interest using a 6550 Q-TOF LC/MS.
Compound identiﬁcation and structure
elucidation were achieved using twoapproaches. First, LC/MS/MS data wereprocessed using the Find by Targeted
MSMS tool in the MassHunter Qualitative
Analysis software. The MS/MS spectrum
generated was then compared tothe Agilent-Metlin metabolite library
containing accurate mass MS/MSinformation. Figures 6 and 7 showed adifferential feature that was identiﬁed
as nicotinic acid with high conﬁdence.
The mirror image plot (middle) showed
excellent matches of the acquired sample
spectrum (top) with the library standard
spectrum (bottom) for both m/z values
and abundances of the precursor andfragment ions with reverse match scoresof 97 for the positive ion data and 99 forthe negative ion data.
When an acquired MS/MS spectrum hadpoor or no matches to the Agilent-Metlin
MS/MS library, the other approach using
MSC software was employed. MSC3
proposes theoretical fragment masses
from proposed molecular structuresby using a systematic bond-breaking
approach. It then correlates thosetheoretical fragment masses with theobserved accurate mass fragment
ions for a compound of interest. MSC
calculates an overall correlation score(goodness-of-ﬁt) from the individual
score for each fragment ion; one or
multiple substructure candidates may besuggested.

A Sample MS/MS spectrum

Nicotinic acid

B Mirror image plot

C Library MS/MS spectrum

Figure 6. Compound structure identiﬁcation using MS/MS spectra matching with a reverse match scoreof 97 for nicotinic acid in positive ESI mode. The reverse score reﬂects ion matches for those peaks fromthe library standards that are reﬂected in the spectra acquired from the sample. (A) sample MS/MSspectrum, (B) mirror image plot, (C) library MS/MS spectrum. Collision energy (CE) of 20 eV was used toacquire MS/MS spectra.

A Sample MS/MS spectrum

B Mirror image plotOOH

N
Nicotinic acid

C Library MS/MS spectrum

Figure 7. Compound structure identiﬁcation using the MS/MS spectra matching with a reverse matchscore of 99 for nicotinic acid in negative ESI mode. The reverse score reﬂects ion matches for thosepeaks from the library standards that are reﬂected in the spectra acquired from the sample. (A) sample
MS/MS spectrum, (B) mirror image plot, (C) library MS/MS spectrum. Collision energy (CE) of 10 eV wasused to acquire MS/MS spectra.

6Multiple data ﬁles were batch processedin MSC B.07.00 by searching themagainst the Agilent-Metlin, the Pathway
to PCDL generated KEGG and Biocyc, andweb-based PubChem and ChemSpider
databases. The ability to search multiple
databases increases the chance of ﬁnding
structure hits, but it comes with the costof decreasing selectivity. This is shown inFigures 8 and 9, where the Agilent-Metlin
database was searched ﬁrst, and threecompounds at m/z 187.1114, 203.1041,
and 307.1512 got structure hits (Figure 8).
For the m/z 187.1114 ion, nine structures
were found. By querying the PubChem
database using the same MSC ﬁltering
criteria, structure hits were returned
for all six compounds (Figure 9) and300 structures found for the m/z 187.1114
ion. The structure interpretation of theMS/MS spectrum for the m/z 187.1114
ion is shown in Figure 10. All fragment
ions were labelled with the mostplausible fragment structure proposed by
the MSC. Using this approach, we wereable to quickly interpret the resulting
MS/MS spectra to support annotation for
those signiﬁcant features.

Figure 8. MSC results obtained by searching the Agilent-Metlin database.

Figure 9. MSC results obtained by searching the web-based PubChem database.

Glycyl-L-leucine

Figure 10. Structure interpretation of MS/MS spectrum for m/z 187.1088 ion using MSC andAgilent-Metlin database.

7

Conclusions

References

An easy-to-use Q-TOF LC/MS based
workﬂow is presented for a discoverymetabolomics study of a bacterium. TheAgilent software tools (Proﬁnder, MPP,and MSC) enabled high efﬁciency andhigh quality feature extraction, statisticalanalysis, annotation, and identiﬁcation.
The results from this study revealed
interesting bacterium metabolic variationsin the early versus late stationary phase.

1. Monod, J. The Growth of Bacterial
Cultures. Annual Review ofMicrobiology 1949, 3, pp 371-394.
2. MassHunter Proﬁnder: Batch
Processing Software for High
Quality Feature Extraction ofMass Spectrometry Data, Agilent
Technologies Technical Overview,
publication number 5991-3947EN,
2014.
3. S. Joseph, Y. Dai, Pharmaceutical
Impurity Identiﬁcation and Proﬁling
Using Agilent Q-TOF LC/MS Combinedwith Advanced MassHunter Data
Processing Software, Agilent
Technologies Application Note,publication number 5991-1375EN,
2012.

www.agilent.com/chem
This information is subject to change without notice.
© Agilent Technologies, Inc., 2015
Published in the USA, April 28, 2015
5991-5706EN