High-resolution mass spec
for metabolomic analysis
From lipidomics to cell bioenergetics, explore an
integrated, multi-omics approach to metabolism

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Mass spec metabolomics

Introduction
Since its first use to demonstrate theexistence of isotopes, the application ofmass spectrometry (MS) has expanded
immeasurably, and its practitioners are to befound in almost every analytical laboratory.
Driven by ever-increasing demand for greater
performance and speed, particularly for
analyzing complex biological samples, MS
technology has continued to improve and,through coupling with other technologies(chromatography and data processing
software), to yield the cutting-edge,
high-throughput workflows we see today.
In this eBook, we look at some of thewide-ranging applications of the latest MS
technology and workflows used to solve someof the most challenging analytical challenges
faced by biologists working in metabolic
profiling. In particular, we focus on Agilent
Technologies’ latest mass spectrometer,the 6546 LC/Q-TOF, an instrument designed
to provide wide dynamic range while
simultaneously providing high resolution
independent of acquisition speed. DiscoverContents•G aining higher confidence andthroughput in metabolite analysis• A multi-omics approach
to metabolism•E xtracellular flux analysis in
LPS-stimulated macrophages• Improving coverage of theplasma lipidome with Lipid
Annotator software•L ipid profiling workflow
demonstrates disrupted
lipogenesis in leukemia cells•F rom metabolomics to mechanisms
with mass spectrometry•F eatured products

how its integration in workflows can yield
information on metabolic networks, cellbioenergetics, lipid function and much more.

2

Mass spec metabolomics

Metabolomics
High-resolution MS is being applied to thechallenges presented by high-throughput
metabolomics to unravel the intricacies ofmetabolic networks:• Apply Agilent’s 6546 LC/Q-TOF toanalyze a complex biological matrix
with no prior separation for improved
sample throughput. In analyzing thecomplex metabolome of the bacterium
E. coli, Agilent’s technology permits moremetabolic features to be found, profiled
and annotated.
• Learn how the shift from needing tomeasure not only metabolite levels
but reaction rates or metabolic flux has
driven MS technology forward and now
allows biologists to gain insight into
what is happening inside cells undervarious challenges, be they mutations,drug treatments or chemical agents.
Refinement of Agilent’s 6546 LC/Q-TOF
technology has made the systemespecially well-suited for simultaneously
measuring low abundance peaks in thepresence of high-intensity signals.
• View a webinar detailing how to profile
large cohorts with tens of thousandsof samples and how to shift from data
generation to the functional interpretationof results, and exploring novel trends
in metabolomic and 13C metabolic
flux analysis.
Immunometabolism
Gaining access in real time to the bioenergetic
state of a cell is a genuine challenge thatcan provide valuable insight into cell
health and function. Discover a workflow

in which Agilent’s 6546 LC/Q-TOF with
MassHunter VistaFlux software (used toperform 13C stable isotope tracing analysis),is combined with Seahorse XF technology toelucidate the response of macrophages tolipopolysaccharide treatment. This workflow
can uncover the molecular mechanismsof cell metabolism in response to external
triggers or genetic manipulation.
Lipidomics
High-resolution MS finds multiple applications
in lipid profiling, in particular where thecomprehensive characterization of a large anddiverse set of lipid species is required thatspans a wide concentration with a biologicalsample. It allows researchers to overcome theshortcomings of earlier lipidomics techniques
which suffered from limitations in resolution
and reduced dynamic range. Discover:• Improved characterization of the plasma
lipidome by using iterative MS/MS dataacquisition combined with Agilent’s
MassHunter Lipid Annotator software
utilizing the 6546 LC/Q-TOF. Iterative
MS/MS data can be used by Agilent
Lipid Annotator software as part of acomprehensive lipidomics workflow.
•C haracterize lipid alterations in acute
myeloid leukemia cells subjected todifferent drug treatments, through theapplication of a comprehensive lipidomics
profiling workflow. Key to the workflow
is the integration of Agilent’s MassHunter
Lipid Annotator software with the 6546
LC/Q-TOF. This workflow was able toreveal significant differences in lipid classabundance not previously reported andwas more comprehensive than a traditional
shotgun-based lipidomics approach.

3

Mass spec metabolomics

Technical Overview

Agilent 6546 LC/Q-TOF: Gaining
Higher Confidence and Throughput in
Metabolite Analysis

Authors
Karen E. Yannell, PhD
Agilent Technologies, Inc.
Santa Clara, CA, USA
Christian Klein, PhD
Agilent Technologies, Inc.
Santa Clara, CA, USA
Nicola Zamboni, PhD
ETH Zurich
Zurich, Switzerland

Introduction
Metabolomics is constantly pushing the boundaries of complex analysis by mass
spectrometry (MS). For high-throughput profiling of large sample collections, thetrend is to accelerate chromatographic separation, and compensate the loss of
temporal resolution with increased mass resolution and accuracy. In the mostextreme form, chromatography is completely omitted to achieve thousands ofinjections per day with flow injection. Albeit powerful, high-throughput analysis
of complex biological extracts is analytically challenging because of the wide
dynamic range in concentration of metabolites in biological extracts. Whether or
not chromatography is used, mass spectrometers must provide high resolution
and mass accuracy over the full mass range, across many orders of dynamic range
within the same scan, and at high acquisition rates.
This Technical Overview describes the capabilities of the Agilent 6546 LC/Q-TOF.
The improved features of this instrument permit the analysis of a complex biologicalmatrix with no prior separation. E. coli metabolome samples were prepared byethanol extraction of bacteria growing exponentially on glucose minimal medium.
These samples were analyzed by flow injection (1.5 µL) using an Infinity II pumpequipped with restrictive capillary. The 6546 LC/Q-TOF was tuned in negativemode, and data were acquired at 1.5 Hz over a m/z 50 to 1,000 mass range. The
results show resolution >30,000 for analytes greater than m/z 118, improved
mass accuracy, large spectral dynamic range, high sensitivity, and isotopic fidelity.
Ultimately, this increased instrument performance allows more features to be found,profiled, and annotated in the E. coli sample.

4

Mass spec metabolomics

Resolution andmass accuracy

7

50,000

6

40,000530,000log10(ion counts)

60,000Resolution (FWHM)

20,0004
10,000
0

0

200

400
600
Mass-to-charge (m/z)×104157.0255

9

R = 36,000

8
7Counts65157.0506

4
3
2
1
0

×105
2.6
2.4
2.2
2.0
1.8
1.6
1.4
1.2
1.0
0.8
0.6
0.4
0.2
0

800

1,000

773.5336
R = 50,000

CountsThe 6546 LC/Q-TOF achieves aresolution of at least 30,000 for ionsabove m/z 118, and greater than 60,000
for larger ions (m/z 1,521, R = 60,000).
These values were reached in complex
E. coli extracts regardless of the peakintensity and acquisition rate (Figure 1).
Mass accuracy was largely within
0.3 mDa throughout the mass range,
independent of peak intensity (Figure 2).
This performance allows identificationof molecular formulas without priorchromatographic separation and,
therefore, increased throughput.

70,000157.00 157.02 157.04 157.06 157.08
Mass-to-charge (m/z)

774.5370

775.5401

773.5 774.0 774.5 775.0 775.5 776.0
Mass-to-charge (m/z)

Figure 1. Resolution is plotted over the mass range for analytes in an E. coli sample analyzed by flow
injection coupled with the 6546 LC/Q-TOF. Ion intensity is noted by color. Insets show the peak shapes for
low and high mass ions.
0.8
7

0.6

6

0.2
0
-0.2

5

-0.4
-0.6

log10(ion counts)

m/z error (mDa)

0.4

4

-0.8
-1.0

0

100

200

300
400
500
600
Mass-to-charge (m/z)

700

800

900

Figure 2. Mass error (mDa) is plotted over the mass range for extracted analytes in an E. coli
sample analyzed by flow injection with the 6546 LC/Q-TOF. Ion intensity is noted by color.

25

Mass spec metabolomics

Sensitivity and intrascan
dynamic range

Succinate, m/z 117.0193

A dilution series of the E. coli extract was
prepared in water, ranging from a 9X
concentration to an 81-fold dilution. A
concentration of 1X corresponds to theconcentration routinely used on previous
generations of Q-TOF instruments. Eachsample was combined with the sameamount (1X) of uniformly 13C-labeled
E. coli extract to mitigate diverging
matrix effects in the recorded intensities.
Triplicate injections were performed.
Analytes across the mass range wereextracted, and their intensities wereplotted to show the sensitivity of the6546 LC/Q-TOF at low concentrations (ordilutions, Figure 3). The signal intensity
was very stable at lower concentrations,and error only began to increase aroundthe 81-fold dilution. In the analysisof E. coli extracts at the standard 1X
concentration, the sensitivity of the6546 LC/Q-TOF enabled the detectionof several metabolites similar to thoseobtained with the Agilent 6550 iFunnel
Q-TOF LC/MS (Figure 4).

Mevalonate, m/z 147.0664

1 ×10

7

1 ×10

6

1 ×105
×9

×3

×1

:3

:9

:27

:81

×9

ATP, m/z 505.9886

×3

×1

:3

:9

:27

:81

:9

:27

:81

Coenzyme A, m/z 766.1075

1 ×10

5

1 ×104
1 ×104

×9

×3

×1

:3

:9

:27

:81

×9

×3

×1

:3

Figure 3. Ion intensity of four extracted analytes was plotted against the E. coli sample dilution. Error bars
represent standard deviation when n = 3. The grey highlighted sample, 1X, is the standard concentration of
E. coli samples used with earlier Q-TOF generations.

36

Mass spec metabolomics

6546

6550

Figure 4. Metabolite map for features in the 1X E. coli sample when it was analyzed by the 6546 LC/Q-TOF
and the 6550 iFunnel Q-TOF LC/MS. The colored circles represent features (or metabolites) detectedin the sample. The 6546 LC/Q-TOF was able to identify more features (red squares) in several different
metabolite classes compared to the 6550 Q-TOF LC/MS.

7

Mass spec metabolomics

×108
3.5
119.0262[13C2] Succinate-H+3.0

Intensity

2.5

2.0

1.5

1.0

118.0224[13C] Succinate-H+-

0.5

0

117.0

117.5

118.0
Mass-to-charge (m/z)

118.5

117.03117.04Mass-to-charge (m/z)

117.05119.0

10,000x Magnification

×104
9117.0191
Succinate-H+-87
6

Intensity

Additionally, the 6546 LC/Q-TOF has
five orders of in spectrum dynamic
range. This is more than could be
explored using pure E. coli extracts,
so the sample was spiked with
13
C2-succinate. The novel 10 GHz
analog to digital (ADC) acquisition
system of the 6546 LC/Q-TOF enablesprecise detection of peaks in a complex
sample spanning nearly four ordersof magnitude within the same scan
(Figure 5). This allows for quantificationand identification of metabolites over thefull dynamic range of complex extracts.
This aspect is especially important whenmeasuring complex matrices at highthroughputs with little to no sample
preparation or chromatography, sincethese samples often have both low andhigh intensity analytes of interest. In
summary, the 6546 LC/Q-TOF provides
continuous access to a full intrascan
dynamic range without compromises inresolution, accuracy, sensitivity, or scan
speed.

5
4
3

117.0102[13C2] Fumarate-H+-2
10
117.00117.01117.02117.06117.07

Figure 5. Intrascan dynamic range of the 6546 LC/Q-TOF. A 1X 13C-enriched E. coli extract
was spiked with 13C 2-succinate. Three technical replicates are superimposed. Despite the vastdifference in intensities (10 4 to 108 counts), accuracy, and resolution are preserved and allow
precise determination of both abundant and rare peaks within the same spectrum.

58

Mass spec metabolomics

119.0262
13
C2C2H5O4-

16
×106

119.0262
13
C2C2H5O4-

16
14
14
12

119.0809
C3C2H10NO2-

12
10
IntensityIntensity

13

10
8

119.0513
C5H8O2•F-

8
6

119.0513
C5H8O2•F-

119.0809
C3C2H10NO2-

13

119.0909
CC4H12NO2-

13

119.0909
CC4H12NO2-

13

6
4

119.0344
C4H7O4-4
2

119.0344
119.0398
13 C4H7OC
4 2C4H6NaO-

119.013520
119.00119.0703
C3H9N3O2-

119.0135
119.01119.02119.0605
C7H7N2- 119.0703
C3H9N3O2119.0398
119.0605
13CC H NaO-119.05
C7H7N2- 119.07
119.03119.04119.06119.08
2 4 6
Mass-to-charge (m/z)

119.090

119.00119.01resolution
119.02 of proximal
119.03 13C
119.04119.05in the
119.06119.08119.09Figure6. Spectraland 2H peaks
low m/z119.07
range of anenriched
E. coli
Mass-to-charge
extract measured by the 6546 LC/Q-TOF using
flow injection.(m/z)

Figure 6. Spectral resolution of proximal 13C and 2H peaks in the low m/z range of an enriched E. coli
50 measured by the 6546 LC/Q-TOF using flow injection.
extract
7

50
40

7
40
30
6

30
20

6
20
10
5

10
0

log10(ionlog10(ioncounts)counts)

Further
testing evaluated isotopic
and fidelity
labeling performance of the 6546 Q-TOF
Furtherevaluatedisotopic
LC/MS. testing
In principle,
accurateisotopiclabeling
performance
of
the
6546
Q-TOF
analysis depends on spectral resolution
LC/MS.
In
principle,
accurateisotopic
and linear dynamic range. Even in theanalysison spectral separation,
resolution
absence dependsof chromatographic
andlinear thatdynamic
range.
Even in ofthethewe foundthe high
resolution
absenceof chromatographic
6546 LC/Q-TOF
enabled clearseparation,
separation
2 high resolution of thewefound13C
that
the
and
H isotopologues inof most6546
LC/Q-TOF
clear
separation
crowdedregions.enabled
Figure 6
illustrates
13
2
C
and
H
isotopologues
in
of
most
this for the mass range with lowest
crowdedregions.
Figure6
illustrates
resolution (m/z 100). The mass accuracy
this<0.3
for mDathe masswith lowestof
ppm rangeerror acrossthe whole
resolution(m/z 100).
mass accuracy
range
of masses
and Theabundancesof<0.3 mDa
ppm error
across thewholeenablespredictionof molecular
formulaerange
of masses
and abundanceswith great
confidence.
For dozens ofenablespredictionof molecular
formulaerepresentative
metabolites,
the relative
with
great
confidence.
Fordozensisotopic accuracy error (RIAE) wasof
representative
thetherelative
calculated. Formetabolites,
most features,isotopic(RIAE)
was
RIAE wasaccuracy
below 20error% (thatis, inthecalculated.
For most
features,therange obtainedwith triple
quadrupole
RIAE
was below
20error% (thatin thedetectors).
Largerwasis,only
rangeobtainedwith triple
observed
for analytes
withquadrupole
monoisotopic
detectors).
LargerwasThese
only
intensity.
peaks nearing
104 error
observed
foranalyteswithmonoisotopic
analytes have isotopes at or below
intensity.
TheseThe
peaks
nearing
104 intherefore
the baseline.
103, andanalytes
have isotopes
or below
6546 LC/Q-TOF
robustlyatcombines3, andtherefore
the baseline.
10
highresolution
andinisotopic
fidelityThe
6546
LC/Q-TOFhighrobustly
combines
by maintaining
accuracyinhighresolution
andisotopic
fidelityquantification.
by maintaining high accuracy inquantification.

×106

RelativeRelative
isotopicisotopic
accuracy
accuracyerror (RIAE)
error (RIAE)

Isotopic resolution
andfidelity
Isotopic
resolution

5
0
-10
-10
-20

4
0

100

200

300
400
500
Mass-to-charge (m/z)

600

700

800
4

-20

100
300
400
500The RIAE
600 reports
700 the800
Figure0 7. Accuracyof200
isotopic
measurements.
percent error for the ratio between
Mass-to-charge
theoretical and measured M+1/M+0.
RIAE (m/z)
values <20 are considered excellent. Log of the sum of ion
intensity
10 scans
is notedmeasurements.
by color. WorseThe
RIAEvaluesare observed
only
for forpeak
ofbetween
low
Figure 7. overAccuracyof isotopic
RIAE
reports
the percent
error
theions
ratio
intensity,
with
M+1
ion
counts
of
about
5
to
10
%
of
the
monoisotopic
peaks
and
falling
in
the
range
of thetheoretical and measured M+1/M+0. RIAE values <20 are considered excellent. Log of the sum of ion
baseline.
intensity over 10 scans is noted by color. Worse RIAE values are observed only for peak ions of low

www.agilent.com/chem

intensity, with M+1 ion counts of about 5 to 10 % of the monoisotopic peaks and falling in the range of thebaseline.

www.agilent.com/chem
This information is subject to change without notice.
© Agilent Technologies, Inc. 2019
This information is subject to change without notice.
Printed in the USA, February 21, 20195994-0724EN© Agilent Technologies, Inc. 2019
Printed in the USA, February 21, 20195994-0724EN

9

Mass spec metabolomics

Agilent case study: Asking Nuanced Questions
Agilent case study: Asking Nuanced Questions

Agilent Collaborator Takes
Agilent Collaborator Takes
an Integrated, Multi-omics
an Integrated, Multi-omics
Approach to Metabolism
Approach to Metabolism

Adam Rosebrock was feeling unsettled. For years he had
Adam Rosebrock was feeling unsettled. For years he had
been using genomics to study what cells do when they do,
been using genomics to study what cells do when they do,well … what cells do. That is, which genes turn on or off
well … what cells do. That is, which genes turn on or off
as they go through cell division, for example, or various
as they go through cell division, for example, or various
external insults.
external insults.
Simply put, he was not getting enough information. So Rosebrock, a
Simplyput,trained
he wasmicrobiologist
not getting enoughinformation.
So Rosebrock,classicallyand biochemist,began
to expandathe scopeclassicallytrained microbiologist and biochemist, began to expand the scopeof his investigations.
of his investigations.
“My push into metabolomics and mass spectrometry was my unsettled“My
pushinto metabolomics
and mass
spectrometry
my unsettled
feelingcomingto a head,” he says
now. “Ifigured, if wewas
wantedto measure
feelingcomingtoahead,”
he
says
now.
“I
figured,if
wewantedto measure
what the cell was doing biochemically, we should just buckle down
and
what
the
cell
was
doing
biochemically,
weshouldjustbuckledown
andmeasure metabolites directly.”
measure metabolites directly.”
He started to focus on measuring small molecules and the direct output ofHestarted tousing
focuswhat
on measuring
small molecules
and the direct
outputofmetabolism
he calls “kick-butt
mass spectrometers
andfantastic
metabolism
using
what
he
calls
“kick-butt
mass
spectrometers
andfantasticliquid chromatographs” from Agilent.
liquid chromatographs” from Agilent.
One of the biggest shifts over the past few years, Rosebrock notes, is that
One of thebegan
biggestovernot
thejustpastmetabolite
few years,levels
Rosebrocknotes, rates,is thator
scientists
toshifts
measurebut reaction
scientists
began
tomeasurenotjustmetabolite
levelsbut
reactionrates,
ormetabolic flux.
metabolic flux.
“What that has required is really a shift in instrumentation, from thinking of“Whatthat has required
is really a shift
in instrumentation,from thinking
mass spectrometry
for metabolites
as beinga triple quadrupole
questionofto
mass
spectrometry
for metabolites
assays.
being“Mya triplequadrupole
question to
being anaccurate massquestion,” helab hasbeen a proponent
forbeing an accurate
says.
“My
lab hasI’m
been
a proponent
foraccurate
mass for massa longquestion,”time, andheoneof thereasons
reallyexcited aboutaccurate
mass
for
a
long
time,
and
one
of
the
reasonsI’mreallyexcitedabout
Agilent’s portfolio right now is the continued excellence and refinement of theAgilent’s
portfolio rightnow is especiallythe continued
excellence
refinementof themass spectrometry
platform,
the Agilent
6546and
Q-TOF
system.”mass spectrometry platform, especially the Agilent 6546 Q-TOF system.”

Adam Rosebrock, PhD
Adam Rosebrock, PhD

Assistant Professor of Pathology
Director
Metabolomics
SharedAssistantofProfessorof Pathology
Resource,
Brook Cancer
CenterDirector ofStony
Metabolomics
SharedRenaissance
Schoolof Cancer
Medicine
Resource, Stony
Brook
CenterStony
Brook, NewYork
Renaissance
Schoolof Medicine
Co-founder,
Flavored Solutions
Stony Brook,Maple
New York
Co-founder, Maple Flavored Solutions

10

Mass spec metabolomics

The
The platform
platform is
is allowing
allowing Rosebrock
Rosebrock to
to askask better,better, more
more nuanced
nuancedquestions.
questions.
“One“One ofof thethe most
most amazing
amazing ways
ways thatthat mass
mass spectrometry
spectrometry has
has helped
helped me
me as
as
a
biologistisgetting
insight
intowhat’shappeninginside
cells
as
they
are
a biologist is getting insight into what’s happening inside cells as they arehealthyhealthy andand asas they
they areare unhealthy,
unhealthy, either
either by
by mutation
mutation oror by
by environmental
environmental
exposure,orbytreatment
with
drugs
or
chemical
agents,”he
exposure, or by treatment with drugs or chemical agents,” he says.
says.
“For
“For aa longlong time,
time, ourour phenotype,phenotype, forfor geneticists
geneticists inin particular,
particular, was:was: Did
Did my
my
cell
live
or
die?
Or
did
my
cell
shrivelup
andlook
reallyunhappyundercell live or die? Or did my cell shrivel up and look really unhappy under thethemicroscope?
Those
are
broad
descriptive
terms.
If
the
cell
is
dead,
it
can’t
microscope? Those are broad descriptive terms. If the cell is dead, it can’t
reallyreally tell
tell youyou anything
anything else,”
else,” Rosebrock
Rosebrock notes.
notes. “Using“Using mass
mass spectrometry,
spectrometry,
though,
we
can
start
to
ask
about
the
biochemical
state
of
the
though, we can start to ask about the biochemical state of the cell
cell before
before the
the
cell
has
really
shut
down
and
died.
We
can
look
at
transitions
betweencell has really shut down and died. We can look at transitions between healthy
healthycells
cells andand diseased
diseased cells.
cells. We
We cancan look
look at
at thethe changes
changes inside
inside thethe cellcell itself.”
itself.”Rosebrock
Rosebrock says
says thatthat hehe waswas blown
blown away
away byby thethe ability
ability ofof thethe 6546
6546 totomeasurecompounds
acrossawide
dynamic
range
when
the
measure compounds across a wide dynamic range when the systemsystem was
was
first
introduced.
Through
continued
refinement,
he
adds,
the
systemisfirst introduced. Through continued refinement, he adds, the system is now
now
especially
especially adept
adept at
at simultaneously
simultaneously measuring
measuring low
low abundanceabundance peaks
peaks andanddealingdealing with
with high-intensity
high-intensity signals.
signals.
“Using“Using thethe 6546,
6546, we
we cancan measure,measure, at
at aa givengiven retention
retention time,
time, notnot just
just oneone
mass-to-charge
ratio,butwegetareallyfantastic
high-resolution
mass-to-charge ratio, but we get a really fantastic high-resolution view,
view, aahigh-dynamic
range
view,
of
different
co-eluting
analytes,”hesays.
high-dynamic range view, of different co-eluting analytes,” he says.
“We“We now
now have
have anan easiereasier time
time distinguishingdistinguishing crowded
crowded mass
mass spectra
spectra andandisolatingindividualisotopologuesofourcompoundsinflux
isolating individual isotopologues of our compounds in flux experiments
experiments
using
using thethe 6546.
6546. Chromatography
Chromatography isis still
still critical,
critical, but
but wewe havehave increased
increased ourourbiologicalfeaturecountsandhave
better,more
consistent,
mass
biological feature counts and have better, more consistent, mass spectral
spectral peak
peakshapes
on
the
6546.
The
system’sincreased
resolution
gives
us
confidence
shapes on the 6546. The system’s increased resolution gives us confidence
in
in individual
individual mass
mass spectral
spectral featuresfeatures as
as being
being isotopologues
isotopologues rather
rather than
than somesomecoelutinginterference.
This
hasenabledustogenerate
higher-quality
flux
coeluting interference. This has enabled us to generate higher-quality flux data,
data,
higherhigher quality
quality isotope-labelingisotope-labeling data,data, andand bebe moremore confident
confident inin thethe results
results wewegenerate.”
generate.”

Rosebrock’s
Rosebrock’s lab
lab analyzes
analyzes thethe data
data using
using
homegrown
software
along
with
what
homegrown software along with what hehedescribes
describes as
as thethe unsung
unsung hero
hero ofof Agilent’s
Agilent’s
metabolomics
metabolomics portfolio:
portfolio: MassHunter
MassHunter
Profinder
software.
Profinder software.
"Cells"Cells maintain
maintain constant,constant, homeostatic,
homeostatic,levelsofmanymetabolites
levels of many metabolites acrossacross different
different
environmental
environmental conditions
conditions andand whenwhen
faced
faced with
with various
various intrinsic
intrinsic andand extrinsic
extrinsic
perturbations,”
he
says.
"The
perturbations,” he says. "The underlying
underlying
biochemistry
biochemistry is
is anything
anything but
but static,
static, however.
however.
Wearefindingthat
cells
drastically
We are finding that cells drastically changechangethethe rates
rates ofof metabolic
metabolic reactions
reactions inin orderorder totokeep
metabolite
concentrations
stable."keep metabolite concentrations stable."To
To makemake mattersmatters even
even more
more complicated,complicated,scientists
arealso
re-learningscientists are also re-learning thatthat
metabolism
metabolism isis incredibly
incredibly diversified
diversified andandspecialized.
specialized.
“Even“Even inin ourour own
own bodies,bodies, there’s
there’s very
very different
different
metabolism
that
occursacrossdifferent
metabolism that occurs across different
tissues.
tissues. YourYour muscles
muscles areare metabolicallymetabolicallydifferent
different fromfrom your
your liver
liver oror your
your kidneys.
kidneys.
Even
you
and
I
will
have
differences
Even you and I will have differences inin ourour
metabolicprocesses—and
thereisgenetic
metabolic processes—and there is genetic
variation
variation thatthat underlies
underlies why
why your
your liver
liver
might
work
differently
thanmymight work differently than my liver
liver does,”
does,”
Rosebrocksays.
Rosebrock says.
So
So hehe andand hishis fellow
fellow researchresearch scientists
scientists
areare usingusing everything
everything they
they know
know andandeverything
they
can
learn
from
microbiology,
everything they can learn from microbiology,
biochemistry,
biochemistry, genetics,
genetics, genomics,
genomics, andandmetabolomics
tounderstandthedifferences
metabolomics to understand the differences
betweenbetween individuals
individuals andand populations.
populations. All
All in
in
the
hope
of
findingdifferences
that
can
be
the hope of finding differences that can be
exploited
exploited toto selectively
selectively kill
kill pathogens,
pathogens, oror
selectively
killtumors,while
sparingtheselectively kill tumors, while sparing the host.
host.

www.agilent.com
www.agilent.com
For Research Use Only. Not for use in diagnostic procedures.
For Research Use Only. Not for use in diagnostic procedures.
This information is subject to change without notice.
This information is subject to change without notice.
© Agilent Technologies, Inc. 2020
© Agilent Technologies, Inc. 2020
Published in the USA, January 23, 2020
Published in the USA, January 23, 2020
5994-1630EN
5994-1630EN
DE.2789467593
DE.2789467593

11

Mass spec metabolomics

Application Note
Cell Analysis andMetabolomics

Extracellular Flux Analysis and 13C
Stable-Isotope Tracing Reveals
Metabolic Changes in LPS-Stimulated
Macrophages
Authors
Agnieszka Broda and Gerald
Larrouy-Maumus
MRC Centre for MolecularBacteriology and Infection,
Department of Life Sciences,
Faculty of Natural Sciences,
Imperial College London,
London, SW7 2AZ, UK
Sufyan Pandor
Agilent Technologies, Inc.
Cheadle, UK

Abstract
Eukaryotic cells are known to generate ATP by mitochondrial (oxidative
phosphorylation) and non-mitochondrial (glycolysis) metabolism. Agilent Seahorsetechnology is an invaluable tool to gain access in real time to the bioenergetics
activity of the cell by measuring cellular oxygen consumption rates (OCR) andextracellular acidification rates (ECAR) as measures of mitochondrial respirationand glycolysis, respectively. Even though Seahorse provides phenotypic information
about the bioenergetic state of the cell, an in-depth understanding of the changes
in the metabolic pathways requires measuring the abundances and rates ofmetabolite interconversion of the molecules involved in the key metabolic pathways(e.g. glycolysis, tricarboxylic acid cycle (TCA) cycle, pentose phosphates pathway).
This is achieved by performing stable-isotope tracing analysis on an LC/Q-TOF,
providing a detailed view of cellular metabolism. Advancements in LC/Q-TOF
technology coupled with integrated qualitative flux analysis software reveal moredetails by tracking the in vivo turnover of metabolites through pathways. In thisapplication note, we present an example of using both Seahorse XF technology
and 13C stable-isotope-tracing analysis with Agilent MassHunter VistaFlux
software to study the response to lipopolysaccharide (LPS) treatment of RAW
264.7 macrophages. The aim of this application note is to demonstrate thecomplementarity of phenotypic analysis using Seahorse technology and targetedstable-isotope-tracing analysis using an Agilent 6546 LC/Q-TOF.

12

Mass spec metabolomics

Introduction
Immunometabolism research hassignificantly expanded our understandingof immune cell function in recent years.
Modulation of metabolism appears
to be crucial for controlling cell fate.1,2
In this context, Seahorse technology
enables insight into the bioenergetic
state of the cell in response to stimuli.
This is achieved by simultaneously
measuring, in real time, the changesin oxygen consumption rate (OCR, aqualitative indicator of mitochondrial
oxidative phosphorylation) and the rateof extracellular acidification (ECAR, aqualitative indicator of glycolysis), as
shown in Figure 1.
To gain further insight into themechanism leading to changes in
OCR and ECAR in response to stimuli,
metabolomics allows a direct snapshotof pathway activities and metabolite
regulation. For yet more detailed
information on metabolic pathwaychanges, stable-isotope-tracing analysis(e.g. 13C, 15N, 2H) can be applied.
Stable-isotope labeling provides
a different picture of intracellular
metabolism than metabolomics.
Although untargeted metabolomics
provides the abundance of different
metabolites within metabolic pathways,
several metabolic changes do nota fortiori result in an increase or adecrease in the metabolite level.
Stable-isotope-tracing provides
information not revealed by conventional
untargeted metabolomics by measuring
the rates of metabolite interconversion
as a readout of metabolic
enzyme regulation. This makes
stable-isotope-tracer studies a powerful
option to probe metabolic changes incomplex biological systems.
Insights into the full picture of cell
metabolism from the combination of
Seahorse and LC/Q-TOF data can informbiological research as answers fromone platform can drive experiments on
the other, allowing a feedback loop forfollow-up experiments (Figure 2).

(specific assays on the Seahorse andpathway-driven experiments on the
LC/Q-TOF).

Such workflows can provide accurate
answers to biological questions much
faster as both experiments are targeted

Within immunometabolism research,macrophages are known to play a central
role in pathogen recognition, exhibiting

Figure 1. Schematic illustration of cellular metabolism pathways and the link between phenotypicmeasurements using Agilent Seahorse (ECAR and OCR) and the metabolites from relevant metabolic
pathways (e.g., glycolysis, TCA cycle, and mitochondrial respiration) using stable-isotope-tracing
analysis by LC/Q-TOF. G-6-P: glucose-6-phosphate; R-5-P: ribose-5-phosphate; G-3-P: glyceraldehyde-3phosphate; PEP: phosphoenolpyruvate; ECAR: extracellular acidification rate; OCR: oxygen consumption
rate; ETC: electron transport chain.

Biological question
Phenotypic in vitro assay• Grow cells• Biochemistry/immunohistochemistry

Follow-up
experiment

Extracellular flux analysis• Grow cells• Determine phenotype

Stable-isotope-tracing analysis• Grow cells with labelled substrate• Determine flux for target metabolites

Answers and mechanisms
Figure 2. Workflow used to address biological questions by combining conventional phenotypic assays,
Agilent Seahorse measurements, and stable-isotope-tracing analysis on an LC/Q-TOF.

2

13

Mass spec metabolomics

an array of immediate physiologicalremodeling in response to pathogen
stimuli. Elucidation of the mechanisms
regulating metabolic pathways andthe balance of metabolites is crucial
for understanding macrophagefunction and their remodeling upon
external stimuli. One such example
is macrophage activation. Classicalactivation of macrophages is performedby pro-inflammatory stimuli such as
LPS and/or interferon gamma (IFNγ).
Several studies have demonstrated
that activated macrophages undergoa metabolic switch that includes
immediate up-regulation of glycolysis,
remodeling of the TCA cycle, and theinhibition of mitochondrial respiration.3
These processes follow an orchestrated
sequence of metabolic adaptions
that can be followed in real time via
extracellular flux analysis.10
For example, itaconic acid, a
non-amino organic acid derived fromcis-aconitate by the action of cisaconitate decarboxylase (cadA), is
known to be one of the most highly
induced metabolites following LPS
stimulation in bone marrow derived
macrophages (BMDMs).4,5 This process
is known to be energy costly for themacrophages as they lose the ability toperform mitochondrial substrate-level
phosphorylation.6
In this application note, we present
an example of the combination of
Seahorse XF technology with 13C
stable-isotope-tracing analysis using
MassHunter VistaFlux software to

Extracellular flux analysis on the
Seahorse XFp Analyzer

understand the response of RAW 264.7
macrophages to 4 hours of LPS
treatment. The presented workflow
can be adapted to different cell types
to decipher the molecular mechanisms
underpinning immunometabolism.

Experimental
Cell culture and reagentsRAW 264.7 cells were prepared from thefrozen stock (–80 °C) and cultured in atissue culture flask (~0.5 × 105 cells/mL)in DMEM medium (Dulbecco’s Modified
Eagle’s Medium, Sigma-Aldrich)
supplemented with 10% heat-inactivatedfetal bovine serum (FBS). After 48 hours
of incubation at 37 °C in CO2, confluent
cells were split and cultured in fresh
media. The process of splitting was
repeated five to six times until the cells
were fully recovered from being frozen.

Phenotypic in vitro assay-cytokine
measurement by ELISAThe RAW 264.7 cells were cultured ina six-well plate and incubated at 37 °C
in CO2 for three to five hours to adhere,
then treated with LPS at 100 ng/mL finalor plain media for the next four hours.
The supernatant was collected forcytokine TNFα analysis. The TNFα
production by RAW 264.7 macrophages
was determined using the mouse TNFαELISA kit (Ready-SET-Go; Affymetrix
eBioscience; Invitrogen 88732486)
according to the manufacturer’s
instructions. Absorbance readings were
measured at 450 nm.

RAW 264.7 cells were seeded on aneight-well miniplate at cell densitiesof 4 × 105 cells/mL and left to adhere
overnight. Cells were or were not treatedwith LPS at 100 ng/mL final or plain
media, and after four hours, the OCR
and ECAR were measured using an
Agilent Seahorse XFp Analyzer. The day
before the assay, the sensor cartridge
was hydrated by filling the utility plate’smoats and wells with 400 and 200 μL of
double-distilled sterile water, respectively,
and incubating overnight at 37 °C in a
non-CO2 incubator. On the day of theassay, the water was replaced with
200 μL of prewarmed Agilent XF calibrantand incubated for another one hour. Also,
the culture medium was replaced with180 μL of Agilent Seahorse XF RPMI
medium, pH 7.4, that was supplemented
with 10 mM glucose, 2 mM sodium
pyruvate, and 2 mM glutamine, andthe cells were incubated at 37 °C in a
non-CO2 incubator for one hour. The
Seahorse miniplate with cells was placed
into the XFp Analyzer for subsequent
analysis at 37 °C. Measurement cycles
consisted of three minutes mixing andthree minutes measuring. Baseline
rates were measured at 37 °C threetimes before sequentially injecting
the following mitochondrial inhibitors:
oligomycin (2 μM), carbonyl cyanidep-(trifluoromethoxy) phenylhydrazone(FCCP, 2 μM), and a rotenone/antimycin
A mixture (0.5 μM) all from the Agilent
Seahorse XFp Cell Mito Stress Test kit.
After the addition of each inhibitor, three
readings were taken. OCR and ECAR

Seahorse XFp Analyzer workflow-18 hours-4 hours

Seed cells
Hydrate cartridge

Pretreat cells-1 hours
Change media
Load and calibrate
cartridge

0 hours

Acquire data

1 hourExtract phenotype
parameters

314

Mass spec metabolomics

were automatically calculated by the
Seahorse XFp Wave software. Each data
point represents an average of threedifferent well measurements that werenormalized to the number of cells per
well. For a more complete understandingof the general metabolic changes, aphenotype analysis was conducted using
the Cell Mito Stress Test data result file.

Qualitative flux analysis
Metabolite extraction
RAW 264.7 cells were seeded ona six-well plate at a cell density of
5 × 105 cells/mL and incubated for three
to five hours to adhere. After this time,the media was changed for the media
supplemented with [U-13C6]-glucose
at 4.5 mg/mL. Cells were or were nottreated with LPS at 100 ng/mL media
concentration and in the presence of[U-13C6]-glucose at a concentrationequivalent to a 1:1 ratio of 12C and 13C
glucose present in the culture medium,
then incubated for four hours. The
microplate was placed on ice, andthe media was removed carefully.
The adhered cells were washed threetimes with cold PBS before additionof a metabolite extraction buffer(acetonitrile, methanol, and water at aratio of 40:40:20 v/v/v at –20 °C) at adensity of 107 cells/mL. Cells were thenscraped and transferred to an Eppendorf
vial. A 100 μL aliquot of the metabolite
solution was then mixed with 100 μL
of acetonitrile with 0.2% acetic acid at–20 °C and centrifuged for 10 minutes at
17,000 g at 4 °C. The final concentrationof 70% acetonitrile was compatible withthe starting conditions of the HILIC
chromatography. The supernatant wasthen transferred to an Agilent LC/MS
V-shape vial (p/n 5188-2788) and 4 μL
was injected into the LC/MS.

LC/Q-TOF data acquisition

Target metabolite list creation

Data were acquired on an Agilent
1290 Infinity II LC coupled toan 6546 LC/Q-TOF system.
Chromatographic separation
was performed on an Agilent
InfinityLab Poroshell 120 HILIC-Z,
2.1 × 100 mm, 2.7 μm column(part number 675775-924). The HILIC
methodology was optimized for polar
acidic metabolites (details in Table 1).
For easy and consistent mobile phase
preparation, a concentrated 10x solution
consisting of 100 mM ammonium
acetate, pH 9.0, in water was prepared to
produce mobile phases A and B.

As Seahorse provides phenotypic
information about glycolysis (ECAR) andmitochondrial oxidative phosphorylation(OCR), a target list of metabolites related
to those pathways was created using
Agilent MassHunter Pathways to PCDL
software. From a public pathways
database, the software generates atarget personal compound database(PCDL) that contains names, formulas,and various identifiers. Addition of
metabolite retention times in the target
PCDL were provided from a pre-existing
in-house database.

Table 1. Liquid chromatography conditions.
LC Conditions
ColumnAgilent InfinityLab Poroshell 120 HILIC-Z, 2.1 × 100 mm, 2.7 μm (p/n 675775-924)

Mobile PhaseA) 10 mM ammonium acetate in water, pH 9 with 5 μm Agilent InfinityLab Deactivator
Additive (p/n 5191-4506)
B) 10 mM ammonium acetate, pH 9 in 10:90 (v:v) water/acetonitrile

Flow Rate

0.5 mL/min

Gradient

Time (min)
0
11.5
12
15
20

Column Temperature

30 °C

Injection Volume

1 μL

Multisampler Temperature

6 °C

%A
%B
0
100
30
70
0
100
0
100
Post time

Table 2. Mass spectrometry conditions.
MS Conditions
MS System

Agilent 6546 LC/Q-TOF

SWARM Autotune Selection

m/z 50 to 750 fragile ion mode

Ionization Source

Agilent Jet Stream

Polarity

Negative

Gas Temperature

200 °C

Drying Gas Flow

10 L/min

Nebulizer Pressure

40 psig

Sheath Gas Temperature

300 °C

Sheath Gas Flow

12 L/minCapillary Voltage

3000 V

Nozzle Voltage

0V

Fragmentor

115

Acquisition Range

m/z 40–1000

Reference Massm/z 68.995758 and 980.016375

4

15

Mass spec metabolomics

Statistical analysis
Data are presented as the mean ±standard error of the mean fromtwo biological replicates and four
technical replicates per condition
for all experiments. Unpaired
two-tailed Student’s t-tests wereused to compare values, with p <0.05
considered significant.

Results and discussion
LPS stimulation induces a large
increase in TNFα
RAW 264.7 cells are widely used tounderstand molecular mechanismsof macrophage activation. Here, we
assessed macrophage activation bymeasuring the amount of tumor necrosis
factor alpha (TNFα) released in the cell
culture medium in response to LPS
stimulation. As expected, activation ofVistaFlux workflow
Acquire LC/MS
data

1.0E+04

1.0E+03

1.0E+02

1.0E+01

Extract
isotopologue
results

Create target
metabolite list

RAW 264.7 cells by 100 ng/mL of LPS for
four hours leads to a 100-fold increasein the abundance of secreted TNFα,
confirming the activation state of thecells (Figure 3).

TNFα (pg/mL)

Agilent MassHunter Profinder 10 is thebatch-processing module of VistaFlux.
Data files were imported and assigned totreatment groups. The chromatographic
data were time-aligned against an
in-house database of compoundswith known retention times. The batch
isotopologue extraction wizard wasused to extract isotopologues for thetarget metabolites list as well as tocorrect for natural abundance of 13C andfor the tracer purity. In stable-isotope
tracing, results are self-normalized foreach metabolite as the isotopologue
enrichment percentage is calculated
relative to the other isotopologues. The
13
C tracer purity in this experiment was
50%, so the result of these corrections isthat 100% label incorporation represents
full incorporation of 13C into themetabolite. The results from Profinder
were transferred to Omix Premium,
another module of VistaFlux, forvisualization of metabolic fluxes onpathways.

Control

LPS-treated

Visualize resultson pathway

LPS stimulation induces a shift
towards a glycolytic phenotype
To demonstrate how LPS activation
impacts cell bioenergetics in RAW 264.7,
we investigated the cell phenotype with
the Seahorse XFp Analyzer (Figure 4).
The Seahorse phenotype analysis
software shows the metabolic activityof cells under basal conditions andunder stressed conditions to reveal
the metabolic potential. The analysis
showed a general shift of the RAW 264.7
towards a more glycolytic phenotype
when stimulated with LPS in the basal
as well as in the stressed phenotype.
This is in line with the literature showingthat a glycolytic switch is an essential
component of the pro-inflammatory
function of macrophages.7-9

Figure 3. Level of TNFα in response to four hours of
stimulation by 100 ng/mL of LPS in RAW 264.7 cells.
30

Aerobic

Energetic

25
Mitochondrial respiration
OCR (pmol/min/10k cells)

Batch isotopologue data analysis

Control
LPS
20

15

10

5

Quiescent00Glycolytic
246ECAR (mpH/min/10k cells)
Glycolysis

Figure 4. OCR versus ECAR plot for metabolic phenotype analysis measured intriplicates. Open quadrats represent the basal phenotype and closed quadrats thestressed phenotype (metabolic capacity after injection of oligomycin and FCCP).
Four hours of stimulation by 100 ng/mL of LPS in RAW 264.7 cells induce a shift
towards increased glycolytic activity.

516

Mass spec metabolomics

LPS stimulation results in aredirection of carbon flux towards
itaconic acid synthesis
As shown previously by Seahorse XFp
analysis, LPS stimulation of RAW 264.7
cells increases ECAR. As discussed
earlier, ECAR informs on glycolysis.
Therefore, to gain insight into thechanges in metabolite turnover in
response to LPS stimuli leading tochanges in ECAR, we undertook[U-13C6]-glucose stable-isotope tracing
focusing on metabolites involved incentral carbon catabolism and the TCA
cycle (Table 3). RAW 264.7 cells were
simulated or not with LPS for four hours
in a culture medium containing[U-13C6]-glucose, and incorporation of 13C
was determined by LC/MS.
As shown in Figure 5, both lactate andpyruvate display very high levels of
13
C incorporation, reaching up to 95%
labeled independently of the treatment
applied. However, the major change
occurs at the level of cis-aconitate,and specifically itaconic acid, where
LPS activation drastically alters theisotopologue distribution. Effectively,
upon LPS stimulation, a large increasein 13C incorporation is observed for M+1,
M+2, M+4, and M+5 concomitant witha decrease in M+0. This is consistent
with the literature where LPS activation
is known to promote the productionof itaconic acid.4,5 Based on the XF
data presented here, LPS stimulation
leads to an increase in glycolysis. We
therefore investigated the change in theisotopologue distribution of metabolites
involved in this pathway. As shown in
Figure 5, we observed an increase in
13
C incorporation in metabolites involved
in glycolysis as well as the oxidative
branch of the TCA cycle leading towards
the production of itaconic acid. An

associated decrease in metabolites
found in the reductive branch of the
TCA cycle was observed, confirming an
inhibition of the turnover of metabolites
from the reductive branch of the TCA
cycle upon LPS stimulation. Taken
together, these data suggest that upon
LPS stimulation, RAW 264.7 cells redirect
their carbon flux towards itaconic acidproduction, leading to an alteration in13
C turnover in the TCA cycle.

Table 3. Target metabolite list for isotopologue extraction showing mean measured retention
time of eight control samples.
Information Pathway

Metabolite
β-D-Glucose 6-phosphate

ECAR

OCR

FormulaRT (min)CAS

C6H13O9P

9.60

15209-12-8

D-Fructose 6-phosphate

C6H13O9P

8.22

643-13-0

β-D-Fructose 1,6-bisphosphate

C6H14O12P2

10.2

488-69-7
591-57-1

D-Glyceraldehyde 3-phosphate

C3H7O6P

7.03

Dihydroxyacetone phosphate

C3H7O6P

7.95

57-04-5

Pyruvic acid

C3H4O41.40

127-17-4
50-21-5Lactic acid

C3H6O3

2.21

Citric acid

C6H8O7

8.88

77-92-9cis-Aconitic acid

C6H6O6

7.86

585-84-2

D-threo-Isocitric acid

C6H8O7

6.94

6061-97-8

Oxoglutaric acid

C5H6O5

6.04

328-50-7

L-Glutamate

C5H9NO4

6.25

56-86-0

Succinic acid

C4H6O4

6.54

110-15-6

Fumaric acid

C4H4O46.88

110-17-8

L-Malic acid

C4H6O5

6.79

97-67-6

L-Aspartic acid

C4H7NO4

1.22

56-84-8

Itaconic acid

C5H6O4

6.68

97-65-4

6

17

Mass spec metabolomics

Figure 5. Summary of [U-13C6]-glucose stable-isotope-tracing analysis of RAW 264.7 cells stimulated for four hours by 100 ng/mL of LPS compared to untreated
RAW 264.7 cells (control). Results are displayed as quilt plots on the pathways where each quilt plot shows corrected abundance for each isotopologue for control
and treated cells. Statistical significance is denoted by an *.

718

Mass spec metabolomics

Conclusion
Combining real-time extracellular
Conclusion
flux information with 13C
stable-isotope-tracing
allows greater
Combiningreal-time extracellular13
insightintothemechanismofflux information with C
macrophageactivation.
Where
Seahorse
stable-isotope-tracing allows greater
technology
providesinformation
insight into the mechanism ofabout
the metabolic
phenotypes
andmacrophageactivation.
Where Seahorsetheactivities
of
the
two
major
energytechnology provides information
13
pathwaysfrom a macroscopic
view,
about
the metabolic
phenotypes
and C
stable-isotope-tracing
allows atheactivities of the twoanalysis
major energy
microscopic
insight
intothepathwayspathways from a macroscopic view, 13C
leading
to these changes.
stable-isotope-tracing
analysis allows amicroscopic
the pathways
Seahorse XF insightassaysinto
providereal-time,leadingthese changes.
live-cell tometabolicanalysis. With thistechnology,
glycolytic
shift inthe
Seahorse XFa assays
providereal-time,
phenotypeof the living
macrophages
live-cell
metabolicanalysis.
With thiswasshown,whichisaprerequisite
technology, a glycolytic shift in theforpro-inflammatory
macrophage
phenotypeof the living
macrophages
activation.
This
wasconfirmedwhen
was shown, which is a prerequisite
looking
into
themetabolicdetails
for pro-inflammatory macrophageby
qualitativeflux analysis.
The increased
activation. Thiswas confirmed
when
glycolytic
rate
confirmedby thelooking
into
thewasmetabolicdetailsby13
into
increased incorporation
qualitativeflux analysis. of
The Cincreased
glycolysis
metabolites
relativeby
to thetheglycolytic rate
was confirmed
13 showed
basal
case.
Flux
analysisalso
increased incorporation of C intoanincreased
productionof itaconic
glycolysis
metabolites
relativeto theacid,apotentantimicrobial
compoundbasal case. Flux analysis also showed
producedby pro-inflammatory
an increased
production of itaconic acid,macrophages,
and thereby
provided aapotent antimicrobial
compoundmore
detailed
pictureofthechanges
produced by pro-inflammatoryinmetabolism.
macrophages, and thereby provided amore detailed picture of the changes
in metabolism.

This workflow and the technologies
used can be applied to other celltypes
to study andcell metabolism
at theThis workflow
the technologies
phenotypeandmetabolite
levelsused can be applied to other cell to
uncovermolecular
mechanismsatofthe
cell
types
to study
cell metabolism
metabolism
inresponsetoexternal
phenotype and metabolite levels totriggers
genetic manipulations.
uncover ormolecular
mechanisms of cell

6.
6.

metabolism in response to external
References
triggers
or genetic manipulations.

7.

1.

O’Neill, L. A.; Kishton, R. J.;

7.

1.

Immunometabolism
O’Neill, L. A.; Kishton, for
R. J.;
Immunologists.
Nat. toRev. Immunol.
Rathmell, J. A Guide2016,
16, 553–65. for
Immunometabolism

8.

References
Rathmell, J. A Guide to

2.
2.

3.
3.

4.
4.

5.
5.

Immunologists.
Nat.D.
Rev.
Loftus, R. M.; Finlay,
K. Immunol.
2016,
16, 553–65. Cellular
Immunometabolism:
Metabolism
Loftus,R. M.;Turns
Finlay,Immune
D. K.
Regulator.
J.
Biol.
Chem.
2016, 291,
Immunometabolism: Cellular
1–10.
Metabolism Turns Immune
Regulator.
J. Biol.
2016, 291,
Kelly, B.; O’Neill,
L. Chem.
A. Metabolic
1–10.
Reprogramming in Macrophages
and
Cells
InnateKelly,Dendritic
B.; O’Neill,
L. A.inMetabolic
Immunity. Cell Res.
2015, 25,
Reprogrammingin Macrophages
771–84.
andDendritic Cells in Innate
Immunity.
Cell Res.
25,
Lampropoulou,
V. et2015,
al. Itaconate
771–84.
Links Inhibition of Succinate
DehydrogenasewithMacrophage
Lampropoulou, V.
et al.
Itaconate
MetabolicRemodelingandLinks Inhibition of Succinate
Regulationof Inflammation.
Cell
Dehydrogenase
with Macrophage
Metab. 2016,
24, 158–66.
MetabolicRemodelingandRegulation
Inflammation.
Cell
Mills, E. L. etofal.
Itaconate is an
Metab. 2016, 24, 158–66.
Anti-Inflammatory
Metabolite that
Activates
AlkylationofMills, E. L.Nrf2
et al.via
Itaconate
is an
KEAP1.
Nature2018,
556,
113–117.
Anti-Inflammatory Metabolite that

8.

9.
9.

Nemeth, B.et al. Abolition of
mitochondrial substrate-level
phosphorylation
by itaconic
Nemeth, B.et al. Abolitionof acid
producedby
LPS-induced
Irg1
mitochondrial substrate-level
expression in cells
murineacid
phosphorylation
byof
itaconic
macrophagelineage.
FASEB
J 2016,
produced by LPS-induced
Irg1
30,
286–300.
expression in cells of murine

macrophagelineage.
Van den Bossche,
J. etFASEB
al. J 2016,30, 286–300. Dysfunction Prevents
Mitochondrial
Repolarizationof Inflammatory
Van den Bossche,
J. et al.
Macrophages.Dysfunction
Cell Rep. 2016,17,
Mitochondrial
Prevents
684–696.
Repolarizationof Inflammatory
Macrophages.
CellJ.;
Rep.
2016, 17,J.;
Van den Bossche,
Baardman,
684–696.
de Winther, M. P. Metabolic
Characterization
M1J.;
Van den Bossche,ofJ.;Polarized
Baardman,
and
M2
Bone
Marrow-Derived
de Winther, M. P. Metabolic
Macrophages
Using
Real-timeM1
Characterizationof Polarized
Extracellular
Analysis. J. Vis.
andM2 BoneFlux
Marrow-Derived
Exp.
2015,
doi:10.3791/53424.
Macrophages Using Real-time
Extracellular
Analysis. J. Vis.
Artyomov, M.Flux
N.; Sergushichev,A.;Exp.
2015,J.doi:10.3791/53424.
Schilling,
D. Integrating
Immunometabolism
andArtyomov, M. N.; Sergushichev,
A.;
Macrophage
Diversity.
Semin.
Schilling, J. D. Integrating
Immunol. 2016, 28, 417–424.
Immunometabolism
andDiversity.
10. MacrophageKam, Y. et al. RealTimeSemin.
Immunol.
2016,of28,
417–424.
Discrimination
Inflammatory
Macrophage
Activation
10. Kam, Y. et al. Real Time Using Agilent
Seahorse
XF Technology.
Agilent
Discriminationof Inflammatory
Technologies
applicationnote,
Macrophage Activation Using Agilent
publicationXFnumber
5991-8342EN,
Seahorse
Technology.
Agilent
2018.
Technologies
application note,publication number 5991-8342EN,
2018.

Activates Nrf2 via Alkylation of
KEAP1. Nature 2018, 556, 113–117.

www.agilent.com/chem
For Research Use Only. Not for use in diagnostic procedures.

www.agilent.com/chem
DE.5524421296

For Research Use Only. Not for use in diagnostic procedures.
This information is subject to change without notice.
DE.5524421296© Agilent Technologies, Inc. 2020
Printed in the USA, April 14, 2020
This information is subject to change without notice.
5994-1846EN© Agilent Technologies, Inc. 2020
Printed in the USA, April 14, 2020
5994-1846EN

19

Mass spec metabolomics

Application Note
Lipidomics

Improving Coverage of the Plasma
Lipidome Using Iterative MS/MS
Data Acquisition Combined with
Lipid Annotator Software and6546 LC/Q‑TOF
Authors
Jeremy Koelmel
Department of Chemistry,
University of Florida,
Gainesville, FL, USA
Mark Sartain, Juli Salcedo,
Adithya Murali, Xiangdong Li,
and Sarah Stow
Agilent Technologies, Inc.
Santa Clara, CA, USA

Introduction
A major challenge in mass spectrometry-based lipidomics is the comprehensive
characterization of a large and diverse set of lipid species, spanning a wide
concentration range within a biological sample. While shotgun lipidomics has
advanced the field of lipid analysis, it suffers from limitations including the failure
to distinguish isobaric species, which may be of biological importance and reduced
dynamic range due to ionization suppression. This led to chromatography‑based
lipid profiling approaches using high-performance liquid chromatography (HPLC)
coupled to high resolution mass spectrometry (MS).
To enable product‑ion spectral matching against in silico generated databases,
confident lipid annotation requires data acquisition at the MS/MS level. However,while LC separation helps elucidate isomeric lipid species and reduce complexity,
data-dependent high-resolution MS/MS data are limited by the number of precursors
that can be selected for fragmentation during chromatographic elution. Therefore,
it is not possible to acquire all the MS/MS spectra of interest in a single analysis forcomplex samples. Due to concentration bias, this strategy often misses important
lipid species of low abundance.
This Application Note demonstrates solutions to these challenges. We usedreversed-phase (RP) chromatography, which is well suited to resolve many casesof isomeric lipids, and is a popular choice for profiling plasma1, tissue2, and cellularlipids in a comprehensive manner. We coupled this LC separation to the Agilent 6546
LC/Q‑TOF, a mass spectrometer designed to provide wide dynamic range while
simultaneously providing improved resolution independent of acquisition speed.
We also evaluated the fully automated Q‑TOF Iterative MS/MS acquisition mode, inwhich a sample is injected multiple times, and precursors previously selected for
MS/MS fragmentation are excluded on a rolling basis. These results demonstratethat plasma lipidome coverage can be significantly improved with Iterative MS/MS.
Iterative MS/MS data can be used by Agilent Lipid Annotator software as part of acomprehensive lipidomics workflow.

20

Mass spec metabolomics

Experimental
Experimental

septa
septa(p/n(p/n5181‑1212)
5181‑1212)were
wereused,used,andand
22µLµLinjections
injectionswere
weremade.
made.For
Fornegative
negativemode
modeanalysis,
analysis,PTFE/Silicone/PTFE
PTFE/Silicone/PTFE
septa
septa(p/n(p/n5185‑5861)
5185‑5861)were
wereused,used,andand
55µLµLinjections
injectionswere
weremade.
made.

Reagents
Reagentsand
andchemicals
chemicals

MS
MSsystemsystemAgilent
Agilent6546
6546LC/Q‑TOF
LC/Q‑TOFwith
withanan
Agilent
AgilentJet
JetStream
StreamTechnology
Technologysource
sourceAll
Allreagents
reagentsand
andsolvents
solventswere
wereHPLC
HPLC
MethodMethodororLC/MS
LC/MSgrade.
grade.Acetonitrile,
Acetonitrile,methanol,
methanol,
Data
Datawere
wereacquired
acquiredeither
eitherusing
usingaaandandisopropanol
isopropanolwere
werepurchased
purchasedfrom
fromInstrumentation
Instrumentation
conventional
conventionalAutoMS/MS
AutoMS/MSmethod
methodHoneywell
Honeywell(Morristown,(Morristown,NJ,
NJ,USA).
USA).
or
oran
anIterative
IterativeMS/MS
MS/MSmethod,
method,asas
LC
LCsystemsystemUltrapure
Ultrapurewater
waterwaswasproduced
producedwith
withindicated.
indicated.
TablesTables
1
1
and
and
2
2
provideprovidethethe
Agilent
Agilent1290
1290Infinity
InfinityIIIILC
LCincluding:including:aaMilli‑Q
Milli‑QIntegral
Integralsystemsystemequipped
equipped
chromatography
chromatography
and
and
6546
6546
Q‑TOF
Q‑TOF
Table
2.
6546
Q‑TOF
AutoMS/MS
parameters.
MS parametersin thewithwithaaLC‑Pak
LC‑PakPolisher
Polisherandandaa0.22
0.22µmµm•• Agilent
Agilent1290
1290Infinity
InfinityIIIIHigh
HighSpeed
Speed
conditions
conditionsand
andparameters.
parameters.
methodeditorwere
set up as filter
point-of-use
point-of-usemembrane
membranefiltercartridge
cartridge ParameterPump
Pump(G7120A)(G7120A)
6546 LC/Q-TOF(EMD(EMDMillipore,
Millipore,Billerica,
Billerica,MA,
MA,
USA).
USA).
200II°C
Gas Temperature•• Agilent
Agilent1290
1290Infinity
Infinity
IIVialsampler
Vialsampler
AmmoniumAmmoniumfluoride
fluorideand
andLC/MS
LC/MSgrade
grade
with
withthermostat
thermostat(G7129B)(G7129B)
10 L/minGas Flowammoniumammoniumacetate
acetatewere
werepurchased
purchased
50 IIIIMulticolumn
Nebulizer (psig)•• Agilent
Agilent1290
1290Infinity
Infinity
Multicolumn
from
fromMillipore
MilliporeSigma
Sigma(St.
(St.Louis,
Louis,MO,
MO,
300
°C
Sheath Gas Temperature
Thermostat
Thermostat(G7116B)(G7116B)
USA).
USA).NISTNISTSRM
SRM1950
1950human
humanplasma
plasmaSheathGas Flowwas
waspurchased
purchasedfrom
fromMillipore
Millipore
Sigma.
Sigma.

SampleSamplepreparation
preparation

VCap

12 L/min

Table
Table1.1.Chromatographic
Chromatographicconditions.
conditions.
3,500 V (+), 3,000 V (–)

Nozzle Voltage

0V

Parameter
Parameter
Agilent
Agilent1290
1290Infinity
InfinityIIIILCLCNISTNISTSRM
SRM1950
1950plasma
plasmawas
wasthawedthawedonon
150 V
Fragmentor
rative ice,
M/MS,
the
Acquisition
Agilent
Agilent
InfinityLabInfinityLab
Poroshell
Poroshell
120
120
EC-C18,EC-C18,
3.0
3.0
×
×
100
100
mm,
mm,
2.7
2.7µm
µm(p/n(p/n695975-302)
695975-302)
Analytical
Analytical
ColumnColumnice,andandplasma
plasmalipids
lipidswere
wereextracted
extracted
65 V
Skimmer
set upwith
asfollows:
Agilent
AgilentInfinityLabInfinityLabPoroshell
Poroshell120
120EC-C18,
EC-C18,3.0
3.0××55mm,
mm,2.72.7µmµm(p/n(p/n823750-911)
823750-911)
Guard
GuardColumn
Columnwithaamodified
modifiedFolch
Folchextraction
extraction
750 V
Octopole RF Vpp
ck, andselect Add
Columns.
procedure.
procedure.
Methanol
Methanol(400(400µL)µL)waswasadded
added
50
50°C
°C
ColumnColumnTemperature
Temperature
m/z 121.050873, m/z 1221.990637 (+)
tofromaa50
50availableµLµLaliquot
aliquotof
ofthawed
thawedReference
plasmaplasmaMass
ininµLµL(positive),(positive),
55µLµL(negative)(negative)
Injection
InjectionVolume
Volumem/z22
119.03632,
m/z
980.016375(–)
erativeto
22mL
mL
Eppendorf
Eppendorftube,
tube,vortexed
vortexed
briefly,briefly,
50°C°C
40–1700(+)
underaaMS
Parameter
AutosamplerTemperature
Temperature m/z50
MS and MS/MS Range Autosampler
thenthenbath‑sonicatedbath‑sonicatedforforfive
fiveminutes.
minutes.
Type (Figure
1).
15
15seconds
secondsininwash
washportport(50:50(50:50methanol/isopropanol)
methanol/isopropanol)
3 spectra/s
NeedleNeedleWash
Wash
Min MS and MS/MS Acquisition
Rate
Chloroform
Chloroform(800(800µL)µL)waswasadded,
added,and
and
A)
A)10
10
mM
ammoniumacetate,acetate,0.2
0.2mMmMammonium
ammoniumfluoride
fluorideinin9:1
9:1water/methanol
water/methanol
Narrow(~mM
1.3ammonium
m/z)
Isolation Width
tart or Reset in the Iterative
vortexed
vortexedfor
foroneoneminute.
minute.To
ToinduceinduceB)B)1010mMmMammonium
ammoniumacetate,acetate,0.2
0.2mMmMammonium
ammoniumfluoride
fluorideinin2:3:5
2:3:5
MobileMobilePhase
Phase
20 eV acetonitrile/methanol/isopropanol(+),
25 eV (–)
(Figure 2) indicates the
Collision Energyacetonitrile/methanol/isopropanol
phase
phasepartitioning,partitioning,240
240µL
µLof
of
water
waterwaswasg of an iteration set. This
3 0.6
Max
Precursors
Per
Cycle
0.6
mL/minmL/min
Flow
Flow
Rate
Rate
added.
added.The
Themixture
mixturewas
wasthenthenvortexed
vortexed
ny prior rolling exclusion list,
Yes,Time
target25,000%B
counts/spectrum
Precursor
Time
(min)
(min)
%B
for
foroneoneminute,
minute,and
andcentrifugedcentrifugedatat Abundance-Based Scan Speed
0.00
0.00
70
70
ns a new exclusion list.
Yes
Use
Accumulation Time Limit
16,00016,000××ggfor
fortwotwominutes
minutesat
at44MS/MS°C.
°C.The
The
1.00
1.00
70
70
3.50
86
86
erativeor any
other
word
lower
lower
layer
layer
was
was
carefully
carefullyremoved
removedwithwithaa That Cannot Reach Target TIC No 3.50
Reject Precursors
10.00
10.00
86
86
Gradient
GradientProgram
Programo specifythesubsequentgas-tight
gas-tightglass
glasssyringe,
syringe,andandThreshold
transferredtransferred
11.00
11.00
100
100
5,000
counts and 0.001%
for MS/MS
17.00
17.00
100
100
injections
useA‑Lineand ambertotoaa2that
2mL
mLbothAgilent
Agilent
A‑Line
amber
glass
glass
Yes,
one
repeat,then
Active Exclusion Enabled
17.10
17.10
70
70exclude for 0.05 minutes
he exclusion
vial.
vial.To
Tolist.
re‑extract
re‑extractthetheremaining
remaining
19.00
19.00
70
70
Stringency 70 %, cut off 0 %Purityinterphaseinterphase
and
and
upper
upper
phase
phase
layers,layers,19
19
minutesminutes
Stop
Stop
Time
Time
cell indicates the injection
Common organic molecules
Isotope Model
900
900
µL
µLchloroform/methanol/water
chloroform/methanol/water
None
None
Post
PostTimeTime
ses noradds
to the1, 2, unknown
Sort Precursors(86:14:1)(86:14:1)
waswasreset
added,
added,and
andthethemixture
mixturen list, butdoes not170toto330
330barbar
Observed
ObservedColumn
ColumnPressure
Pressurem/z170
40 to 151 (+)
Staticwaswasvortexed
vortexed
forforoneoneminute
minuteandandExclusion Ranges
list. However,note thatm/z 40 to 210 (–)
centrifugedagain.
again.The
Thecombined
combinedlower
lower
partialcentrifuged(time segment)
layerslayers
from
from
the
the
two
two
50
50
µL
µL
extractions
extractions
d MS/MS or Scan (MS only)
were
werecombined
combinedandanddried
driedbybyaavacuum
vacuumon methodwill
reset theconcentrator.
concentrator.
Dried
Dried
lipid
lipid
extracts
extracts
xclusion list.
were
werereconstituted
reconstitutedwith
with100
100µL
µLof
ofaa
methanol/chloroform
methanol/chloroformmixture
mixture(9:1,(9:1,v/v),
v/v),
vortexed
vortexedfor
foroneoneminute,
minute,andandtakentakentotodeactivateddeactivated250
250µLµLautosampler
autosamplerglass
glass
inserts
insertsbefore
beforeLC/MS
LC/MSanalysis.
analysis.For
For
positive
positivemodemodeanalysis,
analysis,synthetic
syntheticrubber
rubberst setup for Iterative MS/MS.
22

Figure 1. The Add Columns dialog box.

21

Mass spec metabolomics

Iterative MS/MS parameters in the
Acquisition method editor were set up as
follows:

Table 2. 6546 Q‑TOF AutoMS/MS parameters.
Parameter

6546 LC/Q-TOF

Gas Temperature

200 °C

Gas Flow

10 L/min

Nebulizer (psig)

50

Sheath Gas Temperature

300 °C

Sheath Gas Flow

12 L/minVCap

3,500 V (+), 3,000 V (–)

Nozzle Voltage

0V

To invoke Iterative M/MS, the Acquisition
worklist was set up as follows:

Fragmentor

150 V

Skimmer

65 V•

Right-click, and select Add Columns.

Octopole RF Vpp

750 V

•

Select Iterative from available
columns under MS Parameter
Column Type (Figure 1).

Reference Mass

m/z 121.050873, m/z 1221.990637 (+)
m/z 119.03632, m/z 980.016375 (–)

MS and MS/MS Range

m/z 40–1700 (+)

Min MS and MS/MS Acquisition Rate

3 spectra/s

Typing Start or Reset in the Iterative
column (Figure 2) indicates the
beginning of an iteration set. This
resets any prior rolling exclusion list,
and begins a new exclusion list.

Isolation Width

Narrow (~ 1.3 m/z)

Collision Energy

20 eV (+), 25 eV (–)

Max Precursors Per Cycle

3

Precursor Abundance-Based Scan Speed

Yes, target 25,000 counts/spectrum

Use MS/MS Accumulation Time Limit

Yes

Reject Precursors That Cannot Reach Target TIC

No

Threshold for MS/MS

5,000 counts and 0.001%

Active Exclusion Enabled

Yes, one repeat, then exclude for 0.05 minutes

PurityStringency 70 %, cut off 0 %Isotope Model

Common organic molecules

Sort Precursors

1, 2, unknown

Static Exclusion Ranges

m/z 40 to 151 (+)
m/z 40 to 210 (–)

•

•

•

Typing Iterative or any other word
is used to specify the subsequent
iterative injections that both use andadd to the exclusion list.
A blank cell indicates the injection
neither uses nor adds to the
exclusion list, but does not reset
the worklist. However, note thata full or partial (time segment)
Targeted MS/MS or Scan (MS only)
acquisition method will reset therolling exclusion list.

Figure 2. Worklist setup for Iterative MS/MS.

Figure 1. The Add Columns dialog box.

3

22

Mass spec metabolomics

Agilent MassHunter Q‑TOF Data
Acquisition Version 10 was used tooperate the 6546 LC/Q‑TOF system.
Agilent MassHunter Lipid Annotator
Version 1.0 was used for all other data
analyses. Default method parameters
were used, except only [M+H]+ and[M+NH4]+ precursors were considered
for positive ion mode analysis, andonly [M-H]– and [M+HAc-H]– precursors
were considered for negative ion
mode analysis. Agilent MassHunter
PCDL Manager Version B.08 SP1 wasused to manage and edit the exported
annotations.

Previously, a Q‑TOF Iterative MS/MS
acquisition mode was shown to beeffective for in-depth peptide mappingof monoclonal antibodies5. We applied
this mode of Iterative Acquisitionon the 6546 LC/Q‑TOF to a complex
lipid sample. Figure 3 illustrates thestrategy of Iterative MS/MS. The first
injection is performed as a traditional
data-dependent (conventional)

Auto MS/MS analysis, where the top N
most abundant precursors are selected
for fragmentation in consideration ofan active exclusion list. In subsequent
injections, precursors selected for
MS/MS fragmentation in the previous
injections are excluded on a rolling basiswith customizable mass error tolerance
and retention time exclusion tolerance.

Counts (%)

SoftwareResults and discussion
Lipid Annotator software analysis ofplasma Iterative MS/MS data
Confident lipid annotation requires
data acquisition at the MS/MS level toenable product ion spectral matching
against in silico‑generated databases.
This study used a novel software tool
(Lipid Annotator) with a combination ofBayesian scoring, a probability density
algorithm, and non-negative leastsquares fit to search a theoretical lipid
library (modified LipidBlast) developedby Kind; et al.3,4 to annotate the MS/MS
spectra. Lipid Annotator takes special
care not to over-annotate lipid entities
by only providing the level of structural
information confidently informed by the
MS/MS spectra.

1

2

3

4

5

6

7
8
9
10 11
Acquisition time (min)

12

13

14

15

16

LC/MS
Injection 1

m/z

LC/MS
Injection 2

Additional Injections…

Precursors selected for MS/MS

m/z

Rolling excluded precursors

Figure 3. Principle of Iterative MS/MS.

4

23

17

18

Mass spec metabolomics

Lipid Annotator provides the ability toanalyze multiple MS/MS data files fromthe same sample origin together as abatch. Figure 4 illustrates an analysis offive plasma Iterative MS/MS data files.

There were 355 specific lipids (includingisomers with different RTs) representing
14 classes annotated across the fivepositive ion mode data files. Separately, abatch of five negative ion mode Iterative

MS/MS data files was analyzed, resulting
in 326 specific lipids representing 20 lipid
classes (not shown).

Five iterative MS/MS injections(+) TIC (all)—Plasma iterative MS/MS injection 1

Mass-to-charge (m/z)

1,000

355 Lipid annotations across five datafiles

800

600

400

1

2

3

4

5

6

7

8
9
10
11
Retention time (min)

12

13

14

15

16

17

18

Lipid classes
Number of lipids per classSM (17.5 %)PE (3.9 %)TG(22 %)

CE (2.8 %)LPE (0.8 %)Cer_NS (2.8 %)DG (2 %)
SHexCer (0.3 %)Cholesterol(0.3 %)

ACar(8.2 %)CerP (0.3 %)LPC(11.8 %)PC (27.3 %)Figure 4. Typical total ion chromatogram (TIC) of plasma in positive ion mode aligned with view of Lipid Annotator
software. Results shown are the combined analysis of five Iterative MS/MS data files. Annotated lipid features areplotted as m/z versus retention time, and colored by lipid class corresponding to the pie chart, where the numbers ofannotated lipids are shown as percentages.

524

Mass spec metabolomics

Iterative MS/MS increases
lipid annotations

A

Conventional AutoMS/MS
Iterative MS/MS

350

Lipids annotated

300
250
200
150
100
50
0

0

1

2

3

4

5

6

Injections
400

B

Conventional AutoMS/MS
Iterative MS/MS

350
300
Lipids annotated

The number of cumulative lipid
annotations in plasma across multiple
Iterative MS/MS data acquisition files
increased compared to conventional
AutoMS/MS files (Figure 5). These
results suggest that 3 to 5 Iterative
MS/MS injections are sufficient forcomprehensive lipid annotation inplasma with the method parameters
used in this study. While plasma
represents a common and complex
biological sample, it is important
to note that the optimal number of
injections may depend on sample
complexity and LC/MS acquisition
method parameters. For plasma
extracts in positive mode, applying
Iterative MS/MS to five sequential
injections increased the coverage of
unique annotated lipids by 69 % (n= 355) compared with conventional
AutoMS/MS acquisition (n = 223) acrossfive sequential injections (Figure 5A).
Likewise, in negative mode analysis ofplasma, 34 % more lipid annotations
were obtained with five injections of
Iterative MS/MS (n = 326) compared
to conventional MS/MS (n = 243)
(Figure 5B). Taken together, theseresults show that due to the spectral
density of numerous lipid precursors
in a chromatographic run, especially inpositive ion mode, a substantial benefit
is obtained using Iterative MS/MS for
LC/MS/MS-based lipidomics dataacquisition.

400

250
200
150
100
50
0

0

1

2

3

4

5

6

Injections

Figure 5. Cumulative unique annotated lipid features from Lipid Annotator software across multiple dataacquisition files for positive (A) and negative (B) ionization modes.

625

Mass spec metabolomics

Due to the iterative exclusion ofabundant precursors, we observedthat Iterative MS/MS enriched lipid
classes that were of low abundance(for example, diacylglycerols), ionized
less efficiently (for example, freecholesterol), or located in spectral-dense
regions of the chromatogram (forexample, triacylglycerols). Table 3
shows examples of lipid classes thatwere highly enriched with sequential
injections of Iterative MS/MS compared
to conventional AutoMS/MS.

10

A

9
8
7

B

9
8

Injection 1
Injection 2

6

Injection 1

7

Injection 3
5
4

Log10 abundance

In agreement with the sequential
exclusion of highly abundant lipid
precursors in Iterative MS/MS mode, we
observed that the mean peak abundanceof triggered lipid precursors decreased
across the first three injections ofplasma in both positive (Figure 6A) andnegative (Figure 6B) polarity datasets.
Additionally, the peak abundances ofannotated lipids in the second injection
were significantly lower than the initial
injection for both positive and negative
polarity datasets (t‑test p‑value <0.001).

10

Log10 abundance

Iterative MS/MS enriches lipid ionsof low abundance and specific lipid
classes6

Injection 2
Injection 3

5
4

3

3

2

2

1

1

0

0

Figure 6. Box plots of feature abundances corresponding to annotated lipids from three sequential
Iterative MS/MS injections in positive (A) and negative (B) ionization modes.

Table 3. Lipid classes highly enriched with Iterative MS/MS. Numbers of cumulative annotated lipids
are provided for five sequential injections acquired with conventional AutoMS/MS compared to Iterative
MS/MS data acquisition.

Lipid Class (polarity)

Number of Annotations
AutoMS/MS

Number of Annotations
Iterative MS/MS

Ceramide Nonhydroxy Fatty Acid-Sphingosines (+)

2

10Cholesterol Esters (+)

4

10Free Cholesterol (+)

01

Diacylglycerols (+)

1

7

Phosphatidylethanolamines (+)

2

14

Triacylglycerols (+)

46

78

Ether-linked phosphatidylcholines (–)

1728

Lysophosphatidylinositols (–)

5

9Phosphatidylethanolamines (–)

9

20

7

26

Mass spec metabolomics

Figure 7 further demonstrates a specificexample of lipid class enrichment, where
precursors corresponding to cholesterolesters of low abundance were selected
for MS/MS fragmentation in sequential
injections.

MS/MS acquisition parameter
optimization with consideration oflipid isomers
To ensure optimal coverage of plasma
lipids, MS/MS acquisition methodparameters were found to be critical.
As a first step, Lipid Annotator uses
a feature-finding algorithm on the
AutoMS/MS or Iterative MS/MS data
file. Feature finding is performed at
the MS1 level, and MS/MS spectra
are associated with each feature ina subsequent step. Only featureswith associated MS/MS spectra are
included in the resulting feature tables.
A minimum of four MS1 data points
across a chromatographic peak isrecommended for feature finding with
Lipid Annotator. Therefore, the MS/MS
acquisition parameters (acquisition rates
and number of precursors per cycle)
must be optimized to ensure a cycle timethat meets this minimum requirement.
With the chromatographic method andplasma sample used in this study, weobserved the lipid chromatographic
base peak widths to range from ~6 to
14 seconds, with an average peakwidth of ~8 seconds. Therefore, given
the minimum observed peak width ofsix seconds, the MS/MS parameters
were adjusted to yield a cycle time of
1.43 seconds. This ensured a minimumof four points across the narrowest
chromatographic peaks.
Iterative MS/MS acquisition parameters
were found to be important for lipidome
annotation coverage, particularly in thecase of lipid isomers. In this context,
we define lipid isomers as cases wheremultiple annotated lipid features havethe same sum composition (and same
precursor m/z), but different retention
times. However, in some of thesecases, the MS/MS spectra provided
further differentiation of isomers at theconstituent level, for instance providing
information on the esterified fatty acylgroups (for example, PC 18:2_18:2
versus PC 16:0_20:4). Analysis of plasma
resulted in significant numbers of lipid
isomers, where 164 out of 355 (positivemode), and 143 of 326 (negative mode)annotated lipids represented lipid
isomers. To ensure lipid isomers are
not missed in the workflow, the active
exclusion window and the iterative RT
exclusion tolerance must be set low
enough so that closely eluting isomers
with the same precursor mass havethe opportunity to be triggered for
MS/MS. Figure 8 demonstrates a typicalscenario for a pair of closely eluting
lysophosphatidylcholine (LPC) isomers
with narrow peak widths.

0.9

0.9

0.8

0.8

0.7

0.7

0.7

0.6

0.6

0.6

0.5
0.4

B

CE 20:4

Injection 1

×102
1.0

0.8

CE 18:1

0.5
0.4

0.5
0.4

0.3

0.3

0.3

0.2

0.2

0.2

0.1

0.1

0

CE 20:3

14.0

14.5 15.0 15.5 16.0
Acquisition time (min)16.5

CE 18:3
CE 16:1
CE 20:5

0.1

0
13.5

Injection 2

C0.9
CE 18:2

Counts (%)

Counts (%)

×102
1.0

Counts (%)

×102
1.0 A

0
14.0

14.5
15.0
15.5
16.0
Acquisition time (min)

16.5

14.0

14.5
15.0
15.5
16.0
Acquisition time (min)16.5

Figure 7. Increased cholesterol ester (CE) precursor selection with Iterative MS/MS from a plasma lipid extract. A) Overlaid extracted MS/MS chromatogramsof a retention time region dense with annotated triacylglycerol (TG) lipids (black features). B) Four unique CE lipid precursors (blue features) were selected
for fragmentation in the first injection. C) After exclusion of CEs of higher abundance (x symbols) and TGs (not shown), three more unique CE precursors of
low abundance (green features) were selected in the subsequent injection. Red diamonds indicate MS1 scans in the chromatograms where the CE [M+NH4]+
precursors were selected for MS/MS.

827

Mass spec metabolomics

Conclusion

Lipid Annotator software provides theability to leverage Iterative MS/MS data
to quickly and automatically generate
custom PCDL libraries with deep
annotation coverage. These libraries
with RT information are a criticalcomponent of an Agilent lipidomics
software workflow that covers targeted
and untargeted lipid profiling, from lipid
annotation to differential analysis.

RT 2.08
Injection 1

1.0
0.9
0.8
0.7
Counts (%)

This Application Note demonstrates that
the Iterative MS/MS mode of LC/Q‑TOF
data acquisition provides a substantial
benefit for improving lipid annotationof complex samples. When applied to aplasma lipid extract, the total number ofannotated lipids increased significantly,
and lipid classes of low abundance areenriched with Iterative MS/MS.

LPC 20:4/0:0
×102

0.6
0.5

0.1 minutes

0.4
LPC 0:0/20:4
RT 1.93
Injection 2

0.3
0.2
0.1
0
1.85

1.90

1.95

2.00

2.05

2.10

2.15

2.20

Acquisition time (min)Figure 8. Iterative MS/MS parameters optimized for lipid isomers. Extracted ion chromatograms areshown for two lysophosphatidylcholine (LPC) 20:4 isomers annotated with Lipid Annotator software. The
red diamonds indicate hypothetical MS1 scans where the LPC 20:4 [M+H]+ precursor m/z 544.3398 is
triggered for MS/MS. Setting the Iterative RT exclusion tolerance to ±0.1 minutes ensures that the same
precursor m/z (±20 ppm) of the neighboring LPC isomer was picked for MS/MS in subsequent injections.

928

Mass spec metabolomics

References
1.

Cajka, T.; Fiehn, O. LC/MS Method

1.

PlasmaTechnologies
Cajka, T.;Lipids.
Fiehn,Agilent
O. LC/MS
MethodApplication
Note,publication
numberfor Comprehensive Analysis of5991‑9280,
2018.
Plasma Lipids. Agilent Technologies

References
for Comprehensive Analysis of

2.
2.

3.
3.

4.
4.

5.
5.

Application
Sartain, M.; Note,
Sana, publication
T. Impact number
5991‑9280,
2018. on Lipidof Chromatography
ProfilingM.;of Liver
Extracts.
Sartain,
Sana,Tissue
T. Impact
Agilent
Technologieson
Applicationof
Chromatography
Lipid
Note,
publication
number5991‑5494,
Profilingof Liver Tissue
Extracts.
2015.
Agilent Technologies Application

Note,publication
numberKind, T.;et al. LipidBlast
in 5991‑5494,
silico2015. mass spectrometry
tandemdatabase
identification.
Kind, T.; etfor
al. lipid
LipidBlastin silico
Nature Methods
2013, 10(8),tandemmass spectrometry755–758 for lipid identification.
database
NatureMethods
2013,
10(8),
Tsugawa,H.; et al.
MS‑DIAL:
755–758
data-dependent
MS/MSdeconvolution
Tsugawa, H.; etfor
al. comprehensive
MS‑DIAL:metabolome
analysis.
Naturedata-dependent MS/MS
Methods 2015,for12(6),
523–526.
deconvolution
comprehensive

metabolome
NatureWu, L.; Wong,analysis.
D. L. An Integrated
Methods
12(6), Mapping
523–526.
Workflow2015,
for Peptide
ofWu,Monoclonal
L.; Wong, D.Antibodies.
L. An Integrated
Agilent
Technologies
Workflowfor PeptideApplication
MappingNote,publication
number 5991‑8633,
of Monoclonal
Antibodies.
2017.
Agilent Technologies Application
Note, publication number 5991‑8633,
2017.

www.agilent.com/chem
For Research Use Only. Not for use in
www.agilent.com/chem
diagnostic procedures.
For Research Use Only. Not for use inThis information is subject to change without notice.
diagnostic procedures.
© Agilent Technologies, Inc. 2019
Printedin the USA,This
informationis March
subject26,
to 2019change without notice.
5994-0775EN© Agilent Technologies, Inc. 2019
Printed in the USA, March 26, 20195994-0775EN

29

Mass spec metabolomics

Application Note
Lipidomics

Lipid Profiling Workflow
Demonstrates Disrupted Lipogenesis
Induced with Drug Treatment inLeukemia Cells
Using an Agilent 6546 LC/Q-TOF and
MassHunter Lipid Annotator Software

Authors
Mark Sartain,
Genevieve Van de Bittner, andSarah Stow
Agilent Technologies, Inc.
Santa Clara, CA

Abstract
The Agilent lipidomics profiling workflow, using the Agilent 6546 LC/Q-TOF
and Agilent MassHunter Lipid Annotator software, was applied to the study of
drug-treated acute myeloid leukemia (AML) cells. The results confirmed previously
reported observations, and revealed lipid differences made evident through the
expanded coverage of this comprehensive workflow.

30

Mass spec metabolomics

Introduction
A previous study found that a drug
combination (BaP) of the lipid-lowering
drug bezafibrate (BEZ) and thecontraceptive medroxyprogesterone
acetate (MPA) had potent anticancer
properties for AML, an aggressive bloodcancer.1 The authors further showedwith a series of experiments, including
lipid analysis, that BaP slows de novo
fatty acid and phospholipid biosynthesis
through downregulation of lipogenic
enzymes, and suggested dysregulationof lipogenesis as a major contributor to
the anticancer effect of BaP.
As a proof-of-principle study, weapplied a lipidomics profiling workflow
to analyze lipid alterations in the AML
K562 cell line in response to BEZ, MPA,
and the BaP drug combination. The
Agilent lipid analysis workflow was
performed with the 6546 LC/Q-TOF,
a mass spectrometer designed tohave wide dynamic range while
simultaneously providing improved
resolution independent of acquisition
rate. Key to the workflow is MassHunter
Lipid Annotator software, which quickly
annotates lipid MS/MS spectra andeasily generates a custom library ofdetected lipids, with deep annotation
coverage. These libraries are a criticalcomponent of the complete lipid analysis
workflow, and support targeted anduntargeted lipidomics profiling.

Four replicate wells were preparedfor each treatment. After incubationfor 24 hours, cells were pelleted bycentrifugation, washed with PBS (–Ca,–Mg, 1 mL, 4 °C), repelleted, flash-frozen,
and stored at –80 °C. Figure 1 depicts
the cell culture strategy.

Lipid extraction
Cell pellets were thawed on ice, andlipids were extracted with a modified
Folch extraction procedure. Methanol(200 µL) was added to each cell pellet
in a 2 mL Eppendorf tube, and tubes
were mixed using a vortex mixer for
two minutes. Chloroform (400 µL) was

added, and tubes were mixed using
a vortex mixer for two minutes. To
induce phase partitioning, 120 µL ofwater was added to each sample. The
mixture was then mixed with a vortex
mixer for two minutes, and centrifuged
at 16,000 × g for five minutes at 4 °C.
The lower layer was carefully removed
with a gas-tight glass syringe, andtransferred to a second Eppendorf
tube. To re-extract the remaining
interphase and upper phase layers,
450 µL of a chloroform/methanol/water(86:14:1, v/v/v) mixture was added,
mixed with a vortex mixer for two
minutes, and centrifuged again.

K562 cells growing in
RPMI medium

24 hours
treatment

Vehicle

BEZ

MPA

BaP

Harvest(centrifuge, PBS wash, frozen at –80 °C)

Experimental
Cell culture
AML K562 cells were cultured insupplemented RPMI medium. Six-wellplates were seeded with 2.4 × 105
cells/mL (3 mL/well) and four different
treatments were applied: 0.5 mM
BEZ, 5 mM MPA, BaP (a combinationof 0.5 mM BEZ and 5 mM MPA), or
vehicle control (1:1 ethanol/DMSO).

Lipid extraction(Modified Folch method)

×16
To LC/MS

Pooled extract
To LC/MS/MS
Figure 1. Experimental design for studying effects of drug treatments on cancer cell lipidome.

231

Mass spec metabolomics

The lower layers were combined, and•MS system: Agilent 6546 LC/Q-TOF
2. Ten-microliter aliquots from600 µL of a chloroform/methanol/waterwith an Agilent Jet Stream
each of the reconstituted(3:48:47 v/v/v) mixture was added, thetechnology ion source
50 µL samples were combined
solutions were then mixed using a vortex
in a single glass vial insert
Methodmixer for two minutes and centrifuged.
(16 samples = 160 µL). The
A pooled K562 lipid extract representing
The lower layer was transferred topooled aliquot was concentrated
the 16 samples (four conditions × foura fresh Eppendorf tube, dried by a
by drying in a vacuum
replicates) was acquired by Iterative
vacuum concentrator, resuspended
concentrator and resuspended inwith 200 µL of reconstitution solvent
50 µL of reconstitution solvent for MS/MS, a fully automated Q-TOF
acquisition mode in which a sample is(methanol/chloroform (9:1 v/v)), andAutoMS/MS (Iterative MS/MS)
injected
multiple times, and precursors
septa (p/nbriefly mixed with a vortex mixer.
data5181‑1212)
acquisition.were used, andMS
systemExperimental
selectedfor MS/MS
fragmentationin2
injections wereinjections
made. For
negative
Extracts were divided and concentrated
Agilent 6546
LC/Q‑TOF
with an• µL Five-microliter
were
made
the
previous
injectionsareexcludedmode
PTFE/Silicone/PTFE
for differentand
LC/MS
acquisition methods
Agilent Jet Stream Technology source
Reagents
chemicals
foranalysis,
all samples.
on a rolling basis. The value of Iterative
septa (p/n 5185‑5861) were used, andas follows:
All reagents and solvents were HPLC
MS/MS in obtaining larger numbers of
MethodInstrumentation5µL
injections
were
made.
• LC/MS
Samplesfor Acetonitrile,
positive ion modeor
grade.
methanol,froma single
Data
were acquired
eitherusingsamplea•LC system: Agilent 1290 Infinity II LC lipid annotations
LC/MS:and isopropanol
were purchased from2
Instrumentation
wasdemonstrated
previously.
conventional AutoMS/MS method
including:
Honeywell
NJ,
USA).
1. Fifty(Morristown,
microliters oftheextracts
or an Iterative MS/MS method, asLCsystem• Agilent 1290 Infinity II High Speed Detailed experimental methods for
Ultrapurewater
was produced
with
were
transferred
to deactivatedindicated.
Tables and
1 and2 provide thechromatography
AutoMS/MS
Agilent 1290
II LC including:
PumpInfinity(G7120A)
a Milli‑Qglass
Integralvialsysteminserts equippedforMS1
data Q‑TOF AutoMS/MS
chromatography
and6546
Q‑TOF as
Table
2.
6546
parameters.
mass spectrometry were followed/MS parametersin thewith a LC‑Pak
Polisherandindividual
a 0.22 µm•
Agilent
1290
Infinity
II Highacquisitionon the• Agilent
1290
Infinity
II Speed
2and parameters.
conditions
described; parameters are provided inmethodeditor weremembrane
set up as filter cartridge
point-of-use
Pump(G7120A)with thermostat
replicates.
Parameter
6546 LC/Q-TOF
Vialsampler
Tables 1 and 2. Additionally, MS-only(EMD Millipore, Billerica, MA,Gas
USA).
(G7129B)
200II°CVialsampler
Temperature•
Agilent
1290 Infinity
2. Ten-microliter aliquots fromdata were acquired on the individual
Ammonium fluoride and LC/MS gradewiththermostat(G7129B)
10 L/minII
Gas Flowwere
each of the 50 µL samples• Agilent
1290 Infinity
samples, with an MS acquisition rate ofammonium acetate were purchased
combined in a singleNebulizer
glass vial
50
(psig)
Multicolumn
Thermostat
three spectra/second.
•
Agilent 1290 Infinity II Multicolumn
from Millipore Sigma (St. Louis, MO,insert (16 samples =Sheath
160 µL).
The(G7116B)
300°C
Gas Temperature
Thermostat (G7116B)
USA). NIST SRM 1950 human plasmapooled aliquot was concentrated
12 L/minSheathGas
Flow
was purchased from Millipore Sigma.
by drying in a vacuum
Table 1. Chromatographic conditions.
3,500 V (+), 3,000 V (–)
VCap
Table 1. Chromatographic conditions.
in
Sampleconcentrator
preparationand resuspended
0V
Nozzle Voltage
50 µL
of reconstitution
solventonfor
Parameter
Agilent 1290 Infinity II LCNIST SRM
1950
plasma wasFragmentor
thawedParameter
Agilent 1290 Infinity II LC
150 V
erative ice,
M/MS,
the
Acquisition
AutoMS/MS(Iterative
MS/MS)
Agilent
InfinityLab
Poroshell
120 EC-C18, 3.0 × 100 mm, 2.7 µm (p/n 695975-302)
Analytical
Columnand plasma lipids were extracted
Agilent
InfinityLab
Poroshell
120 EC-C18, 3.0 × 100 mm, 2.7 µm (p/n 695975-302)
Analytical
Column65 V
Skimmers set upwith
as follows:data acquisition.
Agilent InfinityLab Poroshell 120 EC-C18, 3.0 × 5 mm, 2.7 µm (p/n 823750-911)
Guard Column
a modified
Folch extraction
Octopole RF Vpp

Guard Column

Agilent InfinityLab Poroshell 120 EC-C18, 3.0 × 5 mm, 2.7 µm (p/n 823750-911)
750 V

ick, and
Add
Columns.
procedure.
Methanol(400 µL) mode
was added• select
Samples
for negative-ion
50
Column
50°C°C
Column Temperature
Temperature
m/z 121.050873, m/z 1221.990637 (+)
Reference
Mass
to
a
50
µL
aliquotofthawedplasmain
LC/MS:µL
5 µL(negative)(–)
Injection Volume
m/z25
119.03632,
m/z
980.016375µL(positive),
terative from available
2 mL
Eppendorf
tube,150
vortexed
briefly,
50°C°C
40–1700(+)
s undera MS
MSof
and
MS/MS Range Autosampler Temperature m/z50
1.Parameter
The remainingµL
eachthen bath‑sonicatedfor five minutes.
Type (Figure
1).
15seconds
secondsininwash
washportport(50:50(50:50methanol/isopropanol)
methanol/isopropanol)
Needle Wash
extract was transferred
to a15
3 spectra/s
Min MSand MS/MS Acquisition
Rate
Chloroform
(800
µL)
was
added,
and
A)10
10
mM
ammoniumacetate,acetate,0.2
0.2mMmMammonium
ammoniumfluoride
fluorideinin9:1
9:1water/methanol
water/methanol
glass vial
insertWidthandA)
Narrow(~mM
1.3ammonium
m/z)
Isolation
Start or Reset deactivatedin the Iterative
10mM
mMammonium
ammoniumacetate,acetate,0.2
0.2mMmMammonium
ammoniumfluoride
fluorideinin2:3:5
Mobile Phase
Phase
vortexeddriedfor oneminute.
Toconcentrator.
induceB)B)10
Mobilebyavacuum20 eV acetonitrile/methanol/isopropanol(+),
25 acetonitrile/methanol/isopropanoleV (–)
2:3:5(Figure 2) indicates thephase partitioning,
240 µL ofCollisionwaterEnergy
wasTheset.
samplesng of an iteration
This were resuspended
0.6
mL/min
Flow
Rate
3
Max
Precursors
Per
Cycle
0.6
mL/minFlow Rate
added. The
was then vortexed
50mixtureµL of reconstitution
solvent
ny prior rollinginexclusion
list,
Time
%B counts/spectrum
Yes,Time
target25,000Precursor
Abundance-Based Scan Speed(min)%Bfor one forminute,andcentrifuged
at
0.00
70
MS1
data acquisition on the
0.00
70gins a new exclusion
list.
Yes 1.00
70
Use4MS/MS
Accumulation Time Limit
16,000 ×individual
g for tworeplicates.
minutes at°C. The
1.00
70
3.50
86
86
terativeor any
other
lower
layer
waswordcarefully removed
with a That Cannot
No 3.50
Reject Precursors
Reach
Target TIC
10.00
86
Gradient
Program10.00
86Gradient Program
11.00
100
to specify
thesubsequentgas-tight glass syringe, and Threshold
transferred
11.00
100
5,000
counts and 0.001%
for MS/MS
17.00
100
17.00
100
injections
useA‑Lineand amberto a 2thatmLbothAgilent
glass
17.10
70
Yes,17.10
one repeat, thenActive Exclusion Enabled
70 exclude for 0.05 minutes
19.00
70
he exclusionvial. Tolist.
re‑extract the remaining
19.00
70
Stringency 70 %, cut off 0 %Purity19 minutes
Stop Timeinterphase
and
upper
phase
layers,19
minutes
Stop Timecell indicates the injection
Common organic molecules
Isotope Model
None
Post Time
900addsµL chloroform/methanol/water
None
Post Time
uses norto the1, 2, unknown
Sort Precursors
170 to 330 bar
Observed Column Pressure(86:14:1)
wasadded,
andthemixtureon list, but does not resetto 330 bar
Observed Column Pressure m/z170
40 to 151 (+)
StaticExclusion
Rangeswas vortexedfor one minute andklist. However,note thatm/z 40 to 210 (–)
again. The combined lower
partialcentrifuged(time segment)
layersfromthe
50 µL extractions
d MS/MS or Scan (MStwo
only)
were
combinedandion method will reset the dried by a vacuum
3
concentrator.
Dried lipid extracts
xclusion
list.
were reconstituted with 100 µL of amethanol/chloroform mixture (9:1, v/v),
vortexed for one minute, and taken to
32
deactivated 250 µL autosampler glass
inserts before LC/MS analysis. For

Mass spec metabolomics

Software•Table 2. Agilent 6546 LC/Q-TOF AutoMS/MS (Iterative) parameters.

Agilent MassHunter Q-TOF Data
Acquisition Version 10.0 was used tooperate the 6546 LC/Q-TOF.

•

Parameter

Agilent MassHunter Lipid Annotator
Version 1.0 with default methodparameters was used, except only
[M+H]+ and [M+NH4]+ precursors
were considered for positive ion
mode analysis, and only [M−H]–
and [M+HAc−H]– precursors wereconsidered for negative ion mode
analysis.

•

Agilent MassHunter PCDL Manager
Version B.08 SP1 was used to
manage and edit the exported
annotated lipid libraries (PCDL).
Specifically, PCDL Manager wasused to remove redundancies fornine Cer_NS lipids, where separate
entries were observed for [M−H]–
and [M+acetate]– molecular ions.
The [M−H]– Cer_NS entries weredeleted, leaving 653 lipids in thenegative ion mode PCDL.

•

Agilent MassHunter Profinder
Version 10.0 was used for feature
extraction.3 The provided “Profinder- Lipids.m” method was used forbatch targeted feature extraction
with the following changes:• Step 1: +H and +NH4 checked(pos), −H and +CH3COO checked(neg); report single ions or
single-ion features with charge
state z = 1: checked• Step 2: Expected data variation
for MS isotope abundance: 12.5%• Step 3: Smoothing function:
Quadratic/Cubic Savitzky-Golay
with function width 8; heightfilter: uncheck; limit to largest (byheight) to maximum: 10 peaks

Agilent 6546 LC/Q-TOF

Gas Temperature

200 °C

Gas Flow

10 L/min

Nebulizer (psig)

50

Sheath Gas Temperature

300 °C

Sheath Gas Flow

12 L/minVCap

3,500 V (+), 3,000 V (–)

Nozzle Voltage

0V

Fragmentor

150 V

Skimmer

65 V

OctopoleRF Vpp

750 V

Reference Mass

m/z 121.050873, m/z 1221.990637 (+)
m/z 119.03632, m/z 980.016375 (–)

MS and MS/MS Range

m/z 40 to 1,700 (+)

Minimum MS and MS/MS Acquisition Rate

3 spectra/s

Isolation Width

Narrow (~1.3 m/z)

Collision Energy

20 eV (+), 25 eV (–)

Maximum Precursors Per Cycle

3

Precursor Abundance-Based Scan Speed

Yes, target 25,000 counts/spectrum

Use MS/MS Accumulation Time Limit

Yes

Reject Precursors that Cannot Reach Target TIC

No

Threshold for MS/MS

5,000 counts and 0.001%

Active Exclusion Enabled

Yes, one repeat, then exclude for 0.05 minutes

PurityStringency 70%, cutoff 0%

Isotope Model

Common organic molecules

Sort Precursors

1, 2, unknown

Static Exclusion Ranges

m/z 40 to 151 (+)
m/z 40 to 210 (–)

Iterative MS/MS Mass Error Tolerance

20 ppmIterative MS/MS RT Exclusion Tolerance±0.1 minutes• Step 4: Unchanged Step 5
uncheck Score (Tgt)•

Agilent MassHunter Mass Profiler
Professional Version 15.1 wasused for differential analysis.
Two experiments (positive or
negative ion) were created with the“Lipidomics” experiment type, andthe corresponding Profinder archives(.pfa) were used as the data source.
A percentile shift normalization
algorithm (75 %) was used, anddatasets were baselined to themedian of all samples.

•

Agilent MassHunter ID Browser
Version 10.0 was used within
MPP to make annotations in theuntargeted workflow, with masses±5 ppm and retention times±0.10 minutes as required criteria.

Workflow
Both the targeted and untargeted
lipidomics workflows were used aspreviously described.4433

Results and discussion

A

Lipid Annotator database creation
with pooled AML cellular lipid extracts

Mass-to-charge (m/z)

Mass spec metabolomics

1,000

500

1

2

3

4

5

6

7
8
9
10
Retention time (min)

11

12

13

14

15

16

17

14

15

16

17

SM(8.9%)PE (8.9%)

CL (4.3%)
DG (2.7%)

LPC (9.1%)

Cer_NS (4.3%)
PS (2.5%)
LDGTS (0.5%)
Cholesterol (0.2%)
LPE (1.1%)
PG (0.7%)
ACar (0.9%)

BMP (1.8%)

TG (14.1%)

CE (3%)PC (37%)B
Mass-to-charge (m/z)

As the first step in the lipidomics
workflow, two sets of five
Iterative MS/MS data files from pooled
AML cell extracts were analyzed with
Lipid Annotator software (Figure 2).
There were 430 lipids representing
17 classes annotated for positive ion
mode, and 653 lipids representing
25 classes annotated for negative ion
mode. Lipid Annotator results were
exported to PCDL (.cdb) files.

1,500

1,500

1,000

500

2

3

4

PC (12.5%)

FA (6.7%)

Cer_NS (8.2%)

PG (6.6%)
LPC (3.2%)

5

6

7
8
9
10
Retention time (min)

11

12

13

LPE (2.6%)
EtherPC (7.3%)
HexCer_NS (2.2%)
OxPI (O.4%)
SM (3.6%)
CL (4.8%)
SHexCer (0.1%)
PA (1.3%)
OxPC (0.1%)
LPI (0.1%)
LPG (0.3%)
MDGD (0.4%)
GM3 (0.4%)
PMeOH (0.1%)
LPS (0.1%)
PI (9.3%)
FAHFA (2.2%)

PS (4.8%)

EtherPE (8.7%)
PE (13.6%)

Figure 2. Lipid Annotator software results for positive (A) and negative (B) ionization modes.
Five Iterative MS/MS data files were analyzed as a batch for each project. For illustration purposes, a
representative total ion chromatogram is overlaid with the m/z versus retention time scatter plot. Lipidfeatures are colored by lipid class corresponding to the pie charts, where the numbers of annotated lipids
are shown as percentages.

534

Mass spec metabolomics

Lipid profiling identifies perturbations
induced with drug treatments

BEZ
BaP
20

MPA

Component 2 (17.69%)

Vehicle

10

0

-10

-20

20

-10

0
Component 1 (42.78%)

10

20

B
BEZ
BaP
MPA
Vehicle10
Component 2 (12.41%)

The PCDL (.cdb) databases were used
for Batch Targeted Feature Extractionin Profinder on the respective batchesof 16 MS1 data files. Critically, both
the database compound formulas andretention times were used as required
criteria to search the MS1 data files forthe lipid features in a targeted manner.
Resulting compounds were reviewed in
Profinder and, in some cases, features
were manually integrated or removed
due to poor or ambiguous featurepeak shapes. After manual curation,
375 compounds and 548 compounds
remained in the positive and negative
ion mode datasets, respectively.
Profinder results (.pfa file) were
imported into Mass Profiler Professional(MPP) for statistical analysis, whereseparate experiments were createdfor positive and negative ion modes.
After normalization and baselining, theresulting PCA plots for both polarities
were similar. Without filtering of entities(keeping all compounds), the results
showed tight clustering of the biologicalreplicates within each condition anddemonstrated clear differences between
the drug treatments. Both BEZ and
MPA contributed separately to the
combination BaP effect (Figure 3).
Separation of the groups along
principal component 1 suggested
that BEZ treatment contributed morethan MPA to the combination BaP
drug effect on the lipidome. Theseobservations were consistent with thosedescribed previously.1

A

0

-10

-20

-10

0
Component 1 (40.92%)

10

20

Figure 3. PCA plots for the positive ion (A) and negative ion (B) datasets.

635

BEZ 2
BEZ 4
BEZ 1
BEZ 3
BaP 1
BaP 4
BaP 2
BaP 3

MPA 1
MPA 2
MPA 3
MPA 4

Vehicle 1
Vehicle 2

Vehicle 3
Vehicle 4

Sample correlation (not shown) andunsupervised hierarchical clustering(Figure 4) provided further support for
the PCA results. Biological replicateswithin the conditions grouped together,and BEZ samples showed a closer
relationship to BaP than MPA samples.
Trends were also observed fromunsupervised clustering on the annotatedlipid features. For example, inspectionof the cluster tree within a region
showing a clear pattern revealed a close
relationship for many triacylglycerol (TG)lipids. These lipids were increased in BEZ
and BaP treatments compared to MPA
and the vehicle control samples.

Zoom view

Mass spec metabolomics

Figure 4. Combined unsupervised hierarchical clustering results on compounds (n = 375) and conditions
for the positive ion dataset. (Right) A zoomed region of the cluster tree enriched with annotated TG lipids
is shown. The color range represents the normalized, transformed abundances for each TG feature
represented on a log2 scale.

736

Mass spec metabolomics

Lipid profiling demonstrates
disrupted lipogenesis
To assess differences across lipid classabundances in more detail, a lipid classmatrix plot (heat map) was createdin MPP from the positive ion mode
dataset (Figure 5). Clear differenceswere observed. In agreement with
Southam et al.,1 TGs were increased anddiacylglycerols (DGs) were decreasedin BaP versus vehicle control. DGs
are intermediates in the de novo
phospholipid biosynthetic pathway, andthe authors suggested that DG depletion
with BaP treatment resulted from
disruption of phospholipid synthesis at
the acyl chain addition stage. Table 3
shows a summary of lipid classes withsignificantly different abundances fromthe positive and negative ion datasets.
In contrast to the previous report, we
did not observe a significant decreaseof lysophosphatidylcholine (LPC) or
lysophosphatidylethanolamine (LPE)
class abundances with BaP treatment.
Multiple reasons could account forthis discrepancy, including differencesin normalization and processing
methods for the MS lipid datasets.
We observed significant lipid classdifferences not reported in the previous
study, most notably increases inlevels of ceramide nonhydroxyfatty
acid-sphingosines (Cer_NS) andhexosylceramide nonhydroxyfatty
acid-sphingosines (HexCer_NS), anddecreased phosphatidylcholine (PC)levels in BaP treated cells versus vehicle
control. While the previous study applieda shotgun lipidomics approach withtargeted scans for a limited panel of lipid
classes, our targeted workflow beganwith a discovery phase to search acomprehensive in silico spectral library,
and then used these results for targeted
data mining. Therefore, our approach
was unrestricted, and likely led to thesefindings in lipid class differences.

BaP
1

2

Vehicle
3

4

1

2

3

4

BMP
CE
CL
Cer_NS
Cholesterol
DG
LPC
LPE
PC
PE
PG
PS
SM
TG

Figure 5. MPP lipid class matrix of total normalized lipid class abundances across BaP treatment andvehicle control sample replicates. The color range represents the sum of normalized, transformed
abundances for all lipid features within a lipid class.

Table 3. Summary of lipid classes with significantly different abundance levels induced with
BaP treatment.
Lipid Class
Ceramide Nonhydroxyfatty Acid-Sphingosines

Abbreviation

BaP EffectPolarity†(+)***, (-)*

Cer_NS

Increased

HexCer_NS

Increased(-)***

Triacylglycerols

TG

Increased(+)***

Gangliosides

GM3

Increased

(-)**
(-)**

Hexosylceramide Nonhydroxyfatty Acid-Sphingosines

Lyso-Phosphatidylglycerols

LPG

Increased

Ether-Linked Phosphatidycholines

Ether PC

Increased(-)*

Lyso-Phosphatidylethanolamines

LPE

Increased(+)*

Lyso-Phosphatidylserines

LPS

Increased(-)*

Sulfatides

SHexCer

Increased

(-)*

DG

Decreased(+)***MGDG

Decreased(-)***

PhosphatidylinositolsPI

Decreased(-)***

Cholesterol Esters

CE

Decreased(+)**

Cardiolipins

CL

Decreased(-)**

Oxidized Phosphatidylcholines

OxPC

Decreased(-)*

Oxidized Phosphatidylinositols

OxPI

Decreased(-)*

Phosphatidic Acids

PA

Decreased(-)*

Diacylglycerols
Monogalactosyldiacylglycerols

A two-tailed t-test was used to determine significance between vehicle and BaP sample groups:* p <0.05, ** p <0.01, *** p <0.001

†

8

37

Mass spec metabolomics

BaP
1

2

3

Vehicle
4

1

2

3

4

PC 18:1_22:6
0.4
Normalized intensity values

Lipid matrices were also created in
MPP to visualize abundance differences
across individual lipid features
within lipid classes. Inspection of
the phosphatidylcholine (PC) matrix
plot revealed some inverse patterns
corresponding to a decrease in PCs
with saturated fatty acyl chains (forexample, PC 16:0_26:0) and an increasein PCs with polyunsaturated fatty acyl
chains (for example, PC 18:1_22:6)
induced with BaP treatment (Figure 6).
These observations are also consistent
with Southam et al.,1 who suggested
that PC lipids with 0 to 2 double
bonds are decreased due to reduced
de novo fatty acid and phospholipid
synthesis induced by BaP treatment.
They further hypothesized that theincrease in polyunsaturated PC lipids
was most likely due to cells obtaining
polyunsaturated fatty acids from an
exogenous source other than glucose.

0.3
0.2
0.1
0
-0.1
-0.2
-0.3
-0.4
BaP

Differential response of lipid isomers
with drug treatment

PC 16:0_26:0

Normalized intensity values

The comprehensive LC-based
lipidomics approach allowed profilingof lipid isomers that had the same sum
composition (that is, same exact mass)
but were resolved with chromatography.
There were significant numbers ofsuch isomers in the datasets: in thepositive mode dataset, 94 of the
430 annotated lipids were isomers, whilein the negative mode dataset, 165 of
653 annotated lipids were isomers. In
many cases, isomers showed markedly
different responses to drug treatment.
Inspection of the ceramide (Cer_NS)
lipid matrix in MPP revealed an inverse
relationship for several pairs of isomers(Figure 7A). As an example, extracted ion
chromatograms for the pair of partiallyresolved Cer_NS 42:2 isomers confirmed
an inverse response to BaP treatment,
where the later-eluting isomer was
decreased in BaP treatment comparedto the early-eluting isomer (Figure 7B).

Vehicle

0.2
0
-0.2
-0.4
-0.6
BaP

VehicleFigure 6. MPP lipid matrix of 137 phosphatidylcholine (PC) lipid features across BaP treatment and vehicle
control sample replicates. Box and whisker plots of two selected PC features are shown to the right.

938

Mass spec metabolomics

A

4
BaP

3
2
1
4

Vehicle

3
2
1

B

C
VehicleCer_NS 42:2
RT 12.38

12.38

646.6159

RT 12.38
24:1
18:1

390.3741
616.6037

237.2224 263.2382 347.3319
BaP

12.38

347.3319

237.2224
263.2380
12.3

12.4

12.5

12.6

100

200

616.6038

390.3741

300

400

646.6144
500

600

700

Mass-to-charge (m/z)
Vehicle12.44

Cer_NS 42:2
RT 12.44

646.6155

RT 12.44
24:0
18:2

392.3898

261.2228

349.3477

616.6030

235.2067 261.2224 349.3476

616.6038

235.2065
BaP
12.45

646.6144

392.3898
12.3

12.4

12.5

12.6

100

200

300

400
500
Mass-to-charge (m/z)

600

700

Figure 7. Differential responses and structural elucidation of ceramide nonhydroxyfatty acid-sphingosine (Cer_NS) isomers. (A) MPP lipid matrix of 39 Cer_NS
lipid features across BaP treatment and vehicle control sample replicates. Pairs of isomers indicated with asterisks have the same exact mass but different
retention times and showed an inverse response. (B) Overlaid extracted ion chromatograms comparing vehicle control (n = 4) to BaP treatment (n = 4) for thepair of Cer_NS 42:2 isomers. (C) Corresponding head-to-tail plots from Lipid Annotator with the major matched product ions additionally labeled (observed, red;
database values, blue). Product ion shifts of plus or minus m/z 2.0156 between the two plots provided the evidence for the difference in the double bond numbersin the sphingosine bases and esterified fatty acids (labeled). The observed spectra matched Cer_NS d18:1_24:1 and Cer_NS d18:2_24:0 database spectra, andthese were indicated as the most likely constituents in the Lipid Annotator software.

1039

Mass spec metabolomics

While the isomers were annotated withthe same sum composition, inspectionof Lipid Annotator results provided
strong evidence that the early- andlate-eluting isomers were Cer_NS
d18:1_24:1 and Cer_NS d18:2_24:0,
respectively (Figure 7C). The biologicalsignificance of the differential responseof ceramide isomers to BaP treatment is
not known, but this type of informationis only revealed with the lipidomics
profiling approach.

The untargeted lipidomics workflow
reveals a highly differential
atypical lipid
As described elsewhere,4 untargeted
workflows are also supported and use
the same PCDL and software as thetargeted workflow previously described.
The major differences are that:•

Untargeted feature finding (recursive
batch feature extraction algorithm)
in Profinder is used, and•Lipid annotation is performed later inthe workflow within MPP using the
ID Browser tool.

In our study, the 16 negative ion MS1
data files were analyzed with therecursive batch feature extraction
algorithm in Profinder, and 2,052 features
were imported into MPP. As a result, 513
out of the 2,052 features were annotatedas lipids with the ID Browser tool using
the same negative-ion PCDL librarycreated above (RT ±0.10 minutes was
specified as the required criteria).

To focus the differential analysis on
reproducible features, the entity list was
filtered by sample variability with a CV
<25% required for all four conditions. This
reduced the entity list to 1,377 features.
A moderated t-test on BaP treatment
versus vehicle control resulted in 93
entities that were significantly different(fold-change cutoff 1.5, p-value 0.05),
and 41 of these entities had beenannotated as lipids (Figure 8A). The mosthighly differential feature showed a 3.93fold increase in BaP cells compared tovehicle control (p-value 3.54 × 10–5), andinspection of the entity showed striking
differences across the four conditions(Figure 8B). The compound of interest
had a neutral mass of 339.2774 Da, andID Browser did not return an annotation.
To gain insight into the nature of thiscompound, a Kendrick mass defect(KMD) plot was created in MPP for the
combined list of 513 annotated lipids andthe 93 differential entities (Figure 8C).
Plotting KMD (Y-axis) against lipid
class (X-axis) revealed that the featureof interest shared a similar KMD to agroup of four Cer_NS lipids each withtwo double bonds. Interestingly, themasses of the four ceramide lipids(masses 707 to 735 Da) were muchlarger than the feature of interest(339 Da). The Lipid Calculator tool within
Lipid Annotator was used to generate
hypothetical Cer_NS lipids to assess
whether a sum composition could be
generated with a resulting mass close

to m/z 339.2774. With this approach,the lipid Cer_NS d18:2_2:0 showed amass within 0.3 ppm of the observed
feature mass. Furthermore, analysis with
Qualitative analysis software showed an
MS/MS product ion specific for the d18:2
sphingosine backbone that provided
further evidence for the candidate
Cer_NS d18:2_2:0 annotation (data notshown). The candidate structure for
Cer_NS d18:2_2:0, also known as C2
ceramide or N-acetylsphingosine, isshown in Figure 8D. This C2 ceramide
is absent in many lipid databases(including Lipid Annotator) and, to ourunderstanding, has not routinely been
identified in lipidomics studies. However,C2 ceramide was found physiologicallyat low levels in a different AML cell line
(HL-60).5 Synthetic C2 ceramide is widelyused as a research tool for its biologically
active properties, including the ability
to inhibit cell proliferation and induce
apoptosis.6 We speculate the increaseof C2 ceramide in the BaP-treated cells
could be related to the anticancer effectsof BaP, and believe this information may
be of interest to the cancer researchcommunity.

1140

Mass spec metabolomics

C

A
4

3

0.3
Kendric mass defect–Log10(corrected P value)

0.4
339.2774 at 2.506

2

0.2
0.1
0
-0.1

339.2774 at 2.506

-0.2
-0.3

1

-0.4

Kendric mass defect

0.101
0.099
0.097

FAHFA

FA

EtherPE

EtherPC

Normalized intensity values

SM

SHexCer

PS

PI

PG

PE

PC

PA

0xPE

MGDG

MPA

LPS

BaP

LPI

Cer_NS 44:2; KMD .10205
Cer_NS 42:2; KMD .10175
Cer_NS 42:2; KMD .10168
Cer_NS 18:2_16:0; KMD .10156

0.103

Cer_NS

BEZ

LPE

Zoom view339.2774 at 2.506, KMD.10141

0.105

CL

-1.0

LPC

-0.5

HexCer_NS

0

GM3

0.5

FAHFA

B

FA

3

EtherPE

2

EtherPC-10
1
Log2(fold change)

Cer_NS-2

CL-3

No annotation0

VehicleD

Figure 8. Elucidation of an unknown differential feature with the untargeted workflow. (A) MPP Volcano plot from a moderated t-test with Benjamini-Hochberg
FDR multiple testing correction for BaP treatment versus vehicle control. Significant features (fold change cutoff >1.5, p-value >0.05) are colored in blue (annotatedlipids) and red (unannotated features). The feature of interest (m/z 339.2774 at 2.506) is circled in green. (B) Box and whisker plot of the feature of interest for thefour drug treatment conditions. (C) MPP Kendrick mass defect (KMD) plot for the combined entity list of 513 annotated lipids with the list of 93 differential features(n = 565). Features that could not be annotated with the PCDL are shown in the first column in red. The zoomed region shows the alignment by KMD of the featureof interest with a group of Cer_NS lipids. (D) Cer_NS 18:2_2:0 candidate structure for the feature of interest.

1241

Mass spec metabolomics

Conclusion

References

This Application Note demonstrates
that the lipidomics profiling workflow,
including Lipid Annotator software,
provides substantial improvement in lipid
annotation and differential analysis ofcomplex samples. We applied a targeted
workflow to study lipidome alterations of
the acute myeloid leukemia K562 cell line
in response to a combination of the BEZ
and MPA drug candidates. The resulting
analysis revealed several cellularchanges in response to drug treatment,
including a decrease in diacylglycerols,
an increase in triacylglycerols, anddifferences in fatty acyl components.
Taken together, these results support
a previous report suggesting thatthe BaP combination may exert its
anticancer properties through disruptionof lipogenesis.

1.

Southam, A. D. et al. Drug
Redeployment to Kill Leukemia
and Lymphoma Cells by Disrupting
SCD1-Mediated Synthesis ofMonounsaturated Fatty Acids.
Cancer Res. 2015, 75(12),2530–2540.

2.

Sartain, M. et al. Improving Coverageof the Plasma Lipidome Using
Iterative MS/MS Data Acquisition
Combined with Lipid Annotator and6546 LC/Q-TOF. Agilent Technologies
Application Note, publication number
5991-0775EN, 2019.

3.

MassHunter Profinder: Batch
Processing for High-Quality Feature
Extraction of Mass SpectrometryData. Agilent Technologies Technical
Overview, publication number
5991-3947EN, 2014.

4.

Lipidomics Analysis with Lipid
Annotator and Mass Profiler
Professional. Agilent Technologies
Technical Overview, publication
number 5994-1111EN, 2019.

5.

Snyder, F. et al. Biosynthesisof N-Acetylsphingosine by
Platelet-activating Factor:
Sphingosine CoA-independent
Transacetylase in HL-60 Cells. The
Journal of Biological Chemistry 1996,
271(1), 209–217.

6.

Hannun, Y. A. et al. Programmed Cell
Death Induced by Ceramide. Science
1993, 259(5102), 1769–1771.

This lipid profiling workflow also
provides more comprehensive lipid
annotation than can be achieved bytraditional shotgun-based lipidomics
approaches. Specifically, we identified
significant differences in lipid classabundances induced with BaP treatmentthat were not previously reported.
We also identified specific cases ofchromatographically separated lipid
isomers that displayed differential
responses to drug treatment. Finally,
with an untargeted approach wedemonstrated the ability to propose
candidate annotations for unannotatedlipid features using supplementary tools.

www.agilent.com/chem
For Research Use Only. Not for use in diagnostic procedures.
This information is subject to change without notice.

13© Agilent Technologies, Inc. 2019
Printed in the USA, October 25, 20195994-1356EN

42

Mass spec metabolomics

Join Dr. Nicola Zamboni, Institute for Molecular System
Biology, ETH Zurich, as he discusses trends and challenges
in high-throughput metabolomics and 13C metabolic flux
analysis in this award-winning on-demand webinar.
For Research Use Only. Not for use in diagnostic procedures.

Click here
to watchthis webinar

43

Mass spec metabolomics

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