- Email: [email protected]
MS using SWATH data-independent acquisition for the study of platelet lipidomes in coronary artery disease
Accepted Manuscript Comprehensive MS/MS Profiling by UHPLC-ESI-QTOF-MS/MS using SWATH Data-independent Acquisition for the Study of Platelet Lipidomes in Coronary Artery Disease Jörg Schlotterbeck, Madhumita Chatterjee, Meinrad Gawaz, Michael Lämmerhofer PII:
S0003-2670(18)31056-0
DOI:
10.1016/j.aca.2018.08.060
Reference:
ACA 236234
To appear in:
Analytica Chimica Acta
Received Date: 23 July 2018 Revised Date:
29 August 2018
Accepted Date: 30 August 2018
Please cite this article as: J. Schlotterbeck, M. Chatterjee, M. Gawaz, M. Lämmerhofer, Comprehensive MS/MS Profiling by UHPLC-ESI-QTOF-MS/MS using SWATH Data-independent Acquisition for the Study of Platelet Lipidomes in Coronary Artery Disease, Analytica Chimica Acta (2018), doi: 10.1016/ j.aca.2018.08.060. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Lipidomics with SWATH
Control
SWATH Optimization
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Platelet collection and extraction
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ESI-
PGPC PAzPC POVPC PONPC
10 8
Application and data evaluation
ESI+
6 4 2
0 SWATH Q1 width Accumulation time
30 Da
5 Da
3 Da
30 Da
5 Da
33 ms
3 Da
100 ms
enh.*
2.5
2x
2.0 1.5
A
1.0
0
0.5
5
10
5 Da
100 ms
33 ms
ESI+
33 ms
PGPC
+ 90 %
EP
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SAP STEMI
+ 30 %
100 ms
9000 8000
+ 53 %
600
POVPC 400
3 Da
5 Da
3 Da
PAzPC
200
5 Da
POVPC
3 Da
5 Da
PONPC
100 ms enh*
Optimized Windows Features for SWATH Tuning
3000 2000
0 0
5
10 15 20 Retention Time (min)
m/z
5 Da
PGPC
6000
cycle 5000 SWATH 4000
1
3 Da
7000
PONPC
m/z
2
10000
enh.*
PAzC
Intensity
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+ 46 %
3
0 SWATH Q1 width Accumulation time
30
3 Da
100 ms
800
4
UHPLCESIQTOF
25
1000
30 Da
33 ms
5
25
30
Retention Time (min)
G Normalized peak intensity
0.0 SWATH Q1 width Accumulation time
Experiment 15 20
70 60 50 40 30 20 10 0 -10 -20
DHEAS q=0.00230
QC
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SAP STEMI
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Comprehensive MS/MS Profiling by UHPLC-ESI-QTOF-
2
MS/MS using SWATH Data-independent Acquisition for
3
the Study of Platelet Lipidomes in Coronary Artery
4
Disease
5
Jörg Schlotterbeck†, Madhumita Chatterjee‡, Meinrad Gawaz‡ and Michael Lämmerhofer†
SC
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†
6
University of Tübingen, Institute of Pharmaceutical Sciences, Pharmaceutical
7
(Bio-)Analysis, Auf der Morgenstelle 8, 72076 Tübingen, Germany ‡
8
1Department of Cardiology and Cardiovascular Medicine, University Hospital Tübingen, Otfried-Müller-Strasse 10, 72076 Tübingen, Germany
10
Keywords.
11
Lipidomics,
SWATH,
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Data-independent
acquisition,
phospholipids, biomarkers
EP
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13
Corresponding Author:
15 16 17 18 19 20 21 22 23 24 25 26 27
Prof. Dr. Michael Laemmerhofer Professor for Pharmaceutical (Bio-)Analysis Institute of Pharmaceutical Sciences University of Tuebingen Auf der Morgenstelle 8 72076 Tuebingen, Germany
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T: +49 7071 29 78793, F: +49 7071 29 4565 e-mail: [email protected] http://www.bioanalysis.uni-tuebingen.de/
1
Clinical
Analysis,
oxidized
ACCEPTED MANUSCRIPT 28
ABSTRACT. An untargeted lipidomics workflow based on C8 core-shell particle ultra high-performance
30
liquid chromatography (UHPLC) hyphenated to ESI-QTOF-MS in data-independent
31
acquisition (DIA) with sequential window acquisition of all theoretical fragment ion spectra
32
(SWATH) was developed and applied to differential platelet lipidomics profiling of
33
cardiovascular disease patients (stable angina pectoris (n=10), ST-elevated myocardial
34
infarction (n=13))
35
comprehensive MS and MS/MS data throughout the entire chromatograms and all study
36
samples. Hence, chromatograms can be extracted based on precursors or fragments which
37
provided some benefits in terms of assay specificity in some cases. SWATH acquisition
38
offers
39
chromatography as well as SWATH settings were optimized to cover the lipidome of human
40
platelets. The flexibility of the SWATH experiment design was utilized to implement target
41
SWATH windows with narrow 5 Da Q1 precursor ion selection width (multiple reaction
42
monitoring (MRM)-like SWATH windows) for the detection of low abundant oxidized
43
phospholipids. Data processing was performed with MS-DIAL, and its feasibilities and
44
caveats are discussed by illustrative examples. Thereby, identification of lipids is still a
45
bottleneck in untargeted lipidomics workflow. MS-DIAL, however, offers automatic
46
identification via spectral matching using an in silico library. In total 1971 molecular features
47
were detected across the samples of which 611 were identified (total score >70 %). The
48
quality of the acquired data was validated with embedded quality control samples (n=11).
49
80.3 % of all features detected in the QC samples showed a coefficient of variation of below
50
30 %. Multivariate statistics were used to visualize differences in the lipidome of distinct
51
sample groups at a false discovery rate of 5%.
healthy controls
(n=10).
DIA with
SWATH generates
experimental
design
with
variable
Q1
isolation
windows.
Liquid
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1
INTRODUCTION. It is generally known that lipids are more than energy providers and membrane building
58
blocks. Lipid metabolism is strongly involved in complex pathways like inflammation,
59
signaling and regulation [1-3]. Changes in the lipidome are correlated to various diseases
60
like diabetes, cardiovascular disease, neurodegenerative diseases and cancer [1, 4-6].
61
Therefore, it is evident that lipidomics is a promising research field since its introduction in
62
the year 2003 [7-9]. Lipidomics studies were promoted through technical progress in
63
analytical chemistry. Lipid Maps database, a major resource of lipid related information and
64
data, lists more than 40,000 lipids and computational methods assume over 100,000
65
possible lipid species [10]. State of the art techniques in mass spectrometry (MS) and (ultra)-
66
high performance liquid chromatography ((U)HPLC) are therefore indispensable in order to
67
cope with this huge complexity.
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Two different analytical approaches are common for non-targeted lipidomics studies. The
69
first approach is direct-infusion MS (shotgun lipidomics) which is suitable for high throughput
70
discovery studies due to methodological simplicity and speed [11-15]. A disadvantage may
71
be the lack of sensitivity (low abundant lipids are often suppressed by highly abundant ones)
72
and limited identification coverage [16]. On the other hand, discovery of unknowns is limited
73
to ultra-high resolution MS [8]. LC-MS based methods allow more reliable identification even
74
at lower concentration levels [17]. Due to the separation, ion-suppression effects are
75
reduced. Even isomers and isobars can be resolved chromatographically which improves
76
assay specificity. For this reason, some authors propagate two-dimensional LC-MS
77
workflows which provide enhanced chromatographic lipid resolution [18]. High resolution
78
mass spectrometry with resolving power higher than 15,000 and typical mass accuracy
79
below 5 ppm reduces isobaric overlaps yielding a better overall lipid coverage and increases
80
reliability of identifications [19]. For exact and unequivocal identification tandem MS (MS/MS)
81
is essential. Specific fragmentation of precursor ions could help to identify single lipid
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3
ACCEPTED MANUSCRIPT structures. Mainly there are two ways to acquire MS/MS data in untargeted lipidomics. (1)
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Data-dependent acquisition (DDA) selects precursor ions for fragmentation based on the
84
information gained in a MS survey scan, therefore also sometimes called information-
85
dependent acquisition (IDA) [20]. This information could be specified intensity thresholds, a
86
defined mass range, target masses or mass defect filters [20]. Typically, the top 10-20
87
precursor ions are triggered for MS/MS [21-25]. A noteworthy disadvantage of DDA is low
88
reproducibility and incomplete analyte coverage [20, 26-28]. (2) Data-independent
89
acquisition (DIA) acquires spectral data of fragments without specific selection of a
90
precursor. A limitation of this technique is the generation of composite spectra of coeluted
91
substances that are simultaneously co-fragmented, which could exacerbate lipid
92
identification through their complexity. This complexity could be reduced on analytical level
93
with optimized LC separation and the application of sequential window acquisition of all
94
theoretical fragment ion spectra (SWATH). This DIA technique was introduced in 2012 by
95
Gillet et al. [26] and presented as a new concept for proteomics. Fragmentation of all
96
precursors is achieved through consecutive Q1 isolation windows for a given mass range.
97
SWATH was promptly adapted for small molecule analysis like metabolomics and lipidomics
98
[28-30]. SWATH-MS increases analyte coverage and generates still good MS/MS spectra
99
quality for metabolomics [20]. DIA with SWATH outperformed DDA methods in forensic,
100
toxicological and pharmaceutical studies with higher detection rate and more complete
101
identification [28, 30-32]. To efficiently adapt SWATH-MS for lipidomics, it was necessary to
102
implement databases for identification. In 2013, LipidBlast, an in silico MS/MS database for
103
lipidomics, was introduced for spectral matching and lipid identification, respectively [33].
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This database was implemented in freely available data processing software MS-DIAL,
105
which allowed automatic peak finding, deconvolution and identification of lipids with SWATH
106
data files [29].
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In this study, we present a workflow which utilizes these recent developments in DIA
108
techniques for a comprehensive lipidomics study of platelets involved in coronary artery 4
ACCEPTED MANUSCRIPT disease. The analysis of platelets instead of blood plasma could lead to additional insights of
110
cardiovascular disease mechanisms as they are responsible for symptomatic thrombus
111
formation during an acute myocardial infarction. Platelets can be regarded as individual
112
cellular compartments with distinctive lipid metabolism from plasma. They are activated by
113
different pathways releasing lipid signaling molecules which regulate thrombotic and
114
inflammatory responses leading to significant alterations of the platelet lipidome in the
115
course of platelet activation [15, 34, 35]. In this work, modern core-shell particle technology
116
for UHPLC separations was combined with high resolution non-targeted SWATH-MS/MS.
117
Variable Q1 isolation windows were utilized to increase selectivity and to reduce cycle time
118
[36]. Additional narrow target windows were implemented to enable combined targeted
119
acquisition for four oxidized phosphatidylcholines (oxPCs), viz. 1-palmitoyl-2-(5-oxovaleroyl)-
120
sn-glycero-3-phosphatidylcholine
121
phosphatidylcholine
122
(PAzPC),
123
approach allowed increased sensitivity and selectivity for targets and non-targeted analysis
124
with a single injection. Non-targeted peak finding and identification of lipids in platelet
125
extracts was possible with MS-DIAL [29]. For data evaluation, a statistical workflow based on
126
multivariate statistics, e.g. principal component analysis (PCA), and univariate statistics was
127
developed [37]. A scheme of the entire workflow is depicted in Fig. 1.
128
2
129
2.1
(POVPC),
1-palmitoyl-2-(9-oxononanoyl)-sn-glycero-3-
1-palmitoyl-2-azelaoyl-sn-glycero-3-phosphatidylcholine
1-palmitoyl-2-glutaryl-sn-glycero-3-
phosphatidylcholine
(PGPC).
This
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(PONPC),
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EXPERIMENTAL SECTION. MATERIALS.
130
Acetonitrile was purchased in ultra-LC-MS grade and 2-propanol as LC-MS grade from
131
Carl Roth GmbH + Co. KG (Karlsruhe, Germany). Ammonium acetate (>99.0 %) was
132
purchased
133
oxovaleroyl)-sn-glycero-3-phosphatidylcholine (POVPC), 1-palmitoyl-2-(9-oxononanoyl)-sn-
134
glycero-3-phosphatidylcholine
(PONPC),
135
phosphatidylcholine
1-palmitoyl-2-glutaryl-sn-glycero-3-phosphatidylcholine
5
from
Sigma-Aldrich
(PAzPC),
Inc.
(Schnelldorf,
Deutschland).
1-Palmitoyl-2-(5-
1-palmitoyl-2-azelaoyl-sn-glycero-3-
ACCEPTED MANUSCRIPT (PGPC),
1-heptadecenoyl-sn-glycero-3-
phosphatidylcholine
(LPC(17:1))
and
137
heptadecenoyl-2-eicosatetraenoyl-sn-glycero-3- phosphatidylcholine (PC(17:0/20:4)) were
138
obtained from Avanti Polar Lipids, Inc. (Alabaster, AL, USA). Dehydroepiandrosterone
139
sulfate (DHEAS) was purchased as part of the MassCheck Steroid Panel 2 from
140
Chromsystems Instruments & Chemicals GmbH (Gräfelfing, Deutschland).
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1-
141
Nomenclature and abbreviation of lipids was done according to the classification system of
142
the LIPID MAPS consortium [35, 36]. Lyso lipid species are indicated with the prefix “L” (e.g.
143
PC
144
glycerophosphatidylethanolamines, PC: glycerophosphatidylcholines, SM: sphingomyelin,
145
DG:
146
PI: glycerophosphatidylinositols, PS: glycerophosphatidylserines, FA: fatty acid. Lipid
147
shorthand notation follows the guideline of Liebisch et al. [38].
148
2.2
lyso
species)).
diradylglycerolipids,
Abbreviations
ChE:
PLATELET ISOLATION.
cholesterol
of
lipids:
AC:
acyl
carnitine,
PE:
SC
for
ester,
TG:
triradylglycerolipids,
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Plasma from coronary artery disease (CAD) patients and healthy donors was collected at
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the University Clinic Tübingen after having obtained informed consent in accordance with the
151
ethics committee of the Medical Faculty of the University of Tübingen. Washed platelets
152
were isolated from healthy subjects (n=10), patients suffering from stable angina pectoris
153
(SAP) (n=10) and acute coronary syndrome (ACS) patients with ST-elevation myocardial
154
infarction (STEMI) (n=13) as mentioned in a previous publication [39]. Blood samples were
155
collected in acid-citrate-dextrose-buffer and centrifuged at 430 × g for 20 min. Platelet-rich
156
plasma thus obtained was added to Tyrodes-HEPES buffer (HEPES-2.5 mM; NaCl-150 mM;
157
KCl-1 mM; NaHCO3-2.5 mM; NaH2PO4-0.36 mM; glucose-5.5 mM; BSA-1 mg mL-1; pH 6.5)
158
and centrifuged at 900 × g for 10 min. The supernatant was removed and Tyrodes-HEPES
159
buffer (pH 7.4; supplemented with 1 mM CaCl2; 1 mM MgCl2) was used to resuspend the
160
platelet pellet. Washed platelets were stored at -80 °C until lipid extraction was carried out.
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2.3
LIPID EXTRACTION.
Lipids were extracted from the platelet pellets as published previously [34]. The resulting
163
platelet pellets (3x108 platelets/sample) from the washing step were resuspended in cold
164
acidic ethanol (ethanol / 0.18 M HCl 75:25; v/v). Gentle sonification in a water bath while
165
cooling on ice was used to lyse the platelets. The platelet lysate was centrifuged to get rid of
166
cellular debris and the supernatant was collected and stored at -20 °C until analysis. Before
167
analysis supernatants were dried under a gentle stream of nitrogen and reconstituted in 500
168
µL of methanol. Samples were centrifuged again, and the supernatant was carefully
169
removed without disturbing the pellet. The entire sample preparation was carried out under
170
light protection. The collected supernatants were spiked with internal standards (LPC(17:1),
171
PC(17:0/20:4)) at a final concentration of 40 ng mL-1 and subjected to UHPLC-MS analysis.
172
2.4
CHROMATOGRAPHY.
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An Agilent 1290 Infinity UHPLC instrument (Agilent Technologies, Waldbronn, Germany)
174
with HTC-xt PAL autosampler (CTC, Zwingen, Switzerland) (samples maintained at a tray
175
temperature of 4 °C in the dark) hyphenated to a SCIEX TripleTOF 5600+ (SCIEX, Concord,
176
Ontario, Canada) hybrid mass spectrometer was used for analysis of platelet lipid extracts. A
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Kinetex C8 core-shell particle column (150 x 2.1 mm; 2.6 µm) (Phenomenex, Aschaffenburg,
178
Germany) equipped with a C8 Security Guard ULTRA cartridge (2.1 mm I.D.) (Phenomenex)
179
was used for chromatographic separation prior to MS detection. The mobile phase was
180
composed of aqueous 10 mM ammonium acetate (A) and a mixture of acetonitrile, 2-
181
propanol and water (55:40:5; v/v/v) containing 10 mM ammonium acetate (B). The gradient
182
was 10 % B to 40 % B in 2 minutes and 100 % B in 20 minutes followed by a cleaning step
183
of 10 minutes 100 % B and 2 minutes equilibration with 10 % B at a flow rate of 400 µL·min-1
184
and 50 °C oven temperature. Injection volume was 2 µL.
185
2.5
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MASS SPECTROMETRY.
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A TripleTOF 5600+ (SCIEX) equipped with a DuoSpray source was operated in positive
187
ion mode with an ion spray floating voltage (ISFV) of 5000 V and in negative ion mode with 7
ACCEPTED MANUSCRIPT an ISFV of -4500 V. Heater temperature was set to 500 °C. Nebulizer gas (GS1), heater gas
189
(GS2) and curtain gas (Cur) were used for both polarities at 50, 40, 30 psi, respectively.
190
Declustering potential was set to 100 V. For both polarities the TOF-MS survey scan with a
191
specified resolution of R≥30.000 (FWHM) was set to 250 ms accumulation time. Non-
192
targeted MS/MS acquisition was carried out using DIA by SWATH with variable Q1 window
193
size covering a range of m/z 30 to 1000. The size of Q1 windows was optimized using
194
swathTUNER [36]. For this purpose, the QC was analyzed in separate runs in DDA mode for
195
both polarities prior to analysis of the entire sequence. The top 20 abundant ions within an
196
m/z range of 30-1000 with a minimum intensity of 50 cps were selected for fragmentation in
197
Q1 operated in unit resolution. Dynamic background filtering was used. The already selected
198
ions were excluded for 5 s. A mass tolerance of 50 ppm was allowed and isotopes were
199
excluded within 4 Da. The collision energy for the DDA experiments was set to 20 ± 10 V.
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The resulting peak lists were used for swathTUNER calculations. Considering the need for
201
ten data points per peak [40] a maximum period cycle time of 1.5 s was tolerated in the
202
course of optimization of the SWATH windows. Therefore, 26 SWATH windows with 30 ms
203
accumulation time were calculated by swathTUNER. Four additional SWATH windows were
204
programmed in positive electrospray ionization (ESI+) mode for target analytes of prime
205
interest (oxPCs) with increased accumulation time of 100 ms and narrow Q1 window size of
206
5 Da. Due to narrow Q1 window width, these SWATH windows were MRM-like. Collision
207
energy was optimized for phosphatidylcholine species as principal constituents of significant
208
interest in this study and set to 35 V for each Q1 window. Fragment mass enhancement of
209
m/z 184.07332 was used for the target windows. In negative polarity mode, MS/MS spectra
210
were acquired in 30 variable sized Q1 windows with 40 ms accumulation time with collision
211
energy of -30 V and a collision energy spread of ± 10 V each. Exact SWATH window
212
information is shown in Table A. 1A for ESI+ and in Table A. 1B for negative electrospray
213
ionization mode (ESI-). All SWATH windows were acquired in high sensitivity mode at an
214
MS/MS resolution of R=15000 (FWHM). Calibration was carried out after every fifth injection
AC C
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ACCEPTED MANUSCRIPT using the auto calibrate function in Analyst TF 1.7.1 (SCIEX). As calibration solution 0.1 mol
216
L-1 sodium acetate in a 1:1 mixture of acetonitrile and water (v/v) was used. The calibrant
217
solution was injected into the mass spectrometer with a flow rate of 0.1 mL/min for 30 s
218
using an external pump and a switching valve before the ESI source. Acetate clusters could
219
be used as calibration masses in positive and negative TOF-MS mode as well as for MS/MS.
220
With this method a constant mass accuracy below 2 ppm was achieved. The full list of
221
calibration masses is reported in Table A. 2.
222
2.6
QUALITY ASSURANCE.
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Data quality was evaluated by QC samples which were embedded throughout the analysis
224
batch. For this purpose, an independent quality control sample (QC), obtained from a
225
previous study, was prepared as a mixture (1/1/1, v/v/v) of platelet extracts from healthy
226
donors: (1) n=10 untreated platelets, (2) n=10 platelets incubated with low-density lipoprotein
227
(LDL) and (3) n=10 platelets incubated with oxidized low-density lipoprotein (oxLDL). IS
228
LPC(17:1) and PC(17:0/20:4) were added to the resulting mixture at a final concentration of
229
40 ng mL-1. The same independent QC sample was used for the Q1 window optimization
230
described above. Before analysis, the column was equilibrated with 3 blank injections of 2-
231
propanol. The whole analysis batch consisted of 47 injections in total with 14 injections of the
232
QC sample (6 × before analysis of the first sample with the first 2 in DDA mode followed by 4
233
QC acquired with SWATH, 3 × after the last sample and the other 5 incorporated within the
234
sample sequence after every fifth injection of a sample). All real samples were injected in a
235
randomized order. The exact sample sequence is shown in Table A. 3. The quality of the
236
method across the sequence was evaluated by calculating coefficients of variation (CVs) of
237
peak heights for all molecular features detected in QCs [41, 42], by superimposing QCs
238
using PCA as well as by the internal standards and the alignment tool in MS-DIAL. Absence
239
of possible carryover of lipids, especially of TGs, was evaluated by analysis of extraction
240
solvent blanks at the beginning (i.e. before) and after the sample sequence (Fig. A. 11).
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ACCEPTED MANUSCRIPT 241
2.7
DATA PROCESSING.
Commercially available software packages, PeakView and MarkerView (SCIEX), and
243
freely available MS-DIAL were used for data processing [29]. PeakView was used for
244
manual data evaluation and for judging the LC-MS performance by comparing periodically
245
measured QC samples. Automatic peak finding, LOWESS normalization and identification
246
via spectra matching were performed in MS-DIAL (version 2.82). Prior to MS-DIAL data
247
processing, SCIEX raw data in the format Analyst .wiff was converted to Analysis Base File
248
(.abf) to import the data into MS-DIAL. The same mass range as covered by all SWATHs
249
was searched for peaks with a minimum peak height of 1000 cps for ESI+ polarity and 500
250
cps for ESI- mode, respectively. MS and MS/MS tolerance for peak centroiding was set to
251
0.01 and 0.05 Da, respectively. LipidBlast database was used to identify lipids via spectral
252
matching [33]. Retention time information was excluded from calculation of the total score.
253
Accurate mass tolerance for identification was 0.01 Da for MS and 0.05 Da for MS/MS. All
254
lipids were assigned using the identification tool of MS-DIAL. The identification is based on
255
mass accuracy, isotopic pattern and spectral matching. In MS-DIAL, these criteria were used
256
to calculate a total identification score. The total identification score cut off was 70 %. All
257
reported spectral matches were manually revised for correct assignment. Additionally, the
258
regular elution pattern of lipids differing only in length and saturation of the fatty acid
259
residues were used for lipid assignment [43]. Identification of oxPCs (PGPC, PAzPC,
260
POVPC and PONPC) was done via spectral matching of in house generated reference
261
spectra. [M+H]+, [M+NH4]+, [M+Na]+, [M+H-H2O]+, [M-H]-, [M-H2O-H]-, [M+Na-2H]-, [M+Cl]-,
262
[M+K-2H]- and [M+CH3COO]- adducts were allowed for adduct correction. The identity of the
263
oxPCs was verified by comparison of exact mass, retention time, isotopic pattern and
264
fragment pattern of reference standards. DHEAS was also verified by a reference standard.
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Since the QC samples did not consist of a mixture of all samples, but of similar platelet
266
samples from a prior study, a randomly chosen real sample (STEMI 12) was selected as a
267
reference file to build up the alignment table. Therefore, QC at least filter was not used. 10
ACCEPTED MANUSCRIPT 268
Peaks were filtered for presence in at least of 51 % of one distinct sample group. In ESI+, no
269
background subtraction was necessary. Since the background in ESI- was stronger, an
270
exclusion list was used to reduce false positive hits of the peak finding. A complete list of all
271
used MS-DIAL parameters is reported in Table A. 3 for ESI+ and in Table A. 4 for ESI-.
272
2.8
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STATISTICS.
Data quality was evaluated by a QC sample which was embedded throughout the analysis
274
batch(see chapter 2.3 Quality assurance). It is a commonly accepted quality attribute for
275
non-targeted profiling methods in lipidomics and metabolomics if the QC samples injected
276
across the entire sequence are closely grouped together and largely superimposed upon
277
each other in the score plot of multivariate statistical procedures like PCA [42, 44, 45]. For
278
sample alignment, missing values were interpolated automatically by MS-DIAL. The
279
interpolated values were considered as outliers if n≥6 of 11 QCs showed a positive peak
280
spotting. Thus, only features detected in n≥6 QCs (88 %) without interpolation were
281
considered for precision control. In Table A. 6 the abundance of features among the QC
282
samples is shown. In the present study eleven QCs were used for LOWESS normalization in
283
MS-DIAL. Normalized results of both polarities were combined and subsequently subjected
284
to MarkerView software for logarithmic transformation (ln) and PCA without any scaling. This
285
projection identified STEMI 1, 2, 5 and Control (healthy subjects) 2 as outlier samples.
286
These samples had to be removed prior to the supervised principal component analysis
287
(PCA-DA). For PCA-DA projection mean centering was used as a scaling method. Further,
288
principal component variable grouping (PCVG) was used to identify altered features with a
289
minimum loadings distance of 0.05 from the origin. Hypothesis testing was done in R
290
statistical language (https://cran.r-project.org) using the non-parametric Mann-Whitney-U-
291
test. The false discovery rate (FDR) of the resulting p-value distribution was controlled using
292
the positive FDR approach of Storey [46]. The q-values were calculated in R statistical
293
language using the Bioconductor q-value package [47]. FDR level was set to 0.05. λ values
294
for estimating
AC C
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were allowed between 0 and 0.95 in steps of 0.001 resulting in
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296
Significant features with 0.05 ≥ q-value were filtered for identified lipids. The significantly
297
altered and positively identified lipids were visualized using hierarchical clustering with the
298
software platform Perseus [48]. The normalized peak intensities were logarithmized (ln,
299
zeros were surrogated with 10-8) and clustered using Euclidean distance [40] and average
300
linkage. Up to 300 clusters were allowed for sample and feature clustering. Outlier samples
301
identified with PCA were excluded from hierarchical clustering.
302
3
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3.1
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CHROMATOGRAPHY.
Reversed-phase chromatography was chosen which separates lipid species and has the
305
capability to resolve potential isobaric, isomeric, and isotopic interferences. The method
306
should cover both polar lipid species like oxidized fatty acids and apolar lipids like TGs.
307
Thus, an LC method based on 2.6 µm C8 core-shell material (Kinetex C8) was developed
308
for separation of lipids. The same eluents and identical gradient profile were used for ESI+
309
and ESI-. Acetate buffer was selected, because it showed better performance in negative
310
mode than formate buffer, and an acetonitrile/2-propanol mixture was used as strong eluent
311
in channel B to be able to elute strongly retained neutral lipids such as TGs and CEs.
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The reported method was able to separate and elute lipophilic species with a wide range
313
of polarities. Polar lipid species like lyso-glycerophospholipids eluted in a retention time
314
window from 7 - 10 minutes. Apolar lipids like TGs eluted from 20-25 minutes. Typical EICs
315
of detected lipids in platelet extract (STEMI patient sample 2) are shown in Fig. 2. Many
316
lyso-glycerophospholipids like LPI, LPS and LPE showed double peaks (Fig. 2B). The
317
MS/MS spectral data of those features is nearly identical. The presence of these peaks may
318
be explained by the presence of glycerol-sn1/sn2 positional isomers [23]. The sensitivity in
319
ESI- was slightly lower. This could easily be compensated by increased injection volume.
320
The minimum peak height for peak detection in MS-DIAL was decreased to 500 for ESI-
321
(1000 in ESI+). A lower threshold for peak finding also increased the noise. An exclusion list
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323
molecular feature. The top 200 peaks detected in the TOF-MS survey scan of a blank run
324
were used as exclusion list. Most lipids formed [M+H]+ adducts in positive and [M-H]- in ESI-.
325
Some lipids were detected as [M+NH4]+ (TGs) or [M+CH3COO]- (PCs) adducts. Several
326
lipids formed Na+ adducts to some extent, which could lead to assay specificity problems
327
(false positives) when direct infusion approaches are carried out with QTOF-MS. For
328
example, a very high mass resolution of more than 200,000 (Orbitrap-MS or FTICR-MS)
329
would be needed to distinguish between the [M+Na]+ adduct of LPC(18:0) (m/z 546.3530)
330
and the [M+H]+ LPC(20:3) (m/z 546.3554). In case of LC-MS, those lipids were
331
chromatographically separated and therefore did not present any assay specificity problems
332
for correct lipid identification (see Fig. A. 2).
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Chromatographic separation is also needed to separate another potential isobaric
334
interference. The marginal mass difference between the M+2 isotopologue of the lipid with
335
an additional double bond and the monoisotopic peak of a lipid with a double bond less does
336
not allow to distinguish them at TOF mass resolving power. This isotopic interference is
337
called
338
chromatographically deal with this problem because compounds differing in the double bond
339
number have different retention on RP phases (see Fig. A. 1). Consequently, the current
340
core-shell C8-based RPLC method assured adequate assay specificity for isobaric and
341
isomeric interferences in this lipid profiling method.
342
3.2
double
bond
overlapping
effect
[49].
The
current
LC
method
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MASS SPECTROMETRY.
343
Detection of the lipids was performed by a QTOF-MS instrument (TripleTOF 5600+). It
344
was used in DIA mode with SWATH [26, 32]. Every SWATH cycle consisted of a high
345
resolution (R≥30.000) TOF-MS scan followed by MS/MS experiments with a sequential
346
precursor selection in Q1 of defined m/z range. This SWATH window is typically about 25 Da
347
wide [26]. A total Q1 range from m/z 30 to 1250 could be covered with up to 100 SWATH
348
experiments per cycle. In this study, lipids up to m/z 1000 were included. Pre-experiments 13
ACCEPTED MANUSCRIPT showed only a few molecular features above m/z 1000 without positive identification. As a
350
compromise between wider coverage and more sensitivity, the mass range was limited to a
351
maximum of m/z 1000 to save total cycle time. SWATH Q1 windows with variable isolation
352
width (SWATH 2.0) were used in this study. Adjusted SWATH isolation widths for each m/z
353
range also allowed increasing overall selectivity for critical mass ranges by narrower
354
precursor selection windows [36]. An m/z range with higher precursor ion density was
355
covered with more SWATH windows with narrow isolation width. The m/z ranges with less
356
precursor ion density, in turn, were inversely covered in one or few SWATH windows with
357
wide isolation width. As shown in Fig. 4, the window size did not affect the signal-to-noise
358
ratio of the four oxPC target analytes (PGPC, PAzPC, POVPC, PONPC). However, peak
359
area was lost for a window size of 3 Da without an overlap of previous and subsequent
360
SWATH window. SWATH windows with a size of 5 Da (3 Da target width plus two times 1
361
Da overlap with neighboring windows) showed a gain of peak area between 30 and 90 % for
362
target analytes compared to the original 3 Da window (Fig. 4C). A 3 Da isolation width with 1
363
Da overlap with the previous and the subsequent SWATH window would result in an
364
effective window size of 1 Da. Analyte peaks would be split into multiple SWATH windows
365
which must be avoided. Hence, a minimum SWATH width of 5 Da is recommended. Such
366
narrow Q1 isolation windows are advantageous in view of assay specificity for specific
367
targets, here oxPCs.
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Due to expected low abundance of the targeted oxPCs, which are oxidative stress
369
biomarkers [50] of particular interest in the present study, other MS parameters such as
370
accumulation time were optimized as well. A higher signal-to-noise ratio was gained with
371
increased accumulation time in the target SWATH windows for the fragment with the m/z
372
184.07332 (Fig. 4A). A proportional increase of the S/N ratio of up to factor 3 was achieved
373
with an increase of accumulation time from 33 to 100 ms. A linear correlation between S/N
374
and MS/MS accumulation time was shown already in earlier work [51]. Therefore, the
375
accumulation time was chosen as long as possible and as short as needed with regard to
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ACCEPTED MANUSCRIPT acceptable cycle time (10 data points per peak requirement). To avoid long total cycle times
377
and guarantee ten data points per peak, only target windows were analyzed using 100 ms
378
accumulation time. All other SWATH windows were set to 33 ms. The settling time between
379
individual SWATH windows took 1 ms. For the TOF-MS experiment 250 ms were used
380
resulting in a total cycle time of 1511 ms. A complete overview of the SWATH strategy is
381
shown in Fig. 5. The sensitivity of the four oxPCs in ESI- was below limit of detection in the
382
clinical samples. Therefore, no target windows were used in ESI-.
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Unlike DDA, SWATH acquires data points comprehensively over the entire peak and
384
throughout the entire chromatogram over the specified m/z range. Thus, comprehensive MS
385
as well as comprehensive MS/MS data are available for uncompromised retrospective data
386
processing. In other words, SWATH provides a full digital map of the lipid profile and
387
molecular archive of a sample. This provides the opportunity to select post-acquisition either
388
precursor ion from MS data, or precursor or fragment ions from MS/MS data for data
389
processing and EIC generation, respectively. From the MS/MS spectra peak groups can be
390
generated (Fig. 3) which allow to use the most sensitive signal for (relative) quantitation and
391
the other signals (peaks) for confirmation, like quantifier/qualifier transitions in MRM.
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Since sensitivity was critical and SWATH generates comprehensive MS/MS data, it was
393
considered to use MS/MS chromatograms for data processing. All phosphatidylcholines
394
including oxPCs showed an intense but unspecific MS/MS fragment of the PC head group at
395
m/z 184.07332 in ESI+. Since the target windows were very narrow, this fragment could still
396
be used as identifier [52, 53]. Using 5 Da isolation width for target SWATH windows provides
397
typically clean spectra or reduces the complexity of the composite spectra to a minimum.
398
When the non-specific head group fragment of phosphatidylcholines, phosphocholine (m/z
399
184.07332), was utilized for EIC generation, the resultant MS/MS fragment chromatograms
400
showed only few additional peaks with low intensities (Fig. 6).
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For PAzPC, four isomers (identical sum formula) are listed in the LIPID MAPS database.
402
All four are phosphatidylserines PS(27:0) and therefore clearly distinguishable because of 15
ACCEPTED MANUSCRIPT the class specific head group fragments. The increased selectivity obtained by the narrow
404
isolation width of the SWATH windows was combined with the application of enhanced
405
product ion mode which can be set individually for each SWATH window like other MS
406
parameters as well. The phosphocholine head group m/z 184.07332 was enhanced in the
407
target SWATH windows. Using this acquisition mode additional sensitivity for MS/MS
408
fragments was gained through trapping ions in the Q2 collision cell, and optimized ejection
409
and pulsing towards the TOF analyzer [54]. This allowed a further gain of sensitivity as
410
shown in Fig. 4A and Fig. 4B. Even up to 50 % of peak area could be gained using
411
enhanced mode. However, this enhancement is possible only for a small m/z range of about
412
400 around the target m/z. The excluded m/z region outside of this range around the target
413
m/z is barely detectable. Fragment mass enhancement together with narrow Q1 SWATH
414
windows can therefore be considered as an MRM-like, targeted acquisition window to some
415
extent. The SWATH technique allowed implementation of the MRM-like target windows
416
within conventional variable SWATHs as indicated in Fig. 5A.
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Furthermore, total cycle time was reduced using the variable SWATH window approach
418
with wider windows in less densely populated precursor ion mass ranges. As shown in Fig.
419
5A for the m/z range in ESI+ wide Q1 windows of 191.6 Da and 166.4 Da were used for the
420
m/z ranges below m/z 221.6 and above m/z 833.6, respectively. Only 4 % of the total
421
detected ions were below or above these m/z values, respectively. Also in ESI- (Fig. 5B)
422
only 4 % of all detected precursors were below m/z 211.6. The upper boundary in ESI- was
423
m/z 916.1 with 4 % of all features. Also in the intermediate m/z range, the SWATH window
424
width was dynamically adjusted. For mass ranges with low ion density, like described before,
425
wide SWATH isolation width was used, instead of adding further SWATH experiments and
426
therefore additional cycle time. This allowed the coverage of a large m/z range without
427
unnecessarily increasing the total cycle time. Concluding, a non-targeted lipid profiling with
428
sensitive targeted acquisition of oxPCs was possible with variable SWATH. The reduced
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complexity of composite spectra made reliable identification and relative quantification
430
possible due to favorable MS/MS assay specificity.
431
3.3
APPLICATION.
The above described method was applied to elucidate the lipidomic profile of platelets in
433
platelet extracts collected from healthy subjects (n=10) as compared to SAP (n=10) and
434
STEMI (n=13) patients. A randomized sample sequence was developed to obtain reliable
435
lipid data for clinical interpretation. The DuoSpray ESI source was cleaned before analysis.
436
Therefore, the surface of the ion source needed an equilibration step before the ionization
437
process was stable. Then, the whole LC-MS system was equilibrated with three blank
438
injections of 2-propanol. The blank injections flushed the capillaries, equilibrated the column
439
pressure and the column oven temperature. Afterwards in total 6 pooled QC samples were
440
injected before the first experimental sample. The first two QCs were acquired in DDA mode.
441
The original idea was to generate high quality MS/MS spectra for cross-checking
442
identification in case the SWATH spectra deconvolution was insufficient for spectral
443
database matching. DDA triggering was generally performed on top 20 abundant ions, but
444
many precursors were still missed for MS/MS fragmentation. In most cases, when the
445
SWATH MS/MS spectra were of low quality, the reason was typically a low precursor
446
intensity. In these cases, also DDA missed to trigger MS/MS spectra. Nevertheless, for
447
some features DDA spectra were helpful to proof correct fragment peak assignment (see
448
Fig. A. 6 and Fig. A 10).
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449
Next, the third QC sample was again used for equilibration of the LC-MS system. The
450
fourth to sixth QCs were the first to be acquired for quality assurance of the LC-MS system
451
and for data normalization by LOWESS. The clinical samples were acquired in randomized
452
order after the first six injections of QC samples. After every fifth real sample a QC was
453
acquired. After the last sample three QCs were acquired consecutively, resulting in 11 QCs
454
in total. After their analysis all QC and sample data were processed in parallel using mainly
455
MS-DIAL. 17
ACCEPTED MANUSCRIPT 456
3.4
DATA PROCESSING AND EVALUATION.
Data processing was done with MS-DIAL software [29]. After converting SCIEX raw files
458
(.wiff) to analysis base files (.abf) a project in MS-DIAL was set up. In case of DIA with
459
SWATH, the exact range and size of MS/MS experiments was defined. For data processing,
460
MS-DIAL offers various parameters that influence the results. Number of detected peaks and
461
identified lipids and especially data quality (accuracy, precision) can fluctuate with different
462
extraction and alignment settings. Therefore, these parameters must be chosen carefully.
463
Optimization of these parameters is necessary to find an optimum between highest numbers
464
of detected and identified peaks with acceptable accuracy and precision. For example, in the
465
ESI- mode, the sensitivity was lower than that compared to ESI+ mode. Therefore, a lower
466
threshold for peak finding was necessary, which increased noise detection too. This problem
467
was solved by using an exclusion list. In the ESI+ mode, no exclusion list was necessary.
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For the identification of lipids, MS-DIAL offers an implemented spectral database. In the
469
database settings, collision-induced dissociation (CID) was selected. Additionally, the used
470
buffer system ammonium acetate was selected. Neutral lipids like glycerides only ionize due
471
to adduct formation with buffer cations in the ESI+ mode. The spectral database for
472
lipidomics offers the manual selection of lipid classes. Lipid classes were selected in
473
dependence on the analyzed sample material. For example, sulfoquinovosyl diacylglycerols
474
(SQDGs) can be only found in photosynthetic organisms and were excluded from the
475
identification process. Further, various adducts were selected for identification of lipids, but
476
mainly [M+H]+, [M+NH4]+, [M+Na]+, [M-H]- and [M+CH3COO]- were found. Finally, MS and
477
RT tolerances for inter-sample peak alignment were determined. These values define
478
thresholds for variations in MS and RT if a peak is identical with a peak in other samples.
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Generally speaking, DIA with SWATH produces cleaner MS/MS spectra than for example
480
all-ion-fragmentation (AIF or MSE), a DIA mode in which all precursors are co-fragmented
481
simultaneously and which produces complex composite spectra. However, if several
482
precursors are co-isolated in the same SWATH window, composite spectra will result in 18
ACCEPTED MANUSCRIPT SWATH as well (Fig. 7A and Fig. 7D). MS-DIAL has implemented a deconvolution tool which
484
allows to generate deconvoluted MS/MS spectra (product ion spectra) in which background
485
noise and contaminating ions are removed [29]. Deconvolution is possible as long as the
486
precursor is detected in the MS1 and does not rely on the presence of the precursor on
487
MS/MS level. First, MS/MS chromatograms of centroid MS/MS spectra corresponding to the
488
precursor ion (from MS1) are extracted. Second, smoothing and baseline correction is
489
applied to MS/MS chromatograms. Third, model peaks are extracted from the MS/MS
490
chromatograms, independent if a precursor was observed or not. Fourth, an ideal model
491
peak is fitted to each chromatogram by means of a least squares method [29]. If a precursor
492
is not detected in the MS1 scan but in the SWATH MS/MS due to increased sensitivity, the
493
precursor will not be present in the peak spotting result and therefore not considered as
494
detected. Only a targeted search on MS/MS level with a respective fragment ion m/z would
495
show a peak.
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As a result of deconvolution, the precursor-product ion link is reestablished which further
497
allows to generate specific MS/MS chromatograms of similar quality as SRM chromatograms
498
[29]. They can be used to verify assay specificity and distinguish a specific target lipid from
499
potential interferences. For example, Fig. 7 shows raw and deconvoluted spectra of PE 38:6
500
in both ESI polarities. The raw spectrum in Fig. 7A shows a fragment ion at m/z 184.0766. It
501
is characteristic for PCs and thus may be misleading. The deconvolution process
502
successfully removes, along with some other background ions, this contaminating fragment
503
ion which belongs to an interfering co-fragmented PC but not to the precursor of m/z
504
764.5302. With the deconvoluted MS/MS spectra (Fig. 7B), identification is more
505
straightforward. In ESI- mode (Fig. 7D and Fig. 7E), the deconvolution of the MS/MS spectra
506
reduces the noise clearly and makes identification via spectral matching more precise. As
507
mentioned above, SWATH holds the promise to select either precursor ion or fragment ions
508
to construct EICs whatever is more selective or sensitive. This is documented in Fig. 7C and
509
Fig. 7F. A chromatogram extracted on the precursor of PE(16:0_22:6) (EIC for m/z
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ACCEPTED MANUSCRIPT 764.5095±0.005) shows several (potentially interfering) peaks (blue trace in Fig. 7C). The
511
same interferences were present when extracting the fragment with m/z 313.2774±0.005
512
(loss of fatty acid side chain FA(22:6)) (orange trace in Fig. 7C).The number of interfering
513
peaks is less for the MS/MS chromatogram extracted for the fragment ion with m/z
514
623.5034±0.01 (obtained by neutral loss of phosphatidylethanolamine) (pink trace in Fig.
515
7C). In sharp contrast, the deconvoluted MS/MS chromatogram based on reestablished
516
precursor-product ion link shows a single peak and provides confidence of satisfactory assay
517
specificity for PE(16:0_22:6) for the peak at a retention time of 17.05 min (green trace in Fig.
518
7C). However, the specificity in the ESI- mode is higher than compared to the ESI+ mode
519
because the fatty acid side chains can be detected as negatively charged carboxylate anions
520
[55].The [M-H]- precursor ion of PE(16:0_22:6) (m/z 762.5195±0.005) shows less
521
interferences in ESI- than in ESI+ (Fig. 7F, blue trace). The EIC of 255.2323±0.005
522
represents the FA(16:0) side chain (Fig. 7F, orange trace). This fragment shows a high level
523
of noise (20 %) because of low abundance. The fragment representing the FA(22:6) side
524
chain with the m/z 327.2669±0.005 (Fig. 7F, pink trace) is nearly identical to the
525
deconvoluted MS/MS peak of m/z 327.2669±0.005 (Fig. 7F, green trace). In this respect,
526
MS-DIAL offers tools to verify assay specificity in data sets acquired by DIA with SWATH. In
527
the shown example for PE(16:0_22:6), the specificity in the ESI- mode was already sufficient
528
in the SWATH MS/MS EICs with m/z 327.2669±0.005 and could not be further improved.
529
However, the deconvoluted MS/MS spectra show less noise and improve lipid identification.
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Peak finding with reported parameter settings (see also Table A. 1) resulted in a total of
531
1971 aligned molecular features for both ESI polarities. In ESI+ mode, 1345 molecular
532
features were detected whereof 34 were annotated without MS/MS spectra. 479 features
533
were assigned automatically with an identification score cut-off of 70 % calculated with MS-
534
DIAL [29]. In ESI negative mode, 626 features were aligned including 132 automatic
535
identifications with a cut off score of 70 %. In Fig. 8 all detected features in ESI+ (Fig. 8A)
536
and ESI- (Fig. 8B) are plotted as m/z vs retention time map (peak spotting plot). Every color 20
ACCEPTED MANUSCRIPT represents a lipid class. Phospholipids (PC, PE, SM) were detected in ESI+ as well as in
538
ESI-. High concentrations of TGs were detected in ESI+ as [M+NH4]+ and to minor extent
539
[M+Na]+. PIs were detected exclusively in ESI- mode. In Fig. 8C and Fig. 8D proportions of
540
the detected features are visualized in pie charts. 1326 (67 %) features remain unknown
541
without any matching precursor mass or MS/MS match. 34 (2 %) features showed an MS
542
precursor match but without MS/MS data. These findings were considered as not identified
543
because of missing MS/MS information for structure elucidation. In total, 611 (31 %) features
544
were automatically identified based on precursor mass accuracy, match of isotope pattern
545
and MS/MS spectra (total score ≥70 %) of which 78 % were detected in ESI+ and 22 % in
546
ESI-, respectively. As to be expected the ESI+ polarity results in more identified lipids than
547
ESI- because of the higher sensitivity and larger lipid coverage. The largest number amongst
548
the annotated lipids belonged to TGs (29 %). TGs were detected as [M+NH4]+ and as
549
[M+Na]+. Therefore, many TGs were reported twice in the resulting alignment file. For this
550
reason, the list of structural annotations was manually corrected for multiple adduct
551
appearance. Besides TGs, phospholipids were the most prominent lipids in platelet extracts.
552
Further, all automated identifications by MSDIAL were manually controlled by revision of the
553
spectral matching. In ESI+ mode 18 and in ESI- mode 7 spectral matches were considered
554
as doubtful. Still, 95 % of the automatically assigned spectral matches were considered as
555
trustworthy.
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The embedded QCs were used for LOWESS normalization. The normalized peak height
557
was used for data evaluation and relative quantification. 82 % of all features (1619) were
558
found in the QC samples. Even if the QC samples were not a pool of all actual test samples,
559
most of the features could be detected. If a feature is below the intensity threshold of the
560
peak detection, the MS-DIAL software interpolates the value (gap filling) with values from a
561
local maximum of the EIC within the defined tolerances for retention time and mass [29]. For
562
evaluation of the precision of the QCs embedded in the sequence, interpolated features
563
within the QCs were deleted. Features not detected in 55 % (n≥6 of 11) of the QCs were
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ACCEPTED MANUSCRIPT 564
discarded (Fig. 9A). CVs for these remaining features were calculated and plotted as
565
histogram in Fig. 9B. 45.5 % of features detected in QCs (n≥6) showed a CV of ≤10 %. 26.4
566
% of the features were between 10 % and 20 % CV and 8.4 % were between 20 % and 30
567
% CV. In total 80.3 % of the features present in the QC samples varied less than 30 %. Additionally, the performance was evaluated regarding two internal standards LPC(17:1)
569
and PC(17:0/20:4). CVs for normalized response were below 10 % for all internal standards
570
(Fig. A. 4A). Retention time was stable over the complete sample sequence with 0.1 % to
571
0.2 % CV (Fig. A. 4B) Commonly, normalization in lipidomics is performed with one internal
572
standard per lipid class. As a drawback of this method large retention time spans must be
573
normalized with the same internal standard. For example, the lipid class of the LPC shows a
574
retention time range of about 3 minutes between 8-11 minutes retention time and would
575
have been normalized to a single internal standard eluting at 8.95 minutes. Applying
576
LOWESS all data points are normalized using multiple non-parametric regression.
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In MS-DIAL aligned and normalized data sets for ESI+ and ESI- were combined for
578
statistical evaluation. The combined data matrix was transformed and exported to
579
MarkerView (SCIEX) for performing multivariate statistics. For evaluating data quality and
580
system stability PCA statistics was used. In a first step, outliers had to be identified.
581
Therefore, a PCA score plot was calculated with log transformed data (zeros were
582
surrogated with 10-8) without scaling (Fig. 10A). The samples STEMI 1, 2, 5 and healthy
583
control 2 were identified as statistical outliers due to their position remote from the other
584
samples of their group on the PCA score plot for both ESI polarities. Even though the QCs
585
were not a mixture of all samples but comparable composition (i.e. pooled platelet samples
586
from another study), they were closely clustered together near the origin of the score plot.
587
The largely superimposed QCs in the PCA score plots of both polarities proved a stable
588
analysis over the whole sample acquisition time and analysis sequence, respectively. It was
589
an evidence for a consistent performance of HPLC and mass spectrometer. With a
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ACCEPTED MANUSCRIPT 590
decreasing performance of the analytical platform due to contamination or carry over effects,
591
a drift of the QC samples would have indicated an unstable performance. For further analysis with supervised PCA-DA, outliers and QC samples were excluded
593
(Fig. 10B). SAP 11 was revealed as an additional outlier using the supervised PCA and was
594
therefore eliminated. Interestingly, the same samples were identified as outliers in both
595
polarities. This is evidence that the outlier origin is linked to the sample itself and not induced
596
due to the analysis. In the PCA-DA plot all distinct sample groups are clearly separated,
597
which underlines the differences in their lipid profiles. Especially the scores of the healthy
598
control samples (Fig. 10A) and B) (green)) underline differences in the lipidome of the
599
distinct sample groups. PCVG (Fig. 10C) was used for visualizing altered molecular features
600
compared to other distinct sample groups. Colored spots in Fig. 10C indicate altered
601
abundance of a specific molecular feature in the respective sample groups. The statistical
602
hypothesis testing using the Mann-Whitney-U-test and subsequent control of the FDR using
603
the q-value approach identified 855 molecular features with a q-value of ≤0.05. In Fig. 10D -
604
Fig. 10G 4 significantly altered lipid species were reported exemplarily. Additionally, boxplots
605
for the QCs were plotted to evaluate the analytical precision of the specific feature. The
606
reported lipids in Fig. 10D - Fig. 10F) were detected in all QCs (n=11) and showed CVs
607
below 10 %. LPI(18:1) was significantly (q-value=0.00395) increased in the disease groups
608
compared to the control group. LPS(20:4) showed an elevated level (q-value=0.0031) in the
609
control group. Additionally, FA(18:1) and DHEAS were increased in the patient groups.
610
LPI(18:1), LPS(20:4) and FA(18:1) were identified automatically in MS-DIAL (spectral
611
matches and identification scores are shown in Fig. A. 5, Fig. A. 7 and Fig. A. 8). The
612
identification of LPI(18:1) was 70.6 %, which is near the lowest acceptable identification
613
score. The MS-DIAL generated spectral match was manually revised and was found as not
614
sufficient for reliable identification (Fig. A. 5). Therefore, the raw data were examined for
615
reconsideration of the identification of LPI(18:1). Accurate mass, isotope pattern and
616
fragmentation pattern supported the correct annotation of this feature as LPI(18:1) ( Fig. A.
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ACCEPTED MANUSCRIPT 6). LPI(18:1) was detected and triggered for MS/MS in a DDA experiment of the QC sample
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in ESI- mode. The specific DDA spectrum (Fig. A. 6C) of precursor m/z 597.3 confirms the
619
background subtracted SWATH spectrum (Fig. A. 6C). Two lipid class specific fragments
620
could be structurally assigned confirming the presence of a glycerophosphatidylinositol (Fig.
621
A. 6).
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DHEAS was detected as an unknown. Further investigation using the MS-FINDER
623
software [56] identified a matching chemical formula and suggested DHEAS as possible hit,
624
based on a matching in silico spectrum. The identification of DHEAS was verified using a
625
reference standard. The exact identification procedure is described in Appendix A. The
626
candidate peak and the DHEAS reference corresponded in retention time, accurate mass,
627
isotope pattern and fragment spectra (Fig. A. 9). Therefore, the peak was considered as
628
positively identified. Unfortunately, DHEAS (Fig. 10G)) was below the peak spotting
629
threshold in the QC samples because of too low concentration. In case of missing values,
630
MS-DIAL performs a gap filling using a local maximum of the EIC at the corresponding
631
retention time [29]. The interpolated values resulted in 29 % CV (n=10) after removal of one
632
outlier value (QC1). 77 identified and significantly altered lipids with QC precisions ≤30 %
633
CV in n≥6 QCs, were used for hierarchical cluster analysis and visualization as heat map
634
(Fig. 10H). Outliers and QC samples were eliminated prior to the cluster analysis. All
635
controls were clustered together. For the two patient groups (SAP and STEMI), no distinct
636
clustering was found and these samples were not clearly distinguished in cluster analysis.
637
The differences between SAP and STEMI are subtle in case of the detected overall platelet
638
lipidome. Yet, they could be distinguished by supervised multivariate statistics. Additionally,
639
the SWATH experiment design allowed detection of oxPC targets PGPC, PAzPC, POVPC
640
and PONPC in the clinical samples. It was found that the oxPC targets were significantly
641
increased in STEMI patients as compared to control samples. Detailed results and clinical
642
interpretation of altered lipids detected in positive ESI polarity were published elsewhere
643
[34]. A list of significantly altered lipids detected in ESI- is reported in Table A. 10.
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4
CONCLUSION. Herein we present a complete workflow for comprehensive lipidomics analysis of human
646
platelet lipid extracts. The developed method covers polar species like fatty acids as well as
647
neutral lipids like TGs. The broad polarity range allows analysis of complex lipid extract with
648
one single chromatographic method. The application of DIA with SWATH allows flexible MS
649
experiment design. Candidate lipids or known targets can be analyzed in MRM-like Q1
650
isolation windows, with increased selectivity and sensitivity. In general, MS/MS data are
651
acquired comprehensively over the entire chromatogram and across all samples, with a
652
maximum of analyte coverage and improved sample-to-sample reproducibility as compared
653
to DDA. Peak detection, lipid identification, sample alignment and normalization were user-
654
friendly automated using MS-DIAL software with the provided spectral database LipidBlast
655
[29, 33]. Embedded QC samples in the sample sequence allowed assessment of the data
656
quality by calculating coefficients of variation for all molecular features detected in the QCs
657
[42]. The system stability was proven by PCA projection resulting in score plots with
658
superimposed QCs [42, 45]. Additionally, the QC samples were used for signal correction
659
using normalization with the LOWESS algorithm. This procedure is superior to normalization
660
with internal standards in case of non-targeted approaches [44, 57]. The demonstrated
661
method was successfully applied to a clinical case study comparing platelet lipid extracts of
662
healthy donors to patients with SAP and acute STEMI. The MRM-like target windows
663
allowed detection of oxPCs with reliable selectivity. Various altered lipids were successfully
664
identified providing fundamental insights into relative disease severity. Detailed results and
665
interpretation of the detection of oxPCs and the findings of altered lipids have been
666
published before [34]. However, lipid identification was still the bottleneck of data evaluation.
667
Although software tools like MS-DIAL were helpful for automatic lipid identification elaborate
668
manual data revision was necessary. The identification of one unknown was possible, again
669
with elaborate effort to verify the identification, but many molecular features still remain
670
unidentified because of missing database hits. Overall, this work shows that lipid profiles are
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significantly different in patient groups which could make this method a valuable tool in
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clinical analysis in the context of personalized medicine in cardiology.
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NOTES.
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The authors declare no competing financial interest.
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ACKNOWLEDGEMENT.
Funding: We acknowledge the financial support by the “Struktur- und Innovationsfonds
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Baden-Württemberg (SI-BW)” and the German Science Funds (DFG no. INST 37/821-1
678
FUGG).
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Appendix A. Supplementary Data.
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FIGURE CAPTIONS. Fig. 1. Schematic illustration of the entire method workflow. Briefly, a suitable lipid
856
extraction method was found for the desired sample material. Next, a chromatography was
857
developed. The SWATH experiments and mass spectrometric parameters were optimized in
858
a targeted manner for 4 target analytes (POVPC, PONPC, PGPC and PAzPC). Non-
859
targeted SWATH window extraction width was optimized using swathTUNER [36]. Both
860
results were combined in a targeted/non-targeted SWATH experiment design (further details
861
are shown in Table A. 1). The developed method, then was used for data acquisition of
862
clinical platelet samples followed by data pre-processing and lipid identification in MS-DIAL
863
[29], method validation, statistical testing, FDR control, data post-processing and finally data
864
visualization.
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Fig. 2. EICs of typical lipid classes in A) ESI+ and B) ESI- mode. The lipids were identified
867
in the platelet extract of a STEMI sample (STEMI 2). Identification of the lipids was done via
868
spectral
869
glycerophosphatidylethanolamine, PC: glycerophosphatidylcholine, POVPC: 1-palmitoyl-2-
870
(5'-oxo-valeroyl)-sn-glycero-3-phosphatidylcholine,
871
diradylglycerolipid,
872
PI: glycerophosphatidylinositol, PS: glycerophosphatidylserine, FA: fatty acid
in
MS-DIAL
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matching
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score
cholesterol
>70
%).
SM: ester,
AC:
acyl
carnitine,
sphingomyelin, TG:
PE:
DG:
triradylglycerolipid,
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ChE:
(total
873 874
Fig. 3. SWATH MS/MS spectra and MS/MS EIC peak groups of A) PGPC in standard
875
solution (250 ng mL-1 in MeOH) in a retention time window of 7.380 to 7.819 min and B)
876
LPC(16:0) in clinical sample (STEMI patient sample 12) in a retention time window from .
877
The SWATH MS/MS spectra were background subtracted with PeakView. All shown EICs
878
were extracted with a width of 5 mDa from the corresponding SWATH window. The correct
879
fragment assignment for LPC(16:0) was proofed by DDA spectra (see Fig. A. 10). 30
ACCEPTED MANUSCRIPT 880 Fig. 4. Effect of SWATH window parameters on sensitivity and signal-to-noise ratio (S/N).
882
A) The size of Q1 SWATH windows did not affect the S/N ratio. Increased accumulation time
883
of 100 ms improved signal-to-noise ratio proportionally [51]. Additional fragment mass
884
enhancement could increase the S/N ratio 2-3 times. B) The reported peak areas were
885
normalized to IS LPC (17:1). The area ratios are not affected by applying an increased
886
accumulation time to the target analyte window. Q1 windows with a width of 3 Da showed
887
lower peak area ratios than wider SWATH windows. Fragment mass enhancement could
888
again increase signals up to 2 times. C) Narrow 3 Da SWATH windows increased selectivity
889
but lost sensitivity up to 90 %. For this study windows were allowed as minimum SWATH
890
size. enh*: Enhanced product ion mode was activated for m/z 184.07332
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Fig. 5. Illustrations of the SWATH isolation window widths for A) positive and B) negative
893
polarity. All cycles consisted of a TOF-MS full scan from m/z 30 to 1000 with 250 ms
894
accumulation time followed by individual SWATH windows. Narrow width (5 Da) Q1 windows
895
were used for target analytes POVPC, PGPC, PONPC and POVPC in ESI+ with 100 ms
896
accumulation time. Non-targeted SWATHs were accumulated for 33 ms. In ESI negative
897
polarity all SWATHs were programmed with 40 ms accumulation time.
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Fig. 6. MS/MS EICs of the PGPC precursor (m/z 610.3715±0.005) and head group
900
fragment (m/z 184.0733±0.005) from platelet extract of STEMI patient sample 2. The
901
masses are extracted from the corresponding SWATH MS/MS experiment #16 with a Q1
902
width from m/z 608.0 to m/z 613.0. The narrow SWATH Q1 window of 5 Da allows only a
903
small range of possible precursors producing interferences which were however
904
chromatographically separated. Therefore, the unspecific head group fragment of
905
glycerophosphatidylcholines (m/z 184.0733, red) could be used for relative quantification
906
because of interference-free detection at the retention time of PGPC.
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Fig. 7. Deconvolution of SWATH MS/MS spectrum in MS-DIAL. A) Raw MS/MS spectrum
909
of PE(16:0_22:6) in ESI+ mode. B) Deconvoluted MS/MS spectrum of PE(16:0_22:6) in
910
ESI+ mode. The fragment peak with m/z 184.0717 was eliminated which prevents a
911
misleading interpretation of the spectrum as possible phosphatidylcholine species.
912
SWATH MS/MS EICs of PE(16:0_22:6) and fragments in ESI+ mode. The fragments of
913
PE(16:0_22:6) are not specific enough for baseline separated fragment EIC peaks.
914
Deconvolution of the MS/MS EICs (green) can help to increase the assay specificity. D) Raw
915
MS/MS spectrum of PE(16:0_22:6) in ESI- mode. E) Deconvoluted MS/MS spectrum of
916
PE(16:0_22:6) in ESI- mode. Noise was clearly reduced due to deconvolution. F) SWATH
917
MS/MS EICs of PE(16:0_22:6) and fragments in ESI- mode. The ESI- mode shows more
918
specificity due to detection of the fatty acid side chain fragments (note, m/z 327.2669
919
corresponds to FA 22:6 and m/z 255.2323 to FA 16:0). The data was extracted from platelet
920
extract of STEMI patient sample 7.
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C)
Fig. 8. Results of the peak spotting and automatic lipid identification with MS-DIAL. A) In
923
ESI+ 1345 features were detected. 479 features were automatically identified (colored) with
924
a total identification score of ≥70 %. 870 features were not identified (grey). B) In ESI- 626
925
were aligned of which 132 were identified (≥70 %). C) Pie chart of detected features. 67 %
926
were not identified, 2 % were without MS/MS and 31 % were identified. Thereof, 78 % were
927
detected in ESI+ and 22 % in ESI-. D) Proportion of identified lipid classes. Mostly TGs,
928
PCs, PEs, and SMs were detected. Abbreviations: AC: acyl carnitine, Cer: ceramides, ChE:
929
cholesterol
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glycerophosphatidylethanolamine,
931
glycerophosphatidylserines, FA: fatty acid, FAHFA: fatty acid ester of hydroxyl fatty acid, PA:
932
phosphatidic acid.
AC C
EP
922
933 32
ester,
DG:
diradylglycerolipids,
PC:
SM: sphingomyelin,
glycerophosphatidylcholines, TG:
triradylglycerolipids,
PE: PS:
ACCEPTED MANUSCRIPT Fig. 9. Quality assurance of LC-MS acquisition. Peak detection result was filtered for
935
peaks above detection threshold in 55 % of QC (n = 6 of 11). Percentage CV was calculated
936
for all feature and plotted in a histogram. 72 % of all features were found above threshold in
937
55 % of all QC samples. 80.3 % of all QC abundant features were detected with CV≤30 %.
938
45.5 % of the features were even below 10 % CV
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Fig. 10. Multivariate statistics for visualization of non-targeted lipidomics results. A) PCA
941
score plot with normalized and ln transformed alignment matrix without scaling. 3 STEMI
942
samples and 1 control sample were identified as outliers and excluded from PCA-DA
943
projection in B). B) PCA-DA score plot separates the distinct sample groups clearly. C) PCA-
944
DA loadings plot. Groups were assigned to each variable using the PCVG function in
945
MarkerView (SCIEX). The peaks were assigned to six groups (green, blue, red, light green,
946
purple and orange). The groups indicate elevated and decreased levels of features in each
947
distinct sample group, respectively. D) to G) Box and jitter plots of significantly altered lipids.
948
LPI(18:1), FA(18:1) and DHEAS were elevated in patients. LPS(20:4) was increased in
949
controls. H) Hierarchical cluster analysis and heat map of 77 identified and significantly
950
altered lipids in positive and negative ESI mode. Outlier samples were eliminated prior to
951
clustering. PI: glycerophosphatidylinositols, PS: glycerophosphatidylserines, FA: fatty acid;
952
DHEAS: dehydroepiandrosterone sulphate.
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AC C
953
33
ACCEPTED MANUSCRIPT
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Appendix A. Supplementary Data
Comprehensive MS/MS Profiling by UHPLC-ESI-QTOF-
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MS/MS using SWATH Data-independent Acquisition for the Study of Platelet Lipidomes in Coronary Artery
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Disease
Jörg Schlotterbeck†, Madhumita Chatterjee‡, Meinrad Gawaz‡ and Michael Lämmerhofer† †
University of Tübingen, Institute of Pharmaceutical Sciences, Pharmaceutical (Bio-)Analysis, Auf der Morgenstelle 8, 72076 Tübingen, Germany
‡
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1Department of Cardiology and Cardiovascular Medicine, University Hospital Tübingen, Otfried-Müller-Strasse 10, 72076 Tübingen, Germany
Author for correspondence:
AC C
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Prof. Dr. Michael Laemmerhofer Professor for Pharmaceutical (Bio-)Analysis Institute of Pharmaceutical Sciences University of Tuebingen Auf der Morgenstelle 8 72076 Tuebingen, Germany
T +49 7071 29 78793, F +49 7071 29 4565 e-mail: [email protected] http://www.bioanalysis.uni-tuebingen.de/
1
ACCEPTED MANUSCRIPT
Table A. 1. Design of the SWATH experiment for A) ESI+ and B) ESI-. B
AC C 2
Experiment MS Type 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
SCAN SWATH SWATH SWATH SWATH SWATH SWATH SWATH SWATH SWATH SWATH SWATH SWATH SWATH SWATH SWATH SWATH SWATH SWATH SWATH SWATH SWATH SWATH SWATH SWATH SWATH SWATH SWATH SWATH SWATH SWATH
Min m/z 30 49.5 211.6 266.6 282.8 310.8 360.8 361.8 393.3 420.7 420.8 422.7 440.8 479.3 501.3 525.8 544.8 581.8 603.4 654.5 684.1 707.6 740 765.5 789.1 810.5 832.1 845.1 866.6 879.2 916.1
Max m/z 1000 212.6 267.6 283.8 311.8 361.8 362.8 394.3 421.7 421.8 423.7 441.8 480.3 502.3 526.8 545.8 582.8 604.4 655.5 685.1 708.6 741 766.5 790.1 811.5 833.1 846.1 867.6 880.2 917.1 1000
Accumulation Time (ms) 250 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40
CE (V) -30±10 -30±10 -30±10 -30±10 -30±10 -30±10 -30±10 -30±10 -30±10 -30±10 -30±10 -30±10 -30±10 -30±10 -30±10 -30±10 -30±10 -30±10 -30±10 -30±10 -30±10 -30±10 -30±10 -30±10 -30±10 -30±10 -30±10 -30±10 -30±10 -30±10 -30±10
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CE (V) 35 35 35 35 35 35 35 35 35 35 35 35 35 35 35 35 35 35 35 35 35 35 35 35 35 35 35 35 35 35 35 35
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Accumulation Time (ms) 250 30 30 30 30 30 30 30 30 30 30 30 30 30 100 30 100 30 100 30 100 30 30 30 30 30 30 30 30 30 30 30
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Max m/z 1000 221.6 260.6 284.6 365.8 409.6 468.3 496.8 516 524.8 530.3 545.8 561 593 597 609 613 649 653 665 669 706.1 736.6 758.9 767.5 782.6 787.1 795.7 809.1 814.2 834.6 1000
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Min Experiment MS Type m/z 0 SCAN 30 1 SWATH 30 2 SWATH 220.6 3 SWATH 259.6 4 SWATH 283.6 5 SWATH 364.8 6 SWATH 408.6 7 SWATH 467.3 8 SWATH 495.8 9 SWATH 515 10 SWATH 523.8 11 SWATH 529.3 12 SWATH 544.8 13 SWATH 560 14 SWATH 592 15 SWATH 596 16 SWATH 608 17 SWATH 612 18 SWATH 648 19 SWATH 652 20 SWATH 664 21 SWATH 668 22 SWATH 705.1 23 SWATH 735.6 24 SWATH 757.9 25 SWATH 766.5 26 SWATH 781.6 27 SWATH 786.1 28 SWATH 794.7 29 SWATH 808.1 30 SWATH 813.2 31 SWATH 833.6
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A
ACCEPTED MANUSCRIPT Table A. 2. Sodium acetate cluster masses used for mass calibration. ESI(m/z)
104.99230
141.01693
351.00152
223.02000
433.00459
305.02307
515.00767
387.02615
597.01074
469.02922
679.01381
551.03230
761.01689
633.03537
843.01996
715.03844
1007.02611
797.04152 879.04459
Analytical Order 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47
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1089.02918
File name QC_IDA_1 QC_IDA_2 QC_Equilibration QC_1 QC_2 QC_3 STEMI_8 SAP_11 STEMI_4 STEMI_10 STEMI_9 QC_4 Control_10 STEMI_12 STEMI_11 Control_8 Control_1 QC_5 Control_7 STEMI_1 SAP_3 STEMI_6 SAP_2 Control_9 QC_6 SAP_8 Control_4 Control_5 SAP_10 SAP_6 QC_7 STEMI_5 SAP_9 STEMI_14 STEMI_13 SAP_7 QC_8 Control_2 Control_6 SAP_4 STEMI_7 SAP_5 STEMI_2 Control_3 QC_9 QC_10 QC_11
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ESI+ (m/z)
Table A. 3. Analysis sequence with randomized sample order (IDA, information-dependent acquisition is DDA).
961.04767
AC C
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1043.05074
3
ACCEPTED MANUSCRIPT Table A. 4. MS-DIAL parameters for ESI+.
Retention time tolerance Accurate mass tolerance Identification score cut off
1 30 30 1000
Advanced setting for identification Relative abundance cut off Top candidate report
Centroid parameters MS1 tolerance
0.01
MS2 tolerance
0.05
Peak detectionbased
TRUE
Smoothing level Minimum width Minimum height
peak peak
Adduct setting [M+H]+ LinearWeightedMovingAverage 3
1000
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Both
AC C
TRUE
MSP file and MS/MS identification setting
F:\MS-DIAL\MS-DIAL program ver. 2.82\MSDIAL-LipidDBs-VS32FiehnO.lbm
MSP file
Retention time tolerance Accurate mass tolerance (MS1) Accurate mass tolerance (MS2) Identification score cut off Text post
4
file
and
40 0.01 0.05 70
TRUE
ion
[M+NH4]+
Alignment parameters setting
0.1
0.5
0
[M+H-H2O]+
5
Exclusion mass list (mass & tolerance) Deconvolution parameters Peak consideration Sigma window value Exclude after precursor
70
[M+Na]+
Peak spotting parameters Mass slice width
0.01
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Peak detection parameters Smoothing method
0.1
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Mass range end
Text file
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Data collection parameters Retention time begin Retention time end Mass range begin
identification (retention time and accurate mass based) setting
Reference file
Untreated Platelets ACD vs SAP vs Healthy14.abf
Retention tolerance
0.1
time
MS1 tolerance
0.025
Retention factor
0.5
time
MS1 factor
0.5
Peak count filter
0
QC at least filter
FALSE
Tracking of isotope labels Tracking of isotopic labels
FALSE
ACCEPTED MANUSCRIPT 0.005
68.9977
0.005
112.9861
0.005
214.0706
0.005
68.99809
0.005
1
268.1086
0.005
Retention time end
30
112.9864
0.005
Mass range begin
30
214.0712
0.005
Mass range end
1000
112.9867
Table A. 5. MS-DIAL parameters for ESI(including exclusion list). Data collection parameters Retention time begin
214.0704 42.00354
Centroid parameters MS1 tolerance
0.01
60.02065
MS2 tolerance
0.05
42.00447
Peak based
TRUE
327.1293
Peak detection parameters Smoothing level
3
Minimum peak width
5
Minimum height
500
peak
Peak spotting parameters 0.1
Exclusion mass list (mass & tolerance) 68.99736
0.005
94.9807
0.005
154.947
0.005 0.005
68.99742
0.005
94.9815
0.005
154.9471
0.005
214.0693
0.005
112.9861
0.005
68.99851
0.005
214.0707
0.005
214.0698
0.005
76.97176
0.005
60.02024
0.005
68.99831
0.005
42.0029
0.005
112.9857
0.005
214.071
0.005
68.99813
0.005
5
0.005 0.005 0.005
42.00406
0.005 0.005
269.104
0.005
293.1748
0.005
112.9863
0.005
214.0705
0.005
42.00357
0.005
269.1063
0.005
327.0742
0.005
327.1283
0.005
42.00446
0.005
327.0749
0.005
214.0712
0.005
42.00418
0.005
214.0706
0.005
42.00571
0.005
327.0742
0.005
303.0851
0.005
76.96989
0.005
214.0701
0.005
42.00375
0.005
327.0738
0.005
249.0977
0.005
42.00484
0.005
214.0704
0.005
249.0974
0.005
327.1284
0.005
249.0964
0.005
267.1084
0.005
268.1098
0.005
42.00367
0.005
0.005
AC C
68.99823
EP
214.0705
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Mass slice width
0.005
0.005
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LinearWeightedMovingAverage
0.005
269.1076
76.97162
Smoothing method
0.005
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detection-
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214.0711
ACCEPTED MANUSCRIPT 0.005
112.9853
0.005
214.0704
0.005
60.02051
0.005
76.97128
0.005
154.9471
0.005
60.02051
0.005
293.1777
0.005
154.948
0.005
112.9858
0.005
269.1056
0.005
68.99925
0.005
214.0712
0.005
421.2794
0.005
249.0975
0.005
112.9863
0.005
42.00356
0.005
154.947
68.99821
0.005
76.97253
214.0693
0.005
112.9866
267.1081
0.005
172.9583
112.9861
0.005
325.1833
214.0708
0.005
421.2778
154.9484
0.005
154.9465
0.005
76.97269
0.005
112.9861
0.005
42.00862
0.005
154.9467
0.005
42.0035
0.005
42.00337
0.005
214.0709
0.005
42.00393
0.005
265.1472
0.005
266.15
0.005
214.0698
0.005
214.0709
0.005
112.9863
0.005
154.9469
0.005
297.1517
0.005
214.0706
0.005
154.9468
0.005
199.1689
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327.1286
0.005
0.005 0.005 0.005 0.005
M AN U
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0.005
0.005
42.00428
0.005
361.259
0.005
421.2244
0.005
361.2568
0.005
227.2016
0.005
112.9858
0.005
58.83761
0.005
227.2018
0.005
361.2566
0.005
232.9228
0.005
421.2223
0.005
361.2579
0.005
0.005
112.9864
0.005
68.99813
0.005
68.99825
0.005
112.9865
0.005
154.9475
0.005
309.1729
0.005
232.9227
0.005
42.00383
0.005
214.0711
0.005
214.071
0.005
361.2578
0.005
154.947
0.005
232.9239
0.005
353.1991
0.005
421.2239
0.005
214.0695
0.005
61.9912
0.005
112.9862
0.005
68.99838
0.005
311.1673
0.005
154.9467
0.005
112.9862
0.005
253.2167
0.005
154.9472
0.005
214.0709
0.005
311.1688
0.005
154.9473
0.005
112.9866
0.005
421.2244
0.005
214.0703
0.005
61.99103
0.005
AC C
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214.0704
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ACCEPTED MANUSCRIPT 421.2793
0.005
255.2322
0.005
Exclude precursor
after
232.924
0.005
241.2164
0.005
61.99246
0.005
MSP file
154.9478
0.005
172.9585
0.005
232.9226
0.005
421.2252
0.005
112.9861
0.005
255.2327
0.005
Retention time tolerance Accurate mass tolerance (MS1) Accurate mass tolerance (MS2) Identification score cut off
154.9474
0.005
214.071
0.005
61.99112
0.005
68.99833
0.005
232.9241
0.005
61.99147
0.005 0.005
232.9233
0.005
112.986
0.005
MSP file and MS/MS identification setting F:\MS-DIAL\MS-DIAL program ver. 2.82\MSDIAL-LipidDBsVS32-FiehnO.lbm 40
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0.01 0.05 70
Text file
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Text file and post identification (retention time and accurate mass based) setting Retention tolerance Accurate tolerance Identification cut off
time
mass
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154.9474
TRUE
score
0.5
0.01 70
255.231
0.005
422.2801
0.005
172.9574
0.005
232.9239
0.005
154.9476
0.005
61.99049
0.005
61.99099
0.005
154.9479
0.005
255.2324
0.005
256.2355
0.005
232.9249
0.005
[M+K-2H]-
399.1693
0.005
[M+Hac-H]-
172.9578
0.005
61.99045
0.005
499.2003
0.005
61.99053
0.005
59.01952
0.005
Retention tolerance
212.0754
0.005
MS1 tolerance
0.025
421.2793
0.005
Retention time factor
0.5
255.2324
0.005
MS1 factor
0.5
361.2587
0.005
Peak count filter
0
267.1084
0.005
QC at least filter
FALSE
Peak consideration
Both
Sigma window value
0.5
Tracking of isotope labels Tracking of isotopic labels
Advanced setting for identification Relative abundance cut off
AC C
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Top candidate report
Deconvolution parameters
7
0 TRUE
Adduct ion setting [M-H][M-H2O-H][M+Na-2H][M+Cl]-
Alignment parameters setting Untreated Platelets ACD vs SAP vs Healthy16.abf
Reference file time
0.1
FALSE
ACCEPTED MANUSCRIPT 1.1
Typical isobaric interferences and assay specificity
Shotgun lipidomics may suffer from isobaric interferences and this is the case for LC-MS lipidomics as well if those lipids which have to be regarded as isobaric compounds in view of the employed mass resolving power of the utilized MS instrument, here QTOF, are not
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sufficiently resolved chromatographically. Therefore, emphasis was directed to develop an LC method which provides chromatographic selectivity for critical lipids that may represent potential interferences.
Amongst others M+2 isotopologues of lipids with one double bond more must be
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chromatographically resolved because the mas resolving power of currently employed QTOF instrument is not sufficient to distinguish this small mass difference by MS. An
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example is given in Fig. A. 1. The EIC of TG 46:0 shows a second peak eluted slightly earlier. It originates from the M+2 isotopologue of TG 46:1 which is the corresponding lipid with one additional double bond. Since these two lipids are chromatographically resolved, there is no interference. However, it would represent a problem, if not sufficiently
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chromatographically resolved, especially the influence of a high abundant lipid with one double bond more on a low abundant lipid with the same carbon number but one double bond less might represent a serious problem in relative quantifications due to lack of assay
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specificity. In general, RPLC provides sufficient selectivity for compounds which differ in the double bond number and this is the case for the current method as well, as documented also
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for early eluted lysophospholipids (as a second example form the other end of the lipophilicity scale) (see Fig. A. 2). LPC18:0 (two peaks due to isomeric sn1/sn2 regioisomers) and LPC18:1 (also two peaks due to regioisomers) are well resolved from each other (blue trace in Fig. A. 2), avoiding assay specificity problems.
8
ACCEPTED MANUSCRIPT 21.64
8000 7000
TG(46:0)
5000 4000
TG(46:1) [M+2]
3000 2000
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Intensity
6000
1000 0 19
20
21 22 23 Retention Time (min)
24
25
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Fig. A. 1.EIC of TG(46:0) with m/z 796.7394±0.005 at a retention time of 21.64 min. Even with a narrow extraction window of 5 mDa (6.3 ppm) an additional peak of TG(46:1) [M+2] at 21.37 min is visible. The mass difference of TG(46:1) [M+2] and TG(46:0) [M+H]+ is 11.6 ppm.
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In fact, for many lipids multiple peaks were observed in the XICs even for narrow extraction widths of ±0.005 Da. For instance, the EIC of [M+H]+ of LPC 20:3 (black trace; superimposed by red trace) gives in total 6 peaks. Both, isotopologue with one double bond more (LPC(20:4), two isomers) as well as sodium adduct of LPC(18:0) ([M+Na]+) are well
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resolved (Fig. A. 2A). Fig. A. 2B shows an overlay of the MS spectra of LPC(20:3) and LPC(20:4) documenting the incomplete mass resolution by QTOF MS, thus necessitating adequate chromatographic resolution. Similar situations exist for MS/MS chromatograms.
A
100% 75% 50%
AC C
LPC(20:3) LPC(18:0) [M+H]+ [M+H]+ LPC(20:4) [M+2] LPC(18:1) [M+2]
25% 0% 6
8
10
LPC(18:0) [M+Na]+
12
Retention Time (min)
LPC(20:3)[M+H]+ 546.3554 ± 0.0025 Da LPC(18:0)[M+H]+ 524.3711 ± 0.0025 Da LPC(18:0)[M+Na]+ 546.3530 ± 0.0025 Da
B
100%
LPC(20:4) LPC(20:3) 546.3608
60%
545.3484
10% 546.3513
40% 20% 0%
14
544.3451
80% Rel. Intensity
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Chromatographic separation is therefore important to assure adequate assay specificity.
0% 545.3484 546.36 548.3659 544 545 546 547 548 549 m/z
Fig. A. 2. Evaluation of typical mass interferences in lipidomics. A) EICs of [M+H]+ and [M+Na]+ of LPC(18:0) and [M+H]+ of LPC(20:3). Only a small signal for [M+Na]+ of LPC(18:0) is visible. Two additional peaks are visible for both lipids at earlier retention time. Those peaks were identified as [M+2] isotopologues of LPC(18:1) and LPC(20:4), respectively. The isotopic interference is explained in B) for LPC(20:3). The mass of the [M+2] isotopologue of LPC(20:4) differs only marginally from the mass of the [M+H]+ of LPC(20:3).
9
ACCEPTED MANUSCRIPT False discovery rate control according to Storey [1].
1.2
The q-values were calculated using the R statistical language and the q-value package of Bioconductor [2]. P-values were calculated likewise in R statistical language using the function wilcox.test. The normalized output of MS-DIAL was reduced to class and variable
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data and was used as input in R. The first column contained the sample information, the second column the sample class. The third until the last column contained all variables (molecular features) and the corresponding normalized intensities. The exact code for the Utest was:
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for(i in 2:ncol(data))
{Wilc <- function(Wilcox) wilcox.test(data[,i] ~ Class, data = data, subset = Class%in%
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Wilcox, paired = FALSE, exact = TRUE)
pvalue[,i-1] <- as.numeric(sapply(apply(v,2,Wilc),"[",3))}
The resulting list of p-values for controls vs. STEMI was used as input for the calculations of the q-values. The exact call was:
qvalue(matrix(as.numeric(pvalue[line,])),lambda=seq(0,0.95,0.001),
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qobj=
0.05,pfdr = TRUE)
The summary of the q-value calculation is reported as follows: 0.4811804
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:
Cumulative number of significant calls: <0.001
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<1E-04
<0.01
<0.025
<0.05
<0.1
<1
p-value
184
372
676
771
872
980
1955
q-value
0
0
589
745
855
995
1969
local FDR
52
166
372
491
612
704
1488
The p-value density histogram and the q-value plots are reported in Fig. A. 3.
10
fdr.level
=
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ACCEPTED MANUSCRIPT
Fig. A. 3. A) p-Value density histogram. results plots.
Precision of Internal Standards
LysoPC(17:1) [M+H]+ : CV 10% PC(17:0/20:4) [M+H]+ : CV 7% LysoPC(17:1) [M+HAc-H]- : CV 7%
1.4 1.2 1.0 0.8 0.6
QC3
QC5
QC7
QC9
B
20
18
LysoPC(17:1) [M+H]+ : CV 0.1% PC(17:0/20:4) [M+H]+ : CV 0.2% LysoPC(17:1) [M+HAc-H]- : CV 0.2%
8 6 QC11
QC1
QC3
QC5
QC7
QC9
QC11
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QC1
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Normalized Response
A
RT (min)
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1.3
was estimated as 0.481. B) q-Value package
Fig. A. 4. Precision of the used internal standards LPC(17:1) and PC(17:0/20:4). A) The normalized response of both internal standards varied below 10 % in both ESI polarities. B) The retention time was stable across the complete sequence with CVs of below 0.2 %.
1.4
Distribution of features in QC samples
Embedded QC samples were used to control the quality of the sample acquisition. CVs were calculated for all features detected in QC samples. Features were considered as reliably detected when a minimum of 6 of 11 QCs (n≥6) showed a positive peak detection of the distinct features. In case of not detected features the MS-DIAL software aligns an 11
ACCEPTED MANUSCRIPT interpolated value based on a local maximum at the retention time of the respective EIC. These interpolated values were found not precise and were therefore regarded as outliers for precision evaluation. A maximum of 5 outliers was tolerated. The distribution of peaks considered for precision control is listed in Table A. 6.
ESI+
ESI-
Sum
Percentage
n=6
76
67
143
10.0 %
n=7
67
61
128
9.0 %
n=8
56
53
109
7.7 %
n=9
67
40
107
7.5 %
n=10
219
55
274
19. 2%
n=11
475
188
663
46.6 %
Sum
960
464
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Found in n of 11 QCs
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Table A. 6. Distribution of features within QC samples used for quality assurance via calculation of CVs.
ACCEPTED MANUSCRIPT 1.5
Identification of Lipidis
The following chapter shows examples of representative identification data of the reported features in the main part of the paper. The automatic lipid identification was validated by reviewing the MS/MS spectral matching of the measured and deconvoluted spectrum to an
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in silico library spectrum (LipidBlast) [3]. MS-DIAL provided a score system for lipid identification. The scores are listed in the following tables for each identified lipid, respectively. The retention time score was not used for calculating the total score. The formulas for the calculation of the scores have been published in the original MS-DIAL paper
Identification of LPI(18:1)
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1.5.1
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[4].
The main document showed an example of lipid identification based on successful spectral deconvolution and assay specificity control based on deconvoluted MS/MS chromatograms (see Fig. 6 of the main document). Here, we wish to show an example in which spectral deconvolution failed. Nevertheless, identification by MS-DIAL was still
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valuable and successful in spite of a low identification score of 70.6 %. The great utility in such cases lies in the fact that it is easier and faster to start identification from a suggestion of lipid annotation rather than to start identification from de novo without any suggestion of a
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lipid structure. If the accurate mass similarity score of MS-DIAL is high, it is probably always worth to control the assignment manually by spectral interpretation as exemplified by the
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following example of LPI(18:1).
[M-H]SN1 acyl H2O loss
[Insositol phosphate H2O]-
-
[FA]
13
ACCEPTED MANUSCRIPT Fig. A. 5. Spectral match of LPI(18:1) Table A. 7. Identification scores of LPI 18:1 7.9 597.3036 999 256 741 617 706
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RT (min) Mass [Da) Accurate mass similarity score Dot product similarity score Reverse dot product similarity score Isotope similarity score Total score
Since LPI(18:1) showed a low quality MS/MS spectrum and the total score was near the minimum tolerated score of 70 %, the identification of LPI(18:1) was manually verified. As
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demonstrated in Fig. A. 6A, a representative MS/MS spectrum (QC9) was used for fragment assignment. If we look at this MS/MS spectrum, the quality seems to be poor and the match
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with the library spectrum is low. It seems that there are major contaminating ions remaining after deconvolution. Furthermore, it is striking that two major characteristic fragment ions (m/z 241.0119 and m/z 315.0487) were completely missing in the experimental deconvoluted MS/MS spectrum (Fig. A. 5) as compared to the background subtracted raw
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MS/MS spectrum (Fig. A. 6A). They have been removed by the spectral deconvolution process. The affiliation of m/z 241.0199 to the precursor of LPI(18:1) was verified by the DDA experiment performed at the beginning of the analysis sequence (Fig. A. 6C). The fragment with m/z 315.0487 was also not present in the DDA spectrum. Peak group analysis
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of the SWATH MS/MS EIC of the precursor and both fragments showed a clear correlation
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of m/z 315.0487 and m/z 214.0199 to the precursor of LPI(18:1)(Fig. A. 6C); the fragment on the other hand may not be selective enough for quantitative purposes.
Obviously, the
spectral deconvolution process failed, most probably because this lipid is relatively low abundant. Since precursor accurate mass similarity score was high and one fragment was perfectly matching as well, a manual process of identification was pursued. For in silico fragmentation the PeakView (SCIEX) function “fragment pane” was used. In Fig. A. 6A three fragments and the precursor peaks were assigned. The measured precursor differs only 5.5 ppm from the theoretical [M-H]- ion of LPI(18:1). The assigned fragments are shown in Fig. 14
ACCEPTED MANUSCRIPT A. 6B). Inositol phosphate minus H2O (m/z 241.0119) (fragment I), fatty acid side chain (18:1) (m/z 281.2486) (fragment II) and [M-H]- minus fatty acid side chain (18:1) minus H2O (m/z 315.0487) (fragment III) were all observed in the background subtracted SWATH MS/MS spectrum. Supporting the correct identification, these fragments are used in MS-
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DIAL for identification (Fig. A. 5). They are reported on liipidmaps.org website and in the literature as PI specific fragments [5, 6]. Relying on the above reported criteria, LPI(18:1) is
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considered as correctly identified.
Fig. A. 6. Manual verification of LPI(18:1). A) Background subtracted raw spectrum of sample QC9 from 7.89 to 8.10 minutes. This spectrum was used for assignment of theoretical calculated fragment masses using the “fragment pane” in PeakView software (SCIEX). Not assigned mass peaks (red) were due to non-deconvoluted (SWATH) composite spectrum (581.8 to 604.4). The precursor (error: 5.5 ppm) and three fragments (IIII) were assigned. B) Proposed structures and assigned m/z of fragments, verifying the identification of LPI(18:1) [5, 6]. C) DDA spectrum of precursor with m/z 597.3 at 7.91 min. The DDA spectrum verifies the affiliation of the fragments to the precursor. D) Peak group analysis of m/z 597.3029 and its fragments. The fragments show a clear correlation with the precursor. Especially the fragment with m/z 315.0502 could be assigned to the proposed precursor. 15
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Identification of LPS 20:4
Fig. A. 7. Spectral match of LPS 20:4 Table A. 8. Identification scores for LPS 20:4
Identification of FA 18:1
897 858
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6.8475 544.2657 970 522 549
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RT (min) Mass [Da) Accurate mass similarity score Dot product similarity score Reverse dot product similarity score Isotope similarity score Total score
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1.5.2
Fig. A. 8. Spectral match of FA 18:1 Table A. 9. Identification scores for FA 18:1 RT (min) Mass [Da) Accurate mass similarity score Dot product similarity score Reverse dot product similarity score Isotope similarity score Total score
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10.7232 281.2471 999 732 750 988 933
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Identification of unknown #1438; RT 2.98; m/z 367.1557
The unknown peak was exported from MS-DIAL alignment result to MS-Finder [7]. The chemical formula C19H28O5S was found with an error of 7 ppm as compared to the
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theoretical mass of the [M-H]- adduct of C19H28O5S. Measured MS/MS spectra were matched against the HMDB database using MS-FINDER [7, 8]. The experimental spectrum showed a good match to the in silico MS/MS spectrum of dehydroepiandrosterone sulfate (DHEAS). Since DHEAS was available as standard (MassCheck Steroid Panel 2, Chromsystems
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Instruments & Chemicals GmbH, Gräfelfing, Deutschland) the retention time, exact mass, isotope pattern and MS/MS fragmentation were compared to the unknown peak using the
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same LC-MS/MS method as for the sample acquisition. As reference sample STEMI 12 was selected, which was also the reference file for the peak alignment in MS-DIAL. As shown in Fig. A. 9 the retention time matched with 0.08 min difference. The small difference in RT could be explained due to a different condition of the chromatographic column. The column
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was used for other projects in the meantime. The MS/MS fragment match was identical even without deconvolution. The mass shift of DHEAS standard compared to the unknown peak was +6.3 ppm. In the real sample the candidate peak is low abundant, so the mass accuracy
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is expectedly not perfect. Therefore, an error of 6.3 ppm is acceptable, especially because of a matching isotope pattern. Four different identification criteria are fulfilled and therefore, the
A
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peak is considered as identified as DHEAS. Real Sample (STEMI 12) DHEAS Standard
Intensity
5e4
2.91
4e4
50%
3e4 2e4 1e4
0%
2.99
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B 96.9618
367.1575
100% 367.1565 50%
96.9616
-50%
368.1602 369.1675
367.1570
0e0
0% 2
4
6
8
10
Retention time (min)
100
200
300 m/z
400
367.5 368.0 368.5 369.0 m/z
Fig. A. 9. Identification of unknown peak #1438 as DHEAS; (RT 2.98; m/z 367.1557). A) The retention time of a real sample (STEMI 12) was compared to the retention time of the standard. B) The fragment pattern of standard and unknown peak were identical. C) The isotope pattern of DHEAS standard and QC9. showed a perfect overlay.
17
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Identified and significantly altered lipids in ESI-
All identified lipids were filtered for a maximum tolerated CV of 30 %. The reported CVs in Table A. 10 were derived from QC samples. In contrast to the precision calculation for method validation (see main document), the CV calculation here included interpolated
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values, because the samples comparison is based on all aligned values without outlier filtering. Therefore, all CV values must show a precision below 30 % even including interpolated values. Only three features needed elimination of one outlier. Precisions with deleted interpolated values would have been even better (see main document). All listed
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results have been found in both ESI polarities showing the same relative quantifications. All identifications except of DHEAS have been assigned with MS-DIAL (total score ≥ 70 %)
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and were further manually validated as reported in the previous chapter. A detailed results list and clinical interpretation was published elsewhere [9].
Table A. 10. Identified and significantly altered lipids in ESISample
Total ID
m/z
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-
RT(min)
p-values
q-values
Fold Change
CV %
n/11 QCs
FA(18:1); [M-H]
1387
10.72
281.2471
0.00086
0.00269
0.68
6%
11
LPC(16:0); [M+CH3COO]-
1605
9.4
554.3431
0.00001
0.0023
0.29
5%
11
-
1602
8.13
552.3275
0.00254
0.00496
0.39
26 %
11
-
LPC(18:0); [M+CH3COO]
1632
11.13
582.3735
0.00005
0.0023
0.19
6%
11
LPC(18:2); [M+CH3COO]-
1626
8.71
578.3431
0.00001
0.0023
0.32
6%
11
LPC(22:6); [M+CH3COO]-
1678
8.51
626.3435
0.00000001
0.0023
0.32
20 %
11
1639
7.77
597.3036
0.00173
0.00395
0.34
9%
11
1599
6.85
544.2657
0.00115
0.0031
3.29
6%
11
1887
18.41
816.5735
0.00012
0.0023
0.15
23 %
11
-
LPI(18:1); [M-H]
LPS(20:4); [M-H]-
EP
LPC(16:1); [M+CH3COO]
-
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PC(16:0_18:2); [M+CH3COO] -
PC(18:_18:2); [M+CH3COO]
1905
19.93
844.6027
0.00086
0.00269
0.22
26 %
10
-
PC(16:0_20:4); [M+CH3COO]
1900
17.96
840.5735
0.00003
0.0023
0.33
13 %
11
PC(16:0_20:5); [M+CH3COO]-
1898
17.19
838.557
0.00000001
0.0023
0.17
25 %
10
-
1921
19.17
868.6032
0.00017
0.0023
0.39
21 %
11
-
1919
18.11
866.5857
0.00003
0.0023
0.18
29 %
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PC(18:0_20:4); [M+CH3COO] PC(18:1_20:4); [M+CH3COO]
PC(16:0_22:6); [M+CH3COO]-
1918
17.65
864.5717
0.00000001
0.0023
0.07
8%
11
DHEAS; [M-H]-
1438
2.98
367.1557
0.00006
0.0023
0.30
29 %
10
18
ACCEPTED MANUSCRIPT 1.5.6
Identification in positive and negative ESI polarity
LPC(16:0) is shown as an example for peak group analysis in the main document (see Fig. 2B). The correct assignment of fragment peaks was verified by comparison of deconvoluted SWATH MS/MS spectra (Fig. A. 10A and B) to raw spectra acquired in DDA mode (Fig. A. 10C and D). The deconvoluted SWATH spectra look nearly identical to the DDA spectra which proofs the good quality of deconvoluted SWATH spectra for identification of lipids via spectral matching.
B
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ESI+
Precursor: 554.3 Da
Precursor: 496.3 Da, CE: 20.0
Intensity
1.0e5 8.0e4 6.0e4
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496.3425 700 258.1092 313.2741 600 500 400 300 200 240.0992 100 0 260 280 300 320 497.3424 104.1059
4.0e4 184.0726 2.0e4 0.0e0 200
300
400
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100
478.3308
m/z
500
9000 8000 7000
Intensity
1.2e5
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6000 5000 4000
1000 800 600 400 200 0
480.3977 255.2975
554.4419 224.1310 200 220 240 260 280
3000 2000
255.2975
1000 0 100
200
300
400
500
600
m/z
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Fig. A. 10. Identification of LPC(16:0). A) Deconvoluted SWATH spectrum in ESI+ of the assigned precursor 496.3 and B) in ESI- of the assigned precursor 554.3. C) DDA spectrum of precursor 496.3 in ESI+ and D) DDA spectrum of precursor 554.3 in ESI-. In the following exemplary spectral matches from MS-DIAL are shown which were used for assignment of lipids:
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ACCEPTED MANUSCRIPT LPC(16:1) ESI-
LPC(18:0) ESI-
LPC(18:2)
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PC(18:0_20:5) ESI-
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SM d14:1/18:0
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Cer[NS] d44:1; Cer[NS](d18:1/26:0)
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DG 36:3; DG(18:1/18:2)
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Carryover evaluation
B
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A
References
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Fig. A. 11. Evaluation of carry over. A sequence according to Table A. 3. but with blanks (extraction solvent) before every QC was run. A) shows the ESI+ peak spotting result of a blank before and B) after analysis of a clinical sample sequence. The data extraction parameters were the same as reported in Table A. 4. No lipid was positively identified.
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[1] J.D. Storey, The positive false discovery rate: A Bayesian interpretation and the q-value, Ann Stat, 31 (2003) 2013-2035. [2] J.D. Storey, A.J. Bass, A. Dabney, D. Robinson, qvalue: Q-value estimation for false discovery rate control. R package version 2.10.0. http://github.com/jdstorey/qvalue, (2015). [3] T. Kind, K.H. Liu, Y. Lee do, B. DeFelice, J.K. Meissen, O. Fiehn, LipidBlast in silico tandem mass spectrometry database for lipid identification, Nat. Methods, 10 (2013) 755758. [4] H. Tsugawa, T. Cajka, T. Kind, Y. Ma, B. Higgins, K. Ikeda, M. Kanazawa, J. VanderGheynst, O. Fiehn, M. Arita, MS-DIAL: data-independent MS/MS deconvolution for comprehensive metabolome analysis, Nat. Methods, 12 (2015) 523-526. [5] N. Zehethofer, T. Scior, B. Lindner, Elucidation of the fragmentation pathways of different phosphatidylinositol phosphate species (PIPx) using IRMPD implemented on a FT-ICR MS, Anal. Bioanal. Chem., 398 (2010) 2843-2851. [6] F.-F. Hsu, J. Turk, Characterization of phosphatidylinositol, phosphatidylinositol-4phosphate, and phosphatidylinositol-4,5-bisphosphate by electrospray ionization tandem mass spectrometry: A mechanistic study, J. Am. Soc. Mass. Spectrom., 11 (2000) 986-999. [7] H. Tsugawa, T. Kind, R. Nakabayashi, D. Yukihira, W. Tanaka, T. Cajka, K. Saito, O. Fiehn, M. Arita, Hydrogen Rearrangement Rules: Computational MS/MS Fragmentation and Structure Elucidation Using MS-FINDER Software, Anal. Chem., 88 (2016) 7946-7958. [8] D.S. Wishart, T. Jewison, A.C. Guo, M. Wilson, C. Knox, Y. Liu, Y. Djoumbou, R. Mandal, F. Aziat, E. Dong, S. Bouatra, I. Sinelnikov, D. Arndt, J. Xia, P. Liu, F. Yallou, T. Bjorndahl, R. Perez-Pineiro, R. Eisner, F. Allen, V. Neveu, R. Greiner, A. Scalbert, HMDB 3.0—The Human Metabolome Database in 2013, Nucleic Acids Res., 41 (2013) D801-D807. [9] M. Chatterjee, D. Rath, J. Schlotterbeck, J. Rheinlaender, B. Walker-Allgaier, N. Alnaggar, M. Zdanyte, I. Muller, O. Borst, T. Geisler, T.E. Schaffer, M. Lammerhofer, M. Gawaz, Regulation of oxidized platelet lipidome: implications for coronary artery disease, Eur. Heart J., (2017). [1] J.D. Storey, The positive false discovery rate: A Bayesian interpretation and the q-value, Ann Stat, 31 (2003) 2013-2035. [2] J.D. Storey, A.J. Bass, A. Dabney, D. Robinson, qvalue: Q-value estimation for false discovery rate control. R package version 2.10.0. http://github.com/jdstorey/qvalue, (2015).
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[3] T. Kind, K.H. Liu, Y. Lee do, B. DeFelice, J.K. Meissen, O. Fiehn, LipidBlast in silico tandem mass spectrometry database for lipid identification, Nat. Methods, 10 (2013) 755758. [4] H. Tsugawa, T. Cajka, T. Kind, Y. Ma, B. Higgins, K. Ikeda, M. Kanazawa, J. VanderGheynst, O. Fiehn, M. Arita, MS-DIAL: data-independent MS/MS deconvolution for comprehensive metabolome analysis, Nat. Methods, 12 (2015) 523-526. [5] N. Zehethofer, T. Scior, B. Lindner, Elucidation of the fragmentation pathways of different phosphatidylinositol phosphate species (PIPx) using IRMPD implemented on a FT-ICR MS, Anal. Bioanal. Chem., 398 (2010) 2843-2851. [6] F.-F. Hsu, J. Turk, Characterization of phosphatidylinositol, phosphatidylinositol-4phosphate, and phosphatidylinositol-4,5-bisphosphate by electrospray ionization tandem mass spectrometry: A mechanistic study, J. Am. Soc. Mass. Spectrom., 11 (2000) 986-999. [7] H. Tsugawa, T. Kind, R. Nakabayashi, D. Yukihira, W. Tanaka, T. Cajka, K. Saito, O. Fiehn, M. Arita, Hydrogen Rearrangement Rules: Computational MS/MS Fragmentation and Structure Elucidation Using MS-FINDER Software, Anal. Chem., 88 (2016) 7946-7958. [8] D.S. Wishart, T. Jewison, A.C. Guo, M. Wilson, C. Knox, Y. Liu, Y. Djoumbou, R. Mandal, F. Aziat, E. Dong, S. Bouatra, I. Sinelnikov, D. Arndt, J. Xia, P. Liu, F. Yallou, T. Bjorndahl, R. Perez-Pineiro, R. Eisner, F. Allen, V. Neveu, R. Greiner, A. Scalbert, HMDB 3.0—The Human Metabolome Database in 2013, Nucleic Acids Res., 41 (2013) D801-D807. [9] M. Chatterjee, D. Rath, J. Schlotterbeck, J. Rheinlaender, B. Walker-Allgaier, N. Alnaggar, M. Zdanyte, I. Muller, O. Borst, T. Geisler, T.E. Schaffer, M. Lammerhofer, M. Gawaz, Regulation of oxidized platelet lipidome: implications for coronary artery disease, Eur. Heart J., (2017).
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PCA (log transformed / no scaling)
10 0
0.1
10
0.1
0
-10
-10
-20
0.0
-0.1
-0.2
-30
-0.2
-30 -20 -10
0 10 20 D1 (50.0 %)
q=0.00395
SAP STEMI
q=0.0031
QC
C
SAP STEMI
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F LPS 20:4
4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0 -0.5
40
-0.10
FA 18:1
q=0.00269
QC
C
SAP STEMI
-0.05 0.00 0.05 D1 (50.0 %)
0.10
G 70 60 50 40 30 20 10 0 -10 -20
DHEAS q=0.00230
QC
C
SAP STEMI
5.0 4.0 3.0 2.0 1.0 0.0 -1.0 -2.0 -3.0 -4.0 -5.0
ln(normalized peak intensity)
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4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0 -0.5
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Normalized peak intensity
D
0
Normalized peak intensity
-70 -60 -50 -40 -30 -20 -10 PC1 (60.3 %) (Control)
Normalized peak intensity
-40
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-30
Normalized peak intensity
↑Control ↑SAP ↑STEMI ↓Control ↓SAP ↓STEMI
-0.1
-20
H
PCA-DA loadings (PC variable grouping)
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20
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20
D2 (50.0 %)
30 PC2 (11.9 %)
PCA-DA (log transformed / mean centered)
B QC Control SAP STEMI Outlier
40
D2 (50.0 %)
A
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B 800
n≤5
118
77
195
12.0%
n≥6
960 464 1424
88.0%
≤10%
579 157 736
45.5%
245 183 428
26.4%
≤ 30%
50
136
8.4%
Sum
874 426 1300
80.3%
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EP
86
700
736
ESI+ ESISum
157
600
428
500 400 300
579
183
136
200
245
100 0
TE D
≤ 20%
Number of molecular features in QCs
ESI+ ESI- Sum Percentage Found in QC 1078 541 1619
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≥10
20
86 50
30
56
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22
16
8
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Highlights Comprehensive MS and MS/MS untargeted lipidomic profiling by UHPLC-ESI-QTOF-MS
•
Workflow for data-independent acquisition of lipidomics data with SWATH
•
SWATH parameter optimization
•
Parallel semi-targeted analysis of oxidized phospholipids
•
Clinical application to patient platelets
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S0003-2670(18)31056-0
DOI:
10.1016/j.aca.2018.08.060
Reference:
ACA 236234
To appear in:
Analytica Chimica Acta
Received Date: 23 July 2018 Revised Date:
29 August 2018
Accepted Date: 30 August 2018
Please cite this article as: J. Schlotterbeck, M. Chatterjee, M. Gawaz, M. Lämmerhofer, Comprehensive MS/MS Profiling by UHPLC-ESI-QTOF-MS/MS using SWATH Data-independent Acquisition for the Study of Platelet Lipidomes in Coronary Artery Disease, Analytica Chimica Acta (2018), doi: 10.1016/ j.aca.2018.08.060. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Lipidomics with SWATH
Control
SWATH Optimization
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ESI-
PGPC PAzPC POVPC PONPC
10 8
Application and data evaluation
ESI+
6 4 2
0 SWATH Q1 width Accumulation time
30 Da
5 Da
3 Da
30 Da
5 Da
33 ms
3 Da
100 ms
enh.*
2.5
2x
2.0 1.5
A
1.0
0
0.5
5
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5 Da
100 ms
33 ms
ESI+
33 ms
PGPC
+ 90 %
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100 ms
9000 8000
+ 53 %
600
POVPC 400
3 Da
5 Da
3 Da
PAzPC
200
5 Da
POVPC
3 Da
5 Da
PONPC
100 ms enh*
Optimized Windows Features for SWATH Tuning
3000 2000
0 0
5
10 15 20 Retention Time (min)
m/z
5 Da
PGPC
6000
cycle 5000 SWATH 4000
1
3 Da
7000
PONPC
m/z
2
10000
enh.*
PAzC
Intensity
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3
0 SWATH Q1 width Accumulation time
30
3 Da
100 ms
800
4
UHPLCESIQTOF
25
1000
30 Da
33 ms
5
25
30
Retention Time (min)
G Normalized peak intensity
0.0 SWATH Q1 width Accumulation time
Experiment 15 20
70 60 50 40 30 20 10 0 -10 -20
DHEAS q=0.00230
QC
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SAP STEMI
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Comprehensive MS/MS Profiling by UHPLC-ESI-QTOF-
2
MS/MS using SWATH Data-independent Acquisition for
3
the Study of Platelet Lipidomes in Coronary Artery
4
Disease
5
Jörg Schlotterbeck†, Madhumita Chatterjee‡, Meinrad Gawaz‡ and Michael Lämmerhofer†
SC
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University of Tübingen, Institute of Pharmaceutical Sciences, Pharmaceutical
7
(Bio-)Analysis, Auf der Morgenstelle 8, 72076 Tübingen, Germany ‡
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1Department of Cardiology and Cardiovascular Medicine, University Hospital Tübingen, Otfried-Müller-Strasse 10, 72076 Tübingen, Germany
10
Keywords.
11
Lipidomics,
SWATH,
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Data-independent
acquisition,
phospholipids, biomarkers
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Corresponding Author:
15 16 17 18 19 20 21 22 23 24 25 26 27
Prof. Dr. Michael Laemmerhofer Professor for Pharmaceutical (Bio-)Analysis Institute of Pharmaceutical Sciences University of Tuebingen Auf der Morgenstelle 8 72076 Tuebingen, Germany
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T: +49 7071 29 78793, F: +49 7071 29 4565 e-mail: [email protected] http://www.bioanalysis.uni-tuebingen.de/
1
Clinical
Analysis,
oxidized
ACCEPTED MANUSCRIPT 28
ABSTRACT. An untargeted lipidomics workflow based on C8 core-shell particle ultra high-performance
30
liquid chromatography (UHPLC) hyphenated to ESI-QTOF-MS in data-independent
31
acquisition (DIA) with sequential window acquisition of all theoretical fragment ion spectra
32
(SWATH) was developed and applied to differential platelet lipidomics profiling of
33
cardiovascular disease patients (stable angina pectoris (n=10), ST-elevated myocardial
34
infarction (n=13))
35
comprehensive MS and MS/MS data throughout the entire chromatograms and all study
36
samples. Hence, chromatograms can be extracted based on precursors or fragments which
37
provided some benefits in terms of assay specificity in some cases. SWATH acquisition
38
offers
39
chromatography as well as SWATH settings were optimized to cover the lipidome of human
40
platelets. The flexibility of the SWATH experiment design was utilized to implement target
41
SWATH windows with narrow 5 Da Q1 precursor ion selection width (multiple reaction
42
monitoring (MRM)-like SWATH windows) for the detection of low abundant oxidized
43
phospholipids. Data processing was performed with MS-DIAL, and its feasibilities and
44
caveats are discussed by illustrative examples. Thereby, identification of lipids is still a
45
bottleneck in untargeted lipidomics workflow. MS-DIAL, however, offers automatic
46
identification via spectral matching using an in silico library. In total 1971 molecular features
47
were detected across the samples of which 611 were identified (total score >70 %). The
48
quality of the acquired data was validated with embedded quality control samples (n=11).
49
80.3 % of all features detected in the QC samples showed a coefficient of variation of below
50
30 %. Multivariate statistics were used to visualize differences in the lipidome of distinct
51
sample groups at a false discovery rate of 5%.
healthy controls
(n=10).
DIA with
SWATH generates
experimental
design
with
variable
Q1
isolation
windows.
Liquid
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1
INTRODUCTION. It is generally known that lipids are more than energy providers and membrane building
58
blocks. Lipid metabolism is strongly involved in complex pathways like inflammation,
59
signaling and regulation [1-3]. Changes in the lipidome are correlated to various diseases
60
like diabetes, cardiovascular disease, neurodegenerative diseases and cancer [1, 4-6].
61
Therefore, it is evident that lipidomics is a promising research field since its introduction in
62
the year 2003 [7-9]. Lipidomics studies were promoted through technical progress in
63
analytical chemistry. Lipid Maps database, a major resource of lipid related information and
64
data, lists more than 40,000 lipids and computational methods assume over 100,000
65
possible lipid species [10]. State of the art techniques in mass spectrometry (MS) and (ultra)-
66
high performance liquid chromatography ((U)HPLC) are therefore indispensable in order to
67
cope with this huge complexity.
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Two different analytical approaches are common for non-targeted lipidomics studies. The
69
first approach is direct-infusion MS (shotgun lipidomics) which is suitable for high throughput
70
discovery studies due to methodological simplicity and speed [11-15]. A disadvantage may
71
be the lack of sensitivity (low abundant lipids are often suppressed by highly abundant ones)
72
and limited identification coverage [16]. On the other hand, discovery of unknowns is limited
73
to ultra-high resolution MS [8]. LC-MS based methods allow more reliable identification even
74
at lower concentration levels [17]. Due to the separation, ion-suppression effects are
75
reduced. Even isomers and isobars can be resolved chromatographically which improves
76
assay specificity. For this reason, some authors propagate two-dimensional LC-MS
77
workflows which provide enhanced chromatographic lipid resolution [18]. High resolution
78
mass spectrometry with resolving power higher than 15,000 and typical mass accuracy
79
below 5 ppm reduces isobaric overlaps yielding a better overall lipid coverage and increases
80
reliability of identifications [19]. For exact and unequivocal identification tandem MS (MS/MS)
81
is essential. Specific fragmentation of precursor ions could help to identify single lipid
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ACCEPTED MANUSCRIPT structures. Mainly there are two ways to acquire MS/MS data in untargeted lipidomics. (1)
83
Data-dependent acquisition (DDA) selects precursor ions for fragmentation based on the
84
information gained in a MS survey scan, therefore also sometimes called information-
85
dependent acquisition (IDA) [20]. This information could be specified intensity thresholds, a
86
defined mass range, target masses or mass defect filters [20]. Typically, the top 10-20
87
precursor ions are triggered for MS/MS [21-25]. A noteworthy disadvantage of DDA is low
88
reproducibility and incomplete analyte coverage [20, 26-28]. (2) Data-independent
89
acquisition (DIA) acquires spectral data of fragments without specific selection of a
90
precursor. A limitation of this technique is the generation of composite spectra of coeluted
91
substances that are simultaneously co-fragmented, which could exacerbate lipid
92
identification through their complexity. This complexity could be reduced on analytical level
93
with optimized LC separation and the application of sequential window acquisition of all
94
theoretical fragment ion spectra (SWATH). This DIA technique was introduced in 2012 by
95
Gillet et al. [26] and presented as a new concept for proteomics. Fragmentation of all
96
precursors is achieved through consecutive Q1 isolation windows for a given mass range.
97
SWATH was promptly adapted for small molecule analysis like metabolomics and lipidomics
98
[28-30]. SWATH-MS increases analyte coverage and generates still good MS/MS spectra
99
quality for metabolomics [20]. DIA with SWATH outperformed DDA methods in forensic,
100
toxicological and pharmaceutical studies with higher detection rate and more complete
101
identification [28, 30-32]. To efficiently adapt SWATH-MS for lipidomics, it was necessary to
102
implement databases for identification. In 2013, LipidBlast, an in silico MS/MS database for
103
lipidomics, was introduced for spectral matching and lipid identification, respectively [33].
104
This database was implemented in freely available data processing software MS-DIAL,
105
which allowed automatic peak finding, deconvolution and identification of lipids with SWATH
106
data files [29].
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In this study, we present a workflow which utilizes these recent developments in DIA
108
techniques for a comprehensive lipidomics study of platelets involved in coronary artery 4
ACCEPTED MANUSCRIPT disease. The analysis of platelets instead of blood plasma could lead to additional insights of
110
cardiovascular disease mechanisms as they are responsible for symptomatic thrombus
111
formation during an acute myocardial infarction. Platelets can be regarded as individual
112
cellular compartments with distinctive lipid metabolism from plasma. They are activated by
113
different pathways releasing lipid signaling molecules which regulate thrombotic and
114
inflammatory responses leading to significant alterations of the platelet lipidome in the
115
course of platelet activation [15, 34, 35]. In this work, modern core-shell particle technology
116
for UHPLC separations was combined with high resolution non-targeted SWATH-MS/MS.
117
Variable Q1 isolation windows were utilized to increase selectivity and to reduce cycle time
118
[36]. Additional narrow target windows were implemented to enable combined targeted
119
acquisition for four oxidized phosphatidylcholines (oxPCs), viz. 1-palmitoyl-2-(5-oxovaleroyl)-
120
sn-glycero-3-phosphatidylcholine
121
phosphatidylcholine
122
(PAzPC),
123
approach allowed increased sensitivity and selectivity for targets and non-targeted analysis
124
with a single injection. Non-targeted peak finding and identification of lipids in platelet
125
extracts was possible with MS-DIAL [29]. For data evaluation, a statistical workflow based on
126
multivariate statistics, e.g. principal component analysis (PCA), and univariate statistics was
127
developed [37]. A scheme of the entire workflow is depicted in Fig. 1.
128
2
129
2.1
(POVPC),
1-palmitoyl-2-(9-oxononanoyl)-sn-glycero-3-
1-palmitoyl-2-azelaoyl-sn-glycero-3-phosphatidylcholine
1-palmitoyl-2-glutaryl-sn-glycero-3-
phosphatidylcholine
(PGPC).
This
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(PONPC),
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EXPERIMENTAL SECTION. MATERIALS.
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Acetonitrile was purchased in ultra-LC-MS grade and 2-propanol as LC-MS grade from
131
Carl Roth GmbH + Co. KG (Karlsruhe, Germany). Ammonium acetate (>99.0 %) was
132
purchased
133
oxovaleroyl)-sn-glycero-3-phosphatidylcholine (POVPC), 1-palmitoyl-2-(9-oxononanoyl)-sn-
134
glycero-3-phosphatidylcholine
(PONPC),
135
phosphatidylcholine
1-palmitoyl-2-glutaryl-sn-glycero-3-phosphatidylcholine
5
from
Sigma-Aldrich
(PAzPC),
Inc.
(Schnelldorf,
Deutschland).
1-Palmitoyl-2-(5-
1-palmitoyl-2-azelaoyl-sn-glycero-3-
ACCEPTED MANUSCRIPT (PGPC),
1-heptadecenoyl-sn-glycero-3-
phosphatidylcholine
(LPC(17:1))
and
137
heptadecenoyl-2-eicosatetraenoyl-sn-glycero-3- phosphatidylcholine (PC(17:0/20:4)) were
138
obtained from Avanti Polar Lipids, Inc. (Alabaster, AL, USA). Dehydroepiandrosterone
139
sulfate (DHEAS) was purchased as part of the MassCheck Steroid Panel 2 from
140
Chromsystems Instruments & Chemicals GmbH (Gräfelfing, Deutschland).
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1-
141
Nomenclature and abbreviation of lipids was done according to the classification system of
142
the LIPID MAPS consortium [35, 36]. Lyso lipid species are indicated with the prefix “L” (e.g.
143
PC
144
glycerophosphatidylethanolamines, PC: glycerophosphatidylcholines, SM: sphingomyelin,
145
DG:
146
PI: glycerophosphatidylinositols, PS: glycerophosphatidylserines, FA: fatty acid. Lipid
147
shorthand notation follows the guideline of Liebisch et al. [38].
148
2.2
lyso
species)).
diradylglycerolipids,
Abbreviations
ChE:
PLATELET ISOLATION.
cholesterol
of
lipids:
AC:
acyl
carnitine,
PE:
SC
for
ester,
TG:
triradylglycerolipids,
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Plasma from coronary artery disease (CAD) patients and healthy donors was collected at
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the University Clinic Tübingen after having obtained informed consent in accordance with the
151
ethics committee of the Medical Faculty of the University of Tübingen. Washed platelets
152
were isolated from healthy subjects (n=10), patients suffering from stable angina pectoris
153
(SAP) (n=10) and acute coronary syndrome (ACS) patients with ST-elevation myocardial
154
infarction (STEMI) (n=13) as mentioned in a previous publication [39]. Blood samples were
155
collected in acid-citrate-dextrose-buffer and centrifuged at 430 × g for 20 min. Platelet-rich
156
plasma thus obtained was added to Tyrodes-HEPES buffer (HEPES-2.5 mM; NaCl-150 mM;
157
KCl-1 mM; NaHCO3-2.5 mM; NaH2PO4-0.36 mM; glucose-5.5 mM; BSA-1 mg mL-1; pH 6.5)
158
and centrifuged at 900 × g for 10 min. The supernatant was removed and Tyrodes-HEPES
159
buffer (pH 7.4; supplemented with 1 mM CaCl2; 1 mM MgCl2) was used to resuspend the
160
platelet pellet. Washed platelets were stored at -80 °C until lipid extraction was carried out.
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2.3
LIPID EXTRACTION.
Lipids were extracted from the platelet pellets as published previously [34]. The resulting
163
platelet pellets (3x108 platelets/sample) from the washing step were resuspended in cold
164
acidic ethanol (ethanol / 0.18 M HCl 75:25; v/v). Gentle sonification in a water bath while
165
cooling on ice was used to lyse the platelets. The platelet lysate was centrifuged to get rid of
166
cellular debris and the supernatant was collected and stored at -20 °C until analysis. Before
167
analysis supernatants were dried under a gentle stream of nitrogen and reconstituted in 500
168
µL of methanol. Samples were centrifuged again, and the supernatant was carefully
169
removed without disturbing the pellet. The entire sample preparation was carried out under
170
light protection. The collected supernatants were spiked with internal standards (LPC(17:1),
171
PC(17:0/20:4)) at a final concentration of 40 ng mL-1 and subjected to UHPLC-MS analysis.
172
2.4
CHROMATOGRAPHY.
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An Agilent 1290 Infinity UHPLC instrument (Agilent Technologies, Waldbronn, Germany)
174
with HTC-xt PAL autosampler (CTC, Zwingen, Switzerland) (samples maintained at a tray
175
temperature of 4 °C in the dark) hyphenated to a SCIEX TripleTOF 5600+ (SCIEX, Concord,
176
Ontario, Canada) hybrid mass spectrometer was used for analysis of platelet lipid extracts. A
177
Kinetex C8 core-shell particle column (150 x 2.1 mm; 2.6 µm) (Phenomenex, Aschaffenburg,
178
Germany) equipped with a C8 Security Guard ULTRA cartridge (2.1 mm I.D.) (Phenomenex)
179
was used for chromatographic separation prior to MS detection. The mobile phase was
180
composed of aqueous 10 mM ammonium acetate (A) and a mixture of acetonitrile, 2-
181
propanol and water (55:40:5; v/v/v) containing 10 mM ammonium acetate (B). The gradient
182
was 10 % B to 40 % B in 2 minutes and 100 % B in 20 minutes followed by a cleaning step
183
of 10 minutes 100 % B and 2 minutes equilibration with 10 % B at a flow rate of 400 µL·min-1
184
and 50 °C oven temperature. Injection volume was 2 µL.
185
2.5
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MASS SPECTROMETRY.
186
A TripleTOF 5600+ (SCIEX) equipped with a DuoSpray source was operated in positive
187
ion mode with an ion spray floating voltage (ISFV) of 5000 V and in negative ion mode with 7
ACCEPTED MANUSCRIPT an ISFV of -4500 V. Heater temperature was set to 500 °C. Nebulizer gas (GS1), heater gas
189
(GS2) and curtain gas (Cur) were used for both polarities at 50, 40, 30 psi, respectively.
190
Declustering potential was set to 100 V. For both polarities the TOF-MS survey scan with a
191
specified resolution of R≥30.000 (FWHM) was set to 250 ms accumulation time. Non-
192
targeted MS/MS acquisition was carried out using DIA by SWATH with variable Q1 window
193
size covering a range of m/z 30 to 1000. The size of Q1 windows was optimized using
194
swathTUNER [36]. For this purpose, the QC was analyzed in separate runs in DDA mode for
195
both polarities prior to analysis of the entire sequence. The top 20 abundant ions within an
196
m/z range of 30-1000 with a minimum intensity of 50 cps were selected for fragmentation in
197
Q1 operated in unit resolution. Dynamic background filtering was used. The already selected
198
ions were excluded for 5 s. A mass tolerance of 50 ppm was allowed and isotopes were
199
excluded within 4 Da. The collision energy for the DDA experiments was set to 20 ± 10 V.
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The resulting peak lists were used for swathTUNER calculations. Considering the need for
201
ten data points per peak [40] a maximum period cycle time of 1.5 s was tolerated in the
202
course of optimization of the SWATH windows. Therefore, 26 SWATH windows with 30 ms
203
accumulation time were calculated by swathTUNER. Four additional SWATH windows were
204
programmed in positive electrospray ionization (ESI+) mode for target analytes of prime
205
interest (oxPCs) with increased accumulation time of 100 ms and narrow Q1 window size of
206
5 Da. Due to narrow Q1 window width, these SWATH windows were MRM-like. Collision
207
energy was optimized for phosphatidylcholine species as principal constituents of significant
208
interest in this study and set to 35 V for each Q1 window. Fragment mass enhancement of
209
m/z 184.07332 was used for the target windows. In negative polarity mode, MS/MS spectra
210
were acquired in 30 variable sized Q1 windows with 40 ms accumulation time with collision
211
energy of -30 V and a collision energy spread of ± 10 V each. Exact SWATH window
212
information is shown in Table A. 1A for ESI+ and in Table A. 1B for negative electrospray
213
ionization mode (ESI-). All SWATH windows were acquired in high sensitivity mode at an
214
MS/MS resolution of R=15000 (FWHM). Calibration was carried out after every fifth injection
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ACCEPTED MANUSCRIPT using the auto calibrate function in Analyst TF 1.7.1 (SCIEX). As calibration solution 0.1 mol
216
L-1 sodium acetate in a 1:1 mixture of acetonitrile and water (v/v) was used. The calibrant
217
solution was injected into the mass spectrometer with a flow rate of 0.1 mL/min for 30 s
218
using an external pump and a switching valve before the ESI source. Acetate clusters could
219
be used as calibration masses in positive and negative TOF-MS mode as well as for MS/MS.
220
With this method a constant mass accuracy below 2 ppm was achieved. The full list of
221
calibration masses is reported in Table A. 2.
222
2.6
QUALITY ASSURANCE.
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Data quality was evaluated by QC samples which were embedded throughout the analysis
224
batch. For this purpose, an independent quality control sample (QC), obtained from a
225
previous study, was prepared as a mixture (1/1/1, v/v/v) of platelet extracts from healthy
226
donors: (1) n=10 untreated platelets, (2) n=10 platelets incubated with low-density lipoprotein
227
(LDL) and (3) n=10 platelets incubated with oxidized low-density lipoprotein (oxLDL). IS
228
LPC(17:1) and PC(17:0/20:4) were added to the resulting mixture at a final concentration of
229
40 ng mL-1. The same independent QC sample was used for the Q1 window optimization
230
described above. Before analysis, the column was equilibrated with 3 blank injections of 2-
231
propanol. The whole analysis batch consisted of 47 injections in total with 14 injections of the
232
QC sample (6 × before analysis of the first sample with the first 2 in DDA mode followed by 4
233
QC acquired with SWATH, 3 × after the last sample and the other 5 incorporated within the
234
sample sequence after every fifth injection of a sample). All real samples were injected in a
235
randomized order. The exact sample sequence is shown in Table A. 3. The quality of the
236
method across the sequence was evaluated by calculating coefficients of variation (CVs) of
237
peak heights for all molecular features detected in QCs [41, 42], by superimposing QCs
238
using PCA as well as by the internal standards and the alignment tool in MS-DIAL. Absence
239
of possible carryover of lipids, especially of TGs, was evaluated by analysis of extraction
240
solvent blanks at the beginning (i.e. before) and after the sample sequence (Fig. A. 11).
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ACCEPTED MANUSCRIPT 241
2.7
DATA PROCESSING.
Commercially available software packages, PeakView and MarkerView (SCIEX), and
243
freely available MS-DIAL were used for data processing [29]. PeakView was used for
244
manual data evaluation and for judging the LC-MS performance by comparing periodically
245
measured QC samples. Automatic peak finding, LOWESS normalization and identification
246
via spectra matching were performed in MS-DIAL (version 2.82). Prior to MS-DIAL data
247
processing, SCIEX raw data in the format Analyst .wiff was converted to Analysis Base File
248
(.abf) to import the data into MS-DIAL. The same mass range as covered by all SWATHs
249
was searched for peaks with a minimum peak height of 1000 cps for ESI+ polarity and 500
250
cps for ESI- mode, respectively. MS and MS/MS tolerance for peak centroiding was set to
251
0.01 and 0.05 Da, respectively. LipidBlast database was used to identify lipids via spectral
252
matching [33]. Retention time information was excluded from calculation of the total score.
253
Accurate mass tolerance for identification was 0.01 Da for MS and 0.05 Da for MS/MS. All
254
lipids were assigned using the identification tool of MS-DIAL. The identification is based on
255
mass accuracy, isotopic pattern and spectral matching. In MS-DIAL, these criteria were used
256
to calculate a total identification score. The total identification score cut off was 70 %. All
257
reported spectral matches were manually revised for correct assignment. Additionally, the
258
regular elution pattern of lipids differing only in length and saturation of the fatty acid
259
residues were used for lipid assignment [43]. Identification of oxPCs (PGPC, PAzPC,
260
POVPC and PONPC) was done via spectral matching of in house generated reference
261
spectra. [M+H]+, [M+NH4]+, [M+Na]+, [M+H-H2O]+, [M-H]-, [M-H2O-H]-, [M+Na-2H]-, [M+Cl]-,
262
[M+K-2H]- and [M+CH3COO]- adducts were allowed for adduct correction. The identity of the
263
oxPCs was verified by comparison of exact mass, retention time, isotopic pattern and
264
fragment pattern of reference standards. DHEAS was also verified by a reference standard.
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Since the QC samples did not consist of a mixture of all samples, but of similar platelet
266
samples from a prior study, a randomly chosen real sample (STEMI 12) was selected as a
267
reference file to build up the alignment table. Therefore, QC at least filter was not used. 10
ACCEPTED MANUSCRIPT 268
Peaks were filtered for presence in at least of 51 % of one distinct sample group. In ESI+, no
269
background subtraction was necessary. Since the background in ESI- was stronger, an
270
exclusion list was used to reduce false positive hits of the peak finding. A complete list of all
271
used MS-DIAL parameters is reported in Table A. 3 for ESI+ and in Table A. 4 for ESI-.
272
2.8
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STATISTICS.
Data quality was evaluated by a QC sample which was embedded throughout the analysis
274
batch(see chapter 2.3 Quality assurance). It is a commonly accepted quality attribute for
275
non-targeted profiling methods in lipidomics and metabolomics if the QC samples injected
276
across the entire sequence are closely grouped together and largely superimposed upon
277
each other in the score plot of multivariate statistical procedures like PCA [42, 44, 45]. For
278
sample alignment, missing values were interpolated automatically by MS-DIAL. The
279
interpolated values were considered as outliers if n≥6 of 11 QCs showed a positive peak
280
spotting. Thus, only features detected in n≥6 QCs (88 %) without interpolation were
281
considered for precision control. In Table A. 6 the abundance of features among the QC
282
samples is shown. In the present study eleven QCs were used for LOWESS normalization in
283
MS-DIAL. Normalized results of both polarities were combined and subsequently subjected
284
to MarkerView software for logarithmic transformation (ln) and PCA without any scaling. This
285
projection identified STEMI 1, 2, 5 and Control (healthy subjects) 2 as outlier samples.
286
These samples had to be removed prior to the supervised principal component analysis
287
(PCA-DA). For PCA-DA projection mean centering was used as a scaling method. Further,
288
principal component variable grouping (PCVG) was used to identify altered features with a
289
minimum loadings distance of 0.05 from the origin. Hypothesis testing was done in R
290
statistical language (https://cran.r-project.org) using the non-parametric Mann-Whitney-U-
291
test. The false discovery rate (FDR) of the resulting p-value distribution was controlled using
292
the positive FDR approach of Storey [46]. The q-values were calculated in R statistical
293
language using the Bioconductor q-value package [47]. FDR level was set to 0.05. λ values
294
for estimating
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11
were allowed between 0 and 0.95 in steps of 0.001 resulting in
=0.48118.
ACCEPTED MANUSCRIPT The exact R code, q-value calculation results and plots are reported in Appendix A 1.2.
296
Significant features with 0.05 ≥ q-value were filtered for identified lipids. The significantly
297
altered and positively identified lipids were visualized using hierarchical clustering with the
298
software platform Perseus [48]. The normalized peak intensities were logarithmized (ln,
299
zeros were surrogated with 10-8) and clustered using Euclidean distance [40] and average
300
linkage. Up to 300 clusters were allowed for sample and feature clustering. Outlier samples
301
identified with PCA were excluded from hierarchical clustering.
302
3
303
3.1
SC
RESULTS AND DISCUSSION
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CHROMATOGRAPHY.
Reversed-phase chromatography was chosen which separates lipid species and has the
305
capability to resolve potential isobaric, isomeric, and isotopic interferences. The method
306
should cover both polar lipid species like oxidized fatty acids and apolar lipids like TGs.
307
Thus, an LC method based on 2.6 µm C8 core-shell material (Kinetex C8) was developed
308
for separation of lipids. The same eluents and identical gradient profile were used for ESI+
309
and ESI-. Acetate buffer was selected, because it showed better performance in negative
310
mode than formate buffer, and an acetonitrile/2-propanol mixture was used as strong eluent
311
in channel B to be able to elute strongly retained neutral lipids such as TGs and CEs.
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The reported method was able to separate and elute lipophilic species with a wide range
313
of polarities. Polar lipid species like lyso-glycerophospholipids eluted in a retention time
314
window from 7 - 10 minutes. Apolar lipids like TGs eluted from 20-25 minutes. Typical EICs
315
of detected lipids in platelet extract (STEMI patient sample 2) are shown in Fig. 2. Many
316
lyso-glycerophospholipids like LPI, LPS and LPE showed double peaks (Fig. 2B). The
317
MS/MS spectral data of those features is nearly identical. The presence of these peaks may
318
be explained by the presence of glycerol-sn1/sn2 positional isomers [23]. The sensitivity in
319
ESI- was slightly lower. This could easily be compensated by increased injection volume.
320
The minimum peak height for peak detection in MS-DIAL was decreased to 500 for ESI-
321
(1000 in ESI+). A lower threshold for peak finding also increased the noise. An exclusion list
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12
ACCEPTED MANUSCRIPT for the peak spotting in MS-DIAL was, used in ESI- to avoid noise being detected as
323
molecular feature. The top 200 peaks detected in the TOF-MS survey scan of a blank run
324
were used as exclusion list. Most lipids formed [M+H]+ adducts in positive and [M-H]- in ESI-.
325
Some lipids were detected as [M+NH4]+ (TGs) or [M+CH3COO]- (PCs) adducts. Several
326
lipids formed Na+ adducts to some extent, which could lead to assay specificity problems
327
(false positives) when direct infusion approaches are carried out with QTOF-MS. For
328
example, a very high mass resolution of more than 200,000 (Orbitrap-MS or FTICR-MS)
329
would be needed to distinguish between the [M+Na]+ adduct of LPC(18:0) (m/z 546.3530)
330
and the [M+H]+ LPC(20:3) (m/z 546.3554). In case of LC-MS, those lipids were
331
chromatographically separated and therefore did not present any assay specificity problems
332
for correct lipid identification (see Fig. A. 2).
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Chromatographic separation is also needed to separate another potential isobaric
334
interference. The marginal mass difference between the M+2 isotopologue of the lipid with
335
an additional double bond and the monoisotopic peak of a lipid with a double bond less does
336
not allow to distinguish them at TOF mass resolving power. This isotopic interference is
337
called
338
chromatographically deal with this problem because compounds differing in the double bond
339
number have different retention on RP phases (see Fig. A. 1). Consequently, the current
340
core-shell C8-based RPLC method assured adequate assay specificity for isobaric and
341
isomeric interferences in this lipid profiling method.
342
3.2
double
bond
overlapping
effect
[49].
The
current
LC
method
can
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MASS SPECTROMETRY.
343
Detection of the lipids was performed by a QTOF-MS instrument (TripleTOF 5600+). It
344
was used in DIA mode with SWATH [26, 32]. Every SWATH cycle consisted of a high
345
resolution (R≥30.000) TOF-MS scan followed by MS/MS experiments with a sequential
346
precursor selection in Q1 of defined m/z range. This SWATH window is typically about 25 Da
347
wide [26]. A total Q1 range from m/z 30 to 1250 could be covered with up to 100 SWATH
348
experiments per cycle. In this study, lipids up to m/z 1000 were included. Pre-experiments 13
ACCEPTED MANUSCRIPT showed only a few molecular features above m/z 1000 without positive identification. As a
350
compromise between wider coverage and more sensitivity, the mass range was limited to a
351
maximum of m/z 1000 to save total cycle time. SWATH Q1 windows with variable isolation
352
width (SWATH 2.0) were used in this study. Adjusted SWATH isolation widths for each m/z
353
range also allowed increasing overall selectivity for critical mass ranges by narrower
354
precursor selection windows [36]. An m/z range with higher precursor ion density was
355
covered with more SWATH windows with narrow isolation width. The m/z ranges with less
356
precursor ion density, in turn, were inversely covered in one or few SWATH windows with
357
wide isolation width. As shown in Fig. 4, the window size did not affect the signal-to-noise
358
ratio of the four oxPC target analytes (PGPC, PAzPC, POVPC, PONPC). However, peak
359
area was lost for a window size of 3 Da without an overlap of previous and subsequent
360
SWATH window. SWATH windows with a size of 5 Da (3 Da target width plus two times 1
361
Da overlap with neighboring windows) showed a gain of peak area between 30 and 90 % for
362
target analytes compared to the original 3 Da window (Fig. 4C). A 3 Da isolation width with 1
363
Da overlap with the previous and the subsequent SWATH window would result in an
364
effective window size of 1 Da. Analyte peaks would be split into multiple SWATH windows
365
which must be avoided. Hence, a minimum SWATH width of 5 Da is recommended. Such
366
narrow Q1 isolation windows are advantageous in view of assay specificity for specific
367
targets, here oxPCs.
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Due to expected low abundance of the targeted oxPCs, which are oxidative stress
369
biomarkers [50] of particular interest in the present study, other MS parameters such as
370
accumulation time were optimized as well. A higher signal-to-noise ratio was gained with
371
increased accumulation time in the target SWATH windows for the fragment with the m/z
372
184.07332 (Fig. 4A). A proportional increase of the S/N ratio of up to factor 3 was achieved
373
with an increase of accumulation time from 33 to 100 ms. A linear correlation between S/N
374
and MS/MS accumulation time was shown already in earlier work [51]. Therefore, the
375
accumulation time was chosen as long as possible and as short as needed with regard to
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ACCEPTED MANUSCRIPT acceptable cycle time (10 data points per peak requirement). To avoid long total cycle times
377
and guarantee ten data points per peak, only target windows were analyzed using 100 ms
378
accumulation time. All other SWATH windows were set to 33 ms. The settling time between
379
individual SWATH windows took 1 ms. For the TOF-MS experiment 250 ms were used
380
resulting in a total cycle time of 1511 ms. A complete overview of the SWATH strategy is
381
shown in Fig. 5. The sensitivity of the four oxPCs in ESI- was below limit of detection in the
382
clinical samples. Therefore, no target windows were used in ESI-.
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Unlike DDA, SWATH acquires data points comprehensively over the entire peak and
384
throughout the entire chromatogram over the specified m/z range. Thus, comprehensive MS
385
as well as comprehensive MS/MS data are available for uncompromised retrospective data
386
processing. In other words, SWATH provides a full digital map of the lipid profile and
387
molecular archive of a sample. This provides the opportunity to select post-acquisition either
388
precursor ion from MS data, or precursor or fragment ions from MS/MS data for data
389
processing and EIC generation, respectively. From the MS/MS spectra peak groups can be
390
generated (Fig. 3) which allow to use the most sensitive signal for (relative) quantitation and
391
the other signals (peaks) for confirmation, like quantifier/qualifier transitions in MRM.
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Since sensitivity was critical and SWATH generates comprehensive MS/MS data, it was
393
considered to use MS/MS chromatograms for data processing. All phosphatidylcholines
394
including oxPCs showed an intense but unspecific MS/MS fragment of the PC head group at
395
m/z 184.07332 in ESI+. Since the target windows were very narrow, this fragment could still
396
be used as identifier [52, 53]. Using 5 Da isolation width for target SWATH windows provides
397
typically clean spectra or reduces the complexity of the composite spectra to a minimum.
398
When the non-specific head group fragment of phosphatidylcholines, phosphocholine (m/z
399
184.07332), was utilized for EIC generation, the resultant MS/MS fragment chromatograms
400
showed only few additional peaks with low intensities (Fig. 6).
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For PAzPC, four isomers (identical sum formula) are listed in the LIPID MAPS database.
402
All four are phosphatidylserines PS(27:0) and therefore clearly distinguishable because of 15
ACCEPTED MANUSCRIPT the class specific head group fragments. The increased selectivity obtained by the narrow
404
isolation width of the SWATH windows was combined with the application of enhanced
405
product ion mode which can be set individually for each SWATH window like other MS
406
parameters as well. The phosphocholine head group m/z 184.07332 was enhanced in the
407
target SWATH windows. Using this acquisition mode additional sensitivity for MS/MS
408
fragments was gained through trapping ions in the Q2 collision cell, and optimized ejection
409
and pulsing towards the TOF analyzer [54]. This allowed a further gain of sensitivity as
410
shown in Fig. 4A and Fig. 4B. Even up to 50 % of peak area could be gained using
411
enhanced mode. However, this enhancement is possible only for a small m/z range of about
412
400 around the target m/z. The excluded m/z region outside of this range around the target
413
m/z is barely detectable. Fragment mass enhancement together with narrow Q1 SWATH
414
windows can therefore be considered as an MRM-like, targeted acquisition window to some
415
extent. The SWATH technique allowed implementation of the MRM-like target windows
416
within conventional variable SWATHs as indicated in Fig. 5A.
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Furthermore, total cycle time was reduced using the variable SWATH window approach
418
with wider windows in less densely populated precursor ion mass ranges. As shown in Fig.
419
5A for the m/z range in ESI+ wide Q1 windows of 191.6 Da and 166.4 Da were used for the
420
m/z ranges below m/z 221.6 and above m/z 833.6, respectively. Only 4 % of the total
421
detected ions were below or above these m/z values, respectively. Also in ESI- (Fig. 5B)
422
only 4 % of all detected precursors were below m/z 211.6. The upper boundary in ESI- was
423
m/z 916.1 with 4 % of all features. Also in the intermediate m/z range, the SWATH window
424
width was dynamically adjusted. For mass ranges with low ion density, like described before,
425
wide SWATH isolation width was used, instead of adding further SWATH experiments and
426
therefore additional cycle time. This allowed the coverage of a large m/z range without
427
unnecessarily increasing the total cycle time. Concluding, a non-targeted lipid profiling with
428
sensitive targeted acquisition of oxPCs was possible with variable SWATH. The reduced
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ACCEPTED MANUSCRIPT 429
complexity of composite spectra made reliable identification and relative quantification
430
possible due to favorable MS/MS assay specificity.
431
3.3
APPLICATION.
The above described method was applied to elucidate the lipidomic profile of platelets in
433
platelet extracts collected from healthy subjects (n=10) as compared to SAP (n=10) and
434
STEMI (n=13) patients. A randomized sample sequence was developed to obtain reliable
435
lipid data for clinical interpretation. The DuoSpray ESI source was cleaned before analysis.
436
Therefore, the surface of the ion source needed an equilibration step before the ionization
437
process was stable. Then, the whole LC-MS system was equilibrated with three blank
438
injections of 2-propanol. The blank injections flushed the capillaries, equilibrated the column
439
pressure and the column oven temperature. Afterwards in total 6 pooled QC samples were
440
injected before the first experimental sample. The first two QCs were acquired in DDA mode.
441
The original idea was to generate high quality MS/MS spectra for cross-checking
442
identification in case the SWATH spectra deconvolution was insufficient for spectral
443
database matching. DDA triggering was generally performed on top 20 abundant ions, but
444
many precursors were still missed for MS/MS fragmentation. In most cases, when the
445
SWATH MS/MS spectra were of low quality, the reason was typically a low precursor
446
intensity. In these cases, also DDA missed to trigger MS/MS spectra. Nevertheless, for
447
some features DDA spectra were helpful to proof correct fragment peak assignment (see
448
Fig. A. 6 and Fig. A 10).
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Next, the third QC sample was again used for equilibration of the LC-MS system. The
450
fourth to sixth QCs were the first to be acquired for quality assurance of the LC-MS system
451
and for data normalization by LOWESS. The clinical samples were acquired in randomized
452
order after the first six injections of QC samples. After every fifth real sample a QC was
453
acquired. After the last sample three QCs were acquired consecutively, resulting in 11 QCs
454
in total. After their analysis all QC and sample data were processed in parallel using mainly
455
MS-DIAL. 17
ACCEPTED MANUSCRIPT 456
3.4
DATA PROCESSING AND EVALUATION.
Data processing was done with MS-DIAL software [29]. After converting SCIEX raw files
458
(.wiff) to analysis base files (.abf) a project in MS-DIAL was set up. In case of DIA with
459
SWATH, the exact range and size of MS/MS experiments was defined. For data processing,
460
MS-DIAL offers various parameters that influence the results. Number of detected peaks and
461
identified lipids and especially data quality (accuracy, precision) can fluctuate with different
462
extraction and alignment settings. Therefore, these parameters must be chosen carefully.
463
Optimization of these parameters is necessary to find an optimum between highest numbers
464
of detected and identified peaks with acceptable accuracy and precision. For example, in the
465
ESI- mode, the sensitivity was lower than that compared to ESI+ mode. Therefore, a lower
466
threshold for peak finding was necessary, which increased noise detection too. This problem
467
was solved by using an exclusion list. In the ESI+ mode, no exclusion list was necessary.
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For the identification of lipids, MS-DIAL offers an implemented spectral database. In the
469
database settings, collision-induced dissociation (CID) was selected. Additionally, the used
470
buffer system ammonium acetate was selected. Neutral lipids like glycerides only ionize due
471
to adduct formation with buffer cations in the ESI+ mode. The spectral database for
472
lipidomics offers the manual selection of lipid classes. Lipid classes were selected in
473
dependence on the analyzed sample material. For example, sulfoquinovosyl diacylglycerols
474
(SQDGs) can be only found in photosynthetic organisms and were excluded from the
475
identification process. Further, various adducts were selected for identification of lipids, but
476
mainly [M+H]+, [M+NH4]+, [M+Na]+, [M-H]- and [M+CH3COO]- were found. Finally, MS and
477
RT tolerances for inter-sample peak alignment were determined. These values define
478
thresholds for variations in MS and RT if a peak is identical with a peak in other samples.
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479
Generally speaking, DIA with SWATH produces cleaner MS/MS spectra than for example
480
all-ion-fragmentation (AIF or MSE), a DIA mode in which all precursors are co-fragmented
481
simultaneously and which produces complex composite spectra. However, if several
482
precursors are co-isolated in the same SWATH window, composite spectra will result in 18
ACCEPTED MANUSCRIPT SWATH as well (Fig. 7A and Fig. 7D). MS-DIAL has implemented a deconvolution tool which
484
allows to generate deconvoluted MS/MS spectra (product ion spectra) in which background
485
noise and contaminating ions are removed [29]. Deconvolution is possible as long as the
486
precursor is detected in the MS1 and does not rely on the presence of the precursor on
487
MS/MS level. First, MS/MS chromatograms of centroid MS/MS spectra corresponding to the
488
precursor ion (from MS1) are extracted. Second, smoothing and baseline correction is
489
applied to MS/MS chromatograms. Third, model peaks are extracted from the MS/MS
490
chromatograms, independent if a precursor was observed or not. Fourth, an ideal model
491
peak is fitted to each chromatogram by means of a least squares method [29]. If a precursor
492
is not detected in the MS1 scan but in the SWATH MS/MS due to increased sensitivity, the
493
precursor will not be present in the peak spotting result and therefore not considered as
494
detected. Only a targeted search on MS/MS level with a respective fragment ion m/z would
495
show a peak.
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As a result of deconvolution, the precursor-product ion link is reestablished which further
497
allows to generate specific MS/MS chromatograms of similar quality as SRM chromatograms
498
[29]. They can be used to verify assay specificity and distinguish a specific target lipid from
499
potential interferences. For example, Fig. 7 shows raw and deconvoluted spectra of PE 38:6
500
in both ESI polarities. The raw spectrum in Fig. 7A shows a fragment ion at m/z 184.0766. It
501
is characteristic for PCs and thus may be misleading. The deconvolution process
502
successfully removes, along with some other background ions, this contaminating fragment
503
ion which belongs to an interfering co-fragmented PC but not to the precursor of m/z
504
764.5302. With the deconvoluted MS/MS spectra (Fig. 7B), identification is more
505
straightforward. In ESI- mode (Fig. 7D and Fig. 7E), the deconvolution of the MS/MS spectra
506
reduces the noise clearly and makes identification via spectral matching more precise. As
507
mentioned above, SWATH holds the promise to select either precursor ion or fragment ions
508
to construct EICs whatever is more selective or sensitive. This is documented in Fig. 7C and
509
Fig. 7F. A chromatogram extracted on the precursor of PE(16:0_22:6) (EIC for m/z
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19
ACCEPTED MANUSCRIPT 764.5095±0.005) shows several (potentially interfering) peaks (blue trace in Fig. 7C). The
511
same interferences were present when extracting the fragment with m/z 313.2774±0.005
512
(loss of fatty acid side chain FA(22:6)) (orange trace in Fig. 7C).The number of interfering
513
peaks is less for the MS/MS chromatogram extracted for the fragment ion with m/z
514
623.5034±0.01 (obtained by neutral loss of phosphatidylethanolamine) (pink trace in Fig.
515
7C). In sharp contrast, the deconvoluted MS/MS chromatogram based on reestablished
516
precursor-product ion link shows a single peak and provides confidence of satisfactory assay
517
specificity for PE(16:0_22:6) for the peak at a retention time of 17.05 min (green trace in Fig.
518
7C). However, the specificity in the ESI- mode is higher than compared to the ESI+ mode
519
because the fatty acid side chains can be detected as negatively charged carboxylate anions
520
[55].The [M-H]- precursor ion of PE(16:0_22:6) (m/z 762.5195±0.005) shows less
521
interferences in ESI- than in ESI+ (Fig. 7F, blue trace). The EIC of 255.2323±0.005
522
represents the FA(16:0) side chain (Fig. 7F, orange trace). This fragment shows a high level
523
of noise (20 %) because of low abundance. The fragment representing the FA(22:6) side
524
chain with the m/z 327.2669±0.005 (Fig. 7F, pink trace) is nearly identical to the
525
deconvoluted MS/MS peak of m/z 327.2669±0.005 (Fig. 7F, green trace). In this respect,
526
MS-DIAL offers tools to verify assay specificity in data sets acquired by DIA with SWATH. In
527
the shown example for PE(16:0_22:6), the specificity in the ESI- mode was already sufficient
528
in the SWATH MS/MS EICs with m/z 327.2669±0.005 and could not be further improved.
529
However, the deconvoluted MS/MS spectra show less noise and improve lipid identification.
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Peak finding with reported parameter settings (see also Table A. 1) resulted in a total of
531
1971 aligned molecular features for both ESI polarities. In ESI+ mode, 1345 molecular
532
features were detected whereof 34 were annotated without MS/MS spectra. 479 features
533
were assigned automatically with an identification score cut-off of 70 % calculated with MS-
534
DIAL [29]. In ESI negative mode, 626 features were aligned including 132 automatic
535
identifications with a cut off score of 70 %. In Fig. 8 all detected features in ESI+ (Fig. 8A)
536
and ESI- (Fig. 8B) are plotted as m/z vs retention time map (peak spotting plot). Every color 20
ACCEPTED MANUSCRIPT represents a lipid class. Phospholipids (PC, PE, SM) were detected in ESI+ as well as in
538
ESI-. High concentrations of TGs were detected in ESI+ as [M+NH4]+ and to minor extent
539
[M+Na]+. PIs were detected exclusively in ESI- mode. In Fig. 8C and Fig. 8D proportions of
540
the detected features are visualized in pie charts. 1326 (67 %) features remain unknown
541
without any matching precursor mass or MS/MS match. 34 (2 %) features showed an MS
542
precursor match but without MS/MS data. These findings were considered as not identified
543
because of missing MS/MS information for structure elucidation. In total, 611 (31 %) features
544
were automatically identified based on precursor mass accuracy, match of isotope pattern
545
and MS/MS spectra (total score ≥70 %) of which 78 % were detected in ESI+ and 22 % in
546
ESI-, respectively. As to be expected the ESI+ polarity results in more identified lipids than
547
ESI- because of the higher sensitivity and larger lipid coverage. The largest number amongst
548
the annotated lipids belonged to TGs (29 %). TGs were detected as [M+NH4]+ and as
549
[M+Na]+. Therefore, many TGs were reported twice in the resulting alignment file. For this
550
reason, the list of structural annotations was manually corrected for multiple adduct
551
appearance. Besides TGs, phospholipids were the most prominent lipids in platelet extracts.
552
Further, all automated identifications by MSDIAL were manually controlled by revision of the
553
spectral matching. In ESI+ mode 18 and in ESI- mode 7 spectral matches were considered
554
as doubtful. Still, 95 % of the automatically assigned spectral matches were considered as
555
trustworthy.
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The embedded QCs were used for LOWESS normalization. The normalized peak height
557
was used for data evaluation and relative quantification. 82 % of all features (1619) were
558
found in the QC samples. Even if the QC samples were not a pool of all actual test samples,
559
most of the features could be detected. If a feature is below the intensity threshold of the
560
peak detection, the MS-DIAL software interpolates the value (gap filling) with values from a
561
local maximum of the EIC within the defined tolerances for retention time and mass [29]. For
562
evaluation of the precision of the QCs embedded in the sequence, interpolated features
563
within the QCs were deleted. Features not detected in 55 % (n≥6 of 11) of the QCs were
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ACCEPTED MANUSCRIPT 564
discarded (Fig. 9A). CVs for these remaining features were calculated and plotted as
565
histogram in Fig. 9B. 45.5 % of features detected in QCs (n≥6) showed a CV of ≤10 %. 26.4
566
% of the features were between 10 % and 20 % CV and 8.4 % were between 20 % and 30
567
% CV. In total 80.3 % of the features present in the QC samples varied less than 30 %. Additionally, the performance was evaluated regarding two internal standards LPC(17:1)
569
and PC(17:0/20:4). CVs for normalized response were below 10 % for all internal standards
570
(Fig. A. 4A). Retention time was stable over the complete sample sequence with 0.1 % to
571
0.2 % CV (Fig. A. 4B) Commonly, normalization in lipidomics is performed with one internal
572
standard per lipid class. As a drawback of this method large retention time spans must be
573
normalized with the same internal standard. For example, the lipid class of the LPC shows a
574
retention time range of about 3 minutes between 8-11 minutes retention time and would
575
have been normalized to a single internal standard eluting at 8.95 minutes. Applying
576
LOWESS all data points are normalized using multiple non-parametric regression.
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In MS-DIAL aligned and normalized data sets for ESI+ and ESI- were combined for
578
statistical evaluation. The combined data matrix was transformed and exported to
579
MarkerView (SCIEX) for performing multivariate statistics. For evaluating data quality and
580
system stability PCA statistics was used. In a first step, outliers had to be identified.
581
Therefore, a PCA score plot was calculated with log transformed data (zeros were
582
surrogated with 10-8) without scaling (Fig. 10A). The samples STEMI 1, 2, 5 and healthy
583
control 2 were identified as statistical outliers due to their position remote from the other
584
samples of their group on the PCA score plot for both ESI polarities. Even though the QCs
585
were not a mixture of all samples but comparable composition (i.e. pooled platelet samples
586
from another study), they were closely clustered together near the origin of the score plot.
587
The largely superimposed QCs in the PCA score plots of both polarities proved a stable
588
analysis over the whole sample acquisition time and analysis sequence, respectively. It was
589
an evidence for a consistent performance of HPLC and mass spectrometer. With a
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ACCEPTED MANUSCRIPT 590
decreasing performance of the analytical platform due to contamination or carry over effects,
591
a drift of the QC samples would have indicated an unstable performance. For further analysis with supervised PCA-DA, outliers and QC samples were excluded
593
(Fig. 10B). SAP 11 was revealed as an additional outlier using the supervised PCA and was
594
therefore eliminated. Interestingly, the same samples were identified as outliers in both
595
polarities. This is evidence that the outlier origin is linked to the sample itself and not induced
596
due to the analysis. In the PCA-DA plot all distinct sample groups are clearly separated,
597
which underlines the differences in their lipid profiles. Especially the scores of the healthy
598
control samples (Fig. 10A) and B) (green)) underline differences in the lipidome of the
599
distinct sample groups. PCVG (Fig. 10C) was used for visualizing altered molecular features
600
compared to other distinct sample groups. Colored spots in Fig. 10C indicate altered
601
abundance of a specific molecular feature in the respective sample groups. The statistical
602
hypothesis testing using the Mann-Whitney-U-test and subsequent control of the FDR using
603
the q-value approach identified 855 molecular features with a q-value of ≤0.05. In Fig. 10D -
604
Fig. 10G 4 significantly altered lipid species were reported exemplarily. Additionally, boxplots
605
for the QCs were plotted to evaluate the analytical precision of the specific feature. The
606
reported lipids in Fig. 10D - Fig. 10F) were detected in all QCs (n=11) and showed CVs
607
below 10 %. LPI(18:1) was significantly (q-value=0.00395) increased in the disease groups
608
compared to the control group. LPS(20:4) showed an elevated level (q-value=0.0031) in the
609
control group. Additionally, FA(18:1) and DHEAS were increased in the patient groups.
610
LPI(18:1), LPS(20:4) and FA(18:1) were identified automatically in MS-DIAL (spectral
611
matches and identification scores are shown in Fig. A. 5, Fig. A. 7 and Fig. A. 8). The
612
identification of LPI(18:1) was 70.6 %, which is near the lowest acceptable identification
613
score. The MS-DIAL generated spectral match was manually revised and was found as not
614
sufficient for reliable identification (Fig. A. 5). Therefore, the raw data were examined for
615
reconsideration of the identification of LPI(18:1). Accurate mass, isotope pattern and
616
fragmentation pattern supported the correct annotation of this feature as LPI(18:1) ( Fig. A.
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ACCEPTED MANUSCRIPT 6). LPI(18:1) was detected and triggered for MS/MS in a DDA experiment of the QC sample
618
in ESI- mode. The specific DDA spectrum (Fig. A. 6C) of precursor m/z 597.3 confirms the
619
background subtracted SWATH spectrum (Fig. A. 6C). Two lipid class specific fragments
620
could be structurally assigned confirming the presence of a glycerophosphatidylinositol (Fig.
621
A. 6).
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617
DHEAS was detected as an unknown. Further investigation using the MS-FINDER
623
software [56] identified a matching chemical formula and suggested DHEAS as possible hit,
624
based on a matching in silico spectrum. The identification of DHEAS was verified using a
625
reference standard. The exact identification procedure is described in Appendix A. The
626
candidate peak and the DHEAS reference corresponded in retention time, accurate mass,
627
isotope pattern and fragment spectra (Fig. A. 9). Therefore, the peak was considered as
628
positively identified. Unfortunately, DHEAS (Fig. 10G)) was below the peak spotting
629
threshold in the QC samples because of too low concentration. In case of missing values,
630
MS-DIAL performs a gap filling using a local maximum of the EIC at the corresponding
631
retention time [29]. The interpolated values resulted in 29 % CV (n=10) after removal of one
632
outlier value (QC1). 77 identified and significantly altered lipids with QC precisions ≤30 %
633
CV in n≥6 QCs, were used for hierarchical cluster analysis and visualization as heat map
634
(Fig. 10H). Outliers and QC samples were eliminated prior to the cluster analysis. All
635
controls were clustered together. For the two patient groups (SAP and STEMI), no distinct
636
clustering was found and these samples were not clearly distinguished in cluster analysis.
637
The differences between SAP and STEMI are subtle in case of the detected overall platelet
638
lipidome. Yet, they could be distinguished by supervised multivariate statistics. Additionally,
639
the SWATH experiment design allowed detection of oxPC targets PGPC, PAzPC, POVPC
640
and PONPC in the clinical samples. It was found that the oxPC targets were significantly
641
increased in STEMI patients as compared to control samples. Detailed results and clinical
642
interpretation of altered lipids detected in positive ESI polarity were published elsewhere
643
[34]. A list of significantly altered lipids detected in ESI- is reported in Table A. 10.
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4
CONCLUSION. Herein we present a complete workflow for comprehensive lipidomics analysis of human
646
platelet lipid extracts. The developed method covers polar species like fatty acids as well as
647
neutral lipids like TGs. The broad polarity range allows analysis of complex lipid extract with
648
one single chromatographic method. The application of DIA with SWATH allows flexible MS
649
experiment design. Candidate lipids or known targets can be analyzed in MRM-like Q1
650
isolation windows, with increased selectivity and sensitivity. In general, MS/MS data are
651
acquired comprehensively over the entire chromatogram and across all samples, with a
652
maximum of analyte coverage and improved sample-to-sample reproducibility as compared
653
to DDA. Peak detection, lipid identification, sample alignment and normalization were user-
654
friendly automated using MS-DIAL software with the provided spectral database LipidBlast
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[29, 33]. Embedded QC samples in the sample sequence allowed assessment of the data
656
quality by calculating coefficients of variation for all molecular features detected in the QCs
657
[42]. The system stability was proven by PCA projection resulting in score plots with
658
superimposed QCs [42, 45]. Additionally, the QC samples were used for signal correction
659
using normalization with the LOWESS algorithm. This procedure is superior to normalization
660
with internal standards in case of non-targeted approaches [44, 57]. The demonstrated
661
method was successfully applied to a clinical case study comparing platelet lipid extracts of
662
healthy donors to patients with SAP and acute STEMI. The MRM-like target windows
663
allowed detection of oxPCs with reliable selectivity. Various altered lipids were successfully
664
identified providing fundamental insights into relative disease severity. Detailed results and
665
interpretation of the detection of oxPCs and the findings of altered lipids have been
666
published before [34]. However, lipid identification was still the bottleneck of data evaluation.
667
Although software tools like MS-DIAL were helpful for automatic lipid identification elaborate
668
manual data revision was necessary. The identification of one unknown was possible, again
669
with elaborate effort to verify the identification, but many molecular features still remain
670
unidentified because of missing database hits. Overall, this work shows that lipid profiles are
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significantly different in patient groups which could make this method a valuable tool in
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clinical analysis in the context of personalized medicine in cardiology.
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NOTES.
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The authors declare no competing financial interest.
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ACKNOWLEDGEMENT.
Funding: We acknowledge the financial support by the “Struktur- und Innovationsfonds
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Baden-Württemberg (SI-BW)” and the German Science Funds (DFG no. INST 37/821-1
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FUGG).
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Appendix A. Supplementary Data.
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FIGURE CAPTIONS. Fig. 1. Schematic illustration of the entire method workflow. Briefly, a suitable lipid
856
extraction method was found for the desired sample material. Next, a chromatography was
857
developed. The SWATH experiments and mass spectrometric parameters were optimized in
858
a targeted manner for 4 target analytes (POVPC, PONPC, PGPC and PAzPC). Non-
859
targeted SWATH window extraction width was optimized using swathTUNER [36]. Both
860
results were combined in a targeted/non-targeted SWATH experiment design (further details
861
are shown in Table A. 1). The developed method, then was used for data acquisition of
862
clinical platelet samples followed by data pre-processing and lipid identification in MS-DIAL
863
[29], method validation, statistical testing, FDR control, data post-processing and finally data
864
visualization.
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Fig. 2. EICs of typical lipid classes in A) ESI+ and B) ESI- mode. The lipids were identified
867
in the platelet extract of a STEMI sample (STEMI 2). Identification of the lipids was done via
868
spectral
869
glycerophosphatidylethanolamine, PC: glycerophosphatidylcholine, POVPC: 1-palmitoyl-2-
870
(5'-oxo-valeroyl)-sn-glycero-3-phosphatidylcholine,
871
diradylglycerolipid,
872
PI: glycerophosphatidylinositol, PS: glycerophosphatidylserine, FA: fatty acid
in
MS-DIAL
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matching
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score
cholesterol
>70
%).
SM: ester,
AC:
acyl
carnitine,
sphingomyelin, TG:
PE:
DG:
triradylglycerolipid,
AC C
ChE:
(total
873 874
Fig. 3. SWATH MS/MS spectra and MS/MS EIC peak groups of A) PGPC in standard
875
solution (250 ng mL-1 in MeOH) in a retention time window of 7.380 to 7.819 min and B)
876
LPC(16:0) in clinical sample (STEMI patient sample 12) in a retention time window from .
877
The SWATH MS/MS spectra were background subtracted with PeakView. All shown EICs
878
were extracted with a width of 5 mDa from the corresponding SWATH window. The correct
879
fragment assignment for LPC(16:0) was proofed by DDA spectra (see Fig. A. 10). 30
ACCEPTED MANUSCRIPT 880 Fig. 4. Effect of SWATH window parameters on sensitivity and signal-to-noise ratio (S/N).
882
A) The size of Q1 SWATH windows did not affect the S/N ratio. Increased accumulation time
883
of 100 ms improved signal-to-noise ratio proportionally [51]. Additional fragment mass
884
enhancement could increase the S/N ratio 2-3 times. B) The reported peak areas were
885
normalized to IS LPC (17:1). The area ratios are not affected by applying an increased
886
accumulation time to the target analyte window. Q1 windows with a width of 3 Da showed
887
lower peak area ratios than wider SWATH windows. Fragment mass enhancement could
888
again increase signals up to 2 times. C) Narrow 3 Da SWATH windows increased selectivity
889
but lost sensitivity up to 90 %. For this study windows were allowed as minimum SWATH
890
size. enh*: Enhanced product ion mode was activated for m/z 184.07332
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Fig. 5. Illustrations of the SWATH isolation window widths for A) positive and B) negative
893
polarity. All cycles consisted of a TOF-MS full scan from m/z 30 to 1000 with 250 ms
894
accumulation time followed by individual SWATH windows. Narrow width (5 Da) Q1 windows
895
were used for target analytes POVPC, PGPC, PONPC and POVPC in ESI+ with 100 ms
896
accumulation time. Non-targeted SWATHs were accumulated for 33 ms. In ESI negative
897
polarity all SWATHs were programmed with 40 ms accumulation time.
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Fig. 6. MS/MS EICs of the PGPC precursor (m/z 610.3715±0.005) and head group
900
fragment (m/z 184.0733±0.005) from platelet extract of STEMI patient sample 2. The
901
masses are extracted from the corresponding SWATH MS/MS experiment #16 with a Q1
902
width from m/z 608.0 to m/z 613.0. The narrow SWATH Q1 window of 5 Da allows only a
903
small range of possible precursors producing interferences which were however
904
chromatographically separated. Therefore, the unspecific head group fragment of
905
glycerophosphatidylcholines (m/z 184.0733, red) could be used for relative quantification
906
because of interference-free detection at the retention time of PGPC.
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ACCEPTED MANUSCRIPT 907 908
Fig. 7. Deconvolution of SWATH MS/MS spectrum in MS-DIAL. A) Raw MS/MS spectrum
909
of PE(16:0_22:6) in ESI+ mode. B) Deconvoluted MS/MS spectrum of PE(16:0_22:6) in
910
ESI+ mode. The fragment peak with m/z 184.0717 was eliminated which prevents a
911
misleading interpretation of the spectrum as possible phosphatidylcholine species.
912
SWATH MS/MS EICs of PE(16:0_22:6) and fragments in ESI+ mode. The fragments of
913
PE(16:0_22:6) are not specific enough for baseline separated fragment EIC peaks.
914
Deconvolution of the MS/MS EICs (green) can help to increase the assay specificity. D) Raw
915
MS/MS spectrum of PE(16:0_22:6) in ESI- mode. E) Deconvoluted MS/MS spectrum of
916
PE(16:0_22:6) in ESI- mode. Noise was clearly reduced due to deconvolution. F) SWATH
917
MS/MS EICs of PE(16:0_22:6) and fragments in ESI- mode. The ESI- mode shows more
918
specificity due to detection of the fatty acid side chain fragments (note, m/z 327.2669
919
corresponds to FA 22:6 and m/z 255.2323 to FA 16:0). The data was extracted from platelet
920
extract of STEMI patient sample 7.
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C)
Fig. 8. Results of the peak spotting and automatic lipid identification with MS-DIAL. A) In
923
ESI+ 1345 features were detected. 479 features were automatically identified (colored) with
924
a total identification score of ≥70 %. 870 features were not identified (grey). B) In ESI- 626
925
were aligned of which 132 were identified (≥70 %). C) Pie chart of detected features. 67 %
926
were not identified, 2 % were without MS/MS and 31 % were identified. Thereof, 78 % were
927
detected in ESI+ and 22 % in ESI-. D) Proportion of identified lipid classes. Mostly TGs,
928
PCs, PEs, and SMs were detected. Abbreviations: AC: acyl carnitine, Cer: ceramides, ChE:
929
cholesterol
930
glycerophosphatidylethanolamine,
931
glycerophosphatidylserines, FA: fatty acid, FAHFA: fatty acid ester of hydroxyl fatty acid, PA:
932
phosphatidic acid.
AC C
EP
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ester,
DG:
diradylglycerolipids,
PC:
SM: sphingomyelin,
glycerophosphatidylcholines, TG:
triradylglycerolipids,
PE: PS:
ACCEPTED MANUSCRIPT Fig. 9. Quality assurance of LC-MS acquisition. Peak detection result was filtered for
935
peaks above detection threshold in 55 % of QC (n = 6 of 11). Percentage CV was calculated
936
for all feature and plotted in a histogram. 72 % of all features were found above threshold in
937
55 % of all QC samples. 80.3 % of all QC abundant features were detected with CV≤30 %.
938
45.5 % of the features were even below 10 % CV
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Fig. 10. Multivariate statistics for visualization of non-targeted lipidomics results. A) PCA
941
score plot with normalized and ln transformed alignment matrix without scaling. 3 STEMI
942
samples and 1 control sample were identified as outliers and excluded from PCA-DA
943
projection in B). B) PCA-DA score plot separates the distinct sample groups clearly. C) PCA-
944
DA loadings plot. Groups were assigned to each variable using the PCVG function in
945
MarkerView (SCIEX). The peaks were assigned to six groups (green, blue, red, light green,
946
purple and orange). The groups indicate elevated and decreased levels of features in each
947
distinct sample group, respectively. D) to G) Box and jitter plots of significantly altered lipids.
948
LPI(18:1), FA(18:1) and DHEAS were elevated in patients. LPS(20:4) was increased in
949
controls. H) Hierarchical cluster analysis and heat map of 77 identified and significantly
950
altered lipids in positive and negative ESI mode. Outlier samples were eliminated prior to
951
clustering. PI: glycerophosphatidylinositols, PS: glycerophosphatidylserines, FA: fatty acid;
952
DHEAS: dehydroepiandrosterone sulphate.
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Appendix A. Supplementary Data
Comprehensive MS/MS Profiling by UHPLC-ESI-QTOF-
SC
MS/MS using SWATH Data-independent Acquisition for the Study of Platelet Lipidomes in Coronary Artery
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Disease
Jörg Schlotterbeck†, Madhumita Chatterjee‡, Meinrad Gawaz‡ and Michael Lämmerhofer† †
University of Tübingen, Institute of Pharmaceutical Sciences, Pharmaceutical (Bio-)Analysis, Auf der Morgenstelle 8, 72076 Tübingen, Germany
‡
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1Department of Cardiology and Cardiovascular Medicine, University Hospital Tübingen, Otfried-Müller-Strasse 10, 72076 Tübingen, Germany
Author for correspondence:
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Prof. Dr. Michael Laemmerhofer Professor for Pharmaceutical (Bio-)Analysis Institute of Pharmaceutical Sciences University of Tuebingen Auf der Morgenstelle 8 72076 Tuebingen, Germany
T +49 7071 29 78793, F +49 7071 29 4565 e-mail: [email protected] http://www.bioanalysis.uni-tuebingen.de/
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Table A. 1. Design of the SWATH experiment for A) ESI+ and B) ESI-. B
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Experiment MS Type 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
SCAN SWATH SWATH SWATH SWATH SWATH SWATH SWATH SWATH SWATH SWATH SWATH SWATH SWATH SWATH SWATH SWATH SWATH SWATH SWATH SWATH SWATH SWATH SWATH SWATH SWATH SWATH SWATH SWATH SWATH SWATH
Min m/z 30 49.5 211.6 266.6 282.8 310.8 360.8 361.8 393.3 420.7 420.8 422.7 440.8 479.3 501.3 525.8 544.8 581.8 603.4 654.5 684.1 707.6 740 765.5 789.1 810.5 832.1 845.1 866.6 879.2 916.1
Max m/z 1000 212.6 267.6 283.8 311.8 361.8 362.8 394.3 421.7 421.8 423.7 441.8 480.3 502.3 526.8 545.8 582.8 604.4 655.5 685.1 708.6 741 766.5 790.1 811.5 833.1 846.1 867.6 880.2 917.1 1000
Accumulation Time (ms) 250 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40
CE (V) -30±10 -30±10 -30±10 -30±10 -30±10 -30±10 -30±10 -30±10 -30±10 -30±10 -30±10 -30±10 -30±10 -30±10 -30±10 -30±10 -30±10 -30±10 -30±10 -30±10 -30±10 -30±10 -30±10 -30±10 -30±10 -30±10 -30±10 -30±10 -30±10 -30±10 -30±10
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CE (V) 35 35 35 35 35 35 35 35 35 35 35 35 35 35 35 35 35 35 35 35 35 35 35 35 35 35 35 35 35 35 35 35
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Accumulation Time (ms) 250 30 30 30 30 30 30 30 30 30 30 30 30 30 100 30 100 30 100 30 100 30 30 30 30 30 30 30 30 30 30 30
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Max m/z 1000 221.6 260.6 284.6 365.8 409.6 468.3 496.8 516 524.8 530.3 545.8 561 593 597 609 613 649 653 665 669 706.1 736.6 758.9 767.5 782.6 787.1 795.7 809.1 814.2 834.6 1000
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Min Experiment MS Type m/z 0 SCAN 30 1 SWATH 30 2 SWATH 220.6 3 SWATH 259.6 4 SWATH 283.6 5 SWATH 364.8 6 SWATH 408.6 7 SWATH 467.3 8 SWATH 495.8 9 SWATH 515 10 SWATH 523.8 11 SWATH 529.3 12 SWATH 544.8 13 SWATH 560 14 SWATH 592 15 SWATH 596 16 SWATH 608 17 SWATH 612 18 SWATH 648 19 SWATH 652 20 SWATH 664 21 SWATH 668 22 SWATH 705.1 23 SWATH 735.6 24 SWATH 757.9 25 SWATH 766.5 26 SWATH 781.6 27 SWATH 786.1 28 SWATH 794.7 29 SWATH 808.1 30 SWATH 813.2 31 SWATH 833.6
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A
ACCEPTED MANUSCRIPT Table A. 2. Sodium acetate cluster masses used for mass calibration. ESI(m/z)
104.99230
141.01693
351.00152
223.02000
433.00459
305.02307
515.00767
387.02615
597.01074
469.02922
679.01381
551.03230
761.01689
633.03537
843.01996
715.03844
1007.02611
797.04152 879.04459
Analytical Order 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47
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1089.02918
File name QC_IDA_1 QC_IDA_2 QC_Equilibration QC_1 QC_2 QC_3 STEMI_8 SAP_11 STEMI_4 STEMI_10 STEMI_9 QC_4 Control_10 STEMI_12 STEMI_11 Control_8 Control_1 QC_5 Control_7 STEMI_1 SAP_3 STEMI_6 SAP_2 Control_9 QC_6 SAP_8 Control_4 Control_5 SAP_10 SAP_6 QC_7 STEMI_5 SAP_9 STEMI_14 STEMI_13 SAP_7 QC_8 Control_2 Control_6 SAP_4 STEMI_7 SAP_5 STEMI_2 Control_3 QC_9 QC_10 QC_11
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ESI+ (m/z)
Table A. 3. Analysis sequence with randomized sample order (IDA, information-dependent acquisition is DDA).
961.04767
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1043.05074
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ACCEPTED MANUSCRIPT Table A. 4. MS-DIAL parameters for ESI+.
Retention time tolerance Accurate mass tolerance Identification score cut off
1 30 30 1000
Advanced setting for identification Relative abundance cut off Top candidate report
Centroid parameters MS1 tolerance
0.01
MS2 tolerance
0.05
Peak detectionbased
TRUE
Smoothing level Minimum width Minimum height
peak peak
Adduct setting [M+H]+ LinearWeightedMovingAverage 3
1000
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Both
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TRUE
MSP file and MS/MS identification setting
F:\MS-DIAL\MS-DIAL program ver. 2.82\MSDIAL-LipidDBs-VS32FiehnO.lbm
MSP file
Retention time tolerance Accurate mass tolerance (MS1) Accurate mass tolerance (MS2) Identification score cut off Text post
4
file
and
40 0.01 0.05 70
TRUE
ion
[M+NH4]+
Alignment parameters setting
0.1
0.5
0
[M+H-H2O]+
5
Exclusion mass list (mass & tolerance) Deconvolution parameters Peak consideration Sigma window value Exclude after precursor
70
[M+Na]+
Peak spotting parameters Mass slice width
0.01
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Peak detection parameters Smoothing method
0.1
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Mass range end
Text file
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Data collection parameters Retention time begin Retention time end Mass range begin
identification (retention time and accurate mass based) setting
Reference file
Untreated Platelets ACD vs SAP vs Healthy14.abf
Retention tolerance
0.1
time
MS1 tolerance
0.025
Retention factor
0.5
time
MS1 factor
0.5
Peak count filter
0
QC at least filter
FALSE
Tracking of isotope labels Tracking of isotopic labels
FALSE
ACCEPTED MANUSCRIPT 0.005
68.9977
0.005
112.9861
0.005
214.0706
0.005
68.99809
0.005
1
268.1086
0.005
Retention time end
30
112.9864
0.005
Mass range begin
30
214.0712
0.005
Mass range end
1000
112.9867
Table A. 5. MS-DIAL parameters for ESI(including exclusion list). Data collection parameters Retention time begin
214.0704 42.00354
Centroid parameters MS1 tolerance
0.01
60.02065
MS2 tolerance
0.05
42.00447
Peak based
TRUE
327.1293
Peak detection parameters Smoothing level
3
Minimum peak width
5
Minimum height
500
peak
Peak spotting parameters 0.1
Exclusion mass list (mass & tolerance) 68.99736
0.005
94.9807
0.005
154.947
0.005 0.005
68.99742
0.005
94.9815
0.005
154.9471
0.005
214.0693
0.005
112.9861
0.005
68.99851
0.005
214.0707
0.005
214.0698
0.005
76.97176
0.005
60.02024
0.005
68.99831
0.005
42.0029
0.005
112.9857
0.005
214.071
0.005
68.99813
0.005
5
0.005 0.005 0.005
42.00406
0.005 0.005
269.104
0.005
293.1748
0.005
112.9863
0.005
214.0705
0.005
42.00357
0.005
269.1063
0.005
327.0742
0.005
327.1283
0.005
42.00446
0.005
327.0749
0.005
214.0712
0.005
42.00418
0.005
214.0706
0.005
42.00571
0.005
327.0742
0.005
303.0851
0.005
76.96989
0.005
214.0701
0.005
42.00375
0.005
327.0738
0.005
249.0977
0.005
42.00484
0.005
214.0704
0.005
249.0974
0.005
327.1284
0.005
249.0964
0.005
267.1084
0.005
268.1098
0.005
42.00367
0.005
0.005
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68.99823
EP
214.0705
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Mass slice width
0.005
0.005
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LinearWeightedMovingAverage
0.005
269.1076
76.97162
Smoothing method
0.005
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detection-
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214.0711
ACCEPTED MANUSCRIPT 0.005
112.9853
0.005
214.0704
0.005
60.02051
0.005
76.97128
0.005
154.9471
0.005
60.02051
0.005
293.1777
0.005
154.948
0.005
112.9858
0.005
269.1056
0.005
68.99925
0.005
214.0712
0.005
421.2794
0.005
249.0975
0.005
112.9863
0.005
42.00356
0.005
154.947
68.99821
0.005
76.97253
214.0693
0.005
112.9866
267.1081
0.005
172.9583
112.9861
0.005
325.1833
214.0708
0.005
421.2778
154.9484
0.005
154.9465
0.005
76.97269
0.005
112.9861
0.005
42.00862
0.005
154.9467
0.005
42.0035
0.005
42.00337
0.005
214.0709
0.005
42.00393
0.005
265.1472
0.005
266.15
0.005
214.0698
0.005
214.0709
0.005
112.9863
0.005
154.9469
0.005
297.1517
0.005
214.0706
0.005
154.9468
0.005
199.1689
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327.1286
0.005
0.005 0.005 0.005 0.005
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0.005
0.005
42.00428
0.005
361.259
0.005
421.2244
0.005
361.2568
0.005
227.2016
0.005
112.9858
0.005
58.83761
0.005
227.2018
0.005
361.2566
0.005
232.9228
0.005
421.2223
0.005
361.2579
0.005
0.005
112.9864
0.005
68.99813
0.005
68.99825
0.005
112.9865
0.005
154.9475
0.005
309.1729
0.005
232.9227
0.005
42.00383
0.005
214.0711
0.005
214.071
0.005
361.2578
0.005
154.947
0.005
232.9239
0.005
353.1991
0.005
421.2239
0.005
214.0695
0.005
61.9912
0.005
112.9862
0.005
68.99838
0.005
311.1673
0.005
154.9467
0.005
112.9862
0.005
253.2167
0.005
154.9472
0.005
214.0709
0.005
311.1688
0.005
154.9473
0.005
112.9866
0.005
421.2244
0.005
214.0703
0.005
61.99103
0.005
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214.0704
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ACCEPTED MANUSCRIPT 421.2793
0.005
255.2322
0.005
Exclude precursor
after
232.924
0.005
241.2164
0.005
61.99246
0.005
MSP file
154.9478
0.005
172.9585
0.005
232.9226
0.005
421.2252
0.005
112.9861
0.005
255.2327
0.005
Retention time tolerance Accurate mass tolerance (MS1) Accurate mass tolerance (MS2) Identification score cut off
154.9474
0.005
214.071
0.005
61.99112
0.005
68.99833
0.005
232.9241
0.005
61.99147
0.005 0.005
232.9233
0.005
112.986
0.005
MSP file and MS/MS identification setting F:\MS-DIAL\MS-DIAL program ver. 2.82\MSDIAL-LipidDBsVS32-FiehnO.lbm 40
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0.01 0.05 70
Text file
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Text file and post identification (retention time and accurate mass based) setting Retention tolerance Accurate tolerance Identification cut off
time
mass
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154.9474
TRUE
score
0.5
0.01 70
255.231
0.005
422.2801
0.005
172.9574
0.005
232.9239
0.005
154.9476
0.005
61.99049
0.005
61.99099
0.005
154.9479
0.005
255.2324
0.005
256.2355
0.005
232.9249
0.005
[M+K-2H]-
399.1693
0.005
[M+Hac-H]-
172.9578
0.005
61.99045
0.005
499.2003
0.005
61.99053
0.005
59.01952
0.005
Retention tolerance
212.0754
0.005
MS1 tolerance
0.025
421.2793
0.005
Retention time factor
0.5
255.2324
0.005
MS1 factor
0.5
361.2587
0.005
Peak count filter
0
267.1084
0.005
QC at least filter
FALSE
Peak consideration
Both
Sigma window value
0.5
Tracking of isotope labels Tracking of isotopic labels
Advanced setting for identification Relative abundance cut off
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Top candidate report
Deconvolution parameters
7
0 TRUE
Adduct ion setting [M-H][M-H2O-H][M+Na-2H][M+Cl]-
Alignment parameters setting Untreated Platelets ACD vs SAP vs Healthy16.abf
Reference file time
0.1
FALSE
ACCEPTED MANUSCRIPT 1.1
Typical isobaric interferences and assay specificity
Shotgun lipidomics may suffer from isobaric interferences and this is the case for LC-MS lipidomics as well if those lipids which have to be regarded as isobaric compounds in view of the employed mass resolving power of the utilized MS instrument, here QTOF, are not
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sufficiently resolved chromatographically. Therefore, emphasis was directed to develop an LC method which provides chromatographic selectivity for critical lipids that may represent potential interferences.
Amongst others M+2 isotopologues of lipids with one double bond more must be
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chromatographically resolved because the mas resolving power of currently employed QTOF instrument is not sufficient to distinguish this small mass difference by MS. An
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example is given in Fig. A. 1. The EIC of TG 46:0 shows a second peak eluted slightly earlier. It originates from the M+2 isotopologue of TG 46:1 which is the corresponding lipid with one additional double bond. Since these two lipids are chromatographically resolved, there is no interference. However, it would represent a problem, if not sufficiently
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chromatographically resolved, especially the influence of a high abundant lipid with one double bond more on a low abundant lipid with the same carbon number but one double bond less might represent a serious problem in relative quantifications due to lack of assay
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specificity. In general, RPLC provides sufficient selectivity for compounds which differ in the double bond number and this is the case for the current method as well, as documented also
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for early eluted lysophospholipids (as a second example form the other end of the lipophilicity scale) (see Fig. A. 2). LPC18:0 (two peaks due to isomeric sn1/sn2 regioisomers) and LPC18:1 (also two peaks due to regioisomers) are well resolved from each other (blue trace in Fig. A. 2), avoiding assay specificity problems.
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ACCEPTED MANUSCRIPT 21.64
8000 7000
TG(46:0)
5000 4000
TG(46:1) [M+2]
3000 2000
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Intensity
6000
1000 0 19
20
21 22 23 Retention Time (min)
24
25
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Fig. A. 1.EIC of TG(46:0) with m/z 796.7394±0.005 at a retention time of 21.64 min. Even with a narrow extraction window of 5 mDa (6.3 ppm) an additional peak of TG(46:1) [M+2] at 21.37 min is visible. The mass difference of TG(46:1) [M+2] and TG(46:0) [M+H]+ is 11.6 ppm.
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In fact, for many lipids multiple peaks were observed in the XICs even for narrow extraction widths of ±0.005 Da. For instance, the EIC of [M+H]+ of LPC 20:3 (black trace; superimposed by red trace) gives in total 6 peaks. Both, isotopologue with one double bond more (LPC(20:4), two isomers) as well as sodium adduct of LPC(18:0) ([M+Na]+) are well
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resolved (Fig. A. 2A). Fig. A. 2B shows an overlay of the MS spectra of LPC(20:3) and LPC(20:4) documenting the incomplete mass resolution by QTOF MS, thus necessitating adequate chromatographic resolution. Similar situations exist for MS/MS chromatograms.
A
100% 75% 50%
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LPC(20:3) LPC(18:0) [M+H]+ [M+H]+ LPC(20:4) [M+2] LPC(18:1) [M+2]
25% 0% 6
8
10
LPC(18:0) [M+Na]+
12
Retention Time (min)
LPC(20:3)[M+H]+ 546.3554 ± 0.0025 Da LPC(18:0)[M+H]+ 524.3711 ± 0.0025 Da LPC(18:0)[M+Na]+ 546.3530 ± 0.0025 Da
B
100%
LPC(20:4) LPC(20:3) 546.3608
60%
545.3484
10% 546.3513
40% 20% 0%
14
544.3451
80% Rel. Intensity
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Chromatographic separation is therefore important to assure adequate assay specificity.
0% 545.3484 546.36 548.3659 544 545 546 547 548 549 m/z
Fig. A. 2. Evaluation of typical mass interferences in lipidomics. A) EICs of [M+H]+ and [M+Na]+ of LPC(18:0) and [M+H]+ of LPC(20:3). Only a small signal for [M+Na]+ of LPC(18:0) is visible. Two additional peaks are visible for both lipids at earlier retention time. Those peaks were identified as [M+2] isotopologues of LPC(18:1) and LPC(20:4), respectively. The isotopic interference is explained in B) for LPC(20:3). The mass of the [M+2] isotopologue of LPC(20:4) differs only marginally from the mass of the [M+H]+ of LPC(20:3).
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ACCEPTED MANUSCRIPT False discovery rate control according to Storey [1].
1.2
The q-values were calculated using the R statistical language and the q-value package of Bioconductor [2]. P-values were calculated likewise in R statistical language using the function wilcox.test. The normalized output of MS-DIAL was reduced to class and variable
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data and was used as input in R. The first column contained the sample information, the second column the sample class. The third until the last column contained all variables (molecular features) and the corresponding normalized intensities. The exact code for the Utest was:
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for(i in 2:ncol(data))
{Wilc <- function(Wilcox) wilcox.test(data[,i] ~ Class, data = data, subset = Class%in%
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Wilcox, paired = FALSE, exact = TRUE)
pvalue[,i-1] <- as.numeric(sapply(apply(v,2,Wilc),"[",3))}
The resulting list of p-values for controls vs. STEMI was used as input for the calculations of the q-values. The exact call was:
qvalue(matrix(as.numeric(pvalue[line,])),lambda=seq(0,0.95,0.001),
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qobj=
0.05,pfdr = TRUE)
The summary of the q-value calculation is reported as follows: 0.4811804
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:
Cumulative number of significant calls: <0.001
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<1E-04
<0.01
<0.025
<0.05
<0.1
<1
p-value
184
372
676
771
872
980
1955
q-value
0
0
589
745
855
995
1969
local FDR
52
166
372
491
612
704
1488
The p-value density histogram and the q-value plots are reported in Fig. A. 3.
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fdr.level
=
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Fig. A. 3. A) p-Value density histogram. results plots.
Precision of Internal Standards
LysoPC(17:1) [M+H]+ : CV 10% PC(17:0/20:4) [M+H]+ : CV 7% LysoPC(17:1) [M+HAc-H]- : CV 7%
1.4 1.2 1.0 0.8 0.6
QC3
QC5
QC7
QC9
B
20
18
LysoPC(17:1) [M+H]+ : CV 0.1% PC(17:0/20:4) [M+H]+ : CV 0.2% LysoPC(17:1) [M+HAc-H]- : CV 0.2%
8 6 QC11
QC1
QC3
QC5
QC7
QC9
QC11
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QC1
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Normalized Response
A
RT (min)
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1.3
was estimated as 0.481. B) q-Value package
Fig. A. 4. Precision of the used internal standards LPC(17:1) and PC(17:0/20:4). A) The normalized response of both internal standards varied below 10 % in both ESI polarities. B) The retention time was stable across the complete sequence with CVs of below 0.2 %.
1.4
Distribution of features in QC samples
Embedded QC samples were used to control the quality of the sample acquisition. CVs were calculated for all features detected in QC samples. Features were considered as reliably detected when a minimum of 6 of 11 QCs (n≥6) showed a positive peak detection of the distinct features. In case of not detected features the MS-DIAL software aligns an 11
ACCEPTED MANUSCRIPT interpolated value based on a local maximum at the retention time of the respective EIC. These interpolated values were found not precise and were therefore regarded as outliers for precision evaluation. A maximum of 5 outliers was tolerated. The distribution of peaks considered for precision control is listed in Table A. 6.
ESI+
ESI-
Sum
Percentage
n=6
76
67
143
10.0 %
n=7
67
61
128
9.0 %
n=8
56
53
109
7.7 %
n=9
67
40
107
7.5 %
n=10
219
55
274
19. 2%
n=11
475
188
663
46.6 %
Sum
960
464
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Found in n of 11 QCs
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Table A. 6. Distribution of features within QC samples used for quality assurance via calculation of CVs.
ACCEPTED MANUSCRIPT 1.5
Identification of Lipidis
The following chapter shows examples of representative identification data of the reported features in the main part of the paper. The automatic lipid identification was validated by reviewing the MS/MS spectral matching of the measured and deconvoluted spectrum to an
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in silico library spectrum (LipidBlast) [3]. MS-DIAL provided a score system for lipid identification. The scores are listed in the following tables for each identified lipid, respectively. The retention time score was not used for calculating the total score. The formulas for the calculation of the scores have been published in the original MS-DIAL paper
Identification of LPI(18:1)
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1.5.1
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[4].
The main document showed an example of lipid identification based on successful spectral deconvolution and assay specificity control based on deconvoluted MS/MS chromatograms (see Fig. 6 of the main document). Here, we wish to show an example in which spectral deconvolution failed. Nevertheless, identification by MS-DIAL was still
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valuable and successful in spite of a low identification score of 70.6 %. The great utility in such cases lies in the fact that it is easier and faster to start identification from a suggestion of lipid annotation rather than to start identification from de novo without any suggestion of a
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lipid structure. If the accurate mass similarity score of MS-DIAL is high, it is probably always worth to control the assignment manually by spectral interpretation as exemplified by the
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following example of LPI(18:1).
[M-H]SN1 acyl H2O loss
[Insositol phosphate H2O]-
-
[FA]
13
ACCEPTED MANUSCRIPT Fig. A. 5. Spectral match of LPI(18:1) Table A. 7. Identification scores of LPI 18:1 7.9 597.3036 999 256 741 617 706
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RT (min) Mass [Da) Accurate mass similarity score Dot product similarity score Reverse dot product similarity score Isotope similarity score Total score
Since LPI(18:1) showed a low quality MS/MS spectrum and the total score was near the minimum tolerated score of 70 %, the identification of LPI(18:1) was manually verified. As
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demonstrated in Fig. A. 6A, a representative MS/MS spectrum (QC9) was used for fragment assignment. If we look at this MS/MS spectrum, the quality seems to be poor and the match
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with the library spectrum is low. It seems that there are major contaminating ions remaining after deconvolution. Furthermore, it is striking that two major characteristic fragment ions (m/z 241.0119 and m/z 315.0487) were completely missing in the experimental deconvoluted MS/MS spectrum (Fig. A. 5) as compared to the background subtracted raw
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MS/MS spectrum (Fig. A. 6A). They have been removed by the spectral deconvolution process. The affiliation of m/z 241.0199 to the precursor of LPI(18:1) was verified by the DDA experiment performed at the beginning of the analysis sequence (Fig. A. 6C). The fragment with m/z 315.0487 was also not present in the DDA spectrum. Peak group analysis
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of the SWATH MS/MS EIC of the precursor and both fragments showed a clear correlation
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of m/z 315.0487 and m/z 214.0199 to the precursor of LPI(18:1)(Fig. A. 6C); the fragment on the other hand may not be selective enough for quantitative purposes.
Obviously, the
spectral deconvolution process failed, most probably because this lipid is relatively low abundant. Since precursor accurate mass similarity score was high and one fragment was perfectly matching as well, a manual process of identification was pursued. For in silico fragmentation the PeakView (SCIEX) function “fragment pane” was used. In Fig. A. 6A three fragments and the precursor peaks were assigned. The measured precursor differs only 5.5 ppm from the theoretical [M-H]- ion of LPI(18:1). The assigned fragments are shown in Fig. 14
ACCEPTED MANUSCRIPT A. 6B). Inositol phosphate minus H2O (m/z 241.0119) (fragment I), fatty acid side chain (18:1) (m/z 281.2486) (fragment II) and [M-H]- minus fatty acid side chain (18:1) minus H2O (m/z 315.0487) (fragment III) were all observed in the background subtracted SWATH MS/MS spectrum. Supporting the correct identification, these fragments are used in MS-
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DIAL for identification (Fig. A. 5). They are reported on liipidmaps.org website and in the literature as PI specific fragments [5, 6]. Relying on the above reported criteria, LPI(18:1) is
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considered as correctly identified.
Fig. A. 6. Manual verification of LPI(18:1). A) Background subtracted raw spectrum of sample QC9 from 7.89 to 8.10 minutes. This spectrum was used for assignment of theoretical calculated fragment masses using the “fragment pane” in PeakView software (SCIEX). Not assigned mass peaks (red) were due to non-deconvoluted (SWATH) composite spectrum (581.8 to 604.4). The precursor (error: 5.5 ppm) and three fragments (IIII) were assigned. B) Proposed structures and assigned m/z of fragments, verifying the identification of LPI(18:1) [5, 6]. C) DDA spectrum of precursor with m/z 597.3 at 7.91 min. The DDA spectrum verifies the affiliation of the fragments to the precursor. D) Peak group analysis of m/z 597.3029 and its fragments. The fragments show a clear correlation with the precursor. Especially the fragment with m/z 315.0502 could be assigned to the proposed precursor. 15
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Identification of LPS 20:4
Fig. A. 7. Spectral match of LPS 20:4 Table A. 8. Identification scores for LPS 20:4
Identification of FA 18:1
897 858
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6.8475 544.2657 970 522 549
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RT (min) Mass [Da) Accurate mass similarity score Dot product similarity score Reverse dot product similarity score Isotope similarity score Total score
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Fig. A. 8. Spectral match of FA 18:1 Table A. 9. Identification scores for FA 18:1 RT (min) Mass [Da) Accurate mass similarity score Dot product similarity score Reverse dot product similarity score Isotope similarity score Total score
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10.7232 281.2471 999 732 750 988 933
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Identification of unknown #1438; RT 2.98; m/z 367.1557
The unknown peak was exported from MS-DIAL alignment result to MS-Finder [7]. The chemical formula C19H28O5S was found with an error of 7 ppm as compared to the
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theoretical mass of the [M-H]- adduct of C19H28O5S. Measured MS/MS spectra were matched against the HMDB database using MS-FINDER [7, 8]. The experimental spectrum showed a good match to the in silico MS/MS spectrum of dehydroepiandrosterone sulfate (DHEAS). Since DHEAS was available as standard (MassCheck Steroid Panel 2, Chromsystems
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Instruments & Chemicals GmbH, Gräfelfing, Deutschland) the retention time, exact mass, isotope pattern and MS/MS fragmentation were compared to the unknown peak using the
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same LC-MS/MS method as for the sample acquisition. As reference sample STEMI 12 was selected, which was also the reference file for the peak alignment in MS-DIAL. As shown in Fig. A. 9 the retention time matched with 0.08 min difference. The small difference in RT could be explained due to a different condition of the chromatographic column. The column
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was used for other projects in the meantime. The MS/MS fragment match was identical even without deconvolution. The mass shift of DHEAS standard compared to the unknown peak was +6.3 ppm. In the real sample the candidate peak is low abundant, so the mass accuracy
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is expectedly not perfect. Therefore, an error of 6.3 ppm is acceptable, especially because of a matching isotope pattern. Four different identification criteria are fulfilled and therefore, the
A
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peak is considered as identified as DHEAS. Real Sample (STEMI 12) DHEAS Standard
Intensity
5e4
2.91
4e4
50%
3e4 2e4 1e4
0%
2.99
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B 96.9618
367.1575
100% 367.1565 50%
96.9616
-50%
368.1602 369.1675
367.1570
0e0
0% 2
4
6
8
10
Retention time (min)
100
200
300 m/z
400
367.5 368.0 368.5 369.0 m/z
Fig. A. 9. Identification of unknown peak #1438 as DHEAS; (RT 2.98; m/z 367.1557). A) The retention time of a real sample (STEMI 12) was compared to the retention time of the standard. B) The fragment pattern of standard and unknown peak were identical. C) The isotope pattern of DHEAS standard and QC9. showed a perfect overlay.
17
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Identified and significantly altered lipids in ESI-
All identified lipids were filtered for a maximum tolerated CV of 30 %. The reported CVs in Table A. 10 were derived from QC samples. In contrast to the precision calculation for method validation (see main document), the CV calculation here included interpolated
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values, because the samples comparison is based on all aligned values without outlier filtering. Therefore, all CV values must show a precision below 30 % even including interpolated values. Only three features needed elimination of one outlier. Precisions with deleted interpolated values would have been even better (see main document). All listed
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results have been found in both ESI polarities showing the same relative quantifications. All identifications except of DHEAS have been assigned with MS-DIAL (total score ≥ 70 %)
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and were further manually validated as reported in the previous chapter. A detailed results list and clinical interpretation was published elsewhere [9].
Table A. 10. Identified and significantly altered lipids in ESISample
Total ID
m/z
TE D
-
RT(min)
p-values
q-values
Fold Change
CV %
n/11 QCs
FA(18:1); [M-H]
1387
10.72
281.2471
0.00086
0.00269
0.68
6%
11
LPC(16:0); [M+CH3COO]-
1605
9.4
554.3431
0.00001
0.0023
0.29
5%
11
-
1602
8.13
552.3275
0.00254
0.00496
0.39
26 %
11
-
LPC(18:0); [M+CH3COO]
1632
11.13
582.3735
0.00005
0.0023
0.19
6%
11
LPC(18:2); [M+CH3COO]-
1626
8.71
578.3431
0.00001
0.0023
0.32
6%
11
LPC(22:6); [M+CH3COO]-
1678
8.51
626.3435
0.00000001
0.0023
0.32
20 %
11
1639
7.77
597.3036
0.00173
0.00395
0.34
9%
11
1599
6.85
544.2657
0.00115
0.0031
3.29
6%
11
1887
18.41
816.5735
0.00012
0.0023
0.15
23 %
11
-
LPI(18:1); [M-H]
LPS(20:4); [M-H]-
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LPC(16:1); [M+CH3COO]
-
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PC(16:0_18:2); [M+CH3COO] -
PC(18:_18:2); [M+CH3COO]
1905
19.93
844.6027
0.00086
0.00269
0.22
26 %
10
-
PC(16:0_20:4); [M+CH3COO]
1900
17.96
840.5735
0.00003
0.0023
0.33
13 %
11
PC(16:0_20:5); [M+CH3COO]-
1898
17.19
838.557
0.00000001
0.0023
0.17
25 %
10
-
1921
19.17
868.6032
0.00017
0.0023
0.39
21 %
11
-
1919
18.11
866.5857
0.00003
0.0023
0.18
29 %
11
PC(18:0_20:4); [M+CH3COO] PC(18:1_20:4); [M+CH3COO]
PC(16:0_22:6); [M+CH3COO]-
1918
17.65
864.5717
0.00000001
0.0023
0.07
8%
11
DHEAS; [M-H]-
1438
2.98
367.1557
0.00006
0.0023
0.30
29 %
10
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Identification in positive and negative ESI polarity
LPC(16:0) is shown as an example for peak group analysis in the main document (see Fig. 2B). The correct assignment of fragment peaks was verified by comparison of deconvoluted SWATH MS/MS spectra (Fig. A. 10A and B) to raw spectra acquired in DDA mode (Fig. A. 10C and D). The deconvoluted SWATH spectra look nearly identical to the DDA spectra which proofs the good quality of deconvoluted SWATH spectra for identification of lipids via spectral matching.
B
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Precursor: 554.3 Da
Precursor: 496.3 Da, CE: 20.0
Intensity
1.0e5 8.0e4 6.0e4
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496.3425 700 258.1092 313.2741 600 500 400 300 200 240.0992 100 0 260 280 300 320 497.3424 104.1059
4.0e4 184.0726 2.0e4 0.0e0 200
300
400
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100
478.3308
m/z
500
9000 8000 7000
Intensity
1.2e5
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6000 5000 4000
1000 800 600 400 200 0
480.3977 255.2975
554.4419 224.1310 200 220 240 260 280
3000 2000
255.2975
1000 0 100
200
300
400
500
600
m/z
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Fig. A. 10. Identification of LPC(16:0). A) Deconvoluted SWATH spectrum in ESI+ of the assigned precursor 496.3 and B) in ESI- of the assigned precursor 554.3. C) DDA spectrum of precursor 496.3 in ESI+ and D) DDA spectrum of precursor 554.3 in ESI-. In the following exemplary spectral matches from MS-DIAL are shown which were used for assignment of lipids:
19
ACCEPTED MANUSCRIPT LPC(16:1) ESI-
LPC(18:0) ESI-
LPC(18:2)
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PC(18:0_18:2)
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ACCEPTED MANUSCRIPT PC(16:0_20:4) ESI-
PC(18:0_20:5) ESI-
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DG 36:3; DG(18:1/18:2)
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Carryover evaluation
B
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A
References
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Fig. A. 11. Evaluation of carry over. A sequence according to Table A. 3. but with blanks (extraction solvent) before every QC was run. A) shows the ESI+ peak spotting result of a blank before and B) after analysis of a clinical sample sequence. The data extraction parameters were the same as reported in Table A. 4. No lipid was positively identified.
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[1] J.D. Storey, The positive false discovery rate: A Bayesian interpretation and the q-value, Ann Stat, 31 (2003) 2013-2035. [2] J.D. Storey, A.J. Bass, A. Dabney, D. Robinson, qvalue: Q-value estimation for false discovery rate control. R package version 2.10.0. http://github.com/jdstorey/qvalue, (2015). [3] T. Kind, K.H. Liu, Y. Lee do, B. DeFelice, J.K. Meissen, O. Fiehn, LipidBlast in silico tandem mass spectrometry database for lipid identification, Nat. Methods, 10 (2013) 755758. [4] H. Tsugawa, T. Cajka, T. Kind, Y. Ma, B. Higgins, K. Ikeda, M. Kanazawa, J. VanderGheynst, O. Fiehn, M. Arita, MS-DIAL: data-independent MS/MS deconvolution for comprehensive metabolome analysis, Nat. Methods, 12 (2015) 523-526. [5] N. Zehethofer, T. Scior, B. Lindner, Elucidation of the fragmentation pathways of different phosphatidylinositol phosphate species (PIPx) using IRMPD implemented on a FT-ICR MS, Anal. Bioanal. Chem., 398 (2010) 2843-2851. [6] F.-F. Hsu, J. Turk, Characterization of phosphatidylinositol, phosphatidylinositol-4phosphate, and phosphatidylinositol-4,5-bisphosphate by electrospray ionization tandem mass spectrometry: A mechanistic study, J. Am. Soc. Mass. Spectrom., 11 (2000) 986-999. [7] H. Tsugawa, T. Kind, R. Nakabayashi, D. Yukihira, W. Tanaka, T. Cajka, K. Saito, O. Fiehn, M. Arita, Hydrogen Rearrangement Rules: Computational MS/MS Fragmentation and Structure Elucidation Using MS-FINDER Software, Anal. Chem., 88 (2016) 7946-7958. [8] D.S. Wishart, T. Jewison, A.C. Guo, M. Wilson, C. Knox, Y. Liu, Y. Djoumbou, R. Mandal, F. Aziat, E. Dong, S. Bouatra, I. Sinelnikov, D. Arndt, J. Xia, P. Liu, F. Yallou, T. Bjorndahl, R. Perez-Pineiro, R. Eisner, F. Allen, V. Neveu, R. Greiner, A. Scalbert, HMDB 3.0—The Human Metabolome Database in 2013, Nucleic Acids Res., 41 (2013) D801-D807. [9] M. Chatterjee, D. Rath, J. Schlotterbeck, J. Rheinlaender, B. Walker-Allgaier, N. Alnaggar, M. Zdanyte, I. Muller, O. Borst, T. Geisler, T.E. Schaffer, M. Lammerhofer, M. Gawaz, Regulation of oxidized platelet lipidome: implications for coronary artery disease, Eur. Heart J., (2017). [1] J.D. Storey, The positive false discovery rate: A Bayesian interpretation and the q-value, Ann Stat, 31 (2003) 2013-2035. [2] J.D. Storey, A.J. Bass, A. Dabney, D. Robinson, qvalue: Q-value estimation for false discovery rate control. R package version 2.10.0. http://github.com/jdstorey/qvalue, (2015).
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[3] T. Kind, K.H. Liu, Y. Lee do, B. DeFelice, J.K. Meissen, O. Fiehn, LipidBlast in silico tandem mass spectrometry database for lipid identification, Nat. Methods, 10 (2013) 755758. [4] H. Tsugawa, T. Cajka, T. Kind, Y. Ma, B. Higgins, K. Ikeda, M. Kanazawa, J. VanderGheynst, O. Fiehn, M. Arita, MS-DIAL: data-independent MS/MS deconvolution for comprehensive metabolome analysis, Nat. Methods, 12 (2015) 523-526. [5] N. Zehethofer, T. Scior, B. Lindner, Elucidation of the fragmentation pathways of different phosphatidylinositol phosphate species (PIPx) using IRMPD implemented on a FT-ICR MS, Anal. Bioanal. Chem., 398 (2010) 2843-2851. [6] F.-F. Hsu, J. Turk, Characterization of phosphatidylinositol, phosphatidylinositol-4phosphate, and phosphatidylinositol-4,5-bisphosphate by electrospray ionization tandem mass spectrometry: A mechanistic study, J. Am. Soc. Mass. Spectrom., 11 (2000) 986-999. [7] H. Tsugawa, T. Kind, R. Nakabayashi, D. Yukihira, W. Tanaka, T. Cajka, K. Saito, O. Fiehn, M. Arita, Hydrogen Rearrangement Rules: Computational MS/MS Fragmentation and Structure Elucidation Using MS-FINDER Software, Anal. Chem., 88 (2016) 7946-7958. [8] D.S. Wishart, T. Jewison, A.C. Guo, M. Wilson, C. Knox, Y. Liu, Y. Djoumbou, R. Mandal, F. Aziat, E. Dong, S. Bouatra, I. Sinelnikov, D. Arndt, J. Xia, P. Liu, F. Yallou, T. Bjorndahl, R. Perez-Pineiro, R. Eisner, F. Allen, V. Neveu, R. Greiner, A. Scalbert, HMDB 3.0—The Human Metabolome Database in 2013, Nucleic Acids Res., 41 (2013) D801-D807. [9] M. Chatterjee, D. Rath, J. Schlotterbeck, J. Rheinlaender, B. Walker-Allgaier, N. Alnaggar, M. Zdanyte, I. Muller, O. Borst, T. Geisler, T.E. Schaffer, M. Lammerhofer, M. Gawaz, Regulation of oxidized platelet lipidome: implications for coronary artery disease, Eur. Heart J., (2017).
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PCA (log transformed / no scaling)
10 0
0.1
10
0.1
0
-10
-10
-20
0.0
-0.1
-0.2
-30
-0.2
-30 -20 -10
0 10 20 D1 (50.0 %)
q=0.00395
SAP STEMI
q=0.0031
QC
C
SAP STEMI
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F LPS 20:4
4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0 -0.5
40
-0.10
FA 18:1
q=0.00269
QC
C
SAP STEMI
-0.05 0.00 0.05 D1 (50.0 %)
0.10
G 70 60 50 40 30 20 10 0 -10 -20
DHEAS q=0.00230
QC
C
SAP STEMI
5.0 4.0 3.0 2.0 1.0 0.0 -1.0 -2.0 -3.0 -4.0 -5.0
ln(normalized peak intensity)
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4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0 -0.5
30
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Normalized peak intensity
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0
Normalized peak intensity
-70 -60 -50 -40 -30 -20 -10 PC1 (60.3 %) (Control)
Normalized peak intensity
-40
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-30
Normalized peak intensity
↑Control ↑SAP ↑STEMI ↓Control ↓SAP ↓STEMI
-0.1
-20
H
PCA-DA loadings (PC variable grouping)
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20
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20
D2 (50.0 %)
30 PC2 (11.9 %)
PCA-DA (log transformed / mean centered)
B QC Control SAP STEMI Outlier
40
D2 (50.0 %)
A
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B 800
n≤5
118
77
195
12.0%
n≥6
960 464 1424
88.0%
≤10%
579 157 736
45.5%
245 183 428
26.4%
≤ 30%
50
136
8.4%
Sum
874 426 1300
80.3%
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86
700
736
ESI+ ESISum
157
600
428
500 400 300
579
183
136
200
245
100 0
TE D
≤ 20%
Number of molecular features in QCs
ESI+ ESI- Sum Percentage Found in QC 1078 541 1619
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≥10
20
86 50
30
56
40
22
16
8
4
9
50 60 CV (%)
70
80
90
3
6
100 >100
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Highlights Comprehensive MS and MS/MS untargeted lipidomic profiling by UHPLC-ESI-QTOF-MS
•
Workflow for data-independent acquisition of lipidomics data with SWATH
•
SWATH parameter optimization
•
Parallel semi-targeted analysis of oxidized phospholipids
•
Clinical application to patient platelets
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•