UHPLC-ESI-QTOF-MS/MS based Lipidomics by Data-Independent Acquisition

Michael Lämmerhofer, Jörg Schlotterbeck

Institute of Pharmaceutical Sciences, Pharmaceutical (Bio-)Analysis, University of Tübingen, Germany

Analytica 2016, May 11, 2016, Munich (Germany)

11.5.2016, 13:15, 15 + 5 min

Sciex Innovation Day

Sources and Cellular Responses to Reactive Oxygen Species (ROS)

Source: Toren Finkel and Nikki J. Holbrook, Nature 408, 239-247(9 November 2000) doi:10.1038/35041687

Endogenous

sources

Mitochondria

Peroxisomes

Lipoxygenases

NADPH oxidases

Cytochrome P450

Exogenous

sources

Ultraviolet light

Ionizing radiation

Chemotherpeutics

Inflammatory cytokines

Environmental toxins

Antioxidant

defences

Enzymatic systems

CAT, SOD, GPx

Non-enzymatic systems

Glutathione

Vitamins (A,E,C)

ROS

.O2

- O2

.OH .

OR .NO

.NO2

H2O2

less more

Impaired

physiological function Homeostasis

Normal growth

and

metabolism

Decreased cellular

proliferation

Defective host

defences

Random

cellular

damage

Specific

signalling

pathways

Aging Disease Cell

death

Impaired

physiological function DNA Damage

Protein Modification

Lipid Peroxidation

The etiology of every

major disease has been

linked to oxidative stress

Cyclization,

Oxidation,

rearrangement

OH

HO

Isoprostanes

O

O

Isolevuglandins

O

O

Isothromboxanes

Isofurans

O

HO

Bochkov et al. ANTIOXIDANTS & REDOX SIGNALING 2010, 12, 1009-10053

Molecular Complexity of Oxidized Phospholipids (OxPLs)

Arachidonate

O

OR

CH3

Hydroperoxide

O

OR

CH3

OOHR = Lyso-PC,

lysoPE etc

PC, Phosphocholine

Oxidized

fatty acyl

Non-fragmented OxPLs

PLs with

multiple

oxidation sites /

functional

groups

-OOH

-OH

O

O

Oxidative

fragmentation (Hock

cleavage, -scission)

Fragmented OxPLs

H

O

OH

H

O

O

HO

O

O

HO

O

OH

,-Unsaturated

Saturated

Furan-containing

HO

O

H

O

O

PUFA sn-2

Peroxyl radical

O

OR

CH3

OO. +O2

Malondialdehyde

4-Hydroxynonenal

Advanced lipid-

oxidation end-

products (ALE):

+R.

Lipid Analysis Workflows

4 |

(Biological) Lipid Sample

Lipid Extraction (e.g. Bligh & Dyer)

(SPE) (group selective) Sample prep

Untargeted Targeted

Shotgun MS/MSAll DDA (IDA) DIA (SWATH)

MS-DIAL MasterView

(Target List)

MRMHR

Combined

Targeted /

Untargeted

SWATH

PeakView /

MultiQuant

MasterView

MS-DIAL

PeakView

MultiQuant

LipidView

MarkerView (Sciex) (PCA, t-test)

SIMCA (Umetrics) (PCA, PLS, PLS-DA, OPLS-DA)

IDA Explorer

TripleTOF 5600+

(Sciex)

Data-Dependent Acquisition

5 | © 2016 Universität Tübingen

Data-Dependent Acquisition (DDA)

Most intensive precursor ions

detected in survey scan are

selected for isolation and

fragmentation in a serial manner

Performance declines with complexity of

samples

Limited reproducibility (stochastic

precursor selection)

Bias towards high abundant analytes

Low sensitivity

Missed trigger for fragmentation

Limited analyte coverage

Information-dependent acquisition (IDA)

10.0 10.5 11.0 11.5 12.0 12.5 13.0 13.5 14.0 14.5

Time, min

0%

50%

100%

12.1

XIC of PONPC 650.439 +/- 0.010 Da

MS/MS triggered at RT = 12.1281

200 400 600 800

Mass/Charge, Da

0

200

400

600

I n t e

n s i t y

650.4428

184.0733

+TOF MS^2 (30 - 1000) from 12.128 min

Precursor: 650.4 Da, CE: 20.0

Experiment 16/20

C33H64NO9P / PONPC

(Δm = 5 ppm) C5H15NO4P+ / Phosphocholine

(Δm = 0.2 ppm)

184.07332

Autosampler

PAL-HTX xt DLW (10µL Injection)

LC

Agilent 1290 Infinity Binary System

Column

Phenomenex Kinetex 2.6 u C8 100 A 150x2.1 mm

Mobile Phase

A: H2O 10 mM NH4Ac

B: 55 % ACN, 40 % IPA, 5 % H2O, 10 mM NH4Ac

Gradient

10 % B – 50 % B in 3 min

50 % B – 100 %B in 17 min

MS

AB Sciex TripleTOF 5600+

MS Parameter

Source:

ESI+, 500°C, 5000 V, Cur 30, GS1 50, GS2 40

Q1 width

= 25 Da

Cycle time

~ 1.s

Retention time

m/z

SWATH 1000

50

Data-Independent Acquisition (DIA)

Improved reproducibility for

identification

Better sensitivity

Improved analyte coverage

Composite/multiplexed fragment

ion spectra

DIA (SWATH): Quantification in

MS/MS mode (sensitivity and

specificity gain)

Retrospective data processing

Data processing more elaborate,

but SW for automated metabolite

ID is being developed

SWATH: Sequential windowed acquisition of all theoretical fragment ion mass spectra

UHPLC-MS/MS Method with DIA

Fragmentation of all detectable

precursor ions systematically

parallelized

Heatmap IDA

7 | Jörg Schlotterbeck © 2016 Universität Tübingen

0

5

10

15

20

25

30

0

5

10

15

20

25

30

1000 800 600 400 200 0 200 400 600 800 1000

min

min

m/z

Untargeted Lipidomics

Intensity 500-2000 Intensity 2000-5000 Intensity 5000-10000 Intensity 10000-30000 Intensity >30000

Intensity 500-2000 Intensity 2000-5000 Intensity 5000-10000 Intensity 10000-30000 Intensity > 30000

ESI + ESI -

SWATH 2.0 Calculation (swathTUNER)

8 |

Enhanced

peak

finding

Window size

normalized

to peaks per

window

0

100

200

300

400

500

600

700

800

900

1000

0 5 10 15 20 25 30 35

m/z

min

LC-MS

POVPC

PGPC

PONPC

PAzPC

0

10

20

30

40

50

60

70

80

90

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31

SW

AT

H S

ize

/ D

a

Experiment No.

Optimized SWATH Windows

+

Targets

5 Da

LPC PC

Two methods for generating variable windows:

Equal precursor ion population (PIP)

Equalized precursor total ion current (TIC)

Calculation of variable SWATH windows

Improvement of MS/MS spectra quality,

esp. of compounds with low intensity

Improved identification score

Improved quantitative reproducibility G. Hopfgartner et al., J. Proteome Res., 2015

MS-DIAL: Data-independent MS/MS deconvolution

9 | Oliver Fiehn & Masanori Arita, et al., Nature Methods, 2015, doi:10.1038/nmeth.3393

Open-source software pipeline (for data processing & automated metabolite identification)

a) b)

c)

d)

Benefits of MS-DIAL

10 | Joerg Schlotterbeck © 2016 Universität Tübingen

Peak in all samples within 0.04 min

Ma

ss a

ccu

racy in

all

sa

mp

les w

ith

in 4

mD

a

User-friendly display of results

Alignment results can be viewed

Deconvoluted spectra with

better spectral quality

Deconvoluted MS/MS

chromatogram with enhanced

assay specificity

Automated lipid identification (of

lipids in LipidBlast database)

Supporting documentation (no

black box system)

Platelet Lipidomics

11 | Jörg Schlotterbeck

Acute Coronary Syndrom (STEMI)

Stable Angina Pectoris

Control

ACS 1 SAP K2 R1

ACS 2 SAP K3 R2

ACS 4 SAP K4 R3

ACS 5 SAP K5 R4

ACS 6 SAP K6 R5

ACS 7 SAP K7 R6

ACS 8 SAP K8 R7

ACS 9 SAP K9 R8

ACS 10 SAP K10 R9

ACS 11 SAP K11 R10

ACS 12

ACS 13

ACS 14

QC QC QC ACS 8 SAP K11 ACS 4 ACS 10 ACS 9 QC R10 ACS 12 ACS 11 R8 R1 QC R7 ACS 1 SAP K3 ACS 6 SAP K2 R9 QC SAP K8 R4 R5 SAP K10 SAP K6 QC ACS 5 SAP K9 ACS 14 ACS 13 SAP K7 QC R2 R6 SAP K4 ACS 7 SAP K5 ACS 2 R3 QC QC QC

• 300 cps

• S/N > 100

• Analysis in randomized run order

• QC sample every 5th injection to validate

stable method performance

• Analysis in positive and negative ionisation

mode

• Software based database search with over

40000 lipid entries (LipidMaps) or MS-

DIAL (LipidBlast)

• 7000-8000 features per sample

Data pre-processing: • Retention time correction to internal standards

• Normalization to internal standards

• Normalisazion to total area sum of peaks

Statistical evaluation: • Principal component analysis (PCA)

• Supervised PCA

• t-test -- > „vulcano“ plot

Statistics used to find significant molecular

features varying between groups of sample set

Assay stability as assessed by QC

12 | Jörg Schlotterbeck

a b

c

CVs of retention times:

0.4 % for LPC(17:1)

0.2 % for PC(17:0/20:4)

CVs of raw signal intensities:

12 % for LPC(17:1)

10 % for PC(17:0/20:4)

Platelet Lipidomics

13 | Jörg Schlotterbeck

Responses are log

transformed and autoscaled

Multivariate statistics:

PCA-DA

Platelets from patients with SAP

and ACS can be distinguished from

healthy ones by their lipid profiles

Score Plot

Volcano Plots p-Value vs log Fold Change

14 | Joerg Schlotterbeck © 2016 Universität Tübingen

p < 0.05

p < 0.05

p < 0.05

t-Test: ACS vs SAP

t-Test: ACS vs Control (Healthy)

t-Test: SAP vs Control (Healthy)

Sta

tistica

l S

ign

ific

an

ce

Decreased

compared

to control

Increased

compared

to control

Identification of Significant Features

15 | Jörg Schlotterbeck

• Hits with p < 0.05 were checked for identification (MS-DIAL)

• Positive identification were boxplotted

Matching reference spectrum for PE

38:4; (18:0/20:4(5Z,8Z,11Z,14Z))

Neutral loss

of

Phosphoethanolamine

627.535

MS/MS Identification

0

5000

10000

15000

20000

25000

30000

35000

40000

Control ACS SAP

Are

a

m/z 768.5538

Heatmap of Lipids Identified with MS-DIAL

16 | Jörg Schlotterbeck © 2016 Universität Tübingen

R1 R10 R2 R3 R4 R5 R6 R7 R8 R9

SAP

K10

SAP

K11

SAP

K2SA

P K3

SAP

K4SA

P K5

SAP

K6SA

P K7

SAP

K8SA

P K9

ACS

1AC

S 10

ACS

11AC

S 12

ACS

13AC

S 14

ACS

2AC

S 4

ACS

5AC

S 6

ACS

7AC

S 8

ACS

9lysoPE 16:0; PE(O-16:0/0:0); [M+H]+

PC 38:3; PC(18:3(6Z,9Z,12Z)/20:0); [M+H]+

SQDG 35:5; SQDG(16:2/19:3); [M+NH4]+

TG 53:7; TG(16:2/18:0/19:5); [M+NH4]+

Sample no.

Met

abol

ite n

ame

0.02000

0.4000

1.000

1.750

4.000

200.0

Response

CE Cer DG

DGDG

DGTS

LysoPC

LysoPE MG

MGDG

PlasmenylPC

PC

PE

SM

TG

ESI(+)

7,500 molecular features

664 lipids with p < 0.05 (ACS vs control)

162 of them unidentified by MS-DIAL

Shown are identified ones (502)

0

100000

200000

300000

400000

500000

Control ACS SAP

Are

a

m/z 790.5593

0

10000

20000

30000

40000

50000

60000

70000

Control ACS SAP

Are

a

m/z 822.5491

0

20000

40000

60000

80000

Control ACS SAP

Are

a

m/z 814.5593

0

100000

200000

300000

400000

500000

Control ACS SAP

Are

a

m/z 762.5643

Platelets of SAP and ACS Patients Show Altered OxPL Levels

PLPC-(OOH) PAPC-(OOH)

PLPC-(OOH)2 PC 33:1-(OH)

Not identified by MS-DIAL

Target list of 40,000 lipids

from LipidMaps db

complemented by target list

of OxPLs

Loaded into MasterView

Level of metabolite Id:

o accurate mass

o isotope pattern

o fragmentation

Standards for verification

not available currently

PLPC: 1-Palmitoyl-2-linoleoyl-sn-glycero-3-phosphocholine; PAPC, 1-palmitoyl-2-arachidonoyl-sn-glycero-3-phosphocholine

Combined Targeted / Untargeted Analysis

18 | Jörg Schlotterbeck © 2015 Universität Tübingen

O

O

O H

O ON

+

P-O

O

OO

H

PONPC

O

O

O H

O ON

+

P-O

O

OO

H

POVPC

O

O

O H

O ON

+

P-O

O

OO

HO

PGPC

O

O

O H

O ON

+

P-O

O

OO

HO

PAzPC

1-palmitoyl-2-(5’-oxovaleroyl)-sn-glycero-3-phosphocholine

1-palmitoyl-2-(9’-oxononanoyl)-sn-glycero-3-phosphocholine

1-palmitoyl-2-glutaroyl-sn-glycero-3-phosphocholine

1-palmitoyl-2-azelaoyl-sn-glycero-3-phosphocholine

LODs (matrix): 0.2 - 2.0 ng/ml

LOQs (matrix): 1 - 6 ng/ml

Good repeatabilities

Targeted and nontargeted analysis in single

workflow supported by SWATH

Quantitative analysis based on precursor or

fragments

Improved selectivity and sensitivity

Combined Targeted / Untargeted Analysis

19 | Jörg Schlotterbeck © 2016 Universität Tübingen

O

O

O H

O ON

+

P-O

O

OO

H

PONPC

1-palmitoyl-2-(5’-oxovaleroyl)-sn-glycero-3-phosphocholine

10.0 10.5 11.0 11.5 12.0 12.5 13.0 13.5 14.0 14.5

Time, min

0%

50%

100%

12.1

XIC of PONPC 650.439 +/- 0.010 Da

MS/MS triggered at RT = 12.1281

10.0 10.5 11.0 11.5 12.0 12.5 13.0 13.5 14.0 14.5

Time, min

0%

20%

40%

60%

80%

100%

% I

n t e

n s i t y (

o f

1 9

1 4

. 4 )

12.1

XIC of PONPC 650.439 +/- 0.010 Da

Fragment XIC of Phosphocholine 184.073 +/-0.01 Da

IDA (20 MS/MS per cycle)

SWATH

IDA: Quantification by TOF-

MS (via precursor ion only)

SWATH: Supports

quantification in MS/MS mode

(via fragment ions)

Enough data points across

peak

SWATH without

deconvolution:

Limited assay specificity

Several lipids with

same/similar tR may yield the

same fragment (interference)

Quantification based on Deconvoluted MS/MS Chromatogram

lysoPC 18:1

9.0 9.5 10.0 10.5 11.0 11.5 12.0 12.5 13.0

Time, min

0%

20%

40%

60%

80%

100%

% I

n t e

n s i t y (

o f

1 . 3

e 5

)

10.466 10.157

Lyso-PC(18:1), 522.3554 +/-0.01 Da

MS/MS triggered at RT = 10.3210

9.0 9.5 10.0 10.5 11.0 11.5 12.0 12.5 13.0

Time, min

0%

20%

40%

60%

80%

100%

% I

n t e

n s i t y (

o f

1 . 1

e 5

) 10.47

10.16

Lyso-PC(18:1), 522.3554 +/-0.01 Da

Fragment XIC 184.073 +/- 0.010 Da

IDA

SWATH

SWATH , deconvoluted

20 | Jörg Schlotterbeck © 2016 Universität Tübingen

Data-independent acquisition (DIA) with SWATH

Better coverage of analytes as compared to DDA (IDA) profiling

MS-DIAL a powerful SW for automated lipid identification

Oxidized PLs not successfully identified, therefore target list approach used

In future, focus on combined targeted/untargeted profiling

Ion mobility and 2D-LC as further selectivity dimensions to resolve complexity

Summary / Conclusions

Platelet lipidomics

SAP and ACS show significant differences in lipid profiling (regulation via

signaling cascades)

Several oxidized PLs showed increased levels in SAP and ACS patients

(oxidative stress or controlled regulation upon platelet activation)

Acknowledgements

Financial Support:

Struktur- und

Innovationsfonds für die

Forschung (SI-BW)

Jörg Schlotterbeck

Adrian Sievers-Engler

Stefan Polnick

Markus Höldrich

Corinna Sanwald

Ulrich Woiwode

Carlos Calderon

Aleksandra Zimmermann

Jeannie Horak

Stefan Neubauer

Heike Gerhardt

Siyao Liu

Bernhard Drotleff

Stefanie Bäurer

Mike Kaupert

Prof. Meinrad Gawaz

Dr. Madhumita Chatterjee