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INTRODUCTION

Spontaneous pre-term birth (sp-PTB) is a leading cause

of perinatal morality with long-term, adverse health

problems such as cerebal palsy, learning disabilities and

respiratory illnesses being common. On average, 15

million babies are born prematurely worldwide per year.

The exact mechanism as to how this occurs is however

not well understood and there are no suitable

assessment measures to accurately predict pre-term

birth. Here, we describe a LC-MS lipidomic approach to

reveal molecular factors that may be involved in these

biomolecular processes and potentially be used as early

indicative markers of sp-PTB during pregnancy.

IDENTIFICATION AND LABEL-FREE QUANTIFICATION OF LIPID BASED MARKERS FOR PRE-TERM BIRTH USING A NOVEL SCANNING QUADRUPOLE DIA ACQUISITION METHOD

Shirish Yakkundi1, Lee A Gethings2, Gregoire Thomas3, Aude-Clare Morillon1, Louise C. Kenny1, James I Langridge2 1INFANT, University Maternity Hospital, Cork; 2Waters Corporation, Wilmslow, United Kingdom; 3Squ4re, Wevelgem, Belgium

METHODS

Sample preparation

Samples were collected from women at 20-weeks who had reached

term gestation. Heparinised plasma samples from a cohort of matched

controls (n=32) and case (n=16) of varying phenotype were prepared as

previously described using either IPA or MTBE-based extraction.1,2

Following extraction, samples were vortexed and transferred to glass

vials in preparation for LC-MS analysis.

LC-MS conditions

Metabolites were chromatographically separated using an ACQUITY I-

class configured with a BEH 1.7 µm C18 reversed phase (RP) 2.1 x 100

mm LC column. Experiments were conducted over 20 mins using a

gradient of 30 to 100% IPA:Acetonitrile (10mM ammounium

formate/0.1% formic acid).

Mass spectral data were acquired using a Xevo G2-XS QToF (Waters

Corporation), Figure 1, operated in SONAR™ mode with a quadrupole

scanning mass range of 450-900 Da and window of 10 Da (Figure 2).

Data were collected in both positive and negative ion mode, using a

scan rate of 0.2 sec over a ToF mass range of 50-1200 m/z. A collision

energy profile of 20-50 eV (+) and 25-55 eV (-) were specified for

fragmentation (Function 2).

Bioinformatics

The LC-MS metabolite data were processed and searched with

Progenesis QI (Non-Linear Dynamics, UK). Normalized label-free

quantification was achieved with additional statistical analysis conducted

using EZInfo (Umetrics, Sweden). Compound searches were conducted

using LipidMaps.

References

1. Sarafian et al. Objective Set of Criteria for Optimization of Sample Preparation Procedures for Ultra-

High Throughput Untargeted Blood Plasma Lipid Profiling by Ultra Performance Liquid

Chromatography-Mass Spectrometry. Anal. Chem. 2014; 86:5766-74.

2. Matyash et al. Lipid extraction by methyl-tert-butyl ether for high-throughput lipidomics. J. Lipid Res.

2008;49;1137-46.

RESULTS

Processed LC-MS data resulting from IPA and MTBE extractions were

compared to assess the reproducibility of each technique and classes of

lipid produced by both techniques. Figure 4 shows representative

chromatograms for both extractions. When processed with Progenesis

QI, the number of identified features for both extraction techniques were

comparable for both ionization modes (Figure 5). Assessment of

extraction reproducibility over a five day period showed IPA to provide

reproducibility on the basis of smaller %CV values.1 The recovery of a

variety of lipid classes were found to be common between both

extraction methods. Of the lipid classes investigated, the diglycerides

showed the largest difference in recovery, with MTBE yielding a higher

percentage.

Chromatographic separation of lipids results in the co-elution of multiple

species thereby making identification challenging. Additional selectivity

is introduced into the workflow by implementing SONAR. Comparative

analysis of data acquired using SONAR versus an alternative DIA

strategy (Figure 6) highlights the additional selectivity provided. The

additional selectivity provides added benefit in terms of identification

confidence. Figure 7 shows increased Progenesis QI confidence and

fragmentation scoring with increasing selectivity provided by SONAR for

a range of example lipids. In particular, TG (52:3) (LMGL03010099) and

SM (d18:1/22:0) (LMSP03010006) show an increase of 70% in

fragmentation score and more than a 10% increase in overall

confidence score.

CONCLUSION

SONARTM

DIA acquisition provides multi dimensional data sets exhibiting improved specificity and over other DIA methods.

Comparison of extraction techniques using either IPA or MTBE, show both techniques to provide similar lipid species of comparable abundance.

Greater extraction reproducibility is observed for IPA based methods when compared to MTBE over consecutive days.

A SONAR™ workflow shows higher confidence scores for co-eluting lipids when compared with alternative DIA methods.

Figure 1. Schematic of the Xevo G2-XS mass spectrometer used for

SONAR™ data acquisition

Figure 2. SONARTM

acquisition method and DIA acquisition parameters

used in the different experiments.

Quad Mass

Figure 3. SONARTM

DIA ToF vs. quadrupole m/z data, showing product

ions (vertical bands) from metabolites eluting over a 1 min window and

the quadrupole sweep (diagonal line).

Figure 4. Example BPI chromatograms of plasma lipid extracts: (a)

MTBE (positive ion), (b) IPA (positive ion), (c) MTBE (negative ion), (d)

IPA (negative ion). Separation is based on a 20 min gradient.

(a)

(b)

(c)

(d)

MTBE194

IPA130 2506

(a)

MTBE765

IPA471

6885

(b)

%CV

Co

un

t

%CV

Co

un

t

0

10

20

30

40

50

60

PC PE TG SM DG Cer

%

Lipid Class

IPA

MTBE

Figure 5. Comparative analysis

of MTBE versus IPA extraction.

Venn diagrams show the num-

ber of identified features for

negative (a) and positive (b) ion

data corresponding to both ex-

traction techniques. Histograms

of the CVs show the reproduci-

bility gained from both extrac-

tion methods over multiple ex-

tracts (5 day consecutively).

Representative negative ion

data shows IPA-based extrac-

tion to be more reproducible

when compared with MTBE.

Figure 6. Improved selectivity is demonstrated for SONAR acquired data over the alternative DIA acquired workflow. The co-elution of TG lipids

(retention time = 13.3 min) generates a fragmentation spectrum containing a mixture of fragment ions (MS2) originating from multiple precursors when

acquired using the alternative DIA strategy (A). Acquisition of the same sample set using a SONAR workflow shows improved selectivity and therefore

allowing fragment ions for multiple, co-eluting TG species to be assigned with greater confidence (B). A two dimensional ion map of quadrupole m/z (vs)

retention time with the scanning quadrupole shows increased specificity for co-eluting TG’s at retention 13.3 min, where three individual lipid species

can be clearly identified.

Figure 7. Improved scoring for SONAR generated identifications com-

pared with the alternative DIA technique. SONAR acquired data shows

higher fragmentation (upper graph) and confidence (lower graph) scores

for example lipid species SONAR. Alternative DIA strategy

(MS2 — fragment ions)

Qu

adru

po

leSc

an

Retention Time (mins)

(A)

(B)