High Throughput Identification and Quantification of Phospholipids in

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High Throughput Identification and Quantification of Phospholipids in Complex Mixtures Nicholas M. Balsgart, Mette Mulbjerg, Zheng Guo, Kresten Bertelsen, and Thomas Vosegaard Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.5b03798 • Publication Date (Web): 21 Jan 2016 Downloaded from http://pubs.acs.org on January 24, 2016

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Analytical Chemistry

High Throughput Identification and Quantification of Phospholipids in Complex Mixtures NICHOLAS M. BALSGART,† METTE MULBJERG,*,‡ ZHENG GUO,* KRESTEN BERTELSEN,‡ THOMAS VOSEGAARD†,a †

Center for ultrahigh-field NMR spectroscopy, Interdisciplinary Nanoscience Center and Department of Chemistry, Aarhus * University, Gustav Wieds Vej 14, DK-8000 Aarhus C, Denmark. Department of Engineering, Aarhus University, Gustav ‡ Wieds Vej 10, 8000 Aarhus C, Denmark, DuPont Nutrition Biosciences ApS, Edwin Rahrs Vej 38, DK-8220 Brabrand, Denmark. 31

Key-words: P NMR; Automatic assignment and identification; Quantitative NMR; Phospholipids

ABSTRACT: We present a fully automatic method, autoP, for identification and quantification of lipids in complex lipid mixtures from 1D 31P and 2D 1H-31P NMR spectra. The 31P chemical shifts in lipids are highly sensitive to experimental conditions such as pH and temperature, so the present method uses the much more unambiguous 1H chemical shifts for assignment and 31P intensities for quantification. By using 2D 1H-31P TOCSY correlation experiments, we demonstrate that approximately 20 different lipids can be automatically and unambiguously assigned and quantified by this automatic method.

TOC Graphics

a

Corresponding author: Thomas Vosegaard, [email protected]

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INTRODUCTION In recent years, NMR techniques have been established for identification and quantification of

compounds in complex mixtures covering diverse applications from metabolomic profiling to identification of impurities. A branch of these experiments exploits the use of 31P NMR for quantifying phospholipids in complex mixtures, such as cell extracts.1–6 With a high gyromagnetic ratio, nuclear spin I = 1/2, and 100% natural abundance of 31P, the NMR spectra are generally characterized by high sensitivity, narrow lines, and good chemical shift dispersion, making 31P NMR an interesting probe for such systems. The sensitivity of the 31P nucleus to its local chemical environment governs, in part, its usefulness to the study of such systems, as it displays reasonable chemical shift dispersion. However, it also creates major challenges when using 31P NMR for the identification and quantification of phospholipids. The 31P chemical shifts are highly sensitive to pH and other parameters associated with sample preparation,1 making one-dimensional (1D) 31P chemical shift alone a poor tool for unambiguous assignment of different phospholipids, which is required for quantification. One of the applications of interest is to investigate the composition of enzymatically digested lipid extracts from deoiled soy lecithin. Using common sample preparation techniques,1–3,5,7 a typical series of 1D 31P NMR spectra obtained for different enzymatic digestion times may look like the those in Figure 1. We would expect to see variations in the intensities, but intriguingly, we also observe significant fluctuations of the 31P chemical resonances because of small variations in the chemical environment of the lipids. In highly congested spectra the fluctuations in the 31P chemical shifts quickly become unmanageable.

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Figure 1. Examples of experimental P spectra of complex mixtures of phospholipids. The different spectra are recorded by gradually increasing the time of enzymatic digestion of a lipid extract from deoiled soy lecithin extracted in CDCl3:MeOH:Cs ethyldiamine tetraacetic acid (CsEDTA) (aq) with volume fractions 4:2:1.

To overcome the problem with the 31P chemical shift fluctuations, we have been inspired by the two-dimensional (2D) 1H-31P heteronuclear total correlation spectroscopy (TOCSY) experiment that Kellogg8 developed for studies of nucleic acids. The advantage of using this experiment is that the 1

H chemical shifts of the lipids turn out to be very resilient to changes in pH and other external per-

turbations. In addition, the TOCSY transfer allows correlation between 31P and a large number of 1H resonances, which creates a unique and recognizable NMR fingerprint of each lipid in the 1H dimension of such experiments. In the present work, we demonstrate that the combination of quantitative 1D 31P NMR and 2D 1H31

P TOCSY experiments is sufficiently reliable for the development of robust algorithms for auto-

matic identification and quantification of phospholipid mixtures. In this procedure, autoP, we first used the heteronuclear-TOCSY 2D NMR experiment to identify the individual phospholipids from the 1H dimension of the 2D spectrum and then use the 1D 31P experiment to quantify the lipids. autoP is fully automated, and thus will be a powerful tool for high-throughput identification and quantification of lipids in complex mixtures.



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EXPERIMENTAL SECTION Preparation of lipids mixtures. A solution of 1% enzyme (LysoMax® Oil; DuPont Industrial Bi-

oscience ApS, Brabrand, Denmark) in water was prepared in Eppendorf tubes, and lecithin, from soybean sourced in India and obtained from Lasenor (Barcelona, Spain), was added. The enzymatic hydrolyses were performed in an Eppendorf ThermoStatTM Comfort (Hamburg, Germany) at 55 °C for up to 2.5 h with a mixing frequency of 1400 rpm. The samples were then transferred to another Eppendorf Thermo-StatTM Comfort pre-set to 99 °C at 1400 rpm for 10 min to deactivate the enzyme. Hydrolysed samples were then centrifuged, resulting in a solid and liquid phase, both of which were used for analysis. Approximately 50 µL of liquid phase and 5 mg of the solid phase were used for the analysis. NMR Sample preparation. Most lipid samples for liquid-state NMR use two-phase systems, where water-soluble lipids are in one phase and other lipids are extracted into a second phase.1–3,5,7 We have adapted a single phase system introduced by Lutz and Cozzone,1 in which we find both water-soluble and –insoluble lipids. The solvent system uses 1 mL 5:4:1 (volume fractions) CDCl3:MeOH:Cesium cyclohexane diamine tetraacetic acid (CsCDTA) (aq).9 This solvent system allows for the simultaneous detection of water-soluble and -insoluble phospholipids, making it an ideal solvent for the present purpose with the general scope to identify all phospholipids in the sample. In addition, we generally observed that the 31P chemical shifts displayed less fluctuations in the

CDCl3:MeOH:CsCDTA solution

than

in other solvent systems,

for example the

CDCl3:MeOH:Cesium ethylendiamine tetraacetic acid (CsEDTA) (aq) (volume fractions 4:2:1) solution used in Figure 1. See for example the fluctuations of the 31P chemical shifts in the hydrolysis series in Figures 1 and 6 and supporting information Figure S5. In addition, the CsCDTA is efficient in exchanging any paramagnetic ions in the lipid sample that would otherwise cause linebroadening in the NMR spectra.9


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Table 1. List of lipids used or referred to in this work. Common name

Abbreviation

Phosphatidylcholine

Ma

PC

770

Lysophosphatidyl-choline

LPC

514

Glycerophosphoryl-choline

GPC

257

Phosphatidylinositol

PI

835

Phosphatidyl-ethanolamine

PE

725

Lysophosphatidyl-ethanolamine

LPE

474

Glycerophosphoryl-ethanolamine

GPE

215

PA

712

Lysophosphatidic acid

LPA

457

Glycerophosphoryl acid

GPA

170

N-acyl lysophosphatidylethanolamine

NAL

730

N-acyl phosphatidylethanolamine

NAP

1011

Triisobutylphosphate

TIBP

266

Phosphatidic acid

a

Average molar weights are in g/mol.

The CsCDTA and CsEDTA solutions were prepared by dissolving CsCDTA (CsEDTA) in milli-Q water to reach a concentration of 1000 mM (200 mM). For both solutions, 0.2 M CsOH·H2O was added to the solution until they reached pH = 10. Figure S6 in the Supplemental Material shows 1D 31

P spectra for samples prepared with different pH values. This value was chosen as it generally gives

well-dispersed 31P spectra. A solution consisting of 160 mg TIBP in 2 mL of the CsCDTA/CsEDTA solvent system was used as an internal standard, as 25 µL (2 mg TIBP equivalent) was added to each lipid sample. The samples were centrifuged at 4400 g for 10 min at 20 °C, then 550 µL was transferred to a 5 mm NMR tube for analysis. Calibration tests of lipid concentrations from 31P intensities. A certified reference sample of L-α-Lecithin 20% (Avanti Polar Lipids, Alabaster, AL, USA) was used to verify quantitative results. Five lipids were detected: LPE, PE, LPC, PI, and PC with certified weight percentages of 0.82, 24.36, 0.73, 14.38, and 27.09 wt/wt %, respectively (see Table 1 for a list of lipids used in this study). The average lipid molar masses used to calculate concentrations are given in Table 1. Two samples were



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prepared with total certified lipid amounts of 38.5 and 78.8 mg in the above solvent for quantitative measurements utilizing automatic identification and quantification. NMR experiments. All NMR experiments were performed on a Bruker Avance III spectrometer (Karlsruhe, Germany) equipped with a double-tuned direct-detection broadband probe (and deuterium lock) and a 14.1 T magnet corresponding to 1H and 31P resonance frequencies of 600 MHz and 243 MHz, respectively. The heteronuclear TOCSY pulse sequence of Kellogg8 was modified to allow detection of 31P, as this provided a superior sensitivity in our setup. For both nuclei we need spectral widths of ca. 6 ppm to avoid folding of signals, but for 1H we can accept linewidths of 0.05-0.10 ppm, while we need linewidths of ~0.02 ppm in the 31P dimension to avoid overlap. Hence, we would need to sample 2.55 times as many t1 increments if observing 1H rather than 31P.10 The employed pulse sequence is shown in Figure 2. Durations of the 1H 90º pulse, 31P 180º pulse, and mixing time were 11.2 µs, 25.0 µs, and 70 ms, respectively, and the pulses were phase cycled according to , , , , , , , ;

, , ̅ , ̅ , , , ̅ , ̅ ; , , , , ̅ , ̅ , ̅ , ̅ ; , , , , , , , ; , ̅ , , ̅ , ̅ , , ̅ , with

the phases referring to Figure 2. A spoiler gradient may be applied prior to the experiment, but we did not see any significant improvements by doing so. The phase sensitive 2D dataset is achieved by altering the phase of the 1H 90 degree pulse through the States-TPPI protocol.11 The TOCSY mixing

was achieved using the DIPSI-2 pulse sequence for the mixing12,13 with an rf field strength of 4.17 kHz on both channels. The 2D experiments were recorded using 100 t1 increments (plus two phases for each t1 increment to achieve a hypercomplex dataset), each consisting of 16 scans with a repetition delay of 1 s and 8 dummy scans. The spectral widths in the ν1 and ν2 periods were 6.1 ppm and 10 ppm, centered at 2.9 ppm and 1.0 ppm, respectively. The t2 acquisition time was 1.4 s.

31

1

31

Figure 2. Pulse sequence used for P detection of H- P correlation experiments. Pulse phases ( ) are given in the text.

The quantitative 1D 31P experiments were recorded under inverse gated decoupling to avoid NOE transfer from 1H to 31P and employed 128 scans and repetition delays of 10 s. This repetition delay


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corresponds to 7T1 for TIBP, which has the longest T1 relaxation time (see supporting information) ensuring that quantitative information can be obtained from the 1D spectra.14 The 1D 31P spectra were recorded with a spectral width of 12175 Hz and the carrier frequency set to 0 ppm. All experiments were recorded using 2H lock, temperature regulation set to 278 K, and 1H GARP-4 decoupling. As the 31P resonance frequencies are temperature dependent, we chose the temperature that generally provided the optimum signal dispersion (see supporting information). The ppm scales are referenced to TMS (1H) and 85% H3PO4 (31P) at 0 ppm via the internal TIBP signal at 0.98 ppm (1H) and -0.36 ppm (31P). To measure the chemical shift of TIBP relative to TMS and 85% H3PO4 as part of the initial setup, it was necessary to keep the 85% H3PO4 in a separate container, as the low pH of H3PO4 most likely would alter the 31P chemical shifts of TIBP. Therefore, we prepared a sample of TIBP dissolved in the CDCl3:MeOD:CsCDTA (aq) buffer with a tiny amount of TMS in 5 mm NMR tube. Inside the tube a capillary containing 85% H3PO4 was placed. This sample allowed us to measure the 1H and 31P chemical shifts of TIBP. The TIBP resonance did not display more than 3Hz drift in 1H chemical shift upon changing the temperature from 278 K to 298 K, so following this experiment we now routinely reference the 1H and 31P chemical shift axes through the characteristic TIBP signals at 0.98 ppm (1H) and -0.36 ppm (31P). Peak picking. Peak picking in the 1D and 2D spectra was implemented in MATLAB (Version 2014a, The MathWorks Inc., Natick, MA, USA). A MATLAB script loads the 1D and 2D datasets as input. Spectra were first processed with relevant apodization, then Fourier transformed, correctly referenced and phased prior to spectra loading in MATLAB. Sine-squared apodization in the indirect dimension of the 2D spectra, exponential apodization in the 1D (1 Hz) and direct dimension of the 2D (2-3 Hz) spectra were preferred. Throughout this study we have used the same processing parameters for all experiments, but we have tested the peak-picking and assignment algorithms, and they turn out to be very stable towards changes in linewidths, etc. In the 2D spectra, peaks were detected by creating a skyline projection along the direct (31P) dimension (i.e. finding the maximum value for each of the points in the direct dimension). Starting with the most intense points in the skyline projection, the points were checked against the surrounding data points to be positive singularities, in which case the data point was identified as a peak. This procedure was repeated for all peaks above the noise level. Approximately 3 to 20 phosphorous resonances were detected in each spectrum using this procedure (see Figure 3 and supporting information).



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For each of the identified peaks in the projection along the direct dimension, the same procedure was repeated in the indirect dimension. The final peak list resulting from this procedure was a list containing a number of 31P shifts each correlated with a number of 1H shifts. This peak list was then passed to the identification and scoring part of the algorithm described below in Results and Discussion. Deconvolution of 1D 31P spectra. In order to quantify the individual lipids, an accurate determination of the 31P peak intensities in the quantitative 1D spectra was utilized. As many of the peaks overlapped, normal numerical peak integration did not provide accurate results in many cases; however, since the 31P chemical shifts from the 2D spectrum, and the 31P resonances are nearly perfect Lorentzian in the 1D spectra, it was suitable to use deconvolution. The deconvolution algorithm fits the spectral regions around the detected peaks to Lorentzians. The algorithm optimizes the chemical shift values, linewidths and intensities, in order to give the best root-mean-square (RMS) deviation between the experimental and simulated spectra. If two or more peaks overlap, the algorithm makes a simultaneous fit of these lines. The algorithm treats lines as overlapping if they are located within 10 Hz of each other, since typical linewidths are on the order of 5 Hz. In cases where the deviation between the experimental spectrum and peak deconvolution is significantly larger than the noise level of the spectrum, the algorithm performs a simple numerical integration of the peak intensity, provided the peak has no close neighbours and that there is baseline on both sides of the peak.

RESULTS AND DISCUSSION The expansion of a 2D 1H-31P TOCSY spectrum for a typical phospholipid mixture from soy leci-

thin in Figure 3 shows approximately 10 different 31P signals in the range 0 to 2 ppm. The corresponding traces in the 1H dimension typically show up to 10 resonances from the different hydrogen

atoms in the vicinity of the phosphorous atoms in the lipids.


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Figure 3. Contour plot of a 2D 1H-31P TOCSY spectrum of a lipid mixture with assignment of the different signals. The colored bands represent the lipids identified during the peak picking, and the individual peaks are highlighted by magenta dots. The width of the coloured bands illustrates that the peak positions may fluctuate in the 31P dimension because of their imperfect lineshapes or overlap with neighbouring peaks. Both the chemical shifts and the intensity distribution in the 1H dimension are important observables for a reliable peakpicking algorithm. The band labelled UI (unidentified) represents signals that do not match any entries in the database.

The first step in the fully automated assignment procedure by autoP was an automatic identification of the peaks in the 2D 1H-31P TOCSY spectrum. To achieve this, a peak-picking algorithm (described in the Experimental section) was developed that reproducibly identifies all signals above the noise level and groups them according to their 31P chemical shift. Within each 31P trace of the 2D spectrum, small variations (~0.01 ppm) in the 31P peak positions were often observed because of non-ideal lineshape; hence the peak picking was made tolerant to such small variations when grouping the signals. To illustrate the tolerance to variations in the 31P line positions, the assign-



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ments are highlighted in Figure 3 as bands through the different 31P traces, with the width of the band indicating the tolerance. In order to automatically assign the lipids, a database with the spectral characteristics of various lipids was established. The database was built by recording 2D 1H-31P heteronuclear TOCSY experiments many different samples containing lipid mixtures from different sources, in different solvent systems, and with different TOCSY mixing times (see supporting information). We found that although the 31P chemical shift may fluctuate significantly, the 1H chemical shifts are very stable. The TOCSY mixing time is an important parameter, as the cross peak intensities vary with this parameter, which implies that a different number of 1H resonances may be observed for each 31P signal depending on the length of the mixing time.8 Shorter mixing times provide higher sensitivity whereas longer mixing times allow correlation to more protons in the head group and glycerol part of the phospholipids. Since intensities vary with the mixing time and the database uses the intensities, the same mixing time was used for all samples. A mixing time of 70 ms yielded a suitable compromise that provided enough signals and sensitivity for unambiguous assignment of the phospholipids. See the supporting information for details on the mixing time. The peak-picking algorithm and manual assignment of the different phospholipids were performed on all samples used to establish the database.

Insert Table 2 here Although the 31P chemical shifts may vary significantly as illustrated in Figure 1, the positions of the 1H shifts showed significantly smaller variations (see Figure S5 in the supporting information). This has allowed us to identify quite narrow 1H chemical shift ranges for all resonances of all lipids as well as reproducible values for the typical intensities of these resonances. These values are summarized in Table 2. Between three and eight 1H resonances were observed for each lipid. Table 2 also summarizes the typical intensity distribution of the individual peaks, as this information is also included in the assignment procedure to improve the reliability of the procedure. To address the choice of mixing time, Table S2 shows the same data as Table 2 but adds peak intensities obtained from different TOCSY mixing times to illustrate the significance choice of this parameter. To emphasize the stability of the 1H chemical shifts as compared to those of 31P, the typical ranges for the 31

P chemical shifts were those given in Table 2 within an interval of ±1 ppm, while the ranges for the

1

H shifts were within an interval of only ±0.05 ppm (approximately). The relative intensities may

fluctuate from spectrum to spectrum, and hence we allow such fluctuations by ±20% of the intensity of the most intense 1H peak for each lipid.


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The numbers in Table 2 represent the database that was used in autoP to make the automatic assignment of the peaks to different lipids from 1D and 2D spectra of unknown lipid samples. The assignment was performed based on a scoring function that is calculated for all observed lipids to each of the lipids in the database. The scoring function (S) ranges from zero in the case of poor agreement with a database entry and one in the case of good agreement with a database entry. It considers the number of 1H peaks, the 31P and 1H chemical shift values, and the relative intensities -

23 db '() 2 db '() 2 1, − 1435 1 "H,$ − "H "7,$ − "7 exp − ! + , × exp 0− + ,. , 6 exp − ! Δ"H Δ1 Δ"7 2

.

(1)

23 represents the value In this equation, the index j refers to the different lipids in the database, "8,

23 23 for the 31P chemical shift of database entry, "9,, and 1, represent the :'th 1H resonance frequency 435 and intensity, ";435 and " min( , 435 ),

where 435 is the number of experimentally observed 1H resonances, and min(A,B) denotes the minimum value of A and B. Before calculating the score, the hydrogen peaks are sorted according to

their resonance frequency. The fact that the scoring function does not depend on an exact match of the number of peaks makes it very robust for spectra with low resolution or subsampled spectra with truncation wiggles. In most cases, only one or two lipids will provide a score above 0.05, and rarely more than one scores above 0.25. Identity is assigned to the highest value above 0.25, but this value is a user-defined variable that can be increased for higher certainty or lowered for identification suggestions. The final step in autoP is quantification of the assigned lipids from the quantitative 1D 31P spectrum. For this part of the procedure, the observed 31P chemical shifts from the 2D TOCSY experiments is used as input for an optimization, where the algorithm first aims at deconvoluting the individual peaks as (potentially overlapping) Lorenzians, and in the case of failure of this, falls back on numerical integration. Figure 4 shows the result of this deconvolution for one such sample. As a visual quality check, peaks successfully deconvolved as Lorentzians are colored in green. Peaks where numerical integration is used because the deconvolution fails for some reason, are colored red. Peaks where the RMS deviation is high, but where numerical integration is not expected to work because the peak has close neighbours are colored yellow. Peaks with too low intensity to per-



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form a reliable quantification are colored orange. Along with the color-coding in Figure 4, a simple deconvolution score of +++, ++, +, –, or X, was introduced to indicate the nature of the deconvolution. Of these, peaks marked – have low intensities and as a result have lower relative precision. Those marked X are flagged as unsuccessfully deconvoluted and should be reviewed for the source of failure, however the numerical integration generally provides reliable concentrations. In all cases, however, the effects of the deconvolution score are minor sources of uncertainty on the measurement of concentration from the 31P NMR intensities. Additional sources to the uncertainty in the determination of the concentrations are minor baseline distortions and background signals. As a consequence, we report all concentrations with same precision.

Figure 4. 1D 31P NMR spectrum of a sample containing a number of phospholipids. All peak positions identified in the 2D TOCSY spectrum and the corresponding deconvolutions are shown in green if they are successful, in yellow if the RMS deviation is high, and red if numerical integration is applied. The inserts show successful deconvolutions and at 2.67 ppm a failed deconvolution, as the peak is split into two. The algorithm automatically turns to numerical peak integration in this case.

To establish the absolute relation between 31P intensity and lipid concentration, two samples from soy lecithin with known certified lipid concentrations were investigated. The results of the measurements on the certified samples are plotted in Figure 5, which reports the measured concentration vs. the certified concentration. The data may be fitted by the linear equation 1.0137 −

12


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0.00017, where x is the certified concentration, y is the measured concentration, both in mM, and

F 0.9966, reflecting the high accuracy of the present method.

Figure 5. Plot of autoP-measured concentration versus certified concentration for two different samples with 38.5 mg (green) and 78.8 mg (blue) total lecithin. The samples have known values of weight percentages of the different phospholipids as listed in the Experimental section.

A known amount of TIBP (2mg) is always added to the lipid samples (corresponding to a molar concentration of 7.5 mM), serving as an internal standard, which is used for both chemical shift referencing and for calibration of the concentrations. Thus, for an unknown sample we will be able to quantify the concentration giving rise to each of the 31P peaks. It should be noted that when doing the quantitative analysis, we always performed the 31P 1D NMR acquisition with a repetition de-

lay of 10 s, which was sufficient for all lipids and TIBP to fully relax (I 7J ).

As an example of the output from the automatic procedure, Table 3 lists the peaks identified, assigned, and quantified as described above for a sample of enzymatically digested soy-bean lipids corresponding to the experimental spectra in Figures 3 and 4. The output contains a list with the lipid label for each peak, the 31P chemical shift and intensity, the corresponding concentration, and the deconvolution score, ranked as +++ for the highest fidelity. This grade is given if the deconvolution shows both low rms between the deconvoluted peak and experimental peak as well as close relationship between the integrated value of the peak intensity and the deconvoluted one. ++ allows a wide margin on both parameters and + accepts an even higher RMS deviation.



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Table 3. Results of the fully automatic analysis of a soy lecithin sample subjected to enzymatic hydrolysis. a

Label

31

P

SNR

ppm

Deconvolution

Assignment

Score

Score

C (mM)

TIBP

–0.36

731

+++

0.6

7.50

PC

0.14

120

++

0.8

2.37

PI

0.37

65

++

0.9

2.55

LPC1

0.50

20

+

0.7

0.80

LPC2

0.60

108

++

0.8

2.94

GPC

0.71

95

++

0.7

3.73

PE

0.75

66

++

0.6

2.10

NAP

1.07

14

+

0.7

0.50

NAP

1.09

7

-

0.3

0.30

LPE1

1.12

9

-

0.6

0.32

LPE2

1.17

37

+

0.7

1.27

GPE

1.26

20

++

0.8

0.69

NAL

1.55

9

-

0.7

0.33

NAL

1.72

2

-

0.3

0.09

NAL

1.77

3

-

0.4

0.08

UI

1.84

3

-

0.0

0.10

PA

2.67

46

X

0.7

1.63

LPA

3.65

29

++

0.6

0.91

UI

3.74

5

-

0.0

0.17

GPA

4.02

8

-

0.6

0.20

a

b

b

The abbreviations for the lipids are defined in Table 1. The precision on the measurement of concentration is estimated to be within ±0.05 mM for the samples investigated.

To illustrate the performance of autoP, we followed the alterations in lipid composition during the enzymatic hydrolysis. Five samples were prepared corresponding to hydrolysis times of 0, 30, 60, 90, and 120 min (the sample investigated above was the sample subjected to enzymatic hydrolysis for 90 min). For each of these extracts, we have prepared the NMR samples as described in the Experimental section and recorded 1D and 2D NMR spectra. The 1D 31P spectra and results of the analysis are summarized in Figure 6, which reports the concentrations of different lipids as a function of the time of hydrolysis. In addition, Table 4 gives the determined concentrations. In this example, we identified 11 different lipids and we have been able to map their concentrations as the hydrolysis progressed. We measured concentrations in the range from 0.1-6.3 mM, corresponding to lipid weight fractions of 0.1% to 12.0%.


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The different samples investigated show up to ~20 31P resonances. Given that the 31P peaks were very narrow, and that 31P peaks showed good chemical shift dispersion resulting in no severely overlapping resonances, we anticipate that 50-100 lipids could be resolved and identified from a single sample using the present method. This opens up interesting perspectives to employ this method for fully automated NMR-based lipidomics. Indeed, the total number of lipids in typical human extracts may be as many as 1000-2000,15 but these may be extracted into different solvents according to their physical properties, so each extract would contains in the order of 100 different lipids. If it turns out that the resolution is not sufficiently high in the present method, the next step would be to do the experiments at different temperatures or to change pH for the sample, as this would perturb the 31P chemical shifts and help in resolving overlapping peaks.

Figure 6 NMR-based quantification of the lipids in soy lecithin subjected to enzymatic hydroly31 sis. In the bottom are the 1D P spectra corresponding to the different time of hydrolysis. The different colors refer to the time progression of the hydrolysis. The spider plot shows the measured quantities of different phospholipids. The thin lines indicate the concentration scale that ranges from 0 to 6 mM.

The high resolution in both the 1H and 31P dimensions also makes the present application an ideal case for non-uniform sampling (NUS) of the 2D datasets, potentially reducing the experiment time



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Page 16 of 25

of the 2D experiments by up to 75%. In the present examples, this would imply an overall time from the sample is prepared to complete quantification of 1 hour.

Table 4. Time series of lipid concentrations in a soy lecithin sample subjected to enzymatic hydrolysis.a Time of hydrolysis (min) Lipid

0

30

60

90

120

Mass

40.9

39.7

40.2

39.4

39.1

PC

6.31

4.41

3.46

2.37

1.86

PI

4.26

3.46

3.05

2.55

2.10

PE

3.92

3.08

2.73

2.10

1.99

PA

2.35

1.65

1.94

1.63

1.33

LPC1

0.56

0.70

0.74

0.80

0.75

LPC2

1.67

2.41

3.07

2.94

2.76

GPC

1.58

2.47

3.27

3.73

3.80

LPE

0.64

0.96

1.23

1.27

1.29

GPE

0.15

0.34

0.47

0.69

0.68

GPA

0.00

0.16

0.14

0.20

0.28

LPA

0.40

0.70

0.90

0.91

0.86

b

a

b

All concentrations are in mmol/L. Total amount of lipid in the sample (mg) dissolved in 1.0 mL of solvent.

CONCLUSION A novel automated method, autoP, was successfully utilized for fast, reliable identification and

quantification of phospholipids and other organic phosphorous compounds from 1D and 2D 31P NMR experiments. The method was also successful in assignment and quantification of ~20 phospholipids. Due to high spectral resolution, this method has the potential to result in the unambuously identity and quantification of many more phospholipids.

Acknowledgements We thank Dr. Anne Laursen for providing the enzymes and soy lecithins used in this work. We thank the Danish National Research Foundation (DNRF 0059), Carlsbergfondet, the Novo Nordisk Foundation, the Danish Natural Science Research Council, Lundbeckfonden, the Danish Ministry of Higher Education and Science (AU-2010-612-181), Aarhus University, DuPont Nutrition & health and DuPont R&D for financial support.


Page 17 of 25

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SUPPORTING INFORMATION AVAILABLE 31

P T1 relaxation data, 1D spectra showing the effect of changing temperature and CsCDTA concentration, representative 1 31 2D spectra used to build the database in Table 2, and the Bruker file for the 2D H- P heteronuclear TOCSY sequence are given as supporting information. This material is available free of charge via the Internet at http://pubs.acs.org.

REFERENCES (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15)

Lutz, N. W.; Cozzone, P. J. Anal. Chem. 2010, 82, 5433–5440. Bosco, M.; Culeddu, N.; Toffanin, R.; Pollesello, P. Anal. Biochem. 1997, 245, 38–47. Culeddu, N.; Bosco, M.; Toffanin, R.; Pollesello, P. Magn. Reson. Chem. 1998, 36, 907–912. Gard, D. R.; Burquin, J. C.; Gard, J. K. Anal. Chem. 1992, 64, 557–561. Schiller, J.; Arnold, K. Med. Sci. Monit. 2002, 8, MT205–MT222. Yao, L.; Jung, S. J. Agric. Food Chem. 2010, 58, 4866–4872. Fuchs, B.; Schiller, J.; Wagner, U.; Häntzschel, H.; Arnold, K. Clin. Biochem. 2005, 38, 925–933. Kellogg, G. W. J. Magn. Reson. 1992, 98, 176–182. Lutz, N. W.; Cozzone, P. J. Anal. Chem. 2010, 82, 5441–5446. Vosegaard, T.; Nielsen, N. C. J. Magn. Reson. 2009, 199, 146–158. States, D. J.; Haberkorn, R. A.; Ruben, D. J. J Magn Reson 1982, 48, 286–292. Shaka, A. .; Lee, C. .; Pines, A. J. Magn. Reson. 1969 1988, 77, 274–293. Cavanagh, J.; Rance, M. J. Magn. Reson. 1969 1992, 96, 670–678. Meneses, P.; Glonek, T. J. Lipid Res. 1988, 29, 679–689. Wenk, M. R. Nat. Rev. Drug Discov. 2005, 4, 594–610.



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Page 18 of 25

Table 2. Phospholipid entries in the database for automatic assignment from 2D 1H-31P heteronuclear TOCSY experiments employing a mixing time of 70ms. Label TIBP PC PI LPC1 LPC2 GPC PE NAP LPE GPE NAL PA LPA GPA

"db 7,$ [ppm] -0.36 0.14 0.36 0.50 0.59 0.70 0.75 1.05 1.16 1.25 1.54 2.69 3.68 4.03

"db H,$,: [ppm] 1 0.98 3.63 4.04 3.63 3.63 3.63 3.18 3.42 3.19 3.20 3.89 3.92 3.85 3.85

2 3.82 3.98 4.19 4.00 4.27 3.84 4.05 3.88 4.07 4.07 3.41 4.19 4.12 3.82

3 1.98 4.26 3.73 4.26 3.86 4.27 3.98 3.96 3.92 3.84 4.12 4.44 4.00 3.58

4 4.16 3.46 3.72 3.92 3.92 4.17 4.41 3.86 3.60 5.24

5 4.42 3.27 4.13 4.42 4.17 3.92

6 5.23 3.88 4.01 5.22

7 5.25

8 4.44

23 1, [%]

1 100 100 100 100 100 100 100 100 100 100 100 100 100 100

2 36 64 40 56 55 54 56 88 82 78 63 15 77 100

3 12 55 39 54 35 53 44 67 38 70 37 15 42 59

4 13 38 43 34 49 12 18 36 56 15

5 13 33 28 12 17 47

6 8 31 14 9

7 28

8 28


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 48 49 50 51 52 53 54 55 56 57 58 59 60


Digestion time

Page 19 of 25

2.5

2.0

1.5 1.0 P Chemical shift

31

0.5

0.0



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 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47

1H

90˚

Page 20 of 25

tm t1

φ1 31P

180˚

DECOUPLING φ3 t2

φ2

φ4


φrec

Page 21 of 25

3.0

H Chemical shift

3.5

LPE

4.0

UI GPE

4.5

NAP

1

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


LPC1 GPC LPC2 PE

PI

PC

5.0

1.8

1.6

1.4

1.2 1.0 0.8 31 P Chemical shift

0.6

0.4

0.2



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

+++/++ + x

LPC2

2.68

0.5

0.3

PE

2.64

LPA

GPE

GPA UI

LPC1

PO4 3.5

3

PI

LPE2

PA

4

TIBP

GPC 0.7

2.72

PC

2.5 2 1.5 31 P Chemical shift

1

0.5

0


Page 22 of 25

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Measured Concentration in mM

Page 23 of 25

30 25

R2 = 0.9966

20

A PC PE PI LPE LPC

15 10 5 0 0

5

10

15

20

25


B PC PE PI LPE LPC

30


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 48 49 50 51 52 53 54 55 56 57 58 59 60

PC

LPA

Page 24 of 25

PI PE

GPA 6

4

2

PA

GPE

LPC1

LPE GPC

LPC2

120 min 90 min 60 min 30 min 0 min

5

4 3 2 1 P Chemical shift (ppm)

31


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 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


autoP - automatic -identification -quantification

PC LPC2 GPC PE PI LPE2 GPE LPC1 1

0.5

0


PC PI PE GPC

2.37 mM 2.55 mM 2.10 mM 3.73 mM …

Page 25 of 25

High Throughput Identification and Quantification of Phospholipids in

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