1H high resolution magic-angle coil spinning (HR-MACS) μNMR metabolic profiling of whole Saccharomyces cervisiae cells: a demonstrative study

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(HR-MACS) µNMR metabolic profiling of whole

Saccharomyces cervisiae cells: a demonstrative study

Alan Wong, Céline Boutin, Pedro M. Aguiar

To cite this version:

Alan Wong, Céline Boutin, Pedro M. Aguiar. 1H high resolution magic-angle coil spinning

(HR-MACS) µNMR metabolic profiling of whole Saccharomyces cervisiae cells: a demonstrative study.

Frontiers in Chemistry, Frontiers Media, 2014, 2, pp.1. �10.3389/fchem.2014.00038�. �hal-01157465�

1

_{H high resolution magic-angle coil spinning (HR-MACS)}

µNMR metabolic profiling of whole Saccharomyces

cervisiae cells: a demonstrative study

Alan Wong1_{*, Céline Boutin}1_{and Pedro M. Aguiar}2 1

CEA Saclay, DSM, IRAMIS, UMR CEA/CNRS 3299 – NIMBE, Laboratoire Structure et Dynamique par Résonance Magnétique, Gif-sur-Yvette, France 2

Department of Chemistry, University of York, Heslington, York, UK

Edited by:

Martina Vermathen, University of Berne, Switzerland

Reviewed by:

Xue-Mei Li, Linyi University, China Valeria Righi, University of Bologna, Italy

*Correspondence:

Alan Wong, CEA Saclay, DSM, IRAMIS, UMR CEA/CNRS 3299 – NIMBE, Laboratoire Structure et Dynamique par Résonance Magnétique, F-91191, Gif-sur-Yvette, France

e-mail: alan.wong@cea.fr

The low sensitivity and thus need for large sample volume is one of the major drawbacks of Nuclear Magnetic Resonance (NMR) spectroscopy. This is especially problematic for performing rich metabolic profiling of scarce samples such as whole cells or living organisms. This study evaluates a 1H HR-MAS approach for metabolic profiling of small volumes (250 nl) of whole cells. We have applied an emerging micro-NMR technology, high-resolution magic-angle coil spinning (HR-MACS), to study whole Saccharomyces

cervisiae cells. We find that high-resolution high-sensitivity spectra can be obtained with

only 19 million cells and, as a demonstration of the metabolic profiling potential, we perform two independent metabolomics studies identifying the significant metabolites associated with osmotic stress and aging.

Keywords: micro-NMR, HR-MACS, Metabolic Profiling, Saccharomyces cervisiae, osmotic stress, cell growth

INTRODUCTION

1_{H nuclear magnetic resonance (NMR) spectroscopy has gained}

recognition as a key analytical technique for metabolic profiling of complex bio-systems (Dunn and Ellis, 2005). The non-destructive nature, simplicity of sample preparation and data acquisition provide advantages over other methods including mass spec-trometry (MS) for high-precision investigation of metabolites in living specimens. Moreover, the observed signal intensities in a typical 1H NMR spectrum provide a direct comparison of the metabolite contents in the samples without the need to construct calibration curves for every analyte, which is often the case for other analytical techniques. For these reasons, 1_{H NMR}

spec-troscopy is widely used in the study of metabolomes, offering a robust tool for rich-metabolic profiling (Reo, 2002). Many sam-ples of interest are highly complex bio-mixtures (e.g., biopsies, whole living cells and organisms) and the heterogeneity in the magnetic susceptibility over the sample volume results in broad-ening of the observed NMR resonances; significantly reducing the ability to identify and quantify the metabolic content using traditional high-resolution NMR techniques. For such samples the application of1H-detected high-resolution magic-angle spin-ning (HR-MAS) NMR, has now emerged as a powerful analytical tool for the investigation of heterogenous samples such as intact biopsies (Lindon et al., 2009; Sitter et al., 2009; Beckonert et al., 2010) and whole living organisms (Blaise et al., 2007; Righi et al., 2010). The rapid spinning of the sample (ca. 2–6 kHz) about an axis at an angle of 54.74◦ (i.e., the magic-angle) with respect to the static magnetic field B0overcomes the broadening and yields

well-resolved signals. 1_{H HR-MAS NMR is considered a near}

universal technique for providing unbiased and high-precision

fingerprints of abundant metabolites in heterogenous biosamples. Despite its utility, there are only a handful of studies on whole cells, mainly with robust eukaryotic cells such as marine unicellu-lar microalgae cells (Chauton et al., 2003a,b) and bacterial cells (Himmelreich et al., 2003; Gudlavalleti et al., 2006; Palomino-Schätzlein et al., 2013; Righi et al., 2013), whose cell membranes provide additional protection and structural support significantly enhancing cell survival rates during the measurement. In a review byLi (2006), he has summarized a progress for in vivo studies of intact bacterial cell using multidimensional NMR experiments with HR-MAS. This paucity of examples for HR-MAS studies of whole cells, compared to tissues, is due in large part to the poten-tial for cell lysis as a result of the large centrifugal forces that the cells are subject to under the rapid sample spinning. This is espe-cially problematic for fragile animal and human cells, and can result in distortion of the intracellular metabolic composition. Thus, high-resolution NMR of cell extracts has been the preferred method for cell studies. However in this case, the cells must be subject to complicated chemical treatment protocols to extract specific metabolites; hydrophilic metabolites in aqueous extracts and lipophilic in organic extracts, and require larger sample quan-tities for the multiple NMR samples and experiments to obtain a rich profile of the metabolic response. Unlike with the analysis of extracts, it is unnecessary to chemically divide the hydrophilic and hydrophobic metabolites for HR-MAS, offering a direct ana-lytical approach. Palomino-Schätzlein et al. have optimized a HR-MAS protocol for the study of whole cells using abundant

Saccharomyces cervisiae cells and reported similar metabolic

pro-files to those obtained with high-resolution NMR of cell extracts (Palomino-Schätzlein et al., 2013).

NMR is an inherently insensitive technique, thus HR-MAS analysis often relies on large sample volumes for detection; typ-ically about 100 million whole cells in a 30–50μl volume for each of 3–5 replicate samples (for statistical analyses). In cases where sample size is limited (such as neuron cells), analysis of fewer cells—in a smaller volume—would ease the sample prepa-ration and may improve the high-throughput efficiency (e.g., coupling with micro-fluidic devices for cell separation tech-niques). One promising approach for volume-, or mass-, limited bio-specimens is the uses of a high-resolution magic-angle coil spinning (HR-MACS) (Wong et al., 2012, 2013). HR-MACS, as with the original MACS experiment (Sakellariou et al., 2007), utilizes a secondary tuned circuit and a simple and robust rotor insert, designed to fit inside a standard MAS sample rotor to convert the MAS probe into aμMAS probe without any probe modification. HR-MACS can be readily coupled with any stan-dard HR-MAS probe making it assessable to any laboratory with HR-MAS facilities. The use of a filling-factor optimized detector, susceptibility-optimized inserts and simultaneous spinning of the sample and detector have been demonstrated to yield high sensi-tivity and excellent spectral resolution (up to 2 ppb) allowing for high-precision metabolomic assessments of a sample volume less than 500 nl (Wong et al., 2013). Moreover, the reduced diameter of the sample also abates the centrifugal forces exerted on the cells under sample spinning diminishing the chances of cell lysis due to sample spinning.

The present study capitalizes on the new development of HR-MACS for high-sensitivity and high-resolution1H μNMR-based metabolomics study of whole cells. We build upon the1_H

HR-MAS study of whole S. cervisiae cells (Palomino-Schätzlein et al., 2013) to evaluate the utility of1H HR-MACS for profil-ing the metabolic composition of a small number of S. cervisiae cells (ca. 19 million cells in a 250 nl volume). We have performed two independent studies, monitoring the metabolic responses in

S. cervisiae wild-type cells under osmotic stress and cell growth.

There are a total of four cell groups submitted to the two studies: 24, 48, and 72-h cell growth and a 24-h cell growth subjected to a 60-min period of osmotic stress.

MATERIALS AND METHODS

SACCHAROMYCES CERVISIAE

Wild-type (WT) cells were grown in a glucose-rich medium YPD (1% yeast extract, 2% peptone, 2% glucose) at 30◦C on a shaker (180 rpm). Each sample was inoculated with the same initial cul-ture and with an initial OD600of 0.1 and grown for 24, 48, and

72 h. For the osmotic cell stress study, 0.5 M NaCl (final concen-tration) was added to the 24-h culture cells for 60 min. Prior to the NMR sample preparation, ca. 300× 106cells were washed 3 times with ion-free distilled water. The cell pellet was re-suspended in 4μl of D2O for an immediate NMR acquisition. The use of high

cell concentration was utilized to ensure significant numbers of cells were being transferred to the microsize capillary.

HR-MACS RESONATOR

A single sample-exchangeable HR-MACS resonator (Figure 1) was used in the study for data acquisition. The resonator was constructed by manually winding a 2 mm long (9-turn solenoid),

FIGURE 1 | A photo-illustration of the sample-exchange HR-MACS setup used in this study.

using 30-μm o.d. round copper wire around a 840/600-μm (outer/inner diameter) quartz capillary. The solenoid was then fixed in place with a thin layer of cyanoacrylate glue. A non-magnetic 2.2 pF capacitor (American Technical Ceramics, US) was soldered to the coil leads and affixed to the end of the capillary with cyanoacrylate glue. The resonator had a resonance frequency of 508 MHz with an unloaded coil quality factor, Q= 26. The resonator was secured in a Kel-F insert which fits tightly inside a Bruker ZrO24-mm rotor allowing for an easy sampling with a

550/400-μm quartz capillary.

SAMPLE-PREPARATION

The sample preparation for HR-MACS was performed under a stereomicroscope. The S. cervisiae cells (ca. 300× 106cells in 4μl of D2O) were pipetted into a 550/400-μm capillary sealed at one

end (with epoxy) using a micropipette equipped with a 20μl GELoader® tip (Eppendorf, US). Very gentle centrifugation was applied for 2–5 s to remove air bubbles and ensure the cells dis-placed into the solenoid region. Within the coil detection region there are ca. 19× 106cells. The top of the sample capillary was then sealed with hot paraffin wax to prevent leakage during sam-ple spinning, and inserted into the HR-MACS resonator already fitted inside the MAS rotor. The entire sample-preparation pro-cedure was restricted to no more than 2 min to minimize the chances of sample tampering and degradation.

1_{H HR-MACS NMR SPECTROSCOPY}

1_{H NMR experiments were performed on a widebore 11.7 T}

magnet equipped with a Bruker Avance II Spectrometer operat-ing at 499.13 MHz with a standard Bruker 4-mm HX CP-MAS probe (with a HR-MACS resonator placed inside the probe). The MAS frequency was set using the manual settings of a standard Bruker MAS spin controller to 300± 1 Hz. The B0

shim-ming was performed on a sucrose-D2O sample under slow MAS

and re-checked every five samples. The 90◦-pulse for HR-MACS was 5.3μs at 2 W of applied radio-frequency amplitude. An 8-step 2D-PASS sequence was used (Antzutkin et al., 1995), as

previously demonstrated with the addition of water-suppression (Wong et al., 2010, 2013), to acquire high-resolution sideband-and water-free spectra for all samples. 1_{H PASS NMR}

spec-tra were acquired with 8 t1 increments with each consisting of

96 co-added scans for 20 k data points over a spectral width of 20 ppm. A 1 s recycle delay was used resulting in a 26-min experiment time. The experiments were performed under sample temperature regulation at 10◦C.1_{H chemical shifts were}

inter-nally referenced to the alanine—CH3—doublet atδ = 1.47 ppm.

For each group,1H NMR spectra of five replicate samples were recorded to ensure reproducibility and reliability of the spectral data for interpretation.

MULTIVARIATE DATA ANALYSIS

To reduce the complexity of the NMR data for the subsequent multivariate analysis, the spectra were reduced to 0.005 ppm-wide buckets over the spectral region between 0.87 and 9.00 ppm, with exclusion of the water region (4.7–5.1 ppm), and normalized by the total sum of intensities using MestReNova v8.1.4. Principal component analysis (PCA) and orthogonal partial least-square discriminant analysis (OPLS-DA) were applied to check the data homogeneity and identify the latent patterns and biomarkers using SIMCA-P 13 (Umetrics, Umea, Sweden). Variable data were centered prior to PCA and OPLS-DA.

RESULTS AND DISCUSSION

1_{H HR-MACS NMR SPECTRA}

The1H HR-MACS NMR spectra for all cell groups in this study (cell growth and osmotic study) are shown in Figure 2A. The

overall spectral profiles and the resolution are consistent with the HR-MAS study using 30μl volumes and ca. 75 million cells (Palomino-Schätzlein et al., 2013). Despite using a CP-MAS rather than a HR-MAS probe with field-locking capabilities, we nonetheless obtained excellent quality as previously shown (Wong et al., 2013), but better performance is expected with a HR-MAS probe. The use of the PASS experiment under slow MAS (300 Hz), which consists a train of 180-degree refocussing pulses within one rotor period (3.3 ms), provides a T2-filter similar to

the T2-edited CPMG experiments for suppressing signals from

large molecules that may mask the metabolite signals and dis-tort the baselines. All cell groups were found to have high glucose (δ = 3.5–4.0 ppm) contents, which is attributed to the use of a glucose-rich growth medium (YPD) for the cultivation of cells. The observable fine J-splittings in many resonances are measure-able from the spectra. For example, the distinct doublets of valine at 0.98 and 1.04 ppm (J= 7.3 Hz), and the triplet of α-glucose at 3.45 ppm (J= 9.6 Hz) are apparent in all HR-MACS spectra. The excellent spectral resolution allows extraction of this vital second parameter—in addition to chemical shift—permitting greater precision in peak assignments. A total of 22 metabolites have been identified from the two studies and are summarized in Table S1 of the supporting information (SI). The capability of such rich-profiling from a sub-microliter sample volume (250 nl) is owed to the fact that HR-MACS offers a 4.8-fold sensitivity enhancement in signal-to-noise (SNR) per-unit-mass compared to the coupled HR-MAS probe. This enhancement factor has been calculated based on BHRMACS₁ /BHRMAS₁ at a given radio fre-quency input power (Hoult, 2000), where the B1 field can be

FIGURE 2 | (A)1_{H HR-MACS NMR spectra of the four different cell groups. (B) PCA score plot of all NMR datasets showing the quality of the sub-spectra}

determined from a standard nutation experiment. We found that with an estimated 19 million cells in 250 nl, we obtained an aver-age SNR (using the 2.5–2.0 ppm spectral region) of 120± 10 for a 26-min experimental time. Such high SNR should allow for further reductions in sample size, or in signal-averaging for sam-ples which are prone to rapid decay. Although, there are multiple factors that contribute to the SNR using the inductively-coupled HR-MACS resonator such as coil volume, sampling spin and volt-age noise (more detailed descriptions can be found in SI), using our measured SNR and a value of 10:1 (SNR) as the minimum required for metabolite analysis, these results would indicate that with this HR-MACS spectra should be obtainable from ca. 2 million cells under the same experimental conditions.

Visual inspection of the spectra in Figure 2A allows discrimi-nation of the spectral differences among the different cell groups. For example, higher intensities are found in the region between 3.5–4.0 ppm (glycerol and α-glucose) in the 24-h-NaCl group (cells under osmotic stress); larger signals at 1.0 ppm (valine) and at 3.2 ppm (phosphorylcholine and glycerophosphocholine) are also observed in the 72-h cells. Upon a more careful inspection, additional metabolites (choline, creatine, tyrosine, and phenylala-nine) are identified in the aging cells as compared to stressed cells (see Table S1). An unsupervised PCA score plot is shown in

Figure 2B. It reveals partitioning into four discrete clusters

corre-sponding to the different cell groups. Interestingly, the plot also reveals a clear distinction between the two independent studies, with the corresponding cells responding in an opposite fashion along the first principal component (PC1). The PCA loading plot (Figure S1b), provides further evidence for metabolite differences between the two studies. As the cells age both phosphorylcholine and glycerophosphocholine increase; whereas, the metabolites glycerol, glucose and glutamine show the largest response to the osmatic shock induced by the additional NaCl.

METABOLIC PROFILING OF S. CERVISIAE WT CELLS UNDER OSMOTIC STRESS

Figure 3A shows a HR-MACS spectral comparison between the

non-stressed and stressed cell groups. Both are average spectra, constructed using 5 replicate samples for each group. The dif-ference spectrum, S, consists only of the metabolites which vary under a 60-min osmotic shock; positive signals indicate an increase, and negative signals a decrease in a given metabolite upon osmotic shock. Substantial increases are found for both glu-cose and glycerol, and a decrease in a few amino acids (lysine, arginine, and valine) is observed. These changes have been quan-tified (Table S2 of SI) and are shown graphically in the spectral integral analysis of Figure 3B. For a more accurate metabolic analysis of the latent patterns, the acquired spectra were sub-jected to a supervised statistical analysis using OPLS-DA. The score plot (Figure 3C) displays a clear discrimination between the two groups. Interestingly, it shows a greater metabolic vari-ation (i.e., along the orthogonal component torth) among the

non-stressed cells agreeing with the observed scatter pattern in the unsupervised PCA (Figure 2B). The corresponding loadings plot (Figure 3D) illustrates the metabolites associated with the sepa-ration in the score plot. As with theS spectrum for Figure 3A, the positive signals are associated with the metabolites in higher

concentration for the stressed cells and the negative peaks rep-resent higher concentration in the non-stressed cells. The color scale provides a measure of the correlation of that spectral region’s (i.e., metabolites’) variation to the multivariate discrimination. Similar to the spectral comparison in Figures 3A,B, significant productions of glucose and glycerol metabolites are evident in the stressed cells (in agreement with the PCA loading plot Figure S2). This is inline with studies which have found that glycerol plays an osmoregulatory role in yeast cells (Tamas et al., 1999; Costenoble et al., 2000; Dihazi et al., 2004), and is produced when the yeast cells are subject to osmotic shock. The OPLS-DA analysis also reveals the latent metabolic changes; decreases in select amino acids (lysine, aspartate, arginine, and valine) may suggest an altered glucose metabolism that involves pyruvate pro-duction. Unfortunately, the chemical shift signature (a singlet at 2.36 ppm) for pyruvate is not readily identifiable in the spectra. The metabolic results obtained from1H HR-MACS are coherent with the previous1H HR-MAS study (Palomino-Schätzlein et al., 2013) using a large sample volume with greater number of cells, validating the used of HR-MACS.

METABOLIC PROFILING OF S. CERVISIAE WT CELLS AT THREE DIFFERENT STAGES OF CELL GROWTH

We also monitored the metabolic profiles at three different stages of cell growth: 24-, 48- and 72-h. Typically, the S. cervisiae cells are in the exponential growth phase between 24- and 48-h inter-val, and reach a steady stage at 48-h. At 72-h, the cells may begin to breakdown the nutrients. Figure 4A shows the average spectral comparison (n= 5) between the different stages, with a differen-tialS spectrum of 24- and 72-h cell groups; the positive signals indicate an increase, and negative peaks a decrease of a given metabolite in the 72-h cell groups. Figure 4A clearly exhibits a metabolic spectral profile that is different from that for the stressed cells, suggesting that the metabolic variations are differ-ent in both studies. Increases in the metabolite contdiffer-ent of ethanol, lipid, lactate, alanine, arginine, creatine, phosphorylcholine, and glycerophosphocholine are accompanied by decreases mainly in glutamate and glutamine. These changes are also found in the OPLS-DA analysis (Figure 4B), but with additional quantifica-tion of their contribuquantifica-tion to the variaquantifica-tion. The producquantifica-tion of ethanol and depletion of glucose are attributed to the fact that the

S. cervisiae cells are cultivated in a glucose-rich medium,

result-ing in initiation of the fermentation process (utilizresult-ing glucose and producing ethanol) and repression of respiration. The biosyn-thesis of the phosphocholine derivatives, the major phospholipid component of eukaryotic cell membranes, as well as choline are derived from its synthesis and catabolism (phosphatidylcholine metabolism) contributing to cell growth (Howe and McMaster, 2001).

It should be emphasized that good spectral quality data is vital for reliable metabolic identification and assessment, and acqui-sition of high quality data from small sample volumes is not a trivial task. It is often hindered by a lack of sensitivity and/or resolution, due to poor sample filling factor, or magnetic sus-ceptibility gradients. The success of acquiring excellent spectral quality with HR-MACS is because it offers a near optimal fill-ing factor and minimizes susceptibility-induced broadenfill-ing by

FIGURE 3 |1_{H HR-MACS NMR study of stressed cells. (A) Spectral}

comparison of averaged spectra (n= 5): red corresponds to stressed cells and blue to non-stressed cells. The S spectrum is the spectral difference between stressed and non-stressed cells, where the positive peaks correspond to the increased in metabolite contents in stressed cell, and the negative peaks in the non-stressed cells. The assignments including the abbreviations are listed in Table S1 in SI. (B) Bar-graph

representation of the relative integral in arbitrary units for different metabolites. The results are reported with the mean values and the standard deviation as error bars. The p-value for each metabolite comparison is <0.001 with the exception of alanine and ethanol, which are stated in the bar-graph. Detailed information can be found in Table S2. (C) OPLS-DA score and (D) loadings plots with Q2_{= 0.987,} R2_{Y(cum)= 0.995 and R}2_{X(cum)= 0.965.}

ensemble magic-angle spinning of the resonator with the sample. To our knowledge, there are no other analytical techniques capa-ble of offering rich-metabolic analyses with such a small sample volume (250 nl) without damaging or interfering with the sample anatomy, including MS.

We would like to briefly discuss the benefits and drawbacks of HR-MACS for small-volume analysis of cells. HR-MACS offers a simple and cost-effective approach to high-qualityμNMR anal-ysis (materials cost for one coil< C20). The combination of slow sample spinning (300 Hz) and small sample diameter (i.e., 400μm i.d. HR-MACS) minimize the centrifugal forces exerted upon the cells under rotation, thus making it amenable to analysis of more fragile cells. Another advantage is reduction in numbers of required cells; a 10-fold reduction in total cell quantity from that typically used (i.e., 20 samples of 100× 106cells) should be

readily achievable.1H-detected multiple-resonance NMR experi-ments are also compatible with the HR-MACS approach (Aguiar et al., 2011), permitting the use of HSQC (and related) experi-ments for in-cell NMR spectroscopic investigations of the bioac-tivities inside the living cells (Li, 2006; Maldonado et al., 2011). The ability to analyze nano-scale volumes of cells opens new opportunities for coupling with cell-sorting microfluidic devices for a potent NMR cell screening. Microfluidic 1_{H NMR has}

recently emerged as a micro-NMR diagnostic device by differ-entiating the spin relaxation mechanisms between healthy and unhealthy cells; however, it yields minimal spectral information due to the poor resolution (Haun et al., 2011).

Although the cost is low, the manufacture of the small and delicate HR-MACS resonator can be challenging. Replacing the manual fabrication of HR-MACS with an automated system may

FIGURE 4 |1_{H HR-MACS NMR study of cell growths. (A) Spectral}

comparison composed of averaged spectra (with n= 5): green corresponds to cell growth at 24-h; red at 48-h; and blue at 72-h. TheS spectrum is the

spectral difference between 72 and 24-h cells. The positive peaks correspond to the increased in metabolite contents in 72-h cells, and the negative peaks in the 24-h cells. Both the assignments and abbreviations are listed in Table S1. (B) OPLS-DA loading with Q2_{= 0.986, R}2_Y(cum)=

0.986 and R2_{X(cum)= 0.857.}

make this technique more widespread (Malba et al., 2003; Badilita et al., 2012). The very-low absolute metabolite contents—despite the fact that HR-MACS enhances the detection sensitivity— hinder their detection, without resorting to greater signal aver-aging and thus, longer experiment times. Coupling with other sensitivity enhancement techniques (i.e., nuclear polarization) would shorten the experimental times. Another option to further improve the sensitivity is to build a standaloneμHR-MAS probe (without HR-MACS) for eliminating the any noise contribution from the HR-MACS resonator itself; however, all above options would involve complex instrumentation developments.

CONCLUDING REMARKS

In this short report, we have demonstrated, for the first time, the

1_{H NMR metabolic profiling of a small quantity of whole cells}

on a nanolitre volume. The1H NMR spectra acquired with HR-MACS are of excellent-quality and exhibit high-reproducibility facilitating a rich-metabolic profiling. The two cellular studies demonstrated here illustrate the ability of1_{H HR-MACS coupled}

with statistical methods to provide high precision metabolic anal-yses of S. cervisiae cells in different conditions. Under an osmotic shock, the bio-production of glucose and glycerol metabolites and depleting glutamate and glutamine in S. cervisiae cells is evident.

Significant increases in phosphatidylcholine and glycerophospho-choline are found in relation to cell growth and the production of ethanol via the fermentation process in the aging cells is also evident. The metabolic results found in the stressed cells using HR-MACS are in agreement with the previous HR-MAS study, validating the use of HR-MACS for small cell quantity study.1H HR-MACS NMR spectroscopy opens new possibilities for high-precision investigation of small-volume sample, and may widen the scope of yeast metabolome and other bacterial cells, and the limited number of neuron cells in organisms. The ability to acquire1H NMR spectra of such small volumes puts HR-MACS closer to the scales utilized for microfluidic-based cell sorting and manipulation techniques, facilitating their coupling for a potent micro-scale cell NMR sample screening pipeline.

ACKNOWLEDGMENTS

We would like to thank the French National Research Agency for financial support under ANR33HRMACSZ; and Mr. Angelo Guiga (Saclay, France) for fabricating the Kel-F insert.

SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: http://www.frontiersin.org/journal/10.3389/fchem. 2014.00038/abstract

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Conflict of Interest Statement: The authors declare that the research was

con-ducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Received: 25 April 2014; accepted: 28 May 2014; published online: 12 June 2014. Citation: Wong A, Boutin C and Aguiar PM (2014)1_{H high resolution} magic-angle coil spinning (HR-MACS)μNMR metabolic profiling of whole Saccharomyces cervisiae cells: a demonstrative study. Front. Chem. 2:38. doi: 10.3389/fchem. 2014.00038

This article was submitted to Analytical Chemistry, a section of the journal Frontiers in Chemistry.

Copyright © 2014 Wong, Boutin and Aguiar. This is an open-access article dis-tributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this jour-nal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

1

_{H High Resolution Magic-Angle Coil Spinning (HR-MACS) µNMR Metabolic Profiling of}

whole Saccharomyces cervisiae cells: A Demonstrative Study

Alan Wong

1

*, Céline Boutin

1

, Pedro M. Aguiar

2

1

_{CEA Saclay, DSM, IRAMIS, UMR CEA/CNRS 3299 – NIMBE, Laboratoire Structure et}

Dynamique par Résonance Magnétique, F-91191, Gif-sur-Yvette Cedex, France.

2

_{Department of Chemistry, University of York, Heslington, York, YO10 5DD, United Kingdom}

Table of Contents

Table S1 : Peak Assignments and J-splittings

S2

Table S2 : Metabolite integrals of stressed and non-stressed cells

S3

Figure S1 : PCA score and Loading plots

S4

Relation of SNR to coil volume and detectable nuclear spin

S5

1

_{H shift / ppm}

(multiplicity)

a

metabolite

abbreviation

observable

J-splitting / Hz

0.84(t)

lipid CH3

Lip

0.88(t)

lipid CH3

Lip

∼7.0

0.93(t)

isoleucine

Ile

–

0.98(d)

valine

Val

7.3

1.01(m)

isoleucine

Ile

–

1.04(d)

valine

Val

7.3

1.19(t)

Ethanol

EtOH

7.4

1.27(m)

lipid –(CH

2

)

n

–

Lip

–

1.32(d)

lactate

Lac

6.4

1.47(d)

alanine

Ala

7.6

1.57(m)

lipid –(CH

2

)

n

CO

Lip

–

1.65(m)

arginine

Arg

–

1.72(m)

lysine/lipid CH

2

CH

2

C=C

Lys/Lip

–

1.90(m)

arginine/lysine

Arg/Lys

–

1.93(s)

acetate

Ace

–

2.01(m)

lipid CH

2

C=C

Lip

–

2.06(m)

glutamate

Glu

–

2.12(m)

glutamine

Gln

–

2.23(m)

lipid CH

2

CO

Lip

–

2.36(t)

glutamate

Glu

–

2.45(m)

glutamine

Gln

–

2.56(m)

unknown

–

2.68(m)

aspartate

Asp

–

2.73(m)

lipid C=CCH

2

C=C

Lip

–

2.76(m)

lipid C=CCH

2

C=C

Lip

–

2.81(dd)

aspartate

Asp

∼17.0, ∼3.2

2.95(m)

Glutathione

GSH

–

3.04(m)

creatine (in 48- and 72-hr)

Cr

–

3.04(t)

lysine

Lys

∼8.0

3.19(s)

choline (in 48- and 72-hr)

Cho

–

3.21(s)

PC

PC

–

3.22(s)

GPC

GPC

–

3.24(m)

arginine

Arg

–

3.26(2)

betaine

Bet

–

3.45(t)

α-glucose

α-Glc

9.6

3.55(dd)

glycerol

Gro

11.8, 6.7

3.61(d)

valine

Val

4.6

3.64(dd)

glycerol

Gro

∼10.3, ∼2.7

3.68(m)

choline (lipid) (in 72-hr)

Cho

–

3.77(m)

α-glucose

α-Glc

–

3.83(m)

α-glucose/glycerol

α-Glc/Gro

–

3.95(m)

creatine (in OPLS-DA 72-hr)

Cr

–

5.19(d)

α-glucose

α-Glc

3.0

5.31(m)

UFA

UFA

–

6.88(d)

tyrosine (in 72-hr)

Tyr

7.3

7.18(d)

tyrosine (in 72-hr)

Tyr

7.0

7.32(m)

phenylalanine (in 72-hr)

Phe

–

7.37(m)

phenylalanine (in 72-hr)

Phe

–

UFA = unsaturated fatty acids; PC = phosphorylcholine; GPC = glycerophosphocholine

a) The assignments are based on previous assignments [1]. Abbreviations: s = singlet; d = doublet; dd

= doublet of doublets; t = triplet; m = multiplet

metabolite

integral region

mean integral

(n=5)

STD

mean integral

(n=5)

STD

∆integral

(stressed – control)

b

p-value

α-Glu

5.202-5.177

3.482–3.420

13.19

0.18

22.23

0.22

+9.04

<0.001

Gro

3.577–3.523

0.60

0.22

5.57

0.61

+4.97

<0.001

GPC/PC

3.228–3.209

3.49

0.06

2.41

0.10

–1.08

<0.001

Lys

3.081–3.029

3.92

0.27

2.56

0.14

–1.36

<0.001

Glu

2.379–2.328

25.31

0.11

25.98

0.29

+0.68

<0.001

Ala

1.491–1.460

6.91

0.82

7.42

0.34

+0.51

0.025

EtOH

1.176–1.136

3.45

0.43

3.98

0.87

+0.53

0.151

Val

1.049–1.019

1.000–0.969

16.74

0.13

11.69

0.28

–5.05

<0.001

(a) Data are plotted in the bar-graph in Fig. 2b. (b) Positive indicates higher metabolite content in stressed cells, negative higher in the control cells. (c)

Analyzed by the Student’s t-test with a significant level at p < 0.05.

Figure S1. PCA score and its corresponding loading plot of all

1

H HR-MACS data acquired for the two studies, cell stress and cell growth. (R

2

X(cum) =

0.793; Q

2

_{(cum) = 0.737).}

PC2

-25

-20

-15

-10

-5

0

5

10

15

20

-30

-20

-10

0

10

20

PC1

72h

48h

24h-NaCl

24h

a) PCA-Score Plot

cell

gr

owth

cel

l str

_es

s

0.87 0.875 0.88 0.885 0.89 0.895 0.9 0.905 0.91 0.9150.92 0.9250.930.940.9350.9450.95 0.955 0.96 0.965 0.97 0.975 0.98 0.985 0.99 0.995 1 1.0051.021.011.015 1.025 1.03 1.035 1.04 1.045 1.05 1.055 1.061.065_1.07 1.075 1.08 1.085 1.091.095 1.1 1.1051.111.1151.121.1251.131.135 1.14 1.1451.15 1.1551.161.1651.17 1.175 1.181.185 1.19 1.1951.2 1.205 1.21 1.215 1.221.2251.231.2351.24 1.2451.25 1.255 1.26 1.265 1.27 1.275 1.28 1.285_1.2951.29 1.3 1.305 1.31 1.3151.32 1.325 1.331.335 1.34 1.345 1.35 1.355 1.36 1.365 1.37 1.375 1.38 1.385 1.39 1.395 1.41.4051.461.411.4151.4551.421.451.4251.4451.431.4351.44 1.465 1.47 1.475 1.48 1.485 1.49 1.495 1.5 1.505 1.511.515 1.521.5251.53 1.5351.541.545 1.55 1.555 1.56 1.565 1.57 1.575_1.58 1.585_1.591.595 1.6 1.6051.611.6151.621.6251.631.641.635 1.6451.65_1.6551.665_1.671.66_{1.6751.681.685}1.691.6951.71.7051.71 1.7151.72 1.725 1.73 1.735 1.74 1.745 1.75 1.755 1.76 1.7651.771.775 1.781.7851.791.7951.81.8051.8151.811.821.841.831.8351.8251.8451.851.8551.861.8651.871.875 1.881.8851.89 1.8951.911.91.905 1.915 1.92 1.925 1.93 1.935 1.94 1.945 1.95 1.955 1.96 1.965 1.97 1.9751.9851.98 1.991.995 2 2.0052.0152.01_2.02 2.025 2.03_2.035 2.04 2.0452.052.055 2.06 2.065_{2.0752.082.085}2.07 2.092.1052.0952.1 2.112.1152.1252.142.1452.122.135_2.1552.152.13 2.16_2.172.165_2.1752.215_2.1852.192.23_2.3052.312.212.222.225_{2.182.1952.205}2.235_2.22.32.2852.2452.24_{2.272.2752.282.292.295}2.25_{2.2552.2652.26} 2.315 2.322.325 2.33 2.335 2.34 2.3452.35 2.355 2.36 2.365 2.37 2.375 2.38 2.385 2.39 2.3952.4 2.405 2.412.415 2.422.4252.432.4352.442.4452.452.4552.462.4652.47_{2.552.5452.555}2.4752.48_2.5352.4852.505_2.532.492.5_2.522.542.4952.51_2.5152.525 2.562.5652.572.5752.5952.582.592.62.5852.6052.61 2.615 2.62 2.625 2.63 2.635 2.64 2.645 2.65 2.655 2.66 2.6652.682.782.675_{2.6952.672.6852.692.7}2.7752.772.7652.762.7052.7552.7352.752.742.732.7452.722.71_2.7252.715 2.7852.792.8052.7952.8 2.81 2.815 2.82_{2.8552.9052.852.862.87}2.875_2.8652.8452.8252.882.89_2.92.842.832.8852.8952.835 2.91 2.9152.922.9252.932.935 2.942.952.945 2.955 2.96 2.9652.972.9752.992.9852.982.99533.005 3.013.0153.023.033.025 3.035 3.04 3.045 3.05 3.055 3.063.0753.0653.073.083.0853.093.0953.123.13.143.1153.1253.133.1553.1353.113.1453.153.1053.163.1653.1753.173.183.1853.193.23.2053.1953.21 3.215 3.22 3.21 3.23 3.235 3.24 3.245 3.25 3.255 3.26 3.265 3.27 3.275 3.28 3.285 3.293.295 3.3 3.305 3.313.315 3.32 3.3253.333.3353.34 3.3453.35 3.3553.363.373.365 3.375 3.383.385 3.39 3.395 3.4 3.405 3.41 3.415 3.42 3.425 3.43 3.435 3.44 3.445 3.45 3.455 3.46 3.465 3.47 3.475 3.48 3.485_3.493.535_3.4953.53.5253.533.5053.523.5153.51 3.543.545_3.55 3.555_3.56 3.565 3.57 3.575 3.58 3.585_3.59 3.5953.6 3.605 3.613.6253.623.615 3.63 3.635 3.64 3.645 3.65_3.655 3.663.665 3.67 3.675 3.68 3.685 3.69 3.695 3.7 3.705 3.71 3.7153.723.725 3.73 3.735 3.74 3.745 3.75 3.755 3.763.765 3.77 3.775 3.78 3.785 3.79 3.795 3.8 3.805 3.81 3.815 3.82 3.825_3.8353.83 3.84 3.845 3.85 3.855 3.86 3.865 3.87 3.8753.88 3.885 3.89 3.895_3.9 3.905 3.91_3.933.915_3.9253.9553.92_{3.9353.943.9453.95} 3.96 3.965 3.97 3.975 3.98 3.985 3.993.9954.054.0554.064.0654.0354.04544.0054.034.044.014.0154.024.025 4.07 4.075 4.08 4.0854.094.0954.14.105 4.11 4.115 4.12 4.125 4.13 4.135 4.14 4.145 4.205 4.21 4.215 4.22 4.225 4.23 4.235 4.244.2454.634.6254.5854.584.574.5654.555_4.544.6354.2954.5454.474.4754.494.4954.54.5054.514.5154.524.5254.534.5354.2854.554.464.284.564.5754.264.594.5954.64.6054.614.6154.624.2554.254.34.444.4554.394.3054.314.3154.324.3454.354.3554.364.374.384.3854.3754.3954.4454.294.4354.44.4254.484.344.434.2654.274.2754.424.3654.4154.454.414.4054.4854.3254.4654.3354.33 4.644.6454.654.664.6654.655 4.674.6754.684.6854.69 4.6955.1455.1655.1055.115.1155.1255.1355.145.155.1555.165.125.13 5.17 5.175 5.18 5.185 5.19 5.195 5.2 5.205_5.21 5.2155.225.2855.285.2755.2955.2255.2455.255.245.27_5.2355.235.295.2555.265.265 5.3 5.305 5.31_5.3155.32 5.325 5.33 5.335 5.34 5.345 5.35 5.3555.4155.425.4255.435.4355.4055.415.44 5.445 5.45 5.4555.465.465 5.47 5.475 5.48 5.4855.495.4955.5 5.505 5.51 5.515 5.52 5.5255.53 5.535 5.545.595.6555.645.615.6055.5455.5955.665.555.575.5855.585.5755.5655.565.6155.6255.635.6355.5555.6455.655.65.62 5.6655.675.675 5.68 5.685 5.69 5.6955.7 5.7055.715.715 5.72 5.7255.73 5.735 5.74 5.7455.755.755 5.76 5.765 5.77 5.775 5.78 5.785 5.79 5.7955.85.805 5.81 5.815 5.825.8255.83 5.835 5.845.845 5.85 5.8555.865.8655.875.8755.885.8855.89 5.895 5.95.9055.91 5.9155.925.9255.935.9355.945.9455.95 5.955 5.965.965 5.97 5.975 5.985.985_5.99 5.995 66.0056.01 6.015 6.02 6.025 6.03 6.035 6.04 6.045 6.05 6.055 6.066.0656.076.0856.096.0956.0756.08 6.16.105 6.116.1156.1256.136.1356.12 6.14 6.1456.15 6.155 6.16 6.1656.17 6.175 6.18 6.185 6.19 6.195 6.2 6.2057.0557.1257.127.1157.117.1057.17.0957.097.0857.0757.077.0657.067.0157.13577.0056.9956.996.9356.9256.9156.916.9056.21_6.5956.576.5656.5457.137.146.5357.517.5957.597.5857.587.5757.5657.567.5557.557.5457.547.5357.537.505_7.1457.57.4957.487.4757.4657.467.4557.257.247.2357.237.2257.227.156.547.66.536.416.326.3256.336.346.3456.3556.366.3656.3756.386.4056.4156.316.426.5256.4356.446.4456.4956.496.4856.486.4756.456.466.3156.3356.3056.2856.2556.256.276.2756.2456.246.2356.5056.516.236.266.286.296.2156.36.526.2656.2956.227.0357.037.0457.057.0256.2257.027.016.4556.4257.087.047.576.396.46.9857.4857.497.457.4457.447.3957.277.267.2557.2456.356.3957.2157.217.167.1557.5157.527.5256.376.3857.476.4656.436.986.5856.8256.6356.8456.8556.866.616.6056.66.596.56.926.936.476.586.5756.9756.5556.966.556.5156.826.6457.396.666.6556.656.6156.646.636.6256.627.2056.567.2656.96.856.9456.836.816.8056.8356.946.846.976.9656.956.9557.46.8157.367.3657.377.3757.386.796.687.4056.6857.4157.427.4257.436.6757.4356.716.7056.86.697.417.3856.7957.287.27.1656.7756.7656.766.7556.8657.2756.786.6656.7856.877.2856.676.8856.776.6956.7156.756.746.7356.7257.296.726.896.8756.8957.176.77.37.3557.357.1857.1956.7456.737.187.2957.3456.887.1757.3057.197.347.317.3357.3157.327.3257.33 7.605 7.617.615 7.62 7.6257.647.637.8458.4458.3958.48.4058.418.4158.428.4258.438.448.4558.458.3858.468.4658.638.6358.648.6458.668.6658.678.6758.398.3758.388.248.198.1958.28.2058.218.2158.228.2258.238.2358.2458.6858.258.2558.268.2958.38.3058.318.328.3258.338.3358.688.78.698.928.878.8758.888.8858.898.8958.98.9058.918.9158.9258.868.938.9358.948.9458.958.9558.968.978.9858.998.9958.8658.8558.188.7758.7158.728.738.7358.748.758.7558.768.7658.778.788.858.798.7958.88.8058.818.8158.828.8258.8358.848.8458.18597.897.777.817.8057.87.7957.797.7857.787.7757.7657.827.767.7557.757.7457.747.9557.967.737.7257.8157.8257.997.947.97.9057.917.9157.8857.9257.937.887.9357.9457.837.957.8757.877.8657.867.858.177.847.8357.9757.988.088.1358.0858.118.0758.078.0658.068.0558.1158.058.128.1258.138.0957.698.18.148.0458.048.1458.158.038.0258.028.0158.1558.168.1658.017.6358.7258.7457.8557.6457.7057.657.6757.7157.728.717.77.7357.6957.6857.687.678.987.6658.837.717.668.7858.9657.6558.9758.1758.3158.7058.5658.4357.9788.0058.478.0357.8958.5058.518.5158.358.098.297.9958.278.288.578.618.6957.928.655_8.658.6258.628.5758.6158.6058.68.5958.1058.2657.9858.3458.348.3558.368.3658.378.2858.2758.57.9658.4758.5858.588.568.558.5458.548.598.538.5258.528.4958.5558.498.4858.488.535 -0.05 0 0.05 0.1 0.15 0.2 -0.25 -0.2 -0.15 -0.1 -0.05 0 0.05 0.1 0.15 0.2

PC1

PC2

_GPC/PC

Glu

α-Glc/Gro

b) PCA-Loading Plot

lipid

arg

etc. may be found in the literature) [2]. Assuming no change in the above contributions, we may work

with a simplified expression for S/N, denoted by the following relation:

€

SNR =

peak signal

RMS noise

∝

ω

0 2

_(B

1

i)N

S

V

_noise

(S1)

where B

1

/i is the radio-frequency field generated by the coil per unit current, N

S

denotes the number of

spins, V

noise

is the voltage noise, and w

0

is the resonance frequency. Since

€

V

_noise

∝ r

coil

and the

power dissipation, P, P = r

coil

i

2

, we may re-write equation S1 as the following:

€

SNR =

peak signal

RMS noise

∝

ω

0 2

N

_S

B

1

P

(S2)

The value

€

B

₁

P

varies with the volume and geometry of the solenoid. For a solenoid of given

aspect ratio (ratio of length to diameter) we can take the definition of

€

B

₁

P

given by Clark:

3

€

B

₁

P

∝

Q

V

_coil

ω

0

(S3)

where, Q is the quality factor of the coil and V

coil

is the volume of the coil and substitute into equation

S2 to yield the following:

€

SNR =

peak signal

RMS noise

∝

ω

0 3 2

N

_S

Q

V

_coil

(S4)

Equation S4 allows a direct assessment of the impact of the coil volume and number of spins present

on the signal-to-noise obtainable. It should be noted that in the case of inductively-coupled

micro-coils additional factors such as the regime of the coupling (i.e., under-, critically- or over-coupled)

and the effects of resonance offset from the exact resonance frequency of the spins also play

important roles and have been examined in detail elsewhere [3,4].

1.

(a) Nicholson, J. K.; Foxall, P. J. D.; Spraul, M.; Farrant, R. D.; Lindon, J. C., 750 MHz 1H

and

1

H-

13

C NMR spectroscopy of human blood plasma. Anal. Chem. 1995, 67, 793-811; (b)

Palomino-Schätzlein, M.; Molina-Navarro, M. M.; Tormos-Pérez, M.; Rodríguez-Navarro, S.;

Pineda-Lucena, A., Optimised protocols for the metabolic profiling of S. cerevisiae by

1

H-NMR and

HR-MAS spectroscopy. Anal. Bioanal. Chem. 2013, 405, 8431-8441.

2.

(a) Hoult, D. I.; Richards, R. E., The signal-to-noise ratio of the nuclear magnetic resonance

experiment. Journal of Magnetic Resonance 1976, 24, 71-85; (b) Peck, T. L.; Magin, R. L.;

Lauterbur, P. C., Design and Analysis of Microcoils for NMR Microscopy. J. Magn. Reson. B 1995,

108, 114-124.

3.

Clark, W. G., Pulsed Nuclear Resonance Apparatus. Rev. Sci. Instr. 1964, 35, 316-333.

4.

Jacquinot, J. F.; Sakellariou, D., NMR Signal Detection in Rotating Microcoil. Concepts