Evaluation and Optimization of Metabolome Sample Preparation

Jan 4, 2013 - Evaluation and Optimization of Metabolome Sample Preparation. Methods for Saccharomyces cerevisiae. Sooah Kim,. †. Do Yup Lee,. ‡...
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Evaluation and Optimization of Metabolome Sample Preparation Methods for Saccharomyces cerevisiae Sooah Kim,† Do Yup Lee,‡ Gert Wohlgemuth,§ Hyong Seok Park,† Oliver Fiehn,§ and Kyoung Heon Kim*,† †

School of Life Sciences and Biotechnology, Korea University, Seoul 136-713, Republic of Korea Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States § Genome Center, University of California, Davis, California 95616, United States ‡

S Supporting Information *

ABSTRACT: Metabolome sampling is one of the most important factors that determine the quality of metabolomics data. The main steps in metabolite sample preparation include quenching and metabolite extraction. Quenching with 60% (v/ v) cold methanol at −40 °C has been most commonly used for Saccharomyces cerevisiae, and this method was recently modified as “leakage-free cold methanol quenching” using pure methanol at −40 °C. Boiling ethanol (75%, v/v) and cold pure methanol are the most widely used extraction solvents for S. cerevisiae. In the present study, metabolome sampling protocols, including the above methods, were evaluated by analyzing 110 identified intracellular metabolites of S. cerevisiae using gas chromatography/time-of-flight mass spectrometry. According to our results, fast filtration followed by washing with an appropriate volume of water can minimize the metabolite loss due to cell leakage as well as the contamination by extracellular metabolites. For metabolite extraction, acetonitrile/water mixture (1:1, v/v) at −20 °C was the most effective. These results imply that the systematic evaluation of existing methods and the development of customized methods for each microorganism are critical for metabolome sample preparation to facilitate the reliable and accurate analysis of metabolome. first method that has been used to arrest the cellular metabolism and enzymatic reactions in S. cerevisiae10 and is still the most commonly used quenching method for the yeast.8,11−21 However, when cold methanol quenching was applied, serious losses of intracellular metabolites due to cell leakage were observed in bacteria.22−24 Furthermore, the introduction of a washing step after cold methanol quenching to remove extracellular metabolites was found to worsen metabolite losses in bacterial cells.22 Although cold methanol quenching has been used for S. cerevisiae in numerous studies, only a few studies assessed the metabolite losses or the effect of washing after cold methanol quenching in S. cerevisiae.15,18 A modification of the original cold methanol quenching method,10 known as “leakagefree cold methanol quenching” for yeast metabolomics, was reported to prevent intracellular metabolite losses in S. cerevisiae.18 In bacteria, as an alternative to cold methanol quenching, the fast filtration method was developed and shown to be effective in minimizing the losses of intracellular metabolites both in Gram-negative and Gram-positive bacteria.22,23,25 However, a systematic evaluation of the fast filtration method has not been reported for S. cerevisiae yet.

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he yeast Saccharomyces cerevisiae has traditionally been one of the most popular industrial fermentation microorganisms, especially in alcoholic beverage production. It has recently received increased attention for the production of fuel ethanol and industrial biochemicals.1,2 To produce such bulk commodity products, vigorous manipulation of yeast metabolism is inevitable to achieve high productivity and product yields using cheap and renewable substrates such as lignocellulose hydrolysate.3,4 In this context, S. cerevisiae is currently a popular subject for metabolic engineering and metabolomics in the suite of systems biology. Metabolomics is the study of global changes in the entire metabolome of a single living organism, and its workflow comprises the metabolite sample preparation including sampling, quenching, and extraction, the analysis of metabolites using various analytical and statistical tools, and finally, the biological interpretation of experimental data.5,6 Metabolomics has been suggested as a more rational and systematic approach for microbial strain improvement via identification of key target metabolites.7 However, metabolomics is relatively new in strain improvement, and only a few cases of applications in metabolic engineering are available to date.8 To obtain representative and accurate metabolome data, reproducible, reliable, nonselective, and efficient sample preparation methods are essential given that metabolites are the products of actual events occurring in dynamic living systems.5,9 The cold methanol quenching was the © 2013 American Chemical Society

Received: October 4, 2012 Accepted: January 4, 2013 Published: January 4, 2013 2169

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To prepare the metabolite extract, quenching or fast filtration is followed by the solvent extraction of intracellular metabolites from the separated cells. Ideally, all intracellular metabolites should be completely, nonselectively, and reproducibly extracted by an extraction solvent.19,26 For the extraction of intracellular metabolites from S. cerevisiae, boiling ethanol8,12−15,18,19,27,28 has been most frequently used as an extraction solvent. Cold methanol,11,15,19,29 cold chloroform,10,15,16,19 and cold perchloric acid15,30 have also been frequently used. Recently, a mixed solvent system containing acetonitrile with a medium polarity has been applied to S. cerevisiae.21 In the above studies, the numbers of metabolites identified from S. cerevisiae were usually less than 50, and only two studies reported 80 and 95 metabolites, respectively.11,21 To systematically evaluate extraction solvents for nondiscrimination, reproducibility, and high metabolite recovery yields, a greater diversity of chemically classified metabolites should be taken into consideration. In this study, using gas chromatography/time-of-flight mass spectrometry (GC/TOF MS), a global metabolite profiling platform that enabled the detection of more than 100 metabolites of S. cerevisiae, we evaluated and optimized the quenching (particularly, leakage-free cold methanol quenching),18 fast filtration, washing, and extraction steps. These results can be used as a customized protocol or an experimental strategy for S. cerevisiae metabolome analysis.

supernatant was then collected and concentrated to dryness. The dried metabolite sample was kept at −80 °C until derivatization for GC/TOF MS analysis. Fast Filtration and Subsequent Evaluation of Extraction Solvents. The fast filtration method was also explored to compare with leakage-free cold methanol quenching. Briefly, 1 mL of S. cerevisiae culture grown for 9 h was collected and vacuum-filtered through a nylon membrane filter (0.45 μm pore size, 30 mm diameter; Whatman, Piscataway, NJ) and washed with 1, 2, 5, or 16 mL of distilled water at room temperature. The entire procedure including fast filtration and washing was completed within 30 s. Both the filtered cell mass and the used filter were quickly mixed with 20 mL of a metabolite extraction solvent such as pure methanol at −20 °C, acetonitrile/water mixture (1:1, v/v) at −20 °C, acetonitrile/methanol/water mixture (2:2:1, v/v/v) at −20 °C, or boiling ethanol (75%, v/v) at 95 °C, and the extraction mixture was then immersed in liquid nitrogen. The extraction mixture was then thawed on ice, vortexed for 3 min, and centrifuged at 16 100 rcf for 5 min at 4 °C. The supernatant was collected and vacuum-dried. The extract was resuspended in 500 μL of fresh acetonitrile/water mixture (1:1, v/v) at 0 °C to eliminate lipids and wax and then dried. Derivatization of Metabolites. Prior to GC/TOF MS analysis, metabolites were derivatized by methoxyamination and silylation. Briefly, 5 μL of 40 mg/mL methoxyamine hydrochloride in pyridine (Sigma-Aldrich, St. Louis, MO) was added to metabolite samples and incubated for 90 min at 30 °C. The metabolites samples were then mixed with 45 μL of N-methyl-Ntrimethylsilyltrifluoroacetamide (Fluka, Buchs, Switzerland) and incubated for 30 min at 37 °C. After derivatization, a mixture of fatty acid methyl esters was added to the derivatized metabolites as retention index markers. Metabolite Analysis. The derivatized metabolite samples were analyzed by GC/TOF MS using an Agilent 7890 A GC (Agilent Technologies, Wilmington, DE) coupled with a Pegasus HT TOF MS (LECO, St. Joseph, MI). An RTX-5Sil MS column (30 m × 0.25 mm, 0.25 μm film thickness; Restek, Bellefonte, PA) and an additional 10 m long integrated guard column were used. A 1 μL aliquot of the metabolite was injected into the GC in splitless mode, with the oven temperatures programmed at 50 °C for 1 min, followed by ramping to 330 °C at 20 °C/min, and a final holding for 5 min. Mass spectra of the metabolites were in the mass range of 85−500 m/z at an acquisition rate of 10 spectra/s. The temperatures of the ion source and transfer line were 250 and 280 °C, respectively, and the electron ionization was carried out at 70 eV. Data Processing and Statistical Analysis. GC/TOF MS data were preprocessed using the LECO Chroma TOF software (ver. 3.34; St. Joseph, MI) to detect peaks and to deconvolute the mass spectra. The preprocessed data were processed using BinBase, an in-house programmed database built for metabolite identification, as previously described.31,32 The spectral data were normalized to cell dry weight of each culture. Statistica (ver. 7.1; StatSoft, Tulsa, OK) was used for principal component analysis (PCA) and univariate analysis.31−33 Hierarchical clustering analysis (HCA) was performed using MultiExperiment Viewer to visualize and organize metabolite profiles.34 Confocal Laser Scanning Microscopy. To visualize and evaluate possible leakage of cells after leakage-free cold methanol quenching or fast filtration, quenched or fast-filtered cells were stained with fluorescent dyes, such as SYTO 9 (Invitrogen, Eugene, OR) and propidium iodide (PI, Invitrogen). SYTO 9

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EXPERIMENTAL SECTION Strain and Cultivation. Wild-type S. cerevisiae BY4741 was grown in 100 mL of YPD broth [1% (w/v) yeast extract, 2% peptone, and 2% glucose] in a shaking flask (200 rpm) at 30 °C. Culture samples were collected after 9 h of cultivation to obtain metabolome samples during the exponential growth phase. Quenching, Cell Disruption, and Metabolite Extraction. To evaluate the quenching methods for the yeast, the leakage-free cold methanol quenching method developed for S. cerevisiae by Canelas et al.18 was compared with fast filtration. Additionally, four different extraction solvents including boiling ethanol and cold methanol, which have been frequently used in S. cerevisiae studies, were evaluated to select a suitable extraction solvent for S. cerevisiae. For each step of the metabolite sample preparation process, five or six independent replicate samples were taken at each condition and analyzed using GC/TOF MS. Cold Methanol Quenching. In this study, we used the modified cold methanol quenching method as the leakage-free method for S. cerevisiae, as suggested by Canelas et al.,18 in which the original cold methanol quenching conditions such as 60% (v/ v) methanol, the ratio of cell culture to quenching solvent of 1:6 to 1:4 (v/v), and −40 °C10 were modified to pure methanol, 1:5 (w/v), and −40 °C, respectively. Briefly, 1 mL of S. cerevisiae culture grown for 9 h was quenched by quick injection into 5 mL of pure methanol at −40 °C in this study. The cells were centrifuged at 16 100 relative centrifugal force (rcf) for 5 min at 4 °C, and the cell pellet was collected. The cells were then vacuumdried for 6 h using a speed vacuum concentrator (Labconco, Kansas City, MO). The dried cells were suspended in 500 μL of pure methanol at −20 °C and disrupted by sonication at 4 °C for 5 min using an ultrasonicator (Sonics & Materials, Newtown, CT). The disrupted cells were then vortexed for 3 min and stirred for 10 min in an ice bath. After centrifugation of the disrupted cells at 16 100 rcf for 5 min at 4 °C, the supernatant was vacuumdried for 6 h. To remove lipids and waxes, the metabolite extract was re-extracted in 500 μL of an acetonitrile/water mixture (1:1, v/v) at 0 °C and centrifuged at 16 100 rcf for 5 min at 4 °C. The 2170

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from cells for accurate determination of intracellular metabolites.18,22 In a Gram-negative bacterium, the washing performed after cold methanol quenching resulted in significant losses of intracellular metabolites.22 Similarly, great losses of intracellular metabolites were also observed in the present study when cold methanol quenching was followed by a washing step (data not shown). In fact, washing may not be necessary after cold methanol quenching, because disposal of the supernatant from the quenched cell culture mixture virtually removes most of the extracellular metabolites in cold methanol quenching for bacteria.22 However, when fast filtration is used as an alternative to cold methanol quenching, a washing step needs to be applied between the fast filtration and metabolite extraction steps,23,25 because fast filtration itself lacks a procedure for removal of the supernatant of the quenching solvent and quenched culture broth mixture before metabolite extraction. As shown in Figure 1, stable isotopes such as sorbitol-13C6, alanine-d4, glutamic acid-d5, and leucine-d3 were added to the cell

(green) labels both live and membrane-damaged cells, whereas PI (red) stains only membrane-damaged cells. After either cold methanol quenching or fast filtration, the cell pellet was collected and washed twice with sterile phosphate-buffered saline (PBS; KH2PO4 0.24 g/L, KCl 0.2 g/L, NaCl 8 g/L, and Na2HPO4 1.44 g/L at pH 7.4; Sigma, St. Louis, MO). The washed cells were resuspended in 100 μL of PBS and then added with 0.5 μL of SYTO 9 and 7.2 μL of PI. The mixture of cells and the dyes was incubated in a dark room at 30 °C for 30 min. After centrifugation, stained cells were collected and washed three times with 100 μL of PBS. The washed cells were resuspended in 100 μL of PBS and vortexed. Ten microliters of each cell suspension was transferred to a microscope slide and dried in a dark room at 30 °C for 30 min. To prevent bubble formation, mounting oil was added dropwise onto the stained cells and carefully covered with a cover glass. Fingernail polish was brushed on the edges of the cover glass to fix the cover glass to the microscope slide. Stained cells were visualized by confocal laser scanning microscopy (CLSM; LSM 5 Exciter; Zeiss, Jena, Germany) with a 488 nm Ar laser (green channel) and a 543 nm He−Ne laser (red channel). The microscopic images were analyzed at a magnification of 80×. A band path 505−530 nm filter and a long path 560 nm filter were used as excitation filters for the green (SYTO 9) and red channels (PI), respectively. Determination of the Effect of Washing Using Extracellular Markers. To evaluate the effect of washing on the quenching process, four isotopes such as sorbitol-13C6, alanine-2,3,3,3-d4, glutamic acid-2,3,3,4,4-d5, and leucine-2,3,3d3, which were all purchased from Sigma-Aldrich, were used as external markers. These isotopes were added to the cell culture at the final concentration of 0.08 mg/mL each in the cell culture. The residual concentration of each standard after the washing step was calculated using calibration curves for each standard after GC/TOF MS analysis of intracellular metabolites. The residual percentage of each external marker is presented as a ratio of the residual amount of the external marker to its initially added amount.

Figure 1. Effect of washing water volume after fast filtration on the removal of stable isotopes added to the cell culture. Residual percentages of stable isotopes after leakage-free cold methanol quenching (pure methanol at −40 °C) without washing are presented for comparison.

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RESULTS AND DISCUSSION Identification of Metabolites. More than 1000 compounds are known to be endogenous metabolites in S. cerevisiae.35 As shown in Supporting Information Table S-1, 110 intracellular metabolites were identified in this study, and these metabolites were classified into various chemical classes including amino acids, sugars and sugar alcohols, fatty acids, organic acids, amines, phosphates, and others. A significantly higher number of metabolites were identified in this study, compared to previous studies that evaluated metabolite sample preparation methods for S. cerevisiae with less than 50 identified metabolites using GC/ MS15 and 35 identified metabolites using liquid chromatography−mass spectrometry and GC/MS.18 Therefore, the evaluation of metabolite sample preparation methods in this study may be less biased due to the higher number and the greater chemical diversity of metabolites identified. Of the 110 identified metabolites, amino acids were most commonly detected, accounting for 26% of the total number of identified metabolites. The numbers of sugars and sugar alcohols, fatty acids, organic acids, amines, and phosphates constituted 22%, 15%, 13%, 9%, and 8% of the total number of identified metabolites, respectively. Effect of Washing after Quenching or Fast Filtration. After quenching microbial cells, a washing step may be necessary to remove extracellular metabolites and medium components

culture sample just before quenching. The residual amounts of all of the stable isotopes after cold methanol quenching (pure methanol at −40 °C) without washing and metabolite extraction (pure methanol at −20 °C) were less than 0.3% of their initially added amounts. Therefore, a washing step to remove extracellular metabolites was not necessary after cold methanol quenching. To study the effect of washing procedure after fast filtration, the stable isotopes were also added to the cell culture just before fast filtration. The fast-filtered cell mass was subjected to washing with different volumes of washing water before metabolite extraction with pure cold methanol. As shown in Figure 1, the residual amounts of isotopes decreased as the volume of washing water increased. Especially, the amounts of residual isotopes with 1 mL of washing water were significantly larger than those with larger washing volumes (i.e., 2, 5, and 16 mL) at the 99% confidence level. However, when the amount of washing water increased to 2, 5, or 16 mL, the residual amounts fell within the experimental error range such as 0.11%−2.25% of their initial amounts. Then, we compared the intracellular metabolite recovery yields of representative metabolites of various chemical classes obtained from washing with different volumes of water after fast filtration with metabolite extraction with pure cold methanol (Supporting Information Figure S-1). The increase in the volume of washing water did not significantly 2171

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and consistently lower the recovery yield of intracellular metabolites. For subsequent experiments, 5 mL of washing water was selected as the optimal volume for washing after fast filtration, which yielded satisfactory results both for removal of extracellular metabolites and recovery of intracellular metabolites. Cold Methanol Quenching versus Fast Filtration in Metabolite Recovery. To investigate the intracellular metabolite recovery yields of the different quenching methods, the modified cold methanol quenching method using pure methanol at −40 °C without washing, developed as leakage-free quenching for S. cerevisiae,18 was compared with fast filtration followed by washing with 5 mL of water. In this experiment, pure cold methanol at −20 °C was used as the extraction solvent for both quenching methods. The profiles of the 110 intracellular metabolites obtained using leakage-free cold methanol quenching without washing or fast filtration with washing were subjected to PCA, an unsupervised multivariate analysis. The PCA score plot showed a clear difference between the two groups (fast filtration vs cold methanol quenching) (Supporting Information Figure S-2A). The difference in metabolite profiles between cold methanol quenching and fast filtration was obviously explained by PC1. The values of R2 of variables (R2X) and Q2 from the PCA model involving PC1 and PC2 were 0.73 and 0.85, respectively, which are 1 − (residual sum of squares/sum of squares) and 1 − (predictive residual sum of squares/sum of squares), respectively. The fitting and predicting capabilities of the PCA model, which were shown by R2X and Q2, respectively, were excellent since Q2 was much higher than 0.5.36 In the PCA loading plot (Supporting Information Figure S-2B), each metabolite was colored based on its XlogP value, which is a measure of the hydrophobicity of the compound determined using its octanol/ water partition coefficient.37 Significantly more metabolites in the loading plot (Supporting Information Figure S-2B) were positively correlated with the fast filtration group compared to the cold methanol quenching group in the score plot (Supporting Information Figure S-2A) regardless of their XlogP values. These results indicate the lower recovery of intracellular metabolites from the leakage-free cold methanol quenching method, possibly due to leakage of metabolites during the quenching process. In addition to the comparison of fast filtration with leakagefree cold methanol quenching using PCA, the peak intensities of representative intracellular metabolites of various chemical classes, such as amino acids, sugar and sugar alcohols, fatty acids, organic acids, amines, and phosphates, were compared between the fast filtration and cold methanol quenching methods. As shown in Figure 2A, the normalized peak intensities of various amino acids in the leakage-free cold methanol quenched samples ranged only from 2.1% to 49.3% of those in the fast filtration samples. The normalized peak intensities of intracellular metabolites representing sugars and sugar alcohols, organic acids, amines, and phosphates in the leakage-free cold methanol quenched samples ranged from 10.2% to 31.6% of those in the fast filtration samples (Figure 2B). The normalized peak intensities of various fatty acids in the leakage-free cold methanol quenched sample ranged from 31.7% to 156.9% of those in the fast filtration samples (Figure 2C); some fatty acids, such as palmitic acid, octadecanol, and palmitoleic acid, were more abundant in the leakage-free cold methanol quenched samples than in the fast filtration samples. These results imply that some of the hydrophobic metabolites were lost to a lesser extent with cold methanol quenching, as shown in a previous

Figure 2. Comparison of fast filtration and leakage-free cold methanol quenching in intracellular metabolite abundances from S. cerevisiae when using cold pure methanol (−20 °C) as the extraction solvent: (A) amino acids; (B) sugars and sugar alcohols, organic acids, amines, and phosphates; (C) fatty acids. Normalized peak intensities of some metabolites were multiplied by 0.01−100 for data visualization.

study on a Gram-negative bacterium.22 However, most metabolites belonging to various chemical classes showed significantly higher abundances with fast filtration in accordance with the PCA results. Note the significantly higher losses of intracellular metabolites with leakage-free cold methanol 2172

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Figure 3. Effect of (A) fast filtration and washing with water or (B) leakage-free cold methanol quenching (pure methanol at −40 °C) without washing on membrane integrity of S. cerevisiae analyzed by confocal laser scanning microscopy (left, SYTO 9; right, PI).

damaged the cell membrane of the yeast, thereby enabling PI staining (right, Figure 3B). Since the original cold methanol (60%, v/v at −40 °C) quenching method was first developed for S. cerevisiae,10 numerous researchers used this method as the standard protocol for yeast but dismissed its cell leakage problem. However, two recent studies15,18 investigated the leakage of intracellular metabolites from S. cerevisiae that was subjected to the original cold methanol quenching method.10 Canelas et al.18 suggested using pure methanol in cell culture at a 1:5 (cell culture/ methanol, w/v) ratio at −40 °C or lower, instead of 60% (v/v) methanol, for “leakage-free quenching” of S. cerevisiae. It was reported that this modified pure methanol quenching method significantly reduced the loss of intracellular metabolites compared with the original cold methanol quenching protocol using 60% (v/v) methanol.18 However, the 35 metabolites identified were all hydrophilic compounds, and some metabolites were even lower in abundance in the pure methanol-quenched cells compared to that in the fast-filtered cells. Some studies used the leakage-free quenching method for S. cerevisiae.8,19 However, the results from the present study indicate that the leakage-free cold methanol quenching method still faces the metabolite loss problem due to reduced membrane integrity caused by the cold quenching process. This problem can be overcome by using the fast filtration method, which has been evaluated and optimized in this study. Evaluation of Extraction Solvents. Following the use of boiling ethanol as the extraction solvent for S. cerevisiae by Gonzalez et al.,12 boiling ethanol has been the most frequently used solvent for S. cerevisiae. A comparison of boiling ethanol and cold methanol extractions in S. cerevisiae revealed similarly good

quenching even without a washing step when compared with that of fast filtration. These metabolite losses may be attributed to cellular leakage caused by damage to membrane integrity, as seen in the previous study on a Gram-negative bacterium.22 The previous good assessment results of the modified cold methanol quenching as a leakage-free method18 may be partly due to the fact that a lower number of metabolites (i.e., 35 metabolites) and only the hydrophilic ones were taken into account in that study, compared to our present study (110 metabolites). The larger number of metabolites representing a wider range of chemical classes in this study may have detected metabolite losses more comprehensively. Cold Methanol Quenching versus Fast Filtration in Cell Membrane Integrity. Although numerous studies have used cold methanol quenching for metabolite sampling in S. cerevisiae as mentioned earlier, few reported the leakage of intracellular metabolites when using cold methanol quenching for S. cerevisiae, which may be the direct cause of low metabolite recovery yield.15,18 In this study, to investigate cell leakage as the possible cause for lower intracellular metabolite recovery of leakage-free cold methanol quenching, the cell membrane integrity of S. cerevisiae was analyzed and compared for the leakage-free cold methanol quenching and the fast filtration by CLSM using SYTO 9 and PI. In yeast cells prepared by fast filtration, the intensity of PI staining (right, Figure 3A) was much lower than that of SYTO 9 staining (left, Figure 3A). However, similar proportions of SYTO 9- (left, Figure 3B) and PI-stained cells (right, Figure 3B) were observed when using the leakage-free cold methanol quenching method. The fact that PI permeates only cells lacking cell membrane integrity38 suggests that cold methanol quenching 2173

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Figure 4. Hierarchical clustering analysis of 110 intracellular metabolites from S. cerevisiae using pure methanol (PM) at −20 °C, acetonitrile/water (50ACN; 1:1, v/v) at −20 °C, acetonitrile/methanol/water (AMW; 2:2:1, v/v/v) at −20 °C, or boiling ethanol (BE; 75%, v/v) at 95 °C as the extraction solvent. Similarity assessment for clustering was based on the Pearson correlation coefficient and average linkage methods. Each column and each row represent an extraction solvent and an individual metabolite, respectively.

detected for metabolites using pure cold methanol as the extraction solvent than boiling methanol.15 To identify the optimal extraction solvent capable of extracting metabolites at a

overall performances by both methods, taking only 44 metabolites into account;19 however, a much higher number of gas chromatography peaks including unknowns (>150) were 2174

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15.3, respectively, implying the highest and lowest reproducibilities for acetonitrile/water and acetonitrile/methanol/water, respectively. However, there were no significant differences in the %CVs of extraction solvents even at the 99% confidence level. On the basis of the three evaluation criteria of metabolite profile, metabolite abundance, and reproducibility of metabolite extraction, acetonitrile/water (1:1, v/v) at −20 °C was the best solvent for extracting the intracellular metabolites of fast-filtered S. cerevisiae. Boiling ethanol, the most frequently used solvent for metabolite extraction of S. cerevisiae, and the second most commonly used solvent for S. cerevisiae, pure methanol, were not found to be ideal in this study. The evaluation outcome of extraction solvents for S. cerevisiae herein strikingly differed from that for a Gram-negative bacterium in a previous study,22 in which acetonitrile/water was the poorest solvent among acetonitrile/water, pure methanol, acetonitrile/methanol/ water, and water/2-propanol/methanol (2:2:5, v/v/v). The low performance of acetonitrile/methanol/water in S. cerevisiae was also reported previously.19 These results strongly suggest that metabolite sample preparation methods need to be specifically developed, evaluated, and optimized for microorganisms.

higher yield with a greater diversity, it is advantageous to consider a larger number of metabolites. Furthermore, in the previous studies,15,19 cold methanol quenching, which caused serious metabolite losses in S. cerevisiae, was used when evaluating extraction solvents. In this study, 110 intracellular metabolites of various chemical classes were taken into account to evaluate four different extraction solvents including pure methanol at −20 °C, acetonitrile/water mixture (1:1, v/v) at −20 °C, acetonitrile/ methanol/water mixture (2:2:1, v/v/v) at −20 °C, and boiling ethanol (75%, v/v) at 95 °C in combination with fast filtration and washing. To evaluate the extraction solvents, three criteria were used: the metabolite profile differences by statistical analyses such as PCA and HCA, the sum of peak intensities of identified metabolites, and the reproducibility of metabolite quantification. The metabolite profiles of the pure methanol, acetonitrile/ water, acetonitrile/methanol/water, and boiling ethanol groups were clearly distinguishable by PC1 and PC2, which accounted for 31.1% and 11.3% of the total variance, respectively (Supporting Information Figure S-3). The average PC1 and PC2 scores significantly differed depending on the extraction solvents: pure methanol, 2.07 and −0.27, respectively; acetonitrile/water, 8.66 and −1.58, respectively; acetonitrile/ methanol/water, −2.59 and 5.74, respectively; and boiling ethanol, −8.13 and −3.88, respectively. As shown in Figure 4, HCA revealed that overall acetonitrile/water and acetonitrile/ methanol/water yielded the highest and lowest metabolite abundances, respectively. Furthermore, the five independent replicates for each extraction solvent yielded high intragroup similarity of metabolic profile patterns. The metabolite profiles of pure methanol and acetonitrile/water were closely related to each other as those of acetonitrile/methanol/water and boiling ethanol were in the HCA results. Next, the sums of normalized peak intensities of all the metabolites in each chemical class, in which the peak intensity of each metabolite was subtracted from the mean and then divided by the standard deviation for normalization by unit variance scaling, were compared between the extraction solvents (Supporting Information Figure S-4). The means of the summed values for pure methanol, acetonitrile/water, acetonitrile/ methanol/water, and boiling ethanol were 1.9, 14.4, −10.4, and −5.9, respectively. Thus, acetonitrile/water showed the highest peak intensity, whereas acetonitrile/methanol/water showed the lowest peak intensity. These results are consistent with the HCA results shown in Figure 4. Extraction using acetonitrile/water, in particular, produced high metabolite recovery yields for amines, amino acids, organic acids, phosphates, and sugars and sugar alcohols, whereas fatty acids were most effectively extracted by boiling ethanol. Each extraction solvent exhibited a preference for specific metabolites, likely due to the different chemical properties of the solvents. For instance, proline was efficiently extracted with pure methanol, whereas ribose was preferentially extracted with acetonitrile/methanol/water. Sucrose exhibited a higher abundance in boiling ethanol, and fumaric acid in acetonitrile/water. The reproducibility in metabolite extraction was evaluated by percent coefficients of variation (%CVs) for the abundances of the identified metabolites with each extraction solvent. Supporting Information Figure S-5 shows the frequency distributions of %CVs for the four different extraction solvents, for which intracellular metabolites were quantified in 10% CV accuracy intervals. The median %CVs of the identified metabolites for pure methanol, acetonitrile/water, acetonitrile/ methanol/water, and boiling ethanol were 20.9, 13.1, 22.0, and

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CONCLUSIONS Cold methanol quenching, the most commonly used method for bacteria and yeasts, was found to cause serious intracellular metabolite losses due to a damaged membrane in S. cerevisiae, even when the modified method developed as a leakage-free quenching method was applied. Fast filtration followed by washing with 5 mL of water for fast-filtered cells from 1 mL of culture has solved both the problems of metabolite losses and contamination by extracellular metabolites. For extraction, acetonitrile/water (1:1, v/v) at −20 °C was identified as the optimal solvent with respect to metabolite profile, abundance, and data reproducibility. To obtain high-quality metabolomic data, evaluation and optimization of metabolome sampling methods for each microorganism of interest are necessary.

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ASSOCIATED CONTENT

S Supporting Information *

Additional information as noted in text. This material is available free of charge via the Internet at http://pubs.acs.org.

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AUTHOR INFORMATION

Corresponding Author

*Phone: +82 2 3290 3028. Fax: +82 2 925 1970. E-mail: [email protected]. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This work was supported by the Pioneer Research Center Program (2011-0002327) and the Advanced Biomass R&D Center of Korea (2011-0031353), both funded by Korean Government (MEST). Facility support by the Institute of Biomedical Sciences and Food Safety, Korea University, is acknowledged.

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REFERENCES

(1) Ha, S.-J.; Galazka, J. M.; Kim, S. R.; Choi, J.-H.; Yang, X.; Seo, J.-H.; Glass, N. L.; Cate, J. H. D.; Jin, Y.-S. Proc. Natl. Acad. Sci. U.S.A. 2011, 108, 504−509.

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dx.doi.org/10.1021/ac302881e | Anal. Chem. 2013, 85, 2169−2176