Simultaneous Determination of Multiple Intracellular Primary

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Simultaneous determination of multiple intracellular primary metabolites by ultrahigh performance liquid chromatography coupled with a Q Exactive HF mass spectrometer Nannan Qiu, Di Wu, Xia Cui, Guoliang Li, Sai Fan, Dawei Chen, Yunfeng Zhao, and Yongning Wu Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.6b02417 • Publication Date (Web): 08 Sep 2016 Downloaded from http://pubs.acs.org on September 9, 2016

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Analytical Chemistry

Simultaneous determination of multiple intracellular primary metabolites by ultrahigh performance liquid chromatography coupled with a Q Exactive HF mass spectrometer

Nannan Qiua, Di Wua, b, Xia Cuia, Guoliang Lia, d, Sai Fanc, Dawei Chena, Yunfeng Zhaoa, Yongning Wua* a

Key Laboratory of Food Safety Risk Assessment, Ministry of Health, China National

Centre for Food Safety Risk Assessment, Beijing 100021, China b

School of Life Sciences, Xiamen University, Xiamen 361005, China

c

Institute of Nutrition and Food Hygiene, Beijing Centre for Disease Control and

Prevention, Beijing 100013, China d

Key Laboratory of Life-Organic Analysis of Shandong Province, Qufu Normal

University, Qufu 273165, China

AUTHOR INFORMATION *Corresponding Author Phone/fax: +86-10-83235402, E-mail: [email protected] (Y.-N. Wu).

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ABSTRACT In this study, we established an ultrahigh performance liquid chromatography- Q Exactive HF MS (UHPLC-HF MS) method for the simultaneous determination of 25 targeted metabolites relating to a broad coverage of central metabolic pathways, such as glycolysis pathway, tricarboxylic acid cycle (TCA), serine biosynthesis pathway (SSP), glutaminolysis pathway and closely related biosynthetic reactions. A Shodex Asahipak NH2P-50 2D column was used to separate the targeted compounds, and Full MS+PRM detection using an electrospray ionization source in negative mode was employed. The method also integrated a sample purification step by passing through a Waters Sirocco™ 96 plate to remove protein impurities, ensuring the better resolution and sensitivity of the proposed method. The calibration curves of the method showed good linearity within the range of 1-10000 µg L-1 with the correlation coefficient no less than 0.99. The method can be used for routine quantification of primary metabolites in a wide variety of cell extract samples. With the help of the method, for the first time, we successfully separate the isomers of 3-phosphoglycerate (3-PG) and 2-phosphoglycerate (2-PG), which lay the groundwork for the accurate quantification of metabolites of the tumor cells, the study of PGAM1 inhibitors and the development of neotype anti-cancer drugs. Keywords: UHPLC, Q Exactive HF MS, targeted metabolites, cultured cells, 3-phosphoglycerate, 2-phosphoglycerate Introduction Cellular metabolism is important for maintaining fundamental requirements of biological life. The organization of metabolic networks is essential to capitalize energy sources, convert nutrition into biomass and support developmental programs from unicellular organisms to advanced plants and animals.1 Furthermore,

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comprehensive metabonomics and its applications become an effective way to identify biomarkers or biochemical pathways, which can be used to characterize physiological or pathological states in the biological system.2, 3 Considering the high complexity of dynamic characteristics of the compounds present in a metabolome and within a wide concentration range, it is challenging to establish analytical methods for identifying and quantifying of all the metabolites in vivo. Driven by considerable development

of

both

chromatography

and

mass

spectrometry

techniques,

metabonomics has now become a valuable approach for profiling of pathological samples to identify specific biomarkers for diagnosis, prognosis or treatment efficacy.4, 5 It is acknowledged that chromatography coupled with mass spectrometry technologies are used for the robust measurement of numerous intracellular metabolites in extracts of cultured cells.6 In particular, chromatographic separation technique plays an important role in metabonomics to achieve a comprehensive profiling analysis.7 The strength of MS-based metabonomics is interpreted when coupling to a separation technique, such as capillary electrophoresis (CE),8 gas chromatography (GC),9 liquid chromatography (LC)10 or ion chromatography (IC).11 LC combined with MS equipped with an electrospray ionization source (LC-ESI-MS) was identified as the predominant analytical methodology used for metabonomics.12,13 So far, several LC-MS-based methods for targeted and quantitative analysis of intermediates from primary metabolism have been reported, however, these methods have lower sensitivity and selectivity and cannot fully meet the requirements in complex mateixes. At the same time, almost all of these methods are time consuming, some of which even cost 80 min per run.14 The latest research showed that the phosphoglycerate enzyme 1 (PGAM1) is an

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important enzyme in the glycolysis and gluconeogenesis pathway, which can participate in tumor growth and immune regulation.15-18 The mRNA expression of PGAM1 significantly increases in many tissues during the occurrence of carcinogenesis, such as lung cancer, colon cancer, liver cancer, breast cancer and so on.19-23 These results indicated that PGAM1 could be used as a potential tumor marker while the inhibition of PGAM1 may be considered as a new therapeutic target for developing novel promising antitumor drugs. PGAM1 is able to catalyze 3-phosphoglycerate (3-PG) into 2-phosphoglycerate (2-PG), thus we can determine whether the inactivation of PGAM1 might negatively affect the conversion of 3-PG into 2-PG. Hence, we worked toward a rapid, reproducible and wide dynamic range LC-MS-based method for targeted and quantitative analysis of anionic intermediates from primary metabolism. In this study, an ultra-high performance liquid chromatography (UHPLC) system combined with Q Exactive HF MS has been employed and validated. The method provides high resolution in a short analysis time and allows simultaneously detecting and quantifying a large number of compounds including a broad coverage of intermediates from glycolysis pathway, TCA cycle, SSP and glutaminolysis pathway (Figure 1). Taking HepG2 cells as an example, current method has been successfully applied to simultaneous identification and quantification of the above intracellular metabolites in various cell extract samples. Besides, we chromatographically separated 3-PG and 2-PG at baseline levels, which lay the groundwork for the accurate quantification of metabolites in tumor cells, the study of inhibition of PGAM1 and the development of neotype anti-cancer drugs. EXPERIMENTAL SECTION Chemicals section and Instrumentation section can be found in the Supporting

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Information. Sample preparation HepG2 cells used in the present study were cultured in Dulbecco’s modified eagle medium (DMEM) plus 10% fetal bovine serum, 100 U/mL penicillin, and 100 U/mL streptomycin. The cells were maintained at 37 ºC in a humidified 5% CO2 incubator and passaged when they reached 90- 95% confluence. Cellular metabolites were extracted with methanol/acetonitrile/dH2O using dry ice snap-freezing method as described previously.24, 25 In the present study, we used the similar extraction method to prepare metabolome samples from HepG2 cells in our metabonomics analysis. Briefly, the cells were harvested and quickly washed for three times with ice-cold phosphate-buffered saline (PBS) to remove residual medium components. After the removal of PBS, the metabolites were extracted using the mixture of methanol/acetonitrile/dH2O (5:3:2, v/v/v) (2-4×106 cells per mL), followed by the incubation of culture dishes on dry ice for 15min. Cellular metabolite extracts were then collected by scraping and removing the supernatant following centrifugation at 3,750 rpm for 30 min at 4 ºC. The supernatant was passed through a Sirocco™ 96 plate (Waters, USA), and then 0.5 mL of the filtrate was dried under a gentle nitrogen stream and reconstituted with 0.5 mL of aqueous acetonitrile (97%, v/v), followed by the centrifugation at 12000 rpm for 10 min at 4 ºC. Supernatants were then transferred to new sample vials and submitted for analysis. Samples were treated and analyzed in quintupicate. In addition, cells in two parallel dishes were trypsinized and counted, while subsequent metabolite measurements were normalized to cell counts. Method validation Current method was validated via the following parameters: precision, accuracy,

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carry over, linearity, recovery, limit of detection (LOD) and limit of quantification (LOQ). The method precision has been calculated by the method intra-day repeatability (RSDr) and inter-day reproducibility (RSDR), in terms of % RSDr and RSDR. Intra-day repeatability was evaluated by analyzing and calculating the relative standard deviation (RSD) of the retention times of all analytes in the same run of the day. For the inter-day reproducibility, the RSD of all the analytes was investigated in three different days. The accuracy of the method (percentage recoveries) was evaluated by recovery experiments in cell samples. The LOD and LOQ as the characterization of the method were estimated for a signal-to-noise (S/N) ratio of more than 3 and 10 respectively from the chromatograms of samples spiked at the lowest concentration validated. Due to the presence of endogenous compounds in biological matrices, the content of endogenous compounds in cells should be reduced to a minimum as much as possible by cell starvation experiment. The spiking experiment was performed respectively at 1 times, 2 times and 5 times as the concentration level of the samples. Recoveries (R) test have been calculated by comparing the results of the assigned and measured spiking levels of analyzed metabolites, the latter of which was the difference of the concentration levels of analyzed metabolites with and without spiking the target metabolite standards. Calibration curves were prepared for each analyte starting from the LOQ level and the linearity has been assessed for each analyte by calculating the regression coefficient (R2). The coefficient of determination (R2) was determined by means of the least square approach. Matrix effect was determined by constructing calibration curves in blank extract and in the pure solvent. The effects were expressed in terms of signal suppression/enhancement (SSE) and calculated as follows: SSE = slope of spiked extract/slope of pure solvent standard. 26

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RESULTS AND DISCUSSION Optimization

of

chromatographic

conditions

of

3-phosphoglycerate

and

2-phosphoglycerate The separation and retention of the target analytes on different columns, including DIKMA HILIK column (2.1 mm×250 mm, 3 µm), DIKMA HILIK column (2.1 mm×100 mm, 3 µm) and NH2P-50 2D column (2.0 mm×150 mm, 5 µm) were initially assessed.27 The DIKMA HILIK columns with different specifications under the operating conditions described in Chromatographic conditions in the Supporting Information provided good separation between organic acids and amino acids. Figure S1 showed the merged peak of 3-PG and 2-PG using DIKMA HILIK columns with different lengths and different concentrations of concentrated ammonia. However, the use of these columns failed to achieve chromatographic separation between 3-PG and 2-PG. Alternatively, the NH2P-50 2D (2.0 mm×150 mm, 5 µm) column was chosen for the most appropriate chromatographic column due to the achievement of baseline separation between 3-PG and 2-PG. Given the critical role of mobile phases in the ionization efficiency, different solutions including methanol/water, acetonitrile/water and aqueous solution mixed with 1.5mmoL/L ammonium bicarbonate (pH 9.0, adjusted by 0.1% concentrated ammonia) were initially used as the candidates of mobile phases (Figure S2). Both 3-PG and 2-PG were not identified by using aqueous solution without the addition of buffer salt as the mobile phase. Compared to the mobile phase use of 1.5mmoL/L ammonium bicarbonate and 0.1% concentrated ammonia in aqueous solution, the chromatographic separation of 3-PG and 2-PG needed longer run time when considering 1.5mmoL/L ammonium bicarbonate and 0.1% concentrated ammonia in acetonitrile/water (10:90) or methanol/water (10:90)

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as the mobile phases. Therefore, the mixture of 1.5mmoL/L ammonium bicarbonate and 0.1% concentrated ammonia in aqueous solution was ultimately selected as the mobile phase to obtain sufficiently good performance for ionization of organic acids, sugar phosphates and amino acids along with good peak symmetry. Optimization of MS acquisition modes Full MS scan and targeted-SIM modes are two kinds of commonly-used acquisition models for Q Exactive. The Full MS is a full scan of the precursor ions in the specified mass range collected in the C trap, while the targeted-SIM is the ions from an inclusion list filtered in the quadrupole at a specified isolation width. The targeted-SIM is generally more sensitive than Full MS mode. However, the number of data points (scans) per chromatographic peak are insufficient under the targeted-SIM mode when multiple analytes are simultaneously detected, which needs many selected ion channels to monitor corresponding signals and thus lead to poorly-shaped chromatographic peak that reduces quantitative accuracy. In this study, 25 targeted intracellular metabolites were acquired by Full MS scan and targeted-SIM mode, respectively. Taking L-glutamic acid as an example, the result showed that targeted-SIM mode could cause the loss of data points (scans) per chromatographic peak compared with the chromatographic peak under the Full MS scan mode (Figure S3). Meanwhile, considering isotopic tracer technique must be used for additional confirmation in the subsequent metabolic pathway testing experiments, the Full MS scan mode was also selected as the quantitative method. When operating the Full MS we could increase the signal to noise ratio of the analytes by reducing the mass range as much as possible over the retention time of the analyte. Nevertheless, when the cell samples as complex biological matrices are analyzed only by means of Full MS scan mode, the matrix interferences may reduce qualitative

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accuracy. In order to develop the confirmation method, it is necessary to establish fragmentation patterns for each compound, which include Full MS/dd-MS2 mode and Full MS+PRM mode. In Full MS/dd-MS2 (Top N) mode, an integrated investigation consists of a Full MS of ions event followed by MS/MS scan events, where the ion specified in the inclusion list (a list of targeted accurate masses) was selected by quadrupole, fragmented in HCD cell, collected in the C-trap and analyzed in the Orbitrap. An isolation width of 1.5 Da was used for the quadrupole. In each Full MS/dd-MS2cycle, a Full MS event is followed by MS/MS events of the N most abundant ions.28 However, the N multiple ions fragmentation all entered Orbitrap, some fragmentation patterns may be from matrix rather than the analyte. Thus this acquisition model was useful for less complex background, which relied on high resolution for selectivity, the resolving power of 70,000 was used for both Full MS and MS/MS events. In Full MS+PRM mode, the first scan is a Full MS of ions event followed by a tMS/MS event, where the precursor specified in the inclusion list is selected by quadrupole fragmented in HCD cell with specific fragmentation energy and collected in C trap.28 An isolation width of 1.5 Da was used for the quadrupole. A resolving power of 70,000 was used for Full MS, and 13,500 was used for tMS/MS events. Contrary to Full MS/dd-MS2, in each Full MS+PRM cycle, a Full MS event is followed by MS/MS events of each ion in the inclusion list. The target list needs to be scheduled, while such an acquisition mode depends on the retention time of the analyte than the Full MS/dd-MS2. Thus, this acquisition model, by improving signal-to-noise rather than absolute signal, is most sensitive and selective even in complex matrices (Figure 2). Figure 2A1 extracted PRM total ion chromatogram, while Figure 2A2 and Figure 2A3 extracted dd-MS2 total ion chromatogram. Figure

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2A1 and Figure 2A2 highlighted that in complex biological samples, more data points (scans) per chromatographic peak and better shaped chromatographic peak appeared in the Full MS+PRM mode (Figure 2A1) than in the Full MS/dd-MS2 mode (Figure 2A2, 2A3)), which increases quantitative accuracy. The number of data points (scans) per chromatographic peak appeared only a few (Figure 2A2), or even none (Figure 2A3) when operated in the Full MS/dd-MS2 mode in different cell samples. Therefore, The Full MS+PRM mode was selected as the qualitative and quantitative model for organic acids, sugar phosphates and amino acids. Sample preparation The current method was applied to analysis of the 25 targeted metabolites in HepG2 cells. HepG2 cell samples were prepared according to previously reported methods.19, 20 However, these sample preparation methods without any purification step may inevitably contaminate the mass spectrometer, thus result in a decrease of resolution and sensitivity. To cope with the above defects, a sample purification step was conducted by passing through a Sirocco™ 96 plate (Waters, USA), which could effectively remove protein impurities. The comparison experiments were carried out and results were shown in Figure 3. After treatment with the Sirocco™ 96 plate, the response intensities of most of the compounds were improved in different degrees, except lactate. Meanwhile, the matrix effects were reduced, which can avoid contaminating the mass spectrometer. Figure S4 taking Glucose as an example, maked comparisons between treatment with and without purification step of Sirocco™ 96 plate. Method validation The present method was established for analysis of 25 intracellular metabolites including organic acids, sugar phosphates and amino acids. Figure 4 showed that

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there were no interfering peaks at the retention times of the 25 targeted metabolites. The standard calibration curves were obtained at thirteen concentrations, ranging from 1 to 10000 µg L-1 (1, 2, 5, 10, 20, 50, 100, 200, 500, 1000, 2000, 5000 and 10000 µg L-1). Calibration curves were constructed by plotting the peak area of the compound against the concentration of the compound. Such a wide linear range for the calibration curves can simultaneously meet the determination of high amino acid contents and low organic acid contents. Standard calibration curves showed good linearity within the range of 1-10000 µg L-1 with the correlation coefficient (R2) no less than 0.99 for all analytes (Table 1). The LODs and LOQs for 25 intracellular metabolites were within the range of 0.0031–0.7641 mg L−1 and 0.0102–2.5517 mg L−1, respectively. The intraday precision of the retention time was lower than 0.6%, whereas the inter-day precision was below 5.3%. The accuracy of the method defined as recovery varied from 65.1% to 95.7%, and the RSD of method ranged from 1.1% to 11.0%, which indicated that current method was robust and suitable for the determination of the intracellular metabolites in cells. According to Frenich et al.,29 signal suppression or enhancement effect was considered tolerable if the value was between 0.8 and 1.2, and the values outside this range indicate a strong matrix effect.30 In this study, matrix effects were evaluated by taking 3-PG and 2-PG as an example, The SSE results for cell samples were 0.91 and 0.87. It can be concluded that there was a slight matrix effect for 3-PG and 2-PG in cell samples. Therefore, the pure solvent standard calibration was used for this method and there was no need to use the matrix matched calibration. This method was accurate and reliable for quantify. Applications of the method The validated method was applied to the analysis of the intracellular metabolites in

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cell extracts from HepG2. The studies on the poisonous effects of ethyl carbamate on HepG2 cells suggested that after ethyl carbamate exposed to HepG2, the contents of serine, glycine and GSH decreased, and the content of GSSG increased. The result showed that ethyl carbamate could affect the energy metabolism and induce the redox imbalance of cells, which may lead to oxidative damage to the cells. The significance of separation of 3-phosphoglycerate and 2-phosphoglycerate PGAM1 is an important enzyme in glycolysis and gluconeogenesis pathways, catalyzing the transformation from 3-PG into 2-PG. Recent research showed that PGAM1 participates in tumor growth and immune regulation,31-33 and the mRNA expression of PGAM1 significantly increases in many cancer tissues, such as lung, colon, liver and breast. These results indicated that PGAM1 could be used as a potential tumor marker and therapeutic target.34 Considering that PGAM1 can catalyze the 3-PG into 2-PG, we can determine whether inhibitor of PGAM1 played a role by the changes of the content of 3-PG and 2-PG. Using the method developed in this article, 3-PG and 2-PG can be separated and quantified accurately, which lay the groundwork for the accurate quantification of metabolites of the tumor cells, the study of inhibitor of PGAM1 and the development of neotype anti-cancer drugs. The optimal UHPLC-HF MS method established in current study was successfully applied to the simultaneous analysis of 25 targeted metabolites. Furthermore, we achieved the baseline separation between 3-PG and 2-PG, which were qualitatively and quantitatively evaluated together with other organic acids, and amino acids involved in glycolysis, TCA cycle, glutaminolysis and serine synthesis pathway. This method just needs 20 min for each run. Compared with previously reported methods regarding tedious run time (36~80 min),11, 14, 35 the current method only costs a short run time (20 min) and contributes to the large-scale comprehensive metabonomics

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analysis involving hundreds of samples. CONCLUSION In conclusion, an UHPLC-HF MS method has been established for identification and quantification of 25 targeted intracellular metabolites involved in glycolysis, TCA cycle, glutaminolysis and serine synthesis pathways. This method successfully achieved the baseline separation and accurate quantification for the isomers of 3-PG and 2-PG. The sample preparation procedure comprised a purification step by passing through a Sirocco™ 96 plate (Waters, USA). This method offers significant technical advantages for metabolite analysis, including good linearity, exquisite sensitivity, high speed and reproducibility and wide dynamic range. The ultrahigh performance LC provides fast separation of cellular metabolites within 20 min. In addition, the method has been successfully applied to analyze the intracellular metabolites in cell extracts from HepG2, as well as Hep3B, and a series of breast cancer cells. ACKNOWLEDGMENTS This study was supported by the National Basic Research Program (973, No. 2012CB720804) ASSOCIATED CONTENT Supporting Information This material is available free of charge on the ACS Publications website. Chemicals, chromatographic conditions, mass spectrometry, table of gradient profile for the developed UHPLC-HF MS method, figure of extracted ion chromatograms for 3-PG and 2-PG using different columns of different specifications under different mobile phase conditions, figure of Q Exactive mass chromatograms of Full MS scan and targeted-SIM modes, and figure of Q Exactive mass chromatograms of treatment with or without purification step of Sirocco™ 96 plate.

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Competing Financial Interest No competing financial interests.

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Int J Oncol, 2013, 43, 586-590. (24) Lorenz, M. A.; Burant, C. F.; Kennedy, R. T. Anal. Chem. 2011, 83, 3406-3414. (25) Maddocks, O. D. K.; Berkers, C. R.; Mason, S. M.; Zheng, L.; Blyth, K.; Gottlieb, E.; Vousden, K. H. Nature 2013, 493, 542-546. (26) Chen, D.; Zhao, Y.; Miao, H.; Wu, Y. Mol. Cancer. J. Chromatogr. A 2014, 1374, 268-272. (27) Tufi, S.; Lamoree，M.; Boer, J. D.; Leonards, P. J. Chromatogr. A 2015, 1395 79-87. (28) Kumar, P.; Rúbies, A.; Centrich, F.; Granados, M.; Cortés-Francisco, N.; Caixach, J.; Companyó, R. Anal. Chim. Acta 2013, 780, 65-73. (29) Frenich, A. G.; Romero-Gonzalez, R.; Gómez-Pérez, M. L.; Vidal, J. L. M. J. Chromatogr. A 2011, 1218, 4349-4356. (30) Li, H.; Chen, D.; Miao, H.; Zhao, Y.; Shen, J.; Wu, Y. J. Chromatogr. A 2015, 1410, 99-109. (31) Li, C.; Xiao, Z.; Chen, Z.; Zhang, X.; Li, J.; Wu, X.; Li, X.; Yi, H.; Li, M.; Zhu, G.; Liang, S. Proteomics 2006, 6, 547-558. (32) Shen, J; Wang, W.; Wu, J.; Feng, B.; Chen, W.; Wang, M.; Tang, J.; Wang, F.; Cheng, F.; Pu, L.; Tang, Q.; Wang, X.; Li, X. PLoS One. 2012, 7, e47476. (33) Lei, Y.; Huang, K.; Gao, C.; Lau, Q. C.; Pan, H.; Xie, K.; Li, J.; Liu, R.; Zhang, T.; Xie, N.; Nai, H. S.; Wu, H.; Dong, Q.; Zhao, X.; Nice, E. C.; Huang, C.; Wei, Y. Mol. Cell. Proteomics 2011, 10, M110.005397. (34) Evans, M. J.; Saghatelian, A.; Sorensen, E. J.; Cravatt, B. F. Nat Biotech nol 2005, 23, 1303-1307.

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(35) Buescher, J. M.; Moco, S.; Sauer, U.; Zamboni, N. Anal. Chem. 2010, 82 4403-4412.

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Figure caption Figure 1. The profile of 25 targeted metabolites relating to central metabolic pathways. Figure 2. Q Exactive mass chromatograms and spectra of L-glutamic acid ([M-H]m/z 146.0453) from the Full MS+PRM and Full MS/dd-MS2experiment: (A1) extracted PRM total ion chromatogram in sample No.1; (A2) extracted dd-MS2 total ion chromatogram in sample No.1; (A3) extracted dd-MS2 total ion chromatogram in sample No.2; (B1) and (B2) mass spectrum from chromatograms A1 and A2 respectively. Figure 3. Comparison of the purification effect. Line 1 is the responses of HepG2 cell extract without any purification; Line 2 is the responses of HepG2 cell extract with a purification step by passing through a Sirocco™ 96 plate (Waters, USA). Figure 4. Chromatograms of 25 targeted metabolites at the concentration of 1 mg L−1. These targeted metabolites

include Glucose, Lactic acid, Malic acid, Critrate,

Succinate, 3-Phosphoglycerate, 2-Phosphoglycerate, α-hydroxyglutarate, GSH, Phosphoenolpyruvate,

Hydroxypyruvate

phosphate,

3-Phosphoserine,

Glucose-6-phosphate, GSSH, Phenylalanine, Tryptophan, Methionine, Valine, Alanine, Threonine, Glycine, Glutamic acid, Glutamine, Serine and Lysine.

19



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Page 20 of 25

Table 1.

Validation parameters of the analytical method

Compounds

RT

RSD%; RT Intraday

RSD%; RT Interday

Linear range (mg L-1)

Linearity equation

Correlation coefficients (R2)

LODs (mgL-1/ 103CFU)

LOQs (mgL-1/ 103CFU)

R%

RSD% Method

Glucose

1.66

0.6

2.2

0.001-10

Y =( -4.8836E+4)+2.0799E+3X

0.9983

0.0061

0.0188

71.7

4.2

Lactic acid

2.47

0.2

1.6

0.001-10

Y =( 6.6226E+6)+5.6371 E+3X

0.9998

0.0046

0.0171

68.1

5.9

Malic acid

5.21

0.1

1.7

0.001-10

Y = (4.3571E+7)+4.5841E+5X

0.9981

0.0198

0.0662

83.3

1.8

Critrate

16.10

0.4

5.3

0.01-10

Y = (2.0192E+7)+8.0329E+5X

0.9954

0.0602

0.2008

74.7

2.1

Succinate

5.19

0.6

3.8

0.05-10

Y = (6.3335E+6)+1.08838E+5X

0.9993

0.0209

0.0617

79.2

2.3

3- Phosphoglycerate

15.72

0.1

2.4

0.01-10

Y = (1.3427E+6)+4.4031E+4X

0.9999

0.1108

0.3013

71.0

2.2

2- Phosphoglycerate

17.14

0.3

3.2

0.01-10

Y = (3.9090E+6)+8.7986E+4X

0.9996

0.1092

0.3511

78.2

1.9

α-hydroxyglutarate

5.11

0.5

5.2

0.001-10

Y = (1.0136E+8)+1.6858E+5X

0.9992

0.0301

0.1003

78.0

2.4

GSH

7.02

0.2

2.1

0.05-10

Y = (-8.3326E+6)+5.9836E+4X

0.9989

0.0721

0.2256

92.4

1.1

Phosphoenolpyruvate

20.00

0.5

4.2

0.2-10

Y = (-1.5589E+6)+1.2136E+4X

0.9991

0.2636

0.8775

68.4

5.1

Hydroxypyruvate phosphate

4.40

0.2

1.8

0.5-10

Y = (-8.8182E+4)+2.8927E+2X

0.9977

0.5104

1.6629

69.3

3.6

3-Phosphoserine

6.61

0.1

1.5

0.001-10

Y = (3.8840E+6)+5.8395E+4X

0.9988

0.7641

2.5517

78.5

2.8

Glucose-6-phosphate

4.66

0.4

3.7

0.001-10

Y = (6.4169E+6)+1.1775E+5X

0.9992

0.0609

0.2151

95.4

1.9

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GSSH

15.25

0.1

2.1

0.2-10

Y = (-9.6859E+5)+5.5189E+3X

0.9994

0.4535

1.5036

77.6

3.1

Phenylalanine

3.50

0.6

2.8

0.001-10

Y = (-2.2912E+5)+7.4619E+4X

0.9999

0.0088

0.0313

93.8

1.2

Tryptophan

7.10

0.2

2.1

0.001-10

Y = (2.8768E+5)+6.0729E+4X

0.9997

0.0038

0.0131

96.3

1.6

Methionine

2.47

0.2

1.6

0.001-10

Y = (2.6872E+6)+2.7372E+4X

0.9941

0.0031

0.0102

84.4

1.4

Valine

2.13

0.1

1.6

0.001-10

Y = (-2.4931E+5)+7.2943E+4X

0.9995

0.0063

0.0209

90.7

2.3

Alanine

1.97

0.3

2.7

0.001-10

Y = (-1.4252E+6)+2.6304E+4X

0.9994

0.0239

0.0794

91.1

1.4

Threonine

2.21

0.2

3.1

0.001-10

Y = (4.5619E+6)+3.7268E+4X

0.9964

0.0023

0.0077

87.6

3.6

Glycine

2.03

0.5

4.4

0.005-10

Y = (1.7537E+4)+1.7789E+4X

0.9998

0.0055

0.0190

73.4

2.4

Glutamic acid

3.23

0.1

1.9

0.001-10

Y = (6.5614E+6)+2.2433E+5X

0.9985

0.0073

0.0243

82.8

1.8

Glutamine

2.17

0.1

1.3

0.001-10

Y = (1.9687E+7)+9.8452E+4X

0.9916

0.0036

0.0118

87.2

2.1

Serine

2.19

0.4

2.5

0.001-10

Y =( 2.9889E+6)+2.9348E+4X

0.9974

0.0407

0.1354

95.7

1.7

Lysine

2.16

0.6

2.1

0.5-10

Y = (3.9612E+4)+47.9736*X

0.9982

0.5618

1.5036

65.1

11.0

21



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Figure 1

22


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Page 23 of 25

RT: 0.44 - 5.87 SM: 15G 3.19 3.33

100

3.05

A1

90 2.91

3.47

70 60

1 #1218 RT: 3.19 AV: 1 NL: 3.70E6 NL: 1.89E5 F: FTMS - p ESI Full ms2 [email protected] [50.00-320.00] m/z= 102.05424 146.03800-146.05260 F: FTMS - p ESI Full 3500000 ms2 [email protected] 3000000 [50.00-320.00] MS 1 Relative Abundance

Relative Abundance

80

3.61

2.78

50 40

3.75

2.64

30 3.89

2.50

B1

2500000 128.03357

2000000 1500000

146.04424 1000000

20 4.03

2.36 2.23

10

0.5

RT: 0.52 - 4.51

1.0

1.5

2.0

500000

4.17 4.31

2.09

0

2.5

3.0 3.5 Time (min)

4.0

4.72 4.99 5.27 5.54

4.5

5.0

3.19

3.37

3.09

90

A2

3.73

2.83

2.45

40 2.27

30

90

100

110

120 130 wavelength (nm)

1.60

0 1.0

150

160

170

180

B2

500000 128.03355

400000 300000

146.04414

100000

61.98656

1.78

73.01331

0

1.5

2.0

2.5 Time (min)

3.0

3.5

4.0

4.5

60

70

85.02757 80

97.20959

90

3.14 2.09

A3

80 70

NL: 1.78E4 m/z= 146.03800-146.05260 F: FTMS - p ESI d Full ms2 [email protected] [50.00-320.00] MS 4

60

None

50 40 30 20 10

2.0

2.5

3.0 Time (min)

3.5

4.0

4.5

Figure 2

Figure 3

23


120.04324

100 110 wavelength (nm)

SM: 15G

100

0 1.5

140

2.25 1.96

10

90

80

200000

20

RT: 1.50 - 4.50

70

5 #1601 RT: 3.19 AV: 1 NL: 7.09E5 NL: 6.77E4 F: FTMS - p ESI d Full ms2 [email protected] [50.00-320.00] 102.05423 m/z= 700000 146.03800-146.05260 F: FTMS - p ESI d Full ms2 600000 [email protected] [50.00-320.00] MS 5

60 50

85.02773

61.98652 60

Relative Abundance

RelativeAbundance

80 70

0

5.5

SM: 15G

100

Relative Abundance

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


120

130

140

150

160


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

Figure 4

24


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for TOC only

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Simultaneous Determination of Multiple Intracellular Primary

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