Development of a Postcolumn Infused-Internal Standard Liquid

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Development of a Postcolumn Infused-internal Standard Liquid Chromatography Mass Spectrometry Method for Quantitative Metabolomics Studies Hsiao-Wei Liao, Ching-Hua Kuo, Guan- Yuan Chen, Ming-Shiang Wu, Wei-Chih Liao, and Ching-Hung Lin J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/acs.jproteome.6b01011 • Publication Date (Web): 09 Jan 2017 Downloaded from http://pubs.acs.org on January 17, 2017

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Journal of Proteome Research

Development of a Postcolumn Infused-internal Standard Liquid Chromatography Mass Spectrometry Method for Quantitative Metabolomics Studies Hsiao-Wei Liaoa,b, Guan-Yuan Chena,b, Ming-Shiang Wuc, Wei-Chih Liaoc, Ching-Hung Lin*,†,c,d,e, and Ching-Hua Kuo*,†,a,b,f a

School of Pharmacy, College of Medicine, National Taiwan University, Taiwan The Metabolomics Core Laboratory, Center of Genomic Medicine, National Taiwan University, Taiwan c Department of Internal Medicine, National Taiwan University Hospital, Taiwan d Department of Oncology, National Taiwan University Hospital, Taiwan e Oncology Center, National Taiwan University Hospital Hsin-Chu Branch, Taiwan f Department of Pharmacy, National Taiwan University Hospital, Taiwan b

*Corresponding Author Ching-Hung Lin Address: Department of Oncology, National Taiwan University Hospital, Taiwan (R.O.C.) Tel: +886.2.3123456, ext. 67513 Fax: +886.2.23711174 E-mail:[email protected] *Corresponding Author Ching-Hua Kuo Address: School of Pharmacy, College of Medicine, National Taiwan University, Rm. 418, 4F., No.33, Linsen S. Rd., Chongzheng Dist., Taipei City 100, Taiwan (R.O.C.) Tel: +886.2.33668766 Fax: +886.2.23919098 E-mail: kuoch@ ntu.edu.tw

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ABSTRACT Quantitative metabolomics has become much more important in clinical research in recent years. Individual differences in matrix effects (MEs) and the injection order effect are two major factors that reduce the quantification accuracy in liquid chromatography-electrospray ionization-mass spectrometry (LC-ESI-MS) based metabolomics studies. This study proposed a postcolumn infused-internal standard (PCI-IS) combined with a matrix normalization factor (MNFs) strategy to improve the analytical accuracy of quantitative metabolomics. The PCI-IS combined with the MNF method was applied for a targeted metabolomics study of amino acids (AAs). D8-phenylalanine was used as the PCI-IS, and it was postcolumn infused into the ESI interface for calibration purposes. The MNF was used to bridge the AA response in a standard solution with the plasma samples. The MEs caused signal changes that were corrected by dividing the AA signal intensities by the PCI-IS intensities after adjustment with the MNF. After the method validation, we evaluated the method applicability for breast cancer research using 100 plasma samples. The quantification results revealed that the 11 tested AAs exhibit an accuracy between 88.2 and 110.7%. The principal component analysis score plot revealed that the injection order effect can be successfully removed, and most of the within-group variation of the tested AAs decreased after the PCI-IS correction. Finally, targeted metabolomics studies on the AAs showed that tryptophan was expressed more in malignant patients than in the benign group. We anticipate that a similar approach can be applied to other endogenous metabolites to facilitate quantitative metabolomics studies.

Key words: Postcolumn infused-internal standard (PCI-IS), matrix normalization factor (MNF), amino acids, breast cancer, metabolomics, LC-ESI-MS

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Introduction In recent years, metabolomics has emerged as one of the most important “omics” sciences for biomarker screening1, 2. Biomarkers used in clinical situations should be accurately quantified. Therefore, quantitative metabolomics is gaining much attention in clinical metabolomics3. Liquid chromatography-electrospray ionization-mass spectrometry (LC-ESI-MS) has been widely used in quantitative metabolomics studies due to its high sensitivity, specificity, robustness, and high throughput capability4-6. When using LC-ESI-MS for metabolomics studies, the individual differences of matrix effects (MEs) and the injection order effect are major factors that significantly reduce the quantification accuracy7-9. Quantification errors caused by MEs may lead to biased comparisons in metabolomics studies 9. The individual MEs differ between different samples, which may increase the variation within study groups, thus influencing the statistical test results. The injection order effect is another main source of analytical error in metabolomics studies because the contaminants accumulate in the ESI source and influence the ionization efficiency of the analytes during continuous sample analysis. The internal standard (IS) method is one of the common methods used to correct the injection order effect and MEs. Generally, the target analyte’s structural analogues are chosen as ISs because they have physicochemical properties similar to those of the target analyte. However, the different retention times of the target analytes and ISs may introduce biases into the IS calibration method because of the different extents of the ion suppression effect, resulting in imperfect calibration by the ISs. Accordingly, the stable isotope labeled-internal standard (SIL-IS) method has become the gold standard used to calibrate the MEs or the injection order effect that causes the quantification error. In most metabolomics studies, multiple metabolites are screened to identify potential markers. However, the cost of purchasing the multiple SIL-ISs is extremely high, and this issue is one of the most critical factors that hamper the wider application of quantitative metabolomics in clinical research. Targeted metabolomics aims to identify and quantify selected small molecules involved in specific metabolic pathways2, 5. Recent metabolomics studies have revealed the potential for using amino acids (AAs) as disease markers for diseases such as diabetes, stroke, cardiovascular diseases and cancers10-15. AAs play critical roles in many biological processes, and targeted metabolomics studies on AAs have been applied to various diseases, such as breast cancer and hyperlipidaemia16, 17. For the quantification of AAs, LC-ultraviolet detection, LC-fluorescence detection, and LC-MS are the most widely used techniques18-25. LC-MS provides better specificity 3



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and sensitivity for AA quantification without requiring derivatization18, 26, 27. It has become the mainstream platform used for AA analysis in biosamples. However, the MEs and the injection order effect may reduce the quantification accuracy for AA analysis when using LC-ESI-MS. The SIL-IS method is currently the gold standard used to calibrate the quantification errors caused by MEs. In our previous studies, we have demonstrated that a postcolumn infused-internal standard (PCI-IS) in combination with the matrix normalization factor (MNF) method can greatly improve the quantification accuracy for drugs and endogenous metabolites in various biological fluids when using LC-ESI-MS. The concept of this method involves using the PCI-IS to sense the MEs at the retention time of each analyte, and the MEs cause signal changes for the analytes that can be corrected by dividing the analyte signal intensity by the PCI-IS intensity28, 29. In addition, the MNFs were used to normalize the MEs encountered in various biofluids to the MEs encountered in the standard solutions30. When using the PCI-IS in combination with the MNF method, a single IS can be applied for the calibration of multiple analytes, which would greatly reduce the analytical cost. Considering the emerging need for an accurate and cost-effective approach for quantitative metabolomics, this study employs the advantages of the PCI-IS to resolve the problems in quantitative metabolomics. Due to the biological importance of AAs, we selected AAs as our target metabolites. The power of reducing the injection order and the ME-derived errors in metabolomics studies was demonstrated using 100 plasma samples collected from benign and malignant breast cancer patients.

Materials and Methods Chemicals Alanine, valine, threonine, leucine/isoleucine, glutamic acid, glutamine, methionine, histidine, phenylalanine, tryptophan, glutamine-2,3,3,4,4-d5 (glutamine-d5), Tryptophan-2',4',5',6',7'-D5 (Tryptophan-d5), algal amino acid mixture-13C-15N, ammonium acetate, and formic acid solution (99%) were purchased from Sigma-Aldrich Co. (St. Louis, MO). Deuterated phenylalanine (D8-phenylalanine) was purchased from Cambridge Isotope Laboratories (Andover, MA). MS-grade methanol was purchased from Scharlau Chemie (Sentmenat, Barcelona, Spain). Acetonitrile (ACN) was obtained from J.T. Baker (Phillipsburg, NJ). UHPLC-ESI-MS methods The LC separations were performed using an Agilent 1290 UHPLC system equipped with a binary solvent pump, an auto sampler, a sample reservoir, and a 4


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column oven (Agilent Technologies, Waldbronn, Germany). The coupled mass spectrometer was an Agilent 6460 triple quadrupole system (Agilent Technologies, Waldbronn, Germany). Another Agilent 1260 quaternary solvent pump was applied for the postcolumn-infusion of the PCI-IS. The PCI-IS (D8-phenylalanine) was dissolved in ACN at 1 µg mL-1 and introduced into the ESI interface at a 0.1 mL min-1 flow rate. A SeQuant® ZIC®-HILIC (3.5 µm, 100 Å) PEEK 100 x 2.1 mm column (Merck, Darmstadt, Germany) was employed for the separations. The mobile phase A consisted of 0.2% aqueous formic acid and 10 mM ammonium acetate. The mobile phase B consisted of 0.2% formic acid and 10 mM ammonium acetate in ACN. A 0.4 mL min-1 linear gradient elution was used: 0-0.5 min, 95% mobile phase B; 0.5-1.5 min, 95-60% mobile phase B; 1.5-4 min, 60% mobile phase B; and column re-equilibration with 90% mobile phase B for 2 min. The sample reservoir and column oven were maintained at 4 °C and 40 °C, respectively. The injection volume was 5 µL. The positive electrospray ionization mode was utilized with the following parameters: 350 °C dry gas temperature, 8 L min-1 dry gas flow rate, 241.32 kilopascals nebulizer pressure, 375 °C sheath gas temperature, 11 L min-1 sheath gas flow rate, 4000 V capillary voltage, and 2000 V nozzle voltage. The MS acquisition was executed in the multiple reaction monitoring (MRM) mode. The MRM transitions and detailed parameters of the mass spectrometry for 11 AAs and all of the SIL-ISs are listed in Table S1. Sample preparation Alanine, valine, threonine, leucine/isoleucine, glutamine, glutamic acid, methionine, histidine, phenylalanine, tryptophan, and D8-phenylalanine were prepared separately in 50% methanol at a concentration of 500 µg mL−1 as the stock solution. The working solution was prepared by spiking an appropriate amount of each analyte stock solution into deionized (DI) water to obtain 0.1 to 200 µg mL−1 of the diluted working solution. The plasma samples for method validation were obtained from 3 healthy volunteers. Aliquots of the working solution were spiked into the plasma samples. Protein precipitation was performed by mixing 10 µL of the plasma sample with 90 µL of methanol. The deproteinized sample was centrifuged at 15,000 g for 15 min, and the supernatant was then filtered through a 0.22-µm PP membrane (RC-4, Sartorius, Göttingen, Germany) before UHPLC-ESI-MS analysis. The PCI-IS method combined with the MNF correction method The detailed theory of the PCI-IS in combination with NMF method were 5



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reported in our previous research29,31. The analyte signal intensities in the chromatogram at every time point were divided by the PCI-IS responses at each identical retention time to generate the new adjusted chromatogram. Peak areas obtained by the new adjusted chromatogram were used for amino acid quantification using the following procedures. To calculate the MNFSTD-plasma between the standard solution and plasma matrix, AA concentrations in three plasma samples were first quantified using the standard addition method (SAM). Equation 1 was used to calculate the MNFSTD-plasma for each AA. In equation 1, Aanalyte, STD was obtained by using standard solution, and Aanalyte, M was obtained by using the adjusted chromatogram of plasma samples. AA concentration for obtaining Aanalyte, STD and Aanalyte, M should be the same. To consider inter-individual variation, the MNFSTD-plasma values obtained for the three plasma samples were averaged to obtain the MNFSTD-plasma for each AA. Finally, when testing every plasma samples, the peak areas obtained from the new adjusted chromatogram were then multiplied by the MNFSTD-plasma values to obtain the corrected area for each AA in every plasma sample. The corrected areas were interpolated into the standard curve that was generated by the standard solution for AA quantification.

MNFୗ୘ୈି୮୪ୟୱ୫ୟ =

୅౗౤౗ౢ౯౪౛,౏౐ీ ୅౗౤౗ౢ౯౪౛,౉

Equation 1

Here, A is the peak area, and the concentrations of AAs in the standard solution and the plasma sample are equal. Stable isotope labeled-internal standard (SIL-IS) method Aliquots of 11 SIL-ISs, were mixed in 50% methanol solution to obtain the mixed SIL-IS stock solution. Aliquots of the stock solution were added to deionized water to obtain 3, 10, 30, 100, and 200 µg mL-1 standard solutions to construct the calibration curve for each AA. Each 20 µL plasma sample was spiked with 20 µL of 100 µg mL-1 mixed SIL-IS stock solution, followed by protein precipitation with 160 µL of methanol. The concentrations of 11 AAs in the plasma sample were calculated with the pre-established calibration curves. Assessment of linearity, accuracy, and precision Aliquots of the stock solution were added to deionized water to obtain 3, 5, 10, 30, 50, 100, and 200 µg mL-1 AA standard solutions to build the calibration curve used for the quantification. Linear regressions were established by plotting the corrected ratios of the 11 AAs with D8-phenylalanine (PCI-IS) against the concentration of the AAs. 6


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To test the method precision and accuracy, considering the endogenous concentration, the spiking concentration was designed as 5, 10, and 20 µg mL-1 to make the final concentration lie within the linear range. Three plasma samples obtained from three healthy volunteers were quantified using the SAM and further spiked at concentrations of 5, 10, and 20 µg mL-1. All of these spiked samples were tested for four runs per day for 3 days. Collection of clinical samples The 3 plasma samples used to test the quantification accuracy were collected from 3 healthy volunteers. For the metabolomics study on breast cancer, the samples were all collected from the National Taiwan University Hospital. The benign (n=50) and malignant (n=50) groups were from the Department of Oncology. The study was approved by the institutional review board of the National Taiwan University Hospital. All plasma samples were collected after overnight fasting. The blood samples were collected in EDTA-containing tubes. The blood samples were centrifuged at 3,000 g for 15 min, and the plasma samples were stored at -80 ◦C until use. Data analysis All of the data obtained from the Agilent triple quadruple mass spectrometer were converted into the comma separated value (csv) format and processed using Microsoft Excel 2007 (Albuquerque, NM). The information in the csv file included mass transition, retention time, and signal intensity. The MS acquisition rate was set to 1.5 spectra s-1. The identification of the significant AAs was performed using two sample t-tests between the different groups. Principal Component Analysis (PCA) was performed using a web-based metabolomic data processing tool, MetaboAnalyst 2.0 (Canada) (accessible at http://www.metaboanalyst.ca).

Results Theory of the PCI-IS The original design of the PCI-IS method was utilized for the correction of individual ME differences to improve quantification accuracy. Because the MEs in ESI are primarily retention time dependent32, we postcolumn infused the PCI-IS to reflect the different MEs encountered at each retention time point. Figure 1 displays the use of phenylalanine to demonstrate the performance of the PCI-IS method. Three plasma samples were obtained from different healthy volunteers. The SAM was used to measure the phenylalanine concentration in these samples, and the samples were 7



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adjusted by spiking with the standard solution to make their final phenylalanine concentrations equal. D8-phenylalanine was used as the PCI-IS for signal correction. Before correction (Figure 1(a)), the three plasma samples with the same phenylalanine concentration showed different responses because different endogenous components in the different samples produced different extents of ME-caused signal changes. The MEs caused a similar extent of signal changes as did the D8-phenylalanine signal for the same retention time range (Figure 1(b)). Figure 1(c) displays the correction results of the PCI-IS method. The three plasma samples with the same phenylalanine concentration exhibited the same phenylalanine signal intensity after the correction. Unlike the typically used SIL-IS method, which uses each AA’s respective SIL-IS for the signal correction, a single PIC-IS can effectively correct the signal changes caused by the MEs for multiple AAs. In this study, D8-phenylalanine was used as the PCI-IS for the signal correction for all 11 tested AAs.

Figure 1. MRM chromatogram of (a) phenylalanine, (b) D8-phenylalanine (PCI-IS), and (c) phenylalanine after correction with D8-phenylalanine of three plasma samples with the same phenylalanine concentration. 3.2 Using the PCI-IS method to improve accuracy for quantitative metabolomics There are two main analytical errors in metabolomics studies: one is due to the injection order effect, and the other is caused by the individual ME differences. In this study, we proposed using a PCI-IS as the solution to overcome these two problems encountered in metabolomics studies. 8


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Correction of the injection order effect To test the effectiveness of the PCI-IS method in correcting the injection order effect, we used one plasma sample for 100 continuous injections. The system was first equilibrated by the same plasma sample for 20 injections, before the 100 continuous injections to evaluate the injection order effect. The normalized abundances of AAs before and after the PCI-IS correction are shown in Figure 2. Because the contaminants accumulate in the ESI source and influence the ionization efficiency of the analytes during continuous sample analysis, the deviations of some AAs are higher than 10% in the beginning and after 50 injections. Figure 2 shows that the deviation can be successfully removed after PCI-IS correction. PCA results of the first 20 samples and the last 20 samples obtained (a) before and (b) after the implementation of the PCI-IS method also showed successful removal of the injection order effect (Figure S1). These results lead us to conclude that the injection order effect can be significantly reduced by the PCI-IS method.

Figure 2. Normalized abundance of one plasma sample continuously injected for 100 runs (a) before and (b) after the implementation of the PCI-IS in combination with the MNF correction method. The dots with the same color represent the same AA.

Reducing within-group variation caused by MEs Metabolomics studies investigate metabolite changes according to the physiological, developmental or pathological state of the organism. It is important to provide an accurate concentration of the tested metabolites for the studied individuals. 9



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Typical profiling methods do not consider the signal intensity changes caused by the MEs, but the MEs among each studied individual may markedly differ, which can lead to a biased comparison in metabolomics studies. Because the PCI-IS method is designed to calibrate the signal changes caused by the MEs, a correct comparison for metabolomics studies can be achieved after the PCI-IS correction. We used the coefficient of variation of each AA in the same study group to show the performance of the PCI-IS method. After the PCI-IS correction, the coefficient of variation for most of the tested AAs in the three groups decreased (Figure 3). This decrease revealed that some of the variation before the PCI-IS correction originates from the differences in the individual signal changes caused by the MEs. The decrease was most obvious for glutamine, glutamic acid, and methionine, indicating that these AAs suffered from the most significant MEs.

Figure 3. The PCI-IS adjustment results of the coefficients of variation for the 11 AAs in the (a) benign group and (b) malignant group, before the PCI-IS adjustment (blue bar) and after the PCI-IS adjustment (orange bar). Absolute quantification by the PCI-IS in combination with the MNF method This study used the PCI-IS combined with the MNF method for absolute quantification of AAs. We used MNFs to bridge the ME differences encountered between different matrix types. The detailed procedure to obtain the MNFs for each AA is described in the Materials and Methods section. In this study, the MNFs for 10


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alanine, valine, threonine, leucine/isoleucine, glutamine, glutamic acid, methionine, histidine, phenylalanine, and tryptophan were 0.98, 1.17, 1.11, 1.04, 0.32, 0.81, 1.16, 1.19, 0.98, and 0.79, respectively. For the ME correction, the signal intensity of each AA was divided by the PCI-IS at each retention time point, and the acquired area was further adjusted by the MNFs for each AA. Because the PCI-IS combined with the MNF method corrected the ME-caused signal changes and bridged the differences between the matrix types, a calibration curve constructed with the standard solution was used to quantify the AAs in the plasma samples. The pooled plasma samples obtained from the three healthy volunteers were spiked with the 11 AAs to validate the PCI-IS combined with the MNF method. D8-phenylalanine was used as the PCI-IS to calibrate the analytical errors caused by the MEs. Both precision and accuracy were tested in the spiked plasma samples at 5, 10, and 20 µg mL-1 concentration levels. The repeatability and intermediate precision of the PCI-IS combined with the MNF method were tested four times a day for 3 days. The repeatability (n = 4 runs) and intermediate precision (n = 3 days) of the 11 AAs for the three test concentrations were below a relative standard deviation (RSD) of 6.3% and 13.0%, respectively. The accuracies were evaluated by 3 spiked plasma samples for 3 days, and 11 AAs had recoveries between 88.2% and 110.7% for the three test concentrations (Table 1). SIL-IS method is the current gold standard quantification method in LC-ESI-MS, we additionally compared the quantification results obtained by the PCI-IS method and SIL-IS method. Table S2 shows the quantification results of 3 plasma samples. Over 90% of the quantification results showed less than 20% difference for two methods. The linearity was tested using AA standard solutions at concentrations of 3, 5, 10, 30, 50, 100 and 200 µg mL-1. The coefficient of determination for the 11 AAs were higher than 0.998 within the test range (Table 2). The limit of detection (LOD) was determined to be the concentration at which the signal-to-noise ratio (s/n) equaled 3. The limit of quantification (LOQ) was determined to be the concentration at which the signal-to-noise ratio (s/n) equaled 10. The LODs and LOQs were both tested using the standard solution. The LODs were between 0.5 and 30 ng mL-1 and the LOQs between 1 and 100 ng mL-1 for the 11 AAs. Table 1. The accuracy (Average ± SD, %) of the 11 AAs at the 3 test concentrations of 3 spiked plasma samples. 5 µg mL-1

10 µg mL-1

20 µg mL-1

Alanine

100.8 ± 7.4

98.5 ± 2.6

98.7

± 10.1

Valine

90.4

91.1 ± 9.1

92.7

± 10.3

± 12.4 11



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Threonine

110.2 ± 3.7

105.9 ± 5.3

102.5 ± 1.5

Leucine / Isoleucine

100.4 ± 19.8

102.1 ± 11.6

110.7 ± 12.8

Glutamine

95.0

± 0.8

92.9 ± 3.5

89.3

± 1.7

Glutamic acid

106.0 ± 12.5

101.3 ± 2.2

97.4

± 4.2

Methionine

97.4

± 8.4

89.8 ± 1.9

88.2

± 3.5

Histidine

102.8 ± 10.3

100.9 ± 7.1

100.2 ± 9.5

Phenylalanine

98.4

± 6.6

100.1 ± 4.4

104.6 ± 8.0

Tryptophan

100.7 ± 9.1

103.2 ± 2.3

105.5 ± 9.2

Table 2. The calibration curves, LOQs (s/n = 10), and LODs (s/n = 3) of the 11 AAs. R2

Calibration curves Alanine Valine Threonine Leucine / Isoleucine Glutamine Glutamic acid Methionine Histidine Phenylalanine Tryptophan

LOQ

LOD

(ng mL )

(ng mL-1)

0.9997

100

30

0.9997

30

10

0.9998

50

20

0.9988

100

0.5

y = 0.0072x + 0.86x - 2.50

0.9972

100

30

2

0.9999

100

30

2

y = -0.0005x + 0.92x – 1

0.9998

1

0.5

2

y = -0.0005x2 + 0.87x - 0.69 2

y = -0.0058x + 2.36x - 2.52 2

y = 0.0003x + 0.46x - 0.53 2

y = -0.0227x + 5.34x + 0.79 2

y = 0.00006x + 0.11x + 0.20 y = 0.0035x + 1.52x - 1.29

-1

0.9995

1

0.5

2

0.998

1

0.5

2

0.9997

1

0.5

y = -0.0227x + 5.09x + 2.9 y = -0.0035x + 2.40x - 1.56

Application of the PCI-IS method for breast cancer metabolomics The validated method was applied to a breast cancer study. Plasma samples were obtained from patients with benign (n = 50) and malignant (n = 50) tumors. Table 3 displays the quantification results of the 11 AAs after correction by the PCI-IS combined with the MNF method. AA concentration differences between the two groups were tested by Student’s t-test. Tryptophan exhibited a significant difference between the benign and malignant tumor patients, and its concentration was significantly higher in the malignant group (Table 3). Table 3. Concentration comparison of the 11 AAs for the benign and malignant tumor patients (µg mL-1). Benign

Malignant

p value

Alanine

77.27 ± 23.31

84.46 ± 24.69

0.137

Valine

82.38 ± 20.09

84.89 ± 21.08

0.548

Threonine

21.63 ± 4.25

23.60 ± 5.70

0.054

12


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Leucine/Isoleucine

36.98 ± 10.18

34.40 ± 8.19

0.165

Glutamine

85.44 ± 6.86

86.93 ± 7.75

0.310

Glutamic acid

9.36 ± 2.76

9.75 ± 2.24

0.432

Methionine

3.90 ± 0.59

4.08 ± 0.65

0.143

16.03 ± 3.69

16.75 ± 3.94

0.350

9.22 ± 1.84

9.25 ± 1.76

0.927

22.33 ± 4.53

24.38 ± 5.09

0.036*

Histidine Phenylalanine Tryptophan a

All the concentrations are Mean ± SD (standard deviation).

* Significantly different (p < 0.05)

Discussion Metabolomics is increasingly being applied to the identiﬁcation of biomarkers for disease diagnosis, prognosis and risk prediction. Biomarkers used in clinical situations should be accurately quantified. Therefore, quantitative metabolomics is gaining much attention in clinical metabolomics3. To accurately quantify endogenous metabolites using LC-ESI-MS, SAM and SIL-IS method are currently the most widely used quantification methods. However, in a large-scale metabolomics study, the SAM is time consuming and labor intensive because multiple runs must be performed for each sample. Although the SIL-IS method provides accurate quantitative results and is the current gold-standard quantification method, the cost to purchase multiple isotope ISs is very high. Here, we proposed a novel method that uses a PCI-IS in combination with the MNF method for quantitative metabolomics. AAs were selected as our test metabolites due to their high biological significance. The PCI-IS was used to sense the matrix composition at the retention time of each AA, and the MNF was used to correct the difference in the MEs between the standard solution and the biofluids. Unlike the typically used SIL-IS method, the PCI-IS method does not require an isotopically labeled analog for each AA. A single isotopically labeled AA can be applied for all AAs, which would greatly reduce the analytical costs. To validate the method accuracy, we spiked three different AA concentrations (5, 10, and 20 µg mL-1) into the plasma samples. The quantification results revealed that the 11 AAs have an accuracy between 88.2 and 110.7%. The repeatability (n = 4 runs) and intermediate precision (n = 3 days) of the 11 AAs were below 6.32% and 13% of the relative standard deviation (RSD), respectively. The PCI-IS combined with the MNF method was therefore demonstrated to be an accurate and economic method for AA quantification in biological fluids. The injection order effect and the differences in individual MEs are two of the 13



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major sources of analytical errors in metabolomics studies. The injection order effect is due to the accumulation of contaminants in the ionization source that affect the signal intensity of the analytes. Because the PCI-IS is able to reflect and correct the signal changes in LC-ESI-MS, we demonstrated that the PCI-IS in combination with the MNF method could successfully remove the injection order effect. Currently, only a few metabolomics studies perform accurate quantification of the studied metabolites. For these semi-quantitative metabolomics studies, the MEs were rarely corrected. We observed that variations in the individual matrix components produced different extents of MEs that might have increased the variations within the groups. Most of the studied AAs exhibited decreased within-group variation after the PCI-IS correction (Figure 3). One of the main advantages of reducing the within-group variation is that the differences between groups can more easily be detected. Tryptophan was found to be the only AA exhibiting a significant difference between the benign and the malignant groups. It is an essential AA and has been found to be correlated with cancer cell metabolism12, 14, 33. Tryptophan oxidation via the kynurenine pathway is an important mechanism for tumoral immunoresistance34-36. It is also involved in the metabolism of breast cancer12-15. Moreover, tryptophan metabolism, which is increased via the serotonin pathway, is associated with breast cancer37, 38. An elevation of tryptophan in breast cancer patients has been observed in previous investigations13, 14. In the present study, only slight differences in the tested AAs were found between the two groups, possibly because the control group consisted of patients with benign tumors, rather than healthy control subjects.

Conclusions Quantitative metabolomics has been increasingly important in clinical studies. However, analytical barriers have limited the wider application of this promising omic science for clinical application. This study proposed an accurate and economic PCI-IS in combination with the MNF method for quantitative metabolomics. The PCI-IS in combination with the MNF method removed the injection order effect and corrected the variations in the individual matrix components that caused quantification errors; furthermore, the metabolomics analytical results were more accurate. The method was applied for the absolute quantification of AAs in plasma samples. A single PCI-IS exhibited good calibration performance for the 11 tested AAs, which may greatly reduce the analytical cost. The successful application of the proposed strategy to a breast cancer study demonstrates its applicability to clinical studies. We anticipate that a similar approach can be applied to other endogenous metabolites to facilitate the 14


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translation of metabolomics study results to clinical use.

Associated Content available Table S1. The optimized mass spectrometer parameters and MRM transitions for 11 AAs Table S2. Concentration of 16 amino acids in 3 plasma samples quantified by PCI-IS method, and SIL-IS method (n = 3 runs, µg mL-1). Figure S1. The PCA results of the first 20 samples (red dots) and the last 20 samples (green dots) (a) before and (b) after the implementation of the PCI-IS in combination with MNF correction method.

Acknowledgements This study was supported by the Ministry of Science and Technology of Taiwan (MOST 104-2113-M-002-009; 105-2113-M-002 -013 -). The authors thank the NTU Integrated Core Facility for Functional Genomics of the National Research Program for Genomic Medicine of Taiwan for the technical assistance.

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prognostic immunohistochemical biomarker panel for estrogen receptor-positive breast cancer. J Clin Oncol 2006, 24, (19), 3039-3047.

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For TOC only

20


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Graphic abstract 169x127mm (300 x 300 DPI)



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Figure 1. MRM chromatogram of (a) phenylalanine, (b) D8-phenylalanine (PCI-IS), and (c) phenylalanine after correction with D8-phenylalanine of three plasma samples with the same phenylalanine concentration. 63x47mm (300 x 300 DPI)


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Figure 2. Normalized abundance of one plasma sample continuously injected for 100 runs (a) before and (b) after the implementation of the PCI-IS in combination with the MNF correction method. The dots with the same color represent the same AA. 50x30mm (300 x 300 DPI)



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Figure 3. The PCI-IS adjustment results of the coefficients of variation for the 11 AAs in the (a) benign group and (b) malignant group, before the PCI-IS adjustment (blue bar) and after the PCI-IS adjustment (orange bar). 75x56mm (300 x 300 DPI)


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Development of a Postcolumn Infused-Internal Standard Liquid

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