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Fast quantification of endogenous carbohydrates in plasma using hydrophilic interaction liquid chromatography coupled with tandem mass spectrometry Bangjie Zhu1, Feng Liu1, Xituo Li1, Yan Wang1, Xue Gu1, Jieyu Dai2, Guiming Wang1, Yu Cheng1*, Chao Yan1* 1
School of Pharmacy, Shanghai Jiao Tong University, Shanghai 200240, China;
2
DMPK Department, HD Biosciences Co., Ltd
*
Correspondence to: Professor Chao Yan, Email:
[email protected];
or Dr. Yu Cheng, E-mail:
[email protected] Received: 19-Aug-2014; Revised: 08-Oct-2014; Accepted: 08-Oct-2014 This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as doi: 10.1002/jssc.201400899. This article is protected by copyright. All rights reserved.
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Abstract Objective: Endogenous carbohydrates in biosamples are frequently highlighted as the most differential metabolites in many metabolomics studies. A simple, fast, simultaneous quantitative method for 16 endogenous carbohydrates in plasma has been developed using hydrophilic interaction liquid chromatography coupled with tandem mass spectrometry. Method: In order to quantify 16 endogenous carbohydrates in plasma, various conditions, including columns, chromatographic conditions, mass spectrometry conditions, and plasma preparation methods, were investigated. Different conditions in this quantified analysis were performed and optimized. The reproducibility, precision, recovery, and stability of the method were verified. Result: The results indicated that a methanol/acetonitrile (50:50, v/v) mixture could effectively and reproducibly precipitate rat plasma proteins. Cold organic solvents coupled with vortex for 1 min and incubated at –20°C for 20 min were the most optimal conditions for protein precipitation and extraction. The results, according to the linearity, recovery, precision, and stability, showed that the method was satisfactory in the quantification of endogenous carbohydrates in rat plasma. Conclusion: The quantified analysis of endogenous carbohydrates in rat plasma performed excellently in the sensitivity, high throughput and simple sample preparation, which met the requirement of quantification in specific expanded metabolomic studies after the global metabolic profiling research.
Keywords: endogenous carbohydrate, hydrophilic interaction chromatography, tandem mass spectrometry, plasma This article is protected by copyright. All rights reserved.
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Abbreviations: CE, collision energy; CV, coefficient of variation; DP, declustering potential; ELSD,
evaporative
light-scattering
detection;
HPAEC–PAD,
high-performance
anion-exchange chromatography coupled with pulsed amperometric detection; LLOQ, lower limit of quantification; ULOQ, upper limit of quantification; ACN, acetonitrile; MeOH, methanol
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1 Introduction Carbohydrates, the most abundant natural products, are generally regarded as food sources and structurally fundamental blocks in organisms. Recently, studies of carbohydrates have attracted more attention for their diverse bioactivities and biological functions. Carbohydrates have been described to be useful for improving vibriosis resistance and enhancing immune activity [1]. Yet, many of recent epidemiological studies, clinical trials, and animal studies continue to point to the contribution of excess dietary carbohydrate, especially fructose, to the risk factors for nonalcoholic fatty liver disease [2, 3]. The specific carbohydrate diet and other low-complex-carbohydrate diets may be possible therapeutic options for pediatric Crohn’s disease [4]. Several non-digestible oligosaccharides are also related to a lower risk of infections and diarrhea [5]. Moreover, during the last two decades, metabolomics has become a new approach to study different diseases and carbohydrates were frequently highlighted in the most differential metabolite list in the metabolic profiling studies of biosamples such as plasma, serum and urine [6–8]. Thus, a rapid and simple quantification method to analyze endogenous carbohydrates in the biosamples becomes necessary for expanded studies in the metabolomics approaches. The analysis of complex carbohydrate mixtures is still a challenge, not only because of the low concentration in the plasma or other biosamples, but also because many carbohydrates with similar properties and high hydrophilicity are isomeric, monosaccharides could have different structures (ring opened or closed, different ring size and conformations), structures of oligosaccharides are often branched and most carbohydrates lack chromophores This article is protected by copyright. All rights reserved.
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or fluorophores, a property that makes detection difficult [9]. Chromatographic techniques are commonly used for the analysis of carbohydrates. Complex mixtures of carbohydrates have been appropriately characterized with a relatively good resolution and sensitivity by GC [10] and GC–MS [11, 12]. However, carbohydrates are not suited to direct analysis by GC or GC–MS and require preceding derivatization which is not applicable for a high throughput measurement. Regarding carbohydrate detection, the lack of an effective chromophore or fluorophore group in saccharide structures prevents their direct detection from universal spectrophotometric detectors such as UV and fluorescence detection. This is the reason why the detection of carbohydrates by UV absorption and fluorescent detectors must be performed after a tedious derivatization [13]. High-performance anion-exchange chromatography coupled with pulsed amperometric detection (HPAEC–PAD) is a high-resolution technique for trace analysis of carbohydrates and a powerful alternative to traditional HPLC methods [14–16]. However, some sample pre-treatment may be required to remove interfering compounds that would affect detection, and degradation could become a problem at a high pH required in the analysis. Although evaporative light-scattering detection (ELSD) is used as a semi-universal detector for carbohydrates during the last decade [17–19], quantification by ELSD is less straightforward, since the response factor is generally not linear. The detection of compounds using MS allows the distinction between homologues of different masses, however, isobars such as glucose, fructose cannot be differentiated by mass alone. HPLC can provide a distinct separation of carbohydrate isobars. Reverse-phase chromatographic separation using C18 columns has been used to separate carbohydrates [20, 21], but a simultaneous use of derivatization reagent is necessary to This article is protected by copyright. All rights reserved.
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increase the hydrophobicity of the sugars. Recently, HILIC has bloomed to become an alternative to RPLC for the separation of the polar compounds and high compatibility with MS. In recent years, such HILIC approaches are increasingly being chosen for various polar compounds analysis [22–24]. However, the underivatized hybrid silica (BEH) ACQUITY UPLC HILIC column has been shown to be effective for the retention and separation of polar basic compounds, but limited for other polar compounds. To meet the analytical requirement, we investigated the use of HILIC–MS/MS with HILIC amide column using a new stationary phase, to achieve a simultaneous analysis of over ten carbohydrates. In this work, we aimed to develop a rapid and simple quantitative method to separate 16 carbohydrates and quantify these endogenous metabolites in rat plasma using HILIC–UHPLC–ESI-MS/MS.
2 Materials and methods 2.1 Chemicals Acetonitrile (LC–MS grade), methanol (HPLC grade), formic acid (99.5%, LC/MS), ammonium acetate and ammonium hydroxide solution (≥25% NH3 in H2O) were obtained from Sigma–Aldrich (St. Louis, MO, USA). Melibiose, sucrose, maltose, maltotriose, maltotetraose, glucose, fructose, xylose, ribose, raffinose, fucose, inosine, adenosine, mannitol, erythritol, arabitol and lamivudine were also purchased from Sigma–Aldrich. 13
L-Methionine-
C5 ,
15
N (99%) was from Cambridge Isotope Laboratories (Andover, MA,
USA). Pure water was prepared from a Milli-Q purification system (Millipore, Bedford, MA, USA). This article is protected by copyright. All rights reserved.
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2.2 Standard solutions and endogenous-metabolite-released matrix for calibration standards and QCs Endogenous-metabolite-released plasma for calibration standards and QCs were prepared from rat plasma, which had been stripped of endogenous carbohydrates using neutral decolorizing carbon (Sigma–Aldrich, MO, USA). Charcoal was added to rat plasma at the concentration of 4 g/100 mL. The suspension was stirred at room temperature for 30 min, vortexed for 30 min, and then centrifuged at 4°C, 13 000 rpm for 15 min. The supernatant was transferred to vials to be used as the endogenous-metabolite-released plasma [25, 26]. Stock solutions of the compounds were prepared by dissolving individual compound in pure water with the concentration of 10–100 mM and stored at –80°C until use. The compounds were divided into three groups: A, B and C, depending on their concentration in rat plasma, and standard working solutions were prepared by dilution with 70% acetonitrile at different concentration levels (13 points). Calibration curve were prepared by spiking aliquots of working solutions to plasma treated by a charcoal-stripping technique. The first calibration curve was in the range of 0.001–10 µM, the second within 0.01–100 µM and the third within 0.1–1000 µM. Details on the composition of mixtures A, B, C and the concentrations were given in the Supporting Information Table S1. The QC samples were prepared in charcoal-treated plasma spiked with known amounts of analytes and internal standard at three concentration levels (Table S1). The QC samples were also divided into three groups, including 0.5 µM (LQC), 2 µM (MQC), 10 µM (HQC) in the A group, 5 µM (LQC), 20 µM (MQC), 100 µM (HQC) in the B group, and 50 µM (LQC), 200 µM (MQC), 1000 µM (HQC) in the C group. Lamivudine and L-methionine-13C5, 15N were used as internal standards. This article is protected by copyright. All rights reserved.
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2.3 Sample preparation Before analysis, the plasma samples were thawed to room temperature, then, an aliquot of 100 µL plasma sample was precipitated with 500 µL cold ACN/MeOH (50:50, v/v) containing 2 μM lamivudine and 10 μM L-methionine-13C5,
15
N. The mixture was vortexed
for 1 min, placed at –20°C for 20 min, and then centrifuged at 4°C, 13000 rpm for 15 min. 300 µL supernatant was transferred to 96-well plate for LC–MS/MS analysis. 2.4 UHPLC–MS/MS Conditions The UHPLC–MS/MS system consisted of an ACQUITY ultra performance liquid chromatographic system (Waters, Milford, MA, USA) coupled with an API 4000 triple quadrupole mass spectrometer (AB Sciex, Foster City, CA, USA) equipped with a Turbo Ion Source (ESI) operating with negative mode. Chromatographic separation was conducted on an ACQUITY UPLC BEH amide column (2.1mm 100 mm, 1.7 µm particle size) equipped with an ACQUITY UPLC BEH Amide 1.7 µm Van-Guard Pre-column. The column temperature was maintained at 60°C. Mobile phase A consisted of 10 mM ammonium acetate in H2O/acetonitrile (95:5, v/v), and mobile phase B consisted of 10 mM ammonium acetate in acetonitrile/H2O (95:5, v/v). The separation of carbohydrates was achieved using the following gradient program at a flow rate of 250 µL/min over a course of 10 min: started with a linear increase from 2% A to 40% A in 6.0 min, then maintained at 40% A for 1.6 min. The mobile phase was allowed to return to the initial condition within 0.1 min, followed by column re-equilibration for 2.3 min. The injection volume was 10 µL. The compounds of interest were detected by MS/MS in negative ion multiple reaction monitoring (MRM) mode. In the RP-HPLC–MS/MS analysis, the precursor ions of This article is protected by copyright. All rights reserved.
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carbohydrates are usually detected as formate or acetate adducts, because of the derivatization process or additive conditions. Yet, in the HILIC–MS/MS analysis, the [M–H]– precursor ions were stronger in the MS response among the possible precursor ions in most analytes, as shown in Table S2 and Figure S1. Therefore, the [M–H]– precursor ions were used for the carbohydrates and their internal standards. Turbo V ion source parameters were common to all analytes: The capillary voltage was –4500 V, and the source temperature was at 550°C. The curtain gas (N2) and collision gas (N2) setting were 30 psi and 10 psi, respectively. The pressure for nebulization gas and vaporization gas setting were 60 psi. The entrance potential (EP) and collision cell exit (CXP) were –50 and –10 V, respectively. The declustering potential (DP) and collision energy were optimized for each analyte. The specific mass transition of each carbohydrate and IS are presented in Table S3. Analyst 1.5 software (AB Sciex, Foster City, CA, USA) was used for data acquisition and processing. 2.5 Method validation The method was validated for linearity, LOD, LOQ, precision, recovery, matrix effect, retention time reproducibility and stability. The analytical curves were examined by using internal standard spiked calibration solutions at 13 concentration levels, ranging from the lower limit of quantification (LLOQ) to the upper limit of quantification (ULOQ) with an injection volume of 10 µL. Integrated peak areas of the selected quantification MRM transitions were used to build the curves. Curves were fitted by a weighted (1/X) least squares regression analysis using the Analyst 1.5 software. LOD and LOQ were calculated based on the S/N of 3:1 and 10:1, respectively. The method precision was evaluated using three different concentration points (LQC, MQC, HQC) This article is protected by copyright. All rights reserved.
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that spiked the QC solutions to the treated matrix. Six replicates of each point of QCs were analyzed to determine the intra-day precision. This process was repeated three times over three days to determine the inter-days precision. Precision was expressed as the coefficient of variation (CV) of the determination of QCs. Inter-day precision were calculated similarly for the 18 replicates of each concentration point pooled from the three validation runs. The reproducibility of retention time for each analyte was also evaluated. Recovery was evaluated at low, medium and high QC concentration. The recovery was calculated by dividing the corrected mean peak area of six replicates of each analyte spiked before extraction by that of each analyte spiked after extraction, comparing the peak area ratio of the same concentration of analytes and IS in spiked samples [27]. Matrix effect was evaluated at high QC concentration in six replicates. As carbohydrates in the plasma were endogenous and the endogenous-metabolite-released surrogate matrix of charcoal-stripped plasma was used, the matrix effect was evaluated at two levels: the endogenous plasma sample compared to the endogenous-metabolite-released surrogate matrix, and the endogenous-metabolite-released surrogate matrix compared to the neat standard solution. In details, HQC work solution was spiked in the surrogate matrix, precipitated and got the peak area ratio of the analytes and IS (a); HQC work solution was spiked in the real plasma sample, precipitated and got the peak area ratio of the analytes and IS (b); HQC work solution was spiked in the neat standard solution (standard in 50% acetonitrile/water, v/v), precipitated and got the peak area ratio of the analytes and IS (c); The real plasma sample was precipitated directly, and got the peak area ratio of the analytes and IS, which was the endogenous value of each real plasma (d); The matrix effects were This article is protected by copyright. All rights reserved.
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calculated using a modified version of the equation described by Matuszewski et al. [28]: ME1 % = (b – d) / a × 100% (1) ME2 % = a / c × 100% (2) The stability was investigated in six replicates. HQC post-preparative samples were stored at 4°C in the dark for 0, 24, 48, and 72 h from the time of sample preparation. The peak area of each compounds and IS were determined and compared to the freshly prepared solutions (0 h).
3 RESULTS AND DISCUSSION 3.1 MS/MS condition optimization Sensitivity and specific mass spectrometric detection condition were evaluated and optimized. Most of the carbohydrates showed the MS response only in the negative ESI mode. The [M–H]– precursor ions were used for the carbohydrates and their internal standards, because the [M–H]– precursor ions were stronger in the MS response among the possible precursor ions in most analytes, as shown in Table S2 and Figure S1. The declustering potential (DP) and collision energy were important parameters for each compound and the optimized MS parameters are summarized in Table S2 and Table S3. Ion source values optimized to get proper response are described in Section 2. 3.2 Chromatographic condition optimization Carbohydrates might have different structures with the same precursor ion and daughter ion, even the DP and collision energy. It is difficult to differentiate them by mass alone, and UHPLC system coupled with MS was employed to achieve good separation. Columns, the This article is protected by copyright. All rights reserved.
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mobile phase composition and column temperature were evaluated and optimized. Several columns including a Phenomenex Gemini C18 column, ACQUITY UPLC HSS T3 column, and XBridge BEH C18 column were evaluated. These columns were found not suitable because of unsatisfactory peak shape, low response, poor separation or even no retention on the column. The newly released ACQUITY UPLC BEH Amide Column (2.1 100 mm, 1.7 µm particle size) is a HILIC column suitable for polar chemicals, the carbohydrates were well retained and separated from each other. Different additives of mobile phase, like formic acid, ammonium hydroxide solution, and ammonium acetate, were evaluated and optimized. Better peak shape and separation were obtained when the concentration of ammonium acetate in mobile phase increased from 0 to 10 mM. But higher concentration of ammonium acetate led to decrease MS response of most of analytes (Fig. S2a). The pH value of mobile phase was optimized by the addition of ammonium hydroxide solution, ammonium acetate or formic acid, respectively. It was observed that the reducing sugars were in double-peak in acidic mobile phase (pH=3.0), while the response of most carbohydrates was decreased in basic environment (pH=9.8; Fig. S2b). Therefore, the nearly neutral mobile phase (pH=6.8–7.0) was finally used. Column temperature is also an important parameter in chromatographic separation. Four temperatures of 30, 40, 50, 60°C were evaluated. The reducing sugars, e.g. glucose, fructose, ribose and fucose failed to present good peak shapes at the column temperature of 30, 40, and 50°C. Nevertheless, when the oven temperature increased to 60°C, the analytes performed good separation, high response, and good peak shapes. The reducing sugars exhibited single peaks, possibly due to alpha and beta anomers This article is protected by copyright. All rights reserved.
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formed by mutarotation [29, 30]. The reason for single peaks is the fast interconversion rates as compared to the chromatographic time scale. That means interconversion between alpha and beta anomers is by orders of magnitude faster than the chromatographic equilibria [19, 31]. However, a higher pH value of the eluent resulted in stronger degradation of these carbohydrates [16]. Considering the manufacturer recommendation of pH 2.0–11 and column temperature below 90°C in the use of this BEH Amide column, a column temperature of 60°C in combination with neutral mobile phase would be better for maintaining the column life time. The optimized chromatographic conditions are given in Section 2, with a high throughput of less than 10 min in a run. A typical MRM chromatogram of the analytes spiked in charcoal treated plasma is shown in Fig.1. 3.3 Sample preparation Sample preparation of plasma is a critical step in an accurate and reliable LC–MS/MS assay. The protein was precipitated by organic solvents. The composition of the organic solvents was evaluated and optimized. Cold solvent of ACN/MeOH (50:50, v/v) was used to precipitate the protein and placed at –20°C for 20 min after the vortex of the mixture of solvent and plasma, which was excellent for the preparation of plasma before HILIC–UHPLC–MS/MS. 3.4 Method validation As the analytes in the plasma are endogenous, the removal of the endogenous metabolites was critical for the matrix preparation in the linearity of standards in the quantitative method. A variety of matrices have been widely used for the quantification of endogenous compounds in biological samples, such as artificial matrix. In this assay, charcoal This article is protected by copyright. All rights reserved.
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stripped rat plasma was selected as the endogenous-metabolite-released matrix for the preparation of calibration standards [25]. 3.4.1 Linearity and sensitivity After the optimization of analytical conditions, the linearity was studied. The calibration curves showed a good linearity (mostly, correlation coefficient of r > 0.999), where y is the peak area ratio of the analyte to the IS and x is the plasma concentration of the analyte. All back-calculated standards met the criteria of ≤ ±15% deviation from nominal concentration. LODs and LOQs were calculated based on the S/N of 3:1 and 10:1, respectively. The linear range, coefficients of determination, LODs and LOQs of all analytes are summarized in Table 1. 3.4.2 Matrix effect, recovery and precision The matrix effect, extraction recovery, and intra- and inter-day precision of all analytes at three concentration levels were shown in Table 2. The two levels of matrix effect performed well,
ME1
%
(the
endogenous
plasma
sample
compared
to
the
endogenous-metabolite-released surrogate matrix) ranged from 96.0 to 118.0, and ME2 % (the endogenous-metabolite-released surrogate matrix compared to the neat standard solution) ranged from 73.0 to 120.0. The intra- and inter-day precision (CV) ranged from 0.53–7.50 and from 1.16–20.8%, respectively. The recovery values of three concentration levels of QC samples ranged from 67.2–110%. Precision and recovery were all within the acceptable range, indicating that the current method was reliable and reproducible. 3.4.3 Reproducibility of retention time and stability The reproducibility of retention time for the analytes, expressed as CV(%), was less than This article is protected by copyright. All rights reserved.
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0.25% in one day and below 1.62% over three days. The stability data fall within a 15% deviation range, suggesting that the stability of post-preparative samples was acceptable under routine laboratory analysis without incurring any significant loss of detection. The corresponding data is presented in Table 3. 3.4.4 Application in rat plasma samples The proposed method was applied in the determination of 18 Sprague–Dawley rat plasma samples. Using this analytical method, we were able to measure the concentration of 11 carbohydrates in all 18 plasma samples. Some of the carbohydrates were previously reported in the quantification of the rat plasma and our data was consistent with the results in the literatures. Some of the carbohydrates were first quantified in the rat plasma. The quantified results of carbohydrates are shown in Table 4. 4 Conclusions In this work, HILIC was coupled with negative ion mode ESI-MS/MS for the simultaneous quantitation of 16 polar metabolites, such as sugars, sugar alcohols and nucleosides in rat plasma. This quantification method performed excellently in the sensitivity, high throughput and simple sample preparation, which met the requirement of quantification in specific expanded metabolomic studies after the global metabolic profiling research.
Acknowledgements This work was financially supported by the National Natural Science Foundation of China (21175092 and 21105064), Shanghai Research Fund for the Post-Doctoral Program (13R21415000), Specially-funded program on the development of national key scientific This article is protected by copyright. All rights reserved.
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instrument
and
Page 16
equipment
(2011YQ150072,
Journal of Separation Science
2011YQ15007204,
2011YQ15007207,
2011YQ15007210), Natural Science Foundation of Shanghai (12ZR1413600), National innovative program (12C26213202212), and Suzhou Specially-funded program on the development of Science and technology (SG201214). The work was also financially supported by Shanghai Ziyuan Pharmaceutical Co., Ltd.
Conflict of interest The authors have declared no conflict of interest.
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[21] Wu, X., Jiang, W., Lu, J., Yu, Y., Wu, B., Food Chem. 2014, 145, 976–983. [22] Vilhena, R. D., Pontes, F. L., Marson, B. M., Ribeiro, R. P., Carvalho, K. A., Cardoso, M. A., Pontarolo, R., J. Chromatogr. B. Analyt. Technol. Biomed. Life Sci. 2014, 967C, 41–49. [23] Trivedi, D. K., Iles, R. K., Biomed. Chromatogr. 2014. [24] Yao, X., Zhou, G., Tang, Y., Guo, S., Qian, D., Duan, J. A., Drug Test Anal. 2014. [25] Kinoshita, K., Jingu, S., Yamaguchi, J., Anal. Biochem. 2013, 432, 124–130. [26] Samtani, M. N., Jusko, W. J., Biomed. Chromatogr. 2007, 21, 585–597. [27] Wang, H., Chung-Davidson, Y. W., Li, K., Scott, A. M., Li, W., Talanta 2012, 89, 383–390. [28] Matuszewski, B. K., Constanzer, M. L., Chavez-Eng, C. M., Anal. Chem. 2003, 75, 3019–3030. [29] Brons, C., Olieman, C., J. Chromatogr. 1983, 259, 79–86. [30] Alpert, A. J., Shukla, M., Shukla, A. K., Zieske, L. R., Yuen, S. W., Ferguson, M. A., Mehlert, A., Pauly, M., Orlando, R., J. Chromatogr. A. 1994, 676, 191–122. [31] Hinterwirth, H., Lammerhofer, M., Preinerstorfer, B., Gargano, A., Reischl, R., Bicker, W., Trapp, O., Brecker, L., Lindner, W., J. Sep. Sci. 2010, 33, 3273–3282.
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Table 1 Analytical performance of the method Retention
Linearity
Correlation
LOD
Compound
LOQ (nM) Time (min)
range (μM)
Coefficient, r
(nM)
Ribose
3.06
1-1000
0.999
180
600
Adenosine
3.18
0.003-10
0.9994
0.9
3
Erythritol
3.18
0.1-600
0.9993
30
100
Fucose
3.48
0.3-1,000
0.9993
90
300
Inosine
3.8
0.003-3
0.9991
0.3
1
Xylose
3.83
0.6-1,000
0.9999
180
600
Arabitol
3.99
0.06-60
0.9976
18
60
Fructose
4.22
0.3-60
0.9995
90
300
Mannitol
4.66
0.6-100
0.998
120
400
Glucose
4.82
1-1,000
0.9991
300
1,000
Sucrose
5.6
0.6-100
0.999
180
600
Maltose
5.95
0.06-60
0.9991
18
60
Melibiose
6.3
0.1-100
0.9989
30
100
Raffinose
6.65
0.03-100
0.9996
3
10
Maltotriose
6.74
0.03-100
0.9991
6
20
Maltotetraose
7.31
0.06-100
0.9997
7.5
25
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Table 2 Matrix effect, recovery and precision of the method at low, medium and high concentrations
Compound
Matrix effect 1 (%)
Precision (CV%)
Precision (CV%)
Intra-day assay (n=6)
Inter-day assay (n=18)
Recovery
Matrix effect 2 (%) Low
Medium
High
Low
Medium
High
Low
Medium
High
Melibiose
107
99.7
92.9
97.3
91.4
0.758
2.36
1.92
3.63
11.1
4.50
Sucrose
117
106
100
92.6
92.2
1.76
2.04
2.87
4.05
5.99
3.15
Maltose
102
108
99.7
99.3
103
1.99
2.80
4.80
6.70
12.6
5.09
Maltotriose
115
99.3
102
98.2
91.3
1.83
2.09
0.877
3.42
3.29
2.35
Maltotetraose
98.1
78.2
72.4
67.7
67.2
0.680
1.48
2.73
2.67
5.98
3.90
Glucose
96.0
101
90.9
89.7
105
1.48
2.03
1.37
13.1
7.70
1.90
Fructose
118
98.9
95.5
75.5
110
2.71
2.07
2.06
8.53
10.7
3.55
Xylose
116
73.0
104
105
102
3.07
1.06
0.890
7.85
6.04
5.74
Ribose
100
81.4
95.8
97.4
102
1.37
2.38
2.35
7.76
9.14
4.70
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Journal of Separation Science
Raffinose
110
90.0
92.4
93.5
90.5
2.92
2.22
2.24
2.95
6.81
3.11
Fucose
107
106
93.9
90.1
89.7
1.96
1.28
1.29
8.76
9.60
6.96
Inosine
97.4
103
99.9
96.6
101
3.36
7.50
4.70
8.77
10.8
10.0
Adenosine
97.2
101
98.5
94.3
92.9
3.11
0.366
1.45
1.54
2.88
3.35
Mannitol
115
101
100
99.8
95.7
5.51
3.76
1.26
20.8
18.5
2.42
Erythritol
97.5
104
99.3
97.5
96.7
1.51
0.528
0.984
1.78
1.76
1.16
Arabitol
111
120
99.4
90.5
90.4
2.40
0.721
0.755
3.88
2.77
2.34
Matrix effect 1 %, the spiked endogenous plasma sample compared to the spiked endogenous-metabolite-released surrogate matrix. Matrix effect 2 %, the spiked endogenous-metabolite-released surrogate matrix compared to the neat standard solution.
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Table 3 The reproducibity of retention time and stability of the sixteen carbohydrates.
Compounds
0 h (%)
Retention time
Retention time
Stability
reproducibility
reproducibility
24 h (%) 48 h (%) 72 h (%)
(Intra-day, n=6, (Inter-day, n=18, CV%)
CV%)
Melibiose
100
102
98.9
100
0.000
0.125
Sucrose
100
101
100
96.9
0.000
0.0987
Maltose
100
104
101
98.2
0.000
0.0866
Maltotriose
100
106
103
104
0.000
0.0885
Maltotetraose
100
97.6
100
97.9
0.000
0.0569
Glucose
100
104
104
102
0.0863
0.208
Fructose
100
104
103
97.8
0.119
0.373
Xylose
100
95
96.7
97.4
0.113
1.62
Ribose
100
106
101
102
0.182
0.417
Raffinose
100
108
104
105
0.000
0.144
Fucose
100
105
103
101
0.250
1.05
Inosine
100
113
105
111
0.139
0.236
Adenosine
100
97.8
101
96.7
0.000
0.263
Mannitol
100
98.0
98.6
96.5
0.119
0.225
Erythritol
100
105
103
101
0.130
0.319
Arabitol
100
102
103
100
0.141
0.324
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Table 4 Carbohydrates concentration in SD rat plasma Compounds
Concentration (nM) (n=18)
Melibiose
169±30.8
Sucrose
1937±298
Maltose
1994±228
Maltotriose
BLQ
Maltotetraose
BLQ
Glucose
11,165,000±706,983
Fructose
6103±2065
Xylose
BLQ
Ribose
BLQ
Raffinose
BLQ
Fucose
1894±155
Inosine
60.9±34.8
Adenosine
5.01±3.45
Mannitol
6311±897
Erythritol
1372±92.6
Arabitol
1229±178
Note: BLQ- Below the lower limit of quantitation
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Figure 1 UHPLC–MS/MS chromatographic traces for 16 compounds and two internal standards on the HILIC Amide column. a. Chromatograms of standards spiked in the plasma. b. Chromatograms of endogenous compounds in the plasma sample. 1) ribose (RT: 3.06 min), 2) xylose (RT: 3.83 min), 3) fructose (RT: 4.22 min), 4) glucose (RT: 4.82 min), 5) maltotetraose (RT: 7.31 min), 6) lamivudine (RT: 2.61 min), 7) adenosine (RT: 3.18 min), 8) inosine (RT: 3.80 min), 9) sucrose (RT: 5.60 min), 10) raffinose (RT: 6.65 min), 11) fucose (RT: 3.48 min), 12) methionine (RT: 4.63 min), 13) melibiose (RT: 6.30 min), 14) maltotriose (RT: 6.74 min), 15) erythritol (RT: 3.18 min), 16) arabitol (RT: 3.99 min), 17) mannitol (RT: 4.66 min), 18) maltose (RT: 5.95 min).
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