Pseudotargeted Metabolomics Method and Its Application in Serum

Jul 26, 2013 - coupled with triple quadrupole (QQQ) MS operated in the multiple reaction ... UHPLC/QQQ MRM MS-based pseudotargeted method displayed ...
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Pseudotargeted Metabolomics Method and Its Application in Serum Biomarker Discovery for Hepatocellular Carcinoma Based on Ultra High-Performance Liquid Chromatography/Triple Quadrupole Mass Spectrometry Shili Chen, Hongwei Kong, Xin Lu, Yong Li, Peiyuan Yin, Zhongda Zeng, and Guowang Xu* CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 457 Zhongshan Road, Dalian 116023, China S Supporting Information *

ABSTRACT: Untargeted analysis performed using full-scan mass spectrometry (MS) coupled with liquid chromatography (LC) is commonly used in metabolomics. Although they are commonly employed, full-scan MS methods such as quadrupole-time-of-flight (Q-TOF) MS have been restricted by various factors including their limited linear range and complicated data processing. LC coupled with triple quadrupole (QQQ) MS operated in the multiple reaction monitoring (MRM) mode is the gold standard for metabolite quantification; however, only known metabolites are generally quantified, limiting its applications in metabolomic analysis. In this study, a pseudotargeted approach was proposed to perform serum metabolomic analysis using an ultra highperformance liquid chromatography (UHPLC)/QQQ MS system operated in the MRM mode, for which the MRM ion pairs were acquired from the serum samples through untargeted tandem MS using UHPLC/Q-TOF MS. The UHPLC/QQQ MRM MS-based pseudotargeted method displayed better repeatability and wider linear range than the traditional UHPLC/Q-TOF MS-based untargeted metabolomics method, and no complicated peak alignment was required. The developed method was applied to discover serum biomarkers for patients with hepatocellular carcinoma (HCC). Patients with HCC had decreased lysophosphatidylcholine, increased long-chain and decreased medium-chain acylcarnitines, and increased aromatic and decreased branched-chain amino acid levels compared to healthy controls. The novelty of this work is that it provides an approach to acquire MRM ion pairs from real samples, is not limited to metabolite standards, and it provides a foundation to achieve pseudotargeted metabolomic analysis on the widely used LC/QQQ MS platform.

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groups are identified, and their biological functions are determined. High-resolution full-scan MS methods such as time-of-flight (TOF), quadrupole-time-of-flight (Q-TOF), Orbitrap, and Fourier transform ion cyclotron resonance are commonly used in untargeted metabolomic analysis.6 These types of MS provide accurate mass to facilitate metabolite identification and unbiased full-scan information to enable the detection of as many metabolites as possible. However, there are also some limitations of full-scan MS, such as the ease with which the detector can be saturated,10 which limits its linear range and makes the accurate quantification of metabolites with concentrations across several orders of magnitude challenging. Because the scan time of every consecutive m/z is limited, the quantification repeatability of metabolites is also affected. Additionally, the number of retrieved metabolites and the accuracy of peak integration are greatly influenced by the peak alignment parameters,11,12 and some false results arise from the

etabolomics aims to systematically study the smallmolecule metabolites present in biological samples,1,2 and it is widely applied in disease research to discover biomarkers for early diagnosis,3 to investigate the pathogenesis of diseases,4 and to evaluate treatment and drug effects.5 Metabolomics platforms are mainly based on nuclear magnetic resonance and mass spectrometry (MS),6 among which LC coupled with MS (LC/MS) has become a key technology due to its simple sample pretreatment process, fast and reproducible analysis, ability to analyze metabolites with a wide range of polarity and samples with different complexity, and widespread availability.7,8 Untargeted analysis is the most commonly employed method in LC−MS-based metabolomics studies. No a priori knowledge of the components of the samples is required; the samples only need to be pretreated according to the defined procedure, and the extracted metabolites are separated by LC and detected by full-scan MS.9 After data acquisition, the peaks are aligned by software to obtain the retention time, mass-to-charge ratio (m/ z), and intensity and subjected to subsequent statistical analysis. Then, metabolites with statistically different levels between © 2013 American Chemical Society

Received: June 5, 2013 Accepted: July 26, 2013 Published: July 26, 2013 8326

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samples of the HCC or control groups, respectively, for untargeted tandem MS using UHPLC/Q-TOF MS. The quality control (QC) sample22 was also prepared by mixing an equal aliquot from all of the HCC and control samples for repeatability and linearity testing on UHPLC/Q-TOF MS and UHPLC/QQQ MRM MS and also for the QC of the batch analysis of real samples. For untargeted tandem MS using UHPLC/Q-TOF MS, 200 μL pooled serum was deproteinized with 800 μL acetonitrile. After vortexing for 2 min, centrifugation at 12000g was performed for 15 min at 4 °C, and then 800 μL supernatant was lyophilized and reconstituted in 100 μL water for LC−MS analysis. For the repeatability test, 100 μL QC serum was deproteinized with 400 μL acetonitrile; 400 μL lyophilized supernatant was reconstituted in 100 μL water, and 10 replicates were prepared. For the linearity test, 20, 50, 100, 200, or 400 μL QC serum was deproteinized with four volumes of acetonitrile; 80, 200, 400, 800, or 1600 μL lyophilized supernatant was reconstituted in 100 μL water, and triplicate samples for each volume of serum were prepared. For metabolomic analyses of the real samples, 100 μL serum was deproteinized with 400 μL acetonitrile and 400 μL lyophilized supernatant was reconstituted in 100 μL water. UHPLC/Q-TOF MS for Untargeted Tandem MS and Metabolomic Analysis of Serum Samples. An Agilent 1200 rapid-resolution liquid chromatography (RRLC) system coupled online via electrospray ionization with an Agilent 6510 Q-TOF MS system (Agilent, Santa Clara, CA) was used for untargeted tandem MS. Five microliters of the reconstituted extract was injected into a reversed-phase UPLC ACQUITY T3 column (2.1 mm × 100 mm × 1.8 μm) maintained at 35 °C. Water and acetonitrile were used as mobile phases A and B, respectively, and both contained 0.1% formic acid. The flow rate was 0.3 mL/min, and the gradient elution was as follows: 1% B maintained for 1 min and then linearly increased to 40% B from 1 to 5 min, to 50% B from 5 to 8 min, to 65% B from 8 to 10 min, to 76% B from 10 to 16 min, and to 100% B from 16 to 20 min and maintained for 5 min, followed by equilibration at 1% B for 4.9 min. The mass spectrometer was operated in positive ion mode with a capillary voltage of 4000 V, fragmentor voltage of 175 V, skimmer voltage of 65 V, nebulizer gas (N2) pressure at 45 psi, drying gas (N2) flow rate of 9 L/min, and a temperature of 350 °C. For untargeted tandem MS, the “auto MS/MS” function of the Q-TOF MS system with data-dependent acquisition was performed in positive ion mode for the five most intense precursors within one full scan cycle (0.25 s) with a precursor ion scan range of m/z 100−1000 and a tandem mass scan range of m/z 40−1000. The collision energies were set at 10, 20, and 40 eV, and both the pooled HCC and control samples were analyzed to obtain abundant and complementary product ion information. For untargeted metabolomic analysis of serum samples, the mass spectrometer was operated in positive full scan mode with a scan range of m/z 100−1000. MRM Ion Pairs Selection from Untargeted Tandem MS. After data acquisition, untargeted tandem MS spectra were processed using the “Find by Auto MS/MS” function of MassHunter Qualitative Analysis software to automatically extract ion pair information for subsequent MRM detection. The retention time window was set to 0.15 min; the positive MS/MS threshold was set to 100, and the mass match

imperfect algorithm and the complex data, which will influence the results of data processing and the reproducibility of different batch analyses. Another LC−MS-based metabolite analysis method is targeted analysis, which only measures selected known metabolites. This method is becoming more widespread in the metabolomics field.13,14 Targeted analysis is often performed using an LC coupled with triple quadrupole (QQQ) MS operated in the multiple reaction monitoring (MRM) mode. MRM selects the characteristic precursor ion of the metabolite in the first quadrupole, the selected ion is collided in the second quadrupole to produce the product ions, and the third quadrupole selects the characteristic product ion.15 The combination of the characteristic precursor ion and product ion makes MRM very specific by reducing the number of interfering ions. MRM has a wide linear dynamic range that spans four to five orders of magnitude16 to enable the measurement of metabolites with largely different concentrations in complex samples. The scan time for each selected metabolite is adequate, and the method also has good repeatability and sensitivity, which assures the accuracy of batch sample analysis. Because the metabolites that should be detected are predefined, no complicated data processing is needed, which simplifies and accelerates the data mining procedure and enhances the accuracy of the data analysis results. These advantages have made MRM the gold standard for metabolite quantification.17 Typically, MRM ion pairs are acquired from metabolite standards.18,19 However, metabolomic analysis permits the analysis of all metabolites in a given biological sample. Because it is impossible to obtain a standard for each metabolite, the targeted analysis is restricted by the metabolite coverage. Recently, we have reported an approach to transforming a nontargeted metabolic profiling method to a pseudotargeted method based on gas chromatography/mass spectrometryselected ion monitoring.20 In this study, we developed a pseudotargeted approach to perform metabolomics using ultra high-performance liquid chromatography (UHPLC)/QQQ MS operated in the MRM mode, in which the ion pairs were acquired from the real samples through untargeted tandem MS using UHPLC/Q-TOF MS. To effectively detect hundreds of metabolite ion pairs, dynamic MRM was employed to monitor every MRM ion pair near its expected retention time. To our knowledge, this is the first time that pseudotargeted metabolomics has been performed using a UHPLC/QQQ MS system operated in the MRM mode. As a proof of concept, the developed method was used to discover serum biomarkers for hepatocellular carcinoma (HCC), which is the fifth most common cancer and the third most common cause of cancerrelated mortality worldwide.21

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EXPERIMENTAL SECTION Chemicals. HPLC-grade acetonitrile was purchased from Merck (Darmstadt, Germany). Formic acid was obtained from Sigma-Aldrich (St. Louis, MO). Ultrapure water was filtered through the Milli-Q system. Sample Collection and Preparation. Serum samples from 29 patients with HCC were collected from the First Hospital of Jilin University in Changchun, China, and serum samples from 30 age-matched healthy controls were also collected. An equal aliquot from each patient with HCC or each healthy control was mixed separately to form the pooled serum 8327

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RESULTS AND DISCUSSION Construction of a UHPLC/QQQ MRM MS-based Pseudotargeted Metabolomics Method. In the present study, we aimed to establish a pseudotargeted metabolomics method using a UHPLC/QQQ MS system operated in the MRM mode without using information from metabolite standards. Traditionally, LC/QQQ MS has been used for targeted metabolite analysis, for which metabolite standards are used to obtain the MRM ion pairs and optimize their chromatographic and mass spectrometric parameters. However, it is impossible to acquire standards for most of the metabolites encountered in metabolomics analyses; thus, it is not easy to perform unbiased metabolomics studies using an LC/QQQ MS system operated in the MRM mode. To resolve the issue of ion pairs acquisition, we proposed a strategy using the naturally untargeted instrument, a full-scan Q-TOF MS system, to directly obtain the ion pairs from the real samples in an unbiased manner. As depicted in Figure 1, we

tolerance was set to 0.02 Da. The single mass expansion was set to symmetric 100 ppm, and the persistent background ions, such as reference mass ions, were excluded. After execution, the detected metabolite ions with information about the precursor ion, product ions, retention time, and collision energy were exported to a spreadsheet. Ion pair spreadsheets of pooled sera from patients with HCC or healthy controls with different collision energies were combined, and ion pairs were selected on the basis of the following rules: different precursor ions eluted in the neighboring time range were scrutinized to exclude the isotopic, fragmentation, adduct, and dimer ions; and the product ion that appeared with most of the applied collision energies and with the highest intensity was selected as the characteristic product ion. UHPLC/QQQ MRM MS Method and Analysis of Serum Samples. An Agilent 1290 UHPLC system coupled online via electrospray ionization with an Agilent 6460 QQQ MS was used for UHPLC/QQQ MRM MS-based pseudotargeted metabolomics. The chromatographic column, mobile phases, and injection volume used with this system were the same as those that were used with the UHPLC/Q-TOF MS system, although the gradient elution procedure (Table S1 of the Supporting Information) was based on the RRLC method using Intelligent System Emulation Technology (ISET, Agilent) to ensure similar elution behavior. The QQQ MS system was operated in positive ion mode with a capillary voltage of 4000 V and a nozzle voltage of 600 V. The nebulizer gas (N2) pressure was set at 40 psi with a drying gas (N2) flow rate of 9 L/min and a temperature of 350 °C. The selected ion pairs derived from untargeted UHPLC/QTOF MS/MS were detected in dynamic MRM, in which each ion pair was monitored close to its expected retention time, which ensured adequate scan time for other ion pairs. The fragmentor voltage and collision energy of each ion pair were optimized to obtain the highest response. After optimization, the defined parameters were imported to the workstation and the UHPLC/QQQ MRM MS-based pseudotargeted metabolomics method was established. For metabolomic analyses of serum samples, 5 μL of reconstituted extract was injected into the UHPLC/QQQ MS system, and the QC sample was added to the analysis batch after every six samples. Data Processing and Statistical Analysis. For the metabolites that were detected by the UHPLC/Q-TOF MSbased metabolomics method, peak detection and alignment were performed using XCMS software23 with the default parameters excluding the full width at half-maximum, which was set to 10 (fwhm = 10) and the retention time window, which was set to 7 (bw = 7). Isotopic peaks were excluded. Normalization to the total studied peak area for each sample was performed when necessary. The areas of the peaks measured by UHPLC/QQQ MRM MS were integrated using MassHunter Quantitative Analysis software. Normalization to the total studied peak area for each sample was performed when necessary. Student’s t-test was performed using SPSS statistics (version 13, SPSS Inc.), with the level of statistical significance set at p < 0.05. Principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) were performed using SIMCA-p (Umetrics, Umeå, Sweden) software. A heatmap was generated using MultiExperiment Viewer (MeV, version 4.7.4, Dana−Farber Cancer Institute, MA).

Figure 1. Scheme of the UHPLC/QQQ MRM MS-based pseudotargeted metabolomics method.

used the Q-TOF MS system coupled with UHPLC to perform untargeted tandem MS from the pooled HCC serum sample and pooled control serum sample, after being deproteinized with acetonitrile. Ion pairs information was extracted by MassHunter Qualitative Analysis software for subsequent MRM detection. The detailed procedure of ion pairs selection and construction of the dynamic MRM from Q-TOF MS/MS data is shown in Figure 2. Figure 2A shows the base peak chromatogram of the untargeted tandem MS using a UHPLC/ Q-TOF MS system containing retention time and product ion data for each precursor ion, which comprised the fundamental elements of MRM ion pairs. For example, the precursor ion m/ z 424.34 (Figure 2B) was automatically selected for tandem MS to produce several product ions (Figure 2C), among which the product ion m/z 85.03 with the highest intensity was selected as the characteristic product ion. Combining the retention time, precursor ion, and product ion data, this metabolite can be scanned by UHPLC/QQQ MS in the dynamic MRM mode (Figure 2D). As shown in Figure 1, after the ion pairs were acquired, they were imported to the UHPLC/QQQ MS workstation, and the fragmentor voltage and collision energy were optimized on the 8328

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Figure 2. Ion pairs selection and construction of the dynamic MRM from Q-TOF MS/MS data.

Figure 3. Representative chromatograms of serum metabolomic analyses: (A) base peak chromatogram (BPC) of UHPLC/Q-TOF MS analysis and (B) reconstructed MRM chromatogram of UHPLC/QQQ MRM MS analysis.

basis of the parameters derived from the Q-TOF MS/MS to obtain the highest response for each metabolite ion. The ion pairs were integrated from the pooled serum samples of patients with HCC and healthy controls to enhance the coverage of the ion pairs. Finally, 518 ion pairs were defined for subsequent MRM detection; in detail, 317 of them were acquired from both the pooled control serum and pooled HCC serum, and 103 and 98 ion pairs were acquired only from the pooled control serum and pooled HCC serum, respectively, which demonstrated the advantage of using pooled serum samples from different sample groups to acquire ion pairs through untargeted MS/MS on UHPLC/Q-TOF MS. It should be noted that the retention time on the 1290 UHPLC system is not exactly identical to that on the 1200 RRLC system; however, through our efforts using Intelligent

System Emulation Technology to match the elution behavior of the 1290 UHPLC system to that of the 1200 RRLC system, the inter-differences of the retention times were minimized, and the elution order of the metabolites was the same, which aids the construction of the dynamic MRM method. After the optimization, the defined parameters (fragmentor voltage, collision energy, precursor ion, product ion, and retention time) of the 518 ion pairs were imported to the UHPLC/QQQ MS workstation. It is important to note that it is impossible to scan 518 ion pairs simultaneously within one cycle, as this will reduce the dwell time of each ion pair and the scan rate and consequently affect the sensitivity and peak shape significantly. To avoid this problem, we used a state-of-the-art technique, dynamic MRM mode. Dynamic MRM is also termed as scheduled MRM, in which every ion pair is scanned 8329

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Figure 4. (A) Repeatability and (B) linearity response of the 318 peaks detected by UHPLC/Q-TOF MS and UHPLC/QQQ MRM MS.

the UHPLC/QQQ MRM MS- and UHPLC/Q-TOF MSbased platforms. The repeatability of the peak areas of the 318 peaks in both platforms was calculated as the relative standard deviations (RSD), and the distribution of RSDs is shown in Figure 4A. Thirty four percent of the UHPLC/QQQ MRM MS-detected metabolites exhibited an RSD of less than 5%, whereas only 1% of the UHPLC/Q-TOF MS-detected metabolites had an RSD of less than 5%. Additionally, 76% of the UHPLC/QQQ MRM MS-detected metabolites displayed an RSD of less than 10%, but only 44% of the UHPLC/Q-TOF MS-detected metabolites had an RSD of less than 10%. Moreover, 34% and 76% of the UHPLC/QQQ MRM MS-detected metabolites had RSDs of less than 5% and 10%, respectively, indicating the high repeatability of this platform for metabolomic analysis. When evaluating the original peak area, we found that the total peak area of the 318 peaks that were detected by the UHPLC/Q-TOF MS-based platform varied greatly among the 10 replicates (Figure S1A of the Supporting Information) with an RSD of 8%, which was much larger than that of the UHPLC/QQQ MRM MS-based platform (Figure S1B of the Supporting Information), which had an RSD of only 2%, indicating the long-term stability of the UHPLC/QQQ MRM MS-based platform. When each peak area was normalized to the total peak area of the 318 peaks, 60% of the UHPLC/QTOF MS-detected metabolites had an RSD of less than 5%, and 88% of the peaks had an RSD of less than 10% (Figure S1C of the Supporting Information), which was a significant improvement in repeatability compared with that using the original peak area. However, no obvious difference in repeatability was found using either the original or normalized peak area for the UHPLC/QQQ MRM MS-based platform (Figure S1D of the Supporting Information). These results indicate that when performing untargeted metabolomics using a Q-TOF MS-based platform, normalization prior to data analysis is critical to ensure high data quality. For the QQQ MRM MS-based platform, either the original or normalized peak area can be used for subsequent data analysis, which has an obvious advantage for time series data such as those for cell culture experiments, for which normalization to total peak area is not reasonable because the total amount of metabolites is constantly increasing.

only near the expected retention time instead of being scanned for the entire duration of the run.24 This change significantly improved the detection. The dwell time and scan rate were enhanced, and consequently, the sensitivity and peak shape were improved, which was advantageous for the quantification. To perform dynamic MRM, the retention time range (ΔtR) should be defined. It should be slightly longer than the peak width to ensure that the entire peak is detected, but it should be as short as possible to not affect the detection of the nearby peaks. Because of the advancement of UHPLC technology, the majority of the peak widths are within 1 min; thus, most of the ΔtR values in our method are no more than 1 min. After the ΔtR was defined, the method was finally established. A typical reconstructed MRM chromatogram of UHPLC/ QQQ MS analysis of a serum sample is shown in Figure 3B. The corresponding base peak chromatogram of UHPLC/QTOF MS analysis is also shown in Figure 3A. From these MRM chromatograms, 518 peaks were detected. The peak profile was similar to that of the Q-TOF MS chromatogram, but the peak shapes were improved. Repeatability and Linearity of UHPLC/QQQ MRM MSbased Pseudotargeted Metabolomics. After data acquisition, XCMS software was used to extract the peaks that were detected by the UHPLC/Q-TOF MS-based metabolomics platform, and 1333 peaks were retrieved, among which 318 peaks were identical to those detected by the UHPLC/QQQ MRM MS-based metabolomics platform. To ensure the comparability of the two platforms, we used the commonly retrieved 318 peaks to compare the analytical characteristics of the UHPLC/Q-TOF MS- and UHPLC/QQQ MRM MSbased platforms. The repeatability of instrument detection is very important for metabolomics. If the instrument detection varies significantly, then small but important differences between groups will not be detected. A QQQ mass spectrometer operated in the MRM mode is the gold standard instrument for biological sample analyses,17 and it has good quantification repeatability for targeted metabolites. To determine whether good repeatability can be achieved for MRM for pseudotargeted metabolomic analysis and compare the developed method with the Q-TOF MS-based traditional method, 10 replicates of the QC samples were measured using 8330

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Figure 5. (A) PCA scores plot of patients with HCC, healthy controls and QCs. (B) PLS-DA scores plot of patients with HCC and healthy controls, and (C) the corresponding loadings plot derived from the serum metabolome detected by UHPLC/QQQ MRM MS. (D) Heatmap of identified marker metabolites.

Figure 6. Relative contents of (A) LPCs, (B) acylcarnitines, (C) amino acids, and (D) the ratio of branched-chain to aromatic amino acids in sera of patients with HCC and healthy controls, analyzed by UHPLC/QQQ MRM MS. (* means significantly different between patients with HCC and healthy controls, and the data are expressed as group mean value ± SD).

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Among these identified metabolites, all serum LPCs were downregulated in patients with HCC (Figure 5D and 6A). As an important signaling molecule, LPC is involved in regulating cellular proliferation, cancer cell invasion, and inflammation.26 It is a substrate of lysophospholipase D (lysoPLD)/autotoxin (ATX). LysoPLD/ATX converts LPC to lysophosphatidic acid (LPA), which is involved in cancer development.27 LysoPLD/ ATX overexpression has been observed in several cancers such as breast cancer,28 glioblastoma,29 and HCC,30 which may account for the reduced serum LPC levels in the patients with HCC in the present study. The alteration of serum acylcarnitine levels in patients with HCC was noticeable, as decreased medium-chain acylcarnitine (C8, C8:1, C10, and C10:1) and increased long-chain acylcarnitine (C18:1 and C18:2) levels were observed in this group (Figure 5D and 6B). Long-chain acylcarnitines are important intermediates of the carnitine shuttle, which is responsible for transporting long-chain fatty acids to the mitochondrial matrix for β-oxidation to supply usable energy, whereas medium-chain fatty acids can freely diffuse into the mitochondria matrix for β-oxidation, a process that does not require an active transport mechanism.31 The accumulation of long-chain acylcarnitines may indicate increased energy consumption in patients with HCC, which would result in activation of the carnitine shuttle for the β-oxidation of longchain fatty acids. Among the dysregulated amino acids, methionine, phenylalanine, and tyrosine metabolism was upregulated and that of tryptophan, valine, leucine, and isoleuline was downregulated in patients with HCC (Figure 5D and 6C). The dysregulation of amino acids was found to be associated with cancer development, including sarcosine in prostate cancer progression4 and glycine in rapid cancer cell proliferation.32 In the present study, the serum levels of branched-chain amino acids (BCAAs, including leucine, isoleuline, and valine) were decreased and those of aromatic amino acids (ArAAs, including phenylalanine and tyrosine) were increased in patients with HCC, indicating enhanced BCAA catabolism and reduced ArAA breakdown in the failing liver.33,34 These changes induced a more than 2-fold reduction of the serum BCAA/ ArAA ratio in patients with HCC (Figure 6D), which also reflected the deterioration of liver metabolism and hepatic function.33,35 Several potential metabolite biomarkers were identified for HCC in the present study, verifying the usefulness of UHPLC/ QQQ MRM MS-based pseudotargeted metabolomics method for potential disease biomarker discovery. Meanwhile, we have noticed that some of the metabolites are also altered in other kinds of cancer, which will affect the specific diagnosis of HCC. Further study using a large amount of samples with different clinical backgrounds is very necessary.

Aside from repeatability, linearity is the most important parameter of instruments used for the metabolomic analysis of biological samples. Due to the complexity of biological samples, the metabolite concentration can span several orders of magnitude, which often exceeds the linear range of the full scan MS (Q-TOF MS typically has a linear range of 2 to 3 orders of magnitude). QQQ MS system, when operated in the MRM mode, only detects the defined metabolites in the assigned time instead of scanning the entire m/z range of QTOF MS. The detector is not easily saturated, and it has a wider linear range that can occasionally exceed 4 to 5 orders of magnitude. To test whether the wider linear range of QQQ MS can be retained in the metabolomic analysis, we used real samples because it is difficult to identify standards for each metabolite. Triplicate samples at each concentration were pretreated, and each sample was analyzed three times. The same samples were injected for measurement by both platforms. The linearity between the MS response and the serum concentration were evaluated by the Pearson correlation coefficient. Thirteen percent of the metabolites detected by UHPLC/QQQ MRM MS had a correlation coefficient larger than 0.99, whereas no metabolites detected using the UHPLC/ Q-TOF MS-based platform had a correlation coefficient larger than 0.99 (Figure 4B). Whereas 49% of the metabolites detected by UHPLC/QQQ MRM MS had a correlation coefficient larger than 0.95, only 14% of the metabolites detected by the UHPLC/Q-TOF MS-based platform had a correlation coefficient larger than 0.95. Moreover, 68% and 44% of the metabolites detected by UHPLC/QQQ MRM MS and UHPLC/Q-TOF MS, respectively, had a correlation coefficient larger than 0.9. The UHPLC/QQQ MRM MSbased metabolomics method displayed a markedly better linearity than the UHPLC/Q-TOF MS-based approach. The wider linear range of the new method makes it more suitable for the metabolomic analysis of complex biological samples. Application of Pseudotargeted Method in Serum Biomarker Discovery for Patients with HCC. To test the applicability of the newly developed method to real samples, we used the UHPLC/QQQ MRM MS-based pseudotargeted metabolomics method to discover serum biomarkers for patients with HCC. The serum extracts of the 29 patients with HCC and 30 healthy controls were analyzed by UHPLC/ QQQ MRM MS. After peak integration, the 518 peaks with integrated areas were used for subsequent statistical analyses. The PCA result is shown in Figure 5A. All the 10 QC samples during batch analysis were closely clustered to the center of the scores plot, verifying the good repeatability of the UHPLC/QQQ MRM MS-based platform. The patients with HCC were distinctly separated from the healthy controls in the PCA scores plot, indicating that aberrant metabolism occurred during the pathogenesis and development of HCC. To focus on the important biomarkers distinguishing the patients with HCC from the healthy controls based on the UHPLC/QQQ MRM MS platform, the metabolites with a VIP (variable importance in projection) value exceeding 1 (Figure 5C, marked with a red □ in the loadings plot) in the PLS-DA model (Figure 5B) and a p-value less than 0.05 based on student’s t-test were selected. Finally, 50 metabolites were defined, among which 27, including amino acids, acylcarnitines, and lysophosphatidylcholines (LPCs), were identified on the basis of our previous strategy.25 The heatmap of these identified important marker metabolites is shown in Figure 5D.

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CONCLUSIONS In the present study, we proposed a strategy to perform pseudotargeted metabolomic analysis using a UHPLC/QQQ MS system operated in the MRM mode. The MRM ion pairs were selected from the UHPLC/Q-TOF MS system through untargeted tandem MS of the real samples. As a proof of concept, 518 serum metabolites were detected using the UHPLC/QQQ MRM MS-based pseudotargeted metabolomics method. The newly developed method displayed better repeatability and wider linear range than the commonly used UHPLC/Q-TOF MS-based untargeted metabolomics method, 8332

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Analytical Chemistry

Article

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and no complicated data processing was required. Serum biomarkers identified using the UHPLC/QQQ MRM MSbased method demonstrated that patients with HCC had lower LPC, higher long-chain and decreased medium-chain acylcarnitines, and higher ArAA and lower BCAA levels than healthy controls. These results proved the applicability of the UHPLC/ QQQ MRM MS-based pseudotargeted metabolomics method in biomarker discovery for disease research. Finally, the newly developed UHPLC/QQQ MRM MS-based pseudotargeted metabolomics method provides an opportunity to combine biomarker discovery and quantitative validation using the same technology platform.

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ASSOCIATED CONTENT

S Supporting Information *

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

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AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. Tel: +86 411 84379530. Fax: +86 411 84379559. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS The study has been supported by the national basic research program of China (Grant 2012CB518303), the State Key Science & Technology Project for Infectious Diseases (Grants 2012ZX10002011 and 2012ZX10002009), the foundation (Grant 21175132) and the creative research group project (Grant 21021004) from the National Natural Science Foundation of China.

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REFERENCES

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dx.doi.org/10.1021/ac4016787 | Anal. Chem. 2013, 85, 8326−8333