Zhongda Zeng

Pseudotargeted Metabolomics Method and Its Application in Serum Biomarker Discovery for Hepatocellular Carcinoma Based on Ultra High-Performance Liquid Chromatography/Triple Quadrupole Mass Spectrometry

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 high-performance 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.

A comparative study of volatile components in green, oolong and black teas by using comprehensive two-dimensional gas chromatography–time-of-flight mass spectrometry and multivariate data analysis

The difference of volatile components in green, oolong and black teas was studied by using comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry (GC×GC-TOFMS). Simultaneous distillation extraction was proved to be a suitable technique to extract the analytes with interest. A total of 450 compounds were tentatively identified with comparison to the standard mass spectra in available databases, retention index on the first dimension and structured chromatogram. 33 tea samples, including 12, 12 and 9 samples of green, oolong and black tea were analyzed by using GC×GC-TOFMS. After peak alignment, around 3600 peaks were detected. Partial least squares - discriminant analysis and hierarchical cluster analysis were used to classify these samples, then non-parametric hypothesis test (Mann-Whitney U test) and the variable importance in the projection (VIP) were applied to discover the key components to distinguish the three types of tea with significant difference amongst them. 74 differential compounds are defined to interpret the chemical differences of 3 types of tea. This study shows the power of GC×GC-TOFMS method combined with multivariate data analysis to investigate natural products with high complexity for information extraction.

Study of induction chemotherapy efficacy in oral squamous cell carcinoma using pseudotargeted metabolomics.

The effect of induction chemotherapy on oral cancer is controversial owing to inconsistent results. However, the efficacy of induction chemotherapy is closely related to locoregional recurrence, distant metastasis, and overall survival after the treatment. A pseudotargeted metabolomics revealed that metabolites involved in glycolysis and amino acid metabolism were inversely regulated in patients with different chemotherapy responses, and most fatty acids, steroids, and antioxidant substances were up-regulated in all patients after the treatment. Among the metabolites, lactic acid, glucose, glutamic acid, aspartic acid, leucine, and glycerol were remarkably associated with induction chemotherapy efficacy. Subsequently, lactic acid, glutamic acid, and aspartic acid were defined as potential biomarkers of the suitability and efficacy of induction chemotherapy. Our results show that 100.0 and 84.37% of patients with different chemotherapy efficacy were correctly identified in the training and test sets, respectively. Moreover, patient suitability for treatment was correctly predicted for 100.0, 81.25, and 100.0% of patients in the training, test, and external validation sets, respectively. In conclusion, metabolites related to glycolysis, redox homeostasis, and anabolic progress were indicative of induction chemotherapy efficacy both pre- and post-chemotherapy and beneficial for outcome evaluation and prediction. These results illustrate the potentials of metabolomics in personalized induction chemotherapy.

Multiple reaction monitoring-ion pair finder: a systematic approach to transform nontargeted mode to pseudotargeted mode for metabolomics study based on liquid chromatography-mass spectrometry.

Pseudotargeted metabolic profiling is a novel strategy combining the advantages of both targeted and untargeted methods. The strategy obtains metabolites and their product ions from quadrupole time-of-flight (Q-TOF) MS by information-dependent acquisition (IDA) and then picks targeted ion pairs and measures them on a triple-quadrupole MS by multiple reaction monitoring (MRM). The picking of ion pairs from thousands of candidates is the most time-consuming step of the pseudotargeted strategy. Herein, a systematic and automated approach and software (MRM-Ion Pair Finder) were developed to acquire characteristic MRM ion pairs by precursor ions alignment, MS(2) spectrum extraction and reduction, characteristic product ion selection, and ion fusion. To test the reliability of the approach, a mixture of 15 metabolite standards was first analyzed; the representative ion pairs were correctly picked out. Then, pooled serum samples were further studied, and the results were confirmed by the manual selection. Finally, a comparison with a commercial peak alignment software was performed, and a good characteristic ion coverage of metabolites was obtained. As a proof of concept, the proposed approach was applied to a metabolomics study of liver cancer; 854 metabolite ion pairs were defined in the positive ion mode from serum. Our approach provides a high throughput method which is reliable to acquire MRM ion pairs for pseudotargeted metabolomics with improved metabolite coverage and facilitate more reliable biomarkers discoveries.

Stable Isotope-Assisted Lipidomics Combined with Nontargeted Isotopomer Filtering, a Tool to Unravel the Complex Dynamics of Lipid Metabolism

Investigations of complex metabolic mechanisms and networks have become a focus of research in the postgenomic area, thereby creating an increasing demand for sophisticated analytical approaches. One such tool is lipidomics analysis that provides, a detailed picture of the lipid composition of a system at a given time. Introducing stable isotopes into the studied system can additionally provide information on the synthesis, transformation and degradation of individual lipid species. However, capturing the entire dynamics of lipid networks is still a challenge. We developed and evaluated a novel strategy for the in-depth analysis of the dynamics of lipid metabolism with the capacity for high molecular specificity and network coverage. The general workflow consists of stable isotope-labeling experiments, ultrahigh-performance liquid chromatography (UHPLC)/high-resolution Orbitrap-mass spectrometry (MS) lipid profiling and data processing by a software tool for global isotopomer filtering and matching. As a proof of concept, this approach was applied to the network-wide mapping of dynamic lipid metabolism in primary human skeletal muscle cells cultured for 4, 12, and 24 h with [U-(13)C]-palmitate. In the myocellular lipid extracts, 692 isotopomers were detected that could be assigned to 203 labeled lipid species spanning 12 lipid (sub)classes. Interestingly, some lipid classes showed high turnover rates but stable total amounts while the amount of others increased in the course of palmitate treatment. The novel strategy presented here has the potential to open new detailed insights into the dynamics of lipid metabolism that may lead to a better understanding of physiological mechanisms and metabolic perturbations.

Simultaneous deconvolution and re-construction of primary and secondary overlapping peak clusters in comprehensive two-dimensional gas chromatography

In this study, simultaneous deconvolution and reconstruction of peak profiles in the first ((1)D) and second dimension ((2)D) of comprehensive two-dimensional (2D) gas chromatography (GC×GC) is achieved on the basis of the property of this new type of instrumental data. First, selective information, where only one component contributes to the peak elution window of a given modulation event, is employed for stepwise stripping of each (2)D peak with the help of pure components corresponding to that compound from the neighbouring modulations. Simulation based on an exponentially modified Gaussian (EMG) model aids this process, where the EMG represents the envelope of all (2)D peaks for that compound. The peak parameters can be restricted by knowledge of the pure modulated (2)D GC peaks derived from the same primary compound, since it is modulated into several fractions during the trapping and re-focusing process of the cryogenic modulation system according to the modulation period. Next, relative areas of all pure (2)D components of that compound are considered for reconstruction of the primary peak. This strategy of exploitation of the additional information provided by the second dimension of separation allows effective deconvolution of GC×GC datasets. Non-linear least squares curve fitting (NLLSCF) allows the resolved 2D chromatograms to be recovered. Accurate acquisition of the pure profiles in both (1)D and (2)D aids quantification of compositions and prediction of 2D retention parameters, which are of interest for qualitative and quantitative analysis. The ratio between the sum of squares of deconvolution residual and original peak response (R(rr)) is employed as an effective index to evaluate the resolution results. In this work, simulated and experimental examples are used to develop and test the proposed approach. Satisfactory performance for these studies is validated by minimum and maximum R(rr) values of 1.34e-7% and 1.09e-2%; and 1.0e-3% and 3.0e-1% for deconvolution of (1)D and (2)D peaks, respectively. Results suggest that the present technique is suitable for GC×GC data processing.

Metabolic profiling study of early and late recurrence of hepatocellular carcinoma based on liquid chromatography-mass spectrometry

The objectives of this pilot study were to predict early postoperative recurrence in hepatocellular carcinoma (HCC) patients based on metabolic features and to explore the related metabolic disturbances. Liquid chromatography-mass spectrometry-based metabolic profiling was performed on the plasma of 18 late recurrent and 22 early recurrent HCC patients. Metabolic differences were found to be related to amino acid, bile acid, cholesterol, fatty acid, phospholipid and carbohydrate metabolism. Bile acids, steroids and fatty acids showed significant variation in the early recurrent HCC group compared to the late recurrence group. Decreased levels of polyunsaturated eicosapentaenoic acid, docosahexaenoic acid and linolenic acid were found to be specific metabolic features for early recurrence. With the combination of methionine, GCDCA and cholesterol sulfate, 85% of the early recurrent HCCs can be predicted correctly with the corresponding area under the curve (AUC) equal to 0.95 in the training set, and 80% of the early recurrent HCCs can be predicted correctly with the corresponding AUC equal to 0.91 in the test set.

Study of metabolite differences of flue-cured tobacco from different regions using a pseudotargeted gas chromatography with mass spectrometry selected-ion monitoring method

A pseudotargeted method based on gas chromatography and mass spectrometry with selected-ion monitoring was established to investigate the metabolite differences of flue-cured tobacco from three different growing regions. The mixed solvent of acetonitrile/isopropanol/water (3:3:2, v/v/v) was chosen as the optimal extraction system based on the good repeatability and extraction efficiency. A self-developed software coupled with commercial software was used to establish the pseudotargeted method including 289 peaks and 47 groups. Multivariable statistical analysis indicated that tobacco samples can be obviously separated based on the geographical origins. On the basis of a Mann-Whitney U test, organic acids, phenols, and alkaloids had higher levels in Hunan province. In contrast, a large proportion of amino acids (including L-tyrosine, L-proline, and serine), sucrose, and linoleic acid were the highest in Yunnan province. Meanwhile, multiple metabolic pathways (including carbohydrate metabolism, tricarboxylic acid cycle, and nitrogen metabolism) were influenced by growing regions. Twenty-eight differential metabolites, which had great contributions to the classification of tobacco samples of three growing regions, were further defined. The results demonstrated that the developed pseudotargeted method was a powerful tool to investigate the metabolic profiling of tobacco leaves and discriminate tobacco leaves of different growing regions.

Component correlation between related samples by using comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry with chemometric tools.

A chemometric strategy has been developed to discover component difference and similarity between two chromatograms (correlation) by using comprehensive two-dimensional (2D) gas chromatography with time-of-flight mass spectrometry (GC×GC-TOFMS). It allows for rapid determination of the presence or absence of analytes of interest in both pure and overlapping peak clusters, and then locates elution windows of target components. First, representative elution windows of analytes are extracted from the 2D GC×GC map to characterize the spectral space and further construct an orthogonal projection matrix for analysis. Next, multi-component spectral correlative chromatography (MSCC) is employed to scan the whole or pre-selected GC×GC-TOFMS data range to obtain component features. An auto-correlative projection curve is proposed to assess the projection residual from MSCC by defining a new evaluation index as reference, based on fixed-size moving window evolving factor analysis. In principle, the method can also be utilized to locate specific compounds whose known spectra are available. It is not restricted by data with high homoscedastic and heteroscedastic noise. Simulated GC-MS data and an extremely complicated herbal product mixture comprising 9 herbs demonstrates that the two-dimensional correlative distribution graph is effective for chemical interpretation between GC×GC-TOFMS data. It allows discovery of information buried in this type of highly complex dataset, especially for rapid and effective data comparison, where specific molecular identity might otherwise be hidden.

Simultaneous measurement of laser-induced shock wave and released particle velocities at Mbar pressure

We show the feasibility of simultaneous measurement of shock velocity and released particle velocity after shock at Mbar pressure. The shock wave is driven by a laser pulse of 1.2 ps duration (full width at half maximum), with the intensity of ∼1014 W/cm2 at 785 nm, irradiating a 500-nm-thick aluminum foil. A chirped laser pulse split from the main pulse is applied to detect the shock breakout process at the rear surface of the target based on frequency domain interferometry. The mean shock velocity determination benefits from the precise synchronization (<100 fs resolution) of the shock pump and probe laser pulse, which is calculated from the time the shock takes to travel the 500-nm-thick aluminum. The released particle velocity determination takes advantage of the chirped pulse frequency domain interferometry. The two measured parameters are self-consistent.