AntDAS: Automatic Data Analysis Strategy for UPLC–QTOF-Based Nontargeted Metabolic Profiling Analysis

Authenticity identification of high - Temperature Daqu Baijiu through multi-channel visual array sensor of organic dyes combined with smart phone App

High - temperature Daqu Baijiu faces a challenge from illegal adulteration of high-grade Baijiu bottles with low-grade Baijiu, affecting its quality and value. This study developed a rapid identification method for high temperature Daqu Baijiu with the same aroma type using a four-channel visual array sensor and detection of color changes caused by competition coordination with Zn2+ and color-changing organic dyes. The array sensor demonstrated high stability and repeatability in targeting flavor components and achieved 97.78 % or more accuracy combined with DD-SIMCA model in detecting adulteration across the Baijiu with same aroma type. The results of GC-MS and Quantum Chemical Calculation showed that esters, acids, and pyrazines played a crucial role. The smart phone App could quickly identify the authenticity of Baijiu with accuracy achieved 93 %. This research provides a foundation for rapid and reliable assessment of Baijiu quality and authenticity, enabling the industry to combat fraudulent practices effectively.

Geographical discrimination of Flos Trollii by GC-MS and UHPLC-HRMS-based untargeted metabolomics combined with chemometrics

For centuries, Flos Trollii has been consumed as functional tea and a folk medicine in China's north and northwest zones. The quality of Flos Trollii highly depends on the producing zones. Unfortunately, few studies have been reported on the geographical discrimination of Flos Trollii. This work comprehensively investigated Flos Trollii compounds with an integration strategy combining gas chromatography-mass spectrometry (GC-MS) and ultrahigh-performance liquid chromatography-high resolution mass spectrometry (UHPLC-HRMS) with chemometrics to explore the differences between Flos Trollii obtained from various origins of China. About 71 volatile and 22 involatile markers were identified with GC-MS and UHPLC-HRMS, respectively. Geographical discrimination models were synthetically investigated based on the identified markers. The results indicated that the UHPLC-HRMS coupled with the fisher discrimination model provided the best prediction capability (>97%). This study provides a new solution for Flos Trollii discrimination.

Highly automatic and universal approach for pure ion chromatogram construction from liquid chromatography-mass spectrometry data using deep learning

Feature extraction is the most fundamental step when analyzing liquid chromatography-mass spectrometry (LC-MS) datasets. However, traditional methods require optimal parameter selections and re-optimization for different datasets, thus hindering efficient and objective large-scale data analysis. Pure ion chromatogram (PIC) is widely used because it avoids the peak splitting problem of the extracted ion chromatogram (EIC) and regions of interest (ROIs). Here, we developed a deep learning-based pure ion chromatogram method (DeepPIC) to find PICs using a customized U-Net from centroid mode data of LC-MS directly and automatically. A model was trained, validated, and tested on the Arabidopsis thaliana dataset with 200 input-label pairs. DeepPIC was integrated into KPIC2. The combination enables the entire processing pipeline from raw data to discriminant models for metabolomics datasets. The KPIC2 with DeepPIC was compared against other competing methods (XCMS, FeatureFinderMetabo, and peakonly) on the MM48, simulated MM48, and quantitative datasets. These comparisons showed that DeepPIC outperforms XCMS, FeatureFinderMetabo, and peakonly in recall rates and correlation with sample concentrations. Five datasets of different instruments and samples were used to evaluate the quality of PICs and the universal applicability of DeepPIC, and 95.12% of the found PICs could precisely match their manually labeled PICs. Therefore, KPIC2+DeepPIC is an automatic, practical, and off-the-shelf method to extract features from raw data directly, exceeding traditional methods with careful parameter tuning. It is publicly available at https://github.com/yuxuanliao/DeepPIC.

Using UHPLC-HRMS-based comprehensive strategy to efficiently and accurately screen and identify illegal additives in health-care foods

Accurately and high-thoroughly screening illegal additives in health-care foods continues to be a challenging task in routine analysis for the ultrahigh performance liquid chromatography-high resolution mass spectrometry based techniques. In this work, we proposed a new strategy to identify additives in complex food matrices, which consists of both experimental design and advanced chemometric data analysis. At first, reliable features in the analyzed samples were screened based on a simple but efficient sample weighting design, and those related to illegal additives were screened with robust statistical analysis. After the MS¹ in-source fragment ion identification, both MS¹ and MS/MS spectra were constructed for each underlying compound, based on which illegal additives can be precisely identified. The performance of the developed strategy was demonstrated by using mixture and synthetic sample datasets, indicating an improvement of data analysis efficiency up to 70.3 %. Finally, the developed strategy was applied for the screening of unknown additives in 21 batches of commercially available health-care foods. Results indicated that at least 80 % of false-positive results can be reduced and 4 additives were screened and confirmed.

A novel simultaneous quantitative method for differential volatile components in herbs based on combined near-infrared and mid-infrared spectroscopy

A novel method based on GC-MS, near-infrared (NIR) and mid-infrared (MIR) spectroscopy combined with chemometrics was established to simultaneously analyze differential volatile components (DVCs) of herb samples. Herein, Florists Chrysanthemum was adopted as the representative sample. Through the introduction of Automatic data analysis workflow (AntDAS) and one-class partial least squares discriminant analysis (O-PLSDA) model, five kinds of terpenes and five kinds of alcohols were efficiently screened as DVCs. By using the selected NIR-MIR spectra sections combined with O-PLSDA, it could achieve the accurate identification of Florists Chrysanthemum from Chrysanthemum morifolium Ramat. What's more, since the selected spectra sections were closely related to the structural and content of DVCs, they could be further used for simultaneous quantitative analysis of DVCs combined with optimized variable-weighted least-squares support vector machine based on particle swarm optimization (PSO-VWLS-SVM). This method only adopted the same NIR-MIR sections for multiple component accurate quantification, highlighting its convenience.

A comparison of feature extraction capabilities of advanced UHPLC-HRMS data analysis tools in plant metabolomics

Data analysis of ultrahigh performance liquid chromatography-high resolution mass spectrometry (UHPLC-HRMS) is an essential and time-consuming step in plant metabolomics and feature extraction is the fundamental step for current tools. Various methods lead to different feature extraction results in practical applications, which may puzzle users for selecting adequate data analysis tools to deal with collected data. In this work, we provide a comprehensive method evaluation for some advanced UHPLC-HRMS data analysis tools in plant metabolomics, including MS-DIAL, XCMS, MZmine, AntDAS, Progenesis QI, and Compound Discoverer. Both mixtures of standards and various complex plant matrices were specifically designed for evaluating the performances of the involved method in analyzing both targeted and untargeted metabolomics. Results indicated that AntDAS provide the most acceptable feature extraction, compound identification, and quantification results in targeted compound analysis. Concerning the complex plant dataset, both MS-DIAL and AntDAS can provide more reliable results than the others. The method comparison is maybe useful for the selection of suitable data analysis tools for users.

AntDAS-DDA: A New Platform for Data-Dependent Acquisition Mode-Based Untargeted Metabolomic Profiling Analysis with Advantage of Recognizing Insource Fragment Ions to Improve Compound Identification

Data-dependent acquisition (DDA) mode in ultra-high-performance liquid chromatography-high-resolution mass spectrometry (UHPLC-HRMS) can provide massive amounts of MS¹ and MS/MS information of compounds in untargeted metabolomics and can thus facilitate compound identification greatly. In this work, we developed a new platform called AntDAS-DDA for the automatic processing of UHPLC-HRMS data sets acquired under the DDA mode. Several algorithms, including extracted ion chromatogram extraction, feature extraction, MS/MS spectrum construction, fragment ion identification, and MS¹ spectrum construction, were developed within the platform. The performance of AntDAS-DDA was investigated comprehensively with a mixture of standard and complex plant data sets. Results suggested that features in complex sample matrices can be extracted effectively, and the constructed MS¹ and MS/MS spectra can benefit in compound identification greatly. The efficiency of compound identification can be improved by about 20%. AntDAS-DDA can take full advantage of MS/MS information in multiple sample analyses and provide more MS/MS spectra than single sample analysis. A comparison with advanced data analysis tools indicated that AntDAS-DDA may be used as an alternative for routine UHPLC-HRMS-based untargeted metabolomics. AntDAS-DDA is freely available at http://www.pmdb.org.cn/antdasdda.

UHPLC-QTOF-MS-based untargeted metabolomics and mineral element analysis insight into the geographical differences of Chrysanthemum morifolium Ramat cv. "Hangbaiju" from different origins

Chrysanthemum morifolium Ramat cv. "Hangbaiju" (HBJ), known as one of the "eight flavors of Zhejiang", is commonly used as a classical tea material for both food and medicine over three thousand years in China. The quality of HBJ is closely related to its geographical origins. However, the mechanism underlying the geographical differences of HBJ remains to be elucidated. In this study, an untargeted metabolomic strategy based on UHPLC-QTOF-MS was established to discover the differential metabolites in HBJ samples from four different origins and explore the possible relationship with mineral elements in planting soils by chemometric analysis. Eight compounds were screened and identified as the key differential metabolites in HBJ samples from different origins. Among them, four important pharmacodynamic compounds including L-arginine, rutin, chlorogenic acid and apigenin-7-O-glucoside are the most abundant in HBJ samples from Tongxiang region, which suggests that HBJ planted in Tongxiang has higher medicinal values. Pearson correlation analysis revealed that the contents of soil mineral elements are positively correlated with those of chlorogenic acid, rutin, apigenin-7-O-glucoside in HBJ samples. Furthermore, an interrelationship model based on random forest algorithm was established to successfully predict the contents of differential metabolites in HBJ samples by soil mineral elements. All these results indicated that the contents of differential metabolites in HBJ samples seemed to be affected by soil mineral elements and therefore resulted in the geographical differences of HBJ.

High-throughput screening of secondary metabolites by Sorbus pohuashanensis cells under environmental stress using UHPLC-QTOF combined with AntDAS

Environment stress can promote the synthesis and accumulation of a series of secondary metabolites, which are important quality factors in medicinal plants. However, the data related to metabolites is often too large, making it difficult to screen quickly, accurately and comprehensively various differential compounds. In this study, a high-throughput screening method for differential secondary metabolites produced by medicinal plants under environmental stress has been developed based on ultrahigh performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UHPLC-QTOF) and automatic data analysis strategy. This work uses Sorbus pohuashanensis cells with biotic stress (Harpin protein) and abiotic stress (Cd²⁺ ) as potential environmental stress factors. The results showed that S. pohuashanensis cells could rapidly respond to both Harpin protein and Cd²⁺ within 24 h, and a significant positive correlation was observed between their concentration (within a certain range) and induction time. The proposed screening method can automatically screen the bulk UHPLC-QTOF metabolic data for differential compounds with high-throughput, and also perform preliminary identification of their possible structures. The screening results indicated that the stress response of S. pohuashanensis cells to Cd²⁺ was significantly higher than that of Harpin protein, and all of them could produce a series of biphenyls, terpenes, and other phytoalexins with stress-resistance and physiological functional properties. Overall, the screening method provides an efficient and powerful tool to study the response mechanisms of plants to environmental stress, to improve the resistance of medicinal plants and also to select and breed high-quality Chinese medicinal plants.

Nontargeted metabolomics-based mapping urinary metabolic fingerprints after exposure to acrylamide

Acrylamide classified as a probable carcinogen to humans is a high production volume chemical in industrial applications released to aquatic and environmental ecosystems, and also widely found in the thermal processing of starch-rich foods. To gain insight into the urinary metabolomics that may induce physiological responses stimulated by acrylamide, rats were orally administered with a single dose of 13C3-acrylamide (10 mg/kg bw) in the treatment group and urine samples were continuously collected every 2 h during the first 18 h and every 3 h during the period from 18 h to 36 h. A reliable nontargeted screening method for the analysis of urinary metabolomics in rats was developed using ultra-high performance liquid chromatography coupled to quadrupole-Orbitrap high-resolution mass spectrometry. All metabolites in urine of rats receiving isotope-labeled acrylamide were screened by validated orthogonal partial least squares-discriminant analyses compared to the animals in the control group, while exposure biomarkers were further confirmed according to the characteristic fragmentation rules and time-dependent profiles. Here we identified 2 new specific exposure biomarkers, named N-acetyl-S-(2-carbamoyl-2-hydroxyethyl)-L-cysteine-sulfoxide and N-acetyl-S-(2-carboxyl)-L-cysteine, compared to 4 currently acknowledged mercapturic acid adducts of acrylamide. In addition, our findings on analysis of acrylamide metabolic pathway and identification of exposure biomarkers confirmed that acrylamide could significantly affect energy metabolism and amino acid metabolism by the Kyoto Encyclopedia of Genes and Genomes pathway analysis for key metabolites. Homocysteine thiolactone and hypoxanthine may be potential biomarkers for the cardiotoxicity, while methionine sulfoxide, hippuric acid and melatonin may be specifically related to the neurotoxicity. Thus, the current study provided new evidence on the identification of emerging exposure biomarkers and specific signature metabolites related to the toxicity of acrylamide, and shed light on how acrylamide affected energy and amino acid metabolism by further mapping urinary metabolic fingerprints.

Combining targeted metabolite analyses and transcriptomics to reveal the specific chemical composition and associated genes in the incompatible soybean variety PI437654 infected with soybean cyst nematode HG1.2.3.5.7

BACKGROUND

Soybean cyst nematode, Heterodera glycines, is one of the most devastating pathogens of soybean and causes severe annual yield losses worldwide. Different soybean varieties exhibit different responses to H. glycines infection at various levels, such as the genomic, transcriptional, proteomic and metabolomic levels. However, there have not yet been any reports of the differential responses of incompatible and compatible soybean varieties infected with H. glycines based on combined metabolomic and transcriptomic analyses.

RESULTS

In this study, the incompatible soybean variety PI437654 and three compatible soybean varieties, Williams 82, Zhonghuang 13 and Hefeng 47, were used to clarify the differences in metabolites and transcriptomics before and after the infection with HG1.2.3.5.7. A local metabolite-calibrated database was used to identify potentially differential metabolites, and the differences in metabolites and metabolic pathways were compared between the incompatible and compatible soybean varieties after inoculation with HG1.2.3.5.7. In total, 37 differential metabolites and 20 KEGG metabolic pathways were identified, which were divided into three categories: metabolites/pathways overlapped in the incompatible and compatible soybeans, and metabolites/pathways specific to either the incompatible or compatible soybean varieties. Twelve differential metabolites were found to be involved in predicted KEGG metabolite pathways. Moreover, 14 specific differential metabolites (such as significantly up-regulated nicotine and down-regulated D-aspartic acid) and their associated KEGG pathways (such as the tropane, piperidine and pyridine alkaloid biosynthesis, alanine, aspartate and glutamate metabolism, sphingolipid metabolism and arginine biosynthesis) were significantly altered and abundantly enriched in the incompatible soybean variety PI437654, and likely played pivotal roles in defending against HG1.2.3.5.7 infection. Three key metabolites (N-acetyltranexamic acid, nicotine and D,L-tryptophan) found to be significantly up-regulated in the incompatible soybean variety PI437654 infected by HG1.2.3.5.7 were classified into two types and used for combined analyses with the transcriptomic expression profiling. Associated genes were predicted, along with the likely corresponding biological processes, cellular components, molecular functions and pathways.

CONCLUSIONS

Our results not only identified potential novel metabolites and associated genes involved in the incompatible response of PI437654 to soybean cyst nematode HG1.2.3.5.7, but also provided new insights into the interactions between soybeans and soybean cyst nematodes.

A chemometric strategy for accurately identifying illegal additive compounds in health foods by using ultra-high-performance liquid chromatography coupled to high resolution mass spectrometry

The accurate identification of unknown illegal additive compounds in complex health foods continues to be a challenging task in routine analysis, because massive false positive results can be screened with ultra-high-performance liquid chromatography coupled to high-resolution mass spectrometry-based untargeted techniques and must be manually filtered out. To address this problem, we developed a chemometric-based strategy, in which data analysis was first performed by using XCMS, MS-DIAL, Mzmine2, and AntDAS2, to select those that provided acceptable results to extract common features (CFs), which can be detected by all of the selected methods. Then, CFs whose contents were significantly higher in the suspected illegal additive group were screened. Isotopic, adduct, and neutral loss ions were marked based on the CFs by using a new adaptive ion annotation algorithm. Fragment ions originating from the same compound were identified by using a novel fragment ion identification algorithm. Finally, a respective mass spectrum was constructed for each screened compound to benefit compound identification. The developed strategy was confirmed by using a complex Chinese health food, Goujiya tea. The features of all illegal additive compounds were precisely screened by the developed strategy, and massive false positive features from the current data analysis method were greatly reduced. The constructed respective mass spectra can benefit compound identification and avoid the risk of identifying ions from the same illegal compound as different compounds. Moreover, unknown compounds that are contained in an illegal compound library can be screened.

Deep convolutional autoencoder for the simultaneous removal of baseline noise and baseline drift in chromatograms

Enhancement of chromatograms, such as the reduction of baseline noise and baseline drift, is often essential to accurately detect and quantify analytes in a mixture. Current methods have been well studied and adopted for decades and have assisted researchers in obtaining reliable results. However, these methods rely on relatively simple statistics of the data (chromatograms) which in some cases result in significant information loss and inaccuracies. In this study, a deep one-dimensional convolutional autoencoder was developed that simultaneously removes baseline noise and baseline drift with minimal information loss, for a large number and great variety of chromatograms. To enable the autoencoder to denoise a chromatogram to be almost, or completely, noise-free, it was trained on data obtained from an implemented chromatogram simulator that generated 190.000 representative simulated chromatograms. The trained autoencoder was then tested and compared to some of the most widely used and well-established denoising methods on testing datasets of tens of thousands of simulated chromatograms; and then further tested and verified on real chromatograms. The results show that the developed autoencoder can successfully remove baseline noise and baseline drift simultaneously with minimal information loss; outperforming methods like Savitzky-Golay smoothing, Gaussian smoothing and wavelet smoothing for baseline noise reduction (root mean squared error of 1.094 mAU compared to 2.074 mAU, 2.394 mAU and 2.199 mAU) and Savitkzy-Golay smoothing combined with asymmetric least-squares or polynomial fitting for baseline noise and baseline drift reduction (root mean absolute error of 1.171 mAU compared to 3.397 mAU and 4.923 mAU). Evidence is presented that autoencoders can be utilized to enhance and correct chromatograms and consequently improve and alleviate downstream data analysis, with the drawback of needing a carefully implemented simulator, that generates realistic chromatograms, to train the autoencoder.

ComMSnDB-An Automatable Strategy to Identify Compounds from MS Data Sets (Identification of Gypenosides as an Example)

Gynostemma pentaphyllum (Thunb.) Makino is a popular functional food and is also used as an important medicinal plant in China. Gypenoside, the main active constituent in G. pentaphyllum (Thunb.) Makino, belongs to dammarane-type triterpenoid saponins. Due to its high molecular weight and high polarity, it is difficult to obtain complete compound information for gypenoside extracts via mass spectrometry experiments. In this study, an automated targeted data postprocessing strategy called Compound MSⁿ Database (ComMSⁿDB) was designed and established to elucidate compounds in gypenoside extracts based on ultrahigh-performance liquid chromatography coupled with electrospray ionization quadrupole time-of-flight tandem mass spectrometry (UHPLC-ESI-Q-TOF-MS/MS). As a result, 18 types of and 199 main saponin constituents, including 47 potential novel compounds, were tentatively identified from different habitats. At the same time, 15 gypenoside standard compounds were used to verify the feasibility of the ComMSⁿDB strategy. These results demonstrated that ComMSⁿDB offers practical value for quick, automated, and effective compound identification.

Rapid detection of five pesticide residues using complexes of gold nanoparticle and porphyrin combined with ultraviolet visible spectrum

BACKGROUD

Pesticides are widely used to control insect infestation and weeds in agriculture. However, concerns about the pesticide residues in agricultural products have been raised in recent years because of public interest in health and food quality and safety. Thus, rapid, convenient, and accurate analytical methods for the detection and quantification of pesticides are urgently required.

RESULTS

A nanohybrid system composed of gold nanoparticles (AuNPs) and tetrakis(N-methyl-4-pyridiniumyl) porphyrin (TMPyP) was used as an optical probe for the detection and quantification of five pesticides (Paraquat, Dipterex, Dursban, methyl thiophanate and Cartap). The method is based on the aggregation effect of pesticides on the carboxyl group modified by AuNPs. Subsequently, with the help of particle swarm optimization-optimized sample weighted least squares-support vector machine (PSO-OSWLS-SVM), all the pesticides could be successfully quantified. In addition, partial least squares discriminant analysis (PLS-DA) was applied and the five pesticides were satisfactorily recognized based on data array obtained from the ultraviolet visible (UV-visible) spectra of AuNP-TMPyP complex. Furthermore, the quantitative and qualitative analysis of the five pesticides could be also achieved in the complex real samples, in which all the relative standard deviations (RSDs) were less than 0.3‰ and all the linear absolute correlation coefficients were more than 0.9990. Furthermore, recognition rate of the training set and the prediction set based on multiplicative scatter correction (MSC), or second-order derivative (2nd derivative) UV-visible spectra in PLS-DA model could reach 100%.

CONCLUSION

This method was successfully applied for the rapid and accurate determination of multicomponent pesticide residues in real food samples. © 2020 Society of Chemical Industry.

Differentiating Westlake Longjing tea from the first- and second-grade producing regions using ultra high performance liquid chromatography with quadrupole time-of-flight mass spectrometry-based untargeted metabolomics in combination with chemometrics

There are numerous articles published for geographical discrimination of tea. However, few research works focused on the authentication and traceability of Westlake Longjing green tea from the first- and second-grade producing regions because the tea trees are planted in a limited growing zone with identical cultivate condition. In this work, a comprehensive analytical strategy was proposed by ultrahigh performance liquid chromatography-quadrupole time-of-flight mass spectrometry-based untargeted metabolomics coupled with chemometrics. The automatic untargeted data analysis strategy was introduced to screen metabolites that expressed significantly among different regions. Chromatographic features of metabolites can be automatically and efficiently extracted and registered. Meanwhile, those that were valuable for geographical origin discrimination were screened based on statistical analysis and contents in samples. Metabolite identification was performed based on high-resolution mass values and tandem mass spectra of screened peaks. Twenty metabolites were identified, based on which the two-way encoding partial least squares discrimination analysis was built for geographical origin prediction. Monte Caro simulation results indicated that prediction accuracy was up to 99%. Our strategy can be applicable for practical applications in the quality control of Westlake Longjing green tea.

A metabolomics strategy for authentication of plant medicines with multiple botanical origins, a case study of Uncariae Rammulus Cum Uncis

Source authentication of herbal medicines was essential for ensuring their safety, efficacy and quality consistency, especially those with multiple botanical origins. This study proposed a metabolomics strategy for species discrimination and source recognition. Uncariae Rammulus Cum Uncis, officially stipulating the stems with hooks of five Uncaria species as its origins, was taken as a case study. Firstly, an untargeted MS^E method was developed by ultra-high performance liquid chromatography hyphenated with quadrupole time-of-flight mass spectrometry for global metabolite characterization. Subsequently, data pretreatment was conducted by using Progenesis QI software and screening rules. The obtained metabolite features were defined as variables for statistical analyses. Principal component analysis and chemical fingerprinting spectra suggested that five official species were differentiated from each other except for Uncaria hirsuta and Uncaria sinensis. Furthermore, orthogonal partial least squares discrimination analysis was performed to discriminate confused two species, and resulted in the discovery of nine contributing markers. Ultimately, a Support Vector Machine model was developed to recognize five species and predict origins of commercial materials. The study demonstrated that the developed strategy was effective in discrimination and recognition of confused species, and promising in tracking botanical origins of commercial materials.

LC-MS Analysis of Serum for the Metabolomic Investigation of the Effects of Pulchinenoside b4 Administration in Monosodium Urate Crystal-Induced Gouty Arthritis Rat Model

Gouty arthritis (GA) is commonly caused by deposition of monosodium urate (MSU) crystals within the joint capsule, bursa, cartilage, bone, or other periarticular tissues after chronic hyperuricemia. Clinically, GA is characterized by acute episodes of joint inflammation, which is most frequently encountered in the major joints, and also has a significant impact on quality of life. Pulchinenoside b4(P-b4) has a wide range of biological activities, including antitumor, anti-inflammatory, antiviral and immunomodulatory activities. Currently, the anti-GA activity and metabolomic profiles after being treated by P-b4 have not been reported. In this paper, for the first time, we have performed a non-targeted metabolomics analysis of serum obtained from an MSU crystal-induced GA rat model intervened by P-b4, using ultra-performance liquid chromatography coupled to quadrupole time-of-flight tandem mass spectrometry. In this study, the main pharmacodynamics of different dosing methods and dosages of P-b4 was firstly investigated. Results have shown that P-b4 possesses high anti-inflammatory activity. These results demonstrated changes in serum metabolites with 32 potential biomarkers. Arachidonic acid, sphingolipid, and glycerophospholipid metabolism are considered to be the most relevant metabolic pathway with P-b4 treatment effect in this study. Moreover, the changes of metabolites and the self-extinction of model effects within 24 h reveals important information for GA diagnostic criteria: The regression of clinical symptoms or the decline of some biochemical indicators cannot be regarded as the end point of GA treatment. Furthermore, our research group plans to conduct further metabolomics research on the clinical course of GA.

Automatic data analysis workflow for ultra-high performance liquid chromatography-high resolution mass spectrometry-based metabolomics

Data analysis for ultra-performance liquid chromatography high-resolution mass spectrometry-based metabolomics is a challenging task. The present work provides an automatic data analysis workflow (AntDAS2) by developing three novel algorithms, as follows: (i) a density-based ion clustering algorithm is designed for extracted-ion chromatogram extraction from high-resolution mass spectrometry; (ii) a new maximal value-based peak detection method is proposed with the aid of automatic baseline correction and instrumental noise estimation; and (iii) the strategy that clusters high-resolution m/z peaks to simultaneously align multiple components by a modified dynamic programing is designed to efficiently correct time-shift problem across samples. Standard compounds and complex datasets are used to study the performance of AntDAS2. AntDAS2 is better than several state-of-the-art methods, namely, XCMS Online, Mzmine2, and MS-DIAL, to identify underlying components and improve pattern recognition capability. Meanwhile, AntDAS2 is more efficient than XCMS Online and Mzmine2. A MATLAB GUI of AntDAS2 is designed for convenient analysis and is available at the following webpage: http://software.tobaccodb.org/software/antdas2.

Signal Smoothing with PLS Regression

Smoothing of instrumental signals is an important prerequisite in data processing. Various smoothing methods were suggested through the last decades each having their own benefits and drawbacks. Most of the filtering methods are based on averaging in a certain window (e.g., Savitzky-Golay) or on frequency-domain representation (e.g., Fourier filtering). The present study introduces novel approach to signal filtering based on signal variance through PLS (projections on latent structures) regression. The influence of filtering parameters on the smoothed spectrum is explained and real world examples are shown.

Comparative Metabolomics Analysis of Cervicitis in Human Patients and a Phenol Mucilage-Induced Rat Model Using Liquid Chromatography Tandem Mass Spectrometry

Cervicitis is an exceedingly common gynecological disorder that puts women at high risk of sexually transmitted infections and induces a series of reproductive system diseases. This condition also has a significant impact on quality of life and is commonly misdiagnosed in clinical practice due to its complicated pathogenesis. In the present study, we performed non-targeted plasma metabolomics analysis of cervicitis in both plasma samples obtained from human patients and plasma samples from a phenol mucilage induced rat model of cervicitis, using ultra-performance liquid chromatography coupled to quadrupole time-of-flight tandem mass spectrometry. In addition to differences in histopathology, we identified differences in the metabolic profile between the cervicitis and control groups using unsupervised principal component analysis and orthogonal projections to latent structures discriminant analysis. These results demonstrated changes in plasma metabolites, with 27 and 22 potential endogenous markers identified in rat and human samples, respectively. The metabolic pathway analysis showed that linoleic acid, arachidonic acid, ether lipid, and glycerophospholipid metabolism are key metabolic pathways involved in cervicitis. This study showed the rat model was successfully created and applied to understand the pathogenesis of cervicitis.

New tools and resources in metabolomics: 2016-2017

Rapid advances in mass spectrometry (MS) and nuclear magnetic resonance (NMR)-based platforms for metabolomics have led to an upsurge of data every single year. Newer high-throughput platforms, hyphenated technologies, miniaturization, and tool kits in data acquisition efforts in metabolomics have led to additional challenges in metabolomics data pre-processing, analysis, interpretation, and integration. Thanks to the informatics, statistics, and computational community, new resources continue to develop for metabolomics researchers. The purpose of this review is to provide a summary of the metabolomics tools, software, and databases that were developed or improved during 2016-2017, thus, enabling readers, developers, and researchers access to a succinct but thorough list of resources for further improvisation, implementation, and application in due course of time.