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Optical cross-reactive sensor arrays inspired by the mammalian olfactory system that can realize straightforward discrimination of plasma from cancer patients hold great potential for point-of-care diseases diagnostics. Herein, a pH programmed fluorescence sensor array based on protein-responsive patterns was designed for straightforward discrimination of different types of cancer plasma. It is worth noting that plasma discrimination can be realized only by programming one nanomaterial using different pH values, which greatly simplifies the programmable design of the sensor array, making it an important highlight of this work. In addition, the mechanism of the pH programmed fluorescence sensor array for protein responsiveness was systematically investigated through molecular docking simulation, fluorescence resonance energy transfer (FRET), and fluorescence lifetime experiments. Most importantly, not only can the differences between plasma from healthy people and and from patients with different cancer species including gastric cancer, liver cancer, breast cancer, and cervical cancer be discriminated by this pH programmed fluorescence sensor array, but also the blind test of unknown plasma samples can be well identified with 100% accuracy, indicating its promising prospect in clinical application.

Liquid chromatography-high-resolution mass spectrometry (LC-HRMS) is the most popular platform for untargeted metabolomics studies, but compound annotation is a challenge. In this work, we developed a new LC-HRMS data-targeted extraction method called MetEx for metabolite annotation. MetEx contains the retention time (

Glycosides are a large family of secondary metabolites in plants, which play a critical role in plant growth and development. Due to the complexity and diversity in structures and the limited availability of authentic standards, comprehensive annotation of the glycosides remains a great challenge. In this study, using maize as an example, a deep annotation method of glycosides was proposed based on untargeted liquid chromatography-high-resolution tandem mass spectrometry metabolomics analysis. First, knowledge-based

Direct-infusion nanoelectrospray ionization high-resolution mass spectrometry (DI-nESI-HRMS) is an alternative approach to chromatography-MS-based techniques for nontargeted metabolomics, offering a high sample throughout. However, its annotation accuracy of analytes is still full of challenges. In this study, we proposed a strategy for the annotation and quantitation of nontargeted metabolomic data using a spectral-stitching DI-nESI-HRMS with data-independent acquisition. The metabolite annotation strategy included the isotopic distribution, MS/MS spectrum similarity, and precursor and product ion correlation as well as matching of the extracted metabolite features along with the targeted metabolite precursors. Two groups of mixed standard solutions containing 40 and 79 metabolites were, respectively, used to establish the metabolite annotation strategy and validate its reliability. The results showed that the detected standards could be well annotated at top three explanations and total qualitative percentages were 100% (40 of 40) for the standard solution and 94.9% (74 of 78) for the standards spiked into the serum matrix. The intensity of the precursor ions was used for quantitation except for isomers, which were quantified by the intensities of the characteristic product ions if available. Finally, the strategy was applied to study serum metabolomics in diabetes, and the results demonstrated that it is promising for a large-scale cohort metabolomic study.

Retention time (RT) prediction contributes to identification of small molecules measured by high-performance liquid chromatography coupled with high-resolution mass spectrometry. Deep learning algorithms based on big data can enhance the accuracy of RT prediction. But at different chromatographic conditions, RTs of compounds are different, and the number of compounds with known RTs is small in most cases. Therefore, the transfer of big data is necessary. In this work, a strategy using a deep neural network (DNN) pretrained by weighed autoencoders and transfer learning (DNNpwa-TL) was proposed to efficiently predict RTs of compounds. The loss function in the autoencoders was calculated with features weighted by mutual information. Then, a DNN pretrained by weighted autoencoders (DNNpwa) was produced. For other specific chromatographic methods, the transfer learning model DNNpwa-TLs were built through fine-tuning the DNNpwa with the help of some compounds with known RTs to conduct the RT prediction. With the above strategy, a DNNpwa was first built with the METLIN small molecule retention time data set containing 80 038 small molecule compounds. A median relative error of 3.1% and a mean relative error of 4.9% were achieved. Then, 17 data sets from different chromatographic methods were studied, and the results showed that the performance of DNNpwa-TL was better than those of other deep learning models. Besides, DNNpwa-TL outperformed random forest, gradient boost, least absolute shrinkage and selection operator regression, and DNN for most of the 17 data sets. Therefore, DNNpwa-TL can provide an efficient method to perform RT prediction of small molecule compounds for different chromatographic methods and conditions.

Stable isotope-resolved metabolomics (SIRM) can provide metabolic conversion information of specific targets; it is a powerful tool for cell metabolism studies. The common analytical platform for SIRM is chromatography-mass spectrometry, which requires a large number of cells and is not suitable for precious rare cell analysis. To study a small number of cells, we established a novel SIRM method using chip-based nanoelectrospray mass spectrometry (MS). 13C-glutamine was taken as an example; the unlabeled and 13C-labeled cells were cultured and extracted in a 96-well plate and then directly injected into MS and analyzed in full scan mode and parallel reaction monitoring (PRM) mode targeting 44 glutamine-derived metabolites and their isotopologues. To define focused metabolite-related MS2 fragments produced in the PRM, a new strategy was proposed including MS2 exact m/z matching, MS2 false positive filtering, and MS2 fragment grouping to remove the interfering MS2 ions. In total, 292 and 349 pairs of paired MS2 ions were obtained in positive and negative ionization modes, respectively. By searching spectra databases, 31 targeted metabolites with their isotopologues were identified and their characteristic product ions were confirmed for MS2 quantification. The relative quantification was achieved by MS2 quantification, which showed better sensitivity and accuracy than common MS1-based quantification. Finally, this method was applied to isocitrate dehydrogenase I-mutated glioma cells for revealing the effects of triptolide on glioma cell metabolism using U-13C-glutamine as a labeling substrate.

Complex mixtures of hydrocarbons are ubiquitous as petroleum fuels and, consequently, environmental contaminants. Because they contain thousands of individual components with similar molecular structures, detailed chemical characterization of hydrocarbon mixtures relies on advanced analytical techniques that are not accessible to many researchers. Many analyses of hydrocarbon mixtures instead characterize them as "unresolved complex mixtures", with quantification limited to a small number of resolvable components and/or total observed mass within specified volatility ranges. This work develops a new analytical approach to characterize the hydrocarbon component of petroleum and environmental mixtures by "hydrocarbon group" (defined by carbon number, degree of unsaturation and, in certain cases, degree of branching) using gas chromatography coupled to a unit-mass-resolution electron ionization quadrupole mass spectrometer (GC/EI-MS), a standard and widely available instrument. Average mass spectra of hydrocarbons from a widely used spectral library are combined with chromatographic signal representing the molecular ion of each hydrocarbon group to recreate the magnitude and mass spectra of the chromatogram. Characterization of hydrocarbons in diesel fuel by this approach is in good agreement with state-of-the-art techniques relying on high-resolution and fast-response mass spectrometers. Application of this approach to subsurface soil gas samples from remediated sites of underground storage tank spills demonstrates that composition of hydrocarbons in environmental samples varies significantly and that the total signal of samples from contaminated sites may contain a substantial fraction of oxygenated components.

Nontargeted analysis is a useful strategy for the discovery of unknown risk compounds. However, how to rapidly screen and determine risk substances is still a big challenge. In this study based on high-performance liquid chromatography (HPLC)-high resolution mass spectrometry (HRMS), a strategy for the rapid screening and determination of risk substances was established. First, the distribution characteristic of every feature from HRMS in all samples was studied by the mean deviation ratio (MDR) calculation. Features with MDR more than 20 were thought to be the first match of obvious suspected substances. Second, the structure characteristics of specific classes of substances which were summarized from our in-house risk substance (IHRS) database with about 500 different additives and drugs were used to rapidly screen the unknown suspected substances with specific structure classes. To further identify the above suspected risk substances, IHRS retrieval was carried out. For the unknown features not included in IHRS database, the exclusion of endogenous substances and multiple network databases searching were first performed, and the remaining substances had to be identified with comprehensive methods. To test the usefulness of the established screening and identification method, 42 meat samples were analyzed and 6 risk substances were discovered and identified, with the usefulness of the method confirmed.

Identification of the metabolites is an essential step in metabolomics study to interpret the regulatory mechanism of pathological and physiological processes. However, it is still difficult in LC–MSn-based studies because of the complexity of mass spectrometry, chemical diversity of metabolites, and deficiency of standards database. In this work, a comprehensive strategy is developed for accurate and batch metabolite identification in nontargeted metabolomics studies. First, a well-defined procedure was applied to generate reliable and standard LC–MS2 data, including tR, MS1, and MS2 information at a standard operational procedure. An in-house database including about 2000 metabolites was constructed and used to identify the metabolites in nontargeted metabolic profiling by retention time calibration using internal standards, precursor ion alignment and ion fusion, auto-MS2 information extraction and selection, and database batch searching and scoring. As an application example, a pooled serum sample was...

Carnitines play important roles in fatty acid oxidation and branched chain amino acid metabolism. The disturbance of acylcarnitines is associated with occurrence and development of many diseases. Comprehensive acylcarnitine identification can greatly benefit their targeted detection, following disease differential diagnosis and possible mechanism study. In this study, we developed a novel strategy to identify as many acylcarnitines as possible based on liquid chromatography–high-resolution mass spectrometry (LC–HRMS). The layer–layer progressive strategy first integrated the initial full scan MS/data-dependent MS/MS monitoring (ddMS2) acquisition and the following parallel reaction monitoring (PRM) to analyze a pooled biological sample. Also 733 possible acylcarnitines were identified containing characteristic high-resolution MS/MS features. Further, accurate mass, retention rules, and HRMS/MS information were used to define subclasses and predict undetected acylcarnitine homologues in each subclass, lead...

Hydroxycinnamic acid amides (HCAAs), diversely distributed secondary metabolites in plants, play essential roles in plant growth and developmental processes. Most current approaches can be used to analyze a few known HCAAs in a given plant. A novel method for comprehensive detection of plant HCAAs is urgently needed. In this study, a deep annotation method of HCAAs was proposed on the basis of ultra-high-performance liquid chromatography-high-resolution mass spectrometry (UHPLC-HRMS) and its in silico database of HCAAs. To construct an in silico UHPLC-HRMS HCAAs database, a total of 846 HCAAs were generated from the most common phenolic acid and polyamine/aromatic monoamine substrates according to possible biosynthesis reactions, which represent the structures of plant-specialized HCAAs. The characteristic MS/MS fragmentation patterns of HCAAs were extracted from reference mixtures. Four quantitative structure-retention relationship (QSRR) models were developed to predict retention times of mono-trans-HCAAs (aromatic amines conjugates), mono-trans-HCAAs (aliphatic amines conjugates), bis-HCAAs, and tris-HCAAs. The developed method was applied for identifying HCAAs in seeds (maize, wheat, and rice), roots (rice), and leaves (rice and tobacco). A total of 79 HCAAs were detected: 42 of them were identified in these plants for the first time, and 20 of them have never been reported to exist in plants. The results showed that the developed method can be used to identify HCAAs in a plant without prior knowledge of HCAA distributions. To the best of our knowledge, it is the first UHPLC-HRMS database developed for effective deep annotation of HCAAs from nontargeted UHPLC-HRMS data. It is useful for the identification of novel HCAAs in plants.

Identification of illegal additives in complex matrixes is important in the food safety field. In this study a nontargeted screening strategy was developed to find illegal additives based on ultrahigh-performance liquid chromatography-high-resolution mass spectrometry (UHPLC-HRMS). First, an analytical method for possible illegal additives in complex matrixes was established including fast sample pretreatment, accurate UHPLC separation, and HRMS detection. Second, efficient data processing and differential analysis workflow were suggested and applied to find potential risk compounds. Third, structure elucidation of risk compounds was performed by (1) searching online databases [Metlin and the Human Metabolome Database (HMDB)] and an in-house database which was established at the above-defined conditions of UHPLC-HRMS analysis and contains information on retention time, mass spectra (MS), and tandem mass spectra (MS/MS) of 475 illegal additives, (2) analyzing fragment ions, and (3) referring to fragmentation rules. Fish was taken as an example to show the usefulness of the nontargeted screening strategy, and six additives were found in suspected fish samples. Quantitative analysis was further carried out to determine the contents of these compounds. The satisfactory application of this strategy in fish samples means that it can also be used in the screening of illegal additives in other kinds of food samples.

Chemical etching of gold by thiols has been known to be capable of generating nonluminescent gold(I) complexes, e.g., in size-focusing synthesis of atomically precise gold nanoclusters (GNCs). These nonluminescent gold(I) complexes have usually been considered as useless or worthless byproducts. This study shows a promising potential of thiol etching of GNCs to prepare novel water-soluble and phosphorescent gold(I) materials for sensing application. First, cysteamine-induced etching of GNCs is used to produce nonluminescent oligomeric gold(I)-thiolate complexes. Then, cadmium ion induces the aggregation of these oligomeric complexes to produce highly water-soluble ultrasmall intra-aggregates. These intra-aggregates can phosphoresce both in dilute aqueous solutions and in the solid phase. Studies on the effect of pH on their phosphorescent emission reveal the importance of the interaction between the amino groups of the ligands and cadmium ion for their phosphorescent emission property. Furthermore, Cu(2+) ion is found to quickly quench the phosphorescent emission of the intra-aggregates and simultaneously cause a Cu(2+)-concentration-dependent peak wavelength shift, enabling the establishment of a novel colorimetric sensor for sensitive and selective visual sensing of Cu(2+).

Metabolomics is increasingly applied to discover and validate metabolite biomarkers and illuminate biological variations. Combination of multiple analytical batches in large-scale and long-term metabolomics is commonly utilized to generate robust metabolomics data, but gross and systematic errors are often observed. The appropriate calibration methods are required before statistical analyses. Here, we develop a novel correction strategy for large-scale and long-term metabolomics study, which could integrate metabolomics data from multiple batches and different instruments by calibrating gross and systematic errors. The gross error calibration method applied various statistical and fitting models of the feature ratios between two adjacent quality control (QC) samples to screen and calibrate outlier variables. Virtual QC of each sample was produced by a linear fitting model of the feature intensities between two neighboring QCs to obtain a correction factor and remove the systematic bias. The suggested method was applied to handle metabolic profiling data of 1197 plant samples in nine batches analyzed by two gas chromatography-mass spectrometry instruments. The method was evaluated by the relative standard deviations of all the detected peaks, the average Pearson correlation coefficients, and Euclidean distance of QCs and non-QC replicates. The results showed the established approach outperforms the commonly used internal standard correction and total intensity signal correction methods, it could be used to integrate the metabolomics data from multiple analytical batches and instruments, and it allows the frequency of QC to one injection of every 20 real samples. The suggested method makes a large amount of metabolomics analysis practicable.

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.

This paper describes a visual sensor array for pattern recognition analysis of proteins based on two different optical signal changes: colorimetric and fluorometric, by using two types of novel blue-emitting collagen protected gold nanoclusters and macerozyme R-10 protected gold nanoclusters with lower synthetic demands. Eight proteins have been well-discriminated by this visual sensor array, and protein mixtures after one-dimensional polyacrylamide gel electrophoresis also could be well-discriminated. The possible mechanism of this sensor array was illustrated and validated by fluorescence spectra, X-ray photoelectron spectroscopy (XPS), fluorescence lifetime, isothermal titration calorimetry (ITC), and matrix-assisted laser desorption/ionization-time-of-flight mass spectrometry (MALDI-TOF MS) experiments. It was attributed to that the adsorption of proteins onto the surface of gold nanoclusters (Au NCs), forming the protein-Au NCs complex. Furthermore, serums from normal and hepatoma patients were also effectively discriminated by this visual sensor array, showing feasible potential for diagnostic applications.

Modifications of genes and proteins have been extensively studied in systems biology using comprehensive analytical strategies. Although metabolites are frequently modified, these modifications have not been studied using -omics approaches. Here a general strategy for the nontargeted profiling of modified metabolites, which we call "nontargeted modification-specific metabolomics", is reported. A key aspect of this strategy was the combination of in-source collision-induced dissociation liquid chromatography-mass spectrometry (LC-MS) and global nontargeted LC-MS-based metabolomics. Characteristic neutral loss fragments that are specific for acetylation, sulfation, glucuronidation, glucosidation, or ribose conjugation were reproducibly detected using human urine as a model specimen for method development. The practical application of this method was demonstrated by profiling urine samples from liver cirrhosis patients. Approximately 900 features were identified as modified endogenous metabolites and xenobiotics. Moreover, this strategy supports the identification of compounds not included in traditional metabolomics databases (HMDB, Metlin, and KEGG), which are currently referred to as "unknowns" in metabolomics projects. Nontargeted modification-specific metabolomics opens a new perspective in systems biology.

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.

Fuzzy c-means (FCM) clustering is an unsupervised method derived from fuzzy logic that is suitable for solving multiclass and ambiguous clustering problems. In this study, FCM clustering is applied to cluster metabolomics data. FCM is performed directly on the data matrix to generate a membership matrix which represents the degree of association the samples have with each cluster. The method is parametrized with the number of clusters (C) and the fuzziness coefficient (m), which denotes the degree of fuzziness in the algorithm. Both have been optimized by combining FCM with partial least-squares (PLS) using the membership matrix as the Y matrix in the PLS model. The quality parameters R(2)Y and Q(2) of the PLS model have been used to monitor and optimize C and m. Data of metabolic profiles from three gene types of Escherichia coli were used to demonstrate the method above. Different multivariable analysis methods have been compared. Principal component analysis failed to model the metabolite data, while partial least-squares discriminant analysis yielded results with overfitting. On the basis of the optimized parameters, the FCM was able to reveal main phenotype changes and individual characters of three gene types of E. coli. Coupled with PLS, FCM provides a powerful research tool for metabolomics with improved visualization, accurate classification, and outlier estimation.

We examine the use of thionylphosphazene-based block copolymers as matrixes for oxygen sensor applications. Poly(aminothionylphosphazene)-b-poly(tetrahydrofuran) (PATPy-PTHFx) block copolymers were prepared via reaction of ring-opened poly(chlorothionylphosphazene) with THF and subsequently with excess n-butylamine (to form PBATPy-PTHFx) or methylamine (to form PMATPy-PTHFx). The block copolymers were characterized by NMR, gel permeation chromatography, and differential scanning calorimetry. Films of PBATPy-PTHFx block copolymers containing platinum octaethylporphyrin or [Ru(dpp)3]Cl2 (dpp = 4,7-diphenyl-1,10-phenanthroline) as the oxygen-sensitive chromophore were prepared, and time-scan experiments were carried out to determine the diffusion coefficients, Do2, and solubilities, So2, of oxygen therein. Despite microphase separation, the data fit well to a simple Fick's law description of oxygen diffusion and gave Do2 values smaller than that for the n-butylamino-substituted PBATP635. For films freshly annealed above the melting point of PTHFx, the Do2 values were 35-50% (dye-dependent) larger than after aging 3 days at room temperature. Films with [Ru(dpp)3]Cl2 as the dye were evaluated as media for phosphorescent pressure-sensing. The dye-containing polymer films exhibit linear Stern-Volmer-like plots, even at high dye concentrations, as well as good photostability, and significantly higher sensitivity to oxygen quenching than simple mixtures of the analogous homopolymers.

Steroid hormones are crucial substances that mediate a wide range of vital physiological functions of the body. Determination of the levels of steroid hormones plays an important role in understanding the mechanism of the steroid hormone-related diseases. In this study, we present a novel targeted metabolic profiling method based on the introduction of an easily protonated stable isotope tag to a hydroxyl-containing steroid hormone with a synthesized derivatization reagent, deuterium 4-(dimethylamino)-benzoic acid (d(4)-DMBA), and liquid chromatography-mass spectrometry (LC-MS). Different from other reported derivatization reagents that have been used to enhance the sensitivities for estrogens or androgens, our method is comprehensive with the capability of covering hydroxyl-containing androgens, estrogens, corticoids, and progestogens. Furthermore, the nonderivatized steroid hormones (e.g., 17α-hydroxyprogesterone, progesterone, and androstenedione) were not destroyed during the derivatization process, and their levels could still be obtained in one LC-MS run. We were able to detect 24 steroid hormones at subng/mL levels (the lower limit of detection could reach 5 pg/mL for estrone and 16α-hydroxy estrone, which is equivalent to 0.1 pg on column) with maximum sensitivity enhancement factors of more than 10(3)- to 10(4)-fold after derivatization. The method was successfully applied to the measurement of free (unconjugated) steroid hormones in urine samples of males, females, and pregnant women. Because the significant role the steroid hormone pathway plays in humans, a comprehensive, sensitive, specific, and accurate method for profiling the steroid hormone metabolome shall offer new insights into hormone-related diseases.

The goal of this study was the application of a novel, fully automatic column-switching approach in a metabonomics study combining the orthogonal selectivities of hydrophilic interaction chromatography (HILIC) and reversed-phase chromatography. The temporal, pharmacodynamic effects of the ginsenoside Rg3 on the metabonome in urine of healthy and liver-tumor-bearing rats have been investigated. Within a total analysis time of 52 min we detected 5686 polar, and on the second column an additional 1808 apolar, urinary metabolite ions. The administration of a single, high dose of Rg3 in a beta-cyclodextrin-based formulation led to a considerable change of the metabolic pattern in cancer rats during 3 days studied. Seventeen biomarker candidates including three apolar metabolites, which were not retained on the HILIC column, were detected. Overall, the results suggest that the developed liquid chromatography-mass spectrometry strategy is a promising tool in metabonomics studies for global analysis of highly complex biosamples. It may not only increase the number of discovered biomarkers but consequently improve the comprehensive information on metabolic changes in a fully automatic manner.

Sensitive and high-resolution chromatographic-driven metabonomomics studies experienced major growth with the aid of new analytical technologies and bioinformatics software packages. Hence, data collections by LC-MS and data analyses by multivariate statistical methods are by far the most straightforward steps, and the detection of biomarker candidates can easily be achieved. However, the unequivocal identification of the detected metabolite candidates, including isomer elucidation, is still a crux of current metabonomics studies. Here we present a comprehensive analytical strategy for the elucidation of the molecular structure of metabolite biomarkers detected in a metabonomics study, exemplified analyzing spot urine of a cohort of healthy, insulin sensitive subjects and clinically well characterized prediabetic, insulin resistant individuals. An integrated approach of LC-MS fingerprinting, multivariate statistic analysis, LC-MSn experiments, micro preparation, FTICR-MS, GC retention index, database search, and generation of an isotope labeled standard was applied. Overall, we could demonstrate the efficiency of our analytical approach by the unambiguous elucidation of the molecular structure of an isomeric biomarker candidate detected in a complex human biofluid. The proposed strategy is a powerful new analytical tool, which will allow the definite identification of physiologically important molecules in metabonomics studies from basic biochemistry to clinical biomarker discovery.

Cigarette smoke condensate is a complex chemical matrix, and analysis of its components is very difficult because of the limitation of the peak capacity and sensitivity of conventional chromatography and the extensive and laborious sample preparation that is frequently required. In this study, the acidic fraction of mainstream cigarette smoke condensate has been investigated by using comprehensive two-dimensional gas chromatography/time-of-flight mass spectrometry (GCxGC/TOFMS). Different column systems were tested and compared under proper GCxGC/TOFMS conditions. Auto data processing by TOFMS software combined with manual identification was used to assign the components. Over 1000 compounds, with S/Nor = 100, including 139 organic acids and over 150 phenols were tentatively identified by the developed method.