Study on the correlation and difference of qualitative information among three types of UPLC-HRMS and potential generalization in metabolites annotation.

The variation of qualitative information among different types of mainstream hyphenated instruments of ultra-performance liquid chromatography coupled to high-resolution mass spectrometry (UPLC-HRMS) makes data sharing and standardization, and further comparison of results consistency in metabolite annotation not easy to attain. In this work, a quantitative study of correlation and difference was first achieved to systematically investigate the variation of retention time (tR), precursor ion (MS1), and product fragment ions (MS2) generated by three typical UPLC-HRMS instruments commonly used in metabolomics area. In terms of the findings of systematic and correlated variation of tR, MS1, and MS2 between different instruments, a computational strategy for integrated metabolite annotation was proposed to reduce the influence of differential ions, which made full use of the characteristic (common) and non-common fragments for scoring assessment. The regular variations of MS2 among three instruments under four collision energy voltages of high, medium, low, and hybrid levels were respectively inspected with three technical replicates at each level. These discoveries could improve general metabolite annotation with a known database and similarity comparison. It should provide the potential for metabolite annotation to generalize qualitative information obtained under different experimental conditions or using instruments from various manufacturers, which is still a big headache in untargeted metabolomics. The mixture of standard compounds and serum samples with the addition of standards were applied to demonstrate the principle and performance of the proposed method. The results showed that it could be an optional strategy for general use in HRMS-based metabolomics to offset the difference in metabolite annotation. It has some potential in untargeted metabolomics.

Study on the Common Molecular Mechanism of Metabolic Acidosis and Myocardial Damage Complicated by Neonatal Pneumonia.

Pneumonia is a common clinical disease in the neonatal period and poses a serious risk to infant health. Therefore, the understanding of molecular mechanisms is of great importance for the development of methods for the rapid and accurate identification, classification and staging, and even disease diagnosis and therapy of pneumonia. In this study, a nontargeted metabonomic method was developed and applied for the analysis of serum samples collected from 20 cases in the pneumonia control group (PN) and 20 and 10 cases of pneumonia patients with metabolic acidosis (MA) and myocardial damage (MD), respectively, with the help of ultrahigh-performance liquid chromatography-high-resolution mass spectrometry (UPLC-HRMS). The results showed that compared with the pneumonia group, 23 and 21 differential metabolites were identified in pneumonia with two complications. They showed high sensitivity and specificity, with the area under the curve (ROC) of the receiver operating characteristic curve (ROC) larger than 0.7 for each differential molecule. There were 14 metabolites and three metabolic pathways of sphingolipid metabolism, porphyrin and chlorophyll metabolism, and glycerophospholipid metabolism existing in both groups of PN and MA, and PN and MD, all involving significant changes in pathways closely related to amino acid metabolism disorders, abnormal cell apoptosis, and inflammatory responses. These findings of molecular mechanisms should help a lot to fully understand and even treat the complications of pneumonia in infants.

Rapid authentication of variants of Gastrodia elata Blume using near-infrared spectroscopy combined with chemometric methods.

The variety is one of the most important factors to generate difference of chemical compositions, which unavoidably influences the quality of natural medicine. Thus, simple and rapid authentication of different variants has great academic and practical significance. In this study, the goal was achieved with the help of near infrared spectroscopy (NIR) and chemometrics by using Gastrodia elata Blume as an example. A total of 540 samples including two classes of variants and their forms were investigated as a whole. The mean spectra of samples of each class and their 2-D synchronous correlation spectra were simultaneously applied to discover the difference of chemical characteristics. After hybrid pre-processing of the first and second derivative combined with Savitzky-Golay and Norris filtering, partial least squares discrimination analysis (PLS-DA) on the basis of latent variable projection was used to assess the feasibility for classification. The results show higher prediction accuracy in both internal test set and external prediction set. In order to further improve the robustness for modeling, three methods for wavelength selection were comprehensively compared to optimize PLS-DA models, including variable importance in the projection (VIP), random frog (RF), and Monte Carlo uninformative variable elimination (MC-UVE). The prediction accuracy of combination of the 2nd derivative, Norris, MC-UVE and PLS-DA achieved to 99.11% and 98.89% corresponding to the internal test set and external prediction set, respectively. The strategies proposed in this work perform effectiveness for rapid and accurate authentication of variants of plants with high chemical complexity.

Variational theory of angulons and their rotational spectroscopy.

The angulon, a quasiparticle formed by a quantum rotor dressed by the excitations of a many-body bath, can be used to describe an impurity rotating in a fluid or solid environment. Here, we propose a coherent state ansatz in the co-rotating frame, which provides a comprehensive theoretical description of angulons. We reveal the quasiparticle properties, such as energies, quasiparticle weights, and spectral functions, and show that our ansatz yields a persistent decrease in the impurity's rotational constant due to many-body dressing, which is consistent with experimental observations. From our study, a picture of the angulon emerges as an effective spin interacting with a magnetic field that is self-consistently generated by the molecule's rotation. Moreover, we discuss rotational spectroscopy, which focuses on the response of rotating molecules to a laser perturbation in the linear response regime. Importantly, we take into account initial-state interactions that have been neglected in prior studies and reveal their impact on the excitation spectrum. To examine the angulon instability regime, we use a single-excitation ansatz and obtain results consistent with experiments, in which a broadening of spectral lines is observed while phonon wings remain highly suppressed due to initial-state interactions.

Method comparison of sample pretreatment and discovery of differential compositions of natural flavors and fragrances for quality analysis by using chemometric tools.

Natural flavors and fragrances or their extracts have been widely used in a large variety of areas, including food, cosmetic, and tobacco industrial processes, among others. The compositions and intrinsic attributes of flavors and fragrances were related to many factors, such as species, geographical origin, planting environment, storage condition, processing method, and so on. This not only increased the difficulty in analyzing the product quality of flavors and fragrances, but also challenged the idea of "quality-by-design (QbD)". This work proposed an integrated strategy for precise discovery of differential compounds among different classes and subsequent quality analysis of complex samples through flavors and fragrances used in tobacco industry as examples. Three pretreatment methods were first inspected to effectively characterize the sample compositions, including direct injection (DI), thermal desorption (TD), and stir bar sorptive extraction (SBSE)-TD, coupled with gas chromatography-mass spectrometry (GC-MS) analysis to obtain characteristic information of samples of flavors and fragrances. Then, principal component analysis (PCA) was applied to discover the relation and difference between chromatographic fingerprints and peak table data once significant components were recognized in a holistic manner. Model population analysis (MPA) was then used to quantitatively extract the characteristic chemicals representing the quality differences among different classes of samples. Some differential marker compounds were discovered for difference analysis, including benzyl alcohol, latin acid, l-menthol acid, decanoic acid ethyl ester, vanillin, trans-o-coumaric acid, benzyl benzoate, and so on. Furthermore, partial least squares-discriminant analysis (PLS-DA) and support vector machine (SVM) were respectively applied to construct multivariate models for evaluation of quality differences and variations. It was found that the accuracy attains to 100% for sample classification. With the help of optimal sample pretreatment technique and chemometric methods, the strategy for quality analysis and difference discovery proposed in this work can be widely delivered to more areas of complex plants with good interpretability and high accuracy.

Screening out Biomarkers of Tetrastigma hemsleyanum for Anti-Cancer and Anti-Inflammatory Based on Spectrum-Effect Relationship Coupled with UPLC-Q-TOF-MS.

Tetrastigma hemsleyanum Diels et Gilg. (T. hemsleyanum) is an economically and medicinally valuable species within the genus Tetrastigma. However, the material basis of its pharmacological action and the biomarkers associated with its anti-cancer and anti-inflammatory effects are still unclear. Additionally, the T. hemsleyanum industry cannot grow because there is a lack of a scientific, universal, and measurable quality control system. This study aimed to explore the chemical basis quality markers related to the anti-cancer and anti-inflammatory effects of T. hemsleyanum to establish an effective quality evaluation method. UPLC-Q-TOF-MSE fingerprint profiles of T. hemsleyanum from different origins were established. Pharmacodynamic studies used HepG2 and HuH-7 cells and LPS-induced RAW264.7 to evaluate the anti-tumor and anti-inflammatory effects of the active ingredients. The spectrum-effect relationships between UPLC fingerprints and anti-cancer and anti-inflammatory activities were evaluated using PCA and PLSR statistical methods. Moreover, docking analysis was performed to identify specific active biomarkers with molecular targets associated with cancer and inflammation. Chlorogenic acid, quinic acid, catechin, kaempferol 3-rutinoside, apigenin-8-C-glucoside, and linolenic acid were associated with anticancer activity, while chlorogenic acid, quercetin, quinic acid, kaempferol 3-rutinoside, rutinum, apigenin-8-C-glucoside, and linolenic acid were associated with anti-inflammatory activity. The spectrum-effect relationship of T. hemsleyanum was successfully established, and the biomarkers for anti-cancer and anti-inflammatory effects were preliminary confirmed. These findings provide a theoretical basis for the elucidation of the substance basis of T. hemsleyanum and lay the foundation for its rapid identification, quality control, industrial research, and utilization.

Chemical profile, anti-hepatoma activity, anti-acetylcholinesterase and antioxidant activity of aerial part of Aconitum carmichaeli Debx.

Five extracts of the aerial parts of Aconitum carmichaeli were obtained by different solvent extraction or macroporous adsorption resin purification: ethyl acetate layer extract (EAE), n-butanol layer extract (BuE), water layer extract (WE), extract eluted by 10% ethanol from macroporous resin (10%EE), extract eluted by 80% ethanol from macroporous resin (80%EE). Antioxidant activities of the five extracts were determined by ABTS, DPPH, FRAP assays, anti-AChE activities by modified Ellman's method, in vitro anti-hepatoma activities by CCK-8 assay, and chemical constituents of 80%EE were identified by UPLC-QE-Orbitrap-MS. The results demonstrated that the 80%EE showed the best in vitro anti-hepatoma activity on Huh-7 cell line with an IC50 of 103.91 ± 11.02 µg/mL. 10%EE and 80%EE gave the highest antioxidant activity. Furthermore, current findings demonstrated that the aerial part of Aconitum carmichaeli Debx. has high medicinal value and may be a good natural medicine.

Information Entropy-Based Strategy for the Quantitative Evaluation of Extensive Hyperspectral Images to Better Unveil Spatial Heterogeneity in Mass Spectrometry Imaging.

Hyperspectral images can be generated from mass spectrometry imaging (MSI) data for the intuitive data visualization purpose. However, hundreds of HSIs can be generated by different dimensionality reduction methods, which poses great challenges in selecting the high-quality images with the best intuitive visualization results of the MSI data. Here, we presented a novel approach that objectively evaluates the image quality of the hyperspectral images. The applicability of this method was demonstrated by analyzing the MSI data acquired from human prostate cancer biopsy samples and mouse brain tissue section, which harbored an intrinsic tissue heterogeneity. Our method was based on the information entropy and contrast measured from image information content and image definition, respectively. The heterogeneity of the MSI data from high-dimensional space was reduced to three-dimensional embeddings and thoroughly evaluated to achieve satisfactory visualization results. The application of information entropy and contrast can be used to choose the optimized visualization results rapidly and objectively from an extensive number of hyperspectral images and be adopted to evaluate and optimize different dimensionality reduction algorithms and their hyperparameter combinations. In conclusion, the information entropy-based strategy could be a bridge between chemometrician and biologists.

Comprehensive metabolite quantitative assay based on alternate metabolomics and lipidomics analyses.

Metabolomics-based precision medicine is facing several obstacles including cross-platform data comparison issue and the lack of metabolome benchmark values of healthy population, one of main reasons is the shortage of comprehensive metabolome quantitation methods. Here, we developed an alternate reversed-phase liquid chromatography-mass spectrometry (RPLC-MS) method to quantitatively determine metabolites and lipids. Assisted by a wide set of reference standards and real samples, up to 397 multiple reaction monitoring (MRM) transitions (239 for positive and 158 for negative ion modes) and 1080 MRM transitions (607 for positive and 473 for negative ion modes) were defined respectively in the metabolomic and lipidomic analyses with more than 1000 metabolites and lipids being quantified. Among them, 144 analytes including amines, amino acids, benzenoids, peptides, nucleobases and related, bile acids, carboxylic acids, fatty acids, hormones, indoles and others were absolutely quantified, while carnitines, lyso-phosphatidylcholines, lyso-phosphatidylethanolamines, free fatty acids, sphingomyelins, phosphatidylcholines (PCs), alkyl and alkenyl substituted PCs, phosphatidylethanolamines (PEs), alkyl and alkenyl substituted PEs and triacylglycerols were semiquantified. The developed method was validated to have good analytical characteristics. Analytical results of standard reference material 1950 human plasma had a good agreement with literature data. As a proof of application, this method was used to study serum metabolic pattern changes of patients with hyperuricemia and nonalcoholic fatty liver. This alternate RPLC-MS method for quantitative metabolites and lipids analysis can further be used to provide technology and large-scale data support for precision medicine and life sciences.

Significant metabolic alterations in patients with hepatitis B virus replication observed via serum untargeted metabolomics shed new light on hepatitis B virus infection.

Until now, the metabolic effects of hepatitis B virus (HBV) replication on the progression of hepatic diseases (hepatitis, cirrhosis, and liver cancer) and liver functions have remained unexplored. Thus, a total of 199 hepatic disease patients with active and inactive HBV were enrolled in this study to explore serum metabolic characteristics using untargeted metabolomics. Multiple analyses, including principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), volcano plot and pathway analysis, were used for metabolic data analysis. Additionally, differential metabolites were analysed by commercial databases. A decrease of approximately 0.8-fold in amino acids (L-glutamic acid, D-glutamine and L-tyrosine) and an increase of 2-fold in phosphatidylcholines (PCs) and lysophosphatidylcholines (LPCs) were observed in hepatic disease patients with HBV replication. Moreover, downregulation of arachidonic acid, PC 34:2, sn-glycerol-3-phosphocholine, 1-palmitoylglycerophosphoinositol, and 1-oleoylglycerophosphoinositol by 0.6-fold was also found in the serum of patients with HBV replication. In addition, liver function was significantly different between cirrhosis patients with or without HBV replication (p < .05). In summary, this is the first study to focus on the metabolic changes induced by HBV replication in patients and to compare metabolic alterations in the progression of hepatic disease induced by HBV infection. High levels of amino acid depletion and PC and LPC biosynthesis were primarily observed, which may shed new light on the pathogenesis and treatment of HBV infection.

Quantitative Analysis of a Weak Correlation between Complicated Data on the Basis of Principal Component Analysis.

The mining of weak correlation information between two data matrices with high complexity is a very challenging task. A new method named principal component analysis-based multiconfidence ellipse analysis (PCA/MCEA) was proposed in this study, which first applied a confidence ellipse to describe the difference and correlation of such information among different categories of objects/samples on the basis of PCA operation of a single targeted data. This helps to find the number of objects contained in the overlapping and nonoverlapping areas of ellipses obtained from PCA runs. Then, a quantitative evaluation index of correlation between data matrices was defined by comparing the PCA results of more than one data matrix. The similarity and difference between data matrices was further quantified through comprehensively analyzing the outcomes. Complicated data of tobacco agriculture were used as an example to illustrate the strategy of the proposed method, which includes rich features of climate, altitude, and chemical compositions of tobacco leaves. The number of objects of these data reached 171,516 with 14, 4, and 5 descriptors of climate, altitude, and chemicals, respectively. On the basis of the new method, the complex but weak relationship between these independent and dependent variables were interestingly studied. Three widely used but conventional methods were applied for comparison in this work. The results showed the power of the new method to discover the weak correlation between complicated data.

A graph density-based strategy for features fusion from different peak extract software to achieve more metabolites in metabolic profiling from high-resolution mass spectrometry.

In metabolomics study, it is not easy to extract the metabolites from data of ultra high-performance liquid chromatography-high-resolution mass spectrometry, especially for those with low abundance. Different software for peak recognition and matching use different algorithms, leading to different extract results. Therefore, integration of results from different software can obtain richer metabolome information, but the redundant features should be removed. In this study, an integrated strategy of fusing features and removing redundancy based on graph density (FRRGD) was proposed. A graph is used to cover the ion features generated by two open access software (XCMS, MZmine 2) and a software (SIEVE) from an instrument vendor, and redundant features were removed by searching the maximal complete sub-graphs. A standard mixture containing 41 metabolites and a spontaneous urine were utilized to develop the method and demonstrate its usefulness. For the standard mixture, 19, 19 and 27 metabolites were extracted by XCMS, MZmine 2 and SIEVE, respectively. After fusion by FRRGD, 37 metabolites were obtained. For the diluted spontaneous urine sample, 1103, 1500 and 387 metabolites were extracted by XCMS, MZmine 2 and SIEVE, respectively, FRRGD produced 1619 metabolites which were much more than individual software, significantly increasing metabolome coverage. The proposed FRRGD shows a great prospect as a new data processing strategy for metabolomics study.

Development of a plasma pseudotargeted metabolomics method based on ultra-high-performance liquid chromatography-mass spectrometry.

Untargeted methods are typically used in the detection and discovery of small organic compounds in metabolomics research, and ultra-high-performance liquid chromatography-high-resolution mass spectrometry (UHPLC-HRMS) is one of the most commonly used platforms for untargeted metabolomics. Although they are non-biased and have high coverage, untargeted approaches suffer from unsatisfying repeatability and a requirement for complex data processing. Targeted metabolomics based on triple-quadrupole mass spectrometry (TQMS) could be a complementary tool because of its high sensitivity, high specificity and excellent quantification ability. However, it is usually applicable to known compounds: compounds whose identities are known and/or are expected to be present in the analyzed samples. Pseudotargeted metabolomics merges the advantages of untargeted and targeted metabolomics and can act as an alternative to the untargeted method. Here, we describe a detailed protocol of pseudotargeted metabolomics using UHPLC-TQMS. An in-depth, untargeted metabolomics experiment involving multiple UHPLC-HRMS runs with MS at different collision energies (both positive and negative) is performed using a mixture obtained using small amounts of the analyzed samples. XCMS, CAMERA and Multiple Reaction Monitoring (MRM)-Ion Pair Finder are used to find and annotate peaks and choose transitions that will be used to analyze the real samples. A set of internal standards is used to correct for variations in retention time. High coverage and high-performance quantitative analysis can be realized. The entire protocol takes ~5 d to complete and enables the simultaneously semiquantitative analysis of 800-1,300 metabolites.

Driving driven lattice gases to identify their universality classes.

The critical behaviors of driven lattice gas models have been studied for decades as a paradigm to explore nonequilibrium phase transitions and critical phenomena. However, there exists a long-standing controversy in their universality classes. This is of paramount importance as it implies the question of whether or not a microscopic model and its mesoscopic field theory may possess different symmetries in nonequilibrium critical phenomena in contrast to their equilibrium counterparts. Here, we heat with finite rates two generic models of driven lattice gases across their respective nonequilibrium critical points into further nonequilibrium situations. Employing the theory of finite-time scaling, we are able to unambiguously discriminate the universality classes between the two models. In particular, the infinitely driven lattice gas and the randomly driven lattice gas models belong to different universality classes. These results show that finite-time scaling is effective even in nonequilibrium phase transitions.

Metabolomics in Stem Cell Biology Research.

Stem cell research has been greatly facilitated by comprehensive and integrative multi-omics studies. As a unique approach of functional analysis, metabolomics measures many metabolites and activities of metabolic pathways which can directly indicate cellular energetic status, cell proliferation and fitness, and stem cell fate choices such as self-renewal versus differentiation. Here we describe the methods of applying metabolomics, 13C-labeled glucose and glutamine tracing with mouse embryonic stem cells (ES cells), metabolite analysis using mass spectrometry tools, and the following statistical and computational modeling analysis. Integration of these methods into the more common gene expression and epigenetics analysis toolbox will help to generate a more complete picture and in-depth understanding of one's stem cells of interest.

Removal of false positive features to generate authentic peak table for high-resolution mass spectrometry-based metabolomics study.

In metabolomics research, false positive features from non-sample sources and noises usually exist in the peak table, they will make the results of screening differential metabolites or biomarkers unreliable. In this study, a method to remove false positive features (rFPF) was developed to improve the quality of the peak table. rFPF recognizes real peak profiles based on the information entropy and statistical correlation, and eliminates false positive features from non-sample sources and noises. A standard mixture with 42 standards (14 isotopic labeled internal standards and 28 common standards) and a urine sample were applied to evaluate the effectiveness of the rFPF method. The analysis results of metabolite standards showed that more than 92% false positive features were removed by rFPF, but target standards completely remained. The analysis results of urine sample showed that the number of features was significantly reduced from 7182 to 2522. Interestingly, 98% of the identified metabolites remained after removing false positive features. The proposed rFPF shows great prospects as a new data handling method for metabolomics studies. The MATLAB code and data can be downloaded from http://app.ifc.dicp.ac.cn/Confirmation/Authentication.html.

Ion-Pair Selection Method for Pseudotargeted Metabolomics Based on SWATH MS Acquisition and Its Application in Differential Metabolite Discovery of Type 2 Diabetes.

The pseudotargeted metabolomics method integrates advantages of nontargeted and targeted analysis because it can acquire data of metabolites in the multireaction monitoring (MRM) mode of mass spectrometry (MS) without needing standards. The key is the ion-pair information collection from samples to be analyzed. It is well-known that sequential windowed acquisition of all theoretical Fragment ion (SWATH) MS mode can acquire MS2 information to a maximum extent. To expediently acquire as many ion-pairs as possible with optimal collision energy (CE), an ion-pair selection approach based on SWATH MS acquisition with variable isolation windows was developed in this study. Initially, nontargeted acquisition of all metabolites information in plasma Standard Reference Material (SRM 1950) was performed by ultra high-performance liquid chromatography (UHPLC)-quadrupole time-of-flight (Q-TOF) MS platform with three CEs. With the help of software tool, the ion-pairs of unique metabolites were gained. Then they were validated in scheduled MRM coupled with UHPLC. After removing false positive, the ion-pairs with an optimal CE was integrated. A total of 1373 unique metabolite ion-pairs were obtained at positive ion mode. And repeatability of the established pseudotargeted approach was evaluated by intraday and interday precision. The results demonstrated the method was stable, reliable, and suitable for metabolomics study. As an application example, alterations of serum metabolites in Type 2 diabetes were investigated by using the established method. This work provides a pseudotargeted ion-pair selection method based on SWATH MS acquisition with the characters of increased metabolite coverage, suitable CE, and convenient processing.

Comprehensive Strategy to Construct In-House Database for Accurate and Batch Identification of Small Molecular Metabolites.

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-MS n-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 analyzed to deliver the strategy, and 202 metabolites were identified in the positive ion mode. It shows our strategy is useful for LC-MS n-based nontargeted metabolomics study.

Semiautomated Alignment of High-Throughput Metabolite Profiles with Chemometric Tools.

The rapid increase in the use of metabolite profiling/fingerprinting techniques to resolve complicated issues in metabolomics has stimulated demand for data processing techniques, such as alignment, to extract detailed information. In this study, a new and automated method was developed to correct the retention time shift of high-dimensional and high-throughput data sets. Information from the target chromatographic profiles was used to determine the standard profile as a reference for alignment. A novel, piecewise data partition strategy was applied for the determination of the target components in the standard profile as markers for alignment. An automated target search (ATS) method was proposed to find the exact retention times of the selected targets in other profiles for alignment. The linear interpolation technique (LIT) was employed to align the profiles prior to pattern recognition, comprehensive comparison analysis, and other data processing steps. In total, 94 metabolite profiles of ginseng were studied, including the most volatile secondary metabolites. The method used in this article could be an essential step in the extraction of information from high-throughput data acquired in the study of systems biology, metabolomics, and biomarker discovery.

The development of plasma pseudotargeted GC-MS metabolic profiling and its application in bladder cancer.

Bladder cancer (BC) is a fatal malignancy with considerable mortality. BC urinary metabolomics has been extensively investigated for biomarker discovery, but few BC blood metabolomic studies have been performed. Hence, a plasma pseudotargeted metabolomic method based on gas chromatography-mass spectrometry with selected ion monitoring (GC-MS-SIM) was developed to study metabolic alterations in BC. The analytical performance of the developed method was compared with that of a nontargeted method. The relative standard deviation (RSD) values of 89 and 70.7 % of the peaks obtained using the pseudotargeted and nontargeted methods, respectively, were less than 20 %. The Pearson correlations of 90.7 and 78.3 % of the peaks obtained using the pseudotargeted and nontargeted methods, respectively, exceeded 0.90 in the linearity evaluation. Compared with the nontargeted method, the signal-to-noise ratios (S/N) of 97.9 and 69.3 % of the peaks increased two- and fivefold, respectively. The developed method was fully validated, with good precision, recovery, and stability of the trimethylsilyl (TMS) derivatives. The method was applied to investigate BC. Significant increases in the contents of metabolites involved in, for example, the pentose phosphate pathway (PPP) and nucleotide and fatty acid synthesis were found in the high-grade (HG) BC group compared to the healthy control (HC) group. These differences imply that the activated PPP may regulate BC cell proliferation by promoting lipid and nucleotide biosynthesis and the detoxification of reactive oxygen species (ROS). These results illustrate that the plasma pseudotargeted method is a powerful tool for metabolic profiling. Graphical abstract The plasma pseudotargeted metabolic profiling suggested the metabolic alterations in bladder cancer (BC) and the significantly differential metabolites for BC discrimination.