Search

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. Image 1 • A strategy of fusing features and removing redundancy based on graph density (FRRGD) was proposed. • FRRGD uses a graph to cover features and fuses the features in a maximal complete sub-graph to reduce redundancy. • A metabolite standard mixture and a spontaneous urine were used to develop and validate FRRGD. • FRRGD greatly enhanced the metabolome detection coverage with low abundance. [ABSTRACT FROM AUTHOR]

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. Image 1 • A method for removing false positive features was developed. • A metabolite standard mixture and a urine sample were used to validate the effectiveness of the proposed method. • The proposed method largely reduced the number of false positive features but the true target features still remained. [ABSTRACT FROM AUTHOR]

Liquid chromatography-mass spectrometry (LC-MS) is an important analytical platform for metabolomics study. Peak alignment of metabolomics dataset is one of the keys for a successful metabolomics study. In this work, a MS/MS-based peak alignment method for LC-MS metabolomics data was developed. A rigorous strategy for screening endogenous reference variables was proposed. Firstly, candidate endogenous reference variables were selected based on MS, MS/MS and retention time in all samples. Multiple robust endogenous reference variables were obtained through further evaluation and confirmation. Then retention time of each metabolite feature was corrected by local linear regression using the four nearest neighbor robust reference variables. Finally, peak alignment was carried out based on corrected retention time, MS and MS/MS. Comparing with the other two peak alignment methods, the developed method showed a good performance and was suitable for metabolomics data with larger retention time drift. Our approach provides a simple and robust alignment method which is reliable to align LC-MS metabolomics dataset. [ABSTRACT FROM AUTHOR]

• Multiple molecular representations can provide more information for RT prediction. • The method automatically determines the optimal molecular representation combination. • Experiments showed the method having better performance in most cases. • Transfer learning increases the performance of RT prediction for the small data. Retention time (RT) can provide orthogonal information different from that of mass spectrometry and contribute to identifying compounds. Many machine learning methods have been developed and applied to RT prediction. In application, the training data size is usually small in most chromatography systems. To enhance the performance of RT prediction, this study proposes a RT prediction method based on multi-data combinations and adaptive neural network (MDC-ANN). MDC-ANN establishes the RT prediction model for the target chromatographic system through transfer learning and a base deep learning model trained on a big dataset. It selects the optimal molecular representation combination from the multiple input candidates and automatically determines the neural network structure according to the determined input combination. MDC-ANN was compared with two new efficient deep learning methods, three transferring methods and four popular machine learning methods on 14 small datasets and showed advantages in MAE, MedAE, MRE and R2 in most cases. The experiment results illustrated that integrating multiple molecular representations can provide more information, improve the performance of RT prediction and contribute to compound annotation, different chromatographic systems may use different molecular representation combinations to obtain good RT prediction performance. Hence, MDC-ANN which automatically determines the best combination of molecular representations for a specific system is promising for predicting RTs accurately in real applications. [ABSTRACT FROM AUTHOR]

A growing body of evidence suggests the deleterious effects of perfluoroalkyl substances (PFASs) on kidney, but little is known on the association between PFASs joint exposure and uric acid. Serum PFASs concentrations were measured in 661 participants recruited from Tianjin, China using liquid chromatography/mass spectrometry. The associations of single PFASs exposure with uric acid levels and hyperuricemia were assessed using multivariable linear and logistic regression models, respectively. Restricted cubic spline models were established to investigate the dose-response relationships between PFASs concentrations and uric acid levels. Bayesian Kernel Machine Regression (BKMR) model with a hierarchical variable selection was performed to assess the joint effect of PFASs on uric acid. Potassium perfluoro-1-octanesulfonate (PFOS) and perfluoro- n -octanoic acid (PFOA) were the dominated contributors with median concentrations of 16.80 ng/ml and 9.42 ng/ml, respectively. Increased PFOA concentration (per log 2 -unit) was associated with elevated uric acid level (β = 0.088, 95% CI: 0.033–0.143) and higher risk of hyperuricemia (OR = 1.134, 95% CI: 1.006–1.289). Conversely, the estimated change of uric acid associated with log 2 -unit increment in perfluoro- n -decanoic acid (PFDA) was −0.081 mg/dL (95% CI: -0.154, −0.009). A significant linear dose-response pattern was found between log 2 -transformed PFOA concentration and uric acid level. BKMR analyses indicated a non-significant overall effect of PFASs mixture on uric acid. Significant associations between PFOA and PFDA and uric acid, and between PFOA and hyperuricemia were found in the single-pollutant models, but the joint effect of PFASs mixture on uric acid was not observed in the BKMR model, which provided new insights in regulation policies and risk assessment of PFASs. [Display omitted] • PFOA was positively associated with uric acid level and hyperuricemia. • A linear dose-response relationship of PFOA with uric acid was found. • A negative association of PFDA concentrations and uric acid level was observed. [ABSTRACT FROM AUTHOR]

With the increased attention to processing heavy crude oils, a detailed description of chemical composition is critical for the petroleum refining industry. The current analytical technique such as ultrahigh resolution mass spectrometry has been successfully applied for the molecular level characterization of complex petroleum fractions. But the structural characterization of heavy petroleum feedstock is still a great challenge. In this study, a novel in-depth characterization method of nitrogen heterocycles (N-heterocycles) in heavy petroleum mixtures was proposed by online liquid chromatography coupled with electrospray ionization high resolution energy-resolved mass spectrometry. A series of typical basic aromatic, neutral aromatic and naphtheno-aromatic nitrogen heterocyclic model compounds were synthesized to investigate energy-resolved fragmentation behaviors in high energy collision-induced dissociation at 10–100 eV. Energy-dependent fragmentation pathways were elucidated. Notably, characteristic double bond equivalent (DBE) versus carbon number distributions of N 1 ions and all CH ions were discovered, which were closely related to their core structure. Then a workflow to assign core structures of alkyl-substituted N-heterocycles in petroleum was proposed and validated. The developed method was applied to investigate the structural isomers in feed and product vacuum gas oil (VGO) fractions. Core structural differences in feed VGO and subtle structural variations between feed and product VGOs were recognized. This work can distinguish structural isomers of N-heterocycles with the subtle difference in their core structure in heavy petroleum fractions based on global energy dimensional fragmentation characteristics. [Display omitted] • Energy-dependent fragmentation pathways of nitrogen heterocycles were elucidated. • Characteristic product ions distributions of nitrogen heterocycles were discovered. • A core structural assignment strategy for the nitrogen heterocycles was developed. • Core Structural isomers can be distinguished based fragmentation characteristics. [ABSTRACT FROM AUTHOR]

Liquid chromatography–mass spectrometry (LC–MS) is now a main stream technique for large-scale metabolic phenotyping to obtain a better understanding of genomic functions. However, repeatability is still an essential issue for the LC–MS based methods, and convincing strategies for long time analysis are urgently required. Our former reported pseudotargeted method which combines nontargeted and targeted analyses, is proved to be a practical approach with high-quality and information-rich data. In this study, we developed a comprehensive strategy based on the pseudotargeted analysis by integrating blank-wash, pooled quality control (QC) sample, and post-calibration for the large-scale metabolomics study. The performance of strategy was optimized from both pre- and post-acquisition sections including the selection of QC samples, insertion frequency of QC samples, and post-calibration methods. These results imply that the pseudotargeted method is rather stable and suitable for large-scale study of metabolic profiling. As a proof of concept, the proposed strategy was applied to the combination of 3 independent batches within a time span of 5 weeks, and generated about 54% of the features with coefficient of variations (CV) below 15%. Moreover, the stability and maximal capability of a single analytical batch could be extended to at least 282 injections (about 110 h) while still providing excellent stability, the CV of 63% metabolic features was less than 15%. Taken together, the improved repeatability of our strategy provides a reliable protocol for large-scale metabolomics studies. [ABSTRACT FROM AUTHOR]

• A theoretical database was derived from detected samples to reduce redundancy. • The chromatographic information of each petroleum formula was extracted. • False-positive assignments were removed by chromatographic retention behaviors. • Low abundance components were effectively and reliably mined from LC-HRMS data. • Molecular characterization with high coverage and rich information was realized. Online liquid chromatography coupled with high-resolution mass spectrometry (LC-HRMS) has attracted much attention in the molecular characterization of crude oil. Neither open access nor commercially available petroleomics tools were developed specifically to process LC-HRMS data. Here, a novel data processing pipeline was specifically designed for LC-HRMS-based petroleomics data. A customizable formula database was established deriving from the detected sample, which could avoid the interference caused by a large number of redundant molecules in a conventionally theoretical molecular database. Molecular formula candidates were assigned by the formula database using a low noise threshold, and false-positive assignments were eliminated by the chromatographic retention behaviors. Multi-dimensional information was obtained, including heteroatom class, double bond equivalent (DBE), carbon number, retention time, and MS/MS spectra. The developed method was compared with a popular petroleomics software, similar relative abundance class distribution was obtained, and much more formulas of low abundant components were uniquely extracted by the developed method. Finally, it was applied to reveal variation between feed and product oils in hydrodenitrogenation. Significantly compositional and structural differences were revealed. The developed method provides a useful pipeline for molecular data mining of petroleum samples. [ABSTRACT FROM AUTHOR]

• An online RPLC-Orbitrap MS method was proposed to characterize nitrogen-containing compounds in heavy petroleum faction. • Both basic and neutral nitrogen compounds were well characterized simultaneously in positive ESI mode. • False-positive element composition assignment was excluded by retention behaviors of homologues series and isomers. • Similar characterization results were obtained between RPLC-Orbitrap MS and 15 T FT-ICR MS. Characterization of heteroatom compounds in crude oils is very important in petroleum production and processing. The ultrahigh-resolution Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) is a powerful MS technique for it but with expensive costs. It is necessary to develop a generally accepted alternative analytical approach to replace FT-ICR MS. In this study, a reversed-phase liquid chromatography (RPLC)-high-resolution (HR) MS method was proposed on an Orbitrap MS with electrospray ionization (ESI). The optimal chromatographic and MS parameters and internal mass calibration method were suggested. The sample complexity was greatly reduced by RPLC separation. Mass resolution (240 K at m / z 200) of Orbitrap MS was enough to obtain an accurate elemental assignment in complex petroleum fractions. False-positive element composition assignment could be easily excluded by the RPLC retention behavior of isomers. The performance of RPLC-Orbitrap MS was evaluated in comparison with direct injection (DI)-FT-ICR MS and DI-Orbitrap MS in positive ion mode for heavy oil analysis. The results showed that heteroatom class species, DBE vs carbon number distributions were very similar between RPLC-Orbitrap MS and DI-FT-ICR MS. The developed methods were further used to analyze petroleum oil fractions with boiling ranges at 180–350 °C, 350–530 °C and >530 °C. Detailed heteroatom class species and corresponding DBE vs carbon number distributions were obtained. This study demonstrated that the RPLC-Orbitrap MS technique could successfully be applied for heteroatom compound characterization in petroleum fractions. [ABSTRACT FROM AUTHOR]