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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]

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]

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]

Abstract: Non-targeted metabolic profiling is the most widely used method for metabolomics. In this paper, a novel approach was established to transform a non-targeted metabolic profiling method to a pseudo-targeted method using the retention time locking gas chromatography/mass spectrometry-selected ion monitoring (RTL-GC/MS-SIM). To achieve this transformation, an algorithm based on the automated mass spectral deconvolution and identification system (AMDIS), GC/MS raw data and a bi-Gaussian chromatographic peak model was developed. The established GC/MS-SIM method was compared with GC/MS-full scan (the total ion current and extracted ion current, TIC and EIC) methods, it was found that for a typical tobacco leaf extract, 93% components had their relative standard deviations (RSDs) of relative peak areas less than 20% by the SIM method, while 88% by the EIC method and 81% by the TIC method. 47.3% components had their linear correlation coefficient higher than 0.99, compared with 5.0% by the EIC and 6.2% by TIC methods. Multivariate analysis showed the pooled quality control samples clustered more tightly using the developed method than using GC/MS-full scan methods, indicating a better data quality. With the analysis of the variance of the tobacco samples from three different planting regions, 167 differential components (p <0.05) were screened out using the RTL-GC/MS-SIM method, but 151 and 131 by the EIC and TIC methods, respectively. The results show that the developed method not only has a higher sensitivity, better linearity and data quality, but also does not need complicated peak alignment among different samples. It is especially suitable for the screening of differential components in the metabolic profiling investigation. [Copyright &y& Elsevier]