A Novel Approach of SWATH-Based Metabolomics Analysis Using the Human Metabolome Database Spectral Library.

Metabolomics is a potential approach to paving new avenues for clinical diagnosis, molecular medicine, and therapeutic drug monitoring and development. The conventional metabolomics analysis pipeline depends on the data-independent acquisition (DIA) technique. Although powerful, it still suffers from stochastic, non-reproducible ion selection across samples. Despite the presence of different metabolomics workbenches, metabolite identification remains a tedious and time-consuming task. Consequently, sequential windowed acquisition of all theoretical MS (SWATH) acquisition has attracted much attention to overcome this limitation. This article aims to develop a novel SWATH platform for data analysis with a generation of an accurate mass spectral library for metabolite identification using SWATH acquisition. The workflow was validated using inclusion/exclusion compound lists. The false-positive identification was 3.4% from the non-endogenous drugs with 96.6% specificity. The workflow has proven to overcome background noise despite the complexity of the SWATH sample. From the Human Metabolome Database (HMDB), 1282 compounds were tested in various biological samples to demonstrate the feasibility of the workflow. The current study identified 377 compounds in positive and 303 in negative modes with 392 unique non-redundant metabolites. Finally, a free software tool, SASA, was developed to analyze SWATH-acquired samples using the proposed pipeline.

An improved detection and identification strategy for untargeted metabolomics based on UPLC-MS.

Untargeted metabolomics provides a comprehensive investigation of metabolites and enables the discovery of biomarkers. Improvements in sample preparation, chromatographic separation and raw data processing procedure greatly enhance the metabolome coverage. In addition, database-dependent software identification is also essential, upon which enhances the identification confidence and benefits downstream biological analysis. Herein, we developed an improved detection and identification strategy for untargeted metabolomics based on UPLC-MS. In this work, sample preparation was optimized by considering chemical properties of different metabolites. Chromatographic separation was done by two different columns and MS detection was performed under positive and negative ion modes regarding to the different polarities of metabolites. According to the characteristics of the collected data, an improved identification and evaluation strategy was developed involving fragment simulation and MS/MS library search based on two commonly used databases, HMDB and METLIN. Such combination integrated information from different databases and was aimed to enhance identification confidence by considering the rationality of fragmentation, biological sources and functions comprehensively. In addition, decision tree analysis and lab-developed database were also introduced to assist the data processing and enhance the identification confidence. Finally, the feasibility of the developed strategy was validated by liver samples of obesity mice and controls. 238 metabolites were accurately detected, which was beneficial for the subsequent biomarker discovery and downstream pathway analysis. Therefore, the developed strategy remarkably facilitated the identification accuracy and the confirmation of metabolites in untargeted metabolomics.

On the Use of In-Source Fragmentation in Ultrahigh-Performance Liquid Chromatography-Electrospray Ionization-High-Resolution Mass Spectrometry for Pesticide Residue Analysis.

In this work, a highly efficient pesticide residue screening and quantification method was established using ultrahigh-performance liquid chromatography-tandem quadrupole time-of-flight mass spectrometry based on in-source fragmentation. Over 400 pesticides were tested, among which 96 pesticides displayed in-source fragmentation. A novel concept of in-source fragment fraction was proposed to evaluate the extent of in-source fragmentation, which was found to be chemical structure- and source parameter-dependent. A high-resolution MS/MS library containing 403 pesticides and 126 fragments was created and was applied for library searching of pesticide residues in vegetables and fruits. The introduction of in-source fragments effectively circumvented misannotation and occurrence of false negatives. The quantification ability for the fragments was validated in terms of recovery, linearity, and limit of quantification and its superiority to the parent pesticides was established. Finally, the proposed method was applied for the analysis of real samples and proficiency test samples, and false negative results were successfully avoided in the analysis.

Wide-scope screening of pesticides in fruits and vegetables using information-dependent acquisition employing UHPLC-QTOF-MS and automated MS/MS library searching.

This paper presents an application of ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UHPLC-QTOF-MS) for simultaneous screening and identification of 427 pesticides in fresh fruit and vegetable samples. Both full MS scan mode for quantification, and an artificial-intelligence-based product ion scan mode information-dependent acquisition (IDA) providing automatic MS to MS/MS switching of product ion spectra for identification, were conducted by one injection. A home-in collision-induced-dissociation all product ions accurate mass spectra library containing more than 1700 spectra was developed prior to actual application. Both qualitative and quantitative validations of the method were carried out. The result showed that 97.4 % of the pesticides had the screening detection limit (SDL) less than 50 µg kg-1 and more than 86.7 % could be confirmed by accurate MS/MS spectra embodied in the home-made library. Meanwhile, calibration curves covering two orders of magnitude were performed, and they were linear over the concentration range studied for the selected matrices (from 5 to 500 µg kg-1 for most of the pesticides). Recoveries between 80 and 110 % in four matrices (apple, orange, tomato, and spinach) at two spiked levels, 10 and 100 µg kg-1, was 88.7 or 86.8 %. Furthermore, the overall relative standard deviation (RSD, n = 12) for 94.3 % of the pesticides in 10 µg kg-1 and 98.1 % of the pesticides in 100 µg kg-1 spiked levels was less than 20 %. In order to validate the suitability for routine analysis, the method was applied to 448 fruit and vegetable samples purchased in different local markets. The results show 83.3 % of the analyzed samples have positive findings (higher than the limits of identification and quantification), and 412 commodity-pesticide combinations are identified in our scope. The approach proved to be a cost-effective, time-saving and powerful strategy for routine large-scope screening of pesticides.

Simultaneous screening of targeted and non-targeted contaminants using an LC-QTOF-MS system and automated MS/MS library searching.

Simultaneous high-resolution full-scan and tandem mass spectrometry (MS/MS) analysis using time of flight mass spectrometry brings an answer for increasing demand of retrospective and non-targeted data analysis. Such analysis combined with spectral library searching is a promising tool for targeted and untargeted screening of small molecules. Despite considerable extension of the panel of compounds of tandem mass spectral libraries, the heterogeneity of spectral data poses a major challenge against the effective usage of spectral libraries. Performance evaluation of available LC-MS/MS libraries will significantly increase credibility in the search results. The present work was aimed to evaluate fluctuation of MS/MS pattern, in the peak intensities distribution together with mass accuracy measurements, and in consequence, performance compliant with ion ratio and mass error criteria as principles in identification processes for targeted and untargeted contaminants at trace levels. Matrix effect and ultra-trace levels of concentration (from 50 ng l(-1) to 1000 ng l(-1) were evaluated as potential source of inaccuracy in the performance of spectral matching. Matrix-matched samples and real samples were screened for proof of applicability. By manual review of data and application of ion ratio and ppm error criteria, false negatives were obtained; this number diminished when in-house library was used, while with on-line MS/MS databases 100% of positive samples were found. In our experience, intensity of peaks across spectra was highly correlated to the concentration effect and matrix complexity. In turn, analysis of spectra acquired at trace concentrations and in different matrices results in better performance in providing correct and reliable identification.