The application of in-source fragmentation in ultra-high performance liquid chromatography-electrospray ionization -tandem mass spectrometry for pesticide residue analysis

He, Zhimin; Zhao, Liuqing; Liu, Xiaowei; Xu, Yujuan

doi:10.1016/j.chroma.2020.461637

Cited by 14 publications

(11 citation statements)

References 22 publications

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“…Some biotransformation products, such as sugar or glutathione conjugates, are by default more polar than the parent compound; hence, we can expect their elution at the beginning of the chromatographic run in the given conditions. It was confirmed that weak bonds in a metabolite can be broken during the ionization process, even using the softest ionization method [ 21 , 22 ], while the higher temperatures can increase in source fragmentation, yielding the even greater response of fragments than the parent compounds [ 23 ].…”

Section: Resultsmentioning

confidence: 99%

Use of Passive and Grab Sampling and High-Resolution Mass Spectrometry for Non-Targeted Analysis of Emerging Contaminants and Their Semi-Quantification in Water

et al. 2022

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Different groups of organic micropollutants including pharmaceuticals and pesticides have emerged in the environment in the last years, resulting in a rise in environmental and human health risks. In order to face up and evaluate these risks, there is an increasing need to assess their occurrence in the environment. Therefore, many studies in the past couple of decades were focused on the improvements in organic micropollutants’ extraction efficiency from the different environmental matrices, as well as their mass spectrometry detection parameters and acquisition modes. This paper presents different sampling methodologies and high-resolution mass spectrometry-based non-target screening workflows for the identification of pharmaceuticals, pesticides, and their transformation products in different kinds of water (domestic wastewater and river water). Identification confidence was increased including retention time prediction in the workflow. The applied methodology, using a passive sampling technique, allowed for the identification of 85 and 47 contaminants in the wastewater effluent and river water, respectively. Finally, contaminants’ prioritization was performed through semi-quantification in grab samples as a fundamental step for monitoring schemes.

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Section: Resultsmentioning

confidence: 99%

Use of Passive and Grab Sampling and High-Resolution Mass Spectrometry for Non-Targeted Analysis of Emerging Contaminants and Their Semi-Quantification in Water

et al. 2022

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“…As shown in Figure A, various degrees of ISF features were detected in all four analytical modes, but overall, ISF features account for a small portion of the total LC-MS features. This percentage agrees with previously published literature, in which 26 out of 400 pesticides (6.5%) were found to be sensitive to ISF and produced 33 ISF features . Compared to our study of the standard mixture, the percentage of ISF features dropped significantly in the untargeted metabolomics data.…”

Section: Resultsmentioning

confidence: 99%

“…Therefore, it is important to develop analytical and bioinformatic strategies to recognize them. However, it is worth noting that experimentally confirming ISF features is time-consuming and requires extensive knowledge about the chemical species. − Conventionally, it is possible to confirm ISF features using pure chemical standards or isotope labeled metabolites . Based on that principle, researchers have compiled and summarized possible fragmentations for some well-defined metabolic classes, such as lipids and known metabolites with MS 2 spectra available in database .…”

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confidence: 99%

“…This percentage agrees with previously published literature, in which 26 out of 400 pesticides (6.5%) were found to be sensitive to ISF and produced 33 ISF features. 9 Compared to our study of the standard mixture, the percentage of ISF features dropped significantly in the untargeted metabolomics data. This might be related to the fact that many metabolic features are not real metabolites, but rather artificial background noise that cannot generate ISF features.…”

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confidence: 99%

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ISFrag: De Novo Recognition of In-Source Fragments for Liquid Chromatography–Mass Spectrometry Data

et al. 2021

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In-source fragmentation (ISF) is a naturally occurring phenomenon during electrospray ionization (ESI) in liquid chromatography−mass spectrometry (LC-MS) analysis. ISF leads to false metabolite annotation in untargeted metabolomics, prompting misinterpretation of the underlying biological mechanisms. Conventional metabolomic data cleaning mainly focuses on the annotation of adducts and isotopes, and the recognition of ISF features is mainly based on common neutral losses and the LC coelution pattern. In this work, we recognized three increasingly important patterns of ISF features, including (1) coeluting with their precursor ions, (2) being in the tandem MS (MS 2 ) spectra of their precursor ions, and (3) sharing similar MS 2 fragmentation patterns with their precursor ions. Based on these patterns, we developed an R package, ISFrag, to comprehensively recognize all possible ISF features from LC-MS data generated from full-scan, data-dependent acquisition, and data-independent acquisition modes without the assistance of common neutral loss information or MS 2 spectral library. Tested using metabolite standards, we achieved a 100% correct recognition of level 1 ISF features and over 80% correct recognition for level 2 ISF features. Further application of ISFrag on untargeted metabolomics data allows us to identify ISF features that can potentially cause false metabolite annotation at an omics-scale. With the help of ISFrag, we performed a systematic investigation of how ISF features are influenced by different MS parameters, including capillary voltage, end plate offset, ion energy, and "collision energy". Our results show that while increasing energies can increase the number of real metabolic features and ISF features, the percentage of ISF features might not necessarily increase. Finally, using ISFrag, we created an ISF pathway to visualize the relationships between multiple ISF features that belong to the same precursor ion. ISFrag is freely available on GitHub (https://github.com/HuanLab/ISFrag).

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“…In-source fragmentation (ISF) or in-source collision-induced dissociation (IS-CID) is an inherent and inevitable phenomenon, which occurs at the intermediate pressure region between the ion source (atmospheric pressure) and mass analyzer (vacuum chamber) in mass spectrometer. − The ISF is of nonselectivity and strong dependence on the potential difference between orifice (nozzle) and QJet (skimmer). − The nonselective ISF may complicate the acquired mass spectra, leading to difficulty in assigning the ion peaks in the mass spectra. ,,,, In extreme cases, even molecular ion peak information on analytes cannot be obtained . Therefore, some researchers have made great efforts to eliminate or reduce the negative effect of ISF in liquid chromatography–mass spectrometry (LC-MS) analysis by adjusting the in-source parameters. ,,, …”

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confidence: 99%

Integration of Chemical Derivatization and in-Source Fragmentation Mass Spectrometry for High-Coverage Profiling of Submetabolomes

Zhu

Wang

et al. 2021

Anal. Chem.

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In-source fragmentation-based high-resolution mass spectrometry (ISF-HRMS) is a potential analytical technique, which is usually used to profile some specific compounds that can generate diagnostic neutral loss (NL) or fragment ion (FI) in ion source inherently. However, the ISF-HRMS method does not work for those compounds that cannot inherently produce diagnostic NL or FI in ion source. In this study, a derivatization-based in-source fragmentation-information-dependent acquisition (DISF-IDA) strategy was proposed for profiling the metabolites with easily labeled functional groups (submetabolomes) by liquid chromatography-electrospray ionization-quadrupole time-of-flight mass spectrometry (LC-ESI-Q-TOF MS). As a proof-of-concept study, 36 carboxylated compounds labeled with N,N-dimethylethylenediamine (DMED) were selected as model compounds to examine performance of DISF-IDA strategy in screening the carboxylated metabolites and acquiring their MSn spectra. In ESI source, the DEMD-derived carboxylated compounds were fragmented to produce characteristic neutral losses of 45.0578, 63.0684, and/or 88.1000 Da that were further used as diagnostic features for screening the carboxylated metabolites by DISF-IDA-based LC-Q-TOF MS. Furthermore, high-resolution MSn spectra of the model compounds were also obtained within a single run of DISF-IDA-based LC-Q-TOF MS analysis, which contributed to the improvement of the annotation confidence. To further verify its applicability, DISF-IDA strategy was used for profiling carboxylated submetabolome in mice feces. Using this strategy, a total of 351 carboxylated metabolites were detected from mice feces, of which 178 metabolites (51% of the total) were positively or putatively identified. Moreover, DISF-IDA strategy was also demonstrated to be applicable for profiling other submetabolomes with easily labeled functional groups such as amino, carbonyl, and cis-diol groups. Overall, our proposed DISF-IDA strategy is a promising technique for high-coverage profiling of submetabolomes with easily labeled functional groups in biological samples.

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The application of in-source fragmentation in ultra-high performance liquid chromatography-electrospray ionization -tandem mass spectrometry for pesticide residue analysis

Cited by 14 publications

References 22 publications

Use of Passive and Grab Sampling and High-Resolution Mass Spectrometry for Non-Targeted Analysis of Emerging Contaminants and Their Semi-Quantification in Water

Use of Passive and Grab Sampling and High-Resolution Mass Spectrometry for Non-Targeted Analysis of Emerging Contaminants and Their Semi-Quantification in Water

ISFrag: De Novo Recognition of In-Source Fragments for Liquid Chromatography–Mass Spectrometry Data

Integration of Chemical Derivatization and in-Source Fragmentation Mass Spectrometry for High-Coverage Profiling of Submetabolomes

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