Multicomponent Characterization of the Flower Bud of Panax notoginseng and Its Metabolites in Rat Plasma by Ultra-High Performance Liquid Chromatography/Ion Mobility Quadrupole Time-of-Flight Mass Spectrometry

Yang, Xiaonan; Xiong, Ying; Wang, Hongda; Jiang, Meiting; Xu, Xiaoyan; Mi, Yueguang; Jia, Lina; Li, Xiaohang; Sun, He; Zhao, Yuying; Li, Xue; Yang, Wenzhi

doi:10.3390/molecules27249049

Cited by 5 publications

(4 citation statements)

References 43 publications

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“…As mentioned earlier, CCSbase can be used for CCS prediction of peptides, carbohydrates, lipids, metabolites, and drugs 50,144,147,148,168 . The use of CCSbase has also been reported for the drug molecules metoprolol acid/atenolol acid in water samples by Hinnenkamp et al 173 and for ergot alkaloids (EA) in cereal samples by Carbonell‐Rozas et al 174 Moreover, Yang et al showed that CCSbase presented smaller error than AllCCS with CCS isomer difference of more than 16% on the study of ginsenoside isomers, 172 while in another study, CCSbase showed similar CCS behavior for per‐ and polyfluoroalkyl substances (PFAS) analysis 175 . Interestingly, Lenski et al developed a workflow that combined the CCSbase prediction method with a quantitative structure–retention relationship (QSRR) predictor for the prediction of CCS and RT, respectively, to reduce false positive annotations in nontargeted metabolomics, recently 176 …”

Section: Ccs Modelsmentioning

confidence: 96%

“…170 Despite the higher prediction error found for some compound classes, according to da Silva et al, it is suggested to use AllCCS as the primary tool for confirmation because it has the highest coverage in CCS prediction compared with other tools and has a perfect Pearson correlation coefficient between experimental and theoretical CCSs. 171 Moreover, it can also provide CCS predictions for small molecules such as drugs, 105 natural products, 172 food contact materials, 155,156 and pesticides. Two clusters contained small molecules, one cluster had lipids associated with it, and the last cluster consisted of carbohydrates and peptides.…”

Section: Metccs and Lipidccsmentioning

confidence: 99%

“…Moreover,Yang et al showed that CCSbase presented smaller error than AllCCS with CCS isomer difference of more than 16% on the study of ginsenoside isomers,172 while in another study,…”

mentioning

confidence: 95%

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Collision cross section measurement and prediction methods in omics

Kartowikromo,

Olajide,

Hamid

2023

J Mass Spectrom

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Omics studies such as metabolomics, lipidomics, and proteomics have become important for understanding the mechanisms in living organisms. However, the compounds detected are structurally different and contain isomers, with each structure or isomer leading to a different result in terms of the role they play in the cell or tissue in the organism. Therefore, it is important to detect, characterize, and elucidate the structures of these compounds. Liquid chromatography and mass spectrometry have been utilized for decades in the structure elucidation of key compounds. While prediction models of parameters (such as retention time and fragmentation pattern) have also been developed for these separation techniques, they have some limitations. Moreover, ion mobility has become one of the most promising techniques to give a fingerprint to these compounds by determining their collision cross section (CCS) values, which reflect their shape and size. Obtaining accurate CCS enables its use as a filter for potential analyte structures. These CCS values can be measured experimentally using calibrant‐independent and calibrant‐dependent approaches. Identification of compounds based on experimental CCS values in untargeted analysis typically requires CCS references from standards, which are currently limited and, if available, would require a large amount of time for experimental measurements. Therefore, researchers use theoretical tools to predict CCS values for untargeted and targeted analysis. In this review, an overview of the different methods for the experimental and theoretical estimation of CCS values is given where theoretical prediction tools include computational and machine modeling type approaches. Moreover, the limitations of the current experimental and theoretical approaches and their potential mitigation methods were discussed.

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Section: Ccs Modelsmentioning

confidence: 96%

Section: Metccs and Lipidccsmentioning

confidence: 99%

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Collision cross section measurement and prediction methods in omics

Kartowikromo,

Olajide,

Hamid

2023

J Mass Spectrom

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“…The CCS value predicted by ML provides more possibilities for distinguishing isomers in the absence of reference standards, with a total of 302 compounds identified or initially identified, of which 109 were not reported. With the continuous expansion of the prediction range and improvement of accuracy in the CCS database, its applications in the component characterization of natural products are becoming increasingly widespread [ 168 , 169 ].…”

Section: Ccs Prediction Applicationsmentioning

confidence: 99%

Collision Cross Section Prediction Based on Machine Learning

Wang

Jiang

et al. 2023

Molecules

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Ion mobility-mass spectrometry (IM-MS) is a powerful separation technique providing an additional dimension of separation to support the enhanced separation and characterization of complex components from the tissue metabolome and medicinal herbs. The integration of machine learning (ML) with IM-MS can overcome the barrier to the lack of reference standards, promoting the creation of a large number of proprietary collision cross section (CCS) databases, which help to achieve the rapid, comprehensive, and accurate characterization of the contained chemical components. In this review, advances in CCS prediction using ML in the past 2 decades are summarized. The advantages of ion mobility-mass spectrometers and the commercially available ion mobility technologies with different principles (e.g., time dispersive, confinement and selective release, and space dispersive) are introduced and compared. The general procedures involved in CCS prediction based on ML (acquisition and optimization of the independent and dependent variables, model construction and evaluation, etc.) are highlighted. In addition, quantum chemistry, molecular dynamics, and CCS theoretical calculations are also described. Finally, the applications of CCS prediction in metabolomics, natural products, foods, and the other research fields are reflected.

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