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In-Source CID Ramping and Covariant Ion Analysis of Hydrophilic Interaction Chromatography Metabolomics.

ABSTRACT: A large proportion of the complexity and redundancy of LC-MS metabolomics data comes from adduct formation. To reduce such redundancy, many tools have been developed to recognize and annotate adduct ions. These tools rely on predefined adduct lists that are generated empirically from reversed-phase LC-MS studies. In addition, hydrophilic interaction chromatography (HILIC) is gaining popularity in metabolomics studies due to its enhanced performance over other methods for polar compounds. HILIC methods typically use high concentrations of buffer salts to improve chromatographic performance. Therefore, it is necessary to analyze adduct formation in HILIC metabolomics. To this end, we developed covariant ion analysis (COVINA) to investigate metabolite adduct formation. Using this tool, we completely annotated 201 adduct and fragment ions from 10 metabolites. Many of the metabolite adduct ions were found to contain cluster ions corresponding to mobile phase additives. We further utilized COVINA to find the major ionized forms of metabolites. Our results show that for some metabolites, the adduct ion signals can be >200-fold higher than the signals from the deprotonated form, offering better sensitivity for targeted metabolomics analysis. Finally, we developed an in-source CID ramping (InCIDR) method to analyze the intensity changes of the adduct and fragment ions from metabolites. Our analysis demonstrates a promising method to distinguish the protonated and deprotonated ions of metabolites from the adduct and fragment ions.

SUBMITTER: Su X

PROVIDER: S-EPMC8141260 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Liquid chromatography coupled to mass spectrometry (LCMS) is widely used in metabolomics due to its sensitivity, reproducibility, speed and versatility. Metabolites are detected as peaks which are characterised by mass-over-charge ratio (m/z) and retention time (rt), and one of the most critical but also the most challenging tasks in metabolomics is to annotate the large number of peaks detected in biological samples. Accurate m/z measurements enable the prediction of molecular formulae which provide clues to the chemical identity of peaks, but often a number of metabolites have identical molecular formulae. Chromatographic behaviour, reflecting the physicochemical properties of metabolites, should also provide structural information. However, the variation in rt between analytical runs, and the complicating factors underlying the observed time shifts, make the use of such information for peak annotation a non-trivial task. To this end, we conducted Quantitative Structure-Retention Relationship (QSRR) modelling between the calculated molecular descriptors (MDs) and the experimental retention times (rts) of 93 authentic compounds analysed using hydrophilic interaction liquid chromatography (HILIC) coupled to high resolution MS. A predictive QSRR model based on Random Forests algorithm outperformed a Multiple Linear Regression based model, and achieved a high correlation between predicted rts and experimental rts (Pearson's correlation coefficient = 0.97), with mean and median absolute error of 0.52 min and 0.34 min (corresponding to 5.1 and 3.2 % error), respectively. We demonstrate that rt prediction with the precision achieved enables the systematic utilisation of rts for annotating unknown peaks detected in a metabolomics study. The application of the QSRR model with the strategy we outlined enhanced the peak annotation process by reducing the number of false positives resulting from database queries by matching accurate mass alone, and enriching the reference library. The predicted rts were validated using either authentic compounds or ion fragmentation patterns.

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In-Source CID Ramping and Covariant Ion Analysis of Hydrophilic Interaction Chromatography Metabolomics.

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