Liquid chromatography-mass spectrometry metabolite library for metabolomics: Evaluating column suitability using a scoring approach

Diamantidou, Dimitra; Sampsonidis, Ioannis; Liapikos, Theodoros; Gika, Helen; Theodoridis, Georgios

doi:10.1016/j.chroma.2023.463779

Cited by 3 publications

(3 citation statements)

References 31 publications

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“…Subsequently, other operations such as drying, redissolving and purifying the extract are carried out. Poor chromatographic performance and high ionisation efficiency are considered to be the reasons for the limitation of insufficient coverage of metabolomics and flawed system reproducibility 61,62 . Metabolites include small polar AAs to non‐polar lipids and the selection of chromatographic columns is closely related to the range of metabolites 63 .…”

Section: Discussionmentioning

confidence: 99%

“…Poor chromatographic performance and high ionisation efficiency are considered to be the reasons for the limitation of insufficient coverage of metabolomics and flawed system reproducibility. 61,62 Metabolites include small polar AAs to nonpolar lipids and the selection of chromatographic columns is closely related to the range of metabolites. 63 Various other factors are involved in the metabolomics results, such as instrument parameter settings (separation parameters, ionisation source, data collection parameters, etc.…”

Section: Limitations and Future Research Directionsmentioning

confidence: 99%

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Clinical metabolomics characteristics of diabetic kidney disease: A meta‐analysis of 1875 cases with diabetic kidney disease and 4503 controls

Yuan,

Huang,

et al. 2024

Diabetes Metabolism Res

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AimsDiabetic Kidney Disease (DKD), one of the major complications of diabetes, is also a major cause of end‐stage renal disease. Metabolomics can provide a unique metabolic profile of the disease and thus predict or diagnose the development of the disease. Therefore, this study summarises a more comprehensive set of clinical biomarkers related to DKD to identify functional metabolites significantly associated with the development of DKD and reveal their driving mechanisms for DKD.Materials and MethodsWe searched PubMed, Embase, the Cochrane Library and Web of Science databases through October 2022. A meta‐analysis was conducted on untargeted or targeted metabolomics research data based on the strategy of standardized mean differences and the process of ratio of means as the effect size, respectively. We compared the changes in metabolite levels between the DKD patients and the controls and explored the source of heterogeneity through subgroup analyses, sensitivity analysis and meta‐regression analysis.ResultsThe 34 clinical‐based metabolomics studies clarified the differential metabolites between DKD and controls, containing 4503 control subjects and 1875 patients with DKD. The results showed that a total of 60 common differential metabolites were found in both meta‐analyses, of which 5 metabolites (p < 0.05) were identified as essential metabolites. Compared with the control group, metabolites glycine, aconitic acid, glycolic acid and uracil decreased significantly in DKD patients; cysteine was significantly higher. This indicates that amino acid metabolism, lipid metabolism and pyrimidine metabolism in DKD patients are disordered.ConclusionsWe have identified 5 metabolites and metabolic pathways related to DKD which can serve as biomarkers or targets for disease prevention and drug therapy.

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Section: Discussionmentioning

confidence: 99%

Section: Limitations and Future Research Directionsmentioning

confidence: 99%

Clinical metabolomics characteristics of diabetic kidney disease: A meta‐analysis of 1875 cases with diabetic kidney disease and 4503 controls

Yuan,

Huang,

et al. 2024

Diabetes Metabolism Res

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“…Thus, a broad range of compounds can be covered by using both types. However, as several stationary HILIC and RP phases are available, their choice is crucial, which was already discussed extensively elsewhere (Diamantidou et al, 2023 ; Elmsjo et al, 2018 ; Si-Hung et al, 2017 ; Sonnenberg et al, 2019 ; Wernisch & Pennathur, 2016 ). Not only the different stationary phases but also the geometry and particle size of columns can affect the outcome of metabolomic studies.…”

Section: Introductionmentioning

confidence: 99%

Comparison of reversed-phase, hydrophilic interaction, and porous graphitic carbon chromatography columns for an untargeted toxicometabolomics study in pooled human liver microsomes, rat urine, and rat plasma

Hemmer,

Manier,

Wagmann

et al. 2024

Metabolomics

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Introduction Untargeted metabolomics studies are expected to cover a wide range of compound classes with high chemical diversity and complexity. Thus, optimizing (pre-)analytical parameters such as the analytical liquid chromatography (LC) column is crucial and the selection of the column depends primarily on the study purpose. Objectives The current investigation aimed to compare six different analytical columns. First, by comparing the chromatographic resolution of selected compounds. Second, on the outcome of an untargeted toxicometabolomics study using pooled human liver microsomes (pHLM), rat plasma, and rat urine as matrices. Methods Separation and analysis were performed using three different reversed-phase (Phenyl-Hexyl, BEH C18, and Gold C18), two hydrophilic interaction chromatography (HILIC) (ammonium-sulfonic acid and sulfobetaine), and one porous graphitic carbon (PGC) columns coupled to high-resolution mass spectrometry (HRMS). Their impact was evaluated based on the column performance and the size of feature count, amongst others. Results All three reversed-phase columns showed a similar performance, whereas the PGC column was superior to both HILIC columns at least for polar compounds. Comparing the size of feature count across all datasets, most features were detected using the Phenyl-Hexyl or sulfobetaine column. Considering the matrices, most significant features were detected in urine and pHLM after using the sulfobetaine and in plasma after using the ammonium-sulfonic acid column. Conclusion The results underline that the outcome of this untargeted toxicometabolomic study LC-HRMS metabolomic study was highly influenced by the analytical column, with the Phenyl-Hexyl or sulfobetaine column being the most suitable. However, column selection may also depend on the investigated compounds as well as on the investigated matrix. Graphical abstract

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Probing the polar metabolome by UHPLC-MS

Lioupi¹,

Marinaki²,

Virgiliou³

et al. 2023

TrAC Trends in Analytical Chemistry

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Liquid chromatography-mass spectrometry metabolite library for metabolomics: Evaluating column suitability using a scoring approach

Cited by 3 publications

References 31 publications

Clinical metabolomics characteristics of diabetic kidney disease: A meta‐analysis of 1875 cases with diabetic kidney disease and 4503 controls

Clinical metabolomics characteristics of diabetic kidney disease: A meta‐analysis of 1875 cases with diabetic kidney disease and 4503 controls

Comparison of reversed-phase, hydrophilic interaction, and porous graphitic carbon chromatography columns for an untargeted toxicometabolomics study in pooled human liver microsomes, rat urine, and rat plasma

Probing the polar metabolome by UHPLC-MS

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