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Analysis of polar primary metabolites in biological samples using targeted metabolomics and LC-MS.

ABSTRACT: Primary metabolites are molecules of essential biochemical reactions that define the biological phenotype. All primary metabolites cannot be measured in a single analysis. In this protocol, we outline the multiplexed and quantitative measurement of 106 metabolites that cover the central part of primary metabolism. The protocol includes several sample preparation techniques and one liquid chromatography-mass spectrometry method. Then, we describe the steps of the bioinformatic data analysis to better understand the metabolic perturbations that may occur in a biological system. For complete details on the use and execution of this protocol, please refer to: Costanza et al.,¹ Blomme et al.,² Blomme et al.,³ Guillon et al.,⁴ Stuani et al.⁵.

SUBMITTER: Turtoi E

PROVIDER: S-EPMC10440349 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature

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Analysis of polar primary metabolites in biological samples using targeted metabolomics and LC-MS.

Turtoi Evgenia E Jeudy Jeremy J Henry Sylvain S Dadi Ikrame I Valette Gilles G Enjalbal Christine C Turtoi Andrei A

STAR protocols 20230816 3

Primary metabolites are molecules of essential biochemical reactions that define the biological phenotype. All primary metabolites cannot be measured in a single analysis. In this protocol, we outline the multiplexed and quantitative measurement of 106 metabolites that cover the central part of primary metabolism. The protocol includes several sample preparation techniques and one liquid chromatography-mass spectrometry method. Then, we describe the steps of the bioinformatic data analysis to be ...[more]

PMID: 37590149

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Project description:Cervical cancer remains one of the most prevalent cancers among females worldwide. Therefore, it is important to discover new biomarkers for early diagnosis of cervical intraepithelial neoplasia (CIN) and cervical cancer, preferably non-invasive ones. In the present study, we aimed to identify unique metabolic signatures for CINs and cervical cancers using global and targeted metabolomic profiling. Plasma samples (69 normal, 55 CIN1, 42 CIN2/3, and 60 cervical cancer) were examined by ultra-performance liquid chromatography-quadrupole-time-of-flight mass spectrometry (UPLC-QTOF-MS) coupled with multivariate statistical analysis. Metabolic pathways were analyzed using the integrated web-based tool MetaboAnalyst. A multivariate logistic regression analysis was conducted to evaluate the combined association of metabolites and human papillomavirus (HPV) status with the risk of cervical carcinogenesis. A total of 28 metabolites exhibiting discriminating levels among normal, CIN, and cervical cancer patients (Kruskal-Wallis test p < 0.05) were identified in the global profiling analysis. The pathway analysis showed significantly altered alanine, aspartate, and glutamate metabolic pathways (FDR p-value < 0.05) in both the discovery and validation phases. Seven metabolites (AMP, aspartate, glutamate, hypoxanthine, lactate, proline, and pyroglutamate) were discriminated between CINs and cervical cancer versus normal (area under the curve (AUC) value > 0.8). The levels of these metabolites were significantly high in patients versus normal (p < 0.0001) and were associated with increased risk of developing CIN2/3 and cervical cancer. Additionally, elevated levels of the seven metabolites combined with positive HPV status were correlated with substantial risk of cancer progression. These results demonstrated that metabolomics profiling is capable of distinguishing CINs and cervical cancers from normal and highlighted potential biomarkers for the early detection of cervical carcinogenesis.

Project description:BackgroundLeptomeningeal metastasis (LM) is a devastating complication of cancer that is difficult to treat. Thus, early diagnosis is essential for LM patients. However, cerebrospinal fluid (CSF) cytology has low sensitivity, and imaging approaches are ineffective. We explored targeted CSF metabolic profiling to discriminate among LM and other conditions affecting the central nervous system (CNS).MethodsWe quantitatively measured amino acids, biogenic amines, hexoses, acylcarnitines (AC), cholesteryl esters (CE), glycerides, phosphatidylcholines (PC), lysophosphatidylcholines (LPC), sphingomyelins (SM), and ceramides (Cer) in 117 CSF samples from various groups of healthy controls (HC, n = 10), patients with LM (LM, n = 47), parenchymal brain tumor (PBT, n = 45), and inflammatory disease (ID, n = 13) with internal standards using the Absolute IDQ- p400® targeted mass spectrometry kit. Metabolites detected in > 90% of samples or showing a difference in proportional level between groups ≥ 75% were used in logistic regression models when there was no single metabolite with AUC = 1 for the groups of comparison.ResultsPC and SM had higher levels in LM than in PBT or HC, whereas LPC had lower level in PBT than the other groups. Glycerides and Cer levels were higher in PBT and LM than in HC. Long-chain AC level in PBT was lower than in LM or HC. A regression model including Ala, PC (42:7), PC (30:3), PC (37:0), and Tyr achieved complete discrimination (AUC = 1.0) between LM and HC. In comparison of PBT and HC, twenty-six individual metabolites allowed complete discrimination between two groups, and between ID and HC fourty-six individual lipid metabolites allowed complete discrimination. Twenty-one individual metabolites (18 ACs and 3 PCs) allowed complete discrimination between LM and PBT.ConclusionsUsing a commercial targeted liquid chromatography-mass spectrometry (LC-MS) metabolomics kit, we were able to differentiate LM from HC and PBT. Most of the discriminative metabolites among different diseases were lipid metabolites, for which their CNS distribution and quantification in different cell types are largely unknown, whereas amino acids, biogenic amines, and hexoses failed to show significant differences. Future validation studies with larger, controlled cohorts should be performed, and hopefully, the kit may expand its metabolite coverage for unique cancer cell glucose metabolism.

Dataset Information

Analysis of polar primary metabolites in biological samples using targeted metabolomics and LC-MS.

Publications

Analysis of polar primary metabolites in biological samples using targeted metabolomics and LC-MS.

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