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A comprehensive method for lipid profiling by liquid chromatography-ion cyclotron resonance mass spectrometry.

ABSTRACT: This work aims to combine chromatographic retention, high mass resolution and accuracy, MS/MS spectra, and a package for automated identification and quantitation of lipid species in one platform for lipidomic analysis. The instrumental setup elaborated comprises reversed-phase HPLC coupled to a Fourier transform ion cyclotron resonance mass spectrometer (LTQ-FT), and Lipid Data Analyzer (LDA) software. Data analysis for lipid species quantification in this platform is based on retention time, mass resolution of 200,000, and mass accuracy below 2 ppm. In addition, automatically generated MS/MS spectra provide structural information at molecular level. This LC/MS technology allows analyzing complex biological samples in a quantitative manner as shown here paradigmatically for murine lipid droplets having a huge surplus of triacylglycerol species. Chromatographic preseparation of the bulk lipid class alleviates the problem of ion suppression of lipid species from other classes. Extension of 1D to 2D chromatography is possible, yet time consuming. The platform affords unambiguous detection of lipid species as low as 0.1‰ within major lipid classes. Taken together, a novel lipidomic LC/MS platform based on chromatographic retention, high mass resolution and accuracy, MS/MS analysis, and quantitation software enables analysis of complex samples as demonstrated for lipid droplets.

SUBMITTER: Fauland A

PROVIDER: S-EPMC3220297 | biostudies-literature | 2011 Dec

REPOSITORIES: biostudies-literature

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A comprehensive method for lipid profiling by liquid chromatography-ion cyclotron resonance mass spectrometry.

Fauland Alexander A Köfeler Harald H Trötzmüller Martin M Knopf Astrid A Hartler Jürgen J Eberl Anita A Chitraju Chandramohan C Lankmayr Ernst E Spener Friedrich F

Journal of lipid research 20110929 12

This work aims to combine chromatographic retention, high mass resolution and accuracy, MS/MS spectra, and a package for automated identification and quantitation of lipid species in one platform for lipidomic analysis. The instrumental setup elaborated comprises reversed-phase HPLC coupled to a Fourier transform ion cyclotron resonance mass spectrometer (LTQ-FT), and Lipid Data Analyzer (LDA) software. Data analysis for lipid species quantification in this platform is based on retention time, m ...[more]

PMID: 21960706

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Project description:BackgroundParkinson's disease (PD) is a prevalent neurological disease in the elderly with increasing morbidity and mortality. Despite enormous efforts, rapid and accurate diagnosis of PD is still compromised. Metabolomics defines the final readout of genome-environment interactions through the analysis of the entire metabolic profile in biological matrices. Recently, unbiased metabolic profiling of human sample has been initiated to identify novel PD metabolic biomarkers and dysfunctional metabolic pathways, however, it remains a challenge to define reliable biomarker(s) for clinical use.MethodsWe presented a comprehensive metabolic evaluation for identifying crucial metabolic disturbances in PD using liquid chromatography-high resolution mass spectrometry-based metabolomics approach. Plasma samples from 3 independent cohorts (n = 460, 223 PD, 169 healthy controls (HCs) and 68 PD-unrelated neurological disease controls) were collected for the characterization of metabolic changes resulted from PD, antiparkinsonian treatment and potential interferences of other diseases. Unbiased multivariate and univariate analyses were performed to determine the most promising metabolic signatures from all metabolomic datasets. Multiple linear regressions were applied to investigate the associations of metabolites with age, duration time and stage of PD. The combinational biomarker model established by binary logistic regression analysis was validated by 3 cohorts.ResultsA list of metabolites including amino acids, acylcarnitines, organic acids, steroids, amides, and lipids from human plasma of 3 cohorts were identified. Compared with HC, we observed significant reductions of fatty acids (FFAs) and caffeine metabolites, elevations of bile acids and microbiota-derived deleterious metabolites, and alterations in steroid hormones in drug-naïve PD. Additionally, we found that L-dopa treatment could affect plasma metabolome involved in phenylalanine and tyrosine metabolism and alleviate the elevations of bile acids in PD. Finally, a metabolite panel of 4 biomarker candidates, including FFA 10:0, FFA 12:0, indolelactic acid and phenylacetyl-glutamine was identified based on comprehensive discovery and validation workflow. This panel showed favorable discriminating power for PD.ConclusionsThis study may help improve our understanding of PD etiopathogenesis and facilitate target screening for therapeutic intervention. The metabolite panel identified in this study may provide novel approach for the clinical diagnosis of PD in the future.

Project description:Metabolomics is an interdisciplinary field that aims to study all metabolites < 1500 Da that are ubiquitously found within all organisms. Metabolomics is experiencing exponential growth and commonly relies on high-resolution mass spectrometry (HRMS). Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) is a form of HRMS that is particularly well suited for metabolomics research due to its exceptionally high resolution (105-106) and sensitivity with a mass accuracy in parts per billion (ppb). In this regard, FT-ICR-MS can provide valuable insights into the metabolomics analysis of complex biological systems due to unique capabilities such as the easy separation of isobaric and isomeric species, isotopic fine structure analysis, spatial resolution of metabolites in cells and tissues, and a high confidence (<1 ppm mass error) in metabolite identification. Alternatively, the large and complex data sets, long acquisition times, high cost, and limited access mainly through national mass spectrometry facilities may impede the routine adoption of FT-ICR-MS by metabolomics researchers. This review examines recent applications of FT-ICR-MS metabolomics in the search for clinical and non-human biomarkers; for the analysis of food, beverage, and environmental samples; and for the high-resolution imaging of tissues and other biological samples. We provide recent examples of metabolomics studies that highlight the advantages of FT-ICR-MS for the detailed and reliable characterization of the metabolome. Additionally, we offer some practical considerations for implementing FT-ICR-MS into a research program by providing a list of FT-ICR-MS facilities and by identifying different high-throughput interfaces, varieties of sample types, analysis methods (e.g., van Krevelen diagrams, Kendrick mass defect plot, etc.), and sample preparation and handling protocols used in FT-ICR-MS experiments. Overall, FT-ICR-MS holds great promise as a vital research tool for advancing metabolomics investigations.

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A comprehensive method for lipid profiling by liquid chromatography-ion cyclotron resonance mass spectrometry.

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A comprehensive method for lipid profiling by liquid chromatography-ion cyclotron resonance mass spectrometry.

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