Instrumental and experimental effects in LC–MS-based metabolomics

Burton, Lyle; Ivosev, Gordana; Tate, Stephen; Impey, Gary; Wingate, Julie; Bonner, Ron

doi:10.1016/j.jchromb.2008.04.044

Cited by 62 publications

(48 citation statements)

References 21 publications

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“…In addition, metabolites need to be properly annotated to obtain consistent and useful results. Whereas, for primary metabolites, it is much facilitated due to the availability of several public libraries for GC/MS studies such as [30,31], it is still a challenging task in the case of secondary metabolites, since no comprehensive database exists up to date [32–34]. Therefore, a future objective to achieve in these techniques is the standardization and annotation of data from multiple metabolomics technologies in public databases [35].…”

Section: Metabolomics Within the Context Of Systems Biologymentioning

confidence: 99%

Metabolomics as a Tool to Investigate Abiotic Stress Tolerance in Plants

Arbona

Manzi²,

Ollas

2013

IJMS

542

316

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Metabolites reflect the integration of gene expression, protein interaction and other different regulatory processes and are therefore closer to the phenotype than mRNA transcripts or proteins alone. Amongst all –omics technologies, metabolomics is the most transversal and can be applied to different organisms with little or no modifications. It has been successfully applied to the study of molecular phenotypes of plants in response to abiotic stress in order to find particular patterns associated to stress tolerance. These studies have highlighted the essential involvement of primary metabolites: sugars, amino acids and Krebs cycle intermediates as direct markers of photosynthetic dysfunction as well as effectors of osmotic readjustment. On the contrary, secondary metabolites are more specific of genera and species and respond to particular stress conditions as antioxidants, Reactive Oxygen Species (ROS) scavengers, coenzymes, UV and excess radiation screen and also as regulatory molecules. In addition, the induction of secondary metabolites by several abiotic stress conditions could also be an effective mechanism of cross-protection against biotic threats, providing a link between abiotic and biotic stress responses. Moreover, the presence/absence and relative accumulation of certain metabolites along with gene expression data provides accurate markers (mQTL or MWAS) for tolerant crop selection in breeding programs.

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Section: Metabolomics Within the Context Of Systems Biologymentioning

confidence: 99%

Metabolomics as a Tool to Investigate Abiotic Stress Tolerance in Plants

Arbona

Manzi²,

Ollas

2013

IJMS

542

316

View full text Add to dashboard Cite

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“…Furthermore, as there are more than 1000 compounds detected in one run, these compounds are not all resolved (e. g. Fig. 3) and this will also lead to ion-suppression/enhancement [27].…”

Section: Urine Analysismentioning

confidence: 99%

Profiling of polar metabolites in biological extracts using diamond hydride‐based aqueous normal phase chromatography

Callahan

Souza

Bacic

et al. 2009

J of Separation Science

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Highly polar metabolites, such as sugars and most amino acids are not retained by conventional RP LC columns. Without sufficient retention low concentration compounds are not detected due ion suppression and structural isomers are not resolved. In contrast, hydrophilic interaction chromatography (HILIC) and aqueous normal phase chromatography (ANP) retain compounds based on their hydrophilicity and therefore provides a means of separating highly polar compounds. Here, an ANP method based on the diamond hydride stationary phase is presented for profiling biological small molecules by LC. A rapid separation system based upon a fast gradient that delivers reproducible chromatography is presented. Approximately 1000 compounds were reproducibly detected in human urine samples and clear differences between these samples were identified. This chromatography was also applied to xylem fluid from soyabean (Glycine max) plants to which 400 compounds were detected. This method greatly increases the metabolite coverage over RP-only metabolite profiling in biological samples. We show that both forms of chromatography are necessary for untargeted comprehensive metabolite profiling and that the diamond hydride stationary phase provides a good option for polar metabolite analysis.

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“…The signal intensity drift of metabolites over time and across different batches is a major confounding factor in large-scale metabolomics studies. The unwanted variations in the measurements of metabolite ion peaks during data acquisition (intra-and inter-batch) are unavoidable and arise from sample handling and preparation, LC column degradation, matrix effects, MS instrument contamination and nonlinear drift over long runs (Leek et al 2010;Burton et al 2008;De Livera et al 2015). Therefore, the development of a normalization method is necessary to remove the unwanted analytical variations occurring in intra-and inter-batch measurements and to integrate multiple batches forming an integral data set for subsequent statistical analysis (De Livera et al 2015;De Livera et al 2012).…”

Section: Introductionmentioning

confidence: 99%

Normalization and integration of large-scale metabolomics data using support vector regression

et al. 2016

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Introduction Untargeted metabolomics studies for biomarker discovery often have hundreds to thousands of human samples. Data acquisition of large-scale samples has to be divided into several batches and may span from months to as long as several years. The signal drift of metabolites during data acquisition (intra-and inter-batch) is unavoidable and is a major confounding factor for largescale metabolomics studies. Objectives We aim to develop a data normalization method to reduce unwanted variations and integrate multiple batches in large-scale metabolomics studies prior to statistical analyses. Methods We developed a machine learning algorithmbased method, support vector regression (SVR), for largescale metabolomics data normalization and integration. An R package named MetNormalizer was developed and provided for data processing using SVR normalization.Results After SVR normalization, the portion of metabolite ion peaks with relative standard deviations (RSDs) less than 30 % increased to more than 90 % of the total peaks, which is much better than other common normalization methods. The reduction of unwanted analytical variations helps to improve the performance of multivariate statistical analyses, both unsupervised and supervised, in terms of classification and prediction accuracy so that subtle metabolic changes in epidemiological studies can be detected. Conclusion SVR normalization can effectively remove the unwanted intra-and inter-batch variations, and is much better than other common normalization methods.

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Instrumental and experimental effects in LC–MS-based metabolomics

Cited by 62 publications

References 21 publications

Metabolomics as a Tool to Investigate Abiotic Stress Tolerance in Plants

Metabolomics as a Tool to Investigate Abiotic Stress Tolerance in Plants

Profiling of polar metabolites in biological extracts using diamond hydride‐based aqueous normal phase chromatography

Normalization and integration of large-scale metabolomics data using support vector regression

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