From sample treatment to biomarker discovery: A tutorial for untargeted metabolomics based on GC-(EI)-Q-MS

Mastrangelo, Annalaura; Ferrarini, Alessia; Rey-Stolle, Fernanda; Garcı́a, Antonia; Barbas, Coral

doi:10.1016/j.aca.2015.10.001

Cited by 152 publications

(103 citation statements)

References 74 publications

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Section: Analysis Of the Labeled Is MIXmentioning

confidence: 95%

“…Carnitine-D3 and sphingosine-D7 were detected only in ESI+ while stearic acid-D5 was only detected in ESI− mode. LPC18:1-D7-which showed two peaks at RT 19.3 and 20.0 min-and isoleucine 13 C, 15 N were detected in both modes. Figure S3 depicts the variation in the relative IS intensities in the experimental samples and QCs as a function of the injection order, in order to evaluate the presence and composition of the IS mix.…”

Section: Analysis Of the Labeled Is MIXmentioning

confidence: 96%

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Troubleshooting in Large-Scale LC-ToF-MS Metabolomics Analysis: Solving Complex Issues in Big Cohorts

et al. 2019

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Metabolomics, understood as the science that manages the study of compounds from the metabolism, is an essential tool for deciphering metabolic changes in disease. The experiments rely on the use of high-throughput analytical techniques such as liquid chromatography coupled to mass spectrometry (LC-ToF MS). This hyphenation has brought positive aspects such as higher sensitivity, specificity and the extension of the metabolome coverage in a single run. The analysis of a high number of samples in a single batch is currently not always feasible due to technical and practical issues (i.e., a drop of the MS signal) which result in the MS stopping during the experiment obtaining more than a single sample batch. In this situation, careful data treatment is required to enable an accurate joint analysis of multi-batch data sets. This paper summarizes the analytical strategies in large-scale metabolomic experiments; special attention has been given to QC preparation troubleshooting and data treatment. Moreover, labeled internal standards analysis and their aim in data treatment, and data normalization procedures (intra-and inter-batch) are described. These concepts are exemplified using a cohort of 165 patients from a study in asthma.

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Section: Analysis Of the Labeled Is MIXmentioning

confidence: 95%

Section: Analysis Of the Labeled Is MIXmentioning

confidence: 96%

Troubleshooting in Large-Scale LC-ToF-MS Metabolomics Analysis: Solving Complex Issues in Big Cohorts

et al. 2019

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“…However, not only thermal exposure of analytes can jeopardize the successful implementation of GC-MS analysis in clinical metabolomics but also other practical factors such as the use of different carrier gases, differences in the applied inlet liners (i.e., differential liner design), different injection techniques (i.e., hot needle versus cold needle injection) or the presence of impurities in the liner or on the column head. Some of the aforementioned points have been previously investigated [62,63]. Practical recommendations for ensuring reliable GC-MS-based metabolomics data include the use of a consistent liner design (i.e., tapered, goose neck or double goose neck), the constant use of deactivated inlet liners as well as glass wool, a regular maintenance schedule, injection of system suitability tests and/or QCs, regular change of GC columns and the use of the highest quality derivatization reagents and solvents [35,63,64].…”

Section: Thermal Degradationmentioning

confidence: 99%

“…Some of the aforementioned points have been previously investigated [62,63]. Practical recommendations for ensuring reliable GC-MS-based metabolomics data include the use of a consistent liner design (i.e., tapered, goose neck or double goose neck), the constant use of deactivated inlet liners as well as glass wool, a regular maintenance schedule, injection of system suitability tests and/or QCs, regular change of GC columns and the use of the highest quality derivatization reagents and solvents [35,63,64].…”

Section: Thermal Degradationmentioning

confidence: 99%

“…Nevertheless, the major advantage of EI is its highly standardized and repeatable performance. Over the years, this has led to extensive spectral libraries containing more than 250,000 compounds, which make GC-EI-MS a very attractive technology for identifying unknown metabolites of interest [63]. Traditionally, a kinetic energy of 70 eV is used for EI, allowing for high spectral comparison.…”

Section: Ionization Techniquesmentioning

confidence: 99%

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Analytical Pitfalls and Challenges in Clinical Metabolomics

et al. 2016

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Metabolomics-based strategies have become an integral part of modern clinical research, allowing for a better understanding of pathophysiological conditions and disease mechanisms, as well as providing innovative tools for more adequate diagnostic and prognosis approaches. Metabolomics is considered an essential tool in precision medicine, which aims for personalized prevention and tailor-made treatments. Nevertheless, multiple pitfalls may be encountered in clinical metabolomics during the entire workflow, hampering the quality of the data and, thus, the biological interpretation. This review describes the challenges underlying metabolomics-based experiments, discussing step by step the potential pitfalls of the analytical process, including study design, sample collection, storage, as well as preparation, chromatographic and electrophoretic separation, detection and data analysis. Moreover, it offers practical solutions and strategies to tackle these challenges, ensuring the generation of high-quality data.

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A Method for Identifying Germplasm that Exhibit Multiple Mutations in the Fatty Acid Synthetic Pathway of Perilla frutescens

Zhang

Shi

Yang

et al. 2020

J Americ Oil Chem Soc

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A method using gas chromatography-mass spectroscopy (GC-MS) was developed to assess phenotypic differences in the fatty acid composition of 143 accessions of Perilla frutescens varieties that originated from different Chinese provinces, Japan, and Britain. Thirty-six compounds identified in the analysis included a number of saturated fatty acids and positional isomers of unsaturated fatty acids. A wide range in phenotypic variation was noted within each of five major fatty acids [16:

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From sample treatment to biomarker discovery: A tutorial for untargeted metabolomics based on GC-(EI)-Q-MS

Cited by 152 publications

References 74 publications

Troubleshooting in Large-Scale LC-ToF-MS Metabolomics Analysis: Solving Complex Issues in Big Cohorts

Troubleshooting in Large-Scale LC-ToF-MS Metabolomics Analysis: Solving Complex Issues in Big Cohorts

Analytical Pitfalls and Challenges in Clinical Metabolomics

A Method for Identifying Germplasm that Exhibit Multiple Mutations in the Fatty Acid Synthetic Pathway of Perilla frutescens

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