A review of strategies for untargeted urinary metabolomic analysis using gas chromatography–mass spectrometry

Khodadadi, Mohammad; Pourfarzam, Morteza

doi:10.1007/s11306-020-01687-x

Cited by 49 publications

(42 citation statements)

References 104 publications

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“…Internal standards were not used in this study because they are not practical for untargeted metabolomics [52][53][54]. Our QC samples were produced by pooling 50 urine samples.…”

Section: Discussionmentioning

confidence: 99%

Optimisation of Urine Sample Preparation for Headspace-Solid Phase Microextraction Gas Chromatography-Mass Spectrometry: Altering Sample pH, Sulphuric Acid Concentration and Phase Ratio

et al. 2020

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Headspace-solid phase microextraction gas chromatography-mass spectrometry (HS-SPME-GC-MS) can be used to measure volatile organic compounds (VOCs) in human urine. However, there is no widely adopted standardised protocol for the preparation of urine samples for analysis resulting in an inability to compare studies reliably between laboratories. This paper investigated the effect of altering urine sample pH, volume, and vial size for optimising detection of VOCs when using HS-SPME-GC-MS. This is the first, direct comparison of H2SO4, HCl, and NaOH as treatment techniques prior to HS-SPME-GC-MS analysis. Altering urine sample pH indicates that H2SO4 is more effective at optimising detection of VOCs than HCl or NaOH. H2SO4 resulted in a significantly larger mean number of VOCs being identified per sample (on average, 33.5 VOCs to 24.3 in HCl or 12.2 in NaOH treated urine) and more unique VOCs, produced a more diverse range of classes of VOCs, and led to less HS-SPME-GC-MS degradation. We propose that adding 0.2 mL of 2.5 M H2SO4 to 1 mL of urine within a 10 mL headspace vial is the optimal sample preparation prior to HS-SPME-GC-MS analysis. We hope the use of our optimised method for urinary HS-SPME-GC-MS analysis will enhance our understanding of human disease and bolster metabolic biomarker identification.

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“…Internal standards were not used in this study because they are not practical for untargeted metabolomics [52][53][54]. Our QC samples were produced by pooling 50 urine samples.…”

Section: Discussionmentioning

confidence: 99%

Optimisation of Urine Sample Preparation for Headspace-Solid Phase Microextraction Gas Chromatography-Mass Spectrometry: Altering Sample pH, Sulphuric Acid Concentration and Phase Ratio

et al. 2020

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“…They also eliminate the variability of injected volumes and the MS signal response due to the effect of sample matrices on ionization. Moreover, loss of some analytes may occur, e.g., due to incomplete extraction, derivatization protocol, or degradation of analytes during storage [72]. During daily operation, fluctuations in the temperature and pressure of the ion source can also affect the MS ionization efficiency of the MS analyzer [69].…”

Section: Internal Standardsmentioning

confidence: 99%

“…This approach has been used for the analysis of steroid metabolites for several decades. Due to the high reproducibility, sensitivity, and availability of mass spectrum databases, GC-MS and GC-MS/MS techniques have often been used in these studies [72]. The most common ionization technique applied in metabolic studies based on GC is electron impact ionization (EI) [81,88,89].…”

Section: Gc-ms Ionization Techniquesmentioning

confidence: 99%

“…GC-MS is a tool that can be used in both targeted and untargeted analysis approaches [80]. Compared to LC-MS, this analytical technique provides higher sensitivity, resolution, reproducibility, reliability, and relatively low cost [72]. For example, profiling of steroids in urine by GC-MS and GC-MS/MS is a suitable tool for the discovery of new steroids, their characterization, and the acquisition of new knowledge useful for the diagnosis of metabolic disorders [51,93].…”

Section: Gc-ms and Their Applicationsmentioning

confidence: 99%

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Analytical Methods for the Determination of Neuroactive Steroids

et al. 2021

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Neuroactive steroids are a family of all steroid-based compounds, of both natural and synthetic origin, which can affect the nervous system functions. Their biosynthesis occurs directly in the nervous system (so-called neurosteroids) or in peripheral endocrine tissues (hormonal steroids). Steroid hormone levels may fluctuate due to physiological changes during life and various pathological conditions affecting individuals. A deeper understanding of neuroactive steroids’ production, in addition to reliable monitoring of their levels in various biological matrices, may be useful in the prevention, diagnosis, monitoring, and treatment of some neurodegenerative and psychiatric diseases. The aim of this review is to highlight the most relevant methods currently available for analysis of neuroactive steroids, with an emphasis on immunoanalytical methods and gas, or liquid chromatography combined with mass spectrometry.

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“…However, these standards can be costly, time consuming to use, and unavailable for certain metabolites [12,17,18]. Using standards may only be feasible for quantifying a few metabolites, but for the purposes of untargeted metabolomics, where one attempts to measure all metabolites [19], it quickly becomes infeasible.…”

Section: Introductionmentioning

confidence: 99%

Leveraging the mass balances of cellular metabolism to infer absolute concentrations from relative abundance metabolomics data

Lee

Styczynski

2021

Preprint

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Motivation: As the large-scale study of metabolites and a direct readout of a system's metabolic state, metabolomics has significant appeal as a source of information for many metabolic modeling platforms and other metabolic analysis tools. However, metabolomics data are typically reported in terms of relative abundances, which precluding use with tools where absolute concentrations are necessary. While chemical standards can be used to determine the absolute concentrations of metabolites, they are often time-consuming to run, expensive, or unavailable for many metabolites. A computational framework that can infer absolute concentrations without the use of chemical standards would be highly beneficial to the metabolomics community. Results: We have developed and characterized MetaboPAC, a computational strategy that leverages the mass balances of a system to infer absolute concentrations in metabolomics datasets. MetaboPAC uses a kinetic equations approach and an optimization approach to predict the most likely response factors that describe the relationship between absolute concentrations and their relative abundances. We determined that MetaboPAC performed significantly better than the other approaches assessed on noiseless data when at least 60% of kinetic equations are known a priori. Under the most realistic conditions (low sampling frequency, high noise data), MetaboPAC significantly outperformed other methods in the majority of cases when 100% of the kinetic equations were known. For metabolomics datasets extracted from systems that are well-studied and have partially known kinetic structures, MetaboPAC can provide valuable insight about their absolute concentration profiles.

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A review of strategies for untargeted urinary metabolomic analysis using gas chromatography–mass spectrometry

Cited by 49 publications

References 104 publications

Optimisation of Urine Sample Preparation for Headspace-Solid Phase Microextraction Gas Chromatography-Mass Spectrometry: Altering Sample pH, Sulphuric Acid Concentration and Phase Ratio

Optimisation of Urine Sample Preparation for Headspace-Solid Phase Microextraction Gas Chromatography-Mass Spectrometry: Altering Sample pH, Sulphuric Acid Concentration and Phase Ratio

Analytical Methods for the Determination of Neuroactive Steroids

Leveraging the mass balances of cellular metabolism to infer absolute concentrations from relative abundance metabolomics data

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