Volatile metabolomic signature of bladder cancer cell lines based on gas chromatography–mass spectrometry

Rodrigues, Daniela; Pinto, Joana; Araújo, Ana Margarida; Monteiro‐Reis, Sara; Jerónimo, Carmen; Henrique, Rui; Bastos, Maria de Lourdes; Pinho, Paula Guedes de; Carvalho, Márcia

doi:10.1007/s11306-018-1361-9

Cited by 33 publications

(28 citation statements)

References 57 publications

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“…The permutation test can be used to determine whether the established PLS-DA model is at risk of overfitting [32,33]. The intercepts generated by 200 random permutation tests for the FT-MIR PLS-DA model permutation test with the selected-class 1 variables were R 2 = 0.395 and Q 2 = −0.861, the values of R 2 and Q 2 of permutation tests with the remaining four categories variables are shown in Figure S1.…”

Section: Resultsmentioning

confidence: 99%

Data Fusion of Fourier Transform Mid-Infrared (MIR) and Near-Infrared (NIR) Spectroscopies to Identify Geographical Origin of Wild Paris polyphylla var. yunnanensis

et al. 2019

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Origin traceability is important for controlling the effect of Chinese medicinal materials and Chinese patent medicines. Paris polyphylla var. yunnanensis is widely distributed and well-known all over the world. In our study, two spectroscopic techniques (Fourier transform mid-infrared (FT-MIR) and near-infrared (NIR)) were applied for the geographical origin traceability of 196 wild P. yunnanensis samples combined with low-, mid-, and high-level data fusion strategies. Partial least squares discriminant analysis (PLS-DA) and random forest (RF) were used to establish classification models. Feature variables extraction (principal component analysis—PCA) and important variables selection models (recursive feature elimination and Boruta) were applied for geographical origin traceability, while the classification ability of models with the former model is better than with the latter. FT-MIR spectra are considered to contribute more than NIR spectra. Besides, the result of high-level data fusion based on principal components (PCs) feature variables extraction is satisfactory with an accuracy of 100%. Hence, data fusion of FT-MIR and NIR signals can effectively identify the geographical origin of wild P. yunnanensis.

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Section: Resultsmentioning

confidence: 99%

Data Fusion of Fourier Transform Mid-Infrared (MIR) and Near-Infrared (NIR) Spectroscopies to Identify Geographical Origin of Wild Paris polyphylla var. yunnanensis

et al. 2019

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“…It known that an acid environment leads to a larger number of VOCs produced compared with a neutral or alkaline environment [6,38]. Despite this, there is a lack of consistency in the approaches described in the literature with either acid, alkali, and/or salts used as a sample treatment [2,8,16,31,[41][42][43][44][45][46].…”

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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“…In addition to other compounds, volatile organic compounds (VOCs) such as fatty acids and organic acids which are important biomarker candidates in biological samples can be successfully achieved by GC-MS. Analysis by GC-MS requires derivatization with reactions of alkylation, acylation, and silylation in order to increase detection or retention of the compound [24].…”

Section: Basic Workflow Of Ms-based Metabolomicsmentioning

confidence: 99%

“…Volatilomics is a new field with adductomics into the metabolomics. For the extraction of VOCs, solvent-free sample preparation/extraction method "solid-phase microextraction (SPME)" is used, which enables extraction of organic compounds from gaseous, aqueous, and solid materials [24].…”

Section: Basic Workflow Of Ms-based Metabolomicsmentioning

confidence: 99%

Metabolomics: Basic Principles and Strategies

Nalbantoglu¹

2019

Molecular Medicine

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Metabolomics is the study of metabolome within cells, biofluids, tissues, or organisms to comprehensively identify and quantify all endogenous and exogenous low-molecular-weight (<1 kDa) small molecules/metabolites in a biological system in a high-throughput manner. Metabolomics has several applications in health and disease including precision/personalized medicine, single cell, epidemiologic population studies, metabolic phenotyping, and metabolome-wide association studies (MWAS), precision metabolomics, and in combination with other omics disciplines as integrative omics, biotechnology, and bioengineering. Mass spectrometry (MS)based metabolomics/lipidomics provides a useful approach for both identification of disease-related metabolites in biofluids or tissue and also encompasses classification and/or characterization of disease or treatment-associated molecular patterns generated from metabolites. Here, in this review, we provide a brief overview of the current status of promising MS-based metabolomics strategies and their emerging roles, as well as possible challenges.

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Volatile metabolomic signature of bladder cancer cell lines based on gas chromatography–mass spectrometry

Cited by 33 publications

References 57 publications

Data Fusion of Fourier Transform Mid-Infrared (MIR) and Near-Infrared (NIR) Spectroscopies to Identify Geographical Origin of Wild Paris polyphylla var. yunnanensis

Data Fusion of Fourier Transform Mid-Infrared (MIR) and Near-Infrared (NIR) Spectroscopies to Identify Geographical Origin of Wild Paris polyphylla var. yunnanensis

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

Metabolomics: Basic Principles and Strategies

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