Quality assurance procedures for mass spectrometry untargeted metabolomics. a review

Dudzik, Danuta; Barbas-Bernardos, Cecilia; Garcı́a, Antonia; Barbas, Coral

doi:10.1016/j.jpba.2017.07.044

Cited by 279 publications

(223 citation statements)

References 215 publications

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“…However, that is not surprising and typically, it is advisable to measure all samples of interest within one run to avoid data comparison of different series. 45 As expected, there was no relevant difference in intra-day precision between the three different scan modes ( Figure 3).…”

Section: Precisionsupporting

confidence: 72%

Analytical considerations for (un)‐targeted metabolomic studies with special focus on forensic applications

Boxler

Schneider

Kræmer

et al. 2018

Drug Testing and Analysis

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Over the past few years, the interest in metabolomics has increased in various fields including forensic toxicology. Forensic analysis typically requires a high degree of accuracy, which is often a problem in metabolomics applications. We aimed for a systematic evaluation of different analytical considerations of a metabolomics workflow allowing a targeted approach within an untargeted setup. Samples with 69 metabolites from different chemical classes were qualitatively and quantitatively analyzed on a high resolution quadrupole time of flight mass spectrometer coupled to liquid chromatography (UHPLC-QTOF). Three issues were addressed: (a) Two different approaches on "blind matrix" a simulated body fluid (SBF) and plasma-filtrate, were tested for calibration samples; (b) comparison of two different HPLC columns, reverse-phase (RP) and hydrophilic interaction chromatography (HILIC); and (c) comparison of three different acquisition modes (TOF-MS, information dependent data acquisition (IDA), and sequential window acquisition of all theoretical fragment-ion spectra (SWATH). Samples were measured repeatedly for method comparison based on sensitivity, accuracy, precision, and detection robustness. The blind matricesshowed similar accuracy for most analytes, while SBF provided an easier preparation with satisfying results. To cover a wide part of the human metabolome, a combination of RP and HILIC showed the best results. The different scan modes performed equally regarding metabolite quantification while TOF-MS was more sensitive but lacked MS/MS spectra generation. IDA and SWATH files were aligned to various databases where IDA showed good MS/MS spectra matches. SWATH seemed to be beneficial in detection rate but was incompatible with many important software tools in metabolomics.

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Section: Precisionsupporting

confidence: 72%

Analytical considerations for (un)‐targeted metabolomic studies with special focus on forensic applications

Boxler

Schneider

Kræmer

et al. 2018

Drug Testing and Analysis

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“…Quality assurance (QA) and quality control (QC) are important processes to ensure robust data acquisition and to maintain confidence in analysis results. Their importance gains increasing recognition in the metabolomics community 5,21,22 and they are required for specific applications by governmental institutions such as the European Medicines Agency (EMA) and the U.S. Food and Drug Administration (FDA) who provide specific guidelines 23,24 . Put simply, while QA defines processes planned and performed to fulfill defined quality requirements, QC comprises measures to report whether these quality requirements have been met.…”

Section: Qc Measures and Metaquac Softwarementioning

confidence: 99%

Concepts and Software Package for Efficient Quality Control in Targeted Metabolomics Studies – MeTaQuaC

Kuhring

Eisenberger

Schmidt³

et al. 2020

Preprint

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Targeted quantitative mass spectrometry metabolite profiling is the workhorse of metabolomics research. Robust and reproducible data is essential for confidence in analytical results and is particularly important with large-scale studies. Commercial kits are now available which use carefully calibrated and validated internal and external standards to provide such reliability. However, they are still subject to processing and technical errors in their use and should be subject to a laboratory's routine quality assurance and quality control measures to maintain confidence in the results. We discuss important systematic and random measurement errors when using these kits and suggest measures to detect and quantify them. We demonstrate how wider analysis of the entire dataset, alongside standard analyses of quality control samples can be used to identify outliers and quantify s ystematic trends in order to improve downstream analysis. Finally we present the MeTaQuaC software which implements the above concepts and methods for Biocrates kits and creates a comprehensive quality control report containing rich visualization and informative scores and summary statistics. Preliminary unsupervised multivariate analysis methods are also included to provide rapid insight into study variables and groups. MeTaQuaC is provided as an open source R package under a permissive MIT license and includes detailed user documentation.

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“…Moreover, GC-MS is a robust platform with high accuracy and reliability in the identification of compounds; however, it presents the drawback that only a few compounds can be volatized and analyzed. 29 Therefore, to get a wider view of the alterations in ameloblastoma metabolic profile and to identify a recurrence metabolic signature, other analytical platforms should be employed to complement our results.…”

Section: Ameloblastoma Exhibits a Higher Membrane And Cytoplasmicmentioning

confidence: 99%

The importance of BRAF‐V600E mutation to ameloblastoma metabolism

Duarte‐Andrade

Silva

Vitório

et al. 2019

J Oral Pathology Medicine

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receive CAPES scholarship and J.G.V. receive CNPq scholarship. RSG, CCG and FPF are research fellows at CNPq.Background: Ameloblastoma is a locally infiltrative, aggressive epithelial odontogenic neoplasm. BRAF-V600E mutation is frequently found in this tumor and has a pivotal role in its pathogenesis, but the consequences of this alteration need to be addressed. An untargeted metabolomics approach was applied to verify whether metabolic disturbances are related to tumor biology and whether BRAF-V600E mutation contributes to these alterations. Methods:Formalin-fixed and paraffin-embedded tissue specimens from thirteen ameloblastoma and six dental follicles were included in this study. BRAF mutational status was determined by competitive allele-specific real-time PCR. Metabolite extracts were analyzed using gas chromatography coupled to mass spectrometry. Univariate and multivariate statistical methods were employed to compare the metabolic profiles of the samples. Results:The abundance of eleven metabolites was significantly higher in ameloblastoma in relation to dental follicles, including amino acids, fatty acids, carbohydrates, inorganic acids, and organoheterocyclic compounds. The presence of BRAF-V600E mutations in ameloblastoma was related to decreased levels of glycerol in comparison with tumors carrying only wild-type alleles of this gene. No metabolic differences were observed between recurrent and primary manifestations of ameloblastoma. Conclusions:Ameloblastoma exhibits a distinct metabolic profile from normal odontogenic epithelium. BRAF-V600E may contribute to metabolic alterations in ameloblastoma. Collectively, our findings suggest that metabolic alterations might play a role in tumor pathogenesis. K E Y W O R D SAmeloblastoma, BRAF, mass spectrometry, metabolomics, odontogenic tumor | INTRODUC TI ONAmeloblastoma is a benign odontogenic neoplasm, most commonly arising in the posterior region of the mandible. 1 The tumor has a slow-growing development, but presents a locally aggressive behavior. 2,3 According to the latest World Health Organization (WHO) classification of odontogenic tumors published in 2017, ameloblastoma was subdivided into three clinicopathological variants, that is,

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Quality assurance procedures for mass spectrometry untargeted metabolomics. a review

Cited by 279 publications

References 215 publications

Analytical considerations for (un)‐targeted metabolomic studies with special focus on forensic applications

Analytical considerations for (un)‐targeted metabolomic studies with special focus on forensic applications

Concepts and Software Package for Efficient Quality Control in Targeted Metabolomics Studies – MeTaQuaC

The importance of BRAF‐V600E mutation to ameloblastoma metabolism

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