Method validation strategies involved in non-targeted metabolomics

Naz, Saima; Vallejo, María S.; Garcı́a, Antonia; Barbas, Coral

doi:10.1016/j.chroma.2014.04.071

Cited by 276 publications

(241 citation statements)

References 72 publications

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“…However, the small size and poor design of some studies drove validation of these biomarkers quite challenging [41]. Critical steps that should be undertaken to avoid any bias, to maximize reproducibility and detection sensitivity, with the final aim to find relevant, specific and robust biomarkers should be addressed.…”

Section: Other Challengesmentioning

confidence: 99%

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The emergence of metabolomics as a key discipline in the drug discovery process

Fillet

Frédérich

2015

Drug Discovery Today: Technologies

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Section: Other Challengesmentioning

confidence: 99%

“…The data pre--processing (calibration, baseline correction, normalization, peak detection and peak alignment) represents a key step for metabolomic analysis [41].…”

Section: Other Challengesmentioning

confidence: 99%

The emergence of metabolomics as a key discipline in the drug discovery process

Fillet

Frédérich

2015

Drug Discovery Today: Technologies

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“…Sampling and sample preparation as well as the method used during LC-MS analysis have a profound effect on the likelihood of generating analytically reliable hypotheses (Gika et al 2014a, b;Vuckovic 2012). A number of publications has focused on assessing and measuring data quality for global metabolite profiling (Gika et al 2016;Naz et al 2014;Naz et al 2013a;Naz et al 2013b;Pandher et al 2009;Simón-Manso et al 2013;Telu et al 2016;Tulipani et al 2013Tulipani et al , 2015Whiley et al 2012), however most of these only focus on a particular step in the workflow.…”

Section: Introductionmentioning

confidence: 98%

LC–MS based global metabolite profiling: the necessity of high data quality

et al. 2016

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LC-MS based global metabolite profiling currently lacks detailed guidelines to demonstrate that the obtained data is of high enough analytical quality. Insufficient data quality may result in the failure to generate a hypothesis, or in the worst case, a false or skewed hypothesis. After assessing the literature, it is apparent that an analytically focused summary and critical discussion related to this subject would be beneficial for both beginners and experts engaged in this field. A particular focus will be placed on data quality, which we here define as the degree to which a set of parameters fulfills predetermined criteria, similar to the established guidelines for targeted analysis. However, several of these parameters are difficult to assess since holistic approaches measure thousands of metabolites in parallel and seldom include predefined knowledge of which metabolites will differ between sample groups. In this review, the following parameters will be discussed in detail: reproducibility, selectivity, certainty of metabolite identification and metabolite coverage. The review systematically describes the generic workflow for LC-MS based global metabolite profiling and highlights how each separate part may affect data quality. The last part of the review describes how data quality can be evaluated as well as identifies areas where additional improvement is needed. In this review, we provide our own analytical opinions in regards to evaluation and, to some extent, improvement of data quality.

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“…On the other hand, inter-day precision reflects small variations in the experimental or operational conditions from repetitive analysis of the same sample within different days [ The accuracy reflects the closeness of agreement between the true value of the analyte concentration and the mean result obtained by applying the analytical method (i.e. reliable quantification) [48,54,57]. In plant metabolomics studies, where no reference materials with known metabolite concentrations are available, the accuracy of the analytical method can be assessed by determining the analytical recoveries of authentic standards or isotopically labelled metabolites spiked into the plant matrix ( §3f) [56].…”

Section: (C) Methods Precision and Accuracymentioning

confidence: 99%

Mass spectrometry as a quantitative tool in plant metabolomics

Jorge

Mata

António

2016

Phil. Trans. R. Soc. A.

110

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Metabolomics is a research field used to acquire comprehensive information on the composition of a metabolite pool to provide a functional screen of the cellular state. Studies of the plant metabolome include the analysis of a wide range of chemical species with very diverse physico-chemical properties, and therefore powerful analytical tools are required for the separation, characterization and quantification of this vast compound diversity present in plant matrices. In this review, challenges in the use of mass spectrometry (MS) as a quantitative tool in plant metabolomics experiments are discussed, and important criteria for the development and validation of MS-based analytical methods provided. This article is part of the themed issue ‘Quantitative mass spectrometry’.

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Method validation strategies involved in non-targeted metabolomics

Cited by 276 publications

References 72 publications

The emergence of metabolomics as a key discipline in the drug discovery process

The emergence of metabolomics as a key discipline in the drug discovery process

LC–MS based global metabolite profiling: the necessity of high data quality

Mass spectrometry as a quantitative tool in plant metabolomics

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