2014
DOI: 10.1007/s11306-014-0740-0
|View full text |Cite
|
Sign up to set email alerts
|

Can we trust untargeted metabolomics? Results of the metabo-ring initiative, a large-scale, multi-instrument inter-laboratory study

Abstract: The metabo-ring initiative brought together five nuclear magnetic resonance instruments (NMR) and 11 different mass spectrometers with the objective of assessing the reliability of untargeted metabolomics approaches in obtaining comparable metabolomics profiles. This was estimated by measuring the proportion of common spectral information extracted from the different LCMS and NMR platforms. Biological samples obtained from 2 different conditions were analysed by the partners using their own in-house protocols.… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
2
1

Citation Types

1
81
0

Year Published

2015
2015
2023
2023

Publication Types

Select...
8
2

Relationship

1
9

Authors

Journals

citations
Cited by 107 publications
(85 citation statements)
references
References 46 publications
(58 reference statements)
1
81
0
Order By: Relevance
“…Advances in the “omics” fields have already led to the publication of validated, highly standardized datasets suitable for data mining/sharing and for inclusion in informative systems complying with the FAIR (Findable, Accessible, Interoperable, and Re‐usable) protocol . In that context, the international metabolomics community has made efforts to determine standard procedures, to evaluate the analytical performance of untargeted metabolomics, and to identify the needs for infrastructure, including data sharing . The Food Biomarker Alliance (FoodBAll), which includes the authors of this article, participates in this effort by fostering the application of novel metabolomic techniques for human nutrition studies as complement to traditional dietary assessment methods such as food frequency questionnaire and 24‐h recall …”
Section: Introductionmentioning
confidence: 99%
“…Advances in the “omics” fields have already led to the publication of validated, highly standardized datasets suitable for data mining/sharing and for inclusion in informative systems complying with the FAIR (Findable, Accessible, Interoperable, and Re‐usable) protocol . In that context, the international metabolomics community has made efforts to determine standard procedures, to evaluate the analytical performance of untargeted metabolomics, and to identify the needs for infrastructure, including data sharing . The Food Biomarker Alliance (FoodBAll), which includes the authors of this article, participates in this effort by fostering the application of novel metabolomic techniques for human nutrition studies as complement to traditional dietary assessment methods such as food frequency questionnaire and 24‐h recall …”
Section: Introductionmentioning
confidence: 99%
“…Thus, the results from this study evidence that in an untargeted LC/ESI‐MS metabolomics ' what you see is what you ionise '. Such a postulation is certainly supported by previous studies in which different ESI conditions have been used for different purposes, and by theoretically sound assumptions based on our current understanding (empirically evidenced) of the complexity of the metabolome and the functionality of ESI sources . The logic may then raise questions about the degree of accuracy and precision in the formulation of a hypothesis based on this data‐driven approach, or if untargeted metabolomics can be trusted in this regard.…”
Section: Resultsmentioning
confidence: 81%
“…Hundreds of metabolites can be separated and measured in samples of interest such as plasma, CSF, urine or cell extracts using a diversity of commonly used metabolomics tools such as NMR, GC–MS and LC–MS detection. Each of these analytical tools carries its own advantages, disadvantages and applications as shown in Table 1 [22]. Table 1 shows the most common analytical tools for analysis of the various classes of compounds, such as lipids, carbohydrates, amino acids, organic acids, sugars, sugar phosphates, biogenic amines, nucleotides, vitamins, purines, fatty acids and steroids.…”
Section: Platforms For Metabolomics Analysismentioning
confidence: 99%