Sample normalization methods in quantitative metabolomics

•Sample-to-sample amount variation could be larger than analytical variation.•Sample normalization is a critical step in quantitative metabolomics.•Sample normalization should be incorporated into a metabolomic profiling workflow.•There is no unified method but a number of methods have been reported...

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Veröffentlicht in:Journal of Chromatography A Jg. 1430; S. 80 - 95
Hauptverfasser: Wu, Yiman, Li, Liang
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Netherlands Elsevier B.V 22.01.2016
Schlagworte:
Analytic Sample Preparation Methods - methods
Analytic Sample Preparation Methods - standards
chromatography
Chromatography, Liquid - methods
Collection
Data processing
Feature extraction
Humans
Liquid chromatography
Mass spectrometry
Mass Spectrometry - methods
Mathematical analysis
Metabolite quantification
Metabolites
Metabolomics
Metabolomics - methods
Metabolomics - standards
Platforms
Quantitative metabolomic profiling
Sample normalization
Specimen Handling - methods
Specimen Handling - standards
Workflow
Metabolomics
Quantitative metabolomic profiling
Liquid chromatography
Mass spectrometry
Sample normalization
Metabolite quantification
ISSN:0021-9673, 1873-3778, 1873-3778
Online-Zugang:Volltext
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Zusammenfassung:•Sample-to-sample amount variation could be larger than analytical variation.•Sample normalization is a critical step in quantitative metabolomics.•Sample normalization should be incorporated into a metabolomic profiling workflow.•There is no unified method but a number of methods have been reported.•The performance of sample normalization methods needs to be carefully considered to select a proper method. To reveal metabolomic changes caused by a biological event in quantitative metabolomics, it is critical to use an analytical tool that can perform accurate and precise quantification to examine the true concentration differences of individual metabolites found in different samples. A number of steps are involved in metabolomic analysis including pre-analytical work (e.g., sample collection and storage), analytical work (e.g., sample analysis) and data analysis (e.g., feature extraction and quantification). Each one of them can influence the quantitative results significantly and thus should be performed with great care. Among them, the total sample amount or concentration of metabolites can be significantly different from one sample to another. Thus, it is critical to reduce or eliminate the effect of total sample amount variation on quantification of individual metabolites. In this review, we describe the importance of sample normalization in the analytical workflow with a focus on mass spectrometry (MS)-based platforms, discuss a number of methods recently reported in the literature and comment on their applicability in real world metabolomics applications. Sample normalization has been sometimes ignored in metabolomics, partially due to the lack of a convenient means of performing sample normalization. We show that several methods are now available and sample normalization should be performed in quantitative metabolomics where the analyzed samples have significant variations in total sample amounts.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
ObjectType-Review-3
content type line 23
ISSN:0021-9673
1873-3778
1873-3778
DOI:10.1016/j.chroma.2015.12.007