Sample normalization methods in quantitative metabolomics

Area of Science:

• Metabolomics
• Analytical Chemistry
• Biochemistry

Background:

• Quantitative metabolomics aims to reveal biological event-induced changes by accurately measuring metabolite concentrations.
• Metabolomic analysis involves pre-analytical, analytical, and data analysis steps, each impacting quantitative results.
• Variations in total sample amount or metabolite concentration between samples can significantly affect accurate quantification.

Purpose of the Study:

• To emphasize the critical role of sample normalization in quantitative metabolomics.
• To review and discuss recently reported normalization methods for mass spectrometry (MS)-based metabolomics.
• To assess the applicability of these normalization methods in real-world metabolomics studies.

Main Methods:

• Focus on mass spectrometry (MS)-based analytical platforms.
• Review of literature for recently reported sample normalization techniques.
• Discussion of the applicability and limitations of various normalization methods.

Main Results:

• Sample normalization is crucial for accurate metabolite quantification, especially when total sample amounts vary significantly.
• Several effective sample normalization methods are now available for quantitative metabolomics.
• Normalization can mitigate the impact of sample variation on the detection of true metabolite concentration differences.

Conclusions:

• Sample normalization is an essential, though sometimes overlooked, step in quantitative metabolomics.
• The availability of convenient normalization methods makes their implementation more feasible.
• Performing sample normalization is critical for reliable metabolomic data analysis, particularly with MS-based platforms.