Best-Matched Internal Standard Normalization in Liquid Chromatography–Mass Spectrometry Metabolomics Applied to Environmental Samples

The goal of metabolomics
is to measure the entire range of small
organic molecules in biological samples. In liquid chromatography–mass
spectrometry-based metabolomics, formidable analytical challenges
remain in removing the nonbiological factors that affect chromatographic
peak areas. These factors include sample matrix-induced ion suppression,
chromatographic quality, and analytical drift. The combination of
these factors is referred to as obscuring variation. Some metabolomics
samples can exhibit intense obscuring variation due to matrix-induced
ion suppression, rendering large amounts of data unreliable and difficult
to interpret. Existing normalization techniques have limited applicability
to these sample types. Here we present a data normalization method
to minimize the effects of obscuring variation. We normalize peak
areas using a batch-specific normalization process, which matches
measured metabolites with isotope-labeled internal standards that
behave similarly during the analysis. This method, called best-matched
internal standard (B-MIS) normalization, can be applied to targeted
or untargeted metabolomics data sets and yields relative concentrations.
We evaluate and demonstrate the utility of B-MIS normalization using
marine environmental samples and laboratory grown cultures of phytoplankton.
In untargeted analyses, B-MIS normalization allowed for inclusion
of mass features in downstream analyses that would have been considered
unreliable without normalization due to obscuring variation. B-MIS
normalization for targeted or untargeted metabolomics is freely available
at https://github.com/IngallsLabUW/B-MIS-normalization.