Alignment and Analysis of a Disparately Acquired Multibatch Metabolomics Study of Maternal Pregnancy Samples

Untargeted liquid chromatography–mass spectrometry
metabolomics
studies are typically performed under roughly identical experimental
settings. Measurements acquired with different LC-MS protocols or
following extended time intervals harbor significant variation in
retention times and spectral abundances due to altered chromatographic,
spectrometric, and other factors, raising many data analysis challenges.
We developed a computational workflow for merging and harmonizing
metabolomics data acquired under disparate LC-MS conditions. Plasma
metabolite profiles were collected from two sets of maternal subjects
three years apart using distinct instruments and LC-MS procedures.
Metabolomics features were aligned using metabCombiner to generate lists of compounds detected across all experimental
batches. We applied data set-specific normalization methods to remove
interbatch and interexperimental variation in spectral intensities,
enabling statistical analysis on the assembled data matrix. Bioinformatics
analyses revealed large-scale metabolic changes in maternal plasma
between the first and third trimesters of pregnancy and between maternal
plasma and umbilical cord blood. We observed increases in steroid
hormones and free fatty acids from the first trimester to term of
gestation, along with decreases in amino acids coupled to increased
levels in cord blood. This work demonstrates the viability of integrating
nonidentically acquired LC-MS metabolomics data and its utility in
unconventional metabolomics study designs.