An Untargeted Metabolomics Workflow that Scales to Thousands of Samples for Population-Based Studies

The
success of precision medicine relies upon collecting data from
many individuals at the population level. Although advancing technologies
have made such large-scale studies increasingly feasible in some disciplines
such as genomics, the standard workflows currently implemented in
untargeted metabolomics were developed for small sample numbers and
are limited by the processing of liquid chromatography/mass spectrometry
data. Here we present an untargeted metabolomics workflow that is
designed to support large-scale projects with thousands of biospecimens.
Our strategy is to first evaluate a reference sample created by pooling
aliquots of biospecimens from the cohort. The reference sample captures
the chemical complexity of the biological matrix in a small number
of analytical runs, which can subsequently be processed with conventional
software such as XCMS. Although this generates thousands of so-called
features, most do not correspond to unique compounds from the samples
and can be filtered with established informatics tools. The features
remaining represent a comprehensive set of biologically relevant reference
chemicals that can then be extracted from the entire cohort’s
raw data on the basis of m/z values
and retention times by using Skyline. To demonstrate applicability
to large cohorts, we evaluated >2000 human plasma samples with
our
workflow. We focused our analysis on 360 identified compounds, but
we also profiled >3000 unknowns from the plasma samples. As part
of
our workflow, we tested 14 different computational approaches for
batch correction and found that a random forest-based approach outperformed
the others. The corrected data revealed distinct profiles that were
associated with the geographic location of participants.