The last decade has seen two complementary trends: (i) technology to perform untargeted metabolomics
with liquid chromatography/mass spectrometry (LC/MS) has become readily available to most investigators,
and (ii) interest in metabolism has continued to heighten in many disparate research fields ranging from cancer
and immunology to neuroscience and aging. Accordingly, the number of investigators who are acquiring
untargeted metabolomic data with LC/MS is dramatically increasing. Yet, informatic tools to analyze the
acquired data have lagged far behind and interpretation of the results remains a serious challenge, even for
experienced users. Thus, there is a substantial number of investigators performing untargeted metabolomics
with LC/MS who either cannot interpret the data generated or, even worse, are interpreting it incorrectly.
When untargeted metabolomics is performed on a typical biological sample, it is common to detect
thousands to tens of thousands of signals (aka features). Translating these signals into metabolite names is
the biggest informatic barrier limiting biomedical applications of the technology. The process is arduous,
particularly for inexperienced investigators, because the majority of signals detected do not correspond to non-
redundant metabolites originating from the biological sample. Rather, most signals (up to 95% in some of our
experiments) are due to complicating factors such as contaminants, artifacts, fragments, etc. Because many of
these complicating signals are not currently in metabolomic databases such as METLIN, they can be
challenging to annotate for inexperienced users. While there are software programs available to annotate the
signals within the data, these tools are beyond the reach of most clinical and biological investigators because
(i) they are not automated with a graphical user interface, and (ii) they rely on a costly experimental design
involving isotopes to find contaminants and artifacts.
We propose to develop an automated solution to name and quantify most of the metabolites detected in
untargeted metabolomic LC/MS experiments. Our strategy is to assume the computational burden of
completely annotating all detected metabolites in untargeted metabolomic data, which only has to be
performed once for a given sample type, so that less-experienced investigators do not have to in their future
experiments. We will completely annotate untargeted metabolomic data sets from different biological samples
using the mz.unity software and credentialing technology developed by the Patti lab. Based on experiments
that we have already performed, we expect to find ~5,000 unique bonafide metabolites per sample. We will
then use these endogenous signals to develop targeted LC/MS methods that enable automated analysis of all
detectable metabolites (i.e., the “reference metabolome”). This will allow investigators with minimal expertise in
metabolomics to profile the unique and bonafide metabolites in their samples at an untargeted scale, but
without informatic barriers that have historically limited progress in the field.
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