Next Gen Metabolomics Technologies: Broader Coverage, Single Cell, Double
Bond Pinpointing, and Spatial Mapping

The highly dynamic nature of metabolites and their abundances makes metabolomics a
powerful endpoint of the ‘omics’ cascade, yielding a molecular profile that is closest to the
physiological phenotype. Metabolomic profiles are therefore sensitive to subtle perturbations
observed in early disease stages or disease progression, which may be difficult to detect at the
proteome or transcriptome levels. Human diseases are multi-factorial in nature, and studying
small parts of their associated molecular changes is generally insufficient for understanding the
full spectrum of disease phenotypes.
The metabolome is the total collection of biologically-active small molecules with molecular
weights lower than about ~1.5 kDa in an organism. This includes endogenous molecules that
are biosynthesized by metabolic networks in “primary metabolism”, specialized “secondary
metabolite” signaling or defense molecules, molecules derived from diet or environmental
exposures (the exposome), and molecules derived from the biosynthetic interactions with
associated microbes (the microbiome). Metabolomics can either be “targeted” to a set of known
compounds, for example certain lipids, or “non-targeted”, which attempts to detect and relatively
quantify as many metabolites as possible.
The vast chemical diversity of the metabolome (lipids, sugars, amino acids, etc.), and its
wide dynamic range (mM to fM) implies that no single analytical method can adequately profile
all metabolites in one metabolomics experiment. Along these lines, the “fusion” of mass
spectrometry (MS) and nuclear magnetic resonance spectroscopy (NMR) is emerging as one of
the most powerful avenues to increase metabolome coverage. Nested separations that work in
a time frame compatible with mass spectrometry, such as those performed by ion mobility, are
also playing a key analytical role in metabolomics as a way of increasing peak capacity, and
identifying metabolites through ion mobility collision cross section measurements. Further,
localization of metabolites at the tissue level with imaging mass spectrometry experiments,
allows linking their abundance with changes observed in biofluids. In this seminar, I will
introduce the typical workflow used in non-targeted metabolomics experiments, describe
potential pitfalls through examples related to our efforts within the Molecular Transducers of
Physical Activity Consortium (MoTrPAC), and showcase the challenges involved in identifying
unknowns with this growing ‘omics approach.

https://umich.zoom.us/j/94087632693?pwd=bW5qMFQ0ZmRHTEdZZTNVUkt2Mmk1UT09
Passcode Fernandez



Facundo Fernandez (Georgia Institute of Technology)