# Experimental Core

> **NIH NIH U2C** · UNIVERSITY OF MICHIGAN AT ANN ARBOR · 2021 · $403,110

## Abstract

Experimental Core - Project Summary
Advances in analytical instrumentation are a key factor that enabled the development of metabolomics as a
systems biology research tool. Continued advances in instrumentation have increased the number of
detectable features in metabolomics data, but the proportion of unidentified features in a typical untargeted
metabolomics study has remained high; this challenge continues to limit interpretation of potentially biologically
informative data. Secondly, most attempts to identify features in metabolomics data are performed in an ad-
hoc manner, and are usually undertaken only when biological data suggests differential abundance of an
unknown feature between sample groups, and even then, identification is only pursued on a few of the highest-
priority targets. While this approach superficially reduces analyst burden by limiting identification to only those
features of biological interest, its actual effect is to increase the overall burden of compound identification,
since these efforts are not carried out in a systematic manner, and their results are rarely indexed in major
databases. Finally, MS/MS spectra are not routinely acquired for all target metabolites, and there exists no
universal, cross-laboratory workflow for routine compound identification, so each laboratory is effectively
required to set up its own extensive library of authentic standards to aid in compound identification. This is a
costly proposition, which results in variable identification of even well-known metabolites.
The Experimental Core of the Michigan Compound Identification Development Core (MCIDC) will help address
these challenges by carrying out the following Specific Aims. First, we will attempt to identify recurrent
unknown features in untargeted metabolomics data using a systematic, prioritized workflow. Secondly, we will
develop and implement novel and cutting-edge analytical techniques and apply them to identification of
unknown features in metabolomics data from biomedically-relevant samples. These techniques will include
sample pre-fractionation and off-line multidimensional liquid chromatography, ultra-high-pressure separations,
chemical derivatization and in-vivo stable isotope labeling, ion mobility mass spectrometry, and high-resolution
NMR analysis. Finally, we will generate a high-quality metabolite library from the unknown features we identify
in addition to previously characterized knowns, which will include a range of empirical details to aid in future
compound identification efforts.

## Key facts

- **NIH application ID:** 10183255
- **Project number:** 5U2CES030164-04
- **Recipient organization:** UNIVERSITY OF MICHIGAN AT ANN ARBOR
- **Principal Investigator:** CHARLES ROBERT EVANS
- **Activity code:** U2C (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $403,110
- **Award type:** 5
- **Project period:** 2018-08-15 → 2023-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10183255

## Citation

> US National Institutes of Health, RePORTER application 10183255, Experimental Core (5U2CES030164-04). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10183255. Licensed CC0.

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