# Universal Metabolite Tagging

> **NIH NIH R01** · SAINT LOUIS UNIVERSITY · 2021 · $440,364

## Abstract

Project Summary/Abstract
A major impediment to mass spectrometry based metabolomics unleashing its full potential is the
complexity of the data which is cluttered with solvent and salt adducts. This is called degeneracy
and gives multiple peaks from one analytes which diminish analyte signal and need to be
discarded using bioinformatic tools. In response to PAR-17-045 which calls for “focused
technology research and development,” a multi-PI team will develop a series of three distinct
chemical tagging platforms based on our recent universal proton affinity tags. These tags react
with virtually all metabolites and eliminate degeneracy, increase signal, allow for multi-charging,
and analysis of ultra-small samples. Aim 1 will develop a universal proton affinity tagging scheme
with multi-dimensional liquid chromatography mass spectrometry platform which allows for pre-
concentrating all metabolites and minimal degeneracy. Aim 2 will synthesize and develop two
sets of isotope labeled tags for ~$2/sample. The first set are isobaric tags for targeted analyses
using low resolution mass spectrometry. The second set are neucode based tags for high
resolution mass spectrometry capable of analyzing up to 60 samples simultaneously. Aim 3 uses
a novel tag which fragments across the carbon-carbon backbone to allow identification of new
metabolites using fragmentation modeling. In the final aim of the proposal we will leverage the
increase in sensitivity and multiplexing of the previous aims to analyze small samples. The
methods developed here will be evaluated for robustness and transferability by comparing
performance across multiple independent laboratories. The outcomes for this proposal are three
distinct technologies which solve multiple critical barriers in metabolomics.

## Key facts

- **NIH application ID:** 10240660
- **Project number:** 5R01GM134081-03
- **Recipient organization:** SAINT LOUIS UNIVERSITY
- **Principal Investigator:** Christopher K Arnatt
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $440,364
- **Award type:** 5
- **Project period:** 2019-09-15 → 2023-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10240660

## Citation

> US National Institutes of Health, RePORTER application 10240660, Universal Metabolite Tagging (5R01GM134081-03). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10240660. Licensed CC0.

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