# Label-free polar metabolite quantification for untargeted metabolomics

> **NIH NIH U2C** · BATTELLE PACIFIC NORTHWEST LABORATORIES · 2021 · $217,754

## Abstract

SUMMARY
The primary focus of the NIH Compound Identification Development Cores (CIDC) is to use untargeted
metabolomics to not only identify novel metabolites but to facilitate and improve the identification of known
metabolites. Furthermore, the CIDC is mandated to promote the accuracy, reproducibility, and interlaboratory
comparison of metabolomics data. One way of promoting reproducibility, improving comparability and enhancing
the confidence of metabolite identification is to improve metabolite quantification -- especially for untargeted
metabolomics. Indeed, as frequently shown by untargeted NMR studies, knowledge of the concentration limits
of a particular metabolite can “rule-in” or “rule-out” a tentative identification. For instance, if a metabolite signal
is tentatively identified as kynurenic acid, but the measured concentration is determined to be 100X times more
than normal, then that tentative identification must be incorrect and thus, “ruled out”. Traditionally compound
quantification in metabolomics (especially absolute quantification) has been limited to targeted metabolomics
while untargeted methods have largely relied on relative quantification. Absolute quantification by LC-MS is
difficult and requires isotopically labeled standards and careful calibration. Isotopic standards are expensive and
difficult to obtain. As a result, the number of metabolites that can be routinely quantified by targeted LC-MS-
based methods is generally less than 500. On the other hand, relative quantification is much easier and it is
possible to use peak intensity comparisons between “cases” and “controls” to relatively quantify thousands of
compounds with little effort. However, relative quantification has many limitations and numerous problems. In
particular, relative values cannot be compared across labs, across platforms, or even over modestly separate
time periods within the same lab (batch effects). This makes relative quantification fundamentally “unFAIR” from
a data sharing or reproducibility perspective. Furthermore, relative quantification only works for certain limited
experimental designs (cases vs. controls) and relative values can never be used in clinical, legal or industrial test
settings. This limits the application of untargeted metabolomics to “research-use only”. If untargeted
metabolomics is ever going to expand beyond the lab and into the mainstream, it will need to develop robust,
label-free quantification methods that can work across different samples, across platforms, across labs and
across time. The challenge is how to perform metabolite quantification via LC-MS without isotopic standards?
Fortunately, there have been a number of recent developments and novel ideas that integrate both experimental
and computation approaches that suggest it may be possible to perform accurate metabolite quantification via
untargeted LC-MS metabolomics without isotopically labeled standards. Our goal is to implement, test and refine
these met...

## Key facts

- **NIH application ID:** 10396924
- **Project number:** 3U2CES030170-04S1
- **Recipient organization:** BATTELLE PACIFIC NORTHWEST LABORATORIES
- **Principal Investigator:** Thomas O Metz
- **Activity code:** U2C (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $217,754
- **Award type:** 3
- **Project period:** 2018-09-01 → 2023-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10396924

## Citation

> US National Institutes of Health, RePORTER application 10396924, Label-free polar metabolite quantification for untargeted metabolomics (3U2CES030170-04S1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10396924. Licensed CC0.

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