# Untargeted Analysis Resource

> **NIH NIH U2C** · RESEARCH TRIANGLE INSTITUTE · 2021 · $2,644,227

## Abstract

ABSTRACT (UNTARGETED ANALYSIS RESOURCE)
As a Children's Health Exposure Analysis Resource (CHEAR) Hub, we have established comprehensive
untargeted methods for detection, annotation, and identification of signals derived from endogenous
compounds, environmentally relevant chemicals, drugs and medications, and ingestion of foods. We use
several systems for Untargeted Analysis, including Liquid Chromatography (LC) coupled to High-Resolution
Orbitrap and Time of Flight (TOF) Mass Spectrometry (MS) systems, and Gas Chromatography (GC) coupled
to TOF MS. Under an Institutional commitment are also installing a GC-Q-Exactive High-Resolution MS system.
We are confident that we can capture tens of thousands of signals, and identify a diverse range of endogenous
compounds (e.g., amino acids, amines, carboxylic acids, sugars, acylcarnitines, nucleosides, fatty acids, and
lysophospholipids), environmentally relevant compounds (e.g., metabolites from alkyl phosphate pesticides,
phthalates, polycyclic aromatic hydrocarbons, volatile organic compounds, perfluoro compounds, metabolites
of tobacco products, environmental phenols, and parabens), metabolites produced by the ingestion of food
(e.g., polyphenols and their metabolites), medications (e.g. acetaminophen, sulfaguanidine, metformin), and
drugs of abuse (e.g., heroin, morphine, opioids, and their metabolites). We also use Lipidomics, UPLC-Ion
Mobility-MS, LC-electrochemical detection (ECD), NMR, and GC- and LC- multiple reaction monitoring methods
to capture signals for analytes that are difficult to detect and identify using untargeted platforms. Using our
methods, we have had outstanding performance in the NIH Common Fund Metabolomics Program Ring Trial,
and in the CHEAR Cross Laboratory Comparison. We will continue to expand the identifications of signals on
the untargeted platforms through using a) Big Data analytics for annotation of signals, followed by confirmation
with standards, b) ensuring that signals are annotated in respect to the metabolic fate of the environmental
compounds, and c) collaborating with the Development Core (DC), and HHEAR program to further develop
broad spectrum methods and panels for analytes not detected using untargeted methods. We use an Ontology
System developed by our laboratory that provides the evidence basis for all signal annotations and metabolite
identifications, ensuring optimal communications of our results to the client, the data analysis center, and data
repositories. Our core uses statistical analysis and modelling approaches to determine metabolites that
distinguish study phenotypes, and to reveal the associations between environmentally relevant chemicals,
endogenous metabotypes, and health phenotypes.

## Key facts

- **NIH application ID:** 10200814
- **Project number:** 5U2CES030857-03
- **Recipient organization:** RESEARCH TRIANGLE INSTITUTE
- **Principal Investigator:** SUSAN J SUMNER
- **Activity code:** U2C (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $2,644,227
- **Award type:** 5
- **Project period:** 2019-09-05 → 2026-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10200814

## Citation

> US National Institutes of Health, RePORTER application 10200814, Untargeted Analysis Resource (5U2CES030857-03). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10200814. Licensed CC0.

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