# Systems Metabolomics for Biomarker Discovery

> **NIH NIH R35** · GEORGETOWN UNIVERSITY · 2022 · $249,993

## Abstract

TSQ Altis Plus Triple Quadrupole Mass Spectrometer for Targeted
Quantitation of Biomarker Candidates
PROJECT SUMMARY
Metabolomics offers a comprehensive analysis of thousands of small molecules in biological samples. It can
play an indispensable role in the growing systems biology approaches to unravel the relationships between
metabolites and diseases. Liquid chromatography coupled to mass spectrometry (LC-MS) and gas
chromatography coupled to mass spectrometry (GC-MS) have been used for high-throughput analysis of
thousands of metabolites. However, the potential values of many disease-associated metabolites discovered by
using these platforms have been inadequately explored in systems biology approaches for biomarker discovery
due to lack of computational tools and resources to: (1) accurately determine the identity of most of the analytes;
(2) investigate the rewiring interactions among the metabolites due to diseases; and (3) integrate metabolite
profiles with those from other omics studies to evaluate the relationships between the metabolites and the
diseases at the systems level. Partly due to these limitations, poor generalizability of previously identified
metabolite biomarker candidates has been observed, especially when they are evaluated through independent
platforms and validation sets. The parent award aims to fill the gaps in metabolite identification and multi-omics
integration by using systems metabolomics approaches that enhance the role of metabolomics in systems
biology approaches for biomarker discovery. Specifically, it utilizes multiple resources (biological databases,
mass spectral libraries, etc.) and innovative statistical, deep learning, and network-based methods for: (1)
developing a comprehensive workflow for ranking putative metabolite IDs; (2) differential analysis of metabolite
profiles and integration of metabolomics data with proteomics, glycomics, glycoproteomics. and
phosphoproteomics data to identify highly promising metabolite biomarker candidates. The selected candidates
are evaluated by targeted quantitation using independent samples and platforms compared to those used for
discovery. However, successful implementation of one of the critical tasks proposed in the parent award relies
heavily on an analytical platform that has high sensitivity for targeted quantitative analysis of selected analytes.
To this end, in this administrative supplement application, we request support to purchase the Thermo Scientific
TSQ Altis Plus Triple Quadrupole Mass Spectrometer, which offers the sensitivity, selectivity, and throughput
needed for targeted quantitation of analytes by selected reaction monitoring (SRM). Availability of this instrument
will not only provide the much needed sensitive, robust, reliable, quantitation platform but also address
unforeseen circumstances that occurred due to the significant amount of machine time required to develop
instrument methods for targeted quantitation of biomarker candidates sel...

## Key facts

- **NIH application ID:** 10581892
- **Project number:** 3R35GM141944-02S1
- **Recipient organization:** GEORGETOWN UNIVERSITY
- **Principal Investigator:** Habtom W Ressom
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $249,993
- **Award type:** 3
- **Project period:** 2021-09-22 → 2026-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10581892

## Citation

> US National Institutes of Health, RePORTER application 10581892, Systems Metabolomics for Biomarker Discovery (3R35GM141944-02S1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10581892. Licensed CC0.

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