# National Center for Quantitative Biology of Complex Systems

> **NIH NIH P41** · UNIVERSITY OF WISCONSIN-MADISON · 2021 · $355,628

## Abstract

PROJECT SUMMARY – TR&D 3
MS is a popular and powerful technique for measuring proteomes, metabolomes, and lipidomes. For each of
these classes, the MS analysis is remarkably similar, consisting of chromatographic separation, mass
measurement, and tandem MS. Despite this commonality, nearly all MS laboratories specialize in analysis of a
single ome, at the exclusion of the others. The integrated workflow we describe will overcome this limitation by
providing a chromatography and MS technology capable of separating and characterizing a complex mixture
containing peptides, metabolites, and lipids. We envision this to happen in a few hours of analysis time and to
provide comprehensive coverage across omes. Here we lay the foundation for this work by eliminating existing
shortcomings in proteomics and lipidomic technologies and commence work on an integrated multi-omic platform
that allows for simultaneous analysis of peptides, lipids, and metabolites. First, work by us and others has
demonstrated that with the latest high MS/MS sampling rates, the primary limitation preventing deeper single-
shot proteome analysis is peptide separations. Second, we have identified two primary obstacles limiting
lipidomic analyses: (1) Poor separation of isomeric and isobaric lipid species, which often co-elute such that the
number of unique lipid species and their quantities cannot be determined. (2) MS/MS bandwidth is limited and
often consumed by redundant sampling of in-source fragments, adducts, and dimers. Finally, presently, most
multi-omic studies split biological samples so that each omic analysis is conducted separately – from sample
preparation to data analysis – by different people with different MS systems. This fragmentation results in
substantial hands-on sample preparation labor and instrument acquisition time. Sample-to-sample
heterogeneity, independent sample handling, and instrument-to-instrument variance all contribute noise, thereby
obscuring otherwise strong biomolecular associations.

## Key facts

- **NIH application ID:** 10089072
- **Project number:** 2P41GM108538-06
- **Recipient organization:** UNIVERSITY OF WISCONSIN-MADISON
- **Principal Investigator:** Joshua J Coon
- **Activity code:** P41 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $355,628
- **Award type:** 2
- **Project period:** 2016-07-05 → 2026-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10089072

## Citation

> US National Institutes of Health, RePORTER application 10089072, National Center for Quantitative Biology of Complex Systems (2P41GM108538-06). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10089072. Licensed CC0.

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