# National Center for Quantitative Biology of Complex Systems

> **NIH NIH P41** · UNIVERSITY OF WISCONSIN-MADISON · 2024 · $172,791

## Abstract

PROJECT SUMMARY – TR&D 2
Processing multiple samples in parallel in a single MS analysis – i.e., sample multiplexing – has become a
preferred method for the application of MS-based systems biology approaches to translational medicine thanks
to numerous key advantages. First, parallel processing of samples in a single mass spectrometric analysis
reduces the amount of material required from any one sample. Second, multiplexing reduces sample preparation
and instrument analysis time requirements. Third, highly plexed analyses facilitate collection of biological
replicate data. Fourth, the ability to combine samples into a single analysis permits fewer MS experiments, which
in turn allows for greater data overlap across all conditions. In this TR&D, we will develop and apply a new class
of isobaric tags for protein and protein post-translational modifications (PTMs) quantitation. Specifically, the tags
will be built upon an amine-reactive N,N-dialkylated amino acid-based scaffold that is compatible with electron
transfer dissociation (ETD). The ultimate goal of this aim is to determine how to most effectively combine the
efficiencies of multiplexed quantification with the advantages of ETD for labile PTM identification. Second, we
will develop and apply chemical tags for multiplexed quantification with data independent acquisition (DIA). To
date, only metabolic labels have been successful in multiplexing analysis for DIA. Here we will continue to
develop our mass defect-based chemical tag, amine-reactive dimethyl pyrimidinyl ornithine (DiPyrO) tags, to
facilitate multiplexed quantitative DIA applications. These tags will offer up to 10-plex DIA quantification, bringing
multiplexed capacity for this emergent technology. Third, we will improve MS hardware for isobaric tag
technology. In particular, we will use gas-phase ion/ion reactions to purify the target analyte and remove
contaminants, thereby improving the quantitative accuracy of isobaric tagging. And, to improve precision, we will
leverage infrared photo-activation to completely release all reporter tags, thereby boosting signal.

## Key facts

- **NIH application ID:** 10878853
- **Project number:** 5P41GM108538-09
- **Recipient organization:** UNIVERSITY OF WISCONSIN-MADISON
- **Principal Investigator:** LINGJUN LI
- **Activity code:** P41 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $172,791
- **Award type:** 5
- **Project period:** 2016-07-05 → 2026-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10878853

## Citation

> US National Institutes of Health, RePORTER application 10878853, National Center for Quantitative Biology of Complex Systems (5P41GM108538-09). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10878853. Licensed CC0.

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