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Updated: Jun 8, 2026

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

Published on: May 27, 2014

High-Throughput Metabolomics using 96-plex Isotope Tagging.

Michael R Armbruster¹, Scott F Grady¹, Kevin Cho²

¹Department of Chemistry and Biochemistry, Saint Louis University, 3501 Laclede Ave, St. Louis, Missouri 63103, United States.

Analytical Chemistry

|July 31, 2024

Summary

This summary is machine-generated.

New chemical tags accelerate liquid chromatography-mass spectrometry (LC-MS) metabolomics, enabling faster analysis and absolute quantitation. This method efficiently screens carboxylic acids in mammalian cells, overcoming previous workflow limitations.

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Published on: December 18, 2020

Area of Science:

Analytical Chemistry
Metabolomics
Biochemistry

Background:

Liquid chromatography-mass spectrometry (LC-MS) is a powerful metabolomics tool.
Current LC-MS workflows face challenges with long analysis times and variable quantitation.
Matrix effects and lengthy duty cycles hinder high-throughput metabolomic studies.

Purpose of the Study:

To develop novel chemical tags for improved LC-MS metabolomics.
To overcome the bottleneck of extended duty cycles in metabolomic workflows.
To enable absolute quantitation of metabolites, reducing matrix dependency.

Main Methods:

Synthesis and application of 96 unique chemical mass tags.
Utilizing LC-MS for high-throughput metabolic profiling.
Performing metabolic screening on nutrient-deprived mammalian cells.

Main Results:

Successfully developed and implemented 96 unique chemical mass tags.
Demonstrated alleviation of the metabolomic workflow bottleneck.
Achieved absolute quantitation of metabolites with reduced matrix effects.
Analyzed 288 samples in 2 hours with 24% relative standard deviation (RSD) for carboxylic acids.

Conclusions:

The novel chemical tags significantly enhance LC-MS metabolomics efficiency.
This approach allows for rapid and accurate absolute quantitation of metabolites.
The method is effective for metabolic screening in biological samples, such as mammalian cells.