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Updated: Dec 24, 2025

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

Published on: May 27, 2014

Development of a high-coverage metabolome relative quantitative method for large-scale sample analysis.

Zhi Zhou¹, Yanhua Chen¹, Yang Gao²

¹State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, 100050, PR China; Center for Bioimaging & System Biology, Minzu University of China, Beijing, 100081, PR China.

Analytica Chimica Acta

|April 8, 2020

Summary

This study introduces an improved quantitative metabolomics method for analyzing large sample sets, successfully quantifying over 1000 metabolites in human plasma and identifying 26 lung cancer biomarkers.

Keywords:

High-coverage analysis Large-scale sample size Liquid chromatography-tandem mass spectrometry Relative quantitative metabolomics

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Untargeted Metabolomics from Biological Sources Using Ultraperformance Liquid Chromatography-High Resolution Mass Spectrometry UPLC-HRMS

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Last Updated: Dec 24, 2025

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

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Published on: May 27, 2014

Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry UPLC-MS

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Published on: March 14, 2013

Untargeted Metabolomics from Biological Sources Using Ultraperformance Liquid Chromatography-High Resolution Mass Spectrometry UPLC-HRMS

Untargeted Metabolomics from Biological Sources Using Ultraperformance Liquid Chromatography-High Resolution Mass Spectrometry UPLC-HRMS

Published on: May 20, 2013

Area of Science:

Metabolomics
Biomarker Discovery
Translational Research

Background:

Quantitative metabolomics is crucial for standardized and translational applications.
Existing methods struggle with large sample sizes and comprehensive metabolite quantification.
Data-independent targeted quantitative metabolomics (DITQM) offers ion pair information for 1324 metabolites.

Purpose of the Study:

To develop an enhanced quantitative metabolomics approach for large-scale studies.
To improve metabolite quantification coverage and data handling efficiency.
To identify potential biomarkers for lung cancer detection.

Main Methods:

Developed scheduled multiple reaction monitoring (MRM) methods for high- and low-abundant metabolites based on DITQM.
Created an open-source program "Quanter_1.0" for efficient data processing.
Applied the method to a large-scale lung cancer study across three analytical batches.

Main Results:

Successfully quantified 1015 metabolites in human plasma with effective relative determination.
Demonstrated improved data quality with reduced intra- and inter-batch variation in quantitative metabolomics.
Identified 26 potential lung cancer biomarkers from the large-scale study.
Achieved a more effective multivariate statistical model due to enhanced data quality.

Conclusions:

The developed quantitative metabolomics approach enables effective analysis of large sample sizes.
The method improves data quality and reduces variability for robust statistical modeling.
This approach is a promising tool for large-scale metabolomics research and clinical applications, including biomarker discovery.