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Updated: Sep 5, 2025

An Integrated Workflow of Identification and Quantification on FDR Control-Based Untargeted Metabolome

An Integrated Workflow of Identification and Quantification on FDR Control-Based Untargeted Metabolome

Published on: September 20, 2022

Enhancing untargeted metabolomics using metadata-based source annotation.

Julia M Gauglitz^1,2, Kiana A West^1,2, Wout Bittremieux^1,2

¹Collaborative Mass Spectrometry Innovation Center, University of California San Diego, La Jolla, CA, USA.

Nature Biotechnology

|July 7, 2022

Summary

This summary is machine-generated.

Human metabolomics studies can now identify more molecular features. A new reference-data-driven analysis method significantly boosts the use of tandem mass spectrometry (MS/MS) data for understanding dietary patterns.

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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
Biochemistry
Bioinformatics

Background:

Untargeted metabolomics studies in humans typically identify only a small fraction (~10%) of detected molecular features.
Accurate annotation of these features is crucial for understanding biological processes and dietary impacts.

Purpose of the Study:

To introduce and validate a novel reference-data-driven analysis approach for human metabolomics.
To enhance the utilization of tandem mass spectrometry (MS/MS) data for feature annotation.
To enable empirical assessment of dietary patterns using untargeted metabolomics data.

Main Methods:

Developed a method to match untargeted metabolomics MS/MS data against metadata-annotated source data, creating a pseudo-MS/MS reference library.
Applied this approach to analyze food source data.
Compared the performance against conventional structural MS/MS library matching.

Main Results:

The reference-data-driven approach increased MS/MS spectral usage 5.1-fold compared to conventional library matching.
Successfully enabled empirical assessment of dietary patterns from untargeted metabolomics data.
Significantly improved the annotation rate of molecular features in human metabolomics studies.

Conclusions:

Reference-data-driven analysis is a powerful strategy to improve feature annotation in human metabolomics.
This method enhances the utility of untargeted metabolomics for investigating dietary intake and patterns.
The approach offers a significant advancement over existing MS/MS library matching techniques.