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Updated: May 4, 2026

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

Scout-triggered MRM from untargeted data: An R-based strategy for multiplexed targeted metabolomics.

Rémy De Boni¹, Guillaume Rossignol², Delphine Arquier¹

¹Université Lyon 1, CNRS, UMR 5280, Lyon, France.

|May 2, 2026

Summary

This summary is machine-generated.

Scout-MRM Builder automates targeted metabolomics method creation from untargeted data. This R package enhances method transferability and reliability for robust biomarker discovery and validation.

Keywords:

Liquid chromatography-mass spectrometry Liver ischemia-reperfusion Method development Scout-triggered MRM Targeted metabolomics Untargeted metabolomics

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

Published on: March 14, 2013

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Last Updated: May 4, 2026

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

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Published on: September 20, 2022

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

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

Published on: March 14, 2013

Area of Science:

Metabolomics
Analytical Chemistry
Biomarker Discovery

Background:

Transitioning from untargeted metabolomics discovery to targeted validation is challenging.
Liquid chromatography-mass spectrometry (LC-MS) methods suffer from poor transferability and retention time (RT) variation.
Automated, robust methods are needed to bridge the discovery-validation gap.

Purpose of the Study:

To present Scout-MRM Builder, an R package for automated targeted method creation from untargeted high-resolution MS2 data.
To implement a Scout-Triggered Multiple Reaction Monitoring (StMRM) strategy using N-Alkylpyridinium-3-Sulfonate (NAPS) standards as dynamic RT markers.
To enable robust and reproducible targeted metabolomics analysis with enhanced method transferability.

Main Methods:

Developed Scout-MRM Builder, an R package utilizing a StMRM strategy.
Employed NAPS standards as dynamic RT markers to trigger specific transition lists.
Automated ion pair extraction, scout identification, and generation of StMRM methods, including pseudo-MRM transitions.
Applied the method to porcine liver extracts and a liver ischemia-reperfusion injury model.

Main Results:

Generated a single StMRM method monitoring 1312 transitions from 558 untargeted features.
Achieved high reproducibility, with 89.9% of transitions showing <20% relative standard deviation (RSD).
Obtained results comparable to untargeted analysis in a liver injury model, identifying a common set of potential biomarkers.

Conclusions:

Scout-MRM Builder effectively bridges the gap between metabolomics discovery and validation.
The StMRM strategy ensures robustness against RT shifts, enhancing method reliability.
Provides a powerful framework for robust, large-scale targeted metabolomics analysis with improved transferability.