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Bago: A Self-optimizing Tool For Lc-ms Gradient Design In Metabolomics.

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Bago: A Self-optimizing Tool For Lc-ms Gradient Design In Metabolomics.

Related Experiment Video

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

Published on: May 27, 2014

BAGO: A Self-Optimizing Tool for LC-MS Gradient Design in Metabolomics.

Huaxu Yu¹, Puja Biswas², Elizabeth Rideout²

¹Zhejiang Provincial Key Laboratory of Pancreatic Disease,The First Affiliated Hospital, Zhejiang University School of Medicine,Hangzhou310003,China.

Analytical Chemistry

|June 15, 2026

View abstract on PubMed

Summary

This summary is machine-generated.

We developed BAGO, a self-optimizing framework for automated liquid chromatography (LC) gradient design in untargeted metabolomics. This data-driven approach enhances metabolite detection by improving compound separation and identification within just 10 iterations.

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Area of Science:

Analytical Chemistry
Metabolomics
Chromatography

Background:

Automated analytical method design is crucial for advancing metabolomics.
Designing optimal liquid chromatography (LC) gradients is complex, hindering untargeted metabolomics.
Current methods struggle to achieve comprehensive separation of all metabolites, known and unknown.

Purpose of the Study:

To develop a self-optimizing framework, BAGO, for automated LC gradient design in mass spectrometry-based untargeted metabolomics.
To enhance global metabolite detection by improving the separation of all compounds.
To enable robust and structure-agnostic optimization across diverse sample types.

Main Methods:

Implemented a data-driven Bayesian optimization process for iterative gradient improvement.

Proposed a global separation index to quantify coelution for annotated and unannotated features.

Benchmarked BAGO across four metabolomics assays with diverse sample matrices and conditions.

Main Results:

BAGO achieved substantial improvements in LC gradient design within 10 optimization iterations.
Optimized gradients increased Gaussian-shaped peaks, MS/MS acquisition rates, and annotated metabolites.
Application to *Drosophila* metabolomics revealed a 41.9% increase in Gaussian peaks and 18 additional significant metabolites.

Conclusions:

BAGO represents a significant step toward fully automated, self-optimizing experimental workflows in untargeted metabolomics.
The framework enhances metabolite discovery and identification through improved chromatographic separation.
BAGO is available as an open-source tool, promoting broader adoption in the field.