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Related Experiment Video

Updated: Nov 1, 2025

Multi-step Preparation Technique to Recover Multiple Metabolite Compound Classes for In-depth and Informative Metabolomic Analysis

Multi-step Preparation Technique to Recover Multiple Metabolite Compound Classes for In-depth and Informative Metabolomic Analysis

Published on: July 11, 2014

Long-Term Metabolomics Reference Material.

Goncalo J Gouveia^1,2, Amanda O Shaver^3,2, Brianna M Garcia^4,2

¹Department of Biochemistry & Molecular Biology, University of Georgia, Green Street, Athens, Georgia 30602, United States.

Analytical Chemistry

|June 22, 2021

Summary

This summary is machine-generated.

A new method, iterative batch averaging (IBAT), creates stable, long-term reference materials (RMs) for metabolomics. This quality control advancement improves data reproducibility and enables the development of biologically relevant RMs for wider scientific use.

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Area of Science:

Metabolomics
Biotechnology
Analytical Chemistry

Background:

Quality control samples are crucial for ensuring data quality, reproducibility, and comparability in metabolomics research.
The limited availability of matrix-matched reference materials (RMs) hinders widespread application of quality assurance/quality control (QA/QC) systems.
There is a significant need within the metabolomics community for accessible and biologically relevant RMs.

Purpose of the Study:

To develop a robust method for producing stable and sustainable reference materials (RMs) applicable to various matrices.
To demonstrate the efficacy of the iterative batch averaging (IBAT) method in creating RMs.
To assess the impact of preanalytical steps on sample variation in metabolomics.

Main Methods:

Developed the iterative batch averaging (IBAT) method for RM production.
Generated 11 independently grown *Escherichia coli* batches and performed 10 IBAT iterations to create an *E. coli* RM.
Used the *E. coli* RM as a food source to produce a *Caenorhabditis elegans* RM and analyzed metabolite variance using nuclear magnetic resonance (NMR).

Main Results:

The IBAT method successfully produced a stable and sustainable *E. coli* RM over time, as confirmed by NMR analysis.
Analysis of *C. elegans* samples revealed that 40% of metabolite variance was attributable to extraction and analysis, while 60% was due to sample-to-sample variation.
The developed RMs demonstrated the utility of IBAT in creating biologically relevant materials.

Conclusions:

The iterative batch averaging (IBAT) method provides a robust solution for producing stable and sustainable reference materials (RMs) for metabolomics.
IBAT facilitates the creation of biologically relevant RMs, addressing a key need in the metabolomics community.
This method enhances the widespread use of RMs, improving data quality and comparability across studies and laboratories.