Related Experiment Video
Updated: Feb 22, 2026
Identification and Quantification of Deranged Metabolites in Critically Ill Patients Using NMR-Based Metabolomics
Published on: November 29, 2024
Evaluation of intensity drift correction strategies using MetaboDrift, a normalization tool for multi-batch
Chanisa Thonusin1, Heidi B IglayReger2, Tanu Soni2
1Department of Internal Medicine, University of Michigan, 6300 Brehm Tower, 1000 Wall St., Ann Arbor, MI 48109, United States; Department of Molecular and Integrative Physiology, University of Michigan, 6300 Brehm Tower, 1000 Wall St., Ann Arbor, MI 48109, United States; Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand.
MetaboDrift is an accessible Excel tool that corrects intensity drift in mass spectrometry metabolomics data. It improves data quality and detects more biologically significant metabolites in large studies.
Area of Science:
- Biochemistry
- Analytical Chemistry
- Epidemiology
Background:
- Mass spectrometry-based metabolomics is valuable for large-scale human studies.
- Intensity drift in data acquired over time or batches challenges accurate analysis.
- Existing drift correction tools are often command-line based and inaccessible.
Purpose of the Study:
- To develop an accessible tool for evaluating and correcting intensity drift in metabolomics data.
- To provide a user-friendly solution for multi-batch liquid chromatography-mass spectrometry (LC-MS) data.
- To enable routine drift correction and batch combination in large metabolomics studies.
Main Methods:
- Developed MetaboDrift, an Excel-based tool for visual drift evaluation and correction.
- Implemented drift correction using quality control (QC) samples or QC-sample independent methods.
- Applied and validated MetaboDrift on clinical metabolomics data from a mixed-meal tolerance test (MMTT).
Main Results:
- QC sample-based drift correction significantly improved correlation with isotope-labeled internal standard normalization.
- MetaboDrift enabled the detection of additional metabolites showing significant physiological responses to the MMTT.
- The tool offers practical approaches for drift correction and batch combination, discussing fitting strategies.
Conclusions:
- MetaboDrift provides a simplified, practical approach to address intensity drift in metabolomics.
- The tool enhances the reliability and scope of findings in large-scale epidemiological metabolomics.
- Accessible software solutions are crucial for broader adoption of advanced metabolomics techniques.
