Faculty, Staff and Student Publications
Publication Date
2-4-2025
Journal
Nature Communications
Abstract
Ion suppression is a major problem in mass spectrometry (MS)-based metabolomics; it can dramatically decrease measurement accuracy, precision, and sensitivity. Here we report a method, the IROA TruQuant Workflow, that uses a stable isotope-labeled internal standard (IROA-IS) library plus companion algorithms to: 1) measure and correct for ion suppression, and 2) perform Dual MSTUS normalization of MS metabolomic data. We evaluate the method across ion chromatography (IC), hydrophilic interaction liquid chromatography (HILIC), and reversed-phase liquid chromatography (RPLC)-MS systems in both positive and negative ionization modes, with clean and unclean ion sources, and across different biological matrices. Across the broad range of conditions tested, all detected metabolites exhibit ion suppression ranging from 1% to >90% and coefficients of variation ranging from 1% to 20%, but the Workflow and companion algorithms are highly effective at nulling out that suppression and error. To demonstrate a routine application of the Workflow, we employ the Workflow to study ovarian cancer cell response to the enzyme-drug L-asparaginase (ASNase). The IROA-normalized data reveal significant alterations in peptide metabolism, which have not been reported previously. Overall, the Workflow corrects ion suppression across diverse analytical conditions and produces robust normalization of non-targeted metabolomic data.
Keywords
Metabolomics, Humans, Algorithms, Asparaginase, Mass Spectrometry, Ovarian Neoplasms, Cell Line, Tumor, Female, Chromatography, Reverse-Phase, Chromatography, Liquid, Ions, Isotope Labeling, Metabolomics, Metabolomics, Metabolomics
DOI
10.1038/s41467-025-56646-8
PMID
39905052
PMCID
PMC11794426
PubMedCentral® Posted Date
2-4-2025
PubMedCentral® Full Text Version
Post-print
Published Open-Access
yes
Included in
Biochemical Phenomena, Metabolism, and Nutrition Commons, Bioinformatics Commons, Biomedical Informatics Commons, Oncology Commons
