Ion suppression correction and normalization for non-targeted metabolomics

Iqbal Mahmud, Bo Wei, Lucas Veillon, Lin Tan, Sara Martinez, Bao Tran, Alexander Raskind, Felice Jong, Yiwei Liu, Jibin Ding, Yun Xiong, Wai-kin Chan, Rehan Akbani, John N. Weinstein, Chris Beecher and Philip L. Lorenzi ()
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Iqbal Mahmud: The University of Texas MD Anderson Cancer Center (MDACC)
Bo Wei: The University of Texas MD Anderson Cancer Center (MDACC)
Lucas Veillon: The University of Texas MD Anderson Cancer Center (MDACC)
Lin Tan: The University of Texas MD Anderson Cancer Center (MDACC)
Sara Martinez: The University of Texas MD Anderson Cancer Center (MDACC)
Bao Tran: The University of Texas MD Anderson Cancer Center (MDACC)
Alexander Raskind: IROA Technologies
Felice Jong: IROA Technologies
Yiwei Liu: The University of Texas MD Anderson Cancer Center (MDACC)
Jibin Ding: The University of Texas MD Anderson Cancer Center (MDACC)
Yun Xiong: The University of Texas MD Anderson Cancer Center (MDACC)
Wai-kin Chan: The University of Texas MD Anderson Cancer Center (MDACC)
Rehan Akbani: The University of Texas MD Anderson Cancer Center (MDACC)
John N. Weinstein: The University of Texas MD Anderson Cancer Center (MDACC)
Chris Beecher: IROA Technologies
Philip L. Lorenzi: The University of Texas MD Anderson Cancer Center (MDACC)

Nature Communications, 2025, vol. 16, issue 1, 1-14

Abstract: 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.

Date: 2025
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DOI: 10.1038/s41467-025-56646-8

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