Ion suppression correction and normalization for non-targeted metabolomics
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 co...
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Nature Portfolio
2025-02-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-56646-8 |
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| author | Iqbal Mahmud Bo Wei Lucas Veillon Lin Tan Sara Martinez Bao Tran Alexander Raskind Felice de Jong Yiwei Liu Jibin Ding Yun Xiong Wai-kin Chan Rehan Akbani John N. Weinstein Chris Beecher Philip L. Lorenzi |
| author_facet | Iqbal Mahmud Bo Wei Lucas Veillon Lin Tan Sara Martinez Bao Tran Alexander Raskind Felice de Jong Yiwei Liu Jibin Ding Yun Xiong Wai-kin Chan Rehan Akbani John N. Weinstein Chris Beecher Philip L. Lorenzi |
| author_sort | Iqbal Mahmud |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-28c36c3b3bf740a880ec97af35df87e2 |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-28c36c3b3bf740a880ec97af35df87e22025-02-09T12:44:15ZengNature PortfolioNature Communications2041-17232025-02-0116111410.1038/s41467-025-56646-8Ion suppression correction and normalization for non-targeted metabolomicsIqbal Mahmud0Bo Wei1Lucas Veillon2Lin Tan3Sara Martinez4Bao Tran5Alexander Raskind6Felice de Jong7Yiwei Liu8Jibin Ding9Yun Xiong10Wai-kin Chan11Rehan Akbani12John N. Weinstein13Chris Beecher14Philip L. Lorenzi15Metabolomics Core Facility, Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center (MDACC)Metabolomics Core Facility, Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center (MDACC)Metabolomics Core Facility, Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center (MDACC)Metabolomics Core Facility, Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center (MDACC)Metabolomics Core Facility, Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center (MDACC)Metabolomics Core Facility, Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center (MDACC)IROA TechnologiesIROA TechnologiesMetabolomics Core Facility, Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center (MDACC)Metabolomics Core Facility, Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center (MDACC)Metabolomics Core Facility, Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center (MDACC)Metabolomics Core Facility, Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center (MDACC)Metabolomics Core Facility, Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center (MDACC)Metabolomics Core Facility, Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center (MDACC)IROA TechnologiesMetabolomics Core Facility, Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center (MDACC)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.https://doi.org/10.1038/s41467-025-56646-8 |
| spellingShingle | Iqbal Mahmud Bo Wei Lucas Veillon Lin Tan Sara Martinez Bao Tran Alexander Raskind Felice de Jong Yiwei Liu Jibin Ding Yun Xiong Wai-kin Chan Rehan Akbani John N. Weinstein Chris Beecher Philip L. Lorenzi Ion suppression correction and normalization for non-targeted metabolomics Nature Communications |
| title | Ion suppression correction and normalization for non-targeted metabolomics |
| title_full | Ion suppression correction and normalization for non-targeted metabolomics |
| title_fullStr | Ion suppression correction and normalization for non-targeted metabolomics |
| title_full_unstemmed | Ion suppression correction and normalization for non-targeted metabolomics |
| title_short | Ion suppression correction and normalization for non-targeted metabolomics |
| title_sort | ion suppression correction and normalization for non targeted metabolomics |
| url | https://doi.org/10.1038/s41467-025-56646-8 |
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