Spectrally accurate quantitative analysis of isotope‐labeled compounds.
Published In: Rapid Communications in Mass Spectrometry: RCM, 2025, v. 39. P. 1 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Kuehl, Don; Wang, Yongdong; Wang, Peter L.; Zhou, Dawei 3 of 3
Abstract
Calculated profile mode mass spectrometry (MS) data are fitted to lineshape‐calibrated liquid chromatography LC/MS data using a Multiple Linear Regression (MLR) model to quantitate the relative concentrations of stable or radiolabeled compound mixtures. This alternative approach significantly improves the precision and accuracy over existing MS methods while providing the much‐needed statistical diagnostics on the goodness‐of‐fit model and thus reliability of the quantitative results obtained. Test compound data containing S/Cl atoms have been measured with either stable deuterium labeling or radioisotope uniform 14C labeling onto an aromatic ring. Since the entire relative distribution of variously labeled compounds is automatically obtained through this approach, it is feasible to directly calculate the Specific Activity (SA) from such mass spectral analysis without radioactivity detection and the usual standard curve quantitation. The applicability of this approach to systematically and accurately accommodate and account for incomplete labeling chemistry or other impurities is also discussed, with wide‐ranging implications including metabolic flux, HDX (Hydrogen/Deuterium Exchange), and quantitative proteomics. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Rapid Communications in Mass Spectrometry: RCM. 2025/05, Vol. 39, p1
- Document Type:Article
- Subject Area:Chemistry
- Publication Date:2025
- ISSN:0951-4198
- DOI:10.1002/rcm.9103
- Accession Number:185068390
- Copyright Statement:Copyright of Rapid Communications in Mass Spectrometry: RCM is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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