Quantitative Integration of Targeted and Nontargeted Metabolomics Using Multi-Stable Isotope Chemical Tagging with UHPLC-QToF MS: Application to Myeloid Leukemia Metabolism

Mass spectrometry-based metabolomics is widely used for comprehensive metabolic profiling. However, most current workflows rely on relative signal intensities, which limit comparability across experiments and prevent quantitative interpretation. This limitation arises from the difficulty in estimating analyte-specific response behavior in the absence of isotopically labeled standards. In this study, we present multistable isotope chemical tagging (MUSIC) as an isotope-resolved internal calibration framework that enables the approximation of response characteristics within a single experimental design. This approach uses multiple isotope-coded tagging reagents as internal calibration points instead of conventional internal standards, enabling the construction of internal calibration curves that account for both tagging efficiency and matrix effects. Internal calibration curves were established for amine-containing metabolites using a dilution series of tagged standard mixtures, enabling robust slope estimation. The resulting calibration framework allows accurate quantification of targeted metabolites and slope-based correction of nontargeted features through reference matching. In validation experiments involving 146 metabolites in serum, the method achieved accuracy and precision within ±15% using only two analytical runs. We further demonstrate that the framework enables the consistent recovery of fold changes across samples and supports comparative metabolic analysis without relying on compound-specific labeled standards. These results establish MUSIC not only as a chemical tagging strategy but also as a quantitative measurement framework that approximates analyte-specific response characteristics for integrated targeted and nontargeted metabolomics for amine-containing metabolites. Following validation, we applied this approach to blood samples to identify the biomarkers of myeloid leukemia.