Untargeted Multiple Reaction Monitoring

Multiple reaction monitoring (MRM) enables robust and sensitive quantification but traditionally requires predefined precursor–fragment transitions, limiting its use in discovery-driven studies. Here, we describe untargeted/micro/universal multiple reaction monitoring (uMRM), a workflow that converts high-resolution untargeted liquid chromatography–mass spectrometry/MS (LC–MS/MS) data into scheduled triple-quadrupole MRM transitions. Pooled-sample LC–MS and stepped-energy DDA MS/MS acquisitions (0, 10, 20, and 40 eV) are used to capture precursor and fragment information representative of each experimental set. Detected features undergo automated deisotoping and empirically validated in-source fragment filtering, followed by spline-based modeling of collision-energy–dependent fragmentation to define optimized precursor–fragment transitions. Transitions are scheduled using retention times observed in pooled samples and deployed on triple-quadrupole instruments without requiring nonlinear retention-time alignment or authentic standards. Across representative biological matrices, including urine, brain tissue, and cultured cells, uMRM enabled automated generation of quantitative MRM methods from untargeted discovery data. Benchmarking across seven triple-quadrupole platforms demonstrated strong agreement between uMRM-derived and experimentally optimized collision energies. By converting discovery-scale data sets into compact transition tables suitable for quantitative deployment, uMRM provides a reproducible approach for linking untargeted LC–MS/MS acquisition with targeted quantitation.