prio-PASEF: Precision and Discovery in MetID Workflows

Significance of the Topic

Metabolite identification workflows are essential for assessing drug safety and efficacy by characterizing both anticipated and unexpected biotransformation products. High-resolution LC-MS platforms have advanced discovery, yet low-abundance metabolites and coeluting species pose challenges to confident annotation. Incorporating ion mobility into data acquisition enhances separation, spectral clarity, and provides collisional cross section measurements for robust structural confirmation.

Objectives and Study Overview

This study introduces prio-PASEF, a novel data acquisition strategy on the Bruker timsTOF platform which schedules prioritized MS/MS events for predicted metabolites while using remaining instrument duty cycle for untargeted data-dependent acquisition. The workflow aims to ensure sensitive detection and high-confidence identification of both known low-abundance metabolites and unexpected transformation products in a single analytical run.

Methodology

Human cryopreserved hepatocytes were incubated with testosterone across eight time points with three biological replicates. Metabolite extracts were analyzed by LC-TIMS-MS using both prio-PASEF and standard dda-PASEF modes. Prio-PASEF leverages a Python API script and timsControl to schedule precursors of interest for fragmentation at optimal retention time apexes. Untargeted DDA fills unscheduled windows, while trapped ion mobility adds an orthogonal separation dimension.

Used Instrumentation

• Bruker timsTOF mass spectrometer with Parallel Accumulation Serial Fragmentation (PASEF)
• Ultrahigh-performance liquid chromatography combined with trapped ion mobility spectrometry (LC-TIMS-MS)
• Python-based APIo script and timsControl software for priority list scheduling
• MetaboScape 2025 software for 4D feature detection and spectral library annotation

Main Results and Discussion

• Precise precursor selection: prio-PASEF selected testosterone precursor seven times at the signal apex versus two selections by standard dda-PASEF, yielding cleaner and more abundant MS/MS spectra.
• Low-abundance detection: testosterone glucuronide, poorly captured by standard DDA, was robustly fragmented in prio-PASEF, confirming identity by accurate mass, retention time, mobility, and diagnostic fragments.
• Comprehensive coverage: untargeted DDA in remaining cycle identified over 480 features with 310 unique MS/MS spectra, enabling discovery of unexpected metabolites.

Benefits and Practical Applications

• Prioritized fragmentation of known targets improves sensitivity for low-abundance metabolites.
• Simultaneous untargeted acquisition maximizes discovery of novel or unexpected compounds.
• Trapped ion mobility separation delivers an extra dimension for spectral clarity and CCS-based confirmation.
• Streamlined workflow on a single run enhances throughput and data richness for drug metabolism studies.

Future Trends and Potential Applications

• Integration with larger and dynamic priority lists for high-throughput screening.
• Enhanced spectral library matching and automated CCS libraries for automated annotation.
• Real-time adaptive acquisition adjusting priorities based on live data feedback.
• Extension of prio-PASEF to diverse matrices and therapeutic classes for broader metabolite profiling.

Conclusion

Prio-PASEF represents an innovative MetID workflow that combines targeted prioritization and untargeted discovery in a single LC-TIMS-MS analysis. By ensuring efficient fragmentation of known metabolites and comprehensive feature detection, this approach enhances confidence, sensitivity, and throughput in drug metabolism research.