Routine Targeted Metabolomic Panel Analysis from Untargeted Acquisition of Differing Mouse Plasma Populations

Importance of the Topic

Data independent acquisition (DIA) in metabolomics streamlines analysis by combining the depth of untargeted screening with the precision of targeted workflows. Applying such approaches to mouse plasma supports biomarker discovery, pathway elucidation, and high‐throughput profiling in preclinical and industrial settings.

Objectives and Study Overview

This work demonstrates an end‐to‐end routine metabolomics workflow using untargeted DIA data acquired on an Agilent Revident LC/Q‐TOF paired with a 1290 Bio HPLC. By analyzing 20 male and 20 female mouse plasma samples, the study aims to:

• Evaluate the feasibility of retroactive targeted panel analysis from untargeted data.
• Assess the performance of the All Ions acquisition method for metabolite identification.
• Compare metabolite profiles across different mouse populations.

Methodology and Instrumentation

Mouse plasma (20 µL) was processed via Captiva EMR-Lipid plates on an automated liquid handler. Four heavy‐labeled internal standards and pooled QCs were interspersed. Chromatography employed an Agilent Poroshell 120 HILIC-Z column (2.1 × 150 mm, 2.7 µm) at 15 °C with a 20 mM ammonium acetate (pH 9.3) plus medronic acid (5 µM) aqueous phase and acetonitrile gradient (0.4 mL/min). The Revident LC/Q-TOF operated with All Ions fragmentation at 0, 10, and 40 V. Data were processed in MassHunter Quantitative Analysis 12.1 using the LC Screener Tool against curated positive (377 compounds) and negative (446 compounds) mode libraries (522 metabolites total).

Main Results and Discussion

The platform achieved:

• A dynamic range spanning four orders of magnitude with coeluting metabolites.
• Mass accuracy within ±5 ppm (average −0.35 ppm) over 225 injections across seven days without recalibration.
• Retention time RSD of 0.81% and QC area RSD averaging 14% (101 compounds under 20% RSD).

Screening flagged 128 compounds as present (93 average mass match score), 101 as possibly present, and 294 absent. Statistical comparison identified 12 metabolites differing >2-fold (p < 0.05) between male and female groups. The combination of All Ions acquisition and LC Screener Tool enabled confident metabolite detection and rapid library mining.

Benefits and Practical Applications

• Retroactive targeted analysis from a single untargeted dataset, removing the need for predefined analyte lists.
• Robust metabolite identification supported by fragment coelution, retention time, and high mass accuracy.
• High‐throughput and reproducible profiling suited for biomarker discovery, QA/QC, and pathway screening.

Future Trends and Opportunities

Advances in DIA strategies and library sharing are expected to expand coverage and confidence in metabolite annotation. Integration with third‐party spectral databases, machine‐learning for feature extraction, and automated statistical workflows will further streamline data interpretation and accelerate applications in precision medicine, toxicology, and systems biology.

Conclusion

The described workflow unifies untargeted acquisition with targeted screening, delivering a flexible, high‐resolution method for routine metabolomic profiling in complex biological matrices. Its combination of dynamic range, mass accuracy, and software tools supports reproducible, comprehensive analysis and comparative studies across sample cohorts.

References

• Yannell KE et al. An End-to-End Targeted Metabolomics Workflow. Agilent Application Note 5994-5628EN, 2023.
• Yannell KE et al. A Comprehensive Untargeted Metabolomics LC/Q-TOF Workflow with an Unknowns Identification Strategy to Identify Plasma Metabolite Shifts in a Mouse Model. ASMS, 2022.