Agilent MassHunter Mass Profiler Software - Familiarization Guide

Importance of the topic

The ability to extract, align, and compare molecular features from complex mass spectrometry datasets is essential for metabolomics, quality control, and biomarker discovery. Automated software workflows significantly reduce manual effort, increase reproducibility, and enable rigorous statistical analysis across large sample cohorts.

Objectives and Overview

This guide describes the main workflows of Agilent MassHunter Mass Profiler Software. It covers project creation, method definition, feature finding and alignment, normalization, statistical filtering, multivariate analysis (PCA), compound identification, result visualization, and data export. Practical examples demonstrate two-sample and group comparisons using LC/MS and LC/IM-MS data.

Methodology and Instrumentation

MassProfiler supports input from GC/MS, LC/MS, and LC/ion mobility-MS (.d raw files, .cef exchange files, and generic .csv). Key algorithmic steps include:

• Feature finding with Molecular Feature Extractor (MFE) or Ion Mobility Feature Extractor (IMFE), isolating isotope and adduct clusters
• Retention time and mass alignment across samples with user-defined tolerances
• Abundance normalization and RT correction using internal standards or global shifts
• Statistical filters (Q-score, frequency, fold-change, significance tests)

Instrument data are processed entirely within MassHunter; identification employs the ID Browser module using accurate mass and optional formula generation against PCDL databases.

Main Results and Discussion

Example workflows show initial feature lists of up to 42 000 entities, refined to under 30 by applying stringent Q-score, occurrence, and fold-change filters. PCA distinguishes control and experiment groups with >90 % variance explained in the first components. Subsequent compound identification confirms sulfa drug spiking in tea extracts, validating the untargeted pipeline.

Benefits and Practical Applications

• High-throughput untargeted profiling for discovery studies
• Batch alignment and normalization for quality assurance and QC
• Integrated PCA for rapid sample grouping and outlier detection
• Seamless export of feature summaries, inclusion lists, and CEF files for downstream analysis or targeted MS/MS acquisition

Future Trends and Opportunities

Advances may include cloud-based processing, machine learning-driven feature annotation, real-time data feedback in acquisition, integration with multi-omics platforms, and expanded libraries for improved compound coverage.

Conclusion

Agilent MassHunter Mass Profiler streamlines complex MS data processing by combining feature extraction, alignment, statistics, multivariate analysis, and identification in a unified environment. Its flexible workflows and export options support diverse analytical goals from biomarker discovery to routine QC.

References

No additional literature references were provided in the source document.