Agilent G3835AA MassHunter Mass Profiler Professional Software - Familiarization Guide

Importance of the topic

Mass spectrometry–based differential analysis is essential for understanding complex biological and environmental systems. Agilent MassProfiler Professional (MPP) leverages high-resolution MS data to detect meaningful differences across sample groups, enabling robust biomarker discovery in metabolomics, proteomics, natural products, food and beverage testing, and environmental monitoring.

Objectives and study overview

This guide uses a Malaria LC/MS ESI+ pH7 demonstration project to introduce the core MPP workflow. Key objectives are:

• Create and configure a new MPP project and experiment
• Import, organize, and preprocess sample data
• Perform initial statistical analysis and fold change filtering
• Identify compounds with MassHunter ID Browser
• Archive and share results

Instrumentation used

This tutorial relies on Agilent software platforms integrated for advanced MS data analysis:

• MassHunter Mass Profiler Professional (G3835AA)
• MassHunter Workstation
• Spectrum Mill
• ChemStation

Methodology and instrumentation

The workflow consists of:

• MS Experiment Creation Wizard – grouping, filtering (abundance, mass, retention time), alignment, normalization, baselining
• Significance Testing and Fold Change Wizard – statistical filtering by p-value and fold change
• MassHunter ID Browser – compound annotation by formula, mass, and retention time

Parameter selection (eg, 0.1% RT window, 5 ppm mass window, minimum abundance 5000 counts) is guided by data quality and instrument resolution.

Key results and discussion

Using the Malaria dataset, alignment yielded 474 features common to all eight samples and 1,283 single occurrences. Filter flags removed low-reproducibility “one-hit wonders.” Significance analysis (p < 0.05, fold change ≥ 2) focused the list on statistically relevant markers. Compound identification integrated seamlessly to annotate features for downstream biological interpretation.

Benefits and practical applications

MPP offers:

• A streamlined end-to-end MS data analysis pipeline
• Integrated compound annotation and pathway analysis for metabolomics and proteomics
• Automated class prediction for unknown samples in QA/QC and research contexts

Future trends and opportunities

Emerging directions include machine learning–driven classification, cloud-based collaboration and data sharing, expanded multi-omics integration, and enrichment of pathway databases for deeper biological insights.

Conclusion

This familiarization guide provides a structured approach to leverage Agilent MPP for reproducible, high-throughput MS differential analysis. By following the stepwise workflow, users can efficiently generate, filter, and identify candidate biomarkers for diverse applications.

Reference

• Keseler et al, Nucleic Acids Research 39:D583–590 (2011)
• Caspi et al, Nucleic Acids Research 40:D742–753 (2012)