Tidy-Direct-to-MS: An Open-Source Data-Processing Pipeline for Direct Mass Spectrometry-Based Metabolomics...

Area of Science:

• Analytical Chemistry
• Biochemistry
• Computational Biology

Background:

• Direct-to-Mass Spectrometry (MS) and ambient ionization techniques offer rapid biochemical fingerprinting.
• Current data processing relies heavily on vendor-specific software, limiting flexibility and accessibility.
• Untargeted metabolomics studies require robust and user-friendly data analysis tools.

Purpose of the Study:

• To develop and introduce Tidy-Direct-to-MS, a novel open-source software for processing direct-to-MS data.
• To provide a user-friendly platform for biochemical fingerprinting and metabolomics data analysis.
• To enhance the efficiency and accuracy of data processing in ambient MS-based studies.

Main Methods:

• Development of Tidy-Direct-to-MS as a Python-based library (part of TidyMS).
• Implementation of modules for sample position detection, m/z drift correction, consensus spectra calculation, and feature abundance determination.
• Automated parameter optimization for maximizing feature detection and validating reference features.

Main Results:

• Tidy-Direct-to-MS enables fast and user-friendly processing of direct-to-MS datasets.
• The tool facilitates automated comparison of parameters to optimize feature detection.
• Demonstrated utility in a marine metabolomics study using transmission mode Direct Analysis in Real Time-Mass Spectrometry (TM-DART-MS).

Conclusions:

• Tidy-Direct-to-MS simplifies and enhances the workflow for untargeted ambient MS-based metabolomics.
• The open-source nature of the tool allows for easy extension and integration.
• This functionality aids in data quality review and subsequent analysis, improving research reproducibility.