DEIMoS: An Open-Source Tool for Processing High-Dimensional Mass Spectrometry Data

Area of Science:

• Analytical Chemistry
• Computational Chemistry
• Biochemistry

Background:

• High-dimensional mass spectrometry data, such as LC-IMS-MS/MS, presents significant analytical challenges.
• Existing tools often struggle with comprehensive data integration and artifact mitigation.
• Efficient and accessible data analysis workflows are crucial for advancing metabolomics research.

Purpose of the Study:

• To introduce DEIMoS (Data Extraction for Integrated Multidimensional Spectrometry), a versatile Python API and command-line tool.
• To provide efficient algorithmic implementations for high-dimensional mass spectrometry data analysis.
• To demonstrate the advantages of a multidimensional data approach in processing complex datasets.

Main Methods:

• Development of DEIMoS, a Python application programming interface (API) and command-line tool.
• Implementation of core functionalities: feature detection, alignment, collision cross section (CCS) calibration, isotope detection, and MS/MS spectral deconvolution.
• Utilization of N-dimensional data, leveraging all acquisition dimensions simultaneously for enhanced analysis.

Main Results:

• DEIMoS successfully processes high-dimensional data, improving feature separation and detection sensitivity.
• Enhanced alignment and feature matching confidence across multiple study samples.
• Mitigation of convolution artifacts in tandem mass spectra through integrated multidimensional analysis.
• Demonstrated efficacy with LC-IMS-MS/MS metabolomics data.

Conclusions:

• DEIMoS offers a powerful and flexible solution for complex mass spectrometry data analysis.
• The tool's ability to leverage multidimensional data significantly improves analytical performance.
• DEIMoS facilitates easier development and access to efficient algorithms for researchers in metabolomics and related fields.