IDSL.CSA: Composite Spectra Analysis for Chemical Annotation of Untargeted Metabolomics Datasets.

Area of Science:

• Metabolomics
• Mass Spectrometry
• Bioinformatics

Background:

• Untargeted metabolomics relies on accurate chemical annotation, which is often limited by the availability of MS2 fragmentation data.
• High-resolution mass spectrometry (HRMS) coupled with liquid chromatography (LC) generates vast datasets, but their full potential is hindered by poor annotation.
• Existing methods struggle to annotate metabolites when MS2 spectra are unavailable, limiting data application.

Purpose of the Study:

• To introduce a novel R package, IDSL.CSA (Integrated Data Science Laboratory for Metabolomics and Exposomics─Composite Spectra Analysis), for enhanced chemical annotation of HRMS data.
• To enable robust metabolite identification from MS1-only data, overcoming the dependency on MS2 fragmentation spectra.
• To demonstrate the utility of IDSL.CSA for analyzing untargeted metabolomics datasets from various LC- or GC-HRMS instruments.

Main Methods:

• Development of the IDSL.CSA R package to generate and search composite mass spectra libraries from MS1 data.
• Utilizing MS1 spectral data to create comprehensive spectral libraries for chemical annotation.
• Validation of IDSL.CSA performance by comparing its annotation rates against traditional MS/MS libraries using human blood samples.

Main Results:

• IDSL.CSA achieves comparable annotation rates for endogenous metabolites in human blood samples versus MS/MS libraries.
• The software successfully generates and searches composite spectra libraries from diverse untargeted metabolomics datasets.
• Demonstrated effectiveness across different high-resolution mass spectrometry platforms coupled with liquid or gas chromatography.

Conclusions:

• IDSL.CSA significantly enhances chemical annotation capabilities for untargeted metabolomics, particularly when MS2 data is absent.
• The cross-applicability of IDSL.CSA libraries across independent studies facilitates novel biological insights.
• This tool expands the utility of existing metabolomics datasets and promotes broader scientific discovery.