A new platform for untargeted UHPLC-HRMS data analysis to address the time-shift problem.

Area of Science:

• Analytical Chemistry
• Biochemistry
• Computational Biology

Background:

• Accurate metabolite identification via UHPLC-HRMS is hindered by significant retention time variations between samples.
• These time shifts complicate the screening and identification of metabolites, impacting metabolomics study reliability.

Purpose of the Study:

• To develop and evaluate a novel coarse-to-refined time-shift correction methodology for UHPLC-HRMS data.
• To introduce a comprehensive data analysis platform for automated feature extraction, registration, and metabolite identification in metabolomics.

Main Methods:

• A coarse-to-refined time-shift correction strategy using multi-fragment ion metabolites as landmarks.
• Pseudo-mass spectra generation for initial coarse time-shift correction.
• Moving window-based multiple-peak alignment for refined peak alignment of extracted ion chromatograms.
• Development of a comprehensive UHPLC-HRMS data analysis platform integrating these correction methods.

Main Results:

• The proposed methodology effectively addresses retention time shifts in UHPLC-HRMS data.
• The new data analysis platform automatically extracts, registers features, and distinguishes metabolite fragment ions.
• Performance evaluation on complex datasets demonstrates the platform's capability to resolve time-shift issues.
• The platform's performance is comparable to established tools like XCMS Online, MS-DIAL, Mzmine2, and Progenesis QI.

Conclusions:

• The developed coarse-to-refined time-shift correction methodology significantly improves metabolite identification accuracy in UHPLC-HRMS.
• The new comprehensive data analysis platform offers an efficient and automated solution for metabolomics data processing.
• This approach provides a valuable tool for researchers in metabolomics, enhancing data reliability and comparability.