QC4Metabolomics: Real-Time and Retrospective Quality Control of Metabolomics Data

Area of Science:

• Metabolomics
• Analytical Chemistry
• Bioinformatics

Background:

• High-quality data is crucial for answering complex biological questions in metabolomics.
• Liquid chromatography-mass spectrometry (LC-MS) data acquisition is complex, often leading to quality control (QC) issues like m/z calibration loss, retention time drift, and ion suppression.
• Current QC practices are often post-processing, which is too late to correct analytical problems, leading to compromised data and wasted resources.

Purpose of the Study:

• To introduce QC4Metabolomics, a novel software for real-time quality control monitoring of untargeted metabolomics data.
• To provide a tool that can identify and flag analytical problems during or immediately after data acquisition.
• To enable immediate mitigation of issues, thereby improving data quality and reducing the need for reanalysis.

Main Methods:

• QC4Metabolomics monitors data files during or retrospectively.
• It tracks user-defined compounds, extracting diagnostic information including observed m/z, retention time, intensity, and peak shape.
• The software also monitors common or user-defined contaminants and presents results via a web dashboard.

Main Results:

• Real-world examples demonstrate QC4Metabolomics' ability to retrospectively identify analytical problems.
• These issues, if monitored in real-time, could have been immediately addressed, preventing compromised sample analysis.
• The software facilitates immediate detection of issues like m/z calibration loss and retention time drift.

Conclusions:

• QC4Metabolomics provides essential real-time quality control for untargeted metabolomics data acquisition.
• Implementing this software can significantly improve data quality and reliability in metabolomics studies.
• The open-source availability and easy deployment options (Docker) promote widespread adoption and accessibility.