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Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry (UPLC-MS)
Published on: March 14, 2013
Protocol for untargeted LC-MS/MS metabolomics annotation and differential abundance analysis using the SIRIUS Python
Jonas A Emmert1, Sebastian Böcker2, Markus Fleischauer3
1Chair for Bioinformatics, Institute for Computer Science, Friedrich Schiller University Jena, Hans-Knöll-Straße 6, 07745 Jena, Thuringia, Germany; International Max Planck Research School "Chemical Communication in Ecological Systems", Max Planck Institute for Chemical Ecology, Hans-Knöll-Straße 6, 07745 Jena, Thuringia, Germany.
This study introduces PySirius for untargeted metabolomics, enabling automated data analysis. It confirms higher rosmarinic acid levels in older rosemary leaves, demonstrating the protocol
Area of Science:
- Computational chemistry and bioinformatics
- Plant metabolomics and natural products research
Background:
- Untargeted liquid chromatography-tandem mass spectrometry (LC-MS/MS) is crucial for comprehensive metabolomic profiling.
- Automated and programmatic analysis of LC-MS/MS data remains a challenge, hindering large-scale studies.
- Accurate feature annotation, including molecular formulas and compound classes, is essential for biological interpretation.
Purpose of the Study:
- To present a novel protocol for programmatic untargeted LC-MS/MS metabolomics analysis using PySirius.
- To detail the steps for data import, feature alignment, and annotation.
- To demonstrate differential abundance analysis and result visualization.
Main Methods:
- Utilized PySirius, a Python client for the SIRIUS software, for automated metabolomic data processing.
- Implemented LC-MS feature alignment for consistent data handling across samples.
- Performed annotation of detected features to determine molecular formulas, structures, and compound classes.
- Conducted differential abundance analysis based on fold change between experimental groups.
- Employed visualization techniques to present analytical results.
Main Results:
- Successfully established a programmatic workflow for untargeted LC-MS/MS metabolomics.
- Validated the protocol by replicating the known finding of increased rosmarinic acid abundance in older rosemary leaves compared to younger ones.
- Demonstrated the capability of PySirius to annotate features and analyze differential compound levels.
Conclusions:
- PySirius provides a powerful and accessible tool for automating untargeted LC-MS/MS metabolomics workflows.
- The protocol facilitates robust feature annotation and differential abundance analysis, aiding biological discovery.
- This approach can be applied to various biological systems for comprehensive metabolomic investigations.
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