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A Strategy for Sensitive, Large Scale Quantitative Metabolomics
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LC-MS metabolomics from study design to data-analysis - using a versatile pathogen as a test case.

Maya Berg1, Manu Vanaerschot1, Andris Jankevics2

  • 1Unit of Molecular Parasitology, Department of Biomedical Sciences, Institute of Tropical Medicine, Nationalestraat 155, 2000 Antwerp, Belgium.

Computational and Structural Biotechnology Journal
|April 2, 2014
PubMed
Summary
This summary is machine-generated.

Liquid chromatography-mass spectrometry (LC-MS) metabolomics effectively distinguishes organism responses to stimuli. This review details the LC-MS pipeline using Leishmania donovani, highlighting data analysis and pitfalls for systems biology integration.

Keywords:
HILICLeishmaniaglobal molecular profilesmass spectrometrysystems biologyunicellular trypanosomatid parasites

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Area of Science:

  • Biochemistry
  • Analytical Chemistry
  • Systems Biology

Background:

  • Liquid chromatography-mass spectrometry (LC-MS) is a powerful technique for analyzing metabolites.
  • Metabolomics enables the study of organismal responses to external factors like drugs and stimuli.
  • Leishmania donovani serves as a versatile model organism for complex biological investigations.

Purpose of the Study:

  • To provide a comprehensive overview of the LC-MS metabolomics pipeline.
  • To illustrate the application of LC-MS metabolomics using Leishmania donovani.
  • To discuss data analysis strategies and potential pitfalls in LC-MS data processing.

Main Methods:

  • Detailed examination of the experimental workflow from sample collection to data generation.
  • Utilizing hyphenated mass spectrometry platforms for enhanced analytical capabilities.
  • Application of state-of-the-art data-analysis software for meaningful result interpretation.

Main Results:

  • Significant improvements in LC-MS technology enhance its utility in biological research.
  • A structured LC-MS pipeline is crucial for reliable discrimination of biological responses.
  • Identification of key considerations for successful LC-MS data acquisition and analysis.

Conclusions:

  • LC-MS metabolomics is an indispensable tool for understanding organismal responses.
  • Careful pipeline management and data analysis are essential for robust findings.
  • Integrating metabolomics into systems biology approaches offers deeper biological insights.