Experimental Design and Workflow for MS-Based Metabolomics and Lipidomics Research (Experimental Design and Workflow for MS-Based Metabolomics and Lipidomics Research)
Owner/Developer: Agilent Technologies
United States of America
28 October 2014
United States of America
The field of Metabolomics is a hypotheses driven scientific “omics” field, that utilizes multivariate statistical analysis software tools to determine how statistically significant endogenous metabolites (or Lipids in the area of Lipidomics), change as a function of phenotype, disease state and/or condition. A typical metabolomics workflow includes many steps starting from Sample Collection/Storage, Sample preparation (protein precipitation/metabolite extraction), separation and detection using both gas phase (GC/MS) and liquid phase chromatography (LC/MS), data mining (unsupervised/supervised), statistical analysis of samples, identification of compounds, and determining if the observed changes match the proposed hypothesis when possible linked to a change in a biochemical pathway. The diverse nature of biological samples (biodiversity/population) adds an additional challenge to these experimental studies, in that the concentration of metabolites can vary greatly from biological sample to sample.
Optional / Voluntary
1 h 11 min
Students, Researchers, Regulators and policy-makers, Teachers and educators, Technicians, Managers, Scientific officers / Project managers, Professionals (e.g. veterinarians), General public
Academia, Industry, Governmental bodies, Contract Research Organizations (CROs), Consulting, SMEs
Continuing Professional Development
Designing procedures and projects, In vitro methods
Full coverage (a dedicated course)
|Details on the topic or technology covered:||
Design of Experiment (DOE) methods described in this presentation are utilized to measure and reduce system/measurement variability and help determine the number of samples required for a particular study. The presentation will discuss how one can reduce system variability and provide confidence in the measurements and hypotheses.
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