Experimental Design and Workflow for MS-Based Metabolomics and Lipidomics Research
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Owner/Developer: Agilent Technologies
Country: |
United States of America |
---|---|
Languages: |
English |
Url: |
https://www.agilent.com |
Created: |
28 October 2014 |
Locations: |
United States of America |
Description: | 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. |
Format: |
Webinars |
Presence: |
Optional / Voluntary |
Access: |
Free |
Content type: |
Theoretical |
Duration: |
1 h 11 min |
Target audience: |
Students, Researchers, Regulators and policy-makers, Teachers and educators, Technicians, Managers, Scientific officers / Project managers, Professionals (e.g. veterinarians), General public |
Target sectors: |
Academia, Industry, Governmental bodies, Contract Research Organizations (CROs), Consulting, SMEs |
Educational level: |
Continuing Professional Development |
3rs relevance: |
Replacement |
Topics covered: |
Designing procedures and projects, In vitro methods |
3rs coverage: |
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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