Harvard/MGH Center on Genomics, Vulnerable Populations, and Health Disparities

Teacher: Clary Clish, PhD (Director, Metabolite Profiling, Broad Institute of MIT and Harvard)

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Course Description: Metabolomics is the comprehensive analysis of endogenous metabolites in biological specimens.  Metabolomics technologies are increasingly used to study metabolism in model systems and for discovery of disease signatures in clinical cohorts.  This workshop presented an overview of liquid chromatography tandem mass spectrometry (LC-MS)-based metabolomics methods and workflows.  We discussed: (1) scientific challenges posed by metabolomics and LC-MS-based measurements as well as utility and limitations of the technology, with a particular focus on the usefulness of this method for understanding health disparities; (2) practical considerations for design of experiments using cellular and animal models, as well as clinical specimen; and (3) state-of-the-art analytical tools and approaches to data analysis. Participants then received individualized help in assessing how this methodology might be incorporated into their proposal under development, with a critical look at how their proposal might advance disparities research.  This workshop was co-sponsored by the Harvard Transdisciplinary Research in Energetics and Cancer Center.

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Click HERE for the video archive.

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This was one of a series of disparities-focused gene-environment research workshops sponsored by the Harvard Catalyst Health Disparities Research Program to support and encourage disparities-focused gene-environment research.  Workshops were tailored to individuals looking to incorporate a novel aim or method into a grant they are currently developing, with a particular interest in supporting faculty writing K-awards, R23s, or R01s.  Participants either (1) had disparities-focused projects under development, but wanted to add cutting edge methods to strengthen their grant proposals, or (2) were developing a proposal that would be strengthened by the addition of a disparities-focused Aim.