This new paper in JPR isn't really a proteomics paper, its more of a metabolomics (metabonomics) study, but it is definitely interesting enough to warrant a quick mention.
In the study, 20 healthy men and 20 healthy women were put on a specific polyphenol-free diet (no caffeine or chocolate for 6 weeks). The exception to this diet was the daily administration of a concentrated tea that was the equivalent of ~5 cups of commercially available tea. The participants then submitted urine samples over a rigorous schedule, with some participants submitting samples 6 times daily.
The urine samples were analyzed with LC-MS using a Waters ACQUITY UPLC system coupled to a Micromass Q-TOF. The samples were also analyzed by GC-MS using an Agilent 6890N GC and a Pegasus HT TOF system.
The study determined the rate of clearance of the polyphenols present in these teas as well as the appearance of compounds that resulted from metabolism of these molecules. One interesting fact the study found was that although caffeine appearance in the urine peaked 1 hour after the tea was ingested, it didn't completely clear the body until 9 hours post ingestion.
The most interesting part of this study, in my opinion, was the use of multivariate statistics for results analysis. I am encouraged when I see the science of statistics infiltrating the analysis of MS data. Its interesting to me how rare this has been so far, especially considering how essential statistics is to genomics.
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