Friday, July 14, 2017

UHPLC-LCMS lipidomics of dog plasma can differentiate breeds!

I admit it. I used this awesome new paper in Springer Metabolomics to justify Google Image searching for pictures of 9 different dog breeds in funny hats and used my favorites to illustrate a figure from the paper. You caught me. However, I also think that this is a really interesting study with results I would not have guessed possible.

First off, I'm no lipidomics expert. I did spend a couple days at Pitt's center for Environmental and Occupational Health which is one of the premier lipidomics centers in the world. What I got out of that visit, however, was mostly that lipidomics is really really hard and I'm glad they were doing it and not me.

The conclusion of the study I'm talking about now is in the title of the post and the paper. I would never have imagined that during our occasionally demented differentiation of wild dogs into this diverse set of physical features that we would also alter their plasma lipidomes, but it looks like we did!

They took plasma from around 100 dogs. n > 9 in all cases. These weren't lab controlled dogs, they were plasma samples from healthy dogs that selflessly volunteered some plasma for science, then went home to their different houses, probably put on funny hats and ate different food. There are a TON of variables here.

The lipids were extracted from the plasma in a very straight-forward manner and separated on a C-18 column and MS1 spectra were acquired at 100,000 resolution on a benchtop Orbitrap (appears to be Exactive "classic" system). Post-processing, lipids of interest were validated by MS/MS via direct infusion using a NanoMate coupled FT-ICR (LTQ-FT Ultra).

All the data was processed in XCMS and FIEmspro (new to me) -- and, you know what? The breeds are distinct. The paper is Open Access. If you're curious, you should totally check out the PCA plots! The breeds are totally distinct!

From the global profiles they are able to hunt down the features/compounds that are driving breed clustering. This is another reason to check out this awesome paper -- in case you're like me -- and don't know how to make that step from prime components to biomarker. I'm still unclear after reading this, but I think this is a cognitive deficiency on my part, because this is a very clear and well-written paper. They provide some great references that I hope will help me bridge this gap eventually.

On the topic of references in this paper -- I spent some time on this open access one as well.

I'm just linking it in case anyone else wants to know what breeds have the highest cholesterol levels and how those vary throughout a dog's life (the n is only high for a few breeds/genders/age groups, but still interesting to me).

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