Monday, September 15, 2014

NeuCode for intact protein quantification!

Soooo...about a year ago someone asked me if I could help them do NeuCode intact protein quan.  And I said...yeah...maybe if you gave me a year or two.

You remember NeuCode, right?  Like SILAC but with tiny shifts in the mass labels.  How tiny?  How bout you can't see them until you get to 100k resolution, and some can't be resolved until you nearly reach a half million resolution.  More resolution reveals more and more channels and allows crazy levels of mutliplexing.

It makes sense to apply tiny shifts like this to proteins, cause you'd have tons and tons of tiny shifts and you wouldn't need so much resolution to separate them?  There is a big problem though.  You wouldn't know the shift unless you knew the protein.  If you had 100 labels you would be looking for a different "heavy" than if you had the ability to integrate 110 "heavy".

The processing would be an absolute nightmare.  Honestly, I had no idea where to start.  This, by itself, could be a damned good Ph.D. project and really, could honestly only come out of one of the very best protein bioinformatics labs.

Wisconsin got there first.  And this is every bit as  complicated and elegant as I figured it would be.

You can read about this in this month's MCP (link to abstract here.)   For those of you (like me) without an MCP subscription who don't want to wait until your library sends it to you (like me), you can read all about the technique on Lloyd Smth's website here.

I'm going to brazenly steal a couple images, but it is explained there better than I can, I just want to impress upon you how freaking smart this is.

So we label and do our intact analysis, but part of our sample goes to RNA-Seq?  What for?  Well, for one, they only want to search for proteoforms that are actually present.  And they get those from the variant call file?  What else do they get from it?

The freaking abundances!!!  Now, some of you will immediately point out the several papers I was talking about recently that suggested that we can't really trust quantitative data from this stuff, but it isn't bad necessarily, it just isn't as good as protein level quan.  And we know that everyone in all those awesome proteomics labs in Wisconsin know that, but there is no reason we couldn't consider that data, right?!?  And it looks like they do.

I really need to sit down and dig through this paper when the library gets it to me, but seems really freaking smart.  Definitely check it out!

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