Finding post translational modifications (PTMS) like phosphorylation is awesome. And maybe its super useful to what you're doing. What is almost always more useful, however, is getting quantification out of your PTMs. If we just go to the peptide level and ignore PTMs we are still at a point where there are more peptides than any mass spectrometer can sequence in a typical cell digest. In the end, we're really just skimming the surface. The absence of a PTM in one sample may seem like the missing key to your puzzle when really it might just be a sampling error.
I'm a big fan of labeled quan. Yes, I know there are drawbacks. The reagents are expensive and they may not be applicable to all experiments and we get ratio suppression in reporter quan experiments. But I'm a busy guy. And I don't have my own Orbitrap. So I've got to steal time on other peoples and nothing out there gets you anywhere near as much data as fast as TMT does.
This cool paper in Open Proteomics from Benedetta Lombardi et al., links the two paragraphs above together. In this paper they evaluate different strategies for doing TMT based quantification of phophorylation. It is a nice little analysis that will save you a great deal of work in case you want to move from compiling lists of phosphorylation events (boring...) to quantifying the shifts in a phospho cascade over a time course following drug treatment or something (way less boring!).
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