The biology in this new study is intense. I know approximately nothing about cardiovascular proteomics (honestly -- as far as I can tell, not many people do -- definitely an underdeveloped field at this point, but there are great people working hard on it!)
What I'm interested in here is how these authors approached the experimental design in terms of combining transcriptomic analysis with their quantitative proteomics.
Don't get me wrong -- there is some solid proteomics work in this. If you are looking for a recent method to enrich mitochondria from cells -- this (and the associated study referenced in the method section) is/are for you. These authors start with an impressively pure mitochondrial fraction before the peptides from it go onto a Q Exactive HF. In just a mass spec nerd note -- this group sacrifices some scan speed and MS1 resolution in order to get higher resolution MS/MS. They use 60,000 resolution for MS1, top10 selected for 30,000 resolution MS/MS. We've been seeing this more often recently.
My inclination is always going to be to get more MS/MS scans -- at the sacrifice of relative quality/scan. In simpler mixtures of peptides, it seems like more researchers prefer to get fewer MS/MS scans if the scans can have higher injection times -- and if you're using longer fill times, you might as well get better resolution MS/MS right? It will be free in terms of cycle time on the lower abundance peptides and will only cost you cycle time on the higher abundance peptides. I should check later to see if anyone has done a comprehensive comparison at different complexities....
Back to the paper -- all the label free proteomics is processed in MaxQuant and the transcriptomics is performed using a commercial kit for heart proteins for the RT-PCR and Hi-Seq analysis using a mouse kit.
The impression you get from this paper at first is that the combination of this staggering amount of data from all of these mutant mice -- is that it was easy. There's no way it was. No way. But they make it look that way till you dig deep into this massive body of work.
And that's why I recommend this paper -- they did this work and laid it out end to end here so I don't have to. If someone hasn't dropped by your lab with samples they've done transcriptomics on and want to combine it with quantitative proteomics -- you'll see them soon. And -- it's tough. We don't have unified pipelines --yet. You need to use some of their genomics tools and our tools and find things like IPA, CytoScape and the right R packages (if they've done the transcriptomics, they probably have an R specialist around) and this study is a great framework for how to pull this off!
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