I started reading this article because I thought parturition was referring to something else entirely.
This nice study was done in by researchers at 2 different facilities in Bristol and if you Google "Bristol, UK" you get this image.
Now that I've written the strangest intro of any blog post to-date, I'm going to actually go into this well-orchestrated paper.
Bloods was taken from fifteen pregnant participants before and during childbirth.
The plasma was separated (and if it was depleted for the Top15 or anything, they don't mention it). The proteins were tryptic digested and labeled with either TMT2 or TMT6 reagent (depending on the experiment). The TMT tagged peptides from each experiment were mixed, SCX fractionated and LC-MS/MSed on an Orbitrap Velos operating in High-High mode. A complex multi-stage LC gradient was used. (Minor note: I think the coverage could have been a tiny bit higher if they moved the MS1 scan from 300 to 380 or 400, but this is a minor point. The operator of this instrument obviously knows his/her way around Xcalibur.
Silliness aside, I've read 4 papers on this plane so far today. Why did this one make the cut for the blog? Because this group did more than TMT 10plex. Look at this, this is 15 pairs. A common criticism of isobaric tagging is that we can't compare beyond our normal dataset. This paper shows that it can be done.
How'd they do it? They ran each experiment separate in PD 1.2 (if anyone knows this group, I hereby volunteer 30 minutes I should be using for sleep to move them up to PD 1.4). If I evaluate their processing scheme, at this point, the observations were what was important. Taking the observations from the different experiments, they ruled out what was significant from what wasn't with a simple T-test. Yup. Like the t-tests that GraphPad lets you do for free if you cite them?
And it works. At the end of this simple analysis they had 40 proteins that appeared important. And 2 checked out via ELISA. That sounds qualified to me. Another argument for why we wouldn't want to do TMT down the drain.
You can read this nice paper at EuPA Open Proteomics here.
thanks for this post, and for your great blog in general!
Just little comment on the stats here: although the t-test is an incredibly useful tool it requires a bit of caution when applied to cases like this. As far as I can see no multiple testing correction was performed in this study, which means that the probability for obtaining significant results just by chance is >99.99% (for the 217 proteins that they are looking at). Looking at the volcano plot such a correction would probably not affect the extreme values but many of the datapoints which show only a small log ratio and where the p-value is between 0.01 and 0.05 would probably not be significant anymore.