Sunday, March 18, 2018
More on the Beadome!
Yesterday I discovered something everyone else knows all about and I'm still fascinated with how I might exploit it to give my collaborators better data -- there is SO much out there on this topic. I think I can be doing better experiments by Monday (today if our IT security people would allow me remote login...not sure why but my head hurts too much for me to drive anywhere)
This study has some amazing insight into how much of a problem the beadome truly is!
In controlled pulldowns less than 1% of the total peptides identified -- and around 1% of the total ion intensity (TIC) come from peptides that are associated with the enrichment! One percent! The rest? Beadome....
The antibody really is trying to just pull down it's targets -- it just sucks at it (what a surprise! antibodies being unreliable? How weird...) But this isn't a Ben hates antibodies post. There is no question at all that when my collaborators do these pull-downs that they enrich their proteins. It's a crude tool, but it works. The important thing here is how do I better get to the 1% of the signal that matters here!?!?
This may not be the first study, but I'm only at least 7 years late to the "Why don't I make a static exclusion list?" party. Check this out!
Unfortunately, it adds some important biological context to everything. Wait. Unfortunate? Oh. It turns out there is a bunch of different ways to do one of these pulldown things. However -- there is tons of potential here for developing lists of your BeadOme junk and eliminating it from fragmentation if you know how the pulldown is done. They go through all sorts of different methods and develop a list for each kind of pull-down thing.
However there is a lot that is shared -- independent of the method -- but it looks like the biggest impact would be from running your experiment while excluding the pulldown things specific static exclusion lists. It bears further investigation for sure!
If we go back to the first paper I linked, the quantification methodology with imputation (random score input imputation) allows them to ignore the beadome impact from the data processing side. This is great -- if you have enough dynamic range to get to that 1% of the signal you really want to! But I still think the use of static exclusion will help a lot to get down to things like PTMs on that 1%....