Saturday, January 5, 2019
Targeted proteomics finds early CSF markers for Alzheimer's disease!
There may easily be 12 good reasons to love this brand new study in MCP (early access version is open!)
however, unless this espresso changes my priorities this morning, I only have time for a couple. Hopefully the ones that will convince you to go right now and check it out.
If you were told to do some discovery proteomics to find some biomarkers for a pre-clinical marker, how would you do it? I predict you'd get some samples, digest them, fractionate them as much as possible and queue them up for days/weeks of instrument acquisition time and matching amounts of data processing time.
Do we always need to do this?
I just went to the @ProteomeXchange Twitter account and from January 2nd to 8:24am EST on Jan 5, 2019 --32 new public proteomics datasets have been reported (tweeted) as newly available.
This is how Lleo et al., (sorry, my keyboard won't make all the right letters) did their discovery --
-- they mined a ton of deposited datasets! I can't pretend to understand the biology here, but they are hunting markers that will show up in CSF before the normal markers that indicate the patient has progressed into the disease. I have this vague understanding that many diseases have better outcome the earlier you realize someone may possess it, and studies were selected that might lead them toward this end.
Some of these proteins were mined directly from the papers (they describe in detail how they used terms to search literature databases) and others were pulled from PRIDE/ProteomeXchange and other public repositories.
I'm sure this takes loads of time and bioinformatic and medical expertise, but there are at least 25 complete disease studies here. This composite represents years of work and centuries of combined skill acquisition that no new study, regardless of instrumentation advances, could replicate in a reasonable time frame.
While writing this, I realized that this group also did discovery proteomics on enriched "synaptosomes" and somehow leveraged this against all the data they mined. Again -- I think a lack of understanding on my part regarding the biology is keeping me from the full picture here. An Orbitrap Velos was used for that part, on fractionated (SCX) samples.
Now it's validation time!
They made heavy peptides for their markers. They got real patient CSF based on classifications from clinicians and they got to work (nanospray on a 5500). About half their chosen markers had too much interference to be used.
Looks like Skyline with MSStats for all SRM work -- and convincing -- useful preclinical biomarkers were found.
This is a great study all around, despite my perhaps confusing description of it.
I think we're going to see more of this in the future, for sure. I was about to runs some stuff and found that a group at Yale had just published a nearly identical discovery experiment! I wrote the authors for clarification regarding which file is which (a problem I have with a lot of PRIDE files, but I think I'm probably just dumb -- anyone else have issues with this?). They used a better instrument than what we have here AND the operator is likely better at her/his job than I am. BOOM! Weeks of instrument time that can be used on something novel. All I have to do is process 80 or so beautiful files and start looking for targets!