Just about every hospital or medical institution has some way of collecting and storing formalin-fixed paraffin embedded (FFPE) tissues. Maybe they keep them on-site themselves. I know of a lab that couldn't get their new Q Exactive installed because someone had filled a room belonging to them with FFPE samples and it took months to find a place for them on-site. These are valuable materials. You don't just put them somewhere you can't find them later! Someone might need them to find the next biomarker or treatment or whatever.
The ideal proteomics clinical samples are flash frozen, but FFPE is sooooo much cheaper to store and there are thousands (millions?) of samples throughout the world.
FFPE has some serious challenges for proteomics. The words Formalin and Paraffin come to mind first for me for some reason. FFPE proteomics is in no way a new idea. Here is a Scholar search I just did requiring both of those terms that kicks back 8,000+ entries. Keeping in mind Scholar's redundancy issues, and the large number of M.D.s who insist on calling western blots and ELISA assays proteomics, there is still a lot of stuff going back years and a lot of it is great, but -- it's proteomics -- from one lab to another I'd confidently bet that no one used the same sample prep methods for their studies.
What if there was a complete workflow that could get near identical data from samples that were stored as either FFPE or as Flash Frozen? That would be worth typing about, right?
You can check out the preprint of HYPER-Sol here.
The data was generated on a Fusion 1 and HF-X and it looks like all the DIA data was processed in SpectroNaut "Pulsar X". DDA data processed in Proteome Discoverer appears to directly import into it as spectral libraries and it handles all the DIA stuff from there.
A lot of time when we're comparing different sample prep workflows we focus on ID overlap, and that's cool and everything. However, we're feeling pressure at all times to be more quantitative about everything. Not only does this workflow get a high degree of ID overlap, but the quan values ALSO line up. This team demonstrates r2(squared....wait...I can't superscript? even Word can superscript... weird...) values that were up in the .9s -- impressive, considering how differently the samples were originally treated!
Okay -- and a big shoutout to Markus Hartl at MPL for tipping me off to another big preprint on this topic. This one uses SWATH and variable DIA windows.