My favorite proteomic papers of 2021!

What a fucking year, y'all. This might be just one part of the wrapup that no one has ever asked for! Despite the fact that this was the least active the blog has been since 2012 I still feel the need to close it strong. I've probably read more papers this year than any year of my life, but I've had to focus on things like learning basic biology and figuring out how the hell a cell sorter works so I can better understand my data. 

Enough rambling (not really) but here are my favorite papers in proteomics of 2021, in no particular order. 

1) 38,000 runs and going strong. The mounting evidence that we're overusing the weakest link in proteomics -- the Nanoflow HPLC. Do you have nanograms of protein or picograms of peptides? NanoLC is still critical, but if you've got micrograms of protein the improvements in mass spectrometers over the last decade have largely made gains in sensitivity that you get with Nanoflow HPLC redundant. 

2) RAWBeans -- Rapid, near universal, deep insight into your instrument files and performance from a simple and handy little tool. You can get great insight into metabolomics files using the tool as well. 

3) Multiplexed DIA is real. Maybe this is confusing with actually multiplexing your DIA windows. In this case I'm referring to multiplexing your samples with tags and running DIA so you get data from multiple samples simultaneously. You can use it with SILAC! Or with two cool new methods that use 3-plex tags. This one in ACS earlier in the year and this more recent preprint. And -- TMT?!? Why not?!?

4) Key the groans, but I have used AlphaFold2 a couple of times in December. Does it sometimes output some whacky gibberish? Sure! But with color coding to indicate structural confidence it's pretty easy to rule out and it beats having no structure at all! 

5) MONTE -- a method to get all the materials you could probably want from your cells. Some biological samples are literally priceless. This is the cleanest procedure I've ever seen to make sure that very little goes to waste.

6) GlycoRNAs -- I mostly like this paper because it shows just how much more there is to learn about biology with yet another class of critical new molecules. 

7) Q-MRM might be a bit polarizing, but I think we haven't scratched the surface of the potential this represents for updating 60 year old colorimetric assays used in the clinic today (or...ugh...radioactive ELISAs...) with inexpensive single quads. Hey! I just remembered that I was interviewed about this and I've never seen the article. I'll have to look for it. 

8) I was trying to keep this somewhat vendor neutral, but I do really like these two studies that are definitely not neutral, so I'll give them the same number:

8a) SureQuant-IsoMHC -- stupid levels of sensitivity, selectivity and accuracy in quan for MHC peptides. 

8b) AlphaTIMS -- makes digging through TIMSTOF data intuitive and nearly instantaneous. Data export comes off kind of whacky and I keep meaning to write the authors. Maybe I'll do that now! 

8c) Okay...well...three...this new mass spectrometer has so much novel about it that I'm going to feature it here. I'm also supremely impressed by how good of a secret this was kept. 

9) Inactivating coronaviruses! 

This is largely me just picking things with a little spare time I have while these files transfer. It was another big year for proteomics and this is just some of the great stuff y'all have done this year. Looking forward to reading a lot more of your great stuff in 2022!