What DIA algorithms perform best for single amino acid variants?!?

 

Yes! I have also wondered this exact same question. DIA is great for protein level quan but you get lots more peptides/protein, so it works out if they aren't always as high quality as DDA proteomics.

Protein post-translational modifications still don't work as well in my hands with DIA vs DDA. My last check of human samples ran on a TIMSTOF with DDA vs DIA was like 8x more PTMs with DDA. Come on IRB paperwork! I'd love to write this paper someday. 

What about those annoying single amino acid variants that every human being has? Except for the completely normal in every way Craig Venter who loaned so much early DNA that he's probably just the UniProt human database (there are many inaccuracies in this last sentence). 

Don't do it yourself - read this cool open access paper! 

Probably worth mentioning that this is Orbi-Orbi DDA and DIA. An Eclipse and Exploris were both employed at different points. You could probably assume some variation when you go to faster instruments with lower relative resolution/mass accuracy. So...maybe doing this analysis with a TOF might make sense? 

A minor criticism is that the authors did more work than strictly necessary. You could just ignore COSMIC entirely and just go download the XMAn fasta libraries that an amazing professor (who mentored an enthusiastic weirdo a few years ago - crap, maybe it was more than a few years ago) updates every few years. Original paper here. If you wanted single amino acid variants since the 2020 (?) update, I can see doing the extra work yourself, I guess. 

As an aside (surprise -enthusiasm! - maybe it's back for good?)  we struggled a lot at first with my postdoc's CKB type knockout mice when doing DIA proteomics. There are lots of CKs and they are very similar. We'd see CKM(uscle) in places in mice where there is no muscle and the knockout mice were always down-regulated, not knocked out, because one peptide would be attributed to the wrong CK (creatine kinase). That seemed to get a lot better every time we'd get a new DIA-NN or SpectroNaut update. 

What tool would you use for a fasta with a bunch of SAAVs in it? The paper is open access, check it out yourself. Worth noting, DIA-NN's new update specificaly has words about improving proteoform level quan in the new release notes. 

Mass spec twitter might have found a home - has BlueSky finally taken off?

Do you miss the golden days of us sharing all the newest developments in proteomics along with really dumb gifs? 

Twitter was, in my humble opinion, THE best tool for finding the coolest new papers and quick opinions from people who read them.

Then something weird happened to it, and no one knows what. 

Completely unrelated -

We all ran away a while back and have been trying stuff, but get this - BlueSky has GIFs now. AND I'm getting pings every few minutes of some super cool scientist who just joined. 

Maybe there are better solutions than this one, but I can't find anything on Twitter. Half the time I can't even find the website. I swear it might be on a different address entirely. Now, I may not delete my final account quite yet. I've got this growing Excel sheet of companies that I will never ever buy from and companies actively spending money on Twitter is a great heads up for me to check them out and see if they should go on the list. 

I forget how bluesky works. My browser just remembered everything but I appear to be @proteomicsnews.bsky.social

FFPE proteomics AND metabolomics? Here's a protocol!

 

This paper is my first test for how BlueSky performs as a replacement for the once great ScienceTwitter, MassSpec Twitter.

I'm dropping it here with a backdate just so I don't forget about it. We all do FFPE proteomics all the time! Can we do metabolomics as well? I'd thought the formalin and paraffin and time would mess them all up! What a possible opportunity for multi-omics?? 

What's the best software for DDA analysis of host-cell protein contamination in biotherapeutics?

 

This is an interesting analysis for a critical workflow in biopharma/biotherapeutics! 

It's applicable in other places, but where this is used the most is when an antibody drug is manufactured in a hamster ovary cell line or something and you need to make sure that not much is going along for the ride.

In this case, do our algorithms designed for deep global analysis make the cut? In the end it looks like they all do pretty well (Figure 5 is a great summary) though some are clearly better than others. One commercial package I wasn't even aware of somehow (Byos, from ProteinMetrics) actually has a specific workflow for host cell proteins - and you can imagine if they were thinking about that during the design they'd do a good job - and that does seem to be the case. 

Leveraging proteomics to develop an accurate model system for human fallopian tubes!

 

We've eventually got to get away from animal models for human studies. There are clearly dramatic differences between mouse/rat/nematode/yeast biology and human that lead to all sorts of false discoveries. These are so drastic that some funding agencies have rapidly approaching hard deadlines where they just won't fund the stuff.

But human biology is tough to mimic in a bioreactor, even if those things are increasingly inexpensive and easier to use. Even modeling something as relatively "simple" as the blood brain barrier is not at all simple. How the f(ourier) do you model something as important and complex (and amazingly under-studied) as the human fallopian tubes??? 

Like this! 

 Okay - so one way you could do this would be to get some human donor samples and do some really amazing imaging and then dig deep through previously deposited data to help construct yourself a map. (P.S. I love that the authors did such a nice shoutout to my long time neigbors in the JHU Microscopy Core.) Could you do a lot better? What if you also captured physiological function like oocyte transport?!? Could you end up with a map that provided a dynamic understanding of how the system changes during physiological function? That would help as well, but what if you used that information to build the most accurate in vitro system for studying these tissues as you could? This multi-institute team did something like all of this. Cells were grown out to organoids which were coaxed into "assembloids" (we're far outside of my expertise here, so please forgive my interpretations) and by controlling the matrices and how these cells were coaxed do differentiate and assemble, they get there. Proteomics was used along the way (TIMSTOF HT with EvoSep One, diaPASEF, analysis with SpectroNaut) to verify that the system proteome expression profiles.

Even for someone (me) who couldn't follow a lot of the biology/cell differentiation stuff, this is clearly an exciting work. I'd 1,000x prefer having access to engineered systems for my pharmacology work over mouse models. Up that another 1,000x if I those systems were backed up with proteomic data that they were accurately representing human biology.