We were JUST talking about this in lab meeting last week! I swear.
I said something like "well...sure...we can generate loads of good glycoproteomics data (I've got a tattoo that is almost old enough to drive that shows I've successfully pulled it off at least once on some pretty crappy instrumentation)....but you can't actually interpret what that big pile of glycopeptide stuff means....
Wow.... I do just have to leave this here and move on. I've already forwarded the paper to a bunch of people, though, and can't wait to spend more time on it.
We need to figure out how much DDM you can use before it's a bad thing, though! This group used 6-8x more than what we use, and they get a lot more membrane proteins.....
Totally worth taking a look at!
As an aside, I ran a commercial program for some people recently and it gave me 20% more protein groups than the ones I currently use. Those extra 20% really annoyed my collaborators. They were ...like... biologically very very unlikely...? Not DIA-NN, a commercial thing, but I did re-learn a lesson that more peptides isn't always a better. But DIA-NN has built enough credibility for me to be hesitantly optimistic that I will like this new version.
Get it where you get DIA-NN! Probably here https://github.com/vdemichev/diann
It's amazingly clear with pictures and "did it work? click here!" AND 4 MILLION PERCENT BETTER than letting Adobe's class trailing LLM help you dig through the user manual. If you see a button to turn that pile of poo off, please let me know where that is!
There used to be LCM(s) (laser capture microdissection thingies) everywhere! No joke, they almost died out to the point that one of the leading companies was briefly for sale at the price of a Baltimore/DC suburb house. At ABRF I looked at two very nice new ones and individual systems were in the very nice Pittsburgh house sort of price range.
MR-SP2 takes one of the legacy systems that you can't buy new anymore and optimizes it up for spatial proteomics with all the details you'd need to set it up yourself.
At 1-2 cell cuts they gett over 600 protein groups on a TIMSTOF Flex system equipped with an EvoSep. I had this exact same setup for ore than 2 years and I think my record is less than 400 proteins on a cancer cell. That might have been Whisper (100nL/min) 40SPD or maybe even 20SPD.
AND they get this out of FFPE tissue! Incredible approachable spatial proteomics without buying all brand new stuff.
Then this is the review for you (and you)!
If I have a super power as a person or a scientist, it is that I'm very okay with being wrong. It helps that it happens all the time and the fact that I have friends and a domestic partner who are way way way smarter than me. I'm used to be the dumbest person in the room and I can just discover that I'm wrong.
And boy - was I wrong about this new Illumina Protein Prep thing.
I thought it was just a repackaging of SomaScan, a product that has had the strangest propensity for avoiding the very simple experiment that would make me stop making fun of it. After a decade I was starting to think that 1) They were doing it just to get on my nerves or 2) They had done it - and aptamer off binding could not be used to estimate a protein concentration in a complex mixture in any meaningful way (translation - it doesn't work).
But Illumina has been killing it for years and years! We have petabytes full of Illumina short read sequencing data all over the world. Sure, you could argue they missed the long read sequencing bandwagon and that is a little weird. But a behemoth of an organization like that has the money and the people to avoid becoming complacent.
So when Illumina acquired whatever SomaScan had changed their name to that month, you had to think "wait. maybe there IS something to it!"
And here I sit while turning a TOF after a power outage that caused me to miss the last day of a conference. Embarrassed and corrected.
The A problem with aptamers is that they are only linear within an EXTREMELY narrow linear dynamic range. If sample A has x target and sample B has 2x target, you can basically see that difference. If sample C has 10x target, you're probably okay, but you're at the end of the dynamic range. If sample D has 1,000,000,0000x more protein, you get about the same value as sample C. More on that and other problems with aptamers here.
This new product is so much more than the original product it was based on - because after you have your aptamer readout you NOW do NGS sequencing on tags on those aptamers. And then you do the quantification off of the NGS readout! By counting the reads! And we all know that there is no better way of doing quantification than counting things. And if there is, it's probably counting an indirect measurement of an indirect measurement. Wait. Didn't we do something like that before?
Okay, but that doesn't fix the linear dynamic range issue of the original measurement. But now you've got rock solid absolutely amazing quan on those narrow measurements, right?
And this is where I change my mind about this whole thing!
This group took a good hard look at precision and accuracy in a pile of different ways to do RNASeq, with a special emphasis on low input techniques like scSeq and scNSeq, but lots of work on the bulk as well.
The CVs ARE AMAZING.
Less than 1! Across the board! Okay, fancy mass spec people, tell me how many times that you've reported a CV <1 across an entire dataset. I'd love to say that I only report out proteins with less than 10%, but we use a 20% CV cutoff.
Oh...fuuuuuuuuuuuuuuuck..... they mean CV%, right? Not CV 1 = 100%??
