Deep Visual Proteomics of Pain!

 

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! 

DIA-NN 2.5! Now with 70% more ....70%?? ...more peptides!?!?

 

Okay, we have to take a look at this for real. I do like the color scheme on these plots, though...

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

Troubleshoot your EvoSep step by step with this cool online thing!

 

For the first time in a long time, I had to do some EvoSep troubleshooting. Turns out that ceramic needle thing can get clogged! 

Gabriel at EvoSep led me to this super useful online resource that walks you through step by step to get it all worked out. 

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! 

MR-SP2 - Super affordable high recovery low input spatial proteomics!

 

Yeah! I love this new method at JPR! 

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. 

Proteomics Show 102 - Dr. Jan Mulder and BRAINS

 

Lazy post day! Reviewer comments (....some a little past the due date...sorry....) on ....I shouldn't type how many papers....it'll make me a little stressed out...... on my desktop.....

I am legitimately loving recording this new season. Thank you US HUPO and these amazing guests we have lined up. Did you know a brain...just....unfolds....? I did not know this. With the podcast actually now racking up more listens than this blog, I should probably advertise this on the other thing.... But this took 6 minutes and most of that was trying to decide on a gif. 

Why do immunopeptidomics anyway? And what comes after?

 

Do you wonder why we don't just do immunopeptidomics by genomics technologies? Besides the obvious fact that it's impossible? Or just wonder what happens after you've spent a really long time working on the crappiest peptides you've ever tried to fragment?  

Then this is the review for you (and you)! 

I'm convinced! Illumina Protein Prep might be a game changer!

Brazenly borrowed from this whitepaper. 

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%??

Oh. So...a CV of 1 is a CV% of 100%. Right. So I'm going to puke. Hey! And the new TIMSTOF water pumps reset their temperature after a power outage. That's cool. So..I have more time since I have to set my water cooler temperature to the temp written on it in sharpie (25C) and I assume wait for this thing to re-equilibrate...

Okay, so maybe we need to look at these numbers a little more. 

It's hard to see but there is a red line which is a CV of 0.1 or  CV% of 10. As you might notice. They don't often get very close to those numbers. Now, we could argue this is cheating. The maximum number of cells analyzed in each study was used to generate a pseudobulk metric. So this is averaging thousands or tens of thousands of cells. What we need is - yeah! 

This paper - 

Which features a super duper method for improving RNASeq reproducibility in measurements! 

And - ACROSS A GENE they get to 

Around 22 to 24 CV%. Ouch. 

This is where it gets way weirder. My TOF is finally back so I need to go do work, but do you think they're attaching a huge gene to each aptamer? Or do you think they're attaching a single short oligo? I'm no expert, but I suspect it's the former and this is like global proteomics CV% on a single peptide compared to across a protein. The numbers get better when you've got a higher sequence coverage.

I'll be honest, I started out this post as an April Fool's joke, but it turned out that I learned a lot. 

I'm not going to change the title, though. I think that this product will change the game and I don't think it's going to be in a great way. On paper this product looks like it will still not be able return quantitative protein values, and it looks like when it does, the variability in metrics will be worse than the product it is based on due to the difficulty in reproducing the output data consistently. 

We'll see, though. If you are using this product, or have access to it and you want to do the easy and obvious experiment to show me I'm wrong and this works, please reach out. In the meantime I'll still tell every conference audience and every classroom I'm in front of that there is zero evidence that this stuff can quantify a protein. 

On site at ABRF 2026!

 

This is the first time ABRF has happened in a city that I live in! Man, once upon a time there were so many proteomics people that we had competing initiatives. We were standardizing methods and standardizing standards and comparing software and it was all a lot of fun and it all kind of stopped. But you know what you can talk about anywhere you want to? 

Single Cell Proteomics! Dr. Kyle Swovick, photo taken from the front row, organized a session and, for a small conference with 3 competing sessions at a meeting with sessions with riveting titles like "asset management" I'd call it a hit. Definitely better attendance than last year's session I organized that was called "Yo, Justin Walley and I are totally going to do SCP in core facilities, hold our beers". This was sort of a victory lap for me because we now have paying customers in our core at Pitt for SCP and the first data delivered received this response, that I don't mind at all.

Kyle's core at Rochester also does it! It's a real thing! Kyle talked about what a pain in the butt it was to reproduce methods from the literature that largely forgot important details and I called the authors of a preprint that said "5,000 proteins per cell" in the title "a bunch of assholes" on video for a second year in a row! 

And we had great questions and interactions.

Was that all the proteomics? Nope! It sure wasn't! 

Someone who isn't an ABRF member nominated John Yates for the lifetime achievement in BioAnalysis award, and they were like "wait. didn't we give that to him 15 years ago? Holy shit. Why haven't we given him this award already??" So they did.

And I got to be like 4 rows back for John's history of protein mass spectrometry. Super funny with xeroxes of papers (younger scientists, we used to go to the library and find papers in actual journals and photocopy them) and a lot of self-effacing humor and reflection. And cuttings from papers of the past that seem either predictive or ridiculous in the context of today. Or both. 

Super fun thing I was very glad to catch.

The rest of ABRF? Hmmm....there are some cool new LCM solutions I'm investigating, and some cell sorters. And a great convention center! 

Maybe I'll write more about it when it's over. 

As an aside - 

Conference travel in the US is really really hard right now. Last weekend at BWI TSA screening was taking over 5 (FIVE) hours. I've flown out of BWI easily 300 times in my life. It's a small airport. 30 minutes is a big deal for TSA there. I agreed to speak at an awesome university a 7.5 hour drive from my house. And I don't think I can get there faster in a plane right now, but it'll cost me about the same in gasoline. ...yaaay...

Transfer single cell peptides all over the place with NO peptide loss at all!

 

4 people have sent me this new preprint and I've had to go "...oh...it's in my draft's folder..." but the power went out in my building while I was at a conference and it's sort of a mess and I'm grumpy, so Imma post this. 

So...sometimes I see these papers where someone does something like - 

1) Get better results than anyone in the world has ever gotten with that instrument (or at all) 

2) And when you do that...if you don't make the data publicly available, I have to think....

Let's do background first! 

THE number one challenge in single cell proteomics (or any low input stuff) is informatics, probably, but right there with it is just protein and peptide loss through plain old surface adhesion stuff.

If you put a 200 picograms of peptide diluted in a 4 microliter droplet in a 384 well plate you'll lose about 30 picograms of that material to that plate surface (say..125uL well volume). It's just gone and DDM will help, you you aren't getting that back. Pick that up in a DDM coated pipette tip and you'll lose another 20 picograms. If you take that droplet and you add enough liquid to it to allow it to effectively vortex it and then thoroughly vortex it  - you'll lose a whole lot more! Peptides stick to plastics.

Even when we're dealing with hepatocytes at 400 picogram of protein (single nuclei) we're probably not getting 200 picogram to the instrument because with our workflow. Loss loss loss. And you can buy a more sensitive instrument for $1M or you can cut your losses by 30% and get the same thing.

In this study - this group transferred a single cell peptide load over and over again! Like 4 times! (four, 四, cuatro) and then - got more proteins per cell than anyone has ever reported on that same instrument with nanopots (single transfer, but exceptionally low loss during prep) or with single well digest, pickup, load, on that same instrument. They just pipetted the cells from here to there and used magnetic nanoparticles and then desalted and then transferred it again and then still got huge numbers. That's a big big claim and you generally expect big evidence to support something like that. But...when there isn't any....it's hard to say or think nice things. 

The Proteomics Show is back! Check out chapter one of "All the Parts!"

 

In this US HUPO sponsored season (thank you!) we're being serious scientists who are asking the big questions of bigtime experts of specific tissues and organs. Neely kicks it off in this week's episode with "...What...IS...hair...?" And one we just recorded features a guest who speaks to our level when referencing the activity of adrenaline as - and I quote - "Yo, get down there and dilate, we gotta go!" 

US HUPO is letting us do a big unsupervised season and so far, I'm happy to say I'm enjoying it. Listen to Episode 101 with Glendon Parker, wherever you get podcast things.