Okay, we all need more ways to look at the quality of our data, particularly before we send it out to collaborators who may do who-knows-what with it!
Only one QC tool out there gives you retro visualization options! And it's this one!
https://dia-nn-qc.streamlit.app/
Load a DIA-NN file and choose 80s terminal or 90s webpage or just the boring regular thing. Who says you can't have a creative background while you're making sure you've got the correct number of scans/peak? Not me!
What a great month for method names already! Introducing the...
Otherwise this seems like an interesting method, particularly if you're not always doing phosphoproteomics or glycoproteomics and you have to do them. I don't see any reason why you couldn't digest the peptides with a more traditional approach and then put those peptides into this workflow around step 2 or so.
If you have a "carrier" "boost" "basil" or "oregonO" channel, why couldn't you load that thing with phospho-enriched samples (for example) instead of 200 cells or a a diluted perfectly digested pooled sample? The reason appears to be that your coisolated peptide (or junk) background ends up leading to a preposterous number of false discoveries. Remember that in these workflows your complete and total evidence for that peptide being there in single cells is just your single reporter ion. Since most PTM modified peptides are already in a suppressed region of signal to noise - and you only get one measurement of that phosphopeptide - you're already in trouble. (Wait. Is that too many dashes? Don't need y'all thinking some AI wrote this thing. Meh, I'll fix that in a minute). Throw in the contamination of your reporter ion signal with the isotopic impurities and now you've got tons of phosphopeptides and they may not really make sense at all.
Ready to fix that? I sure am! Except...I don't have this hardware.... hmmm.... okay, but let's do it anyway! Introducing 2026's early entry for best method name......
ShtMtPro!
Super Heavy Tandem! (Sht) Mass Tags! (Mt) Professional (Pro) version! OMG.
(Mandatory)
Okay, so the AMAZING name should not, in any way, distract you from how good these data are. Compare the number of PTMs you can pick up using this workflow vs DDA? ShtMtPro crushes it. Even vs PRM, ShtMtPro squeezes out a narrow victory!
Intelligent - on the fly - targeting of chemically modified peptides IN SINGLE CELLS? Multiplexing so it's super fast?? Incredible idea that I bet no no one tried at all to talk another vendor into for 3 straight years. If you are thinking something dumb like "I can't do single cell proteomics, I just have this old Tribrid..." this is the second paper on this blog this week that should put you on the right track. If, however, someone offered you $75 and a pack of big red for that old Tribrid, I would happily give you twice that for it!
It's a smart concept and a very nice thing to think about as proteomics becomes more trusted as a diagnostic.
What if you had a random patient come in and you could do global untargeted plasma proteomics on their sample? Not inside of a controlled cohort that you planned 2 years ago and pulled all the samples from the repository? Just that one sample that just came in. That's how clinical stuff might work. A sleepy 22 year old might be working nights to save for grad or med school and be studying and run those 12 samples that came into the lab (typically because it's super important) at 3am. Could you do anything with global data?
If the answer is no, then the future is not very bright for diagnostic untargeted proteomics. If the answer is shmaybe, then you're getting somewhere, and if it's a yes, then let's start building on this idea right this second.
To simulate it they pulled some traditional proteomic studies where they had a discovery cohort and then a validation cohort and someone did it all the traditional way. Found the markers in batch 1 and focused on how well that marker seemed to be predictive in batch 2. So these authors loaded those data, pretended they didn't know what went where and use the machine learning things to try and sort it out - and it totally ends up doing okay!
We've got ourselves a shmaybe here!
I appreciate the transparency of the authors, the conclusions almost read like a "limitations" section. The rest of the paper reads like someone was sending a secret code to Olga Vitek that only she would be able to decipher. If that was really what this was, Nature page fees may be the absolute most expensive way to do it....
Here is the thing, though, it didn't outperform the traditional human thing when the experiment is done really well (the example data they used is superb, probably outliers) but it did reasonably well, and that's still a huge deal.
And everything to reproduce it yourself is reasonably well annotated in these notebooks.
You've got secretion proteomics, and neat plasma and depleted plasma, and some SomaScan data from a related study that they used, and normalized, but don't go into much. They describe the statistics and provide the output data as an excel spreadsheet, which I very very much appreciate. Really nice super high speed targeted work (5 minute gradients on a SCIEX) and just a whole pile of really cool stuff to dig through!
Oh. This is really cool. I'm so glad that SCIEX is finally getting some traction with their super cool ZenoTOF hardware. The high speed high resolution mass spec world is really super competitive right now. You really can't make a bad choice (aside from Agilent, obviously - hey, I didn't tell them to abandon global proteomics instrument market -they're doing fine in their chosen niches) for getting amazing proteomics data.
Could you do even more with EAD, though? What if while you had those ions you also applied another collision energy? Could you really bust those molecules up to get complete coverage of the peskiest ones? Importantly, could it still be way faster than other democratic fragmentation methods (that use chemical based fragmentation)?
Pretty much! Best I can tell they optimize these glycopeptides out and they do need to slow it down some. It looks like the best data is coming off between 13 Hz and 19 Hz (my math from the method section details). They have some time for the EAD and some time for the CID and some accumulation time and that sums up.
I don't know what the new Exedrin ETD benchtop instruments are getting with their improved (and seemingly incredible) new Orbitrap hardware. Given I'm used to 100ms reaction times for ETD not counting the Orbitrap scan times, and internal HCD cells (IRMs?) stop, gate, go times. I think this has to still wildly competitive.
Probably also worth considering that the 7600 this was tested on is now 2 generations behind the faster and more sensitive ones. So...I suspect you could go even faster on the new ones?
Are you ready for deep visual lipidomics? No? Okay what about normal spatial lipidomics with deep visual proteomics?
The diagram pretty much shows what the paper did, but the co-registration of the spatial data from the laser capture microdissection work (now called deep visual proteomics, y'all, get on the hip terminology) to the lipidomics is a star. This does come from the very small but valuable bank of post-mortem human brain tissue in Baltimore that one of these authors definitely didn't steal from another facility.
Lipidomics was at 50um resolution on a Bruker TOF I'm not familiar with, but they did both MS1 and MS/MS analysis. The deep visual proteomics was done on approximately 500 cell cuts. Pretty cool since we learned yesterday that brain cells are small. That's 25nanogram or so? Small!
TMTPro was used for the proteomics with MS2 on an Orbitrap Fusion II (Lumos) system and found around 300 differential proteins of interest that the authors seem interested in. >40k peptides are reported, which is pretty darned good from sections this small on this hardware. If you're thinking of taking the spatial proteomics plunge this seems like a great resource for taking that step.
Big thanks to Matt MacDonald for sending this last night with a "Wow" as the total email content so that I got to sleep at like 1am after I felt like I'd finally gotten through it.
Honestly, "wow" is still the correct word for it.
Before I get into it, this is the paper.
Something I argued for years was that I'd much rather have more cells than more coverage, but I think I've fallen headlong into the coverage race along with everyone else.... this paper is a solid smack in the face because they did A LOT with a few hundred proteins per cell.
They say they get 800 on average in most of the cells. I'm spot checking in DIA-NN and before bioinformagic, I'm getting 450 or so. Probably by the time you match between runs and stuff you probably can double that. I ain't reprocessing 1,500 files, so I have a clear sample bias.
Edit after this post blew up - I kept forgetting to mention the size of the cells, which is a big deal. They think some of these are like 50 picograms of protein! These are like 1/3 the size of the cells we use as our control cell line in my lab. This is a big deal.
And - this is going to sound critical - and I don't mean it to be that way, because this is just a stunning work - but mass spec proteomics people may really just care far too much about quantitative accuracy. This isn't the first time one of our key tenets of proteomics has been really challenged. A Slavov lab study made the heretical decision of not fully resolving TMT reporter ions at baseline. Something that has been unthinkable for a decade or more. It still totally worked. We may try it ourselves sometime here.
This study did around 2,000 single cells, about 1,500 of them from "brain cell types" by cranking their resolution and ion injection time to the moon on an Orbitrap Fusion III (Eclipse) with 40SPD "Whisper" on an EvoSep (the 100nL/min one)
I'm not looking at the paper now, but my notes say that it was DIA with 50Da windows and 250(!!) milliseconds of fill time at 120,000 resolution per MS/MS event. With 12(!!) windows.
12 x .25s x 1 MS1 (which might have been 240,000 resolution) so 3.25 SECONDS? per cycle. Someone somewhere in Seattle was shown this line I just typed - and threw up. All over the place.
But hear me out. For real, what if your quan doesn't have to be good?
Whew! Files finally downloaded so I could look at some of them and --- yeah -- you're going to get a lot of peptides, maybe most of them, with 3 scans/peak...
ftp://ftp.pride.ebi.ac.uk/pride/data/archive/2025/12/PXD071075
Here is the point to stick output of the peptide above. It looks like a triangle, but it isn't actually as good as a triangle would be.
OMG, and I have laughed for real multiple times about S.Table 2. Man, did they throw some shade at just about every label free single cell study that did fewer cells than this one did! Wow. I would like to thank these authors for not citing me, therefore I did not appear on their Table of Shame. They wouldn't want my stuff there because with SCoPE-MS/SCoPE2 this is actually a very normal number of cells analyzed, but the authors made it very clear that label free quan, regardless of how poor, is the superior option. They might be right. They certainly convinced Nature Biotechnology to accept $12,300 to convert this work to PDF and post it on their website. And if that isn't evidence of a good study in 2026, I don't know what is.
Okay, but the take aways here should be
1) You can do a lot with a lot of cells
Even if!
2) You only get a few hundred proteins per cell
3) And the proteins you detect aren't all that well quantified!
A good experimental design and cool samples and solid informatics can push you through to an amazing study.
Quick math, btw, at 40SPD these 2,300 runs or whatever ran - with no blanks, no QCs, and not stopping to calibrate and no failed cells (there are always failed cells) a little more than 2 months on a system that is a couple generations back. That's...not bad....
And they used FACs so the cell prep was inexpensive. I don't have our calculator in front of me, but I'm going to go with this being in the $20/cell range in total costs/cell before any labor. Possibly less.
Kid's up, gotta run. Super super super cool paper you should check out!
I want to close this tab on my desktop so I can see other tabs.
For real, I think this box is cheap. Like I've seen it second hand for less than an HPLC. Even if you needed to buy some expensive reagents, it could be a legit way to get yourself into the world of MS imaging...
Backtracking this post, not sure how I forgot to put it here!
