opt-TMT -scale down everything so you aren't wasting so much reagent!

 

There is another optTMT, but that one doesn't have a dash and it's for designing smart multi-batch mutiplexed experiments. You can read about that one here. 

This new one is about how a lot of TMT labs are labeling 400 pounds of peptide (181 kg) and then injecting 200 micrograms per injection on their Orbitraps and 1000 micrograms on their Astrals. 

If you wanted to just label 10x more peptide than you'd possibly use instead of 10 million times more peptide, how would you do it? That's what the dash is for! 

While this might seem just a little silly since there are protocols out there that have been replicated dozens of times for labeling single human cells, they are actually a lot more convenient than you'd think. We know how much reagent in our lab to use for 1 cell or 25 cells and it's a drag when we have to break out the peptide quan kits and borrow someone's plate reader. This study gives you that in-between concentration fully optimized. 

Another funny solvent is better than formic acid for proteomics?

First off -- 

CHECK WITH YOUR HPLC MANUAL OR MANUFACTURER!!

Is the resolution of GIFs getting worse all the time? If so, it's the only change I've personally seen from this whole "AI revolution", except people saying "I asked ChatGPT" when they would have said "I did a Google search" back before Google reorganized and put their search algorithm teams under the control of their marketing teams. True story, that's why Google really doesn't work well anymore and AskJeeves is back, but now it needs more electricity than all of Spain will sue this year to look up stuff on Wikipedia for you. 

Okay, so someone at some time decided formic acid was a pretty good compromise. Pretty sure it was people in the John Yates lab. TFA gave you the best possible HPLC peaks for peptides, but it lowered your ionization efficiency. Acetic acid gave you the best ionization efficiency but if you were doing MuDPiT (which was a 2D chromatography system for proteomics best left forgotten today but it provided unprecedented proteomic coverage with the awful HPLCs we had at the time), acetic acid messed up your peaks too bad. So...formic acid it is.

Worth noting, formic acid has some drawbacks like poor stability in light, particularly when diluted. So when a lab dropped a paper showing acetic acid should be revisited, we jumped on it. My lab doesn't use formic acid in our HPLCs at all. We do have vendor permission and we have several thousand runs to demonstrate it hasn't been a bad idea at all. 

So when I was contacted by a researcher who was like - "yo, we have something better!"  we borrowed someone else's HPLC and tested it out. In our hands on (nanoflow) it's only marginally better than acetic acid, and possibly so marginal that on the sub-nanogram loads it wasn't significant by student's t-test. I forget, and Cameron actually did the work while I was visiting collaborators. But when you crank up the flow rates? 

Okay, so someone at some time decided formic acid was a pretty good compromise. Pretty sure it was people in the John Yates lab. TFA gave you the best possible HPLC peaks for peptides, but it lowered your ionization efficiency. Acetic acid gave you the best ionization efficiency but if you were doing MuDPiT (which was a 2D chromatography system for proteomics best left forgotten today but it provided unprecedented proteomic coverage with the awful HPLCs we had at the time), acetic acid messed up your peaks too bad. So...formic acid it is.

New Nature Genetics study comparing pQTLs is....worth reading....

 

Ummm.....so...Imma just leave this here and not talk about it any more, maybe. Wait. Maybe just this - if your technology is producing results that can be validated 30% of the time then you could save a lot of time and just pick a gene or protein and flip a coin and go read up on other technologies....

DIA Multiplexed proteomics with off-the-shelf TMTPro reagents!

 

This is obviously interesting - and surprisingly easy to pull off. The data is processed in FragPipe and one of the output sheets is put into these python tools to identify the complementary fragment ions. 

I like the figure above because they use 2 very similar peptides labeled with TMT and demonstrate that they can clearly find clean complementary fragment ion pairs. Oh yeah, here is the paper. 

They really really don't want to do any spectral deconvolution so they only used 3 of the TMTProC tags that give them clusters of complementary ions 4 Da apart. The open suggestion is here the whole time that if you aren't afraid of deconvoluting your complementary ion clusters - you can obviously do more than a 3-plex DIA experiment. 

This is a really nice read with the appropriate controls included as well as a way to dramatically increase the throughput of some DIA proteomics workflows on basically any mass analyzer. Worth a read for sure. 

If you type "TMTc" into the blog search bar you'll find a lot of stuff over the years. This is one old post that goes more into what this is and why it can be valuable. 

Y-MRT - a new prototype TOF with 1 million resolution and 300 Hz?!?

 

Ummmmm......okay so....these specs are amazing....

How do you increase mass resolution? Generally just increase the flight path, right? But you can only go so far before there isn't enough electricity on earth to generate the appropriate vacuum. Reflectrons double the path and the W-TOFs from Pegasus that a big vendor acquired recently can really push those numbers up by multiple reflectrons.

The Y-TOF takes that concept to 11. It's one thing to say "I can make my instrument do 1 million resolution". Give me 45 minutes with your Q-Exactive and I can make it do 1 million resolution. Each scan will take about 8 minutes. (more like 4 seconds, I forget) but it's completely impractical. 

AND you can tune a Time of Flight to get really good mass resolving power at one particular m/z. My Q-TOF gets incredible resolving power in a mass range that isn't exactly where I need it.

The Y-TOF did a 30 minute proteomics run and averaged 600,000 to 800,000 resolution across the usable peptide range!!!  

AND sub-PPM mass accuracy. Parts per BILLION mass accuracy. ON A TOF. 

Obviously a prototype, but more obviously something we should keep our eyes on. Worth noting, they do have to use Astral level loads for bulk proteomics (1 microgram of peptides for the best data) and that this prototype isn't going to smoke your recently purchased $1M instrument, but it's starting in a very nice spot. 

Prosit-PTM! Deep learn modified peptides???

 

We all know other great protein informatics teams are working on the holy grail for DIA proteomics - deep learning and prediction of modified peptides.

Am I extra excited because the team that gave us Prosit is working on it? Yes. Yes, I unfairly am, when I should be evaluating this preprint purely on it's own merits and not the historic success of one of our field's most historically reliable teams.  And not just because of their informatics skills. What makes me excited the most is their long history of making tools that anyone can use. 

Check out this preprint here! 

Breaking through barriers with an Orbitrap-TOF instrument!

Thanks to all the journals allowing Open Peer Review and allowing me to sign about half of the 30 or so papers I've reviewed recently, it's pretty clear to people how unproductive I think things like this title are. Even if, as in here, I really do like the paper. 

I think I'm just old, for real, but I do think that if you've got cool biology in your paper but you've got the instrument front and center you're doing yourself a disservice. 10 years from now that instrument is going to be $100k from second party vendors or $50k on Ebay without an ionization source or accompanying PC and no one is going to look at the biology in your paper. 

However - this is some pretty amazing crosslinking data. And that's my point, I guess. It's a nice study. FAIMS helps a lot with crosslinking on both an Eclipse and Astral, but stepped collision energy - while helpful on the Tribrid, has minimum effects on the Astral. Higher CE helps a lot. There is also a neat toolkit I heard mentioned at iHUPO called "Raw Vegetable" which I assumed someone said but actually meant "Raw Beans" (which I love). You also get a cool step-by-step breakdown on how to optimize crosslink data analysis in Proteome Discoverer. They do some filtering inside the software and break it down at every level. Super helpful for anyone using that toolkit in their lab (I think they use 3.1).

Worth noting, they used the freely available MSAnnika node for the crosslinking, which is pretty cool to see it in use - and optimized through. 

Plasma fractionation increases proteomic coverage!

 

Y'all aren't going to believe this one. For real. 

Everyone out there complaining about the number of proteins you can identify in plasma proteomics and no one has ever tried fractionating it??? What is wrong with us? 

Whoops. Not everyone got that this is a joke. Okay...so...literally for all of time everyone has fractionated plasma in some way to get higher coverage. That makes the title funny. 15 years ago I was running SDS-PAGE gels and cutting fractions and running them separately on an LCMS. 13 years ago I was using something called an OFF-GEL to first fractionate the proteins at the intact level by isoelectric focusing and then digesting the proteins in those fractions and fractionating them at the peptide level to get proteomic depth. The problem with fractionating is that mass spec time is expensive and if each sample takes 144 hours to analyze (example) you only complete 60 samples per year if you never run a QC, a blank, your instrument never needs maintenance and you work every single day of the year. The UK Biobank study 1 would take 833 years. Most people aren't that patient and we're all sort of looking for ways to get a lot of samples analyzed before we retire. 

Hypothetical multiplex tag works in single cells?

 

Did you know there are other tags out there for multiplexing proteomics? Younger people probably don't and I can't tell you where they to get them because I actually truly can't. Let's change the subject entirely.

Did you know lawyers are seriously expensive? Like, for real expensive. If you're struggling with science salaries maybe you should check it out. Okay, let's go back to this paper.

If hypothetical multiplex tags did exist in some places where I couldn't tell you about could those ficitious tags be used for single cell proteomics?  Are you thinking....ummm...yes...? why wouldn't they be? These people found a team of peer reviewers who thought it was useful to check - at Analytical Chemistry! 

And they compared it directly to the commercial reagents that we all know and love. They used the same intrument Orbitrap Fusion Eclipse using 120kDa MS1 and 30kDa MS/MS. The only difference is that the fictitious tags that don't exist and if they did I couldn't tell you where to get them used a slightly lower m/z cutoff. They also optimized at 5:1 tag to peptide. 

For the experimental design here, they sorta mailed it in. 3 tags were used for cells and other tags were used for different controls. They also optimized the carrier to single cells by basically not saying "...why would it be different for one multiplexed reagent when 15 different papers already optimized this on the same Orbitrap hardware...? and said you could go about 100x - 200x to one? 

Then they actually did some interesting stuff by labeling mouse spleen cells with 13 of their available 16 channels. The most interesting part is where they find that if they don't use FDR at all ("set to 100%") they can get 12,543 proteins in mouse spleen cells!!! Someone said "ummm....wtf....you need to use FDR..." and they get 3,991 peptides and 3,602 proteins. So....1.1 peptides/protein on average. Ouch. The FDR calculation scheme is ...nonconventional.... and I almost want to download their data and reprocess it in FragPipe and see if the data is good but the data analysis is unnecessarily strange. Oh. The fun I had when I had free time....

Interestingly, however, the authors get those 3,602 one-hit-wonder proteins to clearly separate the different cells in the mouse spleen into their originating cells, and generate a beautiful T-SNE or U-Map plot, and that's what we came here for anyway, right? The authors suggest some follow-up experiments where they plan to combine both their tagging solution with the amazing commercial one....

GlyCounter - find all those glycopeptides whether you fully sequenced them or not!

 

If you've ever tried to look for a glycopeptide in any type of MS/MS spectra you know how very very rare it is that you get all of the information that you're looking for.

If you want to get full sequence coverage of everything it's probably going to take ETD and 2 different energies of collision dissociation of some kind. The clever combinations of energies certainly help get you more fragments, but they also increase the background complexity. "Is that 8ppm away from the b5 ion or could that actually be the NeuNaC is the third sugar in which case that's the 3 ppm off of the z4 after the loss of a less likely HexNaC at the end? (I possibly made that up because it's equally funny to me if that is a chemical impossibility or if it isn't).

Do you need ALL the info, though? Sometimes I just want to know things like "did this drug increase the number of spectra with glycan related oxonium ions".  I definitely do want to know more than that, but that's what I know how to do with some clever R scripts Conor Jenkins wrote me almost 10 years ago. 

How do you get to real information - and spectra - for glycopeptides in your data in an easy way? 

You don't.

Until now! Hello GlyCounter! 

You're probably assuming "cool, now I just need someone to download some crappy python scripts, fix them and then make me a dummies guide on how to run them." 

NOPE! Check this out! I wouldn't write about it if it was the python thingy, probably.

It's a slick little GUI that takes straight RAW files or mzMLs! Click your options (including whether you used UVPD or ETD(!!!!!!!) and it does the rest, including kick out handy IPSA annotated spectra! 

Important - if you are using a non-Thermo format and convert your data to mzML you don't want to compress them. In MSConvert, turn off that thing. Honestly, that thing messes up a lot of other workflows. If you're converting through FragPipe, it might convert them by default depending on what version of FragPipe you're using. 

Gotta run, but if you need a solid new and approachable toolkit for glycan modifications, you should absolutely check this out.