Introducing the official mascot of the Human Proteomics Organizations!

I've got a lot going on and I've had to take a step back on a lot of things, including the amount of service that I do. Don't worry, the US HUPO VMO is in great hands with Pratik Jagtap and Jordan Burton taking over as chair and vice chair, respectively. I'll still pitch in as part of the coolest committee in all of the Human Proteomics Organization's respective histories.

The last thing I get to do is announce that the HUMAN PROTEOMICS ORGANIZATIONS now have an offical mascot. I've led this important initiative almost completely on my own. It is the least I could do. 

Introducing - 

THE HUPO HOOPOE! (Specifically the African Hoopoe, or Upupa africana)

(Image above generated by the DeepDreamAI. I pay for a subscription, you can use it, but the ones at the bottom are the official logos!) 

A lot of thought went into selecting this majestic animal to represent the prestigious HUPO and US HUPO organizations, but check out the number of boxes that this one ticks off!

1) It nests in dark holes or caves! Just like most proteomics people stuffed in basements because once-upon-a-time mass spectrometers needed big magnets and that made sense. And now it's convenient for real scientists who would rather prefer that proteins (and especially mass spectrometrists) aren't a thing! 

2) When they are out of their caves they make a lot of noise! You can listen to HUPO HOOPAE sounds at this YouTube video!

3) The Hoopoe have majestic crowns upon their heads, which....hmmm....it can't match every characteristic. 

4) All animals (and even bacteria) avoid these, not just because they're saying "transcript numbers have no relationship whatsoever to the amount of protein around" over and over again, but because they smell very very bad. 

5) Perhaps most importantly, things don't bug HOOPAE, particularly not within their caves. They have evolved not only the unique passive defense mechanism mentioned in 4, but they also have unique active defense mechanisms! More details on those here! 

Here are some of the new official HUPO logos. These will also be available on the official US HUPO and HUPO websites soon, but you can download them directly here. 100% reusable art. I actually generated the vector image myself with no AI. 

Admittedly the "action logos" have been somewhat more controversial, but evolution is responsible for the development of our mascot's unique defense mechanism! I just pointed out the obvious. 

I'll put up a folder later. I'm currently just adding the new art as I generate it. 

Microsoft Designer is going all out to help!

Fragment ion intensity prediction boosts TIMSTOF peptide ID rates!

 

This was on here as a preprint, I think, but I also think it got better during peer review.

The idea is pretty simple. The thing about the millions of spectra that things like PROSIT use for deep learning is that they were generated on Orbitraps. Makes sense. Super high quality spectra. However, fragmentation energies and energy ramps and mass analyzer architectures are all different between this and other instruments. Heck, it's probably not out of the question to think that the Orbitrap-TOF Asstral fragmentation patterns are probably not perfectly matched to Orbitrap spectra. Maybe they are, but there are definitely differences between other vendor TOFs and these library spectra.

This group works through those differences and finds they can drastically (3-fold more!) improve identification rates by retraining the models! 

US HUPO Early Career Researcher Seminars May 17th!

 

Need some valuable insight from a panel of researchers from across the range of academic career stages? 

BOOM. Here ya go! 

https://us02web.zoom.us/webinar/register/WN_RNWI3ua4TN-jhz65DJJQuw#/registration

De Novo Multi-Omics Pathway Analysis outperforms gene based pathway data!

 

I can't follow the maths in this paper, but I appreciate the results! 

Most of the stuff we use for "pathway analysis" is based on historic data from....somewhere.... and the details of that somewhere can have some effects on your results. That ...somewhere.... is often data from the past like RNA Microarrays or yeast 2 hybrids or things that have become very niche things because they were replaced with technologies that are more effective for most applications. 

Since things like Ingenuity Pathway Analysis are drawing from thousands of RNA Microarrays - nearly all of which were used for studies of cancer - those tools often aren't all that helpful for things that are not cancer. Do we need these historic datasets? Could we do better by just throwing in our own multi-omics data? 

This group sure thinks so, and the results are very encouraging.

But you don't have to believe them. Neither do I. DMPA has been compiled as an Executable program (or you can run it in something called MuttLab? Woof). and you can get it here! 

JHU Mass Spectrometry day is back! With remote options!

 Thought I posted this a few days ago and couldn't find it. 

Interested? You can register here!  

DEEP LEARNING DEATHMATCH - What actually predicts peptide fragmentation best?

There are all sorts of thing out there now that can learn from millions (billions?) of MS/MS spectra and can take your sequence and predict how it will fragment.

Which one is best? Who knows? Maybe we should have an algorithm DEATH MATCH! 

Okay, again I'm scrambling to wrap up things before they take my keys and kick me out on the street, so I'm leaving this here mostly for you (well...me...maybe...cause I do want to be able to find this later).

HOWEVER their analysis turns out this tables 1 and table 2 are ridiculously valuable. 

They're summaries of immunopeptidomics datasets! Which is what they tested prediction capabilities against. 

Profile rare cell populations with the sacrifice of 1 (one) lab animal!

 

If you've never done it, it absolutely sucks to kill a lab animal to get samples. Not only is it awful but an increasing body of evidence suggests that those millions of years or evolution make it not make very much sense at all to do it for many mechanisms. However, there are some systems where you absolutely have to do it to get that information. What if instead of needing to murder dozens of animals you could learn everything you wanted from just one? 

Hey, wait, this is one of the few labs on earth who are doing real single cell proteomics with mass spectrometers? What are they doing using more than one cell? 

Answer: I suspect having a much better day with their 500 cells than they had with most of their days of working with one! 

Besides being easier, this makes a lot of sense, right? Outside of marketing literature and some people studying single cells that are larger than my dogs, we aren't getting what we expect in terms of today's proteomic depth. But you can FACs sort 500 cells (assuming you have good cell markers) from just about anything.

This group goes through and quantifies something north of 7,000 proteins from every known cell type in rare c-kit+ progenitor population! (review on that here, I didn't know what it was either). 

Now, a fair argument that you could draw from reading other work from this group is that we might not actually know every cell type that is there, but at 500 cell resolution and as good as they appear to be at FACs you can learn a ton. 

While saving animals is a super worthy endeavor and some biologists might be like - ummm....what about animal to animal variation.... - it's one hell of a proof of concept. Some of those things where we can't possibly do human research is because taking a big chunk out of someone is generally not good for them. But you can lose 500 cells from just about anywhere without it being a big deal. Pretty easy to start imaging the future of low input proteomic diagnostics, right? 

Stellar preprint. Highly recommend you spend more time on it than I did this morning! 

Got last generation's instruments? Here's how you optimize them for ultra-low concentration samples!

 

There it is! It was ASAP when I read it on my phone the other day and it moved to this month's issue. 

I might be getting old because the HF-X and Lumos both seem pretty recent to me, but in a review we're putting together we refer to them as "previous generation instruments" and that does appear to be the case.

So...what if that's what you have and someone wants you to run 1 nanogram of peptides or less? Do you want an okay number of peptides and proteins? Or would you rather have 15-fold MORE? Probably the latter, but you do you, yo. 

The reason I took screenshots of the paper with my phone, however, was where and how the peptide "supercharging agents" (as you'll see them referred to in some other studies). DMSO and NBA are employed here to improve reproducibility. I'm pressed for time, but if you're interested in low concentration sample optimization there are a lot of gems in this study. 

Extracted ion chromatograms to improve peptide ID and quan!

 

Now, this study is very clear that this workflow is simple for me or you to add into your or my workflow by just following 3 steps in the materials and methods section and Imma take that as face value. 

because I really like the conclusions. Such as -

and 

as well as 

So when we get through the very last set of mass spectrometry experiments that I will ever run at the Johns Hopkins University (on the systems right now! eeeeeeeeeek! cross your fingers for us that we can get just a couple more weeks without another flood) I'd really like to try this out. 

Molecular pixelation - single cell proteomics (of about a dozen targets) with subcellular resolution (sortof)!

 

I read this while not even Jalen Brunsen dismantling the Pacers could keep me from falling asleep (rough spring so far) but it is totally worth thinking about. 

Coincidentally, this paper prominently features another New Yorker (AOC) which is the only abbreviation I'll now ever remember for antibody oligonucleotide conjugates.

There are a whole ton of single cell technologies where you put an oligo on an antibody and you mix that with your single cells and analyze them. In our experience with these so far - THEY ARE NOWHERE NEAR AS TURNKEY AS SALESPEOPLE WHO WANT TO USE UP YOUR TIME WILL TELL YOU. If you're interested in extremely clean CD8 positive cells in culture and you want to measure the number of cells with CD8 (or whatever) there are solutions. If you want to take some tissue and homogenize it, you might give them a whole lot of money and then get calls from their apps teams asking you to give them tips and tricks because no one has ever pulled it off before. (Reasonably accurate story). 

However - ain't no one giving you spatial estimates of their "quantitative" in a very very vague and possibly inaccurate use of the word single cell proteomics data! (Very very fast, though!!) 

So this new one providing some spatial context on where the proteins ARE inside the cells? That's new to me! 

Okay - then right after I hit post on this the first time I remembered this one that I absolutely need to read! For some reason I can't post pictures right now and I don't have time to spend uploading. Gotta go go go. 

Sharing Proteomics Data While Safeguarding Individuals’ Privacy!

 

Well....at the top of the "I'd rather not talk about this one, but I know we absolutely need to have this conversation..." list! 

Unlike some of the other times this has come up in the literature where someone is like "omg, I can totally figure out who this person is from this dandruff we found!" This one suggests some well-considered solutions. 

Gary Siuzdak dropped a new book - Activity Metabolomics!

Thanks r/massspectrometry! I didn't know about this. I have Gary's Mass Spectrometry for Biotechnology which was a key part of Iulia Lazar's class on bioanalytical instrumentation when I was in grad school. 

This is on an obviously inferior -omics but I bet it's a solid read and it's like $14 in paperback! 

What are R&D instrument depreciation rates based on?

 

If you're just wondering if the guys in suits who say words like "depreciation" and "gestation lag" (and might seem like the sort of guys having fun in college while you were doing enzyme kinetic curves) are just making stuff up - they do appear to have some math to rely on here and there.

This comes from what might be the worst name of an academic journal I've ever heard of, but it's funny how much (what I can understand of it) seems to line up with reality.

That Orbitrap Velos didn't get more valuable while you were using it. It likely got less. And how long was it before you felt you really had to upgrade it to make your customers happy or to get to those low copy number proteins where all the cool stuff seems to hide? 

Carrier channel pollution - the other downside of "boosting"!

This is a thoughtful addition to the challenges with carrier channel boosting proteomics samples. The isotopic impurities are something you can largely ignore when your signal is roughly equivalent, but when it isn't, it's a big problem.

It does make you wonder if there might be a way to make expensive multiplexing tags that are...I dunno.... actually pure...? 

The explosion of "single cell proteomics" papers is not matched by single cell data!

As part of a big review we've been working on for a while, and might be a little late (sorry) we've been really digging into the data behind "single cell proteomics" studies. I really really like this review from friends in Austria because I don't know how to do these publication search term analyses and I can just steal theirs.

To people in the outside world it probably looks like we've completed hundreds - or at least....dozens.... of studies where single normalish-sized single cells were analyzed by LCMS. 

Our list does not have even 2 dozen actually studies. I'm sure that will change at some point. 

Two valuable new entries in everyone's favorite topic - quality control in proteomics!

 

Ready to get super pumped about leaving the lab and not thinking about proteomics for a few days? I'll help you get started with 

QUAAAAAALLLLIIIIIIIITTTTTYYYYYY  COOOOONNNTRRRROOOOOLL PREEEPRIINNNTTS!

Let's start with this great new Primer! 

Just like how you have to put primer on right after you sand most things and before you do the actual hard part of applying the paint people will see --- this'll get you ready for the this bit of important misery --

The definition and analysis of proteomes should be based in reality - not convenience!

 

Got a few minutes to read through a summary of 210+ references and a thoughtful perspective of what proteomics does -and doesn't - do well today? 

100% recommended! 

DecryptM - 31 cancer drugs in 13 cell lines = 1.8 MILLION dose response curves!

 

I'm re-reading this paper from last spring and - wow - 

Don't want to read? Just interested in how a bunch of relevant drugs alter the proteome, phosphoproteome, ubiquitinome or acetylome of different cell lines? 

Check out this ridiculously nice data portal here! 

Is MS Office trying to save all your stuff in some imaginary "Cloud" place?

 

I had another mandatory Microsoft update thing which 1) enabled their ....sub-performing.... blend of ChatGPT and Bing in the lower right corner of my screen where the button should be to get me back to my desktop... and 

2) Makes it so that if I open a document from my desktop and go to save it as a new name it defaults to some imaginary "cloud" thing so I will never ever be able to find it again. You can "other save options" or you can:

Open every one of the MS Office things you use 

Go to file

Go allllllllllllllllllllllllllllllllllllll the way down to Options

Go to Save and shut off this toggle that you never turned on in the first place. 

You are welcome. 

Impact of source conditions and flow rates on CCS in TIMS!

 

So...front end ion mobility things like TIMS are gas vs electric pull, right? So what happens if you run 5x the flow rate for your LC input? Will that alter the gas pressure enough to change your perception of the collisional cross section (CCS) and thereby change the observed 1/k0 values? 

Really really cool study on this here! 

You'll note they're using the ESI source, but if you're using the CaptiveSpray you're blasting your solvent directly into your glass capillary, right? I suspect that you'll see something similar when going from 100nL to 2 uL/min! 

2 new "features" in TIMSControl 5.0. Don't load old DDA methods!

 

You know when you finally solve a mystery that is driving you completely bonkers and you get that relief at first that you solved it.

But then you realize how many samples you have to go back and rerun and you think "maybe I should really truly quit being a mass spectrometrist"? I'm having one of those days. 

Quick solution.

If you upgrade your nice TIMSTOF instrument to TIMSControl 5.0 you'll get the thing above that I labeled #1 and - it is AWESOME. You get much better control over your mobilogram windows that can be guided by your real data. Using it right for DDA can give you dramatic gains in number of relevant fragmentation events. Run a file with your dumb mobilogram that you drew freehand. Then load that file into that PASEF precursor region thing and it will load your whole mobilogram average. Then you can draw the smartest possible mobilogram(s) for your actual experiment. It's beautiful and it might be giving me as much at 10-20% more PSMs on some of these samples. I can't wait to try this on TMT (which moves funny in IMS space and TMT and TMTPro are different, see figure 1C and 1D here) 

However, you don't get this for free. You'll have two new glitches to deal with. The first one is sort of funny. Do me a favor and open TIMSControl 5.0 if you have it and turn on Stepping. Then turn it off. (That little toggle I marked with 2). Then note what happens to #3.

What it'll do is swap your fragmentation energies. Honestly, sort of harmless, but just funny. You'll find that you trigger the same number of peptides but you don't identify and of them because you hit them with the appropriate level of energy for fully liberate TMT11-plex reporter ions. BOOOM.

The other one is more nefarious and what I feel the most stupid about. 

If you load older TIMSControl DDA methods what we find is a dramatic reduction in MS/MS events. If I run the exact same sample with a DDA method from TIMSControl 4.0 back to back with a brand new one with TIMSControl 5.0 while trying to keep everything the same, 

TIMSControl 4.0 in 88 minutes ~25,000 MS/MS scans

TIMSControl 5.0 in 88 minutes ~140,000 MS/MS scans

What it looks to me like it is doing, but this isn't my job so I ain't gonna spend more time on it, is incorrectly reading the Target Intensity and Target Threshold values. Like your max target is now your min target.

I thought maybe it would be as simple as skipping a line in the method, but woooooooooooweeeee, the methods files are very very different.

What deserves two "very"s? There is a sum difference in 280 lines of the methods files between the two software builds. So...while I can't say for sure that the intensity and threshold are swapped, I can say FOR SURE, that the method files should probably not be used interchangeably at all.

This isn't meant to be a criticism of the very nice and incredibly small team that is building the software for all of these instruments. 

Two great new proteomics books coming this summer!

Josip Blonder had one of the biggest effects on my development as a scientist in this field. He's got a fancy emeritus/pseudo-retired status at the NIH now, but apparently he's not just fishing off of Hvar, even if he isn't pulling down the surfaceome these days. He's updated Proteomics for Drug Discovery and it will be out in August!

And when I stumbled on this, I also found this one is coming out just before it! 

The first book on single cell proteomics by mass spectrometry! 

This isn't like preodering a video game. The corporate shmucks can't just cut development and bug testing because they've made enough money for their shareholders. Preodering doesn't change the product, it just means I don't forget to get them.