Lupine - Impute TMT missing values across thousands of samples!

 

For some reason I thought lupine had something to do with werewolves, but if that connection exists, I couldn't sort through Google's ads to get there. You can find some pretty colored flower things, though. 

Lupine is the topic of this new preprint where a deep learning model applied to 1,000 or so of the samples in the CPTAC project.  

CPTAC has been going on for a long time and that puts a lot of confounding variables - that's beside even the fact that these proteomes are from diverse cell types and cancer types. So...Figure 5 is pretty darned impressive....

TesorAI Search - Cloud based spectral libraries - no percolating required!

 

Is there finally a small weird splintery group of proteomics people out there who are starting to think about "The Cloud Computing?" Probably not, but this new preprint is really cool! 

As in any bioinformatics paper there are very boring flow charts everywhere BUT they did us all a favor and kept their equations to themselves. No one likes you showing off how many Greek letters you know. 

Why does it belong on this awful blog, though? There are so....many....tools.... Well, I took a dataset that I know exceptionally well and I ran those files in it, and it looks legit.

AND it took me like 2 minutes to figure out the software. 

Go here. https://console.tesorai.com/

Register a fake email address (don't enough bioinformatics people know how to reach you already... no? use your real email, whatever........you do you) 

You'll get 20 credits/tokens and each one of those is worth 1 Thermo .RAW file.

I used 3 single HeLa cells prepped with NanoPots and ran with a ridiculously low flow rate on an Orbitrap Exploris 480 system that some nice people at BYU put up on ProteomeXchange.

The one part that isn't intuitive is that you should load your FASTA file from the file upload then go to the FASTA tab. It looks like you can load it through workflow, but you have to back out.

It only accepts Thermo .RAW files right now and the C+57 and M+/- oxidation so there are no buttons to push, really. Just load that stuff and tell it to run. 

My 3 .RAW files (about 800MB each) took 38 minutes from run to report.

I'm looking at a protein group report with about 2,800 proteins and that seems very reasonable based on my previous analyses of these cells. I'll have to check, but this is probably more than any combination of search tools have ever gotten me on these files, but they're certainly in the right range.  The PSM report seems reasonable and there are all the normal things - intensity values, funny new score metrics. 

Don't take my word for it, dig out that old Hotmail account you use at the grocery store and try it out for yourself! 

Big shoutout to Matt Labenski for tipping me off to this great use of my lunch break! 

......

 

I know a guy who got a nasty version of hepatitis mouth pipetting in the 1980s, but this is still funny to me. 

Borrowed from the great  r/proteomics community, which is rapidly becoming THE place to take your questions about anything that isn't FragPipe. You go here for that. 

GRable - Identifying glycopeptides from full mass MS1!?!?

 

This at first sounds sorta sketchy until you read the rest of the title (...confidence evaluation with MS2 information....). 

Which then makes it sound like a new twist on the classical way of doing these things. 

Clustering potential glycans sounds super smart and MS2 confirmation - great - but what I really like about this paper is that it leads you to this great data portal! 

GlyCosmos is loaded with tools and great information - and you can evaluate your data with GRable on the portal! 

TIMSTOF resolving both positional and structural histone markers!

 

This is just a solid all-around story on how having just a little extra dimension in your data can help you see some past ions that have the same exact mass and charge (and darned close retention times!) 

The figure above at the top is my favorite part. The ions in panels A, B and C all come off the column at right about 26.80 minutes. And they're all +3 ions at 548.658. 

Check out how different the 1/K0 values are for A and B! Something weird is up there, right? A is at/around 0.82 and B is about 0.9. That's well outside the 0.05 window that people seem to beo okay with putting on these data. Looks like different molecules, right? 

And they are. Well - sorta - they're the same linear peptide sequence but the PTMs are on different sites of these derivatized (propionylated) histone peptides.

C isn't quite as clear cut. The 1/k0 is about the same, so you're back to looking at MS/MS fragments and working them out (which, admittedly, you can see in A vs B, however - without the IMS separating them out, there's a pretty solid chance here that you're just going to see one really complicated spectrum with all 3 in them, right? 

I'm pretty sure they ran the same samples on an Exploris 480 and a TIMSTOF and they can work out most with the former, but they get some new ones in the TT data that they didn't see at all. For people who know what to do with these histone codes, getting more peptides in each sample is something that always seems to make them happy. 

FOXO-regulated OSER1extends lifespan in a bunch of species!

 

Wow, y'all. This is such a ridiculously cool and thorough study....

I mean exactly zero slight to Nature Communications, but until I took the above screenshot from the PDF, I really assumed this was one word Nature. The latter seems to really dig aging mechanism studies (as do I, and not just because of my recent funding sources, getting old is dumb and I'd prefer to do less of it than I currently am) and this is the most complete work I've read in the last year or two. 

On the note of high profile aging studies in one word journals, where this one particularly shines in comparison to a whole lot recently is here 

They provided all of the data that they are legally allowed to under the laws of their country and their continent. And even that can be obtained by filing a request to access it, just like getting patient tumor genomics data from the CPTAC consortium. 

AND this study an absurd amount of data. Proteomics is the tiniest part of it. 

In fact - the 34th experiment described in the method section - is the proteomics. 

They looked at knock-downs of the target protein and overexpression in a slew of model organisms. They followed these organisms over their lifespan and found that more of it increases lifespan and less does the opposite. There is fancy microscopy and ROS assays and transcriptomics - and confirmation of those results with qPCR / RTPCR and then they transfected human cells and that's where the proteomics comes in. 

THEN (Get this)

They looked at a cohort of obviously super cool people in their 90s who are like "hell yeah, you can see why I'm still crushing the 75 year olds on the pickleball court" and they looked at sequence variants. 

And they f'ing find some! That's the data you can't have by just clicking on the link in the paper. The SNPs aren't an absolute homerun, but it looks like something is there. And if you consider all of the environmental exposures and differences in diet and experiences that people have were hanging out on this planet being cool before velcro was invented seeing something from single nucleotide polymorphisms of a protein that is basically completely unknown (look it up, you'll get a bunch of nothing) before this paper is a huge deal. 

I could keep going on about what an inspirational work and what a complete and exciting story it is. It's a short read as long as you stay out of the supplemental and source data. And since I don't want you to think I've slipped off of my game as a cantankerous critical blogger, the source data is a little tough to follow. It's all there, but I had to go back and forth between figures and sheets to a couple times to sort out what I was looking at (or scroll way way down).

Again, that's just me being a jerk as this is by far my favorite thing I've read recently. 

Bravo to this team and I'm super pumped for the follow-up studies they're clear are in progress. 

Forensics backdates cocaine use in Europe to the 1600s!

 

This group was like "no one is going to believe this, so we're going to put every chromatogram and fragment ion in the main body of the text as individual figures".  They broke into a mausoleum, took some pieces of previously undisturbed people and looked for cocaine and cocaine metabolites (they've previously found opioids using similar approaches). 

It doesn't sound impossible, because there was clearly "trade" between Europe and the Americas well before that but this moves the needle back more than a century (according to the introduction, I know nothing about this).

They used a TSQ Fortis II and 10 minute gradients, following solid phase extraction. 

I do know there are some confusing entries in the method section for the mass spectrometer, though.... 

I've never had a Fortis, but I'm sure that these are not accurate descriptions of the hardware. I wondered if they meant 70,000 amu/s or something? but I suspect part of this table was carried over from a completely different piece of hardware. A little scary to see an oversight or error this easy to catch when basing a controversial finding solely on the operation and results of the instrument in question, but otherwise an interesting read. 

Electron microscope fast enough to track electrons - or small media stretch?

 

Ummm....I've recently used a couple of the late Stephen Hawking's books as examples of effective scientific communications. As someone who "owns" the digital rights to two of his books -  "A brief history of time" and "A much more brief history of time for a somewhat slower listening audience" I found both frustating. My brain chemistry or my background or something made a lot of the book really hard to follow. Maybe I could follow some of the concepts in the latter if I had some schematics or a coloring book or something? Hopefully? I don't know.

But something I thought I had down about physics in general was the whole "you can't observe an electron" thing. 

This article is making the popsci circuit this week --

And either I didn't get the concept -or - the popsci people missed something. As someone who used an old electron microscope in a core a long time ago to take some pretty pictures of individual bacterial spores sliced down the middle (if you're bored, proof is here, Figure 3B was stylized by a very tolerant tattoo artist and takes up my whole right upper arm - glycopeptides were tough as hell to analyze on a QTrap) and has literally zero idea how fast an electron microscope is, a faster one sounds better! 

But the popsci narrative seems to have landed on the assumption that this thing can freeze frame capture an electron in place

Which - again - as someone who couldn't follow a popsci physics book while driving on I-695 doesn't seem to be exactly what the paper itself concludes (take this with a pound of salt, or other, I don't know what I'm talking about metaphors/analogies/folk sayings, but.....)

Attoseconds sounds darned fast, though! 

Multi-omics demonstrates nonlinear dynamics in human aging!

 

This is new paper in Nature Aging is getting a lot of well-deserved attention! 

Why? Check out what they did! 

I won't lie and say I can follow these data, but the conclusions they come to seem to be what is grabbing attention. You'd sort of assume that those awful aging things would occur in a linear manner, right? There goes some of that elasticity at 0.004% per day from the time you're 26 till it's mostly gone. What the authors point out is that isn't what is suggested by the epidemiology data - you get to a certain age and - BOOM - chance of stroke doubles. That sort of thing. And when they at multiomics they seem to see large molecular changes happening at certain points. Interesting, a little depressing, but overall impressive to see anyone make sense of this much variable human data. Hopefully just a taste of what every human study will look like soon! 

Amazing new (to me) videos on mass spec physics - and operations!

 I'm adding this stuff to the tutorials tabs over there somewhere --> 

Check out this stuff! 

First, the ShortChemistry channel! 

Alan Doucette does this and they are fantastic. Want a walkthrough from sector trajectory to how the fast fourier transform gets you a mass in an Orbitrap - maybe with a short clip from when the Simpson were good to make sure the point gets driven home? This is a fantastic technical chemist with a great grasp on the physics who is an even better instructor. 

And this is actually brand new - and I love it! I don't know when I last watched 3 hours of anything....

 From the title you'd think....that sounds super boring, but this might be the single best walkthrough of 

1) How to operate a TIMSTOF (with a NanoElute2) 

2) How to set up MaxQuant.Live targeting (on an Exploris 480)

3) And how to run the QC scripts (in R...🤮🤮🤮...but for the second time this week, a blog post will say...and it might be worth it.... ) from NorthEastern/PSTI to look at multiplexed single cell data.

Given blog metrics, very very few people care about number 3, but Arturo is obviously an expert level TIMSTOF operator - and here is an hour of his tips and tricks. If you've never hacked operated an instrument with MaxQuant.Live, Andrew provides a better breakdown of how to do it (on an Exploris 480!) than either the original authors or my pitiful slide decks. My only criticism is Andrew's posture when he's sitting. First hand I know there is a price for that stuff and you're lucky if you don't have to pay up for a couple of decades. Otherwise, it's a fantastic walkthrough that should make you jealous you weren't in Boston in June to see it in person. 

TermineR! Find endogenous proteolytic cleavage events in data you already have!

 

This is super smart, and if anything else can do it, I've certainly either forgotten about it or never heard of it.

What ARE all those other weird MS/MS spectra we don't match to anything? Gene variants are some, definitely loads of PTMs we didn't look for. How much of it is endogenous proteolytic cleavage? 

If you dose cells with a drug that induces CASPASEs to start self-destructing some of those cells, it can be a ton of it, right? But all those other enzymes? This is where it gets super cool.

They process some samples in FragPipe, which they tell you how to set up, and then decide that "FragPipe excels at finding weird cleavage sites" (paraphrasing) and then they use TermineR to figure out what is happening at these weird cut-sites. When comparing a WT mouse to a Pkd1fl/fl:Ksp-Cre mouse (copy pasta) which is a model of some nasty sounding kidney disease they find these endogenous protease activities are different between the two! 

Ever think of looking at whether a funny enzyme is cutting your proteome up in weird ways to see if that's your phenotype? ME EITHER. 

The downside is that the second "r" (the big one) in TermineR means you have to use the R thing, but it still might be worth it because I can't come up with another way to get to these data. 

PeptDeep - Deeplearn all the peptide features on your desktop (with GPU support!)

This is 2 years old? Why did I just find it? 

Meh - maybe it wasn't very good until the version posted 3 months ago that is the one I'm using? 

Here's the thing - lots of tools can do the deep learning stuff for you now. We've had PROSIT for years and ElFragmentador is a great web tool.

Why use something else? 

This one installs locally (pretty easily, as well) and not only kicks out your predicted peptides and peptide fragments - you also get your 1/k0 values (if you're using one of those screwy TIMS things).

In TIMSTOF data it's pretty hard to just go into your officially licensed copy of Data Analysis and find a peptide you're looking for. But if you have the licensed version (or you can beg your sales rep into multiple free demos) you can find your peptide if you know your 1/k0 value and retention time and mass. 

Or you can just predict all of that on your desktop with this easy GUI.

A couple targets is no problem at all. Dump in one protein? It digests it and kicks out your predictions. Super straight-forward. 

Got a lot? DON'T DO IT ON YOUR CPU! Unless you want to heat your office or something. PeptDeep can also be configured to use your GPU. I haven't done that part, but I probably will. Why have 8,000 CUDA cores just sitting there  doing nothing? 

Even cooler? You can easily retrain your prediction models based on your own data. One of the recent updates on Github is an HLA model! But you can take any result file that has real data (formatted properly, of course) and drop it in to improve your models. All the coolest bioinformatics kids know how to do that, but this is the first time I've had something easy enough that I could use. 

Deep Visual Proteomics of Signet Ring Cell Carcinoma

 

Damn, this preprint is sort of tough to read because it's pretty clear Signet Ring Cell Carcinoma fucking sucks and I think I know the dude all these samples came from. 

However, it's a great demonstration of what Deep Visual Proteomics can do - and hopefully this information can be used to help beat or control this disease I'm not sure I knew existed. 

1000% recommended reading.  

Beyond protein lists! The possibilities of AI in downstream proteomics!

 

I put the figure in this post - not to make you not want to read it, but because I had trouble finding something funny to put at the top, but this really is a cool commentary in my post backlog! 

I recently had some of the worst "collaborators" of my entire career. You know the ones - where they want to drop off samples for you to prep, run and then stare at you with disbelief, contempt or hatred when you provide a curated list of proteins or metabolites and quantification values. "What do I do with this" they say or think or yell. Which always brings this to mind....

--- because, honestly, what was the goal in the first place? There is a whole permanent page over there somewhere with some of the next steps --->

And what you'll see is that most of those ideas for proteomics require putting protein lists into gene centric programs, and we know that's pretty silly. If you've actually taken a biology study all the way to the end yourself you've probably had 100 of your most differential molecules and typed them one at a time into the Google or the PubMed thing. What if instead of making Space Karen memes to anonymously post on Reddit you were using that to help find patterns in protein regulation data? 

Less funny, but probably a lot more productive! 

Screw it, just 3D print a new quadrupole!

 

I missed this a few months ago, despite the warm welcome it appeared to get! 

As someone who just upgraded to a modern $99 3D printer after my last one got destroyed in an office rainstorm my first month at Johns Hopkins, I can tell you first hand this technology is improving like crazy.

What can you do with a printer that costs more than $99? You can 3D print mass spectrometry components! They do have to electroplate them afterward, but what might this do for our field? Got an idea for a better mass analyzer? 

SimIon is, what, $800? Design your mass spec there. 

(Or - thanks Dr. Steele for this link, there has been some progress in modeling mass spec physics for free with Blender. Reddit post here.) 

You can basically just take sketches you've drawn and feed those to AIs to make solid 3D models in case you aren't great at any of the dozens of (often free) CAD design tools out there. 

3D print it. Nice printers like the one used here are often available as pay-per-use models at universities, libraries, or little companies (like FabLab here in the greatest city in the world). 

BOOM! Better mass spectrometer! Don't think there is room for innovation today? You're absolutely and completely wrong. Thermo just dropped ANOTHER OrbiTOF design ! 

Stellar requires a lot less power - largely due to smarter vacuum pumps!

 

At ASMS 2024 Thermo released the Stellar MS. You can read about it here. It's an ultra fast ion trap system that can provide nominal mass (say +/- 0.5 Da, normally? something like that?) in what sounds like an affordable package.

One thing that I think hasn't got much notice is that it uses a whole lot less power than you'd expect. I don't have a full specification sheet, I was told it would be online soon, but it looks like the vacuum pump has a lot to do with it.

I went down a rabbit hole for a project I'm working on and it makes me wonder why everyone hasn't switched to more modern roughing pump/forepump designs. 

Every mass spec I've ever used as been an oil filled rotary vane pump, I'm pretty sure. I expect those to require around 700-1000W on their own.

Apparently dry scroll pumps have become a lot less expensive recently and the reason for switching is pretty clear --

260W? That's a lot less than 700W!  

The SogeVac SV65 I see a lot is capable of 0.5 mBAR at 700W. The little Edwards can beat that at 0.2 mBAR with 260W power draw. Now, I know how much power you use doesn't matter to most mass spectrometrists, but for startups cutting your power usage in half or to a third can be a big deal. I do know people who really do care about the $1000/month in power a couple of mass specs need while their trying to eek out a living existing in cost margins. 

FFPE Proteomics Sample Preparation! What modern method provides the best data?

 

Did some weird old doctor just tell you they have a crapload of formalin fixed paraffin embedded tissues they'd like to do proteomics on? Or worse....phosphoproteomics? (Eek)

What modern shotgun proteomics prep method is the best today? 

Don't test them all for yourself! That would take forever! Reading this would be faster.

The current status of single cell proteomics - webinar 8/21/2024!

 

Y'all, some uninformed person nominated me for an award earlier this year, which I won and I got a cool trophy and I get to give a 1 hour webinar on what I and my little team worked on at Johns Hopkins from 2020-2024.

I think it makes sense to take a step back and cover some stuff like "what can single cell proteomics ACTUALLY do today" hence the title. I'm most excited to get to stuff that no one has seen yet, particularly Dr. Colten Eberhard's postdoctoral work, which is basically only being held up by how very slow I am at R. Colten's goal was to take SCP to the next level - large scale in vivo work. He spent months developing dissociation protocols with a heavy hitter in the scSeq community to get cells out of mouse brains intact that could be isolated into single cells. He got it to work, but then we hit a new surprise. If you're interested you can register here. I suspect it will be recorded and available for watching later. 

FedProt - Facilitate clinical proteomics while protecting patient confidentiality!

 

I'm sure something like this will need security reviews etc., that are government specific, etc., but I've never seen anything like it.

Most of the hospitals in the US now use a turd sandwich of terribleness known as EPIC Systems for sharing/storing/transmitting all patient information. Google thinks that 78% of all patient information in the US is held within their systems. A large part of why it is so bad is because the absolute center or any system like this has to be making shitloads of money protecting patient privacy. When that is your number one or two focus, making a stable interface that doesn't require a dedicated IT team at every facility to keep it up and running has to fall to your 11th or 183rd priority. 

Imagine most quantitative proteomics workflows today. How well protected are those data? Imagine that you found a BRCA2 mutation absolutely on accident while doing something else. Could a very motivated insurance person trying to get out of paying a $900,000 cancer treatment get to those data? 

FedProt was designed to allow quantitative proteomic data (currently MaxQuant looks like it's the main support, but that's probably header editing) and sharing and transmitting and linking back to patient information. Again, I'm not at all qualified to dig into how secure it is or even some of the fancy maths on the statistics, but I think it might be the first entry to try and fill a critical area of need before we realize proteomics as diagnostics. 

opti-PRM - dramatically higher PRM sensitivity/selectivity with automatic optimization!

 

This new method/paper at MCP makes a ton of sense and it should possibly bum us all out a little.

The thing we all love about PRMs (PARALLEL reaction monitoring) is that the upfront setup is so easy. Put in the mass of your targets, maybe the retention time in your instrument method if you're fancy (or want a much larger number of targets) and run it. Chances are you can find 2 or 3 good fragment ions from your target at/around your measured or predicted retention time to use for very selective quantification. 

On a low resolution targeted system like a triple quadrupole system it's a lot harder. You need to know your retention time well and you generally need to optimize your collision energies so you get a couple of really good fragment ions. BOOOOOOOOOOOOO. 

However, in the back of all of our heads we all know that peptides fragment differentially based on their sequence, length, etc., etc., and we could probably get more signal out of our PRMs by optimizing more (ask the small molecule people if you don't believe me). 

This group finds tremendous gains in sensitivity by optimizing their PRM settings individually. Collision energy makes total sense to me. Particularly for the weird nontryptic peptides that are the focus of the study. I'm not as clear on why the quad isolation is adjusted as a function of the parent ion m/z.....I guess if it's a big +1 you use a bigger window to capture the M+1 isotope. Actually. That probably is it. 

Sounds like a complete drag, right? THEY AUTOMATED IT! And you can get the scripts! 

A high (accelerator based?) dynamic range ion detector for TOFs!

 

Ummmm....