Is the US HUP Ed&Out the coolest committee you can be a part of (used to be VMO)?
YES!
Huge success that started when Amanda Smythers started pulling together amazing silly rhymes last year at a meeting and Dragana Noe ran with it and made this super clutch finished product.
So much work! Who is pumped for the US HUPO video competition this year? Me? You? Really? Submit your videos!
I swear, I thought this was on the blog somewhere and I can't find it.
If you're in Europe or other places, I guess, you can just disregard this entirely. Rumor is you have a variable or variation DIA window button. If you're in the US, there is a trademark thing and only one vendor(?) can give you a V on your instrument by default.
Huge shoutout here to the Slavov lab for posting RAW files in the plexDIA preprint back in 2021 or something because this way of setting it up is more efficient than how I was setting it up.
As you can see above - you need to set up multiple experiments. In this case I'm acquiring MS1s on my old Q Exactive in the dark and generally extremely damp Biophysics basement at The John.
A Q Exactive Classis is slow by today's standards, so I think you'll definitely see people who will drop the MS1, depending on what their software actually requires.
So the trick is to set your loop count to match the number of windows in your "Inclusion list" file.
This is what your inclusion list should look like - I strongly recommend going into this and going File --> Export --> .csv or .tab delineated (whatever makes you happy) then opening that format in Excel or something else. Then you can use quick equations to set you center mass and overlap. Here I think we're getting a 1Da overlap on each side of each window, but don't quote me on that.
I'm 91% sure that you can also have Skyline do this for you, but - yet again - my Skyline expert is off making a lot more money than me, so the infinite cycle will continue. My cycle is this - sometime in 2025 I suspect I will pay to have some super bright young person go hang out with Brendan MacLean and some other brilliant targeted mass spec people and she/he will come back and tell me how cool Mike MacCoss is and for the next couple of years I'll have a Skyline expert! Then we'll have loads of pretty plots and matrix matched curves and then disaster will strike - Asta-Zeneca or Merck or Moderna will offer that brilliant young person more money than I make. I'll be super happy for them and they'll leave and reviewer comments will come in where it's like "can you do this very simple Skyline thing and we'll accept your paper" and I'll say "...no. No, I can't. I'm way way too dumb to do anything in Skyline myself even though I have tried to sign every support document for the software since 2012 or something.
Above you've got the first 25 windows that are 12.5Da and then the next batch is 25Da and the final is great big 62 Da or whatever.
Considering where most of your peptides are in overall m/z ratio, you might want to adjust this. For example, if you were just LysC digesting and you were pushing up the distribution of the m/z of your typical peptides by about 70%, you could see a 4th experiment where you were using wider windows in the lower m/z mass range, narrow in the middle and then bigger then really big. This specific bit of vaguely and probably not usefulness I just typed was inspired by the r/proteomics entry that inspired me to spend 33 minutes before my first meeting today typing this instead of getting a shower and what appears to be a desperately needed second coffee.
Since the Virginia Tech/Google $20M drama continues, my GoogleDrive that basically every link on this blog went to has been deleted. What would be helpful, I think would be if I put the actual .meth and these tables up in links somewhere. I'm now at 36 minutes somehow, so I can't do it now. If you need these, email me, my contact is over there somewhere ---->
and I'll try to get these up where you can direct download them later
Okay - so this is really interesting and I may need to sleep on it, but here is the idea -
Ideally we'd be able to see every proteoform MS1 rapidly and have instruments fast/sensitive enough to sequence them. We can use 2D separations to get there pre- or post- digestion, but in no case are these experiments fast.
Seems to work, too! The proof of concept appears to be an E.coli proteoform atlas.
Not to brag, but I don't really have any hobbies except reading proteomics papers and maybe finding out that there are a bunch of proteomics people together in some city (or ski resort) and going there whether I'm invited or not. As such, I'm sorta plugged into what's going on in the community.
I personally know/knew 1 (one! yes, one!) invited speaker for US HUPO 2025. Aleksandra Nita-Lazar, who is as good of a scientist as our field has. And then - no one else. So we're legitimately interviewing completely new victims doing crazy badass science. Huge props to US HUPO for this year's line up. I need to upgrade my tablet thing because there is no way mine will survive all the notes.
Case in point? This is just about the sickest fucking paper you could read right now and December of 2024 might be the coolest month for proteomics in all of history. I'm not joking. I can't even keep up with mindblowing advance one after another. And - again - lack of hobbies.
How did I miss this??? I legitimately do not know.
TAU?
APEX Interactomics??? (Temporal - hey this protein is hanging out with THESE Proteins! Right now?? Yes, right now)
Neurons generated from IPSC stem cell thingies from healthy patients and those with dementia? (Disclaimer - went to one stem cell conference in 2009, learned/absorbed very little).
So cool, and we got to spend a morning talking with Tara Tracy for the show (next weeks? week after? something soon) and I'd walk to Philly to see her speak. Google helpfully just informed me it's like 12 hours. Meh. Still would do it.
My favorite part about this new Nature news feature was that I thought it was an article from 10 years ago.
No...but the first citation is that article from 10 years ago...
I'm again going to put off blog posts on the 50,000 human proteome cohorts using "next gen" spot-based targeted proteomics.
I get it - I love the idea that we can use alternative technologies and get to this kind of population level protein level studies. But -just to remind you - we don't know the answer to this question -
If a cell makes an alternative form of a protein you can almost guarantee that there is a good fucking reason for it.
Case in point - what if you treated one of the single best characterized proteins on the planet - not as a protein - but as a proteome?
This team took a look at multiple "purified" forms of trusty old bovine serum albumin and treated it like a population of proteoforms - and
1) It definitely is
2) "Purifying" a protein is....umm... something you'd think was 100% super well defined. Let's go with "could use further definition and characterization".
We've seen things like these in the past - here is an old post where a modified Exactive (I think what later became the Exactive EMR, one of my all time favorite little boxes) - pulled out 59 different forms of ovalbumin.
That's hard to look at and really encapsulate. Woodland et al., went full classic protein biochemistry and - this isn't hard to understand. This is a "purified albumin" separated out by isoelectric focusing in dimension 1 and by SDS-PAGE in dimension 2.
This is a super cool and thought provoking study that pokes some holes in more than a couple of our normal assumptions.
STOP. IGNORE THE FLOWCHART ABOVE. These are bioinformatics people, they think this stuff is mandatory. I assume their conferences all have contests where the winner makes the flowchart most likely to make someone in another field throw up.
Again - don't look at it - 'cause this is legitimately important.
You know how the genomics people have been doing things for years with illustrious sounding titles like "The 1,000 Human Genome Project?" Particularly when a lot of those things kicked off and the technology was more expensive, these things absorbed HUGE amounts of research dollars. The goals were to undestand how human genomes vary across us - as a species.
And they did these things and they kept the results 100% secret from everyone forever.
I guess that's not true, but -to me, as a human proteomics reseacher -they have been less than useless. Yay, you did a bunch of stuff. Who does that help? Not me or anyone I know. Even researchers I know who focus on health disparities can't get usable data out of these things.UNTIL NOW.
What these awesome, though flow-chart loving people did was dig into these top secret genomic databases and they assessed -
-you won't believe it -
Protein level changes across human populations! This is where it gets important.
How many peptide level variants could there possibly be in 1,000 genomes? 12? 15?
Try 54,679! Don't believe me? Here is a completely not illegally taken screenshot. Don't sue me!
How many are you looking for in your data? One? Yeah, me too. I mean, unless we're doing deep cancer genomics and then we search for 2 million. Why not normal variants?!?
Okay - are you thinking - "big deal, I probably need to spend the next 10 days downloading klugey python scripts written by proteomics people and finding out that my Docker thing is from 2017? How on earth does this help me?"
And this is where this is super legit.
Go here. https://zenodo.org/records/12671302
Download this -
Use 7-zip or something to unzip it twice. (I don't know, it's right there with the flowchart competition, bioinforomatics people have contests to see who can Zip things the most number of times. Bonus - as in here - instead of naming each Zip .zip you can name them weird things. The first thing you unzip is .gz, then it will make a .tar, and you also unzip that - and you'll get the whole reason I've written this entire thing -
100% check out the paper. They did other smart stuff and there are other (possibly superior files depending on your application.)
If you're using FragPipe (you should be!) check out this advice from Alexey!
1) Super legit
2) Seems really approachable
3) Kind of resets the bar in my head for what we can do right now with today's off-the-shelf technology.
Generally when we see a super impressive top down study I flip through it and then think - cool - maybe I'll be able to replicate it in 10 years? There is often modified instruments or things where you think - if I was able to keep my core scientific team together in a group for a decade we could pull off something this hard.
Not to say there isn't some legit technical firepower on this study (Kevin Gao is a pro's pro mass spectrometrist), but you can read through this protocol and think - wait - could I totally do this?
Instrumentation is an Exploris 240! (Approachable, affordable, clean it yourself hardware!)
The HPLC is a custom Accela....okay, well...I don't have one of those, but it is running at 400nL/min with an interesting combination of buffers. I assume any U3000 RSLC, Eksigent or whatever could assume those same performance metrics.
Custom nanoLC source. Details in references, but you can make a nanoLC source from Legos. Probably not that tough to reproduce (or necessary). There are funny little bars that are necessary for the Exploris systems when you make your own source and those can set you back several hundred $$
They used TopPic suite for the data analysis, which you can get for free here, as long as you sign stuff saying you won't be a jerk. For some of the focused proteoform specific (is the phosphorylation site at this place or this place) they (interestingly) used BioPharma Finder. I've never loaded more than 5 proteins in that at a time and it's super slow with that many. I assume they put in one sequence and a narrow time window in order to really lock down that one target they're trying to localize.
The results are well displayed - really pretty and clear - and, again, really might just change your mind about doing top down proteomics. Bravo to this team, I legitimately loved reading this paper from beginning to end.
Wait - found something to complain about! Whew, I was worried. They haven't unlocked the PRIDE repository so I can look at the files. It was just accepted (JPR ASAP).
I think it is, though I also thought that in 2022....
and...maybe I did win the Chicago one...? No, it looks like I tried to print my own shirt and the company thought I was playing a joke on them? Weird.
Well, if you think you can beat my entry, go ahead and try! Mwhahahahahahaaaa. You can waste your time submitting one here.
Check out one of my favorite techniques of the last few years - the NanoSplits paper here!
If you aren't familiar, what this does is label free preparation of REAL NORMAL SIZED SINGLE ONE (1, uno, um, eins, jeden, yski, en siffra, een, ichi) at a time on glass slides using precision robotics.
THEN the lysed cell is split into 2 fractions with most of the protein going one way and more of the little transcripts going the other way. You do single cell proteomics on the fraction with more protein and you can amplify the transcripts in the other fraction for transcriptomics.
BOOM! You get everything! Now, there are obviously some drawbacks here, including that it is really hard to do. You need the precision robotics. This team features some people with serious instrumentation backgrounds but also people with a history of simplifying methods so mortals can eventually do them. We've written 2 grant applications where the technique has been prominently featured. The scSeq people are a whole lot more comfortable with this measuring protein thing if they can get evidence that you aren't just making stuff up!
What's super cool here is that while multiple groups have shown complementary data by doing stuff like single cell proteomics and single cell seq on the same or very similar populations of cells (my group's first study was dosing the same cell line from the same source with the same drug - in a recent study) - here you get a real - Cell A proteomics and transcriptomics fill in a specific pattern. Cell B the same.
The authors are quick to point out that NanoSplits could be a bridge technique to unify findings between more traditional studies where you either do SCP or scSeq or both on the same population. A small number of cells split could explain discrepancies between these 2 data types, or help you truly link 2 populations together.
Seriously - a phenomenal, clever technique with top notch data collection and informatics and when I resubmit a grant in a couple of months I'm sure my reviewers will be excited to see a prominently published paper rather than a link to a preprint.
WOOOOOOOOOHOOOOOO!
Last year it was long read sequencing or something (they forgot to include it in the 2014 issue, I'm pretty sure).
Check out this special virtual issue (click on the references!) highlighting a bunch of cool people in our field and their work!
Also, you probably shouldn't go past Fengchao and Sarah's paper here. Link might be the preprint.
Quick breakdown, though -
Imagine you run 50 LCMS runs on different patient samples.
In 35 of those runs you fragment and successfully identify PEPTIIIIDEK, it's pretty much 100% +2 charged and 634.3608 and comes off at 15.6 minutes
In the other 15 runs you see a +2 peptide at 15.6 minutes but you don't fragment it or don't get good enough sequence quality for a positive ID.
Match Between Runs (MBR) to the rescue! It donates that identification to the runs where it was not identified.
Perfect idea, right? What's the problem? There are crapload of peptides in any tryptic digest and they coelute a lot. And as the dynamic range of our instruments keeps going up we see lower abundance peptides that we might not have before.
Compound this with shorter LC gradients
And the fact that every mass analyzer has a +/- mass error
And the retention time on nanoLC, which everyone is pretty much using for some reason no one can justify, is probably more accurately, in those 35 runs, that peptide is coming off somewhere between 14.5 and 16.5 minutes - and now you might be quantifying the wrong peptide.
The top link above is to IonQuant which works in FragPipe.
Could you take that idea and build something even better? Maybe! Just do this!
Here, I fixed it for ya! Even though I can clearly see why I also wouldn't have recommended Ben Orsburn as a reviewer for this one, I do actually really like this new study.
"-of course we understand that single cell proteomics is not actually a single cell"
And, while that was not at all unexpected because this was not a dumb group of people. They're up to date and read a lot and obviously they realize that
THE VAST MAJORITY OF "SINGLE CELL PROTEOMICS" PAPERS
DO NOT
DO PROTEOMICS ON ONE (1) CELL.
They don't do one cell because it is still hard to do. Believe me, I don't care what hardware you have you really have to be on your A-game with everything planned out and have some luck (no lab floods helps a lot) and a 384 well plate that is slightly mismanufactured so it sits silly in your CellenOne can break some expensive glass and you walk away with nothing at the end of a 14 hour day in the lab when you didn't get to stop for lunch (true story).
So people do things like flow sort 1,000 cells as in the study above or they stain and cut out 10 or 200 cell regions based on cell type specificity markers (as they also did in this study) and to really boost the impact of their paper they put "single cell proteomics" in the title. Or, if they're super on their marketing game they'll name their not-Single-Cell-Proteomics method something like SCPro. Deliberately confusing?
Again, in this study - which I do seriously like - they do both. The microscopy is nice, the flow sorting looks good. The front end prep on a tip with SCX and C-18 is - in my somewhat professional opinion - probably a whole lot of extra work for very little actual gain over doing something with less steps and places for lower concentration peptides to bind. But the library generation and diaPASEF analysis on a TIMSTOF Pro results in a solid number of IDs. (50 um custom columns, with low flow rates on a nanoElute). When you get down to what looks like to my eye, probably 5-ish cells sliced out, (probably 1ng-ish of protein/peptide) they're getting 500 proteins, which is a solid achievement. At 10-30 cells they're getting above 2k. (maybe 2-6 ng of protein/peptide). Again, nice numbers.
The downstream analysis is well integrated and the files are publicly available for both the LCMS files (I haven't checked, my office PC has software on it that doesn't like the iPROX access portal sometimes).
Again, it's a nice study, working with a grand total of 100 nanograms of peptides from 1,000 flow sorted cells does still require some finesse. It is, however, frustrating to spend weeks optimizing the isolation of one (1) single cell at a time and analyzing them one at a time and then hit these new perception hurdles. Like - well, this other lab is doing single cell proteomics (they aren't) or that no one can actually analyze one single cell when we have whole conferences where the admittedly small number of researchers actually doing one cell at a time do speak about it.
These perception hurdles have always existed, though. I have little scratches in the surface of my relatively new tablet where I've broken the expensive little tips off of the pens that can write on my relatively new tablet when people have said "well, mass spec isn't quantitative." 4 scratches in the last 2 years, for sure.
This would have been a super positive review (aside from the SCX/C-18 tip) without the title and misleading name for the technique.
SPONTANEOUS ACHILLES EXPLOSIONS!
Proteomics to the rescue! (By the way, there is this whole series of bizarre children's books where there will be some silly problem and it's all COWS TO THE RESCUE or something. It's funny by the 11th page and continues through the 6th book somehow.)
Obviously, this group wants to understand why sometimes people's achilles up and explode just for fun, and they are able to get samples from patient who end up getting correction surgeries! Obviously, this is yet another place where genomics/transcriptomics of the tissue will probably tell you nothing - so it's proteomics time!
Interstingly the group breaks out iTRAQ 8-plex and does pooling, offline fractionation (by SCX, I think, but I forget now, did a singing daycare drop between reading it and now) and then analysis of the fractions on a Q Exactive (Classic, I think). All the files are up on PRIDE where they should be. I have no issue with iTRAQ 8-plex here, by the way. They turned up the collision energy, fragmented each target 2x before putting it on the dynamic exclusion list. The 8-plex allows them to run the QE at maximum (vendor permitted) speed with 17,500 resolution at m/z 200.
What I do have an issue with is the surprising pictures of the operation itself!! I was expecting a calibration or volcano plot and - blech.
Seriously, though, it is all pretty intersting. There are structural differences in the ruptured tissue that are clearly visible and they go into depth with IHC. They find a panel of targets that might be indicative of potential rupture candidates? It's a super compelling study all around - on something I now get to think about.
Interestingly...I think this is a dataset that would be a solid candidate for a reanalysis because it looks like this group didn't consider common collagen PTMs. I'm assuming when they considered dynamic oxidations they exclusively mean methionines. Collagens are hyper-modified. In fact, in the BOLT cloud search engine, Amol wrote in a whole crapload of common collagen PTMs in the first pass search because they're just that common. I think he got that idea from the cRAP database guy, whatever his name is. 😇
It'll take me a while to update everything on all the internet things but we can finally wear these new hats openly.
We'll get used to the colors, though orange and maroon will likely always be my favorite combo.
We're moving so I can get started as Assistant Professor in the Department of Pharmacology and Cellular Biology (through the new institute I don't know if I can talk about yet -soon!). I'll also be helping out Stacy Gelhaus as an Associate Director in the phenomenal Health Sciences Mass Spectrometry center.
I JUST ORDERED SUPER EXPENSIVE BIG HEAVY THINGS and saw pictures of crates with MY name on them. Not someone else's. My name. Weird. Normally "care of" would be expected. Huge shoutout to my friends at Bruker Daltronic for helping an Assistant Professor's money stretch out enough to cover what I need to start
I bet the nerds pulling down ribosomes and cutting the attached DNA with nucleases, then busting up the ribosomes and sequencing what didn't get digested (RiboSeq, definitely not convoluted at all) don't either. Sure, RiboSeq is cool - and programs like ProteoFormer have been around for a while combine RiboSeq and proteomics data output.
But I don't know how to do RiboSeq. I'd have to go back to a grant application that wasn't funded a while back to figure out who on our team was the expert on that part to even know who to start asking dumb questions about the technology. OR I could just do this? And have an output I understand that says "your drug is causing the cell to start making proteins A/B/C right now?"
Again, this is an optimization, but the authors use the original long acronym thing you don't need to commit to memory (because theirs is better) and demonstrate that is can also be used with TMT, it seems to work best with SPS/MS3 based TMT quan.
I don't know about you, but I'm waaaaaay dumber than usual when I'm at really high elevations. Not only dumb, but also lazy and tired and 2 glasses of wine and I might just fall right off of a ski lift.
I went to a wine convention thing in Colorado at a Ski resort years ago and found out all of these things.
This study tried to get to the bottom of this by collecting proteomic and metabolomic samples from people who went to work in high elevations for 6 months. Holy crap. Some of this work was at 4,800 meters in elevation. There isn't anything in Colorado that tall. The highest lift I fell off of wasn't even 4,000 meters. I'd be useless up there.
The metabolomics was done on a Q Exactive and there are details. Waters BEH 2.1mm x 10 cm column at 350uL/min for a completely secret gradient length. It literally says "over time" and if you want to know about how they ran the Q Exactive - you did not come to the right authors. No joke, check this out.
We optimized it for best performance and we will never ever tell you what that was and it's weird you'd want to know. The reviewers were not on top of their game for this one. Weird to see something like this in JPR. Nature something or other? Sure. Not JPR or MCP. Meh.
The data does appear to be publicly available if you're all nosy and want to know or...you know...if you thought this was cool and you wanted to reproduce this work.The plots are really nice, though. MSStats was used for the super secret proteomics data and an R wizard was onboard who tells you what packages he/she/they used. Hmm...no version info - but AHA there is a github and it is populated. The biology looks cool and I really dig some of the graphs, so Imma post it. The authors could put the complete mass spec methods in the Github later since it doesn't appear to be published to Zenodo or something that will lock it from alterations.
Image from my kid's very favorite thing to fall asleep looking at - NIH Bioart!
When Single Cell Mass Spectrometry is announced, I'll add it here. Please ping me if there are others. I have no idea how I missed that Asilomar thing a couple of years ago, but I still feel dumb about not even knowing that I should be there.
Here are the first two!
Single Cell Proteomics (US/Boston/NorthEastern) May 27-May 28
European Single Cell Proteomics (I love this one and it crushed me to miss 2024 in one of my all time favorite cities) Vienna! August 26-27!
I'm leaving this here largely for me to check if I can find plasma proteomics by LCMS that I can confidently link back to this same cohort. There is a lot of LCMS proteomics data from these authors, though all I have actively worked with has been muscle biopsies.
It should be noted that the precision of SomaScan has been shown to be solid. In 8 years of watching for it, I still haven't seen any evidence that the system produces results that are an accurate reflection of the amount of protein present.
US HUPO in Philadelphia? (February 22-26!)
ASMS in the greatest city in the world? (June 1-5!)
International HUPO in Toronto? (November 9-13!)
If you're already in North America - Toronto is your one chance to get the amazing artery clogging amazingness that every European takes completely for granted. Toronto has döner kebab places EVERYWHERE! Even in NYC - which has everything - you need to really look, and probably hop on one of their famously clean subways to pop over a couple stops to find one. Toronto? All over the place!
I did my PhD with an amazing chromatographer as a co-mentor. As such, it was just easier for him to handle all the hard stuff and I'd just run the vacuum chamber things. I like the simplest HPLC possible. Less to break and even better if I don't have to EVER look at a diagram like whatever the picture above is trying to explain to me. There are definitely switching valves involved.
However - I could sure think of 4 things I could use ultralow flow rates for right this second. What if I could get that with ONE loading pump by preloading plugs of solvent that naturally form gradients by diffusion? Sounds like magic, but I'm absolutely interested!
Check this craziness out here!
