Every couple of years this goes out and I nearly forgot! This takes 90 9 seconds or so (unless you write a personalized letter, which helps) and helps keep Skyline developing and provided as a great unifier for the research community.
Please click here and follow the instructions, cause Skyline is waaaay too annoying to pay for!
Cyclic ion mobility? That sounds weird, right? I hardly need to use my ion mobility that goes in a straight line and often forget that it is even there.
But what if there are critical phosphorylation sites out there where a phospho on the 8th residue turns the protein on and the 10th and 13th residues all do something completely different?
Maybe it's time to crank up the spin cycle with this thing?
I've had a scholar alert up for quite a while for ALS proteomics and this nice review puts into perspective where proteomics can have a major impact in at terrifying human disease.
HEY YOU. Yes, you. Would you like to help finish it? Contact me! I've got tons of notes and an outline and amazing data from like 10 different groups who all found really interesting things in it.
My favorite is a glycopeptide that almost every group identified that, as best I can tell, has never been seen before (from the ASMS Halloween poster):
Seriously, check out that awesome review to get motivated and if you want to help those authors reach their goals of finding some new proteomic biomarkers for the disease, a huge dataset is sitting right here. I'll send it right over.
At long last THE Proteomics Show is back! Wherever you get podcasts!
What better way to kick off 2023 and the final leg of the Road to Chicago than with the one and only Dr Ying Ge.
I think my first question was if she had 50 postdocs given the productivity of that group. She doesn't. How are they so incredibly successful? You'll have to listen to find out.
30 human brains?
Multi-enrichment to get to the glycoprotein/peptide patterns? Check this out!
Would you be surprised by differing patters of glycan distribution? Me either, but I'd have no idea what to do with those data.
What really shines in this paper, besides the obvious biological importance and loads of work that went into it is how they visualize the patterns of their observations.
If you're wondering what to do with glycoproteomics data this is definitely worth a look. Frustrating things about these PTMs are (among many) the fact that your biology might be that one sugar may be over/under represented in the glycan chains observed. How on earth do you display that?? Like they did.
Well....that was a rabbit hole that ended at some old scifi horror show about tree monster. An ad that said the "root of all evil" made me think they probably gave it the correct level of seriousness.
HEY! Remember when we did like 2 condition proteomics? That was cool, because we could run our control, then our treated and spectral count some stuff. No fancy stats, just crap data! (J/k)
Now, however, we have to do the grown up science with lots and lots of samples. BATCHES of samples, you might say, and now we need to start thinking hard about BATCH EFFECTS.
What's that? Well, that's the differences that aren't your phenotype. Those are the ones that are due to the fact you had a lot more dog dander on yourself when you processed the samples on Monday than the next batch of samples on Friday. Or, if you're in an old dungeon where the HVAC swings your internal temp from 45-95F between those dates....those changes.....
And that brings me back to BIRCH!
It'll try to sort out what effects were caused by that dog dander when it was 95F in your lab and what was actually due to your CRISPR knockout!
What we should do is hunt down the most die hard specialists of each one together -- with some folding chairs -- and leave Chicago with at least one less than we flew in with!
I don't actually know if that is what we're doing, but it is a funny thought.
I have my opinions. I bet everyone who shows up will have theirs. Maybe we still need more than one method for doing LFQ based proteomics. Do we still need 45? Maybe? But I bet a bunch of us in a room with some folding chairs can sort it out!
As an aside, it just came to my attention that when I use the "line through text" button on this blog it DOES NOT DO THE SAME THING AS MICROSOFT TRACK CHANGES.
I got MS Ana in Vienna at when I was lucky enough to speak at the Proteomics Summer School thing about 1 pandemic ago and have mentioned it on the blog a couple of times.
BOOM -- MS Ana restores your ability to do spectral library searches!
Even better? MS Ana gives you cool abilities like choosing how your decoy spectral library is generated. And it generated column after column of statistics on your peptide spectral matches that you can use to evaluate your forward and decoy match data!
Also, are you one of those weirdos who hates Proteome Discoverererer? Guess what, weirdo, there is a rocket fast stand alone version!
You can get both at www.pd-nodes.org or directly at the MS-Ana page here.
I do want to send a special shoutout to this team for helping me with this tool and sharing this manuscript while it was in review. I couldn't have got my first ever paper in JPR without it. (I guess I'm like 14th author on something I honestly did less than 1/14ths of). I also couldn't have gotten that paper without the help of a bunch of other people. The acknowledgement section might have been longer than the paper itself if I hadn't just thanked #MassSpecTwitter as a group.
This is a preprint, so all the preprint disclaimers apply here, but Simion Kreimer is just about the baddest mass spectrometrist on this planet so I'm cool with wagering just about anything that this will be in press soon.
Want to get going in single cell proteomics? Here is even more resources!
Check out how unbelievably cool this JOVE protocol is from the Slavov lab! The video protocol for SCOPE2 is freaking amazing.
No joke. There is like an 11 minute video explained by the scientists at NorthEastern university -- step by step, that would allow you to prep hundreds and hundreds of single cell proteomics samples per day.
How did I find the amazing NorthEastern video? I was considering doing a JOVE video and then I felt like a dumb person for not looking this up first.
Conclusions?
Single cell proteomics doesn't have to be terrifying. It can be approachable today and there are great resources to get you going.
You know...sometimes I see papers with huge titles like this and think....oh no....what are we overselling today....
This is a study that deserves a big big big title.
This relatively small group of authors did proteomics on 875 drugs.
Eight hundred and seventy five.
achthundertfünfundsiebzig
八百七十五
שמונה מאות שבעים וחמש
åttahundrasjuttiofem
ottehundrede og femoghalvfjerds
walóng daán at pitóng pû’t limá
ثمانية مائة وخمسة وسبعون
ochocientos setenta y cinco
How'd they do it? This figure pretty much covers it!
To make it useful, they'd have to have a ridiculously easy web interface that allows you to look at any drug you want and how it affects around 9,000 different proteins.
Welcome to
Check this out. I know someone who studies/develops compounds similar to Olaparib. I know every drug has like 12 different synonyms, but -- I'll type the first 3 letters into the compounds box and BOOOM!
This is so so so so so so so so so good. I'm just floored by how much thought went into this. There are, of course, similar things for drug + mRNA levels that everyone uses (most of the biggest ones are old microarray data...blech....) but people use those all the time. Protein level??? Nothing I've ever seen has ever come close.
Multiomics sample prep is totally doable, but it takes a long time and can have a load of steps!
Some people in Wisconsin who do a lot of multiomics did the math and came up with about 19 hours to do a good multiomics prep.
They asked Emeril and he said...that's too long for anything, you need....
...it might get worse...maybe...it depends on what you think of Emeril. (I've made this recipe about 20 times over the years. I sub out the dead bird for grilled eggplant and it is super legit.)
This is what they came up with!
Some of the cleanest proteomics samples I've ever gotten in my life were left overs from where people had done lipidomics and metabolomics extraction and the proteins were sort of just left overs. CLEAN PROTEIN remains. So, this makes all sorts of sense to me.
Total prep time? 3 hours!
Wow, did I ever wish I had this before!
Have you ever tried to just figure out what peptides you found in a 3D protein context? It is totally possible, but it is a miserable experience (for me anway)!
This is the easiest thing ever!
Don't feel like reading? Just run it here!
There is a great YouTube video that walks you through it here.
BUT
It's 20freaking22 or whatever it is! We obviously know every protein that is involved in DNA damage repair!
This study makes a very good argument that...no...actually...no, we do not and here is a couple more!
Imagine you just got back from the printers the 3rd edition of your textbook you sent out for printing in 2019 and some bozos with a mass spectrometer totally screw up Chapter 4?!?
For those of us who are maybe occasionally still writing 2017 at the bottom of things when we sign them, you might also be heartened to know we aren't the only ones!
2022's method of the year is what I HOPE that the students in our department totally think is the only way of doing sequencing, because I thought that was all anyone had been doing for at least the last 5 years.
I'm very confused. I'm glad for the long read sequencing people. Good for (what I hope is like literally all of you, why would you still be trying to reassemble little tiny oligostrings back into a complete story when you can have very long strings of oligos...?) Hey, good for genomics and really good for proteomics. It is MUCH easier to find peptides linked to long read sequencing data.
I feel like a jerk, but last year's was pretty pertinent and up to date. This seems like one they forgot to get to in 2017, but that's okay, I have things I started in 2017 that I haven't finished. Most pressing is this huge tree I cut down, but it fell into another tree and has just been hanging right over my garage for years. I've had a professional out twice and they've said they'd come back, and then didn't. One day Imma wake up to no garage. I hope, because I'm typing this right now in the office I made in said garage and there is another alternative...
One of the last remaining arguments for the dDa crowd like me is PTMs! When that domino falls (if possible), we'll see a full paradigm shift.
A lot of time when people talk about PTMs in proteomics they just mean "phosphoproteomics" and a lot of tools let try to look for them.
How do they stack up? You don't have to do it yourself, this team tried them all out!
Do you love R?
Do you want to get into top down proteomics?
Wow, do I ever have something for both of you who said yes!
You might want this link (https://www.toppic.org/software/toppic/index.html)
I had a pretty rough month and a shortage of both time and enthusiasm. As I was thinking about not getting up at 4am to have a couple hours to do cool science, I saw this paper in my Scholar Alerts.
Ions pass BY the Orbitrap detector. They do not strike the Orbitrap detector. As such, they are always at a sensitivity disadvantage to virtually anything where ions make contact with the detector.
Once, long long ago there was this open-ish cloud-like data processing pipeline with a name that would, for some reason, make you think of Santa Claus and hair metal. Not the good hair metal, but the stuff that they play on cellphone commercials and in elevators.
It was called the Trans Proteomic Pipeline and I just learned it still exists thanks to this new paper!
Honestly, it looks pretty comprehensive. Since the TPP is hosted on someone else's computer, you don't necessarily need a ThreadRipper on your side of the internet to do really sophisticated data processing and manipulation.
If you are interested in checking out this pipeline and don't know where to get started, check out this page of tutorials! http://www.tppms.org/tutorials/
While class I metabolism (P450 type oxidation) had been previously observed, class II metabolism stuff really had not been, and that was what she was seeing. And that didn't make sense.
The QE couldn't detect enzymes capable of this process. DDA/DIA/PRM
The TIMSTOFs didn't detect enzymes capable of these processes (PASEF/diaPASEF).
Targeting on a TIMSTOF is a miserable experience. The files are huuuuuuuuuuuuge.
Enter ZenoTOF and ZenoPulsing enabled PRM, which they call MRMHR. MRMHR produces tiny output files that are super simple to process in Skyline with ridiculously high sensitivity.
The first author was able to demonstrate that enzymes matching the metabolites observed in the correct brain ER cell types all lined up. I can't remember if this made the paper, but mRNA could back it up as well.
I know this didn't make the paper, but she also fractionated a bunch of peptides from these brain regions and did really comprehensive proteomics using the same instrument. Using the ProteomicRuler we were able to get some ballpark estimates for how rare the enzymes she is seeing are. Current best numbers are these are at less than 300 copies per cell. Which ain't much. Files are all on MASSIVE at MSV000090576.
Organ-on-a-chip technology can allow a better representation of in vivo conditions than 2D cell culture can. It clearly isn't as easy as 2D cell culture, but it can also be a good bit easier to scale than animal models. I didn't know it until I read this paper, but the US EPA plans to stop animal testing in their research plans in a few years and these technologies will likely play a big role.
If you are jumping into this field and want to fully characterize your drug (or in this case -- CHEMICAL WEAPON) on your simulated organs with both proteomics and metabolomics, what would you do? I'd just do what this group does!
Hey! I know some of these people...including the...senior...corresponding....author... Wait, that can't be the kid I know. Common name.
What they do is grow human hepatocytes in scaffold bioreactors to get them to grow into 3D cell cultures. It doesn't look like any funny gelatin things are employed here (whew....all I can see when they grow cells in gels is...their gel monomers...). They take some control little organs and some other ones they treat with "VX" which, according to Google is something like a CHEMICAL WEAPON.
Since they're making tons of these little "organs" they appear to keep their digestions and extractions in plate to keep them scalable. Apparently, though, the size of the "organs" are tough to normalize and since you've got these cells all stuck on scaffolding you can't exactly weigh them. You have to basically lyse them where they are. Proteomics normalization? That's okay, we basically know how to do that (though I'd follow their directions here to make sure that buffers necessary for the "organs" don't interfere with the quan).
Normalizing the small molecule/metabolomics input? I have no idea how I'd to that. You can normalize the TICs, etc., but that is for relatively small variability. You don't have to load too many samples for LFQ proteomics with vastly different loads on accident to discover the limitations of normalizing at the TIC level, right? What this group comes up with is a rapid method to quantify their metabolomics based on something that reminds me of the name of one of these people...
Joint pathway analysis (!!!) for the metabolomics and proteomics was done in Metaboanalyst, cause apparently it does that now!