Taxonomy source identification from proteomics of hair!

 

Are you an investigator who was assigned a bank heist? 

Do you suspect a certain goat, recently out of the pen, with an alibi that seems a little too good to be true? 

If you can find just one hair at the scene of the crime, this is the study and  these are the resources you need! 

Is that goat still baaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaad, or has 20 years on the streets stripped you completely of your idealism about the system and it's ability to reform animals? 

Find out with proteomics! 

More ultrafast proteomics with thermolysin!

 

Wait. Where does thermolysin cut? Does everyone know that and that's why it isn't listed in the methods section of the paper? It's so common knowledge that the Wikipedia page doesn't list them explicitly either? Gemini, which is apparently now installed in my browser without my permission says it's Leu, Phe, Ile, Val, Ala, and Met, so I'll bet you that is NOT where it cuts. However, this is really cool, for real. 

I love fast cheap enzymes for proteomics! Let's go. However, the reason this paper is great is because they went the extra mile and developed a stable isotope labeled protein standard for this enzyme! 

You chuck in the protein they set up and then you throw in your thermolysin and do your fast digestion and now you've got a pile of internal heavy labeled peptide standards. If you don't see them...the digestion didn't work! If you do see them, you can use them for your quantification! This group does a pile of targeted PRMs on an Orbitrap Exploris with an EvoSep on it. So high resolution discriminatory power of the mass spec and reproducible run times with no tinkering allowed. 

TIMSTOF Ultra2 after 1 year - still incredible when it works!

 

I received some questions about my longer term impressions of the amazingly sensitive and somewhat fast TIMSTOF Ultra2. 

It's been a year already? Yikes. I guess I'll reference my earlier TIMSTOF reviews.

Great stuff!

1) The sensitivity of this thing is still absolutely unreal. Multiple people are writing up papers on single cells ran on it with DIA label free and the numbers are cell type specific and amazing.

Even with a high loss, but very inexpensive and very fast sample prep, most cancer cell check in at well above 2,000 protein groups per cell and human hepatocytes are around 2,500. Throw in a big cohort and actual spectral libraries (non-predicted) and you can add 50% to those numbers. Most studies of >100 cells will have 5,000+ protein groups across the study. Some big cells like cultured human neurons will come in >7,000 on their own. Crazy.

Some small blood cells <10 microns have been a challenge, but if you've only got 80 pg of protein and we know our fast sample prep loses about 50pg, that's...involved..... someone FACs isolated 12 different cell types from patient blood and we struggled with getting 500 protein groups in 2 of the cell types. It's tough to tell what's going on from those levels of sample.

Not so great stuf! 

1) Compass data analysis is still the only way you can do things like extract an XIC. Looking for a weird peptide? Good luck. As an aside, Biognosys sells a targeted quan package that we had for several months because I thought it would do that part for us. It will if you're doing prmPASEF, but it won't help you find a peptide in DIA or DDA data. It also doesn't help you predict the IMS so you basically have to identify your peptide to target it. So we traded that software back for more months of SpectroNaut.

2) We've went through more internal electrical components in the last 6 months than HPLC columns. That's not an exaggeration. This board, that board, that other board, the second board's power supply, the TIMS cartridge, etc., etc.,. This is rough because I knew we didn't have a very local FSE here. They have to come in from DC or Jersey or fly in from Bremen or California. So the downtime can be on the long side when they go down. I thought going into 2026 we basically had a brand new instrument thanks to the sleigh load of electric components we got over the holidays so I wasn't super stressed about a short lapse between warranty and service plan starting up but - there went another pile of electronics.....whoops. To be fair they are building one of the biggest hospitals in the country next door to us and we've had a couple of power outages. But...you start to wonder if they're getting these circuit boards from Alibaba....

3) There appear to be even less support people in the field today. This one is a real surprise given the fact that it looks like the company has been doing really well and the prices of some instruments have increased more than 200%. You'd think that would equal a higher number of apps scientists and engineers in the field, but it appears to be just the opposite. The ones we have are INCREDIBLE. But you get the impression that they never sleep or get a day off because there are two of them for planet earth. 

4) Look, I'm going to complain about absolutely everything. And I have absolutely zero regret for this fit-for-my-lab purpose instrument. It's an incredible precision instrument for what it does. We locked down a full workflow 8 months ago and we just do biological discovery on this thing. Same prep, same column, same method and the papers in preparation are a million times better than anything I've ever done. It's nice to have time to write because your one instrument is down, but there is a point where you have to look back at 50-ish weeks of ownership and start counting the weeks that the front of the instrument has been bright red doom and panic a little. 

5) I did a lot of searching as well, and unlike other instruments there still appears to be no second party field apps support. That's a bummer because in some places you can go to a second party like ZefSci and get superior field apps support than what the vendor offers for a lot less money. If you know of someone who services these things PLEASE LET ME KNOW! I'd switch in a heartbeat. 

Single cell metabolomics (by infusion??) with Medusa!

This new paper maybe isn't for everyone, but I'm excited to look at these scripts. 

There are a very small number of supported software packages out there in the world (approaching zero) that can make sense out of direct infusion of flow injection analysis quantitative data. 

We used to have a pile of them. Ion A is 10x higher than ion B in these two matrices, you could extract that a bunch of ways, but that's largely fallen off.

I'm also excited to see how many metabolites look real in direct injection in an Exploris 480. The intrascan linear dynamic range of a D20 Orbitrap is one of the lowest you can find in mass spectrometry so if this looks like it works you could do a lot with it. (Assuming the package works and is supported, of course!) 

This week's podcast on SNOT with Dr. Jennifer Mulligan is way cooler than you'd guess!

 

For real, I've been telling people about this conversation since we recorded it a while back. Snot is way cooler than you'd guess.

Variable (geographic!) distribution of ion species in electrospray ionization!

 

The fact that some labs have some weird background ions that other labs don't (check my repositories for Pug keratin! There's tons of it!) isn't news. Someone a while back showed they could tell when a study was done in the winter due to the amount of wool peptides ionized in their deposited data. I forget who that was.

But this new paper in JASMS shows that it's not just proteomics and peptides. It can even be those nasty adduct things that everyone outside of one group in Madison, Wisconsin ignores is even a thing in proteomics. 

This is the first time I've seen a multi-lab controlled study. It's been more of a "wow..that's fucking weird...wonder why they have axolotls there...?  Totally worth thinking about, though. I wonder if we went back through the tightly controlled and super smart CPTAC studies if we'd see the same things with tools that actually consider such things? Probably! 

Peptide cross-sections are bi-modal?

 

Maybe this was here before? I'm not going to look, but it's definitely out now in JPR.

It makes sense in my head, though. The same way that a single population of a million ions ionized at the exact same second might end up being distributed between mostly +2 charged, some +3 and maybe a barely detectable number of +4. Why wouldn't that population of peptides dissolved in acidic buffer also have 2 different possible shapes (or more?) Is that charge linked in some way? Would make sense. The authors suggest a simple calculator for predicting both modes - which would be amazing - but it doesn't appear to be in the Github. https://github.com/cox-labs/CCS - maybe it's coming? Or maybe I don't have nearly enough time today and all the maths in the paper scared me a little? Probably.

Dissecting honey bee differential development!

 

I'm legitimately knocking out a couple of blogposts to get my brain fired up for writing and my hands used to the new (quieter) keyboard I brought to a super intensive 3 day writing camp. R01 resubmit peer pressure time! As you might guess, both R01s I should be writing on are about the human liver and not honey bees, but you probably have a dumb way of doing things as well. 

Where the f' is the control key? I'd rather look for it here. AND honey bees are super cool! 

Did you know that worker and drones (which I thought were the same thing) develop at very different rates? Neither did I. Do I care? Right now I do. And these authors did and that's what really matters. I'm pretty sure it isn't a great time to be a honey farmer person. 

Want to talk about an experimental sampling procedure that doesn't sound like fun? These authors collected 1,000 developing workers and the same number of developing drones from at least 8 different time points, up to 70 hours. I feel like a gif should go in here, but that would definitely make it clear to everyone around me that I'm not working on my grants. I'm warming up my brain! 

The sample prep is ...interesting....and kind of old fashioned, but that's how they've been doing it in their group. Acetone precipitation and a lot of urea. Probably there's lots of weird stuff in the developing bees. Would I have put them in liquid N2, smacked them with a hammer and S-trapped it and gotten the same or better results? We'll never know, but that's how I'd do it.

The boring stuff is well-described, which is a refreshing change of pace this year. QE HF ran in top20 mode and a gradient I could reproduce without guessing. Yay MCP reviewers! Downstream analysis in PEAKS against a surprisingly complete sounding FASTA. Solid work all around and - screw it. - 

8 time points! 

OmicsMLMentor - A web app for machine learning in -omics data!

 

Interesting! When this group talks about -omics they even include lipids and metabolites. Worth taking a look at for sure. 

Figure 2 is one of the clearest descriptions I've ever seen of machine learning classifiers. 

The link to the web portal in the paper appears to need a user name and passcode, but I ain't got time for that.

Probably faster to pull the code from this Github anyway. 

What is a token? Running AI /LLMs locally for proteomics people?

 

I had a really weird conversation this week when people were talking about how many "tokens" they were using for making AIs do things poorly for them.

Look, I'm also getting AIs to poorly do things for me that I don't know how to do. What I'm not doing is 

1) Paying for them...

2) Letting some money hoarding corporate weirdos see what I don't know how to do by sending my prompts off to some AI datacenter they knocked down a park to build.

And the LLMs on modern hardware can run faster than the cloud based ones because the upload/download speed can be the bottleneck. 

So! Ben's short and poorly written guide to running an AI / LLM thing locally on a new or old PC.

Disclaimer and clarification: I know people have to use these for their jobs and they have their own local instances that are on their own HPCs so their work can control data access, etc., This isn't shade for you at all. I was surprised by all of this and I'm sharing it. 

For this example I'm going to use my GTX 1080T video card I purchased to run PacMan on a really really big screen in/around 2017/2018. Possibly longer ago than that. 

Since I'm dumb, I use a Graphical User Interface (GUI) called 

LM Studio

You can get it here.

Once you install it, you need a Model. For this example I'm just going to use the first one that's famous. It rhymes with Chutney. 

No joke, it's seriously that easy. I like this big old PC that will be retired soon because it 

1) Doesn't have a wifi card

2) I can just disconnect the ethernet cable from it. 

3) It has trouble telling what the year is. I have the same problem. 

Once I know it's offline and I've confirmed I haven't had another head injury or something and I do know what year it is, then I ask it things that I know stuff about. In this example I asked it about single cell proteomics. The answers are seriously no worse than what the ones on the Cloud will give you. It did blow my mind when I realized this. 

For real, if you're paying for one of these things you should try it. The reason I like to have a PC I can physically disconnect is that some of the available AI models written for data centers can't tell if they're online or not. ChutNeyPT will INSIST sometimes that it is running on a GPU farm in Arkansas when I know it's running on a GPU that is roughly 80% cat and Pug fur by actual weight. 

Honestly, the 8GB model that runs on this old GPU does have some very noticeable lag. And the total data it is drawing from is significantly smaller than other models. It's got to squeeze into 8GB so some things have to go. 

If you want it to run faster than the internet/cloud versions you need to get something newer. The 1080 video card is ooooooold.... 5090 is on the market now and they haven't released a new generation every year. More like every 1.5-2 years. An M4 Mac with 24GB of unified memory that I got last year for $1300 is legitimately lag free. So. Fast. 

Which brings up this question. What are all the huge data centers for? 

When I say that I'm doing dumb things with these AIs, I'd like to humbly consider that - as a scientist without any real hobbies except...proteomics.... the stuff I'm doing with these LLMs might be harder than what the average person typing prompts is doing. And....like....I'm also blasting the new At the Gates album on this same PC. I think I've got 40 tabs open and I've got 2 separate Python APIs open because I don't know where the default folders are located and I don't want to save the side scroller I've been tinkering with for 8-10 years and will likely never finish with the work scripts that I'll likely also never finish. So....like what are the 40 zillion core data centers doing other than accelerating the collapse of our climate?  

Is this a tutorial or a rant by someone who is ultimately very confused.