Nanosplit the transcriptome and proteome from single cells (without the hard part!)

 

When I first saw this I thought - okay, so someone copied the nanosplits paper but they had an Asstral.

And it's almost what this is - 

...but nanosplits requires a technically tough step where you split the droplet containing your mostly lysed single cells. This protocol gets around that step. They still use the same silly robot to isolate the cells, but you absolutely don't need it here (where you basically do need it for nanosplits, it's tough to print that droplet array in a FACs core), and that's a huge win for anyone who doesn't have the slow silly robot. 

Library biases still remain in proteomics hardware particularly for low input TIMSTOF data!

 

I was first going to start with something like this - 

When I read this title 

But I realized that 

1) That's sorta mean.

2) I bet a lot of people thought that all the work that has been done to adjust spectral libraries and deep learning algorithms has been successful

3) Not everyone is doing loads of weird cell types by single cell proteomics on TIMSTOFs and probably doesn't run into this every single day that their TIMSTOF happens to be working.

4) The giant red light on the whole front of my instrument is bumming me out. 

Here is the thing. The Orbitraps had a HUGE head start on data on public repositories. And in the libraries we used to train deep learning algorithms. And every other data type is just different. Especially when you're going down to low load. Even there, we know the Orbitraps struggle against high load libraries. I should put a link in but I can't find it. 

We absolutely find that having reference libraries in single cells helps a lot. On an Ultra2 we like a 25, 50 or even a 100 (for very small cells) cell pool that we run a couple of times and include that in our data analysis workflows. For big studies I've had luck making the library with those 100 cell pools and then just searching the single cells against those new libraries. Now...you'll probably miss that rare cell type and what makes it special, but you might not care about that in every experiment. 

Anyway - this group has some really smart tips for how to build these libraries and the observations in different software. Ultimately they report a 90%(!!!!!) improvement in low load peptide ID rates, so...that's absolutely worth looking at!

S100P levels are linked to recurrence in cholangiocarcinoma

 

It might be easier to make a list of things S100 proteins don't appear involved in at this point.

This paper is going to be posted here because I'm personally interested in it and I wish my lab had access to these samples. 

The samples were digested with some amount of trypsin. You'll never find out how much, but I bet it is fine. They were also labeled with some kind of TMT reagents. The TMT labeled (and, presumably, pooled) samples were analyzed with a Q Exactive of some kind, probably, despite the Agilent high flow coupled Fusion system in the diagram above. The files are on ProteomeXchange if you cared to look. A secret length and flow rate of a gradient of some length you could extract from the .raw files if you wanted, was used for what was most likely a very reasonable DDA method. They couldn't share the resolution of the MS/MS because that might tip you off to what TMT reagents were used. And if they said they used a 1.4Da isolation window someone would complain about it, as would another group if they used a 0.4 Da isolation window. The authors avoid all that controversy by not sharing any of the steps necessary to repeat this analysis of these same tissues.

That being said - the files are publicly available. It could be one of those things where a core ran the samples and the group never paid them, and the core subsequently couldn't find the hours to contribute meaningful corrections to the paper. Also, the downstream analysis seems compelling and it looks like they really thought about their stats in this little cohort. We can probably assume that the mass spec stuff was done right. We can also assume that the reviewers and editors had a lot on their plates when this one slid through peer review. And that happens, we're all busy.

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.