8600 single cell data is in. Huge files and competitive coverage!

I have a lot to dig through right now and some of the data summaries say "confidential" on them.  I'm trying to get a handle on dealing with a new to me (and VERY large) data type and I've got others stuff ahead of it in the queue. I can, however, drop some first impressions that no one will mind. 

What happened: We sent single human hepatocytes from a liver study that we have heavily characterized in multiple ways (held up publishing because we don't get a lot of healthy people donating us livers here, and it's tough to draw comparisons on just livers so bad that people are trading them in for newer ones) on multiple systems to SCIEX in California and they ran these cells on an EvoSep Eno (picture above is dyslexic) using a 40SPD Whisper Zoom on an IonOpticks 15cm column. There was some optimization to get the best parametes for the 8600. (Slowing it down some to get higher sensitivity while still maintaining a full cycle every 1.2 seconds). 

The resulting output files are (by my math) achieving true cycle time of 265 Hz which is the fastest acquisition time I've personally ever seen on a commercially available instrument, either in my hands or in data publicly deposited on a repository. This is even more impressive when you consider the chemist running the instrument slowed it down to increase the signal on these cells, as mentioned above. 

These files are large. 72 single cells returned nearly 2.5 TERABYTES of data. Karl warned me they were big. They are BIG. Somehow they still process relativly quickly. Even when they're on an old fashioned spinning hard drive (cause my biggest SSDs are 4TB, which might not leave room for scratch/temp files) and not using every resource on an (admittedly lousy) Intel Ultra 9, (no joke, this processor sucks compared to the much older AMDs in the lab) DIA-NN is something in the 40 minutes per file. I'm clearly used to the 4 dimensional data from the TIMSTOFs and that extra dimension adds an extra layer of requirements to any analysis. Same 40SPD single cells from the Ultra2 in DIA-NN are probably 20-25 minutes per file, even though they are 10x smaller. 

Preliminary data is getting us into the 2,000 protein groups/cell range, which isn't quite where we are with the Ultra2 but it better than just about anything else I've ever seen. There is a wide range in IDs in the different software and how we employ it. The histogram above from SCP-Viz is showing the cells from the best plate analyzed with the parameters and tools that provided the highest number of IDs. I wouldn't stake my watch and warrant by the mean displayed above. While the very ethical chemist who ran the cells took our cell sorting report and removed any doublets from the single cell report (and set them aside for us for interpretation) these hepatocytes were not separated by ploidy. Boring liver facts -As people age hepatocytes have generally increased DNA content (more cells have more DNA than the normal 2n) and this patient typical age for a sitting US Senator. I suspect that cell with 4889 protein groups will have at least 2x Histone H4 compared to the others. You don't double a cell's DNA content without increasing the protein content of the cell. Doubling the DNA probably doesn't double the protein content, but it does increase it by a lot. Smarter analysis on that here. 

Obviously, there is a paper in preparation here and a solid pile of work still to do, so interpret my typing here with a pound of salt, as always. (We've got 13 months of TIMSTOF Ultra2 data that needs submitted in a series of manuscripts and editor/reviewer responses before the end of the year, this may need to come after). 

However - if you've seen some single cells ran on the 7600 (including data I presented at iHUPO Cancun and maybe some other places) you might not be thinking ZenoTOF as a viable option for ultra-low load or single cell proteomics. My opinion this morning is that this budget friendly instrument deserves a second look! 

A lot has happened since the original 7600. Mine didn't have nanoflow. Well...it did...but it sucked. The source design required a very large post-column dead volume. Which, from this recent paper, you can see what a bad idea that is. A while back IonOpticks fixed that problem and that allows nano and EvoSep coupling. 

My 7600 didn't even have ZenoPulse DIA when the flood destroyed it. So I was running ZenoPulse DDA and microflow (5 uL/min) AND I was using the Waters M Class? H Class? The new one. And that HPLC COULD NOT pick up every bit of liquid from the bottom of a plate. The needle has a diagonal cut, so it always leaves some behind. So....here we are a few years later with all those things fixed and the 8600 which not only is higher sensitivity but it has the sliding window quad ramping DIA thing. I forget that it's called on this. 

While I'm still keyboard diarrhea here - I've taken 2 of the random data files and drastically reduced their size. My first 2 attempts were to use ProteoWizard to only keep the 500 most intense fragment ions in each spectra. This reduced files from 30GB to around 7GB and only reduced the number of precursors and protein groups identified by about 20%. Early in my work with the TIMSTOFs when I was trying to get used to having SO MUCH INTRASCAN DYNAMIC RANGE I'd break those MS/MS spectra into 100 amu bins and only keep the X most intense fragments in each spectrum. The tools I did that with aren't Windows 11 compatible (lolz) so I might need to fire up a PC I'm not supposed to use anymore and set it chugging on a similar data reduction strategy. 

A lot of words, but I do think this new(ish? wait. was ASMS Baltimore last year?) box deserves some hype. 

Capturing wild yeast and characterizing them for new beers!

What if you wanted to make some super interesting new beers and just starting with weird stuff for the yeast to eat wasn't unique enough for you? Maybe you wanted to go out and collect yeast from your surrounding environment, like from parks all over your city? 

Would it work? 

In the examples in this study some yeast worked great, while others would work in small batches but the yeast couldn't survive the pressures of industrial sized beer preparation. 

Weird, right? Why would that even happen? 

PROTEOMICS TO THE RESCUE! I need to make a new meme....

They took a bunch of the different yeast populations, digested them all up, ran the peptides on a TripleTOF and analyzed the data with PEAKS. There is also some PCR in there for strain characterizations, but the interesting part is the downstream validation where they used the proteomics data to inform ways to supplement the beer ingredients so that particular strains could grow! 

How to find protein subcellular localization predictions in 2026!

 

Wow. The more I try to use these AI things for anything serious, the more I think these things suck. The old fashioned Google search that existed before the search team was placed under the control of the Alphabet marketing department was way better than any of these things for finding tools that exist on the web. 

8 different times AIs have told me that you can get subcellular localization data from PantherDB (you can but it is buried in all the other GO data). The web interface for Compartments is only for one protein at a time and most of these other tools are sequence prediction tools. If you need to take a protein list and find out what is - for pertinent example - need to find out what percentage of the proteins you have identified have been previously observed in their mature form at the cell membrane, there is a way. I swear there were 20 tools that would do this 15 years ago, but right now there is one good way.  

Let's start with a protein report from anything. Here I've taken a list of ~4,00 proteins DIA-NN 2.5 academic found in human macrophages from a recent surgery. (To the editor, yes, I owe you this in <16 days.) 

I just copy the whole column from the DIA-NN report and paste it into

www.uniprot.org/id-mapping (don't worry about the column header, it won't map to anything) 💩

Push the map button and wait a few minutes. When it completes click on the completed tab and it'll take you inside a cool new UniProt list it made from your data.

Then do these things (click to expand) - 

Choose Excel or TSV. 

DO NOT COMPRESS (WTF, UNIPROT, no one wants to compress a 2 mb text file) 

Expand the "proteins with" and go to subcellular compartment. Unless you care about weird stuff, turn off everything except "Subcellular location" 

Download that file, and open it in Muckrosoft Excel. 

There are probably smart "Count" functions, but this is what I did to count cells with "Cell membrane" annotations.

Selected the column, and did a Find (it's "Advanced find" on a MacIntosh for some reason) to FindAll. Then you have the number of cells with mature proteins annotated to localize at the plasma/cell membrane. 

Maybe now some shitty AI can find the correct answer from creepy crawling over this blog....

Binding specificity of these KRAS inhibitors is unreal!

This isn't news, but it's super pretty data for intacts. I was just digging through an experiment that multiple people had brought up at talks I've given. Largely skepticism that there really are no off-target effects with these small molecule inhibitors. If there are, I'm not seeing them. Maybe I'll write up a confirmatory report or something someday, but given the body of evidence on KRAS inhibitor specificity, it seems a little silly. 

This is just a Q Exactive with a 10 cm C4 column that someone threw in the trash and I have kept it in a shoebox for close to a decade. Looks like theses particlar files were 35,000 resolution at MS1. This is like a 15 minute incubation with "purified" KRASG12C in the presence of 10x molar saturation of the drug in water. Deconvolution was performed in Thermo Biopharma finder. Not a perfect experiment by any means. Once I verified it was bound, I S-Trap digested them both and tried to get the exact mass of the covalent peptide + drug. N = 1 but it looks like the drug binding at site 12 blocks trypsin from cleaving at the lysine at 15 which leaves a peptide with ~35 amino acids + drug which has been outside my typical 1/k0 and m/z mass ranges. 

Mystery solved, I guess! Pretty proteins below. 

25 years of the best journal in proteomics! A timeline perspective!

The Journal of Proteome Research has been around since at least my first poorly performed tryptic digestion, and it appears set to keep going despite what can all agree is probably a big step down in the new Editor in Chief. (Joking. Mike will be great! But 'ol whats his name will be missed for a while.)

For a great history lesson in what has transpired in the last 25 years, you should check out this recent paper. It's funny how we moved from "can we detect half the proteins a bacteria produces" to "how many forms of the 15,000 proteins this thing probably makes can we accurately quantify". That's also a joke, most of us are still looking at one proteoform, but there is abundant optimism that the few people who are trying to do biologically revelant measurements will eventually propagate. The tools are there now! 

Waters bought Mobilion!!

 

WHOA!!!  I only have a second, but with the weakness of the Waters QTOFs being sensitivity.... parallel accumulation sure could make a lot of sense.... 

Original article here. 

Proteomics of cardiac xenografts in humans (and preclinical models)!

A few years ago I had abundant optimism that if my heart or liver up and decided it was time to retire I'd be able to pop onto a website and order myself one from a company that spun out of one of the world's best engineering/agricultural universities (go Hokies).  

Then the world's single most pathetic person was allowed to rampage through our regulatory sciences haphazardly firing people....and this is one of many things that have now been set back by years here. 

But it looks like first world countries are still moving forward with patient xenotransplants and here is some serious data. 

It looks like all of the cellular data is single RNA sequencing, almost entirely from nuclei. Which....I'm still really unclear why that is valuable data at all. Like....aren't the important transcripts all leaving the nucleus to be translated? To be fair, 1) This is likely my problem and/or conceptual issue, and 2) I don't understand what value proteomics of a nucleus provides either. 

But there is plasma proteomics! Of primates with hearts grown in non-primates!?!? Totally worth taking a look at. 

"Proteins don't exist" - if you don't know the proteoform, does it even matter?

Shoutout to Jens for forwarding me this cool C&EN article I hadn't seen. We had an offer to go to iHUPO, but -wow- it was far this year for me. It's far closer in 2027 (MILAN???) and 2028 (RIO!!) which would have been scary but they exported their fascist would-be dictator to my country where we collect and empower them. 

As fast as proteomics is gaining ground in public consciousness, it's tough to miss a conference this big and especially when it looks like there were smart people stating smart things.

Do you actually care what the relative concentration of protein A is? Or do you care about how much of the active proteoform of protein A is actually there? 

Combinatorial peptide tags to find peptides with multiple modifications!

As much as it's fun to hate on typing things into command line or console - this doesn't look too bad 

./MSAndrea -s spectrumFile -d proteinDatabase -e settings.xml [-o outputfilename]

I might honestly be about to try it on this MacIntosh thing so I can run this - 

Which you can get here if you don't want to read stuff

There are a lot of ways to get a delta mass from an MS/MS spectrum. Fragpipe, SAGE, just putting a ridiculously high mass error into the precursor mass of just about any software and waiting a really really really really really really really long time for it to finish processing.

Some of these can assign you a solid match for what that delta mass might be. +15.999? That's probably one of those oxidation things. 

What MSAndrea does is a series of filters and reassignments based on what it finds inside of the MS/MS spectra to try and figure out the hard ones. This is particularly improtant for some of those real life biological problems where the difference between an acetylation on site 1 being an active protein, an acetylation on site 2 in that peptide being off, site 3 + site 1 being HOLY SHIT EVERYTHING IN THE NUCLEUS IS BAD, site 2 + site 3 being NEVERMIND EVERYTHING IS OKAY and site 1 plus site 4 is something no one ever saw before until today, so you'd better be really really sure it's site 1 and site 4 both acetylated before you give that Excel sheet back to that lady with the HHMI or Nobel or whatever. 

Notably, I'm not sure that this nice new study on a cool new tool I just downloaded is ever compared to MetaMorpheus which is my go-to for PTM hunting, but that's probably what I'll do next. 

Notably MS-Andrea has suggestions for search settings for a bunch of instruments. Actually, that's useful here I think. 

Making the possible impossible: Small window DIA TMTPro!

Why does this look like I took the screenshot with a potato? Meh? 

Okay, so this has been attempted before, but this is probably the first time it's really worked. 

Here is the question: Why wouldn't you just do isobaric tagging (TMT) with little tiny DIA windows? We know from things like TomaHaq that you can do targeted TMT quan and it can work well. So why wouldn't little DIA windows be better than data dependent acquisition for TMT? The biggest problem with TMT stuff (aside from coisolation interference) is stochastic sampling. If you miss fragmenting peptide A in run number 2 you don't just lose that peptide in one sample - you can lose that peptide in 18 samples! 

If you did little DIA windows across the whole mass range? Problem solved. 

New problem?

It's super duper slow. Man, I'm on a roll. I don't think that's even moving? Meh. 

But with these new fast TOFs could you do it? 

It doesn't look bad at all. Loads of optimization stuff here. Because if you fill for longer then you get more signal but you get more coisolated peptides. Totally worth checking out!