Monday, May 5, 2025

6,500 proteins per (LARGE) mouse cell (oocyte) and studying age related changes!

 


I wasn't sure if I liked this study or not, but then I got to an interesting and very counter-intuitive observation and then the biology and decided that I did. 


It's not the first single cell oocyte paper we've seen, and it should be noted that they are quite big cells. These authors estimated them at about 2 nanograms of protein, which seems right based on what I remember from another study. 

One thing that I find really surprising here is that - unlike previous studies - this group tried the reduced volume of 384 well plates and found autosampler vials more reproducible. I'm stumped on this one. This is contrary to everything I've seen and Matzinger et al., found and is frankly just counter intuitive across the board. 

The surface area of an autosampler vial is huge, comparatively to the bottom of a 384 well plate. I do find it a complete pain in the neck to calibrate some autosamplers for accurately picking up out of 384 well plates, but I don't know how much that plays in here. Also some glass binds less peptides than some plastics. Insert shrug. 

That aside, the authors put one oocyte into things with the CellenOne and then add digest. Incubate and inject. 60 min run to run on a 50um x 20cm column and running diaPASEF with a 166ms ramp time. 

Data analysis was in SpectroNaut. 

Okay, and the reason this is escaping the drafts folder is because the biology is really cool. They look at both artificial (handling) and natural (aging linked) conditions and how they effect single oocytes. There are a lot of people out there who care about how those things (probably not in mice, but maybe?) change throughout the aging process! 

Editors make statement on proteomics transparency AND a video for how to make your data available!

 


I wonder if this was inspired by some of the same things that I was just complaining about? 

Okay, so rather than just complain about it, I also went crowdsourcing to find resources - and here is a 4 minute video showing you how to make your data publicly available on PRIDE! 



Sunday, May 4, 2025

Use single cell proteomics (SCP) to add biological relevance to single cell sequencing (scSeq) data!

 


Transcript abundance tells you what a cell wants to do.

Peptide/protein abundance tells you what the cell is actually doing.

You can get measurements of the transcripts of tens of thousands of cells with a few hours of effort and passing it off with reports coming back in a few days.

Each single cell proteome is a lot slower and a lot more expensive, but worth it for the whole... biological relevance... thing.... 

What if you could do a scSeq on tons and tons of cells - and single cell proteomics (SCP) on a small number to correct all that scSeq data? Would you be downloading that as fast as you possibly could? 



Saturday, May 3, 2025

New data analysis strategy in SpectroNaut leverages diagonalPASEF features!

 

I've been on the fence about diagonalPASEF, but I guess when my SpectroNaut license goes live it's probably time to try it. 

I legitimately don't know who came up with diagonalPASEF - there were too many cool methods too fast for me to even try them. But it almost looks like 3 groups (all European...of course....) all had very similar ideas. But on my new instrument it's just a button, so Imma just push it and see what happens.

The bummer is that I do have to take my source off and calibrate the instrument with the ESI source - which I haven't done since it was installed - (you can do good mass and TIMS calibration now without the ESI source but you do need to sensitivity tune and/or quad tune for diagonalPASEF with the source).

But this is legitimately smart looking


Whoa! I went to the new DIA-Neural Network website (Aptila.bio) has anything about support for this mode that I should read and found something I didn't know was publicly shared! Also, not sure yet on whether diagonal is supported, but it does look like I can lie to DIA-NN and say that it is SLICE-PASEF. We'll see! 

Y'all, this ASMS is going to be sooooo crazy. Despite the lack of Europeans and the fact none of us in the US have any money to do science.....




Friday, May 2, 2025

Illumina protein prep! For when you truly do not care how much of each protein is in your sample!

 


Everyone, I think it is time to admit that the biologists have different opinions about what is important in proteomics.  And maybe we're the ones that are wrong. This field originated largely in analytical chemistry where they drilled accuracy and precision into us. Sure, there are reasons for accurate protein measurements, like when you're in clinical chemistry, and maybe my time in those dark basement labs ruined my brain to think that when we measure a protein we actually really want to know how much of that protein is there. 

The biologists want to detect a protein and they want to be able to say that in condition 1 vs condition 2 one of those conditions might possibly maybe have more protein. They don't care at all how much more protein. And - again - I'm the one here who is probably wrong. Hannah did this phenomenal thesis project in my lab and she worked out the nanomolar concentrations of 7k or 8k proteins at the blood brain barrier. We were operating under the assumption that absolute concentrations have value. Like - if your are doing medical imaging you know that proteins below xxnM just can't be visualized with any of today's technology. Don't try. And maybe that's just one outlier where we absolutely have to know the protein concentration.

Maybe the other clinical assays, like CRP and troponin and ALT/AST ratios are also outliers. Sure - whether you're going to get a wire jabbed into a blood vessel might be determined by the absolute amount of troponin in your blood right now as compared to 30 minutes ago. But it really appears that for the vast majority of new people in proteomics they want to know - is there probably less protein here and more protein there? 

So if you really just want to detect proteins and you truly do not care how much of the protein is around - and you've got a lot of money - do I have a technology to show you! 

SOMASCAN COUPLED TO NEXT NEXT GEN SEQUENCING! It's called Illumina Protein Prep! 

For real, it's a real thing. 

First of all - let's look at what aptamers are and what they do. I'll back way up because some people looked at me like I was out of my mind when I talked about the proteomics assay with the lowest quantitative dynamic range.

Let's start with this review from ancient history (2010). Don't worry, this is a physical limitation of protein oligonucleotide interactions. Not much has changed but there are more modern references below. 


Aptamers are oligonucleotides and it's really really cool that one of the cheapest and easiest molecular reagents to make in a customized way can bind proteins at all! Not joking. That's cool stuff. And despite what companies will charge you, they are pennies to manufacture. 

The binding, however, is calculated through either the dissociation constant or association constant and this functions in a linear way over an extremely narrow dynamic range. 


This was taken from the review above. Please note the fluorescence intensity of the blank. In this solo interaction of one aptamer vs. one protein we see a relative increase in aptamer binding from 10nM to 150nM. At 150nM of protein, however, you no longer get a linear response. Lots of reasons for this and I haven't taught stoichiometry since....let's go with a long time.....and I don't want to get into it. 

Imagine you have patient A and patient B. And one has 5nm of IgE in their blood? Well....that's probably about where the blank is, so you get a zero. What happens if you have 1,000nM of IgE? Well...you probably register at about 150nM, maybe a little bit more? Again, maybe you do not actually care in any way whether you have 150nM or 10,000nM? Maybe you're just weird for wanting to know.

What's important here, though is that each aptamer is like this. It is designed for a very specific protein and each one has it's own binding and dissociation constants. It's also important to know that in a complex solution, you're dumping in (in the case of Illumina protein prep) about 10,000 of these different aptamers! It is very very likely that the 1 order linear quantitative dynamic range represented in this figure in an isolated 1 vs 1 system is perturbed and not quite as successful as the above.

Edit -5/3/2025 because I'm self conscious about the crazy number of views this 20 minutes of typing has gotten in a single day. 

This is how a pile of aptamer measurements work.

True concentration of protein X - 0 nM - Aptamer readout - Not zero

True concentration of protein X - 5nM - Aptamer readout - Same as blank

True concentration of protein X - 10nM - Aptamer readout - 2x blank

True concentration of protein X - 20nM - 2x of 10nM - This is good! You're in your dynamic range! 

True concentration of protein X - 50nM - 3x of 10nM - It's still higher, but you've already left that little window where you're aptamer binding corresponds linearly to the amount of proteins (your linear quantitative dynamic range). 

True concentration of protein X - 100nM - 4x of 10nM.....It's still higher but you are now need fancy math to have some way of estimating how much of the protein is there based on the aptamer binding response. 

True concentration of protein X  -1000nM - about 5x of 10nM.... You've maxed out your concentration and all you know is that you've got more than 100nM

True concentration of protein X - 10000nM - about 5x of 10nM - same as above. 

This is important because as you'll see at the very last panel, it s pretty common in mass spectometery to get a linear concentration /signal increase across this ENTIRE range. 

So - in aptamer measurements - 

A) You almost always see a signal whether or not there is any protein there at all. So....when someone tells you they can detect 1,000 or 10,000 or 100,000 proteins in a sample you need to keep in mind that that is simply how many aptamers they put into the mixture. That doesn't mean they actually detect that number of proteins. They love to mix those terms up. And maybe you see a measurement for each protein aptamer. That does not necessarily mean protein detection. 

B) You can trust that signal corresponds to how much protein is present in only a very narrow concentration range. 

C) Above that 10x concentration range the value you see has no relationship AT ALL to the amount of protein present. You've simply maxed out. 

End 5/3/2025 edits

Again - the figures and review above are old - what can we do in 1 vs 1 relationships in 2025? Here is what I'd consider the high water mark today


I should go get some lunch and if you want to read this you should, because it is a solid advance in aptamer binding measurements - that last word is key because aptamer binding is not going to change. These are limited by physics and chemistry and they simply won't change. Yes, you can select for more efficient aptamers for your protein, but you aren't going to change the fundamentals of dissocation constants and maintain the proteins in a state in which they can be measured. 

How did they do? Pretty darned good! About 1 order!  


To be fair this study is focused on measuring aptamer binding over a course of time in a single molecule context. This isn't about extending the linear dynamic range of protein measurements. There are things out there about that. In some techniques what they do is have one aptamer that is good at one concentration and another that is better at others. Then you combine the measurements of both to get a better range. There is a preprint somewhere, but I've spent too much time on this.

So....imagine my disappointment when knowing that I couldn't talk about what I knew regarding an illumina - somalogic partnership (I just assume I'm under NDA with every proteomics company in the world now and I just don't share anything until I can google search it) - and I discover that does not appear to be what they did? 

They appear to simply throw in the requirement to own a NovaSeq 6000 or NovaSeq X system to generate - get this - data on up to 384 samples per WEEK, which is 1/3 the speed of O-link? And even slower than mass spectrometry? 

And if you're new here and aren't familiar with the quantitative dynamic range of mass spectrometry - here is the first thing I found searching my desktop. It's a Sciex app note, but this isn't extraordinary data. I can show you real data like this all day. It's actually surprising because normally you think vendor app notes are going to be crazy unachievable data and this is just very normal. 


You spike 0.l ng/mL of this peptide in rat plasma - you can see it. If you put in 5ng/mL you get a peak that is 6e4 tall. If you put in 500ng/mL (100x more) you basically get a peak that is 100x taller. So...if you want to know how much protein is in your sample, you always have mass spectrometry to fall back on! 

Thursday, May 1, 2025

Deep multi-omics from blood spots - real life actionable and translatable methods!


There are a lot of increasingly complex ways to measure proteomics in blood. You can use 100 different nanoparticles or mix in aptamers or use double antibody arrays - all things that can be easily translated to the clinic as long as you're willing to pay 

for your next blood test! 

So...what if you took a step back? Maybe 10? And used actual clinically available material? And then what if you fully embraced heresy and used HPLC methods that someone in a clinic would actually be successful doing? 

Sounds like science fiction? If so, you should check this out



Tuesday, April 29, 2025

Submitting your first proteomics paper? Do this or don't bother submitting it!


We are SO SO SO very excited that you are moving past the GENotype and are intertested in actually measuring PHENOtypic data (or close to it) by doing proteomics. 

For real - so so so so so very excited to see all of these new technologies. Even if you are using SomaScam or something smarter and more accurate like doing 4,000 separate ELISAs. We're all excited that you are doing it rather than filling every repository on earth with petabytes of more short read transcript level data.

However - now that you are here - THERE ARE RULES. And unless you are threatening patient confidentiality you don't get around these rules because you're new here.

There aren't a lot of rules, but if I'm reviewing your paper the first thing I check is 

1) IS YOUR DATA PUBLICLY AVAILABLE FOR ME TO LOOK AT? 

No? 



Save yourself the time and just don't submit it. It saves me SO MUCH TIME to just look for your data and find that you've just put the R plots on Zenodo or your "RAW data" is a .CSV or Excel file and just reject it on the spot. 

And if you're too confident to read about your new field at all and thinking "I should start an initiative and find a way to make proteomics data publicly accessible. 


We have these things! 

We've almost ALWAYS had these things! 



Look - I don't review every paper. But I don't have my ORCID set up properly with every journals stupid format so I review a lot more than it looks like. I've ruined a lot of lunch breaks this year already and I've recommended rejection of more papers in 2025 so far than in the rest of my career combined. It is legitimately not that hard to make your data available. Edit: 5/5/2025, check out this new post that includes heavy hitting editors in proteomics talking about this same issue AND a video that shows you how to upload proteomics data! 

Sunday, April 27, 2025

Rapid proteomics assay development via ion trap!

 


I definitely think there is some confusion out there regarding what you can/can not do with an ion trap, particularly that new one. Stutter? Stinger? Something like that. This helps clear the air - and expectations! 



Saturday, April 26, 2025

Pitt announces plans to do Proteomics /multi-omics of 1 MILLION PEOPLE!

 


My new home is planning to do deep mult-omics - proteomics by MASS SPECTROMETRY - of ONE MILLION PEOPLE! 

The 12 hour launch event appears to be open to open to the public

There is a lot of talk about AI in this but - I'm obviously biased but I swear this school already has the resources in place and people to actually do AI stuff rather than just talk about it. The HPC here is by far the nicest and most powerful one that I've ever used and - I'm doing mass spec proteomics - I'm going to be using a tiny dribble of the power that we have here!