Shake up your sample prep and a get 2x more phosphopeptides/sample!

 

Honestly, I thought this was going to be boring. How many phosphopeptide enrichment strategies do we actually need. However, when you see how much thought went into this, it is hard to not like it. 

The urea based bead elution is inspired. I wouldn't have thought of that and I'm not sure I understand why it works. However, it is fair to note that the biggest gains they get from this prep is from removing C-18 cleanup prior to injecting on column. Also - and this is really cool - this prep doesn't use phosphatase inhibitors! 

If the absolute number of phosphopeptides in total doesn't blow you away, they are doing Orbitrap-Orbitrap scans and the MS2 scans are done at 30,000 resolution with an auto ion injection time. I think on that means something like 10-15 scans/second on that particular tribrid system. They are clearly biasing this study to quality over quantity. So...2x more high confidence phosphopeptides/run? If you do phospho this is definitely worth checking out.

Oh yeah! And even if you don't do phosphoproteomics the introduction is really worth a read because it highlights why everyone seems so obsessed with these finicky things. 

 

The last post was made because I stumbled onto it while looking for this thing I saw on LinkedIn (I have links from LinkedIn disabled so I don't accidentally end up reading too much marketing stuff). 

Then I went down a rabbit hole watching old Kenny Rogers stuff and now I'm out of time to blog this morning. 

Worth it! 

Solvent swapping on HILIC improves polar metabolite recovery!

 

This belongs on the other blog, but this is the one I have open while traveling so it's going here for me for later!

AND I think one of the authors is basketball and country music legend...

ZenoTOF 7600 performance with NanoLC - and applied to cell-type proteomics!

 

If you've also wondered where a ZenoTOF 7600 would stack up against your hardware when running comparable flowrates (cause you're probably running nanoLC). This new preprint did the heavy lifting for you.

Probably formerly counterintuitive, but something we're all getting used to now - a super fast TOF tends to get more IDs at faster gradients. Narrow those peaks to concentrate that signal and they perform better. As the concentration goes up, that effect tends to level out. 

Based on some vendor marketing material you might be quick to disparage these numbers, but I think we all know that our own labs aren't the perfect operating environment of vendor facilities and after full time use running weird stuff in our instruments they don't run the same at install. 

What is interesting to me is that - yes - NanoLC seems to help, but it doesn't really help that much (maybe 10-20%?) and I really enjoyed the ease of use of the Microflow setup on my ZenoTOF before a ceiling waterfall at Johns Hopkins destroyed the shiny thing. It should probably also be pointed out that this is now a 2 generations old ZenoTOF, as the 7600+ and the 8600 are improved versions of this hardware. Still, a really visually appealing study that answers a lot of questions that I (and maybe you?) had about this instrument. 

Save the date - ABRF 2026 is here in Pittsburgh! 3/28 - 3/21!

 

So...while this might seem like a step down from Las Vegas.... I don't gamble and I had responsibilities and I really don't enjoy second hand smoke, so Las Vegas was more of a "WTF is happening here, no thank you, I am looking for a coffee shop, I'm relatively sure MDMA would negatively impact my ability to be a useful participant in this morning's workshop" sort of place, but there are probably more funny conference stories than some other ABRF locations. 

However, Pittsburgh is super cool, if less crazy. And I'm absolutely going to a conference that I can ride my bike to, so I'll see you there for sure! 

More evidence that schizophrenia is a protein aggregation disorder?

 

Whether or not schizophrenia is a protein aggregation/protein folding/protein insolubility disease is a controversial topic right now. I'm on the controversial side because I think that it is one largely because proteins from the post-mortem brains of patients with the disease are no fun at all to work with. A previous MS student who shall not be named here killed a pile of EasySpray columns a few years ago because he didn't take this problem as seriously as I suggested that he should. 

Here is another study on the protein aggregation side - though it should be noted that these authors are the source of the brains that I've worked with in the past as well. 

However - this is the cool thing about this study - they demonstrate the use of a material for clinical diagnostics that I think is really really cool. 

Postmortem brains have the unfortunate side effect of...being postmortem.... what they do here is extract olfactory neurons from the noses(?) of people and grow them out on a dish and then study those! 

While....that honestly sounds like no fun at all (they cut or take a biopsy from the inside of your nose? ouch) ...I sure would prefer to do that if those neurons could provide insight into how the health of neurons inside my skull. I'd rather do minimally invasive than EXTREMELY INVASIVE. 

And from this molecular analysis it looks like they see the same protein aggregation phenotypes in the olfactory neurons. Might be worth thinking about for sure....

MGVB - An elegantly simple approach to PTM assignment and localization?

 

Finding PTMs in complex proteomics data is either 

A) very focused (looking for 1 or 2 PTMs and taking a minor hit in false discovery rate calculations

B) Not focused at all (open searching) and taking a big 'ol hit in your false discovery rate maths

Obviously we've got really cool tools today to help with B). We can recalibrate our MS1 and MS2 spectra to accuracy levels beyond what the instruments are capable of themselves. And we can hunt for diagnostic fragment ions and we can start to adjust our retention time predictions and on and on.

MGVB is a neat new approach that could be integrated into helping make B even better.

I'm going to call it simple because even I can follow the math in re-evaluating the fragmentation spectrum delta masses.

And it seems to help in 3 or 4 validation datasets the author pulled from repositories and applied this to.

While I can't apply this myself, I can draw some attention to it and - this Github (https://github.com/mvm1964/MGVB) - and maybe it'll end up making tools that I use a little better? 

Automate your proteomics sample prep with a relatively affordable robot and 3D printer!

 

I have recently made the observation that I have somehow gotten even worse at pipetting than I was. Automation has ALWAYS been interesting, but now it's even more imperative. But robots cost a lot of money! 

What if you could take a relatively affordable one and 3D print some parts and go? 

Check this out! 

I don't know what a Meso is, but that's okay. I did some deep research on how much the robotics system they used actually costs (I asked BlueSky). 

And here's what one cost the US government! 

That's....not terrible....

What are all the proteins with direct access to DNA?

 

I  just stumbled on this one while trying to find the PI's email to ask questions about previous studies and online resources from his group - and then I had to send this out to all my DNA researching friends.

Wait. We know what protein has direct access to all the DNA, right? It's histones! I can find you 400 nice little sketches right now and can generate a bunch of figures on BioRender that clearly show DNA is wrapped up in Histones 1-4 (with random letters and numbers and the occasional dashes). And that's all the proteins around until everything unwinds for transcription.

Aaaaaaaaaaaaaaaaaaaaaaaaaaaannnnnnnnnnnnnnnnnnnnnnnnndddd..... just like everything in biology it is not at all that simple.....

These authors demonstrate a "zero length" crosslinking method catalyzed (? might not be the correct term) by high power UV irradiation. The device may be a custom rig for this study. Then they wash or digest then wash away all the nucleic acid stuff that isn't crosslinked. 

A combination of nucleotide sequencing and LCMS proteomics are then used to analyze what's left. You're here for the latter thing, which is good because it's 4am and I have a deadline today for something that...isn't....blogging.... The proteomics is done by microflow analysis on a Tribrid running high resolution MS1 and MS2, which is good because this is how they needed search their data --

-and as someone who just got off of a 4 year polyADPr adventure bus - you want the drivers to use the highest resolution MS/MS you can find to open search for mods this big successfully.

Gotta run, but here is the punchline. After multiple complex negative controls - and introducing stable isotope amino acid labels for confidence - they find compelling evidence of around 1,000 (!!) proteins directly interacting with DNA!!

Don't impute those missing values - BIND them!

Mass spectrometry based proteomics people will do just about anything to avoid a zero value. We'll just put in random numbers or poorly estimate our signal to noise - and then put in the noise value. There are probably 10 posts over the years on this blog about how to "impute" that zero to a number. And those posts are the ones that will likely have someone trying to figure out how to put a comment on the blog post (which generally doesn't work, but I can't disable that feature on the blog).

Other fields, like single cell seq operate under the assumption that every zero value is actually telling us stuff. Here is my favorite paper on the topic by Stephanie Hicks, who we should totally invite on the podcast....

BIND is a new preprint that I haven't read, but I am currently poking around in their sweet data portal that seems to take LCMS proteomics data in a similar direction. 

 https://bind.fengslab.com/

Preprint link here

As with anything I read that has a lot of formulas and presumably Greek letters (?) after I'm done rolling my eyes and thinking 

I look for how they validated whatever those things mean. In this case the authors pull down multiple datasets that I know very well including 2 versions of the NCI-60 cancer panel deep proteomes and one of my very favorite label free single cell proteomics studies. (Jurkat vs HeLa Exploris 480). 

And it really looks like this tool kit adds value to there re-analyses. Which I hoped you'd guessed since I took time to write about this preprint. 

I do have a gripe that the preprint says the data is formatted into tables in the supplemental. It's not. 

But you can run demos on the website with some of the data.