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!
now also at www.proteomics.rocks
Worth it!
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!
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.
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.
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!
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....
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.
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?
And here's what one cost the US government!
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.
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 --
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!!
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.
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.
For anyone out there who wants an Orbitrap to go super duper fast and are okay with getting less mass resolution than a Q-Trap in 2002 do I ever have an advance for you!
Introducing the FASTEST ORBITRAP EVER! All you have to do is ....give up....everything else....
Okay - so I'm obviously just being a jerk. I got in all sorts of trouble a while back for ...running some old instruments at whatever resolution I felt like and then putting funny stickers on them because I wanted faster scan speeds.
1) We weren't getting anywhere near enough signal for most molecules
2) The mass accuracy went out the window. Funny to say now because I was super appalled by 20-30ppm mass accuracy, and that's before I started buying TOFs...where that's pretty good...
What this group does is a bunch of magic to fix problem #1. By balancing accumulation times to get more they show they can get enough signal - EVEN at 8ms fill times to get good MS/MS spectra. Impressively, they get a solid number of IDs on an Exploris 480 running 5 - 8 minute active gradients. They get the best coverage at a whopping 500ng of peptides on those 8 minute gradients. Fortunately, you can clean that quadrupole yourself!
Now...how's the mass accuracy...? Better than an Ion Trap!