Fusion 4 Details leaked! It IS real! Introducing the Orbitrap Assend!

 

For legal purposes, we'll be clear up front that this is up on this website. No insider information here ever. Who would tell me things? 

What do we know so far? A little. 

The schematics appear to show two separate ion routing multipoles, which can only increase parallelization capabilities. Oh cool! A full document with schematics is up on the website link above now! 

Some pretty amazing numbers here. The optional HighMassRange option goes to 16,000 m/z. That might be the highest number outside of the E+EMR or UHMR (I forget that latter's upper number). Orbitrap is talking about 45 Hz @ 7,500 resolution? 

The autoready ion source appears to be a self-calibrating option, which would explain some of the marketing stuff we're seeing cool videos of. The document says you can auto schedule calibrations in your queue which is an Orbitrap first.

For anyone hoping that we'd see a TOF back there, you might be disappointed, but it looks like the ion trap was again tinkered with to improve UVPD, and PTCR. 

It seems like full details will release at IMSC which just wasn't on my radar this year and I don't know where I had that cognitive gap to not even consider putting it on the calendar. For the first big launch from this vendor in quite a long time, and a really impressively professional series of marketing hints and links, I suspect it will be quite the official launch. Sad I'll miss it, but hopefully some data will leak and more will show up at that little conference in rural Mexico in December. 

Super expert TMT Post #2 : How to mix plexes by Alejandro Brenes!

 See post #1 yesterday, but if coming from somewhere outside this blog. Here is the link! 

This original paper has appeared on the blog at some point, for sure, maybe twice because it is amazing. But it should be posted here again because it is such an important topic: 

The original paper provides advice for how to mix different plexes, what to definitely avoid and has all the math to back up the fact they are not making this up! 

Why post it again? When this paper was submitted the TMTPro18 reagents weren't yet out. Rather than making you wait for him to wrap up his thesis and find it (I'm sure it will be worth reading!) he made the updated diagrams available for these reagents! 

Higher resolution is available here (I can't embed twitter's new .jiffyjaff format or whatever it is in blogger. Off topic, but Amazon now uses a new image format for everything, including your personal images if you use their cloud backup thingy. I can't embed those either.)

I bugged him about my own personal issues with medium resolution instruments and multiplexing (on the TIMSTOF and 7600 we can only 10-plex using 126, 135 and 127-134 and he made this as well (you can find the higher res in the same Tweeter thread in the link above).  

It is funny because just a couple of years ago when we were limited to 10-plex on ultra high res instruments, a lot of people independently decided that combining plexes wasn't worth it. CPTAC plowed on combining plexes leaving the quiet implication on the table that maybe we should read their papers in their entirety rather than just working through to figure out just how much one of these amazingly expertly analyzed tumor samples cost on average. (Whoever said this last round cost $15,600 per proteome was probably mediocre at arithmetic). 

Whether it was informatics that needed to advance or study design, combining plexes is now common. There are absolutely clearly pitfalls when you do this, but if you've never set one up before this is THE paper you should read first. 

Super expert advice for TMT post #1: TMT Channel Crosstalk by Phil Wilmarth

 

The next two posts will blatantly steal other people's work who are just trying to make sure that we're mixing TMTPlexes and considering all the proper variables

For post #1: This link will direct you to a real proteomics science blog everyone should read!  

Full structure T-Cell receptor ligated by MHC!

 

It's easy to forget about protein 3D structure sometimes when you spend your week trying to tell linear peptide sequences apart. In the end, however, a lot of actual protein chemistry is "if this thing is open then this other thing fits in it", right? 

This stunning new study demonstrates one of the most critical parts of how MHC binding to T-cell receptors works and angles are super important! (CryoEM is getting better all the time!) 

What comes out of this study that is really interesting is how critical the base of the protein complex is and the role that lipids clearly play in facilitating the interaction between the complexes. 

Targeting protein splice variants -- the definitive guide!

This might not be the most exciting thing to absolutely everyone, but I really like this handy step-by-step protocol for targeting those splice variants that we like to ignore in global proteomics data. 
 

Loads of proteins work by cutting other proteins and splicing them together into new forms with altered or completely new functions. If that's what your collaborators are actually interested in, global proteomics can be a pain in the butt. I've had a PC just about bricked for 5 days processing 170 TIMSTOF files with DIA-NN and found out during the data review that my spectral library didn't contain the single peptide that my friends actually cared about. Considering that relative costs per sample of the $1.3M list price TIMSTOF Flex + MALDI 2 that I used for 2 weeks of data acquisition vs. our very nice, and definitely not $1.3M list price Agilent Ultivo (the world's largest HPLC isn't nearly as large in person as it appears in the ads). You might argue successfully that had everyone been using the exact same language, I could have very easily targeted the single peptide variant...

(unrelated, but funny)

...to be fair, however, finding the sequence variant was NOT FUN. It was not listed in UniProt or NCBI as a peptide isoform variant, so I did have to find the nucleotides and work out the codons and make my target peptide, and send it to PROSIT to make my targeted list (I am specifically just reprocessing the diaPASEF data for that variant now). Skyline LOVES 170 diaPASEF files, btw. I expect to have quan well before US HUPO. 

And this is why I liked this new protocol. There are multiple tools listed in the review for converting DNA sequences to splice variants and super easy instructions (and pitfalls) for targeting these in Skyline. Given that these authors say that OVER 90%!?!?!?!? of human genes go through some sort of splicing!?!??!?! I think this protocol might go into the permanent folder on my desktop that just says SUPER USEFU (maybe if it was lower case the L would fit, but I think I need the caps here).

Make optimized windows for diaPASEF (& for phosphoproteomics) with pyDIAID!

As I might have whined about in the past, making pasefDIA windows can be some tricky business. We gave up and had an expert come in, load up his method, and we just run that one. Some other group wasn't satisfied with that method and made a program to make optimal methods!  

The program is really straight-forward to install and run on what appears to be every operating system and you can get it here.

The dividends for optimizing windows for phosphoproteomics shown in the study are seriously impressive compared to just running a canned method. 

Amazing FFPE proteomic resolution -- more single cell tech making it to the ground!

 

I had this great meeting with some really forward thinking grant funding people where we ended up discussing how single cell proteomics itself, right this second, is probably just wasting space in repositories. For real, at the rate of improvement right now are we going to even look at any of 2022s data out there 3 years from now? I already tell new students to just ignore anything in proteomics published before 2017 (there are clearly exceptions). 

The thing about moonshot projects is sometimes the amazing stuff that comes from the tech you had to develop for it. Modern solar panels, cordless vacuum cleaners, athletic footware and pacemakers are all technologies that you can argue were largely a result of innovations driven by the race to the moon.

As another example of something to fall out sideways of the race to the single cell proteome? What about the highest coverage and highest resolution spatial coverage of an FFPE tissue? 

That's one heck of a side effect when all these people seem to no longer even be impressed with how much of the protein is there -- they want to know how much of the protein is there --and now where!?!? 

Seriously cool results from the most abundant historical medical material on earth. I can't recommend this one enough. 

Photoaffinity probe allows heme chemoproteomic mapping!

 

Heme might be one of those things from biochem that you learned, vaguely remember something about 4 things holding iron, and then didn't think about again.

Recent evidence suggests that it is also involved in signaling! 

This group did something super smart with a photoaffinity probe to enrich interactors --

-- and, darned if it don't like heme has a bunch of protein interactions. Really cool approach that raises some interesting fundamental questions about what can and should be considered a signaling molecule! 

SCIEX Road Tour is Back -- in the world's greatest city this week!

 

During the couple of years that don't count toward the age of any adult human beings or dogs (possibly cats, I'm not sure) vendors had to get creative. While some did things like massive layoffs and pay reductions right when Proteomics made Forbes which led to some impressive alterations in the correct email addresses of my contact list, SCIEX put a bunch of instruments in a big ass truck and put it on the road to visit outdoor venues.

The truck is back in Maryland this week with one stop for the DC area and a significantly cooler sounding stop at the Sagamore Distillery in Baltimore! 

The upcoming east coast stops are: 

You can register for this here. 

Clarifications on ULTRA-FAST human proteomics -- more of kind of fast.

If I'm flipping through my hand held digital phone thing and the Twitter icon looks like this

...it generally means that I did something wrong. To combat that anxiety I just don't open Twitter for a while. Turns out a lot of the these notifications had to do with Monday's ULTRA-FAST proteomics paper and subsequent blog post.

While most of these were really funny suggestions on the names of faster methods, a lot of smart people looked at this study and found it a little less impressive than first glance.

First of all -- 5 minutes isn't anything remotely close to the run to run time. It looks like sample to sample on this system you're talking about 17.5 minutes. Which, hey, sometimes your HPLC is really super slow and maybe you just need a faster loading HPLC for the next stage, but 17.5 minutes is ~82 samples per day. 

There is a Bekker-Jensen et al., paper from 2020 that did some even UltraFASTER samples/day on a FAIMS Exploris 480 that realistically got pretty similar data. 

I bet if this data was reprocessed with the fancy neurotic network stuff that we have now with match between runs that this data would be pretty comparable. 

To be clear, I'm not putting down the UltraFast paper at all, but I do think that from a practical sense, this title might not be the most helpful thing in the world.

If this group gets 10,000 samples shipped to them and their collaborators expect that data back in the 35 days it would take to run them all based on the 5 minute gradients, there might be some fallout when they have to explain why it took an extra 3 months (excluding QCs and blanks and calibrating the instrument, etc.,)

Ultra-FAST DIA proteomics!

 ULTRA isn't just a word you should haphazardly toss around. 60,000 ravers descending on Split Beach in Croatia for a 3 day music festival? Fine, you can use the word (where do they put all those people?) 

What kind of a proteomics method would be good enough for the nitpicky reviewers at JPR to allow someone to use "Ultra"? 

5 minute runs with 5,000 proteins? 

Check out how crazy tight those windows are! Talk about taking the top of the histogram for the peptide distributions. But is it Ultra? 

Spatial proteomic profiling enabled with serious robotics!

 

Wow. Okay, so this new study in JPR is kind of a meta-analysis of a previous study that I hadn't heard of until now and features some ideas I'd certainly never thought of.

New paper: 

Earlier study: 

The pictures are cooler in the 2021 study, but the analysis is a lot more mature in the re-analysis. I feel like it was a case of "oh shit everybody, look what I just pulled off!" and everyone on the team said "oh shit! submit it now while we keep working on the analysis"

I might get the following wrong, there is a lot here, but this is my interpretation. 

What they did was take some mouse tissue that was infected with a pathogenic bacteria and they sliced it like they were going to image it. Instead of imaging it they used a super high precision robot (the SciFlex arrayer S3, which is like a CellenOne but way bigger, a lot more capable and a whole lot less expensive, but it doesn't have the cell sorting software. They use this to spot trypsin on the tissue in places where they want to digest and then they use a NanoMate to pick up the peptides from these spots and they do proteomics on the spots. Paper one shows that they can identify bacterial proteins and get feel for where the pathogen makes changes. Paper two is a more in-depth informatic analysis that really starts to work out the host-pathogen interface to get to the really cool biology.

While I love the idea of digesting slices and doing MALDI based proteomics on our instruments here, what we've found is that we're extremely limited by protein copy number and ionization effects. On an imaging TIMSTOF, you can't fragment anything and the TIMS doesn't do anything but some ion mobility separation. What you end up doing is peptide mass fingerprinting at 30,000 resolution and that limits us to a few dozen extremely high abundance peptides. Still can be cool, but if you're looking for somethign with less than 4 million protein copies per cell, you're largely out of luck. 

What this group has done is get real dynamic proteomic range spatially across an actual infected organ. Again, I probably got this wrong, but I'm still super impressed.