Friday, August 19, 2022

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).

Thursday, August 18, 2022

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. 

Monday, August 15, 2022

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. 

Sunday, August 14, 2022

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! 

Saturday, August 13, 2022

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

Thursday, August 4, 2022

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.,)


Tuesday, August 2, 2022

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? 



Monday, August 1, 2022

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. 

Sunday, July 31, 2022

Ion-mobility fractionation?

 

Different types of gas phase fractionation methods pop up here every couple of years, but this is the first one I've seen that breaks up the ion density using TIMS.


The results really illustrate how dense the peptide signal is. Remember when we were trying to get to a point where we could fragment 100,000 peptides and that seemed way off in the future

These authors use extremely narrorow ion mobility ranges for multiple injections on a TIMSTOF Pro and crank their protein IDs way up. To demonstrate further utility they use the sum of these runs to generate a spectral library for pasefDIA and improve those results relative to other methods of library generation. Clever idea with solid results worth thinking about. 

Saturday, July 30, 2022

FlashIDA -- Real time top down deconvolution and targeting (on Fusion 3 or 4)!

 


More smart data acquisition things, and this one is for top down! 

You can read about FlashIDA here!

It works on the Fusion 3 (Eclipse). I'm going to keep talking about the Fusion 4 until I get over my disappointment of not seeing one released this year. (No inside information, I was just expecting a refresh soon.)

If you're doing top down you should definitely check this out! 

Friday, July 29, 2022

Party is over -- guidelines for single cell proteomics!

 



What does an exciting new field that is rapidly evolving need? Probably a bunch of old people making rules about how and where it should go next! Check this out! 

This one is tarnished a little by the fact that they've slipped in some people on this who are younger than I am. I've got a USB drive full of Golden Girl gifs (GGgs) that I thought I was going to get to break out here. 
(Edit: If you don't know me and the GG reference doesn't tip you off, and you just see a profile pic of me that hasn't been updated in....a while....this is one of the jokes, fellow kids. I'm well above the median of this list and I may wear shoes to work today that are likely older than some of these authors). 

What I'm going to break out instead is a metaphor (or analogy, I forget which is what).

Imagine some prominent aptamer research center somewhere that has been just tearing that field up for the last 20 years. They can make DNA oligomers that can bind to just about anything. In the aptamer field this group is right up there with the 5 best groups in the world. All the grants coming their way allow them to pick up their first mass spectrometer so they can check that the masses of their products make sense without sending them out, and they go with a nice ESI ion trap system. After they get used to running this thing, someone brings in an aptamer binding reaction that doesn't make any sense with their traditional assays and they decide to infuse it on the ion trap. What they see is more than one compound and they can't really tell what is going on, it's too complicated, so someone gets the crazy idea of coupling an HPLC to that ion trap to reduce the complexity. It turns out that DNA oligomer made in that new organism (I dunno) has a big mass discrepancy, so these researchers dial in their chromatography and fragment that ion. Get this: What they see is a bunch of ions that correspond to amino acid masses, and if they were in a perfectly linear chain, the masses only line up if the exact same bond (!!) was broken at every amino acid. Shine up that Nobel Prize, because what they find is that a whole lot of their old aptamer things that were discarded actually have these long linear amino acid chains stuck to them. Unbelievably, they can work out EVERY one of them now because these amino acid chains all fragment the exact same way. 

Now we'll step out of this scenario and back to you, strange person who has read this far: 

Imagine how excited you are going to be when Science Magazine's Aptamer Research Journal (SMARJ) and other leading papers in the field publishes twenty five papers (18 of them are reviews from the guy who supervised the guy who first ran the ion trap) that drop in a 2 year period. Linear amino acid chain identification by vacuum chamber accelerated breakage coupled to reversed phase liquid based separation of reacted aptamer products (LAACIbVCABcRpLbS_RAP) is even making mainstream news. Oh crap. They've even got software that can semi-automatically figure out the order of those amino acids now and it's in one-word Nature! Boom. Paper. Boom. Paper. It is 2022 and Ion Trap sales are off the charts. Every aptamer researcher in the world needs their own because LAACIbVCABcRpLbS_RAP is the future of medicine and environmental research. 

And then all those top aptamer researchers in the world get together and start releasing community guidelines. They even indicate in those guidelines that they know that proteomics exists. Heck, they're even thinking about some of the proteomics data (while, of course, focusing almost entirely on how much better LAACIbVCABcRpLbS_RAP is than the Excel sheets of proteomics data they pulled from the supplemental info of a couple of PLOS papers that are making the rounds in their circles). 

Now, assuming that this rambling metaphor has anything at all to do with whatever I started talking about, I should note that this group is still taking suggestions! You can add your community guidelines and recommendations to LAACIbVCABcRpLbS_RAP, wait, I mean, single cell proteomics, at this cool site here: https://single-cell.net/guidelines

Of course, there is absolutely nothing stopping someone who has completed one of the thousands of successful single cell sequencing studies from contributing to these guidelines. Heck, experts from a reasonably mature, standardized, well-accepted, and incredibly impactful field of science might even have some interesting input from their last 20 years of successes into a field that literally didn't exist 5 years ago. And I really have to wonder if all the noise we're making makes us seem like we should type a little less and listen a little more. (The irony of who typed that sentence is, by far, my second favorite part of this entire post). 


Wednesday, July 27, 2022

Dysregulated proteins and RNA in Parkinson's lymphoblasts!

 

I keep going back to this recent study in Proteomes for a couple of reasons

Obviously, if you're going to study Parkinson's you need access to some cerebral spinal fluid or postmortem brain sections, right? Those are so easy to get that there is a guy I know who will provide you with the latter material only under the upfront agreement that he is the senior author on the paper and he gets to pick the author order. You can do a proteomics or a metabolomics study, make every figure, and you might be 7th author. That's the deal. Go get 30 brain sections somewhere else if you don't like it. 

What drew me to this at first was the use of RNASeq and proteomics. Always a draw, and then once I googled what a lymphoblast was (I'd heard of it, give me a break, I just couldn't point it out on a map) then it made me think this group was a little crazy. What do we care about white blood cells for in a neurological disease? 

They find a bunch of differences at both the proteomic and transcript levels. And the talk about how this disease does end up affecting other systems you wouldn't necessarily have thought of. 

The proteomics was on a QE HF and that data was processed by MaxQuant (I'm pretty sure, details have faded a little). More proteins were found altered between healthy and diseases patients than transcripts at the cutoffs they used, which is pretty cool. Chances are only 1 out of 3 of the changing transcripts actually make it to alter a protein anyway, so the less of those to think about the better.

The final reason I kept thinking about this paper was the fact that the data aren't publicly available. Based on the extensive ethics declarations for the study, I had a hunch and I confirmed the senior author that they couldn't get a release from the medical ethics people to make the files available. As much as we have to be proud of as a field for making our data available....

 -- you know, except the few big labs that have decided making a single 1TB zip folder and uploading that still counts as making the data available -- which, it doesn't. If that's you...

PRIDE is amazing but a LOT can happen during an 8 day download. And who has space for a 1,000 GB zip folder that is EVEN bigger after it's unzipped? 

What was I....oh yeah! 

...human samples have a ton of rules and sometimes someone behind a desk somewhere says that .RAW file isn't leaving the lab, and what do you do about that?

Tuesday, July 26, 2022

Questions about alzheimer's data being blown up by the media....

 


If you needed another reason to hate western blots, you should check out this somewhat overstated piece in Science. It's like following Elizabeth Blik's Twitter where she takes apart images. 

The media is running wild with this piece, as if Amyloid plaques and large neural insolubility problems in neurological models don't exist, and that is not true at all. I'm definitely guilty of reading a quick news blurb on it and repeating it to some people before thinking about it. 

No joke, if you get these insoluble chunks from neurological diseases it is crazy how hard they are they are to break up so you can digest them. Someone I won't name here underestimated this a couple of years ago and may have set a new world record for EasySpray column consumption. This Explainer piece from I fucking love science puts it in context



Rather than being a big shakeup that questions a whole ton of things, the best I can tell this should just help clarify existing data. Toss out the assumption that AB-56 oligomer is important and that should help, right? Good thing we've been collecting data on all the peptides that are present, rather than using rabbit blood extracts bonded to the stuff from firefly butts to "measure" one thing at a time! 

Monday, July 25, 2022

Protein adsorption loss (one of) the bottlenecks of single cell proteomics!

 


This new study probably isn't what you think it is. It is still important, but I jumped to some conclusions about it when I saw the title. 


What this is: 

A nice review of the status of single cell proteomics. 

A reminder that we don't have good QC for it. Come on, y'all, we just sorta started doing QC for bulk proteomics!

A review of some things I'd never heard of that might have some sort of application to help us with QC'ing SCP samples

What this is not: 

A study that quantifies absorption loss or provides tips for how to avoid it. 

At least it's not another deceptively written "single cell levels" study that will confuse potential collaborators and grant reviewers and lead to messed up expectations of what we're capable of because it completely ignores that protein adsorption loss is a thing. 

Thursday, July 21, 2022

SimPLIT -- Streamlined workflow for TMT labeling in 96 well plates!

 

Need a step by step protocol to break out the microchannels and 96-well plates for TMT proteomics? Maybe you should check out SimPLIT

What is cool here is how this group is prepping tons of cell culture proteomics in 96-well plates with labeling, offline fractionation (by HPLC) and repooling and running TMT (on a Fusion). 

 This feels like a study that came together out of a lot of replication and thinking about how to streamline sample prep to the maximum possible efficiency. 

One cool thing here is how they're getting their cells lysed with a 8-horn sonicator thing. I've never seen one of these that could sonicate more than one sample at a time and it took me a bit to find it

If I have a criticism of this study, it is that it kind of feels like overkill on the LCMS side. 12x pooled fractions at 150min gradients (30 hours) is starting to seem like a lot to me, 30 hours means that instrument operating 24-7 will complete 292 18-plexes per calendar year. For showing off how great your method is from a number of proteins quantified perspective, this is a great setup and I'm sure that shortening the gradient length or cutting out the 2D fractionation would work great for a lot of projects! 

For real, though, great study, great data, cool new acronym!