Guest bloggers needed for ASMS and SCP!

Ugh. Okay. I'm going to miss both ASMS and SCP2024 this year. Blech blech puke. 

This works once in a while, but it's super cool when it does. 

Are you going to one or more of these conferences? Would you like to provide guest blogging skills? 

Here is one that totally worked from a few years ago! Others have as well. 

What I'd need from you is just some notes. It can be as thorough or not as makes you happy. 

And zero judgement if you provide really great notes on day 1 and then I just see pictures of you on the socials having a blast and I never hear from you again. It totally happens. Taking notes while people talk is how I absorb information, even when I haven't had very much sleep. I know that's not how everyone's brains work. 

If you are interested (sometimes people have guest blogged here as teams as well) please reach out: 

LCMS methods@gmail.com (remove the space) 

The Best of US HUPO 2024: Lightning Talks June 18th!

Did you also miss US HUPO and the phenomenal lightning talks? BTW, here is a fantastic article ON the conference I also missed because I've been sick most of 2024 so far. 

You don't have to miss out entirely (just mostly) because the best lightning talks have been invited to present their work virtually! Don't worry, it's not during ASMS, it's after that AND after SCP. 

Register here!

This has been a US HUPO approved message, mostly. 

Decrypting lysine deacetylase inhibitor (HDACs!) actions by dose-resolved proteomics!

Lysine deacetylase inhibitors (often called histone deacetylase inhibitors, or HDACs, but that might be a stretch for some of them) are super important drugs! They're being investigated as single and combinatorial therapeutics for cancer and neurological diseases and a host of other things. 

Somebody should probably get ALL of them and then work out how they affect the proteome and the acetylome at a very very deep level. 

 What a great idea, said this group! 

Selfishly, if you were waiting for me to turn in responses to your exceptional reviewer comments on a single cell proteomic analysis of the heterogeneity in response of one of these drugs.... I might have been waiting for this paper so I could cite it. Updating Mendeley is a pain in the butt. And this group does such AMAZING chemoproteomics that I happily already deleted my bulk HDAC data.

They did 48 offline fractions and used MS3 Orbitrap based quan. PLUS separately enriched K+acetyl peptides. Beautiful paper and the data has already been integrated into the DecryptM database I've talked about a lot on this blog. 

 

Get your patch clamp AND proteomic data from each single neuron!

 

Say what you want about people who do patch clamp experiments, but those people have been sticking things into single cells for a long long time to get data. I've never done it, I've just never liked a person I met who did, but I think you basically just get stimulus + voltage measurements. 

So....you painstakingly derived a cell type and stuck a probe...in that cell... and you get one (1) measurement? 

Sure would be cool if you could get that and then use the fact you've got a cell fixed to do something else with it, right?? What about also getting proteomics on it? 

What they do is the whatever it is patch clamp thing. And they do it with 140 single cells. In the methods it appears that 108 actually made through. The cells that are just sucked up into the patch thingamajig is then just dumped into some trypsin at 60C and then analyzed label free.

Interesting (to me) a 100um ID column was used. With a lot of the field going to lower bore columns which are increasingly difficult to work with (50 um? y'all crazy. 20um? Imma pretend that doesn't exist). Label free single cell on a 100um column? Sign me up. 

Instrument is Fusion (3? Eclipse? Pretty sure, but you might check that) with 120k MS1 and 60k MS/MS HCD fragmentation.  MaxQuant was used for analysis. Very very approachable label free method, in my opinion, and over 2,200 proteins were quantified in these 100+ cells.

MORE IMPORTANTLY! These are IPSCs (miscapitalized something) and they derived these end cells from stem cells to have different phenotypes - and they get an impressive recovery of proteins and pathways that make sense in a AD model. Super solid study. Is it worth making friends with someone who patch clamps? I'll let you be the judge of that (no), but you could get a whole lot more data out of each cell. 

Need a laugh this morning? Check out the Omics Solutions Provider Market Map!

 

A very confident redditor on r/bioinformatics put this up last night and - you guessed it -declared SONY, Hamilton and Agilent as the #1 Proteomics Instrument providers in the world! 

You can check out the original post here. 

Multiple innovative new designs for compact 3D printed electrospray sources!

 

Most proteomics people don't care what the size of their instrument is. We're all tucked away in the basements of condemned buildings breathing in mold and absestos and we're too busy competing with water than people for space. 

However, for fields like point of care diagnostics or field based analysis they're taking tiny little mass specs to the site or to patient bedsides. Compared to the size of an LTQ FT or a 12 foot tall Bruker, our electrospray sources seem to take up negligible space - but they're pretty darned big compared to a single quad.

This study demonstrates multiple innovative designs for reducing the footprint of electrospray souces, including some setups where inlet flow is perpendicular to the emitter! 

Protein-protein interactions by mass spectrometry - a 2024 review!

 

I forgot to push the "post" button on a bunch of unfinished ....posts.... I guess.

Protein protein interaction analysis by mass spec is something I've been lucky enough to not have to do for a few years. Where is the field in 2024, in case you actually need to pretend you remember how to do it for a grant application? 

Right on time!  True story, there is a 2006 paper with this exact same name! Neither I nor these authors are old enough to have been doing this stuff then, however! 

Processing data from anything in Proteome Discovererererrererer!

 

Proteome Discoverer can be a controversial thing, I get it. But if you've put in the time to learn the best toolkit for Proteome Informatics 😇, and losing access to that is enough to scare you away from other systems, you're generally in luck. PD can take mass spectrometry data in virtually every format.

I've been using TOFs for the last 4 years after making fun of them and the people who would actively choose to use them pretty much nonstop for the 10 years previous to that. I'm probably exaggerating how long some of that was. 

TIMSTOF data? I'm processing it in PD (the free version of the commercial version)

ZenoTOF data? Same thing. 

The big thing you're lacking with these is MS1 feature detection. It exists for TIMSTOFs and you can get it from www.pd-nodes.org but I've never tried it. 

As fast as these TOFs are, one weakness is that they aren't as good as on-the-fly decision making as the Orbis. Sounds bad, but the up side is that they're fantastic spectral counting instruments. 

And....proteome discoverer is great at spectral counting! You don't need that core lab software that makes the very nice pretty plots, either. You just need -- 

This thing! (get it at www.pd-nodes.org)

It might not work for your newest PD versions, I guess. I'm only on 2.5 personally. 

If you are using external data in a universal format there are some things missing. PD probably can't tell what data you're giving it, so it helps to tell it --

Otherwise it will assume that every spectra you give it is ion trap. Maybe it is. But if you'd like to see something behind the decimal point you probably want it to know what you're looking at. 

Is it a sector? 

That was meant to be a joke. It isn't really a sector is it? If it is, you can search it! Dude on Reddit had set his PD workflow to his data being from a single quad. It wasn't. It's only funny to me because I do know who you are. Keep at it, dude. We're all learning! 

Also - my gosh - the TopN peaks filter is awesome. It bins your data (for just your search engine steps) and throws out anything that isn't in the top N in that bin.

For example I like a top 12 per 100 bin. So in an MS/MS spectra that runs from 125-1500, your search engine only sees

The 12 most intense peaks from 125-225, and 225-325, etc. etc., it's a super fast noise reducer. Very very good for TOFs, particularly those with extremely high intrascan linear dynamic ranges like the ZenoTOF. Blatant plug, but I don't think anyone else has published on the ILDR of that device. Having infinite dynamic range isn't always useful. 

You also probably need to get your data into a universal format. MSConvert can do this, but I don't love the default settings. Here is a walkthrough for converting current SCIEX data to a nice centroided version. And if you're on one of those trapped ion thingies, I recommend these settings. 

However, if your instrument isn't in the damp confines of the basement of a condemned building maybe you can maintain a 1/k0 fluctuation of 0.1. I currently can not, but my mass accuracy is generally better than 0.05 at MS1. It definitely isn't within the 0.015 in the default dynamic exclusion settings. I put these the same at 0.03.  

Next up, you'll want to think about your display settings. If you are generating 100+ MS2 scans a second you're going to have a very large MSF file. Do you need to see all of these data? Probably not. 

You can have a default layout that hides the things you don't need to see. What I do is run a couple of files from each study first (mostly for QC) arrange the tables and filters the way I want them and then save them.

You can just put in this little node in your consensus and load those filters and layouts.

Boom. Indestructible bears. 

Nevermind, that's something else. But you can have the data that makes sense for your sector or single quad proteomics instrument (or ultra fast and very sensitive TOF) 

Global detection of human variants by proteomics - do we still need the transcript data at all?

 

This came out around New Year's and I hadn't gotten around to it. It is a very thoughtful analysis and on the surface you can flip through and think - well...shit....I guess we still do need those sequencing people. Particularly after seeing Figure 5A (above).

The transcriptomics does a better job of detecting all of these weird variants than the proteomics does. Way way bigger numbers. 

Oh yeah, this is the paper. Wow, did it have a fun time in peer review.....

And we know that, end of the day, transcriptomics data generation is a lot less expensive than we are. However --- it seems to me like transcriptomics data analysis is not doing the exact same thing. Depth is going up and so are false discoveries and the amount of super computer firepower to dig through all this extra depth. 

This group goes deeeeeeeep on the proteome. Multiple enzymes huge coverage. And here is the funny part, even when they should be able to see some of these variants the transcriptomics they don't. 

I suspect here is where the peer review went from weeks to months to many months -- what is the ground truth here? The authors suggest that the transcript variants simply aren't making it to stable proteins that can be detected. I'm sure some genomics guys as reviewers 2-4 were like - "....yeah, or maybe you and your whole field is not very smart....? We're so SMART, we sorta trademarked it! 

Either way, I think there are some fundamental questions here and some fundamental truths. 

Proteomics can be relatively inexpensive, but it can also be very very expensive. To get anywhere near the detection capabilities of transcriptomics right now you have to do the latter one. Data analysis of either -omics is not cheap. Well, it can be, if labor is free. Skilled labor that can determine the relative likelihood of a detected variant being true in either technology is not free and it probably won't be within my lifetime, regardless of how many NVIDIA boards you inefficiently sit close together and link together with a proprietary cable. You'll probably want someone to look at that and say "yup! ya skipped a big chunk of oligos right there and made a weird proteoform!" 

Fudging the volcano plot!

 

Picture borrowed from this recipe. But this is what we're normally talking about. 

I feel like I only recently learned how to make volcano plots and I know that there are drawbacks to them. They're also probably overused (like PCA and T-SNE, etc. etc.,) but it does feel like each paper should have some pictures or graphs in them. 

Here's a short discussion of strengths/weaknesses and alternatives!