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! 

Introducing the official mascot of the Human Proteomics Organizations!

I've got a lot going on and I've had to take a step back on a lot of things, including the amount of service that I do. Don't worry, the US HUPO VMO is in great hands with Pratik Jagtap and Jordan Burton taking over as chair and vice chair, respectively. I'll still pitch in as part of the coolest committee in all of the Human Proteomics Organization's respective histories.

The last thing I get to do is announce that the HUMAN PROTEOMICS ORGANIZATIONS now have an offical mascot. I've led this important initiative almost completely on my own. It is the least I could do. 

Introducing - 

THE HUPO HOOPOE! (Specifically the African Hoopoe, or Upupa africana)

(Image above generated by the DeepDreamAI. I pay for a subscription, you can use it, but the ones at the bottom are the official logos!) 

A lot of thought went into selecting this majestic animal to represent the prestigious HUPO and US HUPO organizations, but check out the number of boxes that this one ticks off!

1) It nests in dark holes or caves! Just like most proteomics people stuffed in basements because once-upon-a-time mass spectrometers needed big magnets and that made sense. And now it's convenient for real scientists who would rather prefer that proteins (and especially mass spectrometrists) aren't a thing! 

2) When they are out of their caves they make a lot of noise! You can listen to HUPO HOOPAE sounds at this YouTube video!

3) The Hoopoe have majestic crowns upon their heads, which....hmmm....it can't match every characteristic. 

4) All animals (and even bacteria) avoid these, not just because they're saying "transcript numbers have no relationship whatsoever to the amount of protein around" over and over again, but because they smell very very bad. 

5) Perhaps most importantly, things don't bug HOOPAE, particularly not within their caves. They have evolved not only the unique passive defense mechanism mentioned in 4, but they also have unique active defense mechanisms! More details on those here! 

Here are some of the new official HUPO logos. These will also be available on the official US HUPO and HUPO websites soon, but you can download them directly here. 100% reusable art. I actually generated the vector image myself with no AI. 

Admittedly the "action logos" have been somewhat more controversial, but evolution is responsible for the development of our mascot's unique defense mechanism! I just pointed out the obvious. 

I'll put up a folder later. I'm currently just adding the new art as I generate it. 

Microsoft Designer is going all out to help!

Fragment ion intensity prediction boosts TIMSTOF peptide ID rates!

 

This was on here as a preprint, I think, but I also think it got better during peer review.

The idea is pretty simple. The thing about the millions of spectra that things like PROSIT use for deep learning is that they were generated on Orbitraps. Makes sense. Super high quality spectra. However, fragmentation energies and energy ramps and mass analyzer architectures are all different between this and other instruments. Heck, it's probably not out of the question to think that the Orbitrap-TOF Asstral fragmentation patterns are probably not perfectly matched to Orbitrap spectra. Maybe they are, but there are definitely differences between other vendor TOFs and these library spectra.

This group works through those differences and finds they can drastically (3-fold more!) improve identification rates by retraining the models! 

US HUPO Early Career Researcher Seminars May 17th!

 

Need some valuable insight from a panel of researchers from across the range of academic career stages? 

BOOM. Here ya go! 

https://us02web.zoom.us/webinar/register/WN_RNWI3ua4TN-jhz65DJJQuw#/registration

De Novo Multi-Omics Pathway Analysis outperforms gene based pathway data!

 

I can't follow the maths in this paper, but I appreciate the results! 

Most of the stuff we use for "pathway analysis" is based on historic data from....somewhere.... and the details of that somewhere can have some effects on your results. That ...somewhere.... is often data from the past like RNA Microarrays or yeast 2 hybrids or things that have become very niche things because they were replaced with technologies that are more effective for most applications. 

Since things like Ingenuity Pathway Analysis are drawing from thousands of RNA Microarrays - nearly all of which were used for studies of cancer - those tools often aren't all that helpful for things that are not cancer. Do we need these historic datasets? Could we do better by just throwing in our own multi-omics data? 

This group sure thinks so, and the results are very encouraging.

But you don't have to believe them. Neither do I. DMPA has been compiled as an Executable program (or you can run it in something called MuttLab? Woof). and you can get it here! 

JHU Mass Spectrometry day is back! With remote options!

 Thought I posted this a few days ago and couldn't find it. 

Interested? You can register here!  

DEEP LEARNING DEATHMATCH - What actually predicts peptide fragmentation best?

There are all sorts of thing out there now that can learn from millions (billions?) of MS/MS spectra and can take your sequence and predict how it will fragment.

Which one is best? Who knows? Maybe we should have an algorithm DEATH MATCH! 

Okay, again I'm scrambling to wrap up things before they take my keys and kick me out on the street, so I'm leaving this here mostly for you (well...me...maybe...cause I do want to be able to find this later).

HOWEVER their analysis turns out this tables 1 and table 2 are ridiculously valuable. 

They're summaries of immunopeptidomics datasets! Which is what they tested prediction capabilities against. 

Profile rare cell populations with the sacrifice of 1 (one) lab animal!

 

If you've never done it, it absolutely sucks to kill a lab animal to get samples. Not only is it awful but an increasing body of evidence suggests that those millions of years or evolution make it not make very much sense at all to do it for many mechanisms. However, there are some systems where you absolutely have to do it to get that information. What if instead of needing to murder dozens of animals you could learn everything you wanted from just one? 

Hey, wait, this is one of the few labs on earth who are doing real single cell proteomics with mass spectrometers? What are they doing using more than one cell? 

Answer: I suspect having a much better day with their 500 cells than they had with most of their days of working with one! 

Besides being easier, this makes a lot of sense, right? Outside of marketing literature and some people studying single cells that are larger than my dogs, we aren't getting what we expect in terms of today's proteomic depth. But you can FACs sort 500 cells (assuming you have good cell markers) from just about anything.

This group goes through and quantifies something north of 7,000 proteins from every known cell type in rare c-kit+ progenitor population! (review on that here, I didn't know what it was either). 

Now, a fair argument that you could draw from reading other work from this group is that we might not actually know every cell type that is there, but at 500 cell resolution and as good as they appear to be at FACs you can learn a ton. 

While saving animals is a super worthy endeavor and some biologists might be like - ummm....what about animal to animal variation.... - it's one hell of a proof of concept. Some of those things where we can't possibly do human research is because taking a big chunk out of someone is generally not good for them. But you can lose 500 cells from just about anywhere without it being a big deal. Pretty easy to start imaging the future of low input proteomic diagnostics, right? 

Stellar preprint. Highly recommend you spend more time on it than I did this morning! 

Got last generation's instruments? Here's how you optimize them for ultra-low concentration samples!

 

There it is! It was ASAP when I read it on my phone the other day and it moved to this month's issue. 

I might be getting old because the HF-X and Lumos both seem pretty recent to me, but in a review we're putting together we refer to them as "previous generation instruments" and that does appear to be the case.

So...what if that's what you have and someone wants you to run 1 nanogram of peptides or less? Do you want an okay number of peptides and proteins? Or would you rather have 15-fold MORE? Probably the latter, but you do you, yo. 

The reason I took screenshots of the paper with my phone, however, was where and how the peptide "supercharging agents" (as you'll see them referred to in some other studies). DMSO and NBA are employed here to improve reproducibility. I'm pressed for time, but if you're interested in low concentration sample optimization there are a lot of gems in this study. 

Extracted ion chromatograms to improve peptide ID and quan!

 

Now, this study is very clear that this workflow is simple for me or you to add into your or my workflow by just following 3 steps in the materials and methods section and Imma take that as face value. 

because I really like the conclusions. Such as -

and 

as well as 

So when we get through the very last set of mass spectrometry experiments that I will ever run at the Johns Hopkins University (on the systems right now! eeeeeeeeeek! cross your fingers for us that we can get just a couple more weeks without another flood) I'd really like to try this out.