Proteomics of gushing wine!

There are times when carbonated wine like Champagne is supposed to look like the picture above.  Example:

However, if you haven't just won an NBA championship or the World Cup or are the heel in a dumb Hollywood comedy, the wine isn't supposed to just go shooting out of the bottle. You're supposed to drink it. When you intend to drink it, but it all goes shooting out of the bottle this is called "gushing" and it is a problem with the wine making process and people want to know what causes it and how to stop it.

PROTEOMICS TO THE RESCUE!! (Abstract link here.)

This group takes a look at gushing wine and the grapes they came from and compare them to non-effected wine/grapes. The work is mostly done with HPLC separation, SDS-PAGE (for relative quantification) and MALDI-TOF identification.

Turns out a sometimes handy (under the right conditions -- tasty) fungus that infects grapes is to blame here and they work their way down to a really convincing protein band that appears exclusively in the gushing wines.

BOOM! Biomarker identified! If you find this protein at high abundance in your grapes or juice you might not want to spend months/years making wine from it.

I like this paper because 1) it's a problem I didn't know existed 2) this team seems to go right out with some of our technology and solve it 3) it's a really clever use of our technology's in kind of a unconventional way. And they walk away with a biomarker!?!?

More evidence, finally -- is it time to question the dogma of the nanoLC?

If you want to identify a protein in a gel spot on an LCQ Deca or QTrap -- you need nanoLC. You just don't get enough signal from your 100 bar Accela LC running 200 (+/-40) uL per minute into that big round ESI source that has the 11 things you have to attach to it.  If you want to identify that gel spot you need to split your flow down to nano and break out the alligator clips and get to work. (If you don't know what I'm talking about, believe me that you are fortunate. There was a time not that long ago where 1 scan per second at 800 resolution was a big deal.)

Okay -- where are we today? 40+ scans per second is possible with 8,000 resolution! We have benchtop FTICR systems that are more sensitive than triple quads when operated with sensitivity in mind.

DO WE STILL NEED NANOLC?!?  It is 2018. Can the mass spec and good chromatography at realistic flow rates be a success for proteomics?  Maybe I'm just optimistic because I've hated nanoLC from the first time I ever heard of it, but I think it really is time. For further evidence:

Check this out -- these authors use a great term in this awesome new paper in press at ACS -- is the NanoLC "dogma?"

This study is AMAZING. Well written, logical, unconventional, and someone in this group is a real chromatographer. This isn't written by a pretend protein chromatographer (like me). They think about confronting this problem from where they should -- from plates to peak dispersion -- working their way through different materials and conditions to get to -- something really really impressive.

The authors didn't reveal their final results in the abstract so out of respect for an awesome piece of work -- I'm not going to give them away here either.  Let's just say that if you are sitting there changing pump seals and dreaming of tossing that nanoLC off the roof of your parking garage -- this might just give you that final bit of motivation to up and do it. There is a reasonable chance that you aren't getting results with that headache that are as good as this team is getting without it.

I feel like I owe the authors of this paper something after reading this. This totally made my day.

PACOM -- A great new Java tool for comparing different proteomics datasets!

Do you have a bunch of proteomics datasets that you need to compare? Or do you have something awesome you've done that you'd like to check against what somebody else put up on a repository like ProteomeXchange?!?

Are these datasets small enough that you can do it within Java memory limits (processed total <4GB, I think? which is plenty of room for lots of processed universal output files)  Would you love a simple step by step interface that gives you instant feedback when you are just pushing buttons instead of reading the instructions?

If you answered yes to all of these the Proteomics Assay COMparator (PACOM) is a great new tool you should check out here.

Seriously -- it's tough to compare proteomics datasets and there isn't much out there in the way of tools that can do it besides Perseus and it is always nice to have additional options. I do recommend you check it out (and read some of the instructions....it looks very intuitive but you can't just sleepily start filling it with mZidentmL(s) without reading anything and keep clicking the forward button and get anywhere...well...maybe you can, but before noon I'm lucky if I can put socks on without breaking something...)

Again -- seriously -- this looks like a GREAT piece of software to have on your desktop for assessing your data, comparing between sets and generating impressive metrics and figures.

Wait -- I got it. WHO NEEDS INSTRUCTIONS?!?! Not this guy! He just needs espresso! Wow. There is some cool stuff in here. Heatmaps, mapping chromosome coverage, multi-set reproducibility metrics(!), word clouds(!?!?) -- okay -- I love this.

PALEOPROTEOMICS Technology Review!

On a scale from 1 to "THERE IS A MOTHER FLIPPIN' T-REX ON THE COVER OF JPR AND YOU DON'T KNOW WHY FOR MORE THAN ONE DAY", how busy have you been the last week or so, Ben?

THERE HAS BEEN A T-REX ON THE COVER OF JPR FOR AT LEAST A WEEK!!! Because....

We desperately needed an update on today's technology on Paleoproteomics, and there is a great review inside. You can check it out here.

Vimeo proteomics videos are all back up again.

Thanks to the readers who emailed me this morning to let me know that all my videos are down.

I got a new check card with all new numbers and I thought I'd updated everything -- then Netflix didn't work.... and Vimeo removed all my videos.

Maybe I should double check for other surprises....

Comparative proteomics of dying cells!

Why is the resolution so bad on that picture when I post it? @MCP had the same problem....it looks great in the preprint paper, and that is all that matters!

The idea behind this paper in press at MCP is so simple and brilliant it may make you want to kick yourself if you do your own cell culture. (Don't kick anyone, especially if you don't do your own cell culture...come on, that goes without saying, right?)

You've got a flask of cells and you treat them with your potential drug and then you wash your drug off and scrape your cells off the bottom of your flask (or whatever you do) and then you lyse the cells for proteomics.

WHAT IF YOUR DRUG KILLED A BUNCH OF CELLS AND THEY WASHED AWAY WITH YOUR DRUG?!?!? Your information on the subpopulation of cells that actually responded most powerfully to your treatment just went into the biohazard waste and all you have are the cells that, for whatever reason, don't respond!?!?

I should stop shouting because this group takes a number of drug treatments and examines the cells that detach compared to the ones that stay on the flask/plate bottom and they look pretty similar. Closer examination, though finds some proteins that help explain the drug mechanism and decision(?) for death or loss of adherence following drug treatment.

Also in this month's JASMS -- the world's largest HPLC?!?!

I'm not sure what to say....I know miniaturization is making it tough to work on your plumbing on some of these HPLCs, but I definitely didn't anticipate a shift in direction this extreme. It should be like working on an old small block Ford engine!  Great for those of us with freakishly large hands and poor hand-eye coordination! 

At first I was a little concerned about the ESI source on the mass spectrometer being on the floor, but it appears to be fully closed and  I'm sure there is good reason for it. Maybe it helps minimize vibrations? 

I do like the idea of having the LC monitor up in the air. Too many of us spend our time looking down at monitors, but a lot of scientists I know are going to have trouble getting a mobile phase bottle to the top of the stack. Hopefully it can be configured to have those lower on the system or on a table beside it? 

I hope they bring a model to ASMS this year so I can get a perspective! (Maybe it's a little tiger?)

A revolutionary open access proteomics model!

What if I said you should stop running your proteomics core the way you have been doing it and just set it up as an open-access model where your customers and collaborators would run their samples themselves.

What would be the order of your objections?

1) They'll break everything!?!?!

2) The data quality would be terrible??!

3) We'll never ever make what the bean counters who secretly run my university require me to make!!!


This new paper in this month's great JASMS details the results of a multi-year experiment in Australia with an open access model.

And -- it works. Holy cow, it works -- 900% growth works! And it's hard to read this and not think -- this could work anywhere!

The authors use the word "insular" which I think means this US vs THEM mentality that current models have ---

You know what I'm talking about -- cause if it isn't happening to you, it's happening to someone you know --

Your collaborators and customers can't keep up with the growth and changes in our field (who can!?!) or just have conceptual issues with our technology so communication is hard. You probably think that they're kinda dumb. (They probably aren't -- or they wouldn't have the money to pay for mass spec.) Even when you try your best to communicate with them I bet you are still taking lots of things for granted -- like they know where trypsin cleaves, and what TMT is, that you can't just search for every potential glycopeptide in that 1ug of peptide that they sent you -- so from their perspective you come off like the angry mass spec wizard who doesn't get nearly enough sunlight down in your dungeon.

 The subscription model knocks down this door.

You have direct access to your customers and they have direct access to you. You have to train students or postdocs enough that you all have a shared language -- and interpreters between you and the PI.

This lab sets up things as easily as possible. The same LC systems -- the same universal protocols. A minimum training requirement before someone can access the instruments. The users are invested in the projects and know enough to design ones that are much more likely to succeed. The mass spectrometrists are still critical for the training, advising, troubleshooting, and for the really tough experiments and application development. You know....the cool stuff!!  The student biologists who are super invested in their projects do their projects and handle all the boring stuff you trained them to do. They get valuable experience and together you push biology forward.

Over 100+ peer reviewed papers out of this experiment for far!!

900% growth in lab budget.

(And they didn't break the instruments)

BRAVO to this group for attempting this -- and thank you to Dr. Williamson for the clarity in which this experiment is written up. 900% recommend you check this paper out!!

Enrichment profiles to lead us toward better plasma proteomics assays!

I pretty much have to leave this cool new study here, I've nearly shoveled myself out -- I'M SO TIRED OF SHOVELING SNOW!!  And I'm just catching my breath, before I finish shoveling and see how hybrid-car friendly the roads are between here and work.

Big reason I really want to get back to this paper later? Ummm...ever wondered why you couldn't validate something someone else found in plasma samples even though your samples were identical? Did you harvest with EDTA? Did they? Did you heat inactivate? Did they? It can make a huge difference!

By addressing issues like this, these authors start to establish unique profiles that explain a lot of the differences.

As valuable as these authors demonstrate comprehensive profiling can be -- to me it might even be more important that it highlights the value of comprehensive conserved protocols!

BenchSci -- Find antibodies based on published proof that they work!

Antibodies aren't going to go away, but maybe this cool new site can alleviate one of the biggest headaches -- finding one that has been proven to work for your application.

Instead of:
1) Going to a manufacturer website X
2) Seeing if they have an antibody for your protein
3) Checking if that antibody works for immunoprecipitation or whatever
4) Checking if the manufacturer just says it does -- or if anyone has actually published anything on it
5) Checking the paper to see if you believe the author's results...
6) ...sigh.... go back to 1 (rarely, of course! it got through peer review, didn't it?!? I'm just being funny, but still, that's a lot of clicking through webpages!)

This team uses a machine learning algorithm thing to scroll through the literature and find published proof that there are antibodies for a protein and that they work for certain applications

I talked to one of the developers to:
1) Make sure that this is really what they are doing

2) Make sure that they'd considered scientific literature access stuff because this sounds like a great idea -- as long as they don't end up going to prison for it next week. They are actually working with individual publishers so this is all legit.

3) To suggest they contact all the proteomics journals next(!! and they are !!) so their database can have more MS-compatible antibodies.

To use it you put in your protein of interest in the search bar at the top and on the right blue bar you can start adding filtering parameters like what organism, cell type, application, etc.,  This will start to reduce the figures that show proof of the antibody working for your application.

TADAA! You've got the direct link to the peer reviewed evidence of a functional antibody -- then you know what company to go to.

It looks like you need to register for a free account to get some of the info, but as far as I can tell that's the only catch. (Leave me a comment if you find other ones, please.) Right now it looks like all wins -- You get to the right reagent faster and you have your reference for why you selected that antibody in the first place!

Doing MS3-based reporter ion phosphoproteomics? MS3-IDQ!

If you are doing MS3 based reporter ion quantification, you should really check out MS3-IDQ!

The paper is available here.

The idea is really simple. If you are going to be getting both MS2 and MS3 based data on your peptide, you might as well utilize it to increase your identification and localization rates, right!??

I'm sure this isn't the first group to have thought about it -- but implementing it? Umm....yeah..... There are a ton of factors to consider from the instrument side and from the data processing perspective. I, for one, am really glad that they did the work so I don't have to!

Immune system response in a 500 year old mummy!

The overall story here is really sad from the human perspective (spending some time trying to find just the right Scooby Doo gif helped lighten my mood), but the science in this 5 year old paper highlights capabilities we have that we might never think of.

The article is short so I won't go into it too much, but you know how hard it is to get an extra FFPE slice out of your collaborator? Imagine the material limitations that you have when you are getting it from a museum with some National Geographic people watching your every move. The fact that this Orbitrap XL could identify any peptides -- let alone make observations on the immune system response(!!) of material this old and limited and precious is really amazing.

The authors spend a lot of time on the normalization maths. Of course, we always want a control and some biological replicates before you try any sort of quan -- but this is one of the rare cases where I'll cut the team a break. Fancy math on the mass spec data and PCR to back it up leads the team to some really interesting conclusions on what might have happened to a couple of kids 500 years ago!

I love getting reader comments, but this might be my all-time favorite!

From an anonymous author:

Take that, haters.

More on the Beadome!

Yesterday I discovered something everyone else knows all about and I'm still fascinated with how I might exploit it to give my collaborators better data -- there is SO much out there on this topic. I think I can be doing better experiments by Monday (today if our IT security people would allow me remote login...not sure why but my head hurts too much for me to drive anywhere)

This study has some amazing insight into how much of a problem the beadome truly is! 

In controlled pulldowns less than 1% of the total peptides identified -- and around 1% of the total ion intensity (TIC) come from peptides that are associated with the enrichment! One percent! The rest? Beadome....

The antibody really is trying to just pull down it's targets -- it just sucks at it (what a surprise! antibodies being unreliable? How weird...) But this isn't a Ben hates antibodies post. There is no question at all that when my collaborators do these pull-downs that they enrich their proteins. It's a crude tool, but it works. The important thing here is how do I better get to the 1% of the signal that matters here!?!?

This may not be the first study, but I'm only at least 7 years late to the "Why don't I make a static exclusion list?" party. Check this out!

Unfortunately, it adds some important biological context to everything. Wait. Unfortunate? Oh. It turns out there is a bunch of different ways to do one of these pulldown things. However -- there is tons of potential here for developing lists of your BeadOme junk and eliminating it from fragmentation if you know how the pulldown is done. They go through all sorts of different methods and develop a list for each kind of pull-down thing.

However there is a lot that is shared -- independent of the method -- but it looks like the biggest impact would be from running your experiment while excluding the pulldown things specific static exclusion lists. It bears further investigation for sure!

If we go back to the first paper I linked, the quantification methodology with imputation (random score input imputation) allows them to ignore the beadome impact from the data processing side. This is great -- if you have enough dynamic range to get to that 1% of the signal you really want to! But I still think the use of static exclusion will help a lot to get down to things like PTMs on that 1%....

The Beadome! (The crap that sticks in every pull-down!)

I'm new to IP pull-downs, affinity purification, affinity enrichment, all this stuff. My background in the lab is global plasma/serum stuff. I never have enough dynamic range, I never have enough peptide coverage and the instrument is never fast enough.

At my new facility, IP is the way of life. Everything is enriched with big proteins taken from mouse or camel blood or whatever and stuck to beads or something. It's a mystery to me how it works and I'm too busy fixing EasyNanos and looking for the coolest PTMs you've ever heard of to ask.

As I'm running these things my first thoughts have been -- wait -- if we're using an antibody shouldn't we pull down like, I don't know, the one thing that the antibody is specific for?  Aren't antibodies for matching to one single protein? WHY ARE THERE 2000 THINGS!?!?!

Some of this isn't my ignorance. Turns out a lot of it is just crap that will ALWAYS pull down. There have been multiple awesome studies over the last 10+ years on the "BEADOME"

This is a great first starting point (2008)!

This review is more recent and has some interesting new information as well (can't take screenshots I have it in hardback only)....

Okay -- so here's the question I'm going to get to really quickly. When I do plasma proteomics the first thing I do is build the biggest static exclusion list that I can. I have exactly ZERO time to waste fragmenting albumin, transferrin, and about 45 other proteins. If you're really interested in albumin, you'd better tell me about it up front because no mass spec I'm in front of for very long will ever detect an albumin peptide.

How consistent is this BeadOme thing? And should the Q Exactive (which has a maximum static exclusion list of 5,000 ions) and the Fusion (which -- I've yet to run plasma or serum on yet, so I don't know the upper limit -- gotta be more than 5,000, right? It's got 2 PlayStation 4 CPUs inside it!) be continuously ignoring a lot of stuff to boost my dynamic range?!?!?

TagGraph -- I don't get it, but it looks seriously smart!

Yup. I don't get it. It's Saturday afternoon and my plans are to celebrate my 30-ish percent Irish heritage in the stereotypically tacky way us Americans do just about everything.  It is actually coincidental that these graphs are green.

I'm leaving this here so I can take a look at it later. What I do know is that there is a clear bias in the search engines I use daily that are biased against the PTMs I'm looking for and TagGraph seems to take a nice hard swing at that by working completely around the problem in a new way! Honestly, I'm not going to trust my understanding enough to share it, but what I think I get -- seems seriously smart.

You can find the preprint article here. If you figure it out before I do and want to give me a call and set me on the right path, today would be a bad day for it, but I'm open to it later. Tomorrow morning might also be off limits. We'll see!

Oh yeah! Here is the link!

Accurate masses of iTRAQ/TMT reporter ions.

I'm going to turn something from frustration into something useful!! Take that universe! I just wanted to find, in a hurry, the accurate mass reporter fragments for iTRAQ and TMT. Google Images was nice enough to find me 300 pictures with unit mass.

I'm just putting these here so that they'll pop up in Google image searches in the future.

Of course, this should go without saying, but just to be clear: iTRAQ and TMT are the properties of Proteome Sciences and are trademarked to ABI(TM,R) and Thermo Fisher Scientific (TM,R), respectively.

In no particular order here are these poorly drawn images. Go Google Image Crawler go.

EDIT 3/22/18:  TOTALLY WORKED!!  Only image on the front page of Google Images with anything behind the decimal place. 

Benchmarking quantitative strategies for phosphoproteomics!

Shoutout to @PreOmics for tipping me off to this one (great job Google Scholar alerts -- how'd you miss this one?!?)

I can't even get through this one this morning -- daylight savings time is dumb, but considering we're about to fire up a 10 time point 3 replicate per time point phosphoproteomics study on something that sounds super important (I forget what) it couldn't have shown up at a better time!

At 30 samples we're just at the critical junction where TMT 11-plex should be perfect for it. We'll probably miss some channels moving from plex to plex, but we'll save so much time that it has to be worth it (and that 11th channel is our great pooled control!). The next decision -- it's phospho -- do we break out the MS3 SPS to deal with our ratio compression, knowing full well that the lower speed of the method is going to mean fewer total phosphopeptides?

THIS TEAM GOES THROUGH ALL OF IT!!!  THEY EVEN SPECIFICALLY LOOK AT DNA DAMAGE PROTEINS (which may actually be something like what we're doing -- but...again... I can't remember. You know, it's actually better science, I think. All of my experiments are double blinded. I can't interject unintended bias when I can't remember what organism or project I'm working on!)

Speaking of biases -- this study does a pretty nice job of supporting one of mine. TMT MS2 methods look pretty impressive compared to MS3....definitely check it out!

Call the pathologist (or whatever they're called) why don't they fix cells with MS-compatible crosslinkers?!?!

Okay -- umm....what is the downside here?!?!

Check this out and tell me if you find one!

We're constantly trying to work around the fact that all these cool tissues in repositories -- EVERYWHERE -- have fixed tissue. It's formaldehyde then paraffin and I'm always super impressed when anyone gets anything out of them when they go back to re-analyze them.

Sure -- it works okay for imaging (with specific antibodies) but we're often stuck with reversing the crosslinking (with varying degrees of success) or ignoring the crosslinked moeities. (Come on blogger, at least one of those is a word...)

Why don't we toss the dangerous formaldehyde junk and use better reagents to preserve the tissues!?!?  This group looks at preserving the tissues with MS-cleavable/compatible crosslinkers - and it works!

More alkylation discussions!

Last week I put up a post about a simpler method for reduction alkylation used in some recent studies that I liked. That post is here. I don't think I've ever received (real) comments on a post so quickly and I suggest that you check those out.

One comment will lead you to this great recent paper that takes a deep look at these issues.

These authors take a good hard look at 2-chloroacetamide and find that it comes with it's own special annoyances. Massive increases in methionine oxidation compared to iodoacetamide and both singly and double oxidations of tryptophan (I'm having trouble imagining where that second oxidation goes...I should get more coffee). They also examine how different buffer conditions will lead to increased/decreased off-target alkylation effects.

This is obviously a complex issue that requires a lot more consideration than this blogger is willing to do on a Sunday afternoon, but I'm hoping to get to a single standardized protocol for my experiments soon. When I settle on something I'm just going to do it this way for years. I need to be able to go back to historic data and match it to new stuff and any tweaks in sample prep that lead to improved IDs aren't worth sacrificing my ability to easily align and remine old data.

TMTc+ -- specificity at the level of MS3-based methods with MS2 speed!

I always err toward MS2-based reporter ion experiments. I know the accuracy is better with the MS3 based methods, but my problem is always trying to get to the protein(s) that everyone is so interested in and I can't take the speed hit.

This relatively simple looking new approach shows that by deconvolution and intelligent use of the TMT complementary ion fragments you can have it all!

The use of the high mass fragments that still carry TMT fragment tags is not new, these authors described TMTc a few years ago --and there is a free Proteome Discoverer node from IMP that will allow you to utilize them, but TMTc+ takes it a couple steps further. By adjusting conditions to bias toward the formation of the complement ions and by taking the shape of the ion isolation window into account they demonstrate massive improvements in this approach.

How massive? Ummm....more quantified proteins than a label free approach?!?  If true, this is paradigm flipping stuff. Every study I've ever seen has shown throwing in the reporter ion tag to decrease the number of IDs compared to label free approaches and I think we all just accept it as the trade off for being able to multiplex a bunch of samples at once. But...if you actually get more IDs -- or even no loss -- the next question is why wouldn't you use TMT for every experiment (where your n < something crazy, of course)

This study was just accepted by ACS, so I changed the link above to the direct toward that version of the paper. The preprint is still available at bioRxiv here.