Friday, July 23, 2021

Proteomics takes on Fish Fraud!


This isn't the first use of proteomics for fish fraud, but this is the first one I know of that went head to head with genomics based fish fraud! 

What's fish fraud you ask?  Here is a CNN article. Sorry if it's stupid. Here is the thing, though, it makes a lot of sense to take a cheap food product and market it as a more expensive one. 

In the end food counterfeiting is relatively common and mostly benign, unless you get have allergies to one specific food and are served another of your have cultural reasons, etc.,. And, btw, what is the tuna salad at subway actually made of? I know they keep failing tests for actual tuna in the tuna salad.

This is what we've found when we've investigated whether products are actually what they say they are: we generally find a correlation between lies and other bad things. For example, when we examined CBD oil products in the US, we found that products that were the most ridiculously labeled, including statements like "approved by the FDA" there was a pretty good chance the Q Exactive would find something in that oil that doesn't belong. And products that were adulterated were also more likely to have industrial contaminants, etc.,. 

Wait. This was about this paper! 

More proof that proteomics can at least supplement (if not completely replace) DNA technologies! 




Alpha-fold AI solved all the human 3D protein structures! (Sort of...)

 


Who else saw this on their news feed in the middle of the night and realized they had to try a super easy and intuitive way to find 3D structures of...everything from humans?


Whoa, I must have been busy or something. It's been out for a week! Here is the paper (it's 1 page!)

More importantly -- here is the interface

Woooo.....so.....I use the 3D modeling tools in PeptideShaker a lot....I wonder how hard it would be to interface with this data source. Feature request time? 

Okay -- so this is probably worth noting -- you know that wonky peptide that sometimes passes through Percolator's semi-supervised machine learning filters -- it might also happen with AlphaFold. Again, not a 3D expert, but that structure looks a little off....



Thursday, July 22, 2021

Corrections on some TIMSTOF posts!

It has been 8 months since our TIMSTOF Flex was installed. I missed more than a month when my kid was born and I honestly can't remember since then until recently due to sleep deprivation, but it's starting to get a whole lot cooler around here. 

However, with 2 papers from the TIMSTOF  in review it's clearly working. Wiebke (pronounced close to Veebkeh) may even be integrating some clarifications into new software builds based on things that we've found very tricky to understand with the (3?? 3?) of these things we now have on campus since this Flex landed here in November. 

I'm going back over some of the previous TIMSTOF posts and making some corrections that were due to either my own inexperience, or things that have improved since their initial posts. I've put dates on the posts where edits have been made. 

I've made minor edits to my first impressions post here

100% worth mentioning, AlphaTIMS is software from Matthias Mann's lab that is much faster than Bruker Data Analysis, and has a lot of functionality that you might be looking for. 

Major edits need to go into the "Where did all my scans go" post, as well as a new Python script that accurately calculates how many scans that you will get when setting your target and intensity threshold values (thanks Wiebke!). You can get it from my Github here

I feel like there was another one that could use some work. Maybe I'll find it later! 

To anyone out there who has found issues with anything I've posted here and would like to correct or clarify, please feel free to reach out! My contact info is over there somewhere if you don't have it.  I don't have an abundance of free time, but it is important to me that what is here has at least some semblance of accuracy. 😉

Wednesday, July 21, 2021

Webinar -- Process Proteomics Samples with an OpenTrons!


Woooo! 

I've probably rambled about OpenTrons here before. It's a $3500, $4000, $5000 autopipetting robot. If you google it and don't have a great AdBlocker enabled you will never ever forget about it. Their advertising game is fierce. I've bought a couple over the years at various stops for various purposes. (We've actually got a preprint out and paper submitted on another way to use one).

From previous posts you might find on the blog, I can say that the interface has improved markedly since the first one I ordered (which was Python only). There is a really intuitive little interface for it and the library of methods are improving all the time. 

These people (one you might recognize) are going to show you how to process a ton of proteomics samples with one! 

Tuesday, July 20, 2021

MS-PIANO -- A nice step forward in N-glycopeptide fragment annotations!

 



Gotta move fast, and -- full disclaimer -- I haven't downloaded this to run it yet, but Sandy Markey and Steve Stein are on this paper and I'm gonna just assume that the software works as advertised. I WILL be using it soon, though, I've got a bunch of N-glycopeptides to annotate, I've got a talk to give soon! 

My one problem with the MS-Piano paper is the story about the name. 


I may only have a West Virginia 'Murican public school education, but I know the name of a famous piano player when I see one, that dude wrote the song they play at US graduation ceremonies. 

🙈🙉🙊

Don't feel like reading? You can get MS-PIANO here

Thursday, July 15, 2021

Black sheep -- How to handle proteins with extreme ratios (R/Python)!

 


It would be super convenient if every protein that is differential in your model would fit nicely into your volcano plot in such a way that it is clearly differential without messing up your nice visualization.

However, sometimes get a whole list of proteins that say something like 100 because your software default is that is the maximum fold-change to report. How do you deal with those? Do you look at each one manually to see if it is a 112-fold change? Or do you wonder if you should have used imputation or something so you didn't have as many missing values (even if you divide by a quan value that is made up)? 


Black sheep is for the more serious informatics mass spectrometrists, maybe, but it is flexibly provided in both R and through Conda with really clear documentation. The test cases are really smart, including a phosphopeptide data set! 

Wednesday, July 14, 2021

Integrated multiomics to understand yeast alcohol tolerance!

 


Remember when ethanol seemed like a really smart way to help reduce greenhouse gas production? I'm not saying it's not (I don't know) and I do see the warning signs on some gas pumps in the US that say "warning, 10% ethanol" (presumably because old cars can't handle it well). If yeast had a higher tolerance to alcohol and could make a lot more of it, producing alcohol becomes a lot cheaper and easier to do. 

If we could understand it better, maybe we could mess with it and crank it up, right? 

Time for some super smart multi-omics (I'd argue the experimental design might be the star here, though)! 

Since this is a blog (supposedly) about proteomics, we'll focus on that. A Q Exactive HF was used for the proteomics with MaxQuant doing the data processing on these strains that were selected as they were forced to evolve increasing tolerance to ethanol in bioreactors!  How cool is that? 

By selecting multiple different clones as tolerance evolved, they could rule out a lot of the noise of this pressurized selective process, landing on 25 proteins of interest.

I won't lie and say I understand the multi-omics gene copy number stuff but you can check that out. But if you immediately needed to make a yeast strain with a higher tolerance to alcohol than the ones you already have -- there is a short list in this paper of what genes/proteins to start messing with! 





Tuesday, July 13, 2021

ASMS 69 Abstract deadline is today, slackers!


For the first time ever, all of my ASMS abstracts are in -- with hours to spare! 

I know not every one is in. Get on it, yo! We gotta get to Philly for ASMS 69


Monday, July 12, 2021

Time resolved proteomics of COPD of cigarette smoking!

 


This is a really interesting study new study on a lot of levels. Now, it is required under the rules of this blog that I insert at least the following gif. 


This is a BIG study, though and smoking mice were used to get started and to build a set of presumptive targets before moving into human tissue. Oh yeah! This is the study


There is a ton of work here, but why would you want to read it? Well, it does a really good job of integrating a ton of iTraQ 8-plex data from a Q Exactive Plus. Offline fractionation is involved as well as a ton of different time points and conditions. 

Interstingly, the authors do a lot of this with Perseus after processing with Proteome Discoverer which, now that I'm thinking about it, makes a lot of sense to do. 

Around all the great proteomics stuff, the biology comes off as super interesting as well. It's been pretty well established that smokers are biologically older than they are chronologically and this work lands on a really interesting observation I'll just steal from the text here. 

I found this just a really enjoyable work with a complicated series of goals that succeeded thanks to a rigorous experimental design, that pays off in the end with some cool biology. Highly recommended! 


Saturday, July 10, 2021

Proteometabolomics identifies protein bound metabolites in a fungus!

 


Oh. Raise you hand if you hadn't been considering how your metabolites might be directly interacting with your proteins and that it might be really powerful to do so. 



A really cool part of this method is how this can be a one pot solution. Despite how we seem to be divided into different camps between proteomics and metabolomics, basically all of today's high resolution instruments can do both really really well. In this case the authors do both great metabolite ID and proteomics on a Q Exactive Plus instrument. 

Friday, July 9, 2021

Rapid analysis of small amounts of heart tissue with Azo and PASEF!

 


I've got to move fast and can't do this great new study justice. Have you ever tried heart proteomics? It isn't a ton of fun. There are just a couple of proteins that make up just about the entire proteome. Unless there have been new developments, there aren't easy depletion kits. Most high coverage proteomics is long 2D experiments starting with tons of material -- or -- just 4 million Titin peptides. 

But if you want to understand what is going on in the heart, you have to go to the protein level. A lot of the cells rarely divide so you aren't exactly dealing with a lot of genomic instability issues like in cancer, and that's why this new study is so cool!


1) Low amounts of sample (they work down to 1 milligram of heart tissue. Not protein. Starting material!) 

2) It's fast! They optimize a sample prep method that gets the digestion conditions both reproducible and down to 30 minutes? What?

3) It's 1 dimensional! (or 4, depending on how you count, I guess) Two hour gradients on a TIMSTOF Pro. 

And it gets some serious depth of coverage. 4,000 proteins? 

Tuesday, July 6, 2021

Pioneer -- A clear pipeline for generating spectral libraries!

I think I've been using Skyline for close to a decade. Is that possible? I think it must be! And I know how to do exactly 2 things with it. And if I need to make even the slightest modification to those 2 pipelines, I'm more likely to beat the Oregon Trail (which I've never done) than to get Skyline to do it. 

Today I successfully pulled off a 3rd thing in Skyline in only like 11 tries, all thanks to Pioneer! 

You ready? This paper is super awesome


I've never heard of this journal, but if this is the kind of stuff they publish, I'm bookmarking it. This is step by step how to make a spectral library from like 12 search engines if you want. AND with the secret locations of everything in Skyline to allow you to compile spectral libraries WITHOUT the original RAW data! There are legitimately 6 steps highlighted in Pioneer that I'd never have guessed were remotely linked to making a library. 

If you're doing DIA or need spectral libraries for any reason at all, I can't recommend this great paper enough. 


 

Thursday, July 1, 2021

Requested repost -- how to install Fragger and other community nodes in PD!

 


Would you like to have a fully functioning version of the Proteome Discoverer environment on your PC at home (or on multiple PCs throughout your lab, which is a much more normal thing to do)? You obviously can't have the commercial nodes that the manufacturer has to pay royalties on, but you can have lots of tools including:

!!!MS-FRAGGER!!! operating in PD on any Windows PC. 

The SugarQB glycoproteomics workflow (which...I'd argue is as good as ANYTHING available for glycoproteomics for any price right now, with that caveat that you have to have your glycan mod in your database) 

AND TONS MORE!! 

Here is a link to a 20-ish slide walkthrough for setting up PD with a bunch of cool free nodes. I tried to include every relevant link, so your browser or security settings might be mad about all the extensions in the slide deck. 

This might not be completely accurate (duh), but I tried and I hope it helps. 

I put up the Version info in the image above, because maybe I'll update it going forward. 

As an aside, having PD installed at home helps me morally justify having a badass OMICSPCs system in my basement. It's only coincidental that it can run Crysis. 

Wednesday, June 30, 2021

Great (and timely!) review on studying protein-DNA interactions!

 


I suspect that if you've got a mass spec someone is going to come by soon (if they haven't already) and they're gonna be asking about protein-DNA/protein-RNA, glyco-RNA interactions. There is considerable excitement in all of these areas (Thermo is recruiting a postdoc to develop tools for Proteome Discoverer in Bremen, or was...I can't find the link, but it sounded really cool). There is an open postdoc across the street here for that as well.

For a very timely review on the first things in that list, I suggest this very thorough new paper (I hadn't heard of 3/4 of these).

When someone comes by asking about this stuff, nod a lot, pretend you've got another pressing meeting coming up and get back to them after consulting this great paper. 

Tuesday, June 29, 2021

Two weeks till ASMS Abstract cutoff! And SCP2021 program is up!

 


Hey everybody! Hate to interrrupt your summer breaks, but the ASMS abstract deadline is 2 WEEKS AWAY! Get those abstract in so we can hang out in (not as beautiful as Baltimore, the world's greatest city, but only a short drive away) PHILADELPHIA! 

There aren't hospitality suites.

There is something EVEN better! 

The 4 Seasons LandScaping Company (you can't tell me you don't want to go)!

Relive the peak of incompetence that my neighbors dream to return of (for real, I live near a lot of scary nutjobs).

Before that, though, Single Cell Proteomics 2021! August 18-21!

The schedule just posted! You can check it out here


Monday, June 28, 2021

Random walk phosphopeptide data analysis!

 


I've gotten excited more than a few times in the past when I came across what might be a powerful way to make sense of phosphopeptide data. Is this it? If nothing else, the output looks gorgeous (haven't tried it myself yet, but I'm optimistic!)


The math is intimidating. I felt better about reading the paper from the HTML version so I didn't have to look at letters that make more sense on the front of big houses near college campuses. 

What I do get is that it uses random walk, and I absolutely know what that is. 

All this stuff is made in R and you can get it from Github here! 

Sunday, June 27, 2021

Eclipse + FAIMS + 20 nanoliter/minute + NanoPots!

 



I can't remember if I posted on a preprint of this study or if I've just read it so many times that I thought I did



How far can you push an Orbitrap Eclipse for direct single cell (no SCoPE-MS or boost or DIA) just DDA? This might be a good answer! 

One normal-sized human cell, prepared with NanoPots, ran at 20 nanoliter/minute (they used a Dionex RSLC 3000 at 250 nL/min and split the flow lower to get it to 20 nL/min). There are some really interesting comparisons here. Eclipse, so ion trap MS/MS or Orbitrap MS/MS? FAIMS on vs FAIMS off? What fill times? What AGC targets? 

The total gradient time appears to be around 150min, so not nearly as bad as I would have assumed to get to a flowrate that low. A custom 50cm column is used, I think, but I forget the details. I don't believe they used match between runs to get to their total numbers. Data processing was in Proteome Discoverer. A great reference for this field of single cell proteomics that is 


(Zoolander quote moratorium is over! I get 4 more before it kicks in again)

Saturday, June 26, 2021

Weird Forbes article on proteomics?!?!

 


I go to the beach for a couple of days and come back to what?  Some weird museum mass spectrometer(?) on the front of Forbes?

If you didn't know Forbes appears to be a magazine for business people. You can check out the article here. If Windows has recently updated for you there is probably some new internet browser on your computer called "Edgey" or something and it doesn't have ad blockers installed on it. You can use that to view the article!

Thanks to Twitter, we've also been able to identify the mass spectrometer above. It is a MicroMass (pre-dates me, and I'm hella old) that was set up as above by some genomics researchers to demonstrate that proteomics people are clearly insane and the safe bet is still studying 30,000 human genes or transcripts, despite their general lack of biological relevance. 

To the people outside of proteomics who are increasingly stumbling on this bizarre blog, our instruments are not nightmarish setups of weird wires and magnifying glasses shown above -- our cutting edge stuff is professionally designed, polished, and ready for prime time -- some of the newest examples look like this (the one in the middle is in our lab and sometimes people come by to just look at it, because proteomics stuff used to look like the picture in Forbes and, while the box shouldn't matter, it does suggest that maybe proteomics has grown up and might be worth a second look, particularly if in the 1970s or whatever, Frankenstein's mass spec didn't get them the data they wanted). 

Forbes identifies proteomics as the future of medicine??? HOLY COW. Took freaking long enough for someone to goshdarned figure it out. So much money goes into genomics that they have been muddying the water for years. Lots of scientists don't know that there is basically NO correlation between the abundance of a transcript (as measured by things like RNASeq) and the abundance of the protein that actually does the work in the cell....I should get T-shirts printed....

While I'm super excited by the examples the author brings up, like the incredible work by Bateman Lab and collaborators that can detect Alzheimer's proteins 20 freaking years before symptoms develop, I do find this paragraph puzzling: 



I don't know what slow people they were talking to, but I'd go back to industry or back to washing dishes at Big Frank's if I could only get shreds of protein data in 30 hours. Again, I'm hella old. That's way to slow. For proteomics outsiders, using an instrument released in 2012, we can get high depth proteomics (8,000 or so most abundant human proteins and post-translational modifications) on up to 16 human or patient samples in about 24 hours. The instrument in our lab pictured above? Can do twice that many/day with around 1% of the starting material (not even an exaggeration. underestimation, actually) . At lower depth proteomics TODAY is much much much faster. 

While I'm very excited to see Proteomics get mentioned and to see some really promising new companies get funded, I am also seeing some unproven tech generating alarming amounts of money on very little data. I guess this is how it works, but I'm glad I've got a good seat for the show! 




Friday, June 25, 2021

MONTE -- Prep a tumor for basically everything!

 

....so...what if you were tired of wasting tumor samples and wanted to think reeeeeaaaaally hard about how to do the sample prep so you could really maximize everything you could learn about a single tumor sample? 

Welcome to Monte! 



This one!


 

Thursday, June 24, 2021