Cause no one ever asked for it! My favorite papers of 2015!

(Picture from PugsAndKisses.Com)

This is definitely my favorite post of the year. This is where I get to go back through this ridiculous hobby of mine and re-read my interpretations about the amazing work you guys are doing out there!  (An added benefit is that I get to fix typos, errors and even delete some of the dumber things I've typed.)

There was SO MUCH great stuff published this year. I know I only read a tiny fraction, but I now have 17 tabs open that I'm trying to narrow down. I'm going to start with the 2 that really stand out in my mind

PROMIS-QUAN -- The most proteins ever ID'ed in a plasma sample isn't some analysis where someone did 2D fractionation and 288 hours of runs? No, its one single LC-MS run?  When friends outside my field ask me how the technology is progressing, I tell them about this paper. I hope hope hope it is real. I feel equally impressed that this group came up with this and equally stupid for not thinking of it, because it is so simple and so so brilliant.

Intelligent acquisition of PRMs -- I really think PRMs are the future of accurate quantification. You get your ion and you know it really is your target because you have basically ever fragment of one species with accurate mass, typically within 1ppm or 2. Problem is they are kinda slow. So these crazies in Luxembourg go and write their own software so that they can intelligently acquire their targets based on the appearance of heavy labeled internal standards? This is a study that is so good, the PI makes this list even though he didn't respond when I asked him for a slide from his HUPO talk. Tie this in with a lot of mounting data that PRMs can be as sensitive or more than QQQ and you start to wonder what routine labs are gonna look like in the near future...

LC-MS can be both reproducible AND accurate -- The genomics/transcriptomics people get to eat our lunch sometimes due to the belief in general science that we aren't very reproducible. So a bunch of smart people get together and show that our biggest problem, as a field, may be that we don't have common sample prep techniques, cause if you prep samples the same way it doesn't seem to matter where your mass spec is or who runs it...

(within reason, of course)

... you can get the same data.

Speaking of sample prep:

How 'bout massively speeding up FASP reactions with mSTERN blotting, iFASP, or change gears entirely with the SMART digest kits?  Which one should you use? I don't know! I'm just a blogger. How 'bout a bunch of you smart people get together and decide which one and lets shake off this whole "proteomics isn't reproducible" bologna and get all the money people are spending on those weird, shiny (and crazy expensive!) RNA boxes. 

Oh yeah!  On the topic of those RNA boxes, PROTEOGENOMICS!

Probably my favorite primary research paper on this topic this year (man, there were some great ones!) I can think of was this gem in Nature. We also saw several great reviews, but this one in Nature Methods was likely the most current and comprehensive one that I spent time on. Is Proteogenomics still really hard to do? Sure! Does it look worth it? Yeah, I still think it does, and it'll get easier at some point!

There were some proteomics papers that transcended our field this year as well. Probably the biggest one was the pancreatic cancer detection from urine that the good people at MSBioWorks were involved in.  Another one I liked a lot was the Proteomics in Forensics out of the Kohlbacher group. Apparently you guys liked it as well, cause my blurb on it was probably my most read post of the year.

  [Previously my opinions on another paper that were a bit negative occupied this spot. I chose to delete a few minutes after posting. Lets keep this positive! Insert Gusto instead!]

Now it gets a little random! Just things that occur to me this morning as really smart.

How 'bout going after non-stoichiometric peptides and PTMs?  When I mention this to people it still seems a little controversial but biologically it makes an awful lot of sense. This year we also either saw a lot more glycoproteomics because that field is advancing on all fronts or I was just more aware of it.  I think its the former, though. A great example was this paper out of Australia.  It was another big year for phosphoproteomics, with new enrichment techniques, incredibly deep coverage studies, reproducibility analyses, applications of quantification and even new tools to analyze all that phospho data!

Another one that sticks out to me was Direct Infusion SIM (DISIM?). If you need to quantify something fast, turns out you can direct infuse the target and you can get some good relative quantification. Makes sense to me, and they have the data to show it works, so why not!?!

Okay, I've been working on this one for way too long. Ending notes: Holy cow, y'all did some awesome stuff in 2015!  THANK YOU!!! I can't wait to see what you've got for us this year!!!!

CPTAC shows high reproducibility in Orbitrap quan between systems AND methodologies!

Once in a while I run into someone who heard from someone else that Orbitraps aren't good for quantification.

...and I try really hard to not make this face...

Our good friends at CPTAC decided to make the ultimate comparison. Over 1,000 (one-thousand!!!) LC-MS/MS runs. From different mass spectrometers. From different institutes. With different quantification technologies. On xenografts! (That's a human tumor grown on a mouse. You don't get much more variable).

They compared iTRAQ quan with XIC based label free quan (peak area integration) and spectral counting. What did they find? I'll just quote it.

"If laboratories deploy different methodologies to analyze the differences between the same two complex samples, then they will assuredly see differences in the gene or protein lists produced by the two technologies. The degree of conformity observed in this study, however, was encouraging. When label-free data were analyzed by spectral counting rather than precursor intensity, the differences yielded a high degree of overlap. When iTRAQ rather than label-free methods are deployed, the differential genes were again quite similar. These overlaps suggest a degree of maturity in proteomic methods that has grown through years of development along multiple tracks.

At base, biologists need to know that differential proteomics technologies can produce meaningful results. Our assessment showed that biological pathway and network analysis is highly consistent across instruments."

Right?!? Ben's interpretation: We're still getting a subset of the data in something as complex as a human tumor. We can bias this subset by using completely different methodologies, but even on the most complex human samples and experiments, we're at a point where we are HIGHLY reproducible. And this is the global/fractionated stuff....

Protein carbamylation is a hallmark of aging - and how to detect it

A recent paper in PNAS makes the statement in the title "Protein Carbamylation is a Hallmark of Aging. You can find it here.

They find that you can almost assess the age of a mammal by looking at the degree of carbamylation in the proteins of that mammal. I'm not 100% awake yet, so it took me a minute or two to remember what carbamylation was and why it puts up a little alarm in my head. Then I found the image above. Most of the time when I think about carbamylation, its cause its a sample prep issue.

Here is a paper that discusses this modification.  When I run Preview on a sample and it pulls up carbamylation as a modification to consider I've always assumed it is from a protein prep in which either excessive Urea was used, or Urea was used and the prep was performed at too high of a temperature. Turns out, it might be detecting old samples as well? Interesting thought, right?

Detection of this modification is very straight-forward in any search engine. In PD you just need to activate the modification in the Administration --> Maintain chemical modifications tab.

With this valuable new information, I expect y'all to get on reversing this aging stuff 'cause the more I experience it the more I realize I'm NOT a fan of it.

Pinnacle -- the best translational software I've ever seen.

I've been wanting to talk about this one for months!!! Unfortunately, I do have a day job and there are rules I have to follow to keep that day job, so I held my tongue until I found out I was finally allowed to talk about it this week.

At HUPO I got to see Pinnacle. Pinnacle is software specifically meant for all you translational people out there. I know, there is a ton of software out there, but I'm going to argue that you ought to demo this one if:

1) You have so many clinical samples (especially high resolution ones) that you can't process them in anything like a reasonable amount of time
2) You are doing label free quantification
3) You are doing data-independent analysis (DIA, pSMART, WiSIMDIA)
4) You just want to use a piece of software that is graphically pretty.

This software is fast. Sick fast. It-shouldn't-possibly-be-this-fast FAST.  Put in HUNDREDS of Q Exactive Raw files -- targeted, untargeted, DIA, whatever -- and watch it pull the data out in minutes.

Wonder what the data quality is like? Just look at color and shape of the icons on the left (click on the pic above to zoom in) and get a feel for the quality OR look to the right of the peptide sequence where you ACTUALLY SEE THE INTEGRATED PEAKS.  Sorry to shout, but how cool is that?  "Wow, that is crazy upregulated! Should I investigate it? Nope, that is obviously just a poor integration. Better readjust that integration right now". In real time. Without changing the settings and reprocessing the data. Just fixing that peak. Click, click, done.

Pinnacle has a bunch of other functions. Its a thorough software package and you purchase the modules that you need for your work. You can also download a free trial version here that lets you process one dataset and see what I'm talking about.

Interesting, though somewhat morbid, article on elite scientists and progress

I'm not entirely sure what to think of this. Partially because I'm having a little bit of trouble wrapping my head around it. Maybe part of the difficulty is that the article is from the National Bureau of Economic Research. Which, Wikipedia tells me, is a real thing.

Anywho...you can read the article I found on Vox here.

And the abstract for the original article is here (there is a $5 charge to download the complete article)

Christmas Magic -- Multiply charged proteins ionized with no energy!

This is really interesting. What if you could just mix up your proteins, including the big ones with your matrix compound(s) and then magically get multiply charged species into your mass spectrometer? No energy. They just grab some protons and go flying into the air? Well, it sounds like you could save a lot of energy on lasers....AND....maybe you could finally give up on that weird old TOF in the corner that can go to 100kDa (you know...the one that is 8 foot tall and has accuracy within 1kDa...or 2...)

Well, that appears to be exactly what happens. What?!!?  I know!

Check out this paper from Sarah Trimpin for more details. Hey, if nothing else, it has one of the single most amusing abstracts I've ever read.

And its got this great chart!

CaspDB - A database of caspase cleavage products!

Another tool to help find identifications for unmatched MS/MS spectra!  Caspases are proteins that hang around just to destroy other proteins. They are a critical component of apoptosis and normal cell maintenance, and if you believe the recent in silico protein cycle predictions -- they are active constantly. If my mix of proteins I just harvested is full of incomplete, complete, modified AND degraded proteins, then all these unmatched spectra start to make sense.

Caspases have specific substrates for degradation and a bunch of them have been worked out. CaspDB is a new online tool to help you work with this this information. It is described in this new Open Access paper from Sonu Kumar et al.,.

While the paper is totally neat and all, you can go directly to check out this online tool here.  You'll quickly find out that this tool requires a good bit of pre-existing information before it is useful. Once you've got some data, you can use it to run through your protein of interest and different caspases to see if you've got stuff that makes sense. The paper goes forward to show how awesome these prediction tools are by going ahead and proving that a ton of their software predictions are totally true.

This is obviously a very powerful and interesting tool and this will generate some great data from the validation end. But first you need to get some observations....

...(how did we ever get anything done before this...????...)

Check out this thing!!!  Its called Pripper and, wait, we'll need this...

...to go WayBack to 2010....to this paper from Mirva Piippo et al., that describes Pripper. Pripper is a Java tool that will take any FASTA database you give it and will perform in silico caspase cleavages on that database and give you a new FASTA that has all the predicted caspase cleavage products.

If you're thinking "How can I trust a tool that is 5 whole years old?" Never fear, it has been updated multiple times (the version I just unzipped is time stamped from 2013). Oh. If you download Pripper here you might want to right click on the zip file, go to properties, click "unblock" then "apply" and THEN unzip it. Windows Defender on my PC blocked it as a threat.

Now you have a tool that will make you a predicted caspase cleavage FASTA that you can run against your samples. If it comes up with something really cool then you can go to the CaspDB and search those observations against their more advanced prediction models (and validated data!)