Downstream analysis of proteomics data!

Alright!  This painfully thought-out and beautifully executed experiment yielded a big list of differentially regulated proteins! Woooo!  So...now what....?

This review from Karimpour-Fard et al., is a great place to start. This concise little piece in Human Genomics walks you through some tools and approaches that can help you figure out:

1) What is significant in your list (and all the stuff that isn't)

2) What those words mean that that stats and bioinformatics people are always using (Anova?)

3) How to extract some biologically meaningful data out of all that stuff.

A nice short review (and Open Access!) that might help you make that next step forward!

Shoutout to my aunt Beth who took this cool picture downstream off a bridge near home!

SIM-XL -- Lets identify those crosslinked peptides!

Mapping protein-protein interactions in complexes is a tough job. We can go one of two ways with it:
1) The relative way: When I pull down proteins under condition A and under condition B,  I see relative upregulation of this protein, so it must be associated
and
2) The crosslinking way: Under these conditions I throw in a crosslinking compound, then pull everything down, digest and identify the crosslinked peptides.

Both are hard, but the relative way is a good bit simpler from the data processing perspective.  Analysis of crosslinked MS/MS spectra? Thats hard. There are some nice approaches like XComb and StavroX/MeroX.  SIM-XL is a new one. If you wonder why you might want to try a different piece of software, look at this result output (click to Zoom!)

Um....how frickin' cool is that?!?!?

Its a GUI driven interface with all sorts of cool graphical maps to help make sense of your crosslinked data. It'll accept all sorts of MS/MS converted data files (and if you've got the MSFileReader installed, it'll directly read Thermo .RAW files!)  and its even possible to map your data against spatial constraint data obtained from 3D protein structures to see if what you are seeing is possible at the biological level!

You can check out the SIM-XL website here.

And you can find the original paper by Lima et al., here.

Happy halloween!

Remember the time I taught "Advanced Proteome Discoverer" dressed as a realistically scaled Q Exactive? Honestly, it was a little distracting and kinda uncomfortable so I didn't keep it on long!

My co-instructor managed to get a good picture!

Thermo Fisher Cloud. The next step in your processing pipeline?

Alright, so...now I have big list of proteins....what do I do now? What a great question. There are lots of things. If you own an institutional license for an expensive pathway software, you could try that. You could go to KEGG. If you're one of those highly employable people who know R really well, there are ton of cool scripts and on and on.

One thing you might want to check out is the Thermo Fisher Cloud. Why?
Cause it looks pretty cool. And its free. And you get 10GB of free data storage on the Cloud just for registering and checking it out. Oh, and there are these tools I've never seen outside of papers on R scripts like Pathway Over Representation and Pairwise Significants that are super easy to use in this format. And if we generate interest in this then more tools will be added and faster. The bioinformatician behind the scenes in this project has some great insight into what this field needs and I think we'll continue to see more cool things added to this interface all the time.

You can register to use this resource here.

I'm gonna see over 60 of you tomorrow?!?!

I just saw an update on the attendees for tomorrow's NIH PD workshop. 60+ people!  I'm super psyched. Sorry the blog has been slow lately. I started a new role recently for my day job and I've been putting all of my free time into new content for the workshop. There are people flying in from far away to attend!!!!?!?!  I don't want anyone to be disappointed.
Thank you PRIDE Repository and to you guys who put tons of cool experiments in there!

And to everyone who can't make it, I can't make promises yet, but I think at least some of the material should be accessible to you later. I'm working on it! Can not wait to get back to Maryland today!!!

EDIT: 10/29/15  So...I found out the hard way (after lugging a tripod and good camera into the NIH and through 3 security checkpoints..) that all video recording on NIH campuses is done by an organized and unionized group that considers any attempt to record on campus as a threat to their livelihood. However, for the price of a good used car, they will record a workshop for you.   We will have some slides to share, though!

The most thorough review of reporter ion quantification ever?

Need to teach a class about report ion quantification? Holy smokes, Yavin Raunivar and John Yates just put together your lesson plans in this new review in JPR.

Its thorough, up-to-date, and shockingly concise considering the history, reagents and methodologies described. Even if you've done these experiments for years and with different instruments there are still some great insights here!

There is a great section on doing PTM quantification with reporter ions (very phospho-centric) that brings up a really interesting methodology (reversed ammonia gas spray across the front of the instrument (?what?!? I gotta read that) that boosts TMT-phospho IDs (??again, no idea!!).

The highlight in this, for me, is a concept I've never even considered and I feel really dumb for not having come up with myself. TARGETED ANALYSIS with reporter ions.  We're getting more targeted all the time, especially with new high-certainty LC-MS methods like PRM (parallel reaction monitoring). These let us look at hundreds of peptides in a pathway and each MS/MS scan gives us a ton of confirmational data that we're looking at the right target. But what if we multiplexed it with TMT-10? Then you get the sensitivity of the targeted approach and the certainty from the PRM and you can also get relative quan from up to 10 patients at once!!!

Sorry, my mind is kind of blown.  I'd better finish this coffee and get to work....

Quantitative proteomics and lysine acetylomics of astrocytes

Apparently, antioxidants are super important in brain astrocytes. Maybe cause they use a lot of energy and that results in the formation of dangerous oxygen free radicals?

To investigate this, Mariana Pehar et al., did just an awesome job of profiling astrocytes that had a major antioxidant pathway knocked out (or down) sorry, I skimmed the biology here, I was really interested in the method for my own selfish reasons.

The cells were SILAC labeled. So they end up with really nice up/down regulation of their whole proteins. For the whole proteomics they did in-gel digestion with 40ug of protein and cut out 12 sections and double digested (LysC and Trypsin).

For the quantitative lysine acetylomics, the proteins were mixed, SCX fractionated and the peptides were incubated with a bead with an antibody that recognizes acetylated lysines and pulled down. Everything was LC-MS/MS'ed on an Orbitrap Elite.

On the data processing side, the files were ran through once with a recalibration algorithm (similar to the Recalibration node in PD 1.4). Once recalibrated, the files were reprocessed.  The data was processed with various tools including Andromeda in MaxQuant, and MS-Viewer in the Protein Prospector and Perseus. The combination of these analyses is a solid output that gives the changes at the whole protein level as well as the changes at the lysine acetylome level.

Oh, and they worked out a cool variation of anti-oxidant response that appears to be mediated by the super cool Nrf protein(s). Solid paper!  Having trouble getting the PRIDE depository reference number listed in the paper to lead me to anything, unfortunately, cause I'd love to see this RAW data, but super cool paper.