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.

Skyline needs your help!

Ever wondered how the Skyline team has managed to create and support this awesome software? Turns out its got a grant in the background holding it up -- a grant that is up for competitive renewal.  SO...this awesome free software that thousands of us are using (8,000 PCs fired up Skyline just last week!!!) might disappear (or become..gasp!..not free!) if this grant goes away.

If you're thinking "what can I possibly do to protect my access to this software before I finish this extremely large mug full of espresso shots?" you should click on this link.

It takes you to a place where you can download a draft letter of support that, if you get back to Brendan, will be attached to the R01 renewal application. The deadline for their application is coming up real fast, so time is of the essence.

TMT normalization based on variation is real important in clinical studies!

Any time we quantify anything we need to determine what is the priority for the measurement. Is it absolute quantification or is it lots of data points. This is always consistent, whether you're weighing out masses for buffers or doing global -omics quan.

We're absolutely no different. On Monday I worked with an awesome team developing an absolute quantification assay. One biomarker with heavy labeled spike-ins. One data point with precision and accuracy. But ONE measurment!  Can we get that level of perfection with 16,000 measurements? Yeah...if you've got 10 years...(as technology improves...maybe 4 years?). And this will be measurement where we've reduced the level of technical variation.

Okay. So what happens when we look at something from a global level where the technical variation is high, but also the biological variation?  I just learned that you can gain a lot of insight from this output by actually looking at the variation itself!

In this study in PLOS one, Evenlyne Maes et al., take a look at TMT labeled clinical samples and use statistical tools to normalize the samples using measurement variation. And all the sudden the data appears a lot more meaningful.

Variance normalization isn't a new concept in reporter ion based quantification. IsobariQ is a software a friend of mine has evaluated and really likes that was first showing off its algorithm for variance normalization in iTRAQ at HUPO in 2011.  And there is an R package out there that does something similar and, for the life of me, I can never ever remember what it is called. Someone just asked me about it last week and I still haven't come up with the name.

What is cool about this paper is that we actually see the normalization working in clinical samples. Maybe someone else has done this, but I haven't seen it. If you show that something works well in your cool cancer cells that are grown at exactly 37C and get exactly 30% N2, or whatever, that is one thing (and awesome! and I'm not going to put it down ever. Bravo! I can't keep cells alive at all!)

 But if you show me that your technique can extract more meaningful biological data out of people who walked into a clinic -- people who may have eaten 10 minutes ago, or 6 hours, or who have immunity to this virus or that or have any amount of the crazy differences each one of us walk around with every day? Thats gonna make me think we should take a look at what you did!