Full optimization of a QE HF for TMT quan!

Got a QE HF and wondering how you can best optimize that speedy monster for the best possible TMT 10plex quan? Well, you don't have to do the experiment yourself, cause my buddy Tabiwang (et al.,) already did that for you.

You can check out a description of the method on Accelerating Science here.

And this will directly link you to the poster describing the optimized parameters.

Rumor is that an extensive application note may be in development.

Playing with the OpenMS Proteome Discoverer community nodes!

Hot diggity dog!

I got some samples and got to work playing with the OpenMS PD Community nodes for PD 2.0, which you can get here!  BTW, new and improved nodes are coming for PD 2.1!!!

Here is the processing setup. The LFQ nodes require Sequest and Percolator for now. I looked at my samples and picked a good retention time that made sense. The peaks were real nice so I used a typical retention time of 60 seconds.  I would have used a smaller window with other LFQ software, but this stuff is fast enough that I didn't really care.

Note:  In Spectrum selector "MS Order" MUST say "Any" or it won't work.

These are the settings I used for Consensus. It appears that you only need the two nodes on the right, but I don't see any problems when I use the other nodes. The data may not fully integrate, but it doesn't hurt the output. There is a dramatic difference in speed on my PC when I change the number of cores that the Profiler is allowed to use. If I give it 8 cores this thing is faaaaaassssttttt!!!!

Okay. Boring part over!  How's the data look?

Well, you get these sweet new tabs!  Quantified proteins/ quantified peptides and EVEN BETTER?!?!? Quantified features!!! You get quantification even if you didn't identify stuff.
"Hey, whats that thing that's upregulated 27-fold in the tumor?"  Well, sir/madam, that is your biomarker. Figure out what the heck it is now.

Okay. Sorry for all the scribbles. This isn't my data. This anonymous protein is present in all 12 samples analyzed. The files I put in are labeled in order of their "F" value in PD.  My first file is "Abundance number 1".

I can go into quantified peptides and/or features to see the individual quan values, or I can pop over to the PSM tab and see how the original MS1 intensities look.

Okay. But this is the real test. How do the values compare to the RAW intensity values and XIC areas?

 Really really well. Definitely try out this software.

Confirmation of NIA standards for Alzheimer's disease via protein biomarkers

So, I've read 2 biology sciency things today. In both cases, the scientific method was at work (YAY!). Researchers were looking as published results and in the first case (the tardigrade genome I mentioned earlier in the week) striking problems were found with the data.

The second paper is more positive for the studies pre-dating it!  In this paper from Huded et al., some researchers in India decided to test the National Institute on Aging's criteria for Alzheimer's diagnosis and progression.  This requires removal of cerebral spinal fluid (CSF) and testing for a number of known protein biomarkers. Quantification reveals presence and severity of the disease.

Now, there hasn't just been one test on these biomarkers, there have been tons of them. So it would be super weird if they didn't check out properly (or you could blame it on the ELISA assays they were using). But, hey! sometimes you want to verify it yourself, especially if it requires extracting fluid from someone's nervous system!!  On diseases that are this nefarious, every data point is going to help. Lets get early detection and drugs on the market, STAT!

A team at UVA decided to rewrite the textbook on antibody profiling.

This is such a great paper!  AND its Open Access. Several people who occasionally read my ramblings here who need to see this right now are about to get this link emailed directly to them! You're welcome!

The paper is from Lichao Zhang et al., and some guy named Don Hunt was apparently involved which might explain some things about it.

When I visit people who profile antibodies, they are doing 2 things. First they are getting intact masses on the antibody. In big facilities, maybe its a whole group of people figuring out what intact protein masses are there.  The second thing is digesting with trypsin and peptide mapping. Between the two groups they pretty much figure out what they're looking at. Groups that use multiple enzymes get better coverage, but you're looking at a ton of runs.

This approach? Kind of a lower-middle down approach with just enough awesome tweaks to maybe get the whole antibody figured out in one shot!

They start with the whole antibody and then they reduce and alkylate it (more on that in a minute). Then they run it through or over an immobilized enzyme I've never ever heard of, aspergillopepsin I, which instantly cuts the antibody to pieces around 3-9 kDa long.  See? Lower-middle-down! What else would you call it?

What else would you call peptides that are 3-9kDa long? Perfect for ETD!  In this case they used an LTQ Orbitrap Velos with ETD. And these perfectly-sized fragments give off amazing levels of coverage. They process everything with ProsightPC BioMarker search functions.

Okay. Neat, right?  But it gets better.

The digestion occurs with a bioreactor. The antibody goes in and comes out digested...and the reaction quenched. Want bigger fragments? Increase the flowrate. Smaller? Decrease it.

One last thing. They alkylate the cysteines with a new reagent. Its called NAEM

Not only does it alkylate in 10 minutes, but it also puts a positive charge on the cysteines which aids in fragmentation.

How's it work out? Absolutely ridiculous levels of coverage of these huge and hugely important proteins and their PTMs in record instrument time!

What the heck is this metformin stuff, and how is it slowing aging?

So I'm sitting here watching the Q Exactive blow away yet another triple quad (the Q E is like "what's a matrix effect? never heard of it...") in terms of small molecule sensitivity and I have some time to browse Twitter and I see this article from June!  What the Albert heck is this about?  (Shoutout to @attilacordas for the Tweet)

Human beings are being given an Anti-aging drug? Sign me up!  Turns out you have to be suffering from one of three conditions to be eligible - cancer, heart disease, or cognitive impairment. You might argue I qualify under the third condition, but I think they mean decline from baseline and I've always been this way. The drug they are testing is metformin.

Next question is the title of this post. What does this stuff do?

Well, Chengkai Dai says its something like this (in this here paper):

In this paper he argues that HSF1 is repressed by nutrient stress and/or metformin and induces "proteomic chaos" and this messes up tumors. Which sounds pretty awesome. But what does this have to do with aging?

Time to turn off the "since 2015" filter on Google Scholar and travel far, far back in time!

So, this group dosed worms with the drug and did 2D-DIGE and peptide mass fingerprinting to come up with the solution that its all about reactive oxygen species and peroxiredoxin.

Which sounds an awful lot like what the resveratrol people have been thinking their drug does...either way this is super cool.

Can phospho- protein profiling be highly reproducible?

DNA damage response proceeds along a tightly controlled phosphorylation cascade. The main operators are very well studied and predictable enough that immuno assays are used in the clinic for these phosphorylation sites.

Could you quantify the entire cascade with a single LC-MS run? And could you do it with a high level of reproducibility?  Sure looks like it!

In this paper from Jacob Kennedy et al., out of the Fred Hutch, they use a single step IMAC pull-down followed by MRMs and the data looks fantastic. (Max CV on phosphos of ~16%?!?!)

Do 150 Western blots? Or monitor all of this pathway in a single run? Makes the mass spec seem like a pretty cheap option, right? Now, being the resolution snob that I am, I would like to point out that the relatively small number of targets here, this is something that could easily be adapted to a Q Exactive. Again, the data looks seriously fantastic here, but if I was to improve this assay in any way I'd want to see my fragment ions in PRM +/- 1ppm.

Tardigrade genome is done!

How on earth do you guys do proteomics on unsequenced organisms? I know there's BICEPS, which relies on genomes from similar organisms and error tolerant searches, but...besides that?

Okay, here are some good examples. They finally did the tardigrade genome, and its reeeeaaaalllly weird. But if the genome is only just now done, how did:

These groups do the proteomics studies in 2010 and  2011?

Well, the 2010 group did 2D-gels and compare their IDs against the current known protein sequences and the group in 2011 focuses primarily on highly conserved heat shock proteins.

So, you do what you can with the proteins that are in the databases. Or you study something that doesn't change from organism to organism.

Why is this cool, other than the fact that tardigrades are frickin awesome?  Cause this is a perfect example of a great meta-analysis project. Google Scholar pulls up 5 studies that appear to be at least partial proteomic analyses of these ridiculously cool organisms. Every single one of them was performed with imperfect genomic or protein databases. If I was looking to write a nice and reasonably high impact paper this weekend, I'd be downloading this genome here and seeing how many of these papers submitted data to Tranche and PRIDE that I can freely download and meta-analyze. Then maybe we'd know how these weird, indestructible things tick...or dance...or swim....

UPDATE 12/5/15: Ummm...okay, so maybe hold off on that meta-analysis... the work of this study above has been an explosion in the genomics community. It appears there might have been some errors. I LOVE SEEING SCIENCE IN ACTION!!!  Maybe all this is real, and maybe not, but either way we'll be further ahead!  Discussion on the controversy here.