SCX separation inside your nanospray emitter!?!?!!?

I read this abstract yesterday and thought to myself "....great, someone discovered MudPIT..." and promptly forgot about it and got back to work.

This morning I reread it. Unfortunately, I don't have time to read the paper before I go out the door on this ridiculously early morning, but....they appear to be doing SCX in their nanospray emitter....

Considering the way that I learned to do SCX involved a buffer with 8M salts of some kind, they are either doing something very different -- or they are replacing their mass spec every couple of days.

If you'd like to delve into this mystery you can find it here!

Known unknowns of cardiolipin signaling: The best is yet to come

A few years ago I had the pleasure of spending a few days with a bunch of lipidomics experts in Pittsburgh and got to learn: 1) How ridiculously insanely hard lipidomics can be if you aren't going after the "easy" compounds and 2) How important they are 3) How very very little we know about them.

This group just wrapped up a really nice review on one of their tougher problems -- the analysis of cardiolipins. How much fun are cardiolipins to work with? Start with the fact that structurally similar ones tend to cluster in similar mass ranges, but have different functions and you have a good idea.

One example they mention in the paper, 12 of their compounds of interest are within 0.1 Da in MS1 mass and even when they fragment them to figure out which one is which -- MS2 isn't capable of elucidating the location of a functional site -- which is critical to know cause there are a slew of isomers that are within these "12" compounds. They have to employ a 2D LC method and utilize MS3 methods on an Orbitrap Fusion to figure out what they are looking at.

They also show genetics techniques they can use, as well as imaging techniques to localize these things. The problem sounds...daunting...but groups all over the world are chipping away at it. And you can't beat the optimism in the title!

Oh yeah! Paper link here!

Basic Orbitrap physics seminar

Hey! Wanna log on for free and listen to me go on about where and how ions move around in Orbitrap devices?  The goal is to have a better understanding of where the ions are going and when to help understand your instruments better!

Note: -- Part 6 should be more like: From the LTQ-Orbitrap XL through the Orbitrap Fusion Lumos!

You can register here (limited to 1,000 total viewers....so be quick about it)

The videos should be available afterward and I'll post it here!

OpenMS 2.0!!!

Does OpenMS officially have everything now?

Ben, what are your rambling about now?  Oh...just the evolution of OpenMS into something that can do everything, as described in this brand new paper!

OpenMS can already do:
-Peptide ID
-Peptide Quan
-Integration into Proteome Discoverer via the OpenMS PD Community nodes
-Add DNA/RNA binding (to protein) detection capabilities to both OpenMS and to PD
-Allow people to add their own source code and then use the OpenMS downstream workflows (like FDR) to link to whatever upstream source search engines you are using; I think this is how these guys controlled this awesome inference study.
and now?

-INTEGRATION WITH COMPOUND DISCOVERER?  I love HRAM metabolomics and it consumes most of my increasingly rare instrument time these days. As much as I may rant about how easy metabolomics is with an Orbitrap after a beer or two, it is a field that still has its own innate challenges -- challenges that we honestly may not fully understand yet. Flexible software platforms that can address these are going to be critical if metabolomics is every really going to blow up the way we keep thinking its going to. I'm not surprised that the OpenMS team has the capability to add software to Compound Discoverer....considering the cool stuff they've developed for Proteome Discoverer, but...

...I had no idea they'd stated making nodes....and I don't know what the MetaboProfiler is or what it does, but it painlessly installed into my copy of CD 2.0 can't wait to give it a try!!!!

-PROTEOGENOMICS!?!? This study points out a case study where it does, as well as...

-Degradomics!  Have you tried realistically quantifying the degradation of proteins at a global level? No? Well...It. is. not. fun. The tools have to get better before we can track more than a small group in and someone is using OpenMS for that.

-Integration into KNIME and R for collaboration and downstream processing, respectively

-And a bunch of other stuff like Galaxy integration(?!?!), but this list is long enough now.

Does this sound like a sales pitch for OpenMS? It probably does, but this team of talented people are quietly making amazing tools for our community and going to great pains to make these tools as accessible as possible. And I don't mind being loud about it. (There are bunch of new tutorial videos for getting started now!)

You can easily find OpenMS and their spiffy new website with a Google search or directly link here.

Origin of Disagreements in Tandem Mass Spectra!

When you search the same RAW file containing tandem mass spectra versus the same database using different search engines, you are going to see some disagreements in the results.

For example, if I take a proteomic sample from myself and I run it through Mascot and I run it through Sequest separately, the results probably not going to be exactly the same. Mascot will identify some peptides that Sequest won't, and vice versa. It is also likely that I'll see a few MS/MS spectra that Sequest said was one sequence and Mascot said...is something different...

Considering that the database we're searching this against is constructed making some textbook assumptions and is starting from a DNA sequence....that is not mine....we do pretty darned good though!

Where do these disagreements come from? That is the topic of this new paper from Dominique Tessier et al., in this month's JPR.   To evaluate this question, these researchers grab a cancer dataset from PRIDE from Gygi lab and then run some plant samples in house on an Orbitrap Velos using high/low (or...medium/low? 30k MS1 + Top5 ion trap MS/MS).

The RAW files are then searched versus: Mascot, MSGF+, X!Tandem, TPP (presumably, also using X!Tandem) and an analysis of the conflicts are performed between the results.

The results are interesting, and the processed results are more conflicting than I've ever seen. The authors develop a concept of "peptide space" and conclude that optimization of the search parameters for each engine is essential to getting the best and most overlapping data. They also note that in some versions of the software they utilize the parameters that they need to change to get the best data is sometimes not easily user accessible.

I think this is a nice study and a good look at some of the problems we have in the statistics behind the scenes. It is sometimes easy to forget these days what an enormous undertaking from a mathematical perspective developing all these tools has been over the last couple of decades. Today's proteomics researchers coming in can simply push a play button to get good results and its easy to take it for granted!

Minor criticisms:
1) The RAW files were converted by different tools that I believe are quite different in their underlying mechanisms.  I think this is a variable should have been eliminated by using the same tools. Would it have an effect? I dunno...but its a variable that could be knocked out with 5 minutes more work.
2) PD 1.7? Wow, I don't have that one! ;)
3) I think the function of the search engines is something that is being focused on cause its the easiest to implicate. The FDR estimations employed were different for each engine. I think this could have a big impact on these results. I'd suspect that if FDR was controlled the same way for each of these results that the level of agreement would be a little better
4) The in-house generated data is just a little weird. 30k MS1 followed by 5 MS/MS for plant fractions is going to yield only high copy number proteins and using a search parameter of 0.4 Da for the fragments is probably too tight and will affect the downstream results a little.

Again, minor criticisms from somebody who just does proteomics as a hobby. Please feel free to ignore!  I do like this paper and I'm glad Twitter (PastelBio!) recommended it for my breakfast paper today.

2017 EuBIC Winter School on proteomics bioinformatics

What has a bunch of big proteomics bioinformatics speakers...
In Austria...
In January...
At a place called "SportHotel"...
With an organized tobogganing event...
And a Hackathon?!?!?

It must be the EUBIC Winter School!!!  While I'm trying to find some way to find someone else to pay my registration fee, you should check it out and see if you can find someone to do the same for you!

You can find out more about this EuPA sponsored event here. 

Is the Q Exactive HF less sensitive than other models?

Short answer:

At ASMS there was a rumor buzzing around. Earlier in the summer, 2 groups had found --individually -- on their assays, their QE Plus outperformed their QE HF in terms of sensitivity by limits of detection. Therefore, the rumor said, the QE HF wasn't as sensitive as the Plus.

I actively got involved in the first assay and resolved it myself -- it was a minor misconception on the parameters of the two instruments. The second was much more complicated and didn't seem to be my problem...until it was...and it irreparably destroyed a couple weeks of work I did in the spring.

The first one is the easiest to talk about (both thematically, and emotionally) but in the end, its the same issue.

This is a simple schematic of the QE to demonstrate how it works similarly to a triple quadrupole, but it'll do here as well. The focus here is the C-trap, which we control in the instrument software:

The importance of the C-trap in any hybrid Orbitrap system can not be understated. It is a critical and often misunderstood component of the system. In the newest instruments we have 2 ways of controlling this parameter in the instrument method software -- the AGC target and the maximum Injection Time (IT; which I often call "fill time").

The AGC Target is the goal. In this case, I am telling this QE Focus that the goal is for it to obtain 50,000 charges. That is 50,000 +1 ions; or 25,000 +2 ions; or whatever adds up to 50,000 total charges. That is the goal.

The Maximum IT is the backup plan. In this example, the QE is told to obtain 50,000 charges OR to fill for 57 milliseconds before it performs the HCD fragmentation and Orbitrap scan.

57ms is a ton of time to collect ions. Consider the fact that a modern QQQ (triple quadrupole) instrument running at maximum speed only spends 2ms on each target ion. Here, the QE can collect the same ion for >25 times longer than the QQQ.  {A QQQ has an advantage here because the ions actually physically strike the detector, while ions in the Orbitrap pass by the detector many many times, but that is a different conversation.} The fact is that in most experiments you won't ever need 57ms of fill time to collect 5e4 charges. You only need the maximum IT time on extremely low concentration ions.

Lets go to the Q Exactive Family Cycle Time Calculator! (Which you can download here).

QE Plus first:

In this case; MS1s are off, just MS/MS. Here I'm going at maximum speed. This is the most common settings for a QE; where the limiting factor is set to be the slowest part of the experiment -- the actual Orbitrap scan (which is 64ms on a QE or QE Plus); if you consider the 7ms of instrument overhead (to collect, fragment, cool, and inject) 57ms is the most efficient experiment. If you don't hit your AGC target, it will go to the next scan at 57ms anyway.  Lets call this the "High Speed" Experiment.

Lets take a look at the QE HF "High Speed" Experiment settings (please remember the Cycle time calculator is really an unofficial cycle time estimator -- its also a holiday here and I'm noticing this math isn't looking right, but I'm too lazy to check it):

Boom! We're flying here. We went from 12Hz to 18Hz or whatever, so the Orbitrap is faster!  We can get 20 MS/MS faster on the HF than on the Plus. If the AGC target is always easy to hit -- the HF is going to tear through 50% more scans than the QE Plus.

See the problem, though?? What if the AGC target ISN'T easy to hit? What if the maximum injection time is needed? Then the QE HF gets 57ms to collect ions; but the QE HF gets only 32ms --about one-half the amount of time that the Plus did. All the sudden, you're going to be looking at 1/2 the signal!!

The only way to ensure that the 2 instruments are running equally in terms of limits of detection or limits of quantification is to set the maximum injection time to the same number between the two instruments.

Imagine that you had all the same parameters -- same LC; column; sample concentration injection; AGC target; and Maximum IT -- how would that experiment turn out? The sensitivity will be just a little bit higher on the QE HF than on the Plus -- for an entirely different reason.

Imagine the top of your peak -- where the highest intensity of your target is coming out. This is the place where you are most likely to be hitting your AGC target.

Look at this peak I chose, literally at random, from the very first file I found in my Downloads folder. Also, please remember that if someone sends me a file to check out, 99% of the time something is wrong with it and they are asking me for advice on what might be the problem -- regardless, it is an okay example. I've labeled the scan numbers.

Check out lower on the peak --- the scans are further spaced than they are on the top. Near the bottom of this weak signal the Maximum IT is being used. Near the top of the peak, where we have the most ions, the AGC is being reached. The limiting factor at the top of the peak is the scan speed of the instrument.

Imagine now -- if I had a faster scanning instrument -- like a QE HF running this experiment -- here I would be getting 2x the number of scans at the top of this peak. Honestly, here I may have missed the actual top of this peak entirely. The max signal may have came out between 11739 and 11758 -- and I might have missed it. Even if 11739 was the highest that signal ever got, There is definitely some signal missed here between 11713 and 11739 that could have been picked up more accurately by a faster scanning instrument (or a less complex experiment).  Therefore, the faster scanning QE HF would get slightly higher sensitivity on the same experiment than a slower scanning instrument.

I believe this post deserves this image.

The QE HF isn't as sensitive as a QE Plus???

EDITS: Wow!  My 4th most read blog post ever? WTHeck? Okay. So just in case you think I'm making this stuff up. Please go to this paper, where the sensitivity of the QE HF is compared to the QE Classic. The HF can achieve the better fragmentation quality in ONE-HALF the time the Classic requires for the same sample and LC setup.

This study did not compare the Q Exactive Plus, but...this one did....

And...in this deep analysis, no deficiencies in the QE HF in terms of sensitivity were uncovered. Exactly the opposite.

Proteomic analysis of how stink bugs mess up tomatoes!

Out of the 15 or so tomato plants we put in this spring, we've gotten something like 12 good tomatoes. A large part of the problem has been my puppy who will take bites out of any tomato he can reach but the ones he can't reach have also been messed up -- but in a weird and different way.

A little investigation on my part and it turns out its those jerks in the picture above causing the rest of the problems. That is Halyomorpha halys, or the brown marmorated stink bug, an invasive species brought to the U.S. in the 90s that has definitely made its way into the ecosystem of Pug Mountain.

They don't do a lot of obvious physical damage to the fruit, I guessed at first it was coincidental that I had a few wandering around. Not so.

In this PLOS One paper from Michelle Peiffer (not this person) and Gary Felton describe their investigation into this problem. They study both the effects on the tomatoes, as well as doing a proteomic investigation of the salivary glands of the pest.

The stinkbugs make little bites in the tomato and inject enzymes from their salivary glands (GROSS!) into the tomatoes that liquefy the region around the bite. Then they can easily drink the tomato.

When they look at what the salivary glands (and salivary sheaths...which go into the tomatoes (GROSS!)) they found a slew of different proteins, mostly consisting of digestive enzymes.

When they look at the plants, they find that if they take extract from the salivary sheaths and apply them to tomato leaves that they induce a stress response in the tomato plants that they can measure as well.

Solid all-around paper that has some good stats. I particularly like that they looked at both the bug and the host response. What I don't like is the fact that I've eaten 12 tomatoes that probably had bug spit inside of them. In my searching I found some good pesticide recommendations that should fix the problem...though I've still got to do something about the puppy.....

High pH reverse phase StageTip FTW!

Need a quick method to boost your membrane proteomics coverage?

At first glance this paper might seem a little boring or obvious, but I only know one group that uses techniques like this and they've never published it and I'd never guessed it would be as powerful as this new paper suggests.

The idea is that they solubilize with SDS-PAGE buffers (which....despite tons of work on other techniques...is still probably the best technique for high membrane proteome coverage), run a gel and gel extract. I've seen some groups run for only a very short time and then only cut out the very very top piece of the gel (right below the stacking) and digest it out. There is still a TON of stuff in there.

This group (sorry, first author, your name is way too long for me to write it out here et al.,) takes it another step forward, taking the gel slice StageTip high PH reverse phase fractionating it and walk away with a huge number of membrane proteins and peptides with typical membrane spanning domains from a tiny amount of membrane starting material.

Simple and cheap boost to membrane proteomics coverage? Sign me up!

Johns Hopkins launches new proteomics initiatives!

As someone who has been associated with Johns Hopkins since my very first full-time job, the lack of support for proteomics on the campus has been a source of frustration for me (and others...). There has obviously been proteomics capabilities on campus, but the administration has always treated it like an after-thought, especially compared the the $$$.$$$.$$$.$$$ the school has always thrown at genomics technology!

No longer!  Today JHU officially launches the Center for Proteomic Discovery which is definitely the most sophisticated lab in Maryland, and probably on par with anything in the U.S.

With the third Lumos en route and multiple Q Exactives in plan for validation exclusively by high resolution PRMs, JHU isn't messing around.

In order to drive innovation in the center, JHU has introduced a "core coins" program that reminds me very much of the PRIME-XS project in Europe. The JHU school of medicine will sponsor worthy projects for free proteomics work, both at the new Center ran by Dr. Chan-Hyun Na and in the Dr. Cole's proteomics core facility

The center is also open to collaborators and service fee customers outside of the school. You can find out more about this awesome new resource here!