Proteomics goes after the muscle cell secretome!

 

The introduction of this great new study has all sorts of cool biology in it in terms of how skeletal muscle cells and exercise end up secreting (myokines?) that lead to all the positive things our physicians tell us they do. 

I'm most impressed with how clean and smart the experimental design is to help add new information to how a central tenant of middle school health classes actually works. Cells grown in culture are stimulated with different things and the secretome is sampled from the cultures. An HF-X is used for the LCMS and MaxQuant for the data processing. The logic of the study is really clear and well executed, and the figures clearly drive home each point the authors make. 

PepSep -- the best chromatography doesn't have to be the most expensive?

 

I held off on putting up a post about PepSep until I knew I was personally stocked up on columns. 

No disclaimers here -- PepSep doesn't give me anything for free, or even a discount over what is on their website. I've never met the mysterious person behind the best columns I've ever used. 

If you use an EvoSep or have a Bruker, I'm going to guess that you have seen columns that look exactly like these, because I strongly suspect this is the supplier for both. 

Even better than buying from the source? How could that be? Well, what if you could use a column configurator to assemble your dream column from a very straight-forward list of options? Insanity? 

What if you want 3 25cm columns with a 50um internal ID because you are slooooow flooooow weirdooooo and even want smaller bead sides and you want them to plug up directly to your instrument? 

You can do this here! 

3 columns for only 1475 squiggly things? Try beating that! 

Too hot to handle --new antibiotic peptides...from ghost peppers?!?

 

I haven't paid much attention to antibiotic developments recently but when I was in grad school (in a microbiology department) the fact the world was just about out of antibiotics way back then was a pretty big deal. 

Want to go hunting for some new antibiotic peptides in material you wouldn't necessarily suspect would have them? You can't go wrong starting with what is detailed in this great new study!

Ghost peppers have antibiotic properties? I'm sure you're familiar, but this is the crazy hybrid pepper that typically checks in at over 1e6 scoville units. A typical jalapeno is 2,000 to 5,000 depending on how badly you mistreat the plant and different habanero varieties are in the 1e5s.

Before you get too excited, I started this post with the chart above which demonstrated the antibiotic properties each HPLC fraction had against E.coli because I don't think that Taco Bell uses the late eluting fractions. This study shouldn't be used as justification to start another petition. 225 supporters? I'm sure this one impressed a giant global corporation. 

Jokes aside, this is a really well executed study on how to hunt down antibiotic compounds from an extremly complex and largely uncharacterized starting material. This team worked out inhibitory curves against at least 5 different bacteria, both gram positive and negative. And progressively worked their way to enriching and characterizing a large endogenous peptide responsible for this activity. The LCMS work was performed with a SCIEX 5600 and Q Exactive HF-X and the files are up on ProteomeXchange via PXD024605 and -- this is something super cool I wouldn't have thought of at all -- the plant that produced this pepper has also been deposited at a greenhouse at UNC! What a great idea for both reproducibility and in case this specific plant was a one-off, I guess? The Ghost pepper is a recently generated hybrid plant product and plant genomics can be super whacky. 

This blog doesn't have a lot of rules, but one is and will always be: if Randy Savage ever said the title of your paper in an interview it has to be linked here. 

OH YEAH. 

Revisiting the ion trap!

Hopefully you didn't catch my 15 min speed rambling at SCP2021, but one of the things that I tried to emphasize was the value of when an ion actually makes physical contact with the detector of a mass analyzer. 

It happens in most hardware, including the long forgotten and often maligned ion trap. 

Is it time to revisit this relic of ancient history? There might be an argument here. 

This ion trap appears to fair quite well in these comparisons even considering where it is placed within the instrument that they are using. Not that the architecture of a hybrid instrument has anything to do with anything when they accomplished 100% ion transfer efficiency a while back. 

Ice-R lecture is up and the paper is out!

I'm still excited by the potential of IceR even though I posted the preprint pretty recently. You can find it here. A slightly modified version of the study is now available here.

For a walkthrough of what makes this study so very cool, you should be able to click on the video above, or watch the video at the singlecell.net YouTube channel here. 

The outside bioinformatics people are starting to drop in to see what is happening in proteomics.  I swear, 1/3 of the new questions this year on r/bioinformatics have been about proteomics. I can't overstate how noisy (and HUGE) the data that they've been working with is compared to the stuff we generate. I think we're going to see transformative developments on the informatics side very very soon. 

The flipside is that our bad habits are going to be way out in the open. I've witnessed some shock and horror about our seemingly random use, as a field, of two different alkylating reagents that differ in mass by a just about the difference between a carbon-12 and carbon-13 and the common lack of metadata that will tell someone who pulled down 2,000 LCMS files which file used which. 

My unnecessary summary of SCP2021!

This is totally unnecessary because, if history is any indication at all, these talks are all being cleaned up and will be up on YouTube and singlecell.net shortly.

However, despite what I'd call a somewhat extreme case of virtual conference fatigue I made it through every talk of these great 3 days. 

Since I'm confident I saw some unpublished data and I don't know what I can/can't share, I'll only talk about things I know have been preprinted or published. 

Big takeaways from this conference? 

Takeaway #1 Single cell proteomics is: 

There were registrations throughout the conference, so these final number was actually higher by the end than when this was first plotted. 

From the conversations that were public and that I had to the side in the chat bars it is clear that this was new to a lot of people. And there were a LOT of people who were not LCMS proteomics experts. There were real scientists who came to see if this was something that could solve their biological or mechanistic problems.

Takeaway #2 Preparing single cells is still really hard and the best data from single cells is coming from people with the CellenOne. 

There was a jaw dropping advance in liquid handling that John Yates called "ingenious" and I was too flustered to even compose thoughts on a keyboard to support, that Andrew Leduc and Joshua Cantlon pulled off the day before the conference started. (It's also something I can do on our robot! ...theoretically...) 

Takeaway #3 Even at the single cell level, RNA abundance does not correlate with protein abundance. So if you run into someone who thinks that's the answer to that whole riddle, the answer is still no. 

Takeway #4 Maybe I sound like a broken record (whatever that means) at this point, but this next generation of proteomics/mass spectrometrists is just plain scary. I really like this conference because, sure, you get to see John Yates and Matthias Mann show what their groups are doing, but you also get to see talks from enthusiastic students doing cutting edge stuff. From the second talk to the very last one (a great talk on CE-MS at absurdly low levels of peptides) we got to see youthful enthusiasm from scientists who will clearly be much better at this than we are now. 

Takeaway #5 Smart instrument acquisition! I'd like to point out again that at this point the majority of single cell proteomics data publicly available has been generated on the Q Exactive "Classic" system. And we saw EVEN BETTER data from my (personal all time favorite instrument) enabled by smart instrument acquisition through MaxQuant.Live. Yes, we saw some amazing data from Orbitrap Eclipses with real time search and software built to enhance RTS, and Mann and Cox showed data from TIMSTOF Pro systems that were modified to become the prototypes of the TIMSTOF SCP (and I showed data from our lab's $1.3M list system). But we also saw smarter ways of using the now 9 year old (yikes. I think that number is real) Q Exactive in smarter ways to generate data at the same level.

You don't have to be one of the groups in the world with infinite funding to get into this field. It's more important to get the cells sorted and samples prepped write AND

you need smart informatics! Takeaway #6

Slavov Lab has pioneered single cell proteomic informatics, but -wow- is that ball rolling now! I've typed about a lot of this stuff, but it's just getting better.

IceR

SCP (from Gatto Lab, check this out! Make it all a QFeature!)

And worth noting, Cox lab is paying attention and innovating in this field as well. 

There is clearly more on the way.

Takeaway #7 We're not going to get there alone. We need to think outside of "proteomics" to understand the proteome.

From the phenomenal (and somewhat science fiction feeling) recent work of the human protein atlas through the HubMap consortium and the crazy laser capture clinical sample microdissection work going on in the Mann lab it couldn't be more clear that we, as a field, need scientists outside our little LCMS proteomics echo chamber more now than ever. 

As a final thought, the single cell transcriptomics people have a huge head start, and they have not stopped innovating. A couple new papers and preprints dropped this week that have shown really staggering work on trancript intracellular localization and we should probably pay close attention to what they are doing as well. Until we start getting (if possible) a much larger piece of the puzzle, if we can leverage their data in a complementary manner we absolutely should.

As a final final thought -- it came up more than once from outside our field that digesting our proteins is a big problem at both the proteome and single cell proteome level. The outside world wants top down proteomics to make that next step a lot more than even we do.

SCP2021-- Day 1 quick overview!

If you aren't tuned into Single Cell Proteomics 2021, I get it. Virtual conference fatigue has kicked in for just about anyone, but this is my favorite virtual conference I've been in a conference since that dumb virus thing. 

If you just forgot it was August, I think there is still room for today's busy lineup of talks that kick off at 9am EST.

You can check out the lineup and everything here, and since this is a Slavov meeting you know that everything will be as available to everyone has legally possible. 

Day 1 was superb.

1) Sample prep

2) Sample prep

(Automatic prep of thousands of cells with reagents all available right now!) 

3) MaxQuant.Live walk through and demo(!!) 

4) DIA-NN walkthrough and demo

and

5) SCP (single cell proteomics in R!) walkthrough and demo

This was amazingly well curated with data that will automatically load into R for you to try out! 

I'll try to double back in fill in with links to resources as they inevitably post. 

Today and Wednesday look like it's almost all APPLICATIONS of single cell proteomics!

Understand that cell, yo. 

HYPERsol -- Easy, reproducible extraction from FFPE tissue!

Hospitals and other medical research institutions like to store interesting medical samples for analysis by future methods. The most common way to store solid samples seems to be through formalin fixing and paraffine embedding (FFPE). 

Formalin is a crosslinker! 

And paraffin is a wax! 

Who cares? S-Trap it. 

There are basically extra steps involved to remove the wax. 

It is worth noting that some of our potential collaborators have looked at the thickness of the slices used in this study and said that they might be able to provide slices 10% as thick. My rough math says that I need to be in the 5% slice size for plenty of wiggle room for standard label free LCMS, even if I want to donate half to a pool for library building purposes. 

Defining protein contents and variation of some human body fluids

 

This might not be the most exciting "news" topic (who named this thing?) but I had trouble finding this paper and thought it would be worth putting it up (again?). 

How much protein is in saliva again? How much does it vary from person to person? What about plasma when it's depleted, or not? Maybe there are other references, but this is the one I had saved in my old free citation manager and now that I work somewhere that uses the fancy pay for ones, it's now saved in it. 

Dyna-TMT -- Friendly data processing for SILAC-TMT!

 

Combining TMT + SILAC sounds like a great idea, right? I will absolutely run that for you if you do the cell culture and the data processing. 

Thanks to the nice python scripts in this new preprint, I'd actually consider doing the experiment if I really really like you even if I had to do the data processing. You're on your own on the cell culture. 10-12 passages without contamination? Yeah....that's something experts should do, particularly considering the costs of those reagents. 

Check this out! 

Impressively clear differentials in CSF proteomics in ALS!

 

Wait. This is PROTEOMICS clustering!??! Okay, I've got to come back to this open access paper at MDPI later to spend some time on it.

You can get the files at ProteomeXchange via: PXD019910

BTW, there was a recent Twitter survey about JPR vs MCP for publishing mainline proteomics work. Honestly, I think a lot of us should take a look at Proteomes. 

1) Always open access

2) Reasonable page charges (including the open access, it's like $1500 USD)

3) Formatting that isn't a crazy nightmare for submission (and relatively fast turn-around for reviews). There is actually a word template that you download and cut your text into. 

4) Rapidly increasing exposure and climbing impact factor. 

5) As a reviewer, you get discounts on submissions to any of their family of journals that you can accumulate over time. 

Quantitative consequences of carrier channels in p-Tyr and endogenous peptides!

 

The use of carrier channels to boost peptide signal in techniques like SCoPE-MS are being explored by more than a few groups. There currently appear to be some finite limits to the amount that one can "BOOST" a peptide signal prior to there being consequences. Recently, another large study define the "carrier proteome effect" (paper here) and (video lecture by an author here). 

For a second analysis using a D20 high field Orbitrap (the little ones with the increased field curvature) specifically looking at phosphotyrosine (!!) and MHC peptide (!!!!) you should check out this recent study 

This team carefully looks at the advantages and consequences of different levels of carrier channel in two peptide populations that we'd all love to crank the signal up on.

The math closely matches the results from the Rose et al., study linked above. Which, from a technical level is really interesting because it showcases the similarity in the ion measurement and transfer levels in the Orbitrap Eclipse and Exploris 480. This was historically a headache for fine tuning experiments between the LTQ Orbitraps, Orbitrap Fusions, and Q Exactive systems. 

What we see is that while higher carrier channels in these little tiny high field Orbitraps do end up generating lots more peptide identifications, degradation of quantification becomes obvious above a 200-cell carrier somewhere, with 500 cell carrier looking pretty wonky. I would, however, urge some caution in defining this as the absolute carrier limit for all devices. Talking directly to you reviewer #2. Although the proteomics world has been dominated by Orbitraps, a roughly 2 order of magnitude intrascan dynamic range is a well characterized parameter of these devices. 

Ref

Ref

Heck, here is the original description of the device. The goal was to get the system to an intrascan dynamic range >10 and it could do that without a C-trap. It needed the curved trap (introduced later) to get to 300. 

These are not, however, the only mass spectrometers and the unique characteristics of having a curved trap to cool and compress ion packets before introducing them into a second trapping device introduces variables that can not be extended to the ions being measured by other devices. It would be silly to try to apply these same limits as universal to, for example, a triple quad device which has no ion gating or trapping at all, and therefore has an "intrascan" linear dynamic range many orders of magnitude larger.  

For a quick overview of how SCoPE-MS looks on an instrument or two that are not D20 high field Orbitraps (I'm of course, well aware that the majority of single cell data in public repositores at this point is on the D30's at Northeastern), you could check out my short (remote) talk at Single Cell Proteomics 2021 next week. 

Facile sample prep down to 100 cells!

 

Which is easier to say? 

Facile? 

or

n-dodecyl β-d-maltoside? 

These might be some of the questions that will come to you while reading this new paper. 

Another question might be: Wait. Why do we need yet another way to prep a sample?  

Look, I actually really like this study and I don't mean anything against these authors. The reproducibility is spot on and 1,500 protein IDs on a QE Plus from 100 MCF-7 cells from MS2 spectra (they did much better with match between runs) is better than I've personally got on a D30 Orbitrap of any kind with similar numbers. 

Maybe this is the last one, though? We've officially got enough ways to prepare a human proteomics sample, we've ended twenty years of tinkering on a high note! Woooo! Now, let's go out and use these proteomics platforms to do some stuff! 

And NOT inventing a new spectral library format, if that was what you were thinking. Don't. 

MS-SPOOKY!

MS-SPOOKY is clearly a joke, but it is a very good joke, although this comment on Reddit from aTacoParty, might actually be better. 

You can read the full text here.

Proteomics for low cell numbers -- the review!

 

Moving from migrograms to nanograms (or  below) of material isn't as easy as scaling everything down, well, if sort of is, if you don't care about the data quality.

What else is important?!??

This great review has it all covered!