Proteomic and Metabolomic Characterization of COVID-19 Patient Sera!

Okay - I'm finally back after an almost week long struggle with a super sophisticated malware thing that blocked the install of all malware updates that could remove it. All sorts of fun, I promise.

AND -- Now I have proteomic AND metabolomic data from 40+ COVID-19 infected patients and 50+ controls to dig through?!?

Wait -- has this been out for 2 weeks? Okay -- well -- thanks Google Scholar alerts, you're winning it in the pandemic.....

The proteomics is:
TMT Pro labeled (16-plex)
Fractionated into 120 fractions
Concatenated into 40
Ran with microflow (not nano) in 35 minute gradients
QE HF-X

The Metabolomics is:
Separated into 4 batches
QE HF -- I'm unclear as to the data acquisition strategy. If you read the methods it suggests MS1 only at 35,000 resolution, which is not only inaccurate (because that setting doesn't exist on the device), but is a suboptimal way to run metabolomics, but then data dependent acquisition is implied to have happened. (The classical metabolomics world seems to think that lower resolution and mass accuracy is okay -- I disagree. I think low resolution metabolomics is fantastic if you've got a pathway you want to find and whether it's actually there or not is of secondary concern.)

The stats are on-point. I dig the downstream analysis here.

All the data is available at ProteomeXchange via IPX Project ID: IPX0002106000

The ultimate guide to Proximity Labeling!

Moving fast and backdating some posts -- I've been looking for a great review on this explosion of proximity labeling data and variations (just check ProteomeXchange -- tons of this stuff coming in and improving our understanding of (every?) biological system)!

BOOM! Brand new, clear, and with pretty and thorough figures (open ahead of print!) 

What's the deal with birds?

If you've ever published anything you probably have a special folder from "Journals" of an often increasing degree of sophistication offering you publication spots, or Editorial positions, or -- and this is the best one -- inviting you to speak at conferences that they just made up.

There are multiple databases trying to keep up with these "predatory" organizations, but -- again -- some of them are getting better. I seriously almost fell for going to Europe for a predatory conference. I love talking about proteomics and I'm probably going to fall for it eventually.

The best things are when people deliberately mess with these journals -- and this might be one of the best ones yet -- I humbly present my favorite thing I've read today--

NCI Assay Portal -- 150 multiplexed assays from CPTAC!

When you get to go back to lab, you cancer researchers, you'll have some great new ready made resources courtesy of the one hundred zillion dollars spent on the CPTAC inititative!

You can check them out here!

Okay...to be honest, I thought this post was going to be poking fun at this -- like -- hey, what is wrong with Picky and Phosphopedia -- they do this stuff and have been around for years, but the more I jump around, the more this looks like well-spent tax dollars.

LOD/LLOQ on peptide assays? Legit SOPs that you can download? Yeah, you'll have to do more of the heavy lifting than with the two resources I mentioned, but for modeling? this is legit.

US HUPO IS ONLINE NOW. It's APRIL 15th everybody!

You can logon here! 

ASMS 2020 -- Reboot!

Around the really clever wordplay in all directions, ASMS 2020 is go! You can check on the plans here!

SARS-CoV-2 Protein protein interaction webinar THIS FRIDAY!

Want the inside track on how this huge protein protein interaction study was assembled so rapidly?

Figure out what time it is in London and check out this great talk on Friday!

ALSO -- have you heard about the COVID-19 Mass Spectrometry Coalition?


Sign up and let's kill this stupid virus thing!

FAIMSPro -- More proteins, but where did all the peptides go??

Mystery with a short answer (I think) that took me a while to figure out. However, since it isn't detailed precisely in the study above, I feel a little less unsmart. (1,2,3 negatives in that sentence? Meh.)

When you fire up your FAIMS Pro, you're going to be IMpressed. The background noise is great, the nitrogen consumption....less great..... and you're going to see WAY more protein IDs!

However, there is a cost to this. You're going to get less coverage of those proteins. The authors made this pretty green chart, so I don't have to (borrowed without any permission whatsoever)

Regardless of what FAIMS compensation voltage (CV) you use, you're going to end up with fewer peptides identified. But more proteins!

I'll even go one better with another stolen green plot -- the proteins you'll find will be lower abundance! (Deep HeLa is fractionated).

If you spend less time on albumin, titin and keratin, we all win (unless you're a keratin researcher, and you have your own challenges).

I scratched my head about this and plotted stuff a bunch of different ways. Surprisingly the MS1 isolation interference doesn't seem all that different (but -- keep in mind that this is essentially a normalized measurement)

<---No FAIMS left
FAIMS -75 right -->









However, if you take out all the z=1 peptides and your Signal (S) goes up, you're also raising low abundance peptides that are now contributing to your Noise (N), so probably this is all good stuff.

Okay -- so what is actually different between the files?

The stupid charge state distributions!

 No FAIMS left -- FAIMS -75 right. All the sudden you've got a bunch more +3 peptides (relatively)

Okay -- this gets better, I think, because you know what a lot of search engines assume? They assume that your MS/MS fragments will be +1 charged.

Sure, they'll try to look at more, but as awesome as a 1980s TransAm looks and sounds, it's only got 205 horsepower. With age, it's probably closer to 170 at the wheels without a full rebuild by someone good. You can get faster used hybrids on Craigslist.

Is that a long and unnecessary metaphor/analogy/something or other? Absolutely.

Nearly all of the newer search engines that I'm always going on about start by deconvoluting the MS/MS spectra prior to searching.

As a more controlled experiment (n=1! I'm winning science today) -- let's take the same file and run it through Proteome Discoverer with and without first deconvoluting the MS/MS spectra so all the fragments are +1 (this was in PD 2.1, due to the fact I'm using an older PC today thanks to some weird malware issues that I think are Zoom related)

It's safe to assume in a tryptic digest that you've always got your single basic residue at the terminus. There's one. In a +2 you've got one "mobile proton" so probably +1 fragments make sense, but in a +3?

I'm too bored with this post to dig up some MS/MS spectra. (I went down a rabbit hole reading about Trans Am specs to make sure I was right. My Craigslist hybrid is faster 0-60 than a 1985 Trans Am when the two were brand new. (The Trans Am looks way cooler, though).

CV -75 file number 1. No deconvolution/SeQuest+ Percolator


Same file. Changed nothing except added deconvolution of the MS/MS spectra

FAIMS Pro results may not be exactly what everyone wants. If you're looking for the highest coverage, maybe you want to take it off and run without it. If what you want is the highest number of protein IDs because you are willing to sacrifice some coverage of the higher abundance ones to see some of the lower abundance ones....

----particularly if you're willing to tweak your workflow toward this newish kind of data!

Reminder of #ALSMinePTM Challenge -- and some wiggle room on data submission!

To the amazing people who have signed up for the largest proteomic informatic challenge in the history of the universe -- THANK YOU!

Also -- just a reminder that we were targeting this week for data submissions.

However -- if you've found your life a little bit offset by some virus thing, we think it's fair to move back the due date.

We'll have a portal or something set up soon(?) to start submitting data this week, but we'll continue to accept submissions for the next 2 weeks.

If you're just now hearing about the biggest proteomics informatics challenge in history and want to join in -- there's no better time than right now.

You can find the official site here.

I have something permanently stuck to the front page of this blog over there --> somewhere.

And I have an informal description of what we're doing and why this matters (beyond showing off how good your progam is or what a wizard you are at processing data) here!

Rapid gradient single shot FAIMS-Pro Exploris -- 5000 proteins in 20 min!??!

Did I post this once already? Probably, it was in preprint a while back, but -- wow -- I've reprocessed all the DDA data and it's unreal how good this is.

1) EVOSEP
2) FAIMS Pro
3) Exploris 480

Even conservatively on the 20-ish minutes DDA 500ng HeLa runs I'm getting over 3,800 human proteins on the best runs.

Worth noting, on the DDA runs single FAIMS compensation voltages (CV) are utilized. The authors are very clear that the CV voltages of 70/75 that appear to max out the number of identifications as seen here may be very specific to the instrument being at absolute peak performance (i.e., clean and very lightly used).

I haven't processed the DIA data yet, but it appears to produce better results in their hands.

Huge question here that should be addressed based on TIC alone between some runs I have from EasyNLC vs the signal that I'm seeing here.....do you just naturally get more signal with the EvoSep due to the fact that you're loading directly to your separation column, rather than to a trap as is typical in NanoLC (since you need that column to live a little longer?)

If anyone has that kind of data, I'd love to see it.

Oh yeah! Here is the paper.