My argument for why MS should be on the the front line for emerging pathogens!

 The good people at Bioanalysis Zone let me write some of my opinions up and edit them for a more mainstream audience than this weird blog normally appeals to. In this installation, I talk about just a few of the shining successes MS labs that were able to get access to SARS-CoV-2 samples have had. I also whine about why there were so relatively few of them. 

I guess I tried to make an argument that based on the ratio of (access to sample : valuable contributions) that if we had more access there would clearly be more contributions. Not sure if it carried across the way  I meant, or not. I mean...if you clearly know what you're doing (not talking about me, obviously, but other people)...and you want to help, you shouldn't have to go to Craigslist or LinkedIN to find disease samples during a freaking pandemic, right? That's what legitimate people in legitimate roles have had to do. 

Imagine if we'd had an established front line of defense network of labs (featuring mass spectrometry) that had the job of responding to emerging pathogens, providing testing until the next gen technologies could be scaled up and providing drug activity data from day 1.....  How different would it be today? 

MetaMorpheus -- Now with more style (and some cool visualizations)!

I know, I know, not everyone loves Metamorpheus and MSFragger and their every update shouldn't qualify for some kind of a lazy blog post, but they are both getting better all the time. This new update for MM has both a great gain in asthetics as well as some super handy visualization features. 

Ask yourself this -- how many PTMs did you search with your tool of choice today? Did you manually click "static + carbamidomoocowdoacetylatoin" and "dynamic + oxidation" and hit the go-button? Maybe you did the pyro-N-Glu thing? 

Chances are your list looks very similar to what Metamorpheus found in that represents the largest bars on this cool histogram below. 

 I didn't punch in any PTMs at all. I just added my workflows and hit go and I got what you did and all this other stuff!  I was lazier and got more data. Seriously, though, the PTM visualization thing is really cool. 

Toward a proteomics META-DATA standard!

I was just telling people about this in a meeting this morning and I don't think I knew there was a paper! 

 

Have you been thrilled to find that there is publicly available data for a project similar to the one you're about to start and had your hopes dashed against sharp rocks because the files uploaded make little sense? 

Have you thought about testing your new data processing pipeline for someone's old data and read 300 pages of supplemental information to discover they never tell you what file is what? 

The thing that is missing from that experiment is MetaData. (Pronounced like "Mee Ta Da Tay; imagine that 90s star wars character with the big ears saying it. It's like that.) 

And a bunch of busy bodies are trying to fix this problem by annotating data previously deposited and by giving us some templates for how we can annotate the things we upload. Strongly recommend you check this out. It'll be great for all of us! 

Characterisation of protease activity during SARS-CoV-2 infection!

This new preprint  has it all! 

SARS-CoV-2 infected cells.

Drug curves! 

Proteomics!

Weird protease activities (viruses are dumb) 

Pretty graphs! 

Publicly available data! 

A corresponding author who just set up his lab a few months ago and already has data? Seriously impressive work all-around. 

Is proteomics ready for the clinic? Perspectives on Acute Myeloid Leukemia.

This great new review at IJMS asks the question: "Hey! With all this better proteomics stuff, can you help us with leukemia yet?" 

 

I dig this as a summary of both the advances in LCMS technology that could feasibly be used within the confines and limitations of a medical environment (double dog dare you to try pitching offline fractionation to a hospital administrator) as well as a summary of the really promising work that has been done in one specific disease.

Table 2 is beautiful and says a ton about the potential of proteomics and our traditional limitations. It says to me: "Here are great studies! Can you imagine what we could do with larger cohorts...?" We'll get there!

Complex proteomic patterns in chemotherapy response in breast cancer (113 patient FFPE!)!

I can't follow all the cancer terms in this paper despite hearing lots about HER2 and some of these other things over the years. What I can follow is this is a big cohort multi-omics study that appears to have been done very very well. 

It represents a tremendous amount of work, starting with 113 FFPE tissues from patients broken down by genetic representation of their tumors and the treatments they received, as well as how they responded to the treatments they received. I think it represents something like 30 individuals, given the pre- and post- treatment samples. On top of this cell lines were also used for both proteomics and metabolomics. 

Super-SILAC was used for the proteomics from the laser microdissected (pretty sure that's how they did it?) samples as well as the cell lines (?) a little fuzzy on the design here without digging in much further and the metabolomics utilized heavy glucose and glutamine. The LCMS for the metabolomics is completely new to me, and I think it deserves exploration. Something called a zipHILIC was used with a low flowrate (100uL/min with AmBiC/ACN gradient and a 59 min gradient!) 

The study keeps going. They use CRISPR and do some mouse work to support their findings and they even use a SeaHorse (not the mythological creature, the Agilent high throughput metabolism thingy). 

This is a really inspiring amount of work with a great story about the value of proline metabolism in chemotherapeutic response. 

690 RAW files can be downloaded at PRIDE here (PXD012000). I haven't found the metabolomics ones yet. This may just be the proteomics! And...it's somewhere in the range of a freaking TERAbyte of RAW data!!) 

Interpreting peptide fragmentation -- spectral quality overrides software!

This is a FANTASTIC resource for just about anyone! 

Is this peptide identification real? Why or why not? 

This guide is open access, clear, and covers everything! 

100% recommended if you're wondering about that PTM or if you're new to manual interpretation of mass spectra! 

Glycoproteomics of sparkling wine?

 Need more proof that glycopeptides are involved in everything? 

I suspect that this paper is about the fancy stuff that no one is allowed to called Champagne unless the grapes in it can be directly seen through the bathroom window of one specific bureaucrat in a really dreary looking part of France, rather than the sparkling wine that people make into slushies in Pennsylvania. Just in case you needed more reasons to look forward to ASMS 2020 Philly!!! 

Back to the fancy stuff! 

This team uses SWATH to anayze different sparkling wines fermented in different ways, while keeping in mind desirable characteristics. Turns out there are a load of glycopeptides and the amount of time the wine hangs out on the lees has a lot to do with their distribution.

Interestingly they find a lot of nontryptic cleavages, suggesting that there are some proteases from the grapes or wood or yeast or something at work in the bottles! 

The data processing is done with Byonic, which I didn't know could process DIA data. 

hu.MAP -- Learning machine magic on 15,000 proteomics experiments!

 This new preprint outlines a project that could be termed "ambitious"....

Wooo....ummm.....shit..... I was really going to try and write something clever about this that at least created an illusion that I had some idea what was going on here. Nope. Not going to happen.

These people took an absurd amount of data and tried to come up with a better way of predicting protein-protein interactions. We have great actual protein-protein interaction data like BioPlex, so it makes sense that if your fancy artificial netneuro learny machine doohicky could learn from the sets and match the data from BioPlex, you're probably on the right path, since that's kind of the gold standard and everything. I think they show that in one of the curves. 

After kind of being able to follow along for the length of this paper, it seems like when you go to the hu.MAP 2.0 database that you'll be blown away by the awesome and powerful data at your fingertips... so I punched in a couple of proteins I know really well....and....

....I suspect it's way cooler if you read the instructions. If "RAS interacts with RAS Interactor #1" is what came out of 15,000 RAW files, we might still be a lot closer to...

....than Cyberdyne System (Boston Dynamics) rolling out a Terminator. 

MASSIVE.Quant -- Reanalyze, merge, reimagine publicly deposited data!

This is far far too cool for anything I can write to express how cool it is.

This makes it look like we can reanalyze any quantitative proteomic dataset with virtually any tool that we have in our utility belts.....

 You need to check this out.