Two people I know are in ScienceNews talking about AI in Proteomics!

 

Yeah! Proteomics is bigtime! 

Check out this 1) Instanovo is finally out and 2) 2 people I know were interviewed about it and they seem to think it's smart! 

Instanovo final paper here. 

omicsGMF - Why I'm going to have to install R Studio on my new laptop...

 

You know - I was really pumped when my kid's puppy was like "HEY HEY HEY GUY I HATE, WAKE UP OR I'M ABSOLUTELY GOING TO TAKE A DUMP IN YOUR APARTMENT!!" 

So we walked in the pouring rain without either of our coats until she found the absolute perfect place to poop about 3 blocks from our apartment at 4am. 

And then my Inbox was like - "HEY HEY HEY GUESS WHAT! YOU'RE GOING TO HAVE TO TOTALLY INSTALL R STUDIO ON YOUR PERFECTLY OKAY NEW LAPTOP!" 

And here is why I have to stop putting it off....

I don't have an accessible (free) solution for large scale batch effect correction right now. Do you? I guess I can MSStats it, but a long time ago I realized I'm probably just not smart enough to use it. omicsGMF does require me to fire up R which I do have an official certificate saying that I took a bunch of classes in. But it looks to me like I don't have to think about it after I do. It looks like it is smart enough to apply the corrections if I just get everything formatted the right way. 

Now - the upside is that as long as I don't try to rollerblade to work today (Pittsburgh pavement is tough to predict when it's wet) my day absolutely has to get better! 

https://github.com/statOmics/GMFProteomicsPaper

Selected knockouts of the HLA / MHC presentation system!

 

Forwarding this one in just a second for sure! 

Most of what I know about the HLA/MHC immunopeptidomics / neoantigen presentation system comes from this amazing old paper in Cell Immunity. 

The mechanisms for processing and presentation are largely inferred from the large number of high confidence peptides they identify - but again - this is inference.

If we really wanted to understand how this system worked could you just knock out each protein along the way and do proteomics and immunopeptidomics? I mean, that's what a lot of people would tell you to do and that's what this cool new MCP paper does. 

They knock out 11 proteins in the system and there are 3 or 4 that cause big systematic changes in what peptides are expressed on the cell surface. The ramifications are probably beyond me and absolutely beyond what time I've got to think about it today but some collaborators are going to totally dig all of this new insight! 

MASSIVE.UCSD links are all updating! Here's how you find your data!

 

I was freaking out just a little late last night in lab. You ever get those 100GB warnings from MASSIVE and then the time limit goes by and you're like "false alarm, nothing changed?" 

If you're over your FairUse limits with Thermo RAW files those files will disappear.

With Bruker .d file format you have embedded folders. Those folders stay, they just get emptied out. For real - it'll look like you still uploaded like 800 .d files and they're still there - but they...aren't....

As I was trying to figure out what I still had backups of -and what I didn't - none of my FTP links that worked before ABRF seemed to work now. 

Right now you just need to add the -ftp into every address.

For example - Proteome Discoverer files I need to answer a reviewer's question were

ftp://massive.ucsd.edu/v06/MSV000093434/

And the webportal version thinks that's what they still are - 

But they're actually at 

ftp://massive-ftp.ucsd.edu/v06/MSV000093434/search/

That's probably all stuff that will be fixed at the end of the migration. You might need to update some reviewers if you told them they could find the stuff at the former, but it's now at the latter. 

Another...unbelievable.... single cell proteomics study - in situ - even....

 

Would you believe you could sample single cells directly off a plate and lyse/digest and transfer that cell with virtually no losses? 

No? Sounds sort of unbelievable and there is some "extraordinary claims require some amazing evidence" or something.  

Would you believe over 3,000 proteins per single cell with a TIMSTOF Pro 1 system running a 21 minute active gradient on those same cells? 

No? Would that immediatley make you think -wow - that should have ultra super fucking amazingly extraordinary goddamned evidence cause I don't think I've ever gotten more than 500 proteins on a very similar system regardless of how long the gradient has been -even on a cell line I like a lot that has a higher protein content than either of those? 

Would you be really annoyed that those instrument files weren't provided? 

What if when you follow a bunch of links to the Electronic Supplemental Information where you were promised access to the data you instead find an Excel spreadsheet with 20 columns  (10 cells each?) and 2150 rows for protein values and not one single missing value? 

In SINGLE CELLS PROCESSED IN SPECTRONAUT? You can't get 0 missing values if you had 2 ug of each of these injections, yo.

Would you start to wonder if there is actually something of a link between some groups and their ...unbelievable...advances in a new field and the fact they only seem to publish in places where providing your data isn't required? Maybe I'm just a jerk, but this study seemed so cool until about page 3. 

I'm not linking the study, but if you see it, I'd suggest you crank the skepticism up to 11 or so. 

PCA-N -36,000 human plasma proteomics samples by mass spec in 311 days!

 

WHOOOOOOOOOOOOOOOOOOOOOOOOOOOOOAAAAAAAA!!!!!

Dataset for reanalysis alert!! 

EDIT - EEEEEEEEEEEEEEEEEEEEEEEEEEEEKKKKKK! I can't find the data files. Contacting authors now. 

I have some 100SPD files from Matt Foster's Astral and I think they're like 8GB? So....8GB x 36,000? Is that 288 TB? It might just be uploading and they wanted to preprint something this year? 

Mann lab optimized perchlorate based enrichment to get a super cheap sample prep method giving them about 2,000 proteins per sample in human plasma. Then did 36,000 of them?!?! No nanoparticles? Off the shelf stuff? I need to read this, but - whoa. 

Artificial Intelligence (AI) in Proteomics - at ABRF 2025!

Artificial intelligence stuff has come so far in such a short time. Just last year, a sick 3 year old and I could not get either of the ones I pay for to generate an image of a turtle swimming in ketchup. And - today? Check that out! Totally did it! The future is now! 

Okay - so in what might possibly be slightly more useful - what about a killer session at ABRF 2025 on AI in proteomics? This featured two young scientists who actually know what they're talking about. 

Sebastian Paez from Talus bio - who went through where and how we're using AI (surprising places! like inside some of the instruments?? what??) and how our "raw" data has already been manipulated a whole lot. Right now we use our old fashioned search tools and then we go back with AI learning machine things and clean them up. He emphasized new tools where the smart computer is on the front end rather than cleaning stuff up. He also showed a cautionary tale where a modification searched in closed vs open search resulted in completely different results. 

The session ended with Justin Sanders from the Noble lab at University of Washington giving the best description of how Percolator (and derivative tools) work that I've ever seen. Really cool stuff in the context of where these things succeed (how we know) and where they are still not very good (PTMs).

A big surprise in this session was Graham Wiley at the University of Oklahoma. My picture from that was even worse than the ones above. My wife ordered me a new phone this morning. The battery couldn't survive a 5 hour flight home on airplane mode which made finding an uber at 2 in the morning a lot of fun. 

Dr. Wiley is a genomics guy who is doing tons of proteomics now thanks to O-Link technology. Like the really good stuff (5,100 proteins). While the sesssion was AI, as a side effect, I couldn't scribble notes fast enough. 

Like every question that I've had about -

How much does it cost? (If you've already got an Illumina Nova) it'll add about $350k in mandatory robotics. (List price, so probably a lot less). 

What's your throughput? One tech can do about 172 samples in a day (sample prep side) with all the nice robotics. 

What's it cost per sample reagents and labor? His group (which is a certified global service provider) is running about $406/sample in costs. 

What are other limitations to consider? Runs on the Nova thing are very sample specific. Like you can't fire the thing up with plasma in some wells(or equivalent?) and cell lysate in the other. You need those runs to be separate. 

A big advantage for his group has been using the nice robots for other purposes when they aren't running these preps. Again, this seems like I got distracted, but it was a super valuable addition for me for this cool session!  If you're interested in the deep O-link stuff you can find more about his lab and services here. 

LimeLight! Share DDA proteomics data if you can install a docker!

 

Okay - so this is really cool as a user - even if I feel like I'd only ever use it to look at someone else's data because I don't think I meet the minimum requirements to share my data. 

Getting an account and digging through the example data is really intuitive. I'm all about more transparency and so it goes here. Maybe I'm sleepy and moving fast but when we get to a bunch of prompts to install my docker my brain sort of checked out. It seems like every time a software has a docker requirement that means I can no longer look at MALDI imaging data on my PC because I messed up something in software I paid for. On new PCs here I don't think I have the permissions necessary to actually try (which is probably a good thing). 

For real - I'm probably just old and cantankerous and just can't keep up with all this new fangled stuff. But - I tell you what - if you put your data there and I'm the reviewer I'll totally dig through it on LimeLight over downloading all your files locally and digging through them. It looks like it can answer most of my first questions (which is totally a good thing!) 

PTI Turns 2! To celebrate there is a Festival with high plex DIA!!

 

Time flies when you're a new institute! 

Parallel Squared is about to turn 2 and they decided to have a Research Festival! 

Check this out. 

Yes I want to plexDIA, but my mTRAQ kits only let me do 3 channels. With tagging and pooling that's a lot of work for only 3x throughput. 9-plex?? That's almost as much as I can TMT on my TIMSTOF!! 

You can attend in person if you register soon (and there is still space) and remote attendance is an option. Check it out here! 

The first US HUPO 2025 post (by guest blogger Amanda Smythers)!

Ben: (Amanda is a postdoc at Dana Farber and some Harvard place and is the Chair of the US HUPO Early Career Researchers (ECR) Committee. Many of us picked up extra stuff, but Amanda pulled triple duty at US HUPO 2025 filling in for government reasearchers who weren't allowed to present to the public...cause... you know...).  

Amanda: 

The first time I went to US HUPO we were in the beautiful city of Charleston, SC. We had everything you could want in a conference – innovative science, inspiring atmosphere, great weather, a t-shirt cannon, and a Waffle House within walking distance. However, what really caught my attention (and kept me going back year after year), was the commitment to Early Career Researchers throughout the program.

If you haven’t been to US HUPO before, you can expect an ECR event every night. The last few years we have had the same general set up. We start with a “mentorship day” before the plenary, where we focus on skills like marketing yourself, negotiating pay raises, and visually communicating your science. The Monday night of the conference we have a “Speed Dating” night, where we talk about how to give an elevator pitch and then use the time to meet all the other ECRs in attendance. And last but not least, we have a “Lab Pitch Night” on Tuesday, where we put academic and industry/biotech lab heads in the hot seat and give them exactly 3 minutes to pitch why you should go work for them.

After benefiting from the incredible ECR programs in Charleston and Chicago, I joined the leadership committee where I now serve as chair. We were absolutely delighted to bring our largest program yet to Philadelphia and can’t wait to see what we can bring to St. Louis in 2026.

(Photos captioned by Ben, sorry about spelling ) 

.

For the first time, we had two sessions for our annual mentorship day. In the morning, we focused on “Navigating Tough Conversations in the Workplace,” with four fantastic speakers: Dr. Stefani Thomas (University of Minnesota), Dr. Dominique Figueroa (Thermo Fisher Scientific), Dr. Baljit Ubhi (fractional leader and consultant, multiple companies), and Dr. Ben Orsburn (the man, the myth, the legend – now at University of Pittsburgh). One of my favorite pieces of advice was from Stefani, who aptly started by reminding everyone to be careful of advice! Not all advice is applicable and useful for every person. Bal challenged us all to know what we want, and that if you don’t ask for it, you will never get it. We spent a lot of time in the morning talking about negotiation strategies, and how essential it is to strategize what you request in a negotiation. The need to be flexible was certainly underscored, as a failure to achieve what you want in one arena may open you up to more negotiation power in another.  Dominique broke this down succinctly into three steps: 1) focus on one ask and an answer; 2) make the ask clear; and 3) take yourself out of it. Ben added to this with some easy ways to support asks, including looking at market value, knowing your worth, and not being afraid to aim high. I was particularly grateful that Ben reminded us how women frequently aim for lower than their worth at positions where they meet 100% of the qualifications, while men typically aim for higher than their worth at positions where they only meet 60% of the qualification (read more about this here: https://hbr.org/2014/08/why-women-dont-apply-for-jobs-unless-theyre-100-qualified). 

(Photo and captioning by Ben - definitely got the spelling right this time)

In the afternoon, we switched paces to talk about "Innovation 101: How to approach new scientific ideas, follow up on inspiration, and convince others it is worthwhile,” with another four fantastic speakers: Dr. Daniel DeBord (MOBILion), Dr. Michael Krawitzky (Bruker), Dr. Mark Condina (Mass Dynamics), and Dr. Sarah Parker (Cedars Sinai). Mark kicked us off by telling us that we need to know what we are good at, and use this inspire your vision, purpose, and motivation, as well as your ability to communicate with authenticity and create shared value. Daniel added to this further by reminding us that opportunity comes from the confrontation of scientific problems with passion and skill, and encouraged us to follow inspiration and not be afraid of failing (sometimes making something worse shows us how to make it better!). Michael encouraged a start-up mindset which includes adapting and pivoting when needed, accepting failure, embracing persistence, capitalizing on ideas, and trusting yourself. Finally, Sarah reminded us all that innovation can mean so much to so many people- including the implementation of ideas and creating teams. I love this mentality, as it can be overwhelming to always feel that you must invent something new in order to support innovative science.

For those who missed out, Mark put together a phenomenal list of resources here: https://tinyurl.com/53by7tcp. PS: I have to give a shout out to Mass Dynamics, who have constantly been supportive of ECRs and offered to help sponsor this event long before I met Mark!

All in all, US HUPO was yet again my favorite event of the year for getting inspiring and making new friends and connections. Next year in St. Louis!

Insightful article on the proteomics expert shortage!

 

As a lot of people know I've been trying to find the funds and opportunity to launch a formal degree program in proteomics for...a while... I'm cautiously optimistic that I might have found an interested audience with the money and the authority to make it happen. We'll see, though! Honestly - it can almost feel like escapism to put your head down and prep a couple hundred samples for an instrument right now - or submit a crappy grant - but there is only so much protesting and complaining that we can do. (That's what I'm telling myself, anyway!) 

As I'm rebuilding and upgrading the pitch deck from the last time I did it - there is all sorts of cool new stuff out there. Not only overly optimistic market growth numbers, but also things like this insightful article. 

If you don't feel like reading this - you could at least check out some excerpts like this one that I'm clearly going to be using. 

Of course, this, and probably my whole blog and enthusiasm for the growth of my field and science in general is all overshadowed by the terrifying and uninformed decisions being made in Washington DC - but -the US is a small landmass. And proteomics is global! 

Predictive markers of BRAF targeted therapy effectiveness!

 

Title first because - by page 2 I was thinking - 

1)  Can I get another espresso without waking my kid or his puppy up? 

and 

2) What a weird and crappy experimental design! I do need that espresso because I'm clearly misreading Table 1. 

24 solid tumors were analyzed? Across both sexes and a wide age range? They are all tumors? Like no healthy matched controls? Wait - are those tumors from different organs? Weird, right? 

The reason I'm continuing to type isn't (solely) because I'm procrastinating on 10 things I need to be doing. I'm continuing to type because it fucking worked! 

The question was totally a spotlight into the dark to illuminate an important area. The question is something like: We give people these BRAF targeted therapeutics when we find a BRAF 600E mutation and we don't know anything else about whether that's was the best idea or how well it would work. So they had this small set of frozen tumors from people who had this BRAF targeted therapy and they did RNASeq and proteomics (TIMSTOF) on them. They also know what regimen of drugs the patients were on and how well the regimen worked as broken down in how long the therapy kept the cancer from recurring.

The RNASeq pointed out a bunch of mutations but - no surprise to anyone here - it doesn't appear to have pointed out a protein that appears to coincide, maybe even correlate (unclear), with the time that the patient was in remission.

I guess the lesson here is something like - look - sometimes samples are super precious and you just don't have the numbers to assemble a cohort that makes sense - that doesn't mean you shouldn't take a look?

ABRF 2025 is next week! Don't miss the proteomics sessions!

Hey! Are you going to ABRF2025? It starts in 4 days! I should pick a seat for my flight! (Strangely you do that on SouthWest now??? Weird. 

I was asked to amplify some of the mass spec stuff that can sometimes get lost among the FACS and microscopy and inferior technologies like genomics/transcriptomics. 

Honestly, I have 6 different scSeq things circled on my calendar, as well as some microscopy stuff. 

There is still mass spec there! 

Self-serving, the session I designed is the first one that pops up (there will clearly be lightning talks and posters) but the first proteomics session got the super ideal timeslot of 4:30pm on Tuesday! 

I hope that we'll make these available via Webinar as well. Working on it. ABRF had a blanket deal with one of the big webinar companies and the squeeky wheel seems to get the limited time slots).

There will be a talk on technology from a vendor that I haven't gotten to see yet as well! 

Wednesday is a super cool session that I literally changed my flight to be able to catch. 

Artificial intelligence in proteomics? Let's goooooooooooooooo! 

Oh yeah...and there is Metabolomics! (Insert vomiting sounds here)!  

Got 3 minutes? Vote for TalusBio (and proteomics) to win a cool award!

TaluBio is this funky spinout company that has stolen some of the best and brightest young people in our field and is out doing crazy cool proteomics drug discovery work on rare diseases.

You probably knew that, but did you know that they're up for some big award against all sorts of funky startups? It's a funny list to go down, like one company makes autonomous tractors. Hopefully.... by... developing their own algorithms and not using these... no one wants what happened to Jeremy Renner (he got better). 

You can go to GeekyWire and check it out (and vote for TalusBio) here! 

SpectroPipeR - Clean up those .SNE files with this R stuff!

 

I can't wait to try this and compare it to DIA-Analyst! 

Actually tell if it is an ADP-r or a PolyADP-r with insanely hard sample prep!

 

If you're ever feeling really good about your skills as a proteomic mass spectrometrist and need knocked down a peg, I strongly recommend going into the amazing field of ADP-Ribosylation. This amazing modification can be one ADPr or long chains of repeated ADPr. Sounds fun, right? 

Glycoproteomics is great because there are like 1-4 sugars for each mass and they can go on like 5 amino acids? I forget, and each sugar chain can break in like 2 or 3 places. 

ADPr cranks up the fun by each monomer fragmenting in 7 possible places, the mod can exist on 11 different amino acids and - again - each monomer has the exact same mass. 

Look at this awful thing (from this great JASMS paper from a couple years ago) 

Sign me up for citrullination on an Orbitrap (please don't) or phospho vs sulfation vs nitration on a TOF (for real, no, please don't) any day of the week. 

So Anthony and Isabel had this great idea of MAKING THIS HARDER. Because the way people do it is to cleave it down to a much smaller mod, so you know that there was an ADPr, but you don't know if it was one or a chain. 

It took a village - and maybe like 5 years (including pandemic times which we all know like only sorta counts) but Isabel actually pulled it off. 

To really get it to work - you need that ETD + HCD (EThcD?) thing that you can only get on Tribrids. And while the preliminary work on Chan-Hyun Lab Lumos(es) looked good, you can really see the benefits of the enhanced signal and improved fragmentation dynamics on the Eclipse in Ben Garcia's lab. This didn't make the manuscript - but - wow - does this stuff look better with the labile mod workflows in the newer MSFraggers and huge thanks to Dan and Alexey's team for extra help setting those processing things up. 

NIH BioArt has a Q Exactive!

 

If you're rapidly drafting some figures - Biorender can be super cool. But what if you don't have a license? NIH BioArt is adding stuff all the time! 

https://bioart.niaid.nih.gov/

Proof? There is no way that it had a Q Exactive when it first made my blog last year! I would have found it, but now it has it and more free/open art to use to really drive that dumb point home that you're trying to make! 

While you're there check out how much cool new stuff has been put on NIH 3D! 

https://3d.nih.gov/

Mandatory reminder that these government resources are probably at risk right now. I mean... we should be concerned about how we will do science if PubMed is no longer a thing....

EasyPubPlot - Super simple amazing customizable plots in like 90 seconds!

 

Where was this when I was stinking up the NIH submission system with my crappy grant applications? 

You want a 110 pt font axis? You get a 100 pt font axis! 

Introducing EasyPubPlot! 

And I'm not messing around guys. This isn't easy for someone who knows off the top of their head what version of R is on their computer. I'm not talking easy for someone who knows the difference between a .txt and a .tsv and a .csv that only opens right in Notepad++, not NotePad+ or NotePad-

I want to take the vomit inducing plots from SpectroNaut and make a plot that is not vomit inducing? 

I go here. 

https://pharmaco-omicslab.shinyapps.io/EasyPubPlot/

I put in a .csv that contains my protein, my fold change and my p-value and -BOOOM volcano plot! 

Is it going to impress your bioinformatics friend carries a shoebox of hard drives with them wherever they go? No. Because he/she/they can make a volcano plot some other magical way that I can not. 

Did I make the nicest proteomics volcano plot I've ever made in my life in about 90 seconds? HELL yeah I did and then I cranked the fonts up to 110 and turned off the legends and swapped the colors around. I DID THIS WITHOUT LEARNING ANYTHING. AT ALL. 100.00% of my brain was focused on a ridiculously good bibimbap. 

For real, it's like they took everything I need to make plots and simplified it down to just those things. It's like what the Red Elephant did for manual peptide sequencing. Or simplifying Prosit down to actually what I need from Prosit the way EL FRAGMENTADOR does. 

I've been paying for and using GraphPad for 5 years and I can't make something in it that isn't hideous without 4 hours of work. It's eventually okay, but - man does it start out dumb every time you put data in it. EasyPubPlot just makes nice plots! 

It does more stuff than Volcano, too! This is just what I'm most excited about. 

The heatmap feature is nice, but if you need a heatmap the Broad's Morpheus toolkit is probably a little nicer and it has embedded statistics. But if you just want to make a nice publication ready bubble plot or box plot - the tutorials are ON POINT. 

Is there a problem? 

The pub is short for "publish" but could you make a publication ready volcano plot at a pub? There is only one way to find out, probably. 

Too lazy to read? Navigate NCI resources with DrBioRight 2.0!

 

Just like everyone else, I'm absolutely fucking thrilled to see Artificial Intelligence showing up all over the place. A lot of commercial websites now have slow new AI functions that are essentially "ctrl+f" in your web browser - the kind of improvement is assume Elon Musk and his fans are most excited about. 

(Another amazing new success from Mr. Musk this week. Add that with the fact that they're  

now leasing office space for HHS bioinformaticians to log into AWS to do their work...cause that's a cost savings over them logging into AWS from spaces that cost the HHS $0! .... Though my favorite thing this week is the stated attempt to put AI into all government databases.. ..which...run...on.. .COBOL.... if you aren't familiar, when I took Fortran 77 in highschool (the 77 was the year that version of the language was finalized - which is a good summary of Appalachian high schools in the 90s) my teacher would make fun of COBOL being old and useless. Totally going to seamlessly integrate with AI. No worries there. 

While I'm ranting you can absolutely buy this at BestBuy right now. Just in case your "smart washer" from 2018 that is the single worst appliance you've ever owned in your entire life needs downgraded for 5x what a non-smart washer/drier that works costs! 

With that lead in! 

Introducing Dr BioRight 2.0! A large language model to search NCI data! 

If you're thinking something like "oh no, did peer reviewers see the words 'large language models and multi-omics' in the abstract and just accept it without ever looking at it?" you're probably a cynical jerk. Geez.... I mean, that's exactly what I thought, and that's why I spent my morning trying to see if this LLM could actually do things (other than generate "Internal Server" errors, which, to be fair, it generates an awful lot of) 

Short summary? There is some value here, I think. Particularly if you're not a big fan of reading, but you're patient enough to ask an algorithm to dig through some results for you, and you aren't patient enough to dig through said results yourself. 

For a bad example - I went to a pancreatic cancer dataset and I asked Dr. BioRight how often KRAS mutations show up in the cohort. Since it's the most mutated gene in that dataset, I figured it would skim the abstract and give me the answer. It didn't. ChatGPT400 or whatever it's called now will do that for you, that's not why you want to use this tool. 

I got frustrated, saved this blog post in my "do not push the publish button, Ben" folder and moved on. I went back because it popped up in my newsfeed and then I grabbed a random dataset and started asking it stuff about the source data in the study.

 And this is where things get really interesting. In this study with a 65 patient cohort I just asked what the most commonly mutated genes were. It appears to generate R commands and run them?  

That's actually super legit! Slow....but it really sounds impressive. Now - I'm not checking to see if this is actually right, but you can see where this might be an asset.

You don't go to an AI to make art for you because you know how to make art. You don't ask ChatGPT to write something for you if you want it written well and accurately. And you don't want to ask Dr. BioRight 2.0 about a cancer study that you reanalyzed yourself and contributed to a paper on. But if you are interested in skimming publicly available data for stuff you want to dig into depth on, it is faster than pulling the manuscript source data, figuring out which table is the one you want, and looking for it yourself. And that can probably be of use at times. 

Multi-omics of coal miner's exposed to 10+ years of coal dust....

 

I was really torn on using the above .gif because I come from a long line of people who have died either in coal mines or thanks to what they inhaled down there. Unfortunately, all of my cousins that are under the ground somewhere in WV bathe in the political disinformation kool-aid to the point that I can't even talk to them, so we're going to have some fun with this important new study! 

If you think being a coal miner in West Virginia is dangerous just because the governor of the state made his billions of dollars running the least safe mines in the USA and never paying his legal fees - you'd be right. But it's a super dangerous thing to do anywhere in the world and there is always some piece of dog shit who is willing to save $1 by making it a little less safe for you. 

This study found a cohort with the following requirements - you had to be over 40 and you had to have spent more than 10 years breathing coal dust. How'd they know they weren't lying about being down there 10 years? Well...most of them had easily diagnose-able pneumoconiosis. Which, yes, I had to copy and paste. They did have to exclude people who had lung cancer... ugh...so if you know Zoolander, maybe the funniest movie that ever happened that doesn't have Will Ferrell as the lead, you know what .gif I should use next and I probably shouldn't.

They didn't go after plasma - they went after BALF (no, I can't spell it) which is a fluid in the lung that is not fun to get from people. I hope they compensated these volunteers very very well. 

They FASP'ed the BALF (man, if you are thinking of FASP'ing human samples, I'll legit send you S-traps they're like $5 a sample, particularly if you cut someone's lung while they were under anaesthesia) for proteomics using a TIMSTOF Pro and they did the metabolomics on an Exploris 120. Everything looks pretty standard. MaxQuant for the proteomics data and MetaboAnalyst for metabolomics. 

I can't exactly follow where the ELISA came in here in the study - like what materials and why when they had proteomics on the BALF. I figured we'd be doing ELISA on cool blood targets, and maybe that is what they did? Oh - they did ELISA on some BALFs from these patients and an unanalyzed (by LCMS cohort). I think.

Hey - I wonder if the proteomic or metabolomic alterations of breathing coal dust for 10 years is obvious? Oh.

Yeah....it might be obvious.....

I'm not sure what the authors are planning to do with these data. I mean, you could probably put together a powerpoint presentation on - inhaling coal dust for 10 years alters your lungs on a basic molecular level? 

Completely unrelated -