Rapid microglia phagosome proteomics!

 

A big thank you to Aleksandra Nita-Lazar for suggesting this as one of the most impactful papers of 2024 that didn't make my initial list! 

Again, the less I type here the better on the physiology/interpretation point, but from the introduction it's pretty clear that: 1)  microglia phagosomes are super important biologically and in a "plethora of brain pathologies" (stolen from the abstract) and 2) you'd think we'd know a lot about them, and we do not.

The part I do understand here is that a LOT of techniques were employed including proteomics on the purified phagosomes. That part was performed using a QE HF-X using around 100 minute total gradients. I like the fact that a very large inline traps were used (6cm x 100um) and I think that extra pile of chromatographic resolution in the trap helps make a 14cm x 75um separation column better match the relatively long gradients employed. DDA was used and downstream analysis was performed with MaxQuant and Perseus

LCMS metabolomics was also performed on a QE Classic exclusively using HILIC separation (I think) with data analysis through Compound Discoverer. There is a typo in the MS1 resolution, but otherwise it's all really well detailed. 

I love me some multi-omics data integration and there is a lot here to integrate. 

Aleksandra said that there are fantastic biological findings here and the authors seem equally enamored with this whole pile of data we have on these important subcellular compartments. 

Can proteomics data tell us about the ancient common ancestor of all life on Earth?!?!

 

The less I type about this one the better because it is way too far out of my functional working knowledge. It was the paper suggested for super important 2024 paper that should have been on my best of 2024 list! 

My favorite proteomics papers of 2024!

 

It's been...a...year....for sure. 

And for proteomics, this is EASILY the biggest year ever. Mine kicked off with the honor of being invited to speak at the amazing EuBiC Winter School! (Mandatory - you should think about integrating ProteoBench in your lab - it's probably time for protein informatics to grow up and this is one major step forward.)

SO! Because NO ONE EVER ASKED FOR IT (should be the official moto of this blog and about half the episodes of THE Proteomics Show)

Wait.  This is important. This year there will be a winner! I'll send out a trophy and everything. I'll be bugging specific people to help me judge. If you'd just like to contribute your opinion, please email 

THEproteomicsShow@gmail.com. 

And my favorite proteomics papers of 2024 are.....(In no particular order!)

Single ALS neuron proteomics!  Man, I feel like Amanda and I gave this study a hard time because - from a cell by cell basis, it was a really expensive study to pull off. Our review is just trying to add some clarity to people wanting to jump into single cell proteomics. In case it is at all unclear, I fucking love this study and it is a bar that I hope my new group can get to some day. 

ProHap - finally leverage population level genomic variants for humans in standard proteomics experiments! 

NanoSplits should be up here as well - the post on it is the 10th most visited post on this blog - in something like 13 years of me frantically typing into the void. What? For real. 

Steamlined proteome stability - https://proteomicsnews.blogspot.com/2024/10/streamlined-proteome-stability-find.html - because - unfortunately - proteins are more than just linear sequences and there is this whole structural stability aspect to most of them that drugs and other things affect. Get to how condition B is altering proteome stability FAST? 

This obviously has to go here, even though, by blog rules Matthias Mann (who - I finally got to meet this year thanks to him being a guest on THE Proteomics Show! I tell you what, I've met people I've thought were arrogant poopheads with 5% the H-index. He's astoundingly nice and grounded) is ineligible for ProteomicsNews paper of the year because....let's face it, his group basically got Nature's Method of the year. Sure, there's other work, but we all know what paper we're all talking about. This one. Spatial proteomics helped a patient with a lethal skin disease. Come on. This is amazing.

(Also, to be fair, my team's papers aren't eligible for top paper of 2024 even though I think my students did some super amazing stuff this year. So, there we go, I'm in the same category as Mann lab on something.) 

Human proteome is bigger than we ever thought?? The one criticism of this study is - wait, we've all seen evidence that this is a thing for years and years? And....this is the study that pulls all of those observations together and is like "....umm....so here is why this happens..." Huge.  https://proteomicsnews.blogspot.com/2024/09/the-human-genome-and-proteome-are.html

Nonlinear dynamics in aging - https://proteomicsnews.blogspot.com/2024/08/multi-omics-demonstrates-nonlinear.html - This is another great one and you know what the criticism I've seen of this is? "That previous studies saw the same thing at the transcript level? Yo, that's fucking awesome because it means that some of those transcript count things actually matter in the context of aging, which seems to be a protein level disease! 

FedProt - https://proteomicsnews.blogspot.com/2024/08/fedprot-facilitate-clinical-proteomics.html - If we are going to do clinical proteomics we're gonna have to figure out how to process the data without violating patient confidentiality.

How big has proteomics been this year? So big that we've had a really weird case of blatant plagiarism. I mean...this probably doesn't fit the theme of this blog post, but it should be noted, right? 

This one was super cool - DIA based PTM analysis of single cells - and then single NUCLEI proteomics! Such a great study all around. It should be noted that we've also seen another single nuclei proteomics preprint this year as well already. 

Proof that LCMS proteomics can embrace scale? Need that? What about 1,200 FFPE tissues analyzed at depth AND speed?  While trying to find my blog post on this by searching back for "FFPE" it provides some super cool context. Posts where just 2 or 4 years ago I was super psyched that someone pulled off 120 FFPE tissues in one study. 

Actually - on this same note, this in depth optimization of FFPE for PTMs should also be considered here.

What else can proteomics contribute today? What about an entirely new and much more powerful diagnostic panel for Parkinson's Disease? From a personal note, I should really spend more time on this soon. My mother is almost entirely disabled at this point from Parkinson's symptoms and - according to common commercial panels like 27andThee, she absolutely doesn't have it. Protein level diseases need protein level panels, y'all. 

Instruments on the Leading Edge of Proteomics! This year I had a gap between closing down the Hopkins lab, center and core and moving so I spent a lot of time in other people's labs. Man - I saw this review absolutely everywhere I went.  

I'm not going to add space proteomics to the survey, but what a cool year for using proteomics to understand how mammalian physiology changes in space. Multiple super legit studies and a special 

A big problem at the beginning of COVID for those of us trying to do LCMS based diagnostics was how long the digestion stuff took. That led some of us to try intact proteomics, smart people with TOFs and ...well....me....with a MALDI ion trap (sorry, Dr. Phinney). What if you could digest your samples as fast as you can load your traps? Welcome to near real time plasma proteomics! 

DecryptM was one of my favorite papers from last year, and the proteomics resource I visit the most often. This year they added amazing depth to the extremely valuable (lysine) histonde deacetylase inhibitors. HDACs! https://proteomicsnews.blogspot.com/2024/05/decrypting-lysine-deacetylase-inhibitor.html

It was really really hard to narrow this list down to just this many. I might add more and if you have an opinion for something important I missed, please let me know! If I missed your paper, please don't interpret it as a slight. I am literally pulling this together in a barn in WV whenever my kid is bouncing on a trampoline or doing something other than talking to me. 

What party did I miss - I mean....what's a Pi-Hub?

 

I forwarded this new paper around and it was somewhat relieving to see other people as confused as me about it.

Of course, I immediately found the contact page on the Pi-Hub and asked if I could contribute somehow to these huge though somewhat vague and overarching mission(s)! 

Shotgun proteomics workflows for insects!

This might have been useful this time last year, but we worked it out on our own. Could we have done better with some real optimization because chitin is a very unique material? Probably. 

It's part of this cool new - 2025 release! Tissue proteomics book!

MoSTERT - Predict retention times for any(?) modified peptide

 

One of the things on my checklist to discuss with potential new collaborators for LCMS based proteomics is going to come down to how we currently run the instruments based on current informatics challenges.

Data Independent Analysis (DIA) is probably going to give you the highest number of peptides and proteins and best quan.

Then why do anything else? Cause the other one

Data Dependent Analysis (DDA) is way more likely to identify cool PTMs. 

I'm convinced this has very little to do with the instruments or the instrument methods - we just haven't sorted out the informatics yet. Probably because 

1) we don't really understand the whole "proteome" (it's fun to forget that whole dark matter (WTF are all these other peptides?) thing, but I think it/ they stress out the neural networks. and 

2) All our deep learning things for retention time/ion mobility (where applicable) and fragmentation patterns are based on unmodified peptides. RT is a big problem. For real, what do you know about how a PTM shifts a RT? A phospho or a GlcNAc makes the peptide come off earlier? What about an oxidation? 

You know what you need? Shots of monster! 

Nope. Wrong. That's something else).

Zero-shot RT prediction with MoSTERT!

There are lots of RT prediction things out there, why is this one worth typing about? Check this out. This is what the program is thinking about the new PTMs it finds. 

Whenever someone says "PTM" in proteomics, I just assume they probably mean phosphoproteomics, mostly because LCMS proteomics is pretty good at doing those. But there are loads and load of them. And, at the end, they're just chemistry, right? What I like about MoSTERT-2S is that it seems to think about the PTMs at their basic molecular level first and then the authors go back to the biological or proteomic biases imparted in the data that is curated and collected and demonstrate they're onto something.  

Untargeted spatial metabolomics and proteomics with DESI and nanoPOTs!

 

I have things to do, so I'm just gonna drop this here and run, but it's super cool! 

You know those Waters DESI things that sound fantastic but no one you know has one? They're like a MALDI but things multiply charge like an ESI. Now you can get proteins and peptides and fragment things like any other ESI source - you can even use all your normal deconvolution and fragment prediction stuff in a spatial context! It looks like they don't have to matrix it, so they can do their DESI stuff, then capture off the same tissue to do proteomics. It does make me wonder if I can de-MALDI matrix but an almost 4 year old is trying to type on the same keyboard I'm using, so i should probably get going. Cool stuff, though! 

Proteomics Hackathon March 3-5! Remote options

 

This is a save the date I saw on LinkedIn. I'll update when I see a link and registration details! 

Multiplexed spatial proteomics! 50um resolution at 3,500 proteins and >120 slices/day!

 

Ying Zhu is having a solid month for his CV! 

While the resolution might not wow people in the MALDI world, the depth sure should make up for it. 3,500 proteins quantified and more than 120 "pixels" analyzed per day (a pixel is a 50um x 50um square, though it looks like they can cut smaller than this. 

With this little material and an eye on throughput - the fancy tools on the Eclipse for MS3 and Real Time Search didn't improve things.

The other interesting thing I highlighted is the batch effect correction. Original reference goes back to here (small world) with these caveats. 

ChiP-DIP - new post!

 

WOW! Did I ever blow my initial take on this paper. Not a little, but by so much that I'm a little concerned about whether I have some level of impairment in my reading and/or comprehension right now. I'm tempted to write it off as flu like symptoms while moving my family to a new city in the winter, and hoping it isn't a brain tumor, but - if you saw that post it was way off. 

I'm tempted to leave my original post up here because, if there is a strength I have as a scientist, I think it is my ability to rapidly accept and own up when I'm wrong. And I was very very wrong here. However, I think given the amount of traffic this post generated, I should just link some correct information to the paper and an apology to the authors for a legitimately worrying level of inaccuracy in my interpretation of their paper. 

This method doesn't just quantify your histone/histone PTMs, it also provides direct insight where those proteins currently are on the oligonucleotides. I don't know of anything with mass spectrometry that can get you anywhere near that level of insight.  

Here is a link to the paper. 

 (This post will link you to a special issue on histones from MCP.). 

And here is a more recent review. 

Why (and how!) to look at mass spectra! January 29th webinar!

 

Today you can buy a mass spec package and - as long as nothing breaks - and you take really good notes most people can be generating really good proteomics data.

When it's not working - well....that's different. Troubleshooting still seems to require an expert or a lot of work and logical exclusion of components one at a time. 

If your fancy neural network machine intelligence thing that turns those spectra into biology decides to have a headache and not work? Well, sometimes you have to look at the original data. 

Where do you learn that in 2024? While proteomics is growing at the rate it is? We discussed that with DB-J on the Proteomics Show on the last episode of season 5. Ummm....I mean...there are good papers out there....or you can try to get a slot at CSHL in the Spring....? 

Need something now! THIS WEBINAR IS FOR YOU! You can register here! https://www.ushupo.org/Webinars

We are the analyzers!

Follow this if you don't want to watch it in my silly blog interface. https://www.youtube.com/watch?v=wzmJDNmsWK8

Is the US HUP Ed&Out the coolest committee you can be a part of (used to be VMO)? 

YES! 

Huge success that started when Amanda Smythers started pulling together amazing silly rhymes last year at a meeting and Dragana Noe ran with it and made this super clutch finished product. 

So much work! Who is pumped for the US HUPO video competition this year? Me? You? Really? Submit your videos! 

Set up a DIA experiment with variable size windows on any Q Exactive!

 

I swear, I thought this was on the blog somewhere and I can't find it. 

If you're in Europe or other places, I guess, you can just disregard this entirely. Rumor is you have a variable or variation DIA window button. If you're in the US, there is a trademark thing and only one vendor(?) can give you a V on your instrument by default. 

Huge shoutout here to the Slavov lab for posting RAW files in the plexDIA preprint back in 2021 or something because this way of setting it up is more efficient than how I was setting it up. 

As you can see above - you need to set up multiple experiments. In this case I'm acquiring MS1s on my old Q Exactive in the dark and generally extremely damp Biophysics basement at The John. 

A Q Exactive Classis is slow by today's standards, so I think you'll definitely see people who will drop the MS1, depending on what their software actually requires. 

This may actually be exactly the Slavov lab method that I am showing here. It might be worth noting that plexDIA was using mTRAQ tags, so the m/z of the peptides are shifted up a bit by the mass of the tag - but I tell you what - this method will absolutely smoke a standard 20 AMU fixed with DIA method. 

So the trick is to set your loop count to match the number of windows in your "Inclusion list" file.

This is what your inclusion list should look like - I strongly recommend going into this and going File -->  Export --> .csv or .tab delineated (whatever makes you happy) then opening that format in Excel or something else. Then you can use quick equations to set you center mass and overlap. Here I think we're getting a 1Da overlap on each side of each window, but don't quote me on that. 

I'm 91% sure that you can also have Skyline do this for you, but - yet again - my Skyline expert is off making a lot more money than me, so the infinite cycle will continue. My cycle is this - sometime in 2025 I suspect I will pay to have some super bright young person go hang out with Brendan MacLean and some other brilliant targeted mass spec people and she/he will come back and tell me how cool Mike MacCoss is and for the next couple of years I'll have a Skyline expert!  Then we'll have loads of pretty plots and matrix matched curves and then disaster will strike - Asta-Zeneca or Merck or Moderna will offer that brilliant young person more money than I make. I'll be super happy for them and they'll leave and reviewer comments will come in where it's like "can you do this very simple Skyline thing and we'll accept your paper" and I'll say "...no. No, I can't. I'm way way too dumb to do anything in Skyline myself even though I have tried to sign every support document for the software since 2012 or something. 

What was I typing about? Aha! VariableDIA windows! 

Above you've got the first 25 windows that are 12.5Da and then the next batch is 25Da and the final is great big 62 Da or whatever. 

Considering where most of your peptides are in overall m/z ratio, you might want to adjust this. For example, if you were just LysC digesting and you were pushing up the distribution of the m/z of your typical peptides by about 70%, you could see a 4th experiment where you were using wider windows in the lower m/z mass range, narrow in the middle and then bigger then really big. This specific bit of vaguely and probably not usefulness I just typed was inspired by the r/proteomics entry that inspired me to spend 33 minutes before my first meeting today typing this instead of getting a shower and what appears to be a desperately needed second coffee. 

Since the Virginia Tech/Google $20M drama continues, my GoogleDrive that basically every link on this blog went to has been deleted. What would be helpful, I think would be if I put the actual .meth and these tables up in links somewhere. I'm now at 36 minutes somehow, so I can't do it now. If you need these, email me, my contact is over there somewhere ----> 

 and I'll try to get these up where you can direct download them later 

If you knew most of the proteoforms - is MS1 alone enough?

 

Okay - so this is really interesting and I may need to sleep on it, but here is the idea - 

Ideally we'd be able to see every proteoform MS1 rapidly and have instruments fast/sensitive enough to sequence them. We can use 2D separations to get there pre- or post- digestion, but in no case are these experiments fast.

What if we tossed the MS1s? Could we still do good biology? I mean....an intact proteoform mass with 600 amino acids is a lot less likely to occur completely at random than a 10 amino acid peptide...

Seems to work, too! The proof of concept appears to be an E.coli proteoform atlas. 

Tau interactome maps and neurodegeneration!

 

Y'all, I am SO PSYCHED for US HUPO 2025. As you might know, THE Proteomics Show podcast was renewed for a 6th(Six? 6?) season! And the whole reason US HUPO put up with our antics in the first place was that we were highlighting invited speakers, award winners and the big deal plenary people.

Not to brag, but I don't really have any hobbies except reading proteomics papers and maybe finding out that there are a bunch of proteomics people together in some city (or ski resort) and going there whether I'm invited or not. As such, I'm sorta plugged into what's going on in the community. 

I personally know/knew 1 (one! yes, one!) invited speaker for US HUPO 2025. Aleksandra Nita-Lazar, who is as good of a scientist as our field has. And then - no one else. So we're legitimately interviewing completely new victims doing crazy badass science. Huge props to US HUPO for this year's line up. I need to upgrade my tablet thing because there is no way mine will survive all the notes. 

Case in point? This is just about the sickest fucking paper you could read right now and December of 2024 might be the coolest month for proteomics in all of history. I'm not joking. I can't even keep up with mindblowing advance one after another. And - again - lack of hobbies. 

How did I miss this??? I legitimately do not know.

TAU? 

APEX Interactomics??? (Temporal - hey this protein is hanging out with THESE Proteins! Right now?? Yes, right now)

Neurons generated from IPSC stem cell thingies from healthy patients and those with dementia? (Disclaimer - went to one stem cell conference in 2009, learned/absorbed very little).

So cool, and we got to spend a morning talking with Tara Tracy for the show (next weeks? week after? something soon) and I'd walk to Philly to see her speak. Google helpfully just informed me it's like 12 hours. Meh. Still would do it. 

The antibodies don't work....ummm....duh....?

 

My favorite part about this new Nature news feature was that I thought it was an article from 10 years ago.

No...but the first citation is that article from 10 years ago...

Look - antibodies do work. But is that discount mass produced antibody for $400 what you want to stake 15 years of your career on without validating the hell out of it? Probably not, right? It's a really super ultra complex tool. Like any complex tool it needs QC/QA and we don't see enough of it. 

Proteoform level analysis of "purified" albumin reveals shocking levels of complexity!

I'm again going to put off blog posts on the 50,000 human proteome cohorts using "next gen" spot-based targeted proteomics.

I get it - I love the idea that we can use alternative technologies and get to this kind of population level protein level studies. But -just to remind you - we don't know the answer to this question - 

What we do know - without any possible doubt, whatsoever, that evolution is super ridiculously stingy when it comes to making new stuff. Sure, there are excessive things that don't negatively impact the overall survival of a population - but those are exceedingly rare.

If a cell makes an alternative form of a protein you can almost guarantee that there is a good fucking reason for it. 

Case in point - what if you treated one of the single best characterized proteins on the planet - not as a protein - but as a proteome? 

This team took a look at multiple "purified" forms of trusty old bovine serum albumin and treated it like a population of proteoforms - and 

1) It definitely is

2) "Purifying" a protein is....umm... something you'd think was 100% super well defined. Let's go with "could use further definition and characterization". 

We've seen things like these in the past - here is an old post where a modified Exactive (I think what later became the Exactive EMR, one of my all time favorite little boxes) - pulled out 59 different forms of ovalbumin. 

That's hard to look at and really encapsulate. Woodland et al., went full classic protein biochemistry and - this isn't hard to understand. This is a "purified albumin" separated out by isoelectric focusing in dimension 1 and by SDS-PAGE in dimension 2. 

This is a purified protein??? Some of that stuff probably was just tagging along, but a whole ton of that is albumin proteoforms. And - again - there is probably a very good reason for why an organism would expend energy to develop alternative forms of all these proteins, right? 

This is a super cool and thought provoking study that pokes some holes in more than a couple of our normal assumptions. 

ProHap - Search your proteomics data against population variants! Critically important new community resource!

 

STOP. IGNORE THE FLOWCHART ABOVE. These are bioinformatics people, they think this stuff is mandatory. I assume their conferences all have contests where the winner makes the flowchart most likely to make someone in another field throw up.

Again - don't look at it - 'cause this is legitimately important. 

You know how the genomics people have been doing things for years with illustrious sounding titles like "The 1,000 Human Genome Project?" Particularly when a lot of those things kicked off and the technology was more expensive, these things absorbed HUGE amounts of research dollars. The goals were to undestand how human genomes vary across us - as a species. 

And they did these things and they kept the results 100% secret from everyone forever. 

I guess that's not true, but -to me, as a human proteomics reseacher -they have been less than useless. Yay, you did a bunch of stuff. Who does that help? Not me or anyone I know. Even researchers I know who focus on health disparities can't get usable data out of these things.

UNTIL NOW. 

What these awesome, though flow-chart loving people did was dig into these top secret genomic databases and they assessed - 

-you won't believe it -

Protein level changes across human populations! This is where it gets important. 

How many peptide level variants could there possibly be in 1,000 genomes? 12? 15? 

Try 54,679! Don't believe me? Here is a completely not illegally taken screenshot. Don't sue me!

Almost FIFTY-FIVE THOUSAND PEPTIDE VARIANTS?!?

How many are you looking for in your data? One? Yeah, me too. I mean, unless we're doing deep cancer genomics and then we search for 2 million. Why not normal variants?!? 

Okay - are you thinking - "big deal, I probably need to spend the next 10 days downloading klugey python scripts written by proteomics people and finding out that my Docker thing is from 2017? How on earth does this help me?" 

And this is where this is super legit. 

Go here. https://zenodo.org/records/12671302

Download this - 

Use 7-zip or something to unzip it twice. (I don't know, it's right there with the flowchart competition, bioinforomatics people have contests to see who can Zip things the most number of times. Bonus - as in here - instead of naming each Zip .zip you can name them weird things. The first thing you unzip is .gz, then it will make a .tar, and you also unzip that - and you'll get the whole reason I've written this entire thing -

You get a FASTA FILE that represents common peptide level variants that appear in human beings across our population! 

Yeah, it's pretty big. 104MB and 157k entries. But you're encapsulating some much larger percentage of normal human genetics now! 

100% check out the paper. They did other smart stuff and there are other (possibly superior files depending on your application.) 

If you're using FragPipe (you should be!) check out this advice from Alexey! 

And check out this additional resource from his team here!

Top down proteoform analysis of kinase inhibitors in an approachable method!

 

Wow. This new study of kinase inhibitor treatment of cancer cells - using top down (intact protein/ no digestion) proteomics is 

1) Super legit

2) Seems really approachable

3) Kind of resets the bar in my head for what we can do right now with today's off-the-shelf technology.

And I might have a surprise for you. While Neil Kelleher's name is here because it is part of a special issue in his honor - this isn't a Kelleher lab study! 

Generally when we see a super impressive top down study I flip through it and then think - cool - maybe I'll be able to replicate it in 10 years? There is often modified instruments or things where you think - if I was able to keep my core scientific team together in a group for a decade we could pull off something this hard. 

Not to say there isn't some legit technical firepower on this study (Kevin Gao is a pro's pro mass spectrometrist), but you can read through this protocol and think - wait - could I totally do this? 

Instrumentation is an Exploris 240! (Approachable, affordable, clean it yourself hardware!) 

The HPLC is a custom Accela....okay, well...I don't have one of those, but it is running at 400nL/min with an interesting combination of buffers. I assume any U3000 RSLC, Eksigent or whatever could assume those same performance metrics. 

Custom nanoLC source. Details in references, but you can make a nanoLC source from Legos. Probably not that tough to reproduce (or necessary). There are funny little bars that are necessary for the Exploris systems when you make your own source and those can set you back several hundred $$

They used TopPic suite for the data analysis, which you can get for free here, as long as you sign stuff saying you won't be a jerk. For some of the focused proteoform specific (is the phosphorylation site at this place or this place) they (interestingly) used BioPharma Finder. I've never loaded more than 5 proteins in that at a time and it's super slow with that many. I assume they put in one sequence and a narrow time window in order to really lock down that one target they're trying to localize. 

The results are well displayed - really pretty and clear - and, again, really might just change your mind about doing top down proteomics. Bravo to this team, I legitimately loved reading this paper from beginning to end. 

Wait - found something to complain about! Whew, I was worried. They haven't unlocked the PRIDE repository so I can look at the files. It was just accepted (JPR ASAP). 

Is this the year I finally win the US HUPO conference T-shirt design contest?!?

 

I think it is, though I also thought that in 2022....

and...maybe I did win the Chicago one...? No, it looks like I tried to print my own shirt and the company thought I was playing a joke on them? Weird. 

Well, if you think you can beat my entry, go ahead and try! Mwhahahahahahaaaa. You can waste your time submitting one here. 

THE (real) single cell proteomics technique scSeq people love - NanoSplits- is out!

 

Check out one of my favorite techniques of the last few years - the NanoSplits paper here! 

The first preprint of this study is somewhere on the blog, but the work evolved considerably since we initially saw it.

If you aren't familiar, what this does is label free preparation of REAL NORMAL SIZED SINGLE ONE (1, uno, um, eins, jeden, yski, en siffra, een, ichi) at a time on glass slides using precision robotics. 

THEN the lysed cell is split into 2 fractions with most of the protein going one way and more of the little transcripts going the other way. You do single cell proteomics on the fraction with more protein and you can amplify the transcripts in the other fraction for transcriptomics. 

BOOM! You get everything! Now, there are obviously some drawbacks here, including that it is really hard to do. You need the precision robotics. This team features some people with serious instrumentation backgrounds but also people with a history of simplifying methods so mortals can eventually do them. We've written 2 grant applications where the technique has been prominently featured. The scSeq people are a whole lot more comfortable with this measuring protein thing if they can get evidence that you aren't just making stuff up! 

What's super cool here is that while multiple groups have shown complementary data by doing stuff like single cell proteomics and single cell seq on the same or very similar populations of cells (my group's first study was dosing the same cell line from the same source with the same drug - in a recent study) - here you get a real - Cell A proteomics and transcriptomics fill in a specific pattern. Cell B the same. 

The authors are quick to point out that NanoSplits could be a bridge technique to unify findings between more traditional studies where you either do SCP or scSeq or both on the same population. A small number of cells split could explain discrepancies between these 2 data types, or help you truly link 2 populations together. 

Seriously - a phenomenal, clever technique with top notch data collection and informatics and when I resubmit a grant in a couple of months I'm sure my reviewers will be excited to see a prominently published paper rather than a link to a preprint.