Really good new single cell proteomics videos from SpectroNaut!

 

I have limited time and patience, so I just about gave up trying to get access to these new videos from Biognosys. Glad I stuck it out, though, because they're legitimately worth the time. 

Highlights? 

The free Biognosys QuIC QC software that I just requested that appears to be able to provide rapid QC metrics on every instrument platform. Beyond our daily QA samples I still really have no way of figuring out what is going on with our TIMSTOFs when they don't hit our minimum sample cutoffs. Maybe this is the solution?? You do need their iRT samples, but I've got a shitload of those in my 4C, so I'm set!

Chris Adams shows off the Bruker, EvoSep, Cellenion, SpectoNaut collaboration thing where they sort single HeLa cells by size using the CellenOne into 96 well thingies that can be directly loaded into EvoTips. They run them on the SCP and crack 3k proteins with Whisper 20 (about 1 hr per sample). 

Valdemaras (Schoooof Lab) shows off some really innovative thinking on window size and when to inject your MS1 scans for DIA single cell proteomics. For high load proteomics we've been constantly dreaming about faster instruments that can handle smaller window sizes. Is that same logic as important when you've cut your peptide load by 1,000 fold? Do you need to place the same priorities on cycle time, or do you need to think about it differently? I won't tell you, you should see the video and the paper should be out very soon, I think. Some of these results were described here. 

Molecular and Cellular Crosstalk between Bone and Brain! Wait. What?

 

Ummm....hmmm....you need to know some biology to read this great new study. And maybe if you do it already makes sense to you that there is crosstalk between your skeleton and your brain and that changes as you get age. 

What I'm going to do is leave this nice review here that lays down kilos of evidence going back to the 1970s that this is the case. 

I'll steal a snippet from the conclusions for why you should care: 

Stroll down memory lane with the evolution of LCMS for blood proteomics!

 

I was just asking about this the other day on the podcast when there were two blood proteomic experts there! I haven't done much blood proteomics stuff recently (like decade, recently) and I do wonder where today's crazy instruments are. 

This thing goes a lot further than that by talking about all the kits and techniques (besides just getting a new LCMS, gasp!) that can help you dig that much deeper! 

A nice read. I'm still on the depletion fence in most regards, but if you're rusty or thinking about getting a last-gen instrument on Ebay and starting a proteomics biomarker company, this is the review for you. 

OpenCDB -- RNA-Seq + Proteomics data in the easiest way you've ever seen!

 

There are lots of ways to take a RNASeq data and LCMS data and make them work together. I haven't seen an easier one than OpenCBD! You can read about it here. 

Or, you can just go to this website and start clicking buttons! 

There are only 7 places where you have to do anything and one of them is "what is your email address?" I bet you already know the answer to that one! 

A lot of single cell proteomic variation is cell size!

 

How to normalize single cell proteomics data has been a hot topic this week on social media for some reason. I went to link a blog post on this study from Lanz et al., and discovered I'd never actually posted this preprint, even though I've cited it and used their method of normalization.

Considering my Inbox has so many items thumbtacked at the top with red exclamation points I have to scroll down to see anything new, I probably shouldn't have time to post this. HAHA, I'm going to do something I actually want to for 6 minutes today! 

What this group did was attempt to identify normalization factors that would remove some of the variance in single cell measurements in two recent datasets. What they found was that if they simply normalized the histone H4 abundance between cells, a lot of the variability condensed. 

I can say for sure that it definitely helps. For anyone using Proteome Discoverer this is easy to set up. 

You reprocess your consensus report and you make these changes in either of the quantification nodes. You set it for specific protein amount and then you choose the FASTA file of the protein that you want to normalize against. This FASTA must be recognizable as a main FASTA by PD (imported through the admin tab). 

I rambled a lot more about this a few years ago here. 

What would real wide scale proteomics to help patients look like? I think this is the template.

 

Thanks largely to a Super Mario based typing game I ran into when I was 17, I type pretty fast. NOT fast enough for this week, however, but I have to leave this super impressive study here. 

While a lot of people in proteomics seem to not fully understand what the word "clinical" means, there is a group out in LA that are actually doing it. They have new real life protein based assays that will go live soon (or have) that will better inform people making health decisions about patients. 

If we're going to really get out there and use these toys to make a difference for patients, I think it is going to look a lot like this.

Holy cow. I found a gif of this weird game that was in the back of my library? The internet knows too much. 

decryptM -- Drug activity on PTMs!?!?

 

There is lots and lots here, but our childcare is down thanks to COVID and I have about 11 minutes free this week. This is good enough that it deserves some of it! I can't do it justice at all here. The more I look at it the more I think this isn't a hyped up paper at all. If anything, it might be understated. 

Other groups have been working on these sorts of things. I'm pretty sure I talked about the Harvard effort on this thing somewhere.

But phosphos?? 

I only have one RAW file down, but the way they do this is FAAAAAST. 

27 minute runs! Multiplexed phosphos! 

Gotta go! 

Perseus as a R portal for proteomic data (and for people who can't do TIDY!)

 

(That's a UMAP of almost 2,000 single cell proteomes! And I made it without even bothering anyone for help. It almost makes sense!)

Okay, I give Perseus about as hard a time as anyone out there, but every time I absolutely have to sit down and spend several hours remembering how to do something in it, I end up being amazed by it. 

If you aren't familiar, Perseus is a really powerful informatics package that you can use for just about anything. 

It is written in C++ or something, so it can be stupid fast at some things and it is node driven so you think I'd like it better since I love a certain node driven software for proteomics. 

However, it can be sort of daunting because it ends up looking like this before it does what you want it to.

(Stolen from this amazing YouTube video) 

And it absolutely makes sense if you spend enough time with it and you take the time to annotate your individual nodes so you can remember what you did. 

Something that occurs to me a lot when using Perseus for what I mostly use it for is how much friendlier it is than TIDY in R. I've got this thing with rights and lefts that I've been told makes me a terrifying driver. Extend that to a rigorous data format in which you swap everything 90 degrees, and - no chance my brain can do it. 

This is like 2 years old now, but it opened up a whole realm of new capabilities for proteomics data analysis. 

The newer versions of Perseus can be pretty much auto-equipped with a several cool tools where Perseus talks to R or Python and brings the data back when you follow these directions. However, if that isn't enough for you, you can link in your own stuff. And it seems to me like getting your data into the correct format for R is often 90% of the battle, but that might just be my brain. 

How much are other people paying for their mass spectrometers?!?

 

WHOA! Ummm....hmmm....I guess since this guy posted it, he's cool with me reposting it! 

And....so....I have a quote in hand for an instrument on this that is about $350k more than what it's listed for on this thing. I'd think there was a typo in it, but I have quotes from 3 other people that are around that same price. Definitely share this around. Everyone wins when consumers know more! 

Also, WTF is an Exploris MX? Is it a D20 Exactive???

European Single Cell Proteomics Meeting 4 is go! (VIENNA!)

 

Karl Mechtler realized several years ago that a dedicated meeting for single cell proteomics would be a really great idea. Every year I've wanted to go to it and this is finally my chance. Unfortunately, I do have to talk, but if you're interested in single cell proteomics you should consider this one-of-a-kind meeting in beautiful Vienna! 

You can register here!

Want to integrate proteomics in your research? IDEA internships available!

Did you accidentally find this terrible blog looking for actual information on how to add proteomics to your research? If so, I apologize.

However, here is one super cool option. 

If you are a grad student or postdoc you can do an internship in an IDEA lab! You do have to be in states that get funding for this, but you can check out the requirements here. 

Hooray! More reviews on single cell proteomics!

 

I'm sort of keyed in on the developments in single cell proteomics, so a lot of the mass spec side of this didn't surprise me much. Some numbers out of Munich that are mentioned are right up there with some data I saw at a meeting a few weeks ago from some pioneers at BYU. Neither has published, but if you see that and think it's a lighyear ahead, it is definitiely a big jump from what I'm doing (my priorities are different) but it is where the front runners aiming for coverage are going, or are at. 

But this new perspective piece is a really cool read if you aren't keeping up with what is happening with the resurgence of protein arrays. 

Love them or hate them, protein array technology is back with an absolute vengeance. I can't find it right this second but there was a clinical proteomics perspective piece I think Hui Zhang wrote that predicted they'd be the dominant technology in proteomics. It was funny for a while because I think the paper was 2013 or something (I should be able to find it with that much information) but looking at the money and where it is going now, was it phenomenal prescience? 

I think this is the lead in to a special issue comprising a whole lot of reviews on single cell proteomics which you can find here. If you were going to read one of them, I'd check out the imaging one because I get asked about getting the resolution up on MALDI-MS all the time, and there are some great ideas in there. 

Comparison of veinous blood to finger prick blood!

 

Wow. I have a lot of writing here to backtrack! First I had to catch up on my day job where I discovered that I have a limited number of business hours (6) to respond to emails while I'm at a conference before people in my department will search my office and leave the door open to make sure they drive the point home. 

This paper is super important, though and has to go even before my big writeup of US HUPO! 

Veinous blood is a lot trickier to get than finger pricks, but a finger prick is a huge amount of material for us! 

Is it the same, though? It turns out that...mostly....but there are differences that make a ton of sense. Most importantly, though, we need to really think about how the blood was drawn. You could absolutely throw off your data if some samples were gathered from one and some from the others. As I said, super important work! 

MASP -- What if we think about mass spec imaging sideways?

I saw this at US HUPO and I'm thrilled that it is out. It is the sort of sideways innovative thinking that comes out of Jun Qu's lab and it's worth thinking about.

I gotta catch up on piles of work from being away, but here is the paper from long ago 2022. 

I think I've seen some robotics stuff from PNNL recently that was similar, but what MASP is doing is generating amazingly small matrices and then physically isolating slices of biological material.

Now they can take the little cuts from each scaffold, do in-depth proteomics and reassemble the pieces. How little? Well, they're cutting pieces smaller than what our MALDI-Orbitrap can resolve WITH. A. LASER. No joke, they're cutting little boxes at less than 100 um in size.

Crazy? Sort of, but you have to think outside the box if you want to get spatial proteomics. 

We haven't really tried hard to push MALDI-MS proteomics but a postdoc here who worked with it for a while would get the top 100 or 200 proteins as he worked across a tissue. 

MASP is getting 5,000 proteins! 

Final Road to Chicago Podcast! Dr. Nick Riley!

 

Holy crap. If you ignore the last podcast, which I seriously thought Ben was joking about posting, it almost seems like we got better at it as we went along. Nick carried this one, and apparently if you're a postdoc on the podcast, we badger you with questions. 

I'm off to US HUPO now myself and I'm taking a plunge this week. I'm only taking a MacIntosh laptop with me, that I'm gonna figure out how to f'ing use. Maybe there will be blog posts. Maybe I'll be sitting in meetings swearing a lot at a big shiny laptop. Who knows? 

AND -- BTW -- WE SOLD OUT US HUPO! Either this is because of the podcast entirely, in which case, hopefully that means we'll get funding to keep doing it. 

OR

Proteomics is really continuing to grow. Thanks, in some small part, by you nerds out there doing your weird nerd stuff. KEEP IT UP.