Histology guided lipidomics and proteomics with co-registration of spatial information!

 

Are you ready for deep visual lipidomics? No? Okay what about normal spatial lipidomics with deep visual proteomics? 

The diagram pretty much shows what the paper did, but the co-registration of the spatial data from the laser capture microdissection work (now called deep visual proteomics, y'all, get on the hip terminology) to the lipidomics is a star. This does come from the very small but valuable bank of post-mortem human brain tissue in Baltimore that one of these authors definitely didn't steal from another facility. 

Lipidomics was at 50um resolution on a Bruker TOF I'm not familiar with, but they did both MS1 and MS/MS analysis. The deep visual proteomics was done on approximately 500 cell cuts. Pretty cool since we learned yesterday that brain cells are small. That's 25nanogram or so? Small! 

TMTPro was used for the proteomics with MS2 on an Orbitrap Fusion II (Lumos) system and found around 300 differential proteins of interest that the authors seem interested in. >40k peptides are reported, which is pretty darned good from sections this small on this hardware. If you're thinking of taking the spatial proteomics plunge this seems like a great resource for taking that step. 

Single cell proteomics of the developing human brain!

 

Big thanks to Matt MacDonald for sending this last night with a "Wow" as the total email content so that I got to sleep at like 1am after I felt like I'd finally gotten through it. 

Honestly, "wow" is still the correct word for it. 

Before I get into it, this is the paper. 

Single cell proteomics still feels new, but maybe I'm just old, but we're still learning what assumptions we need to make to get to real biological discovery. 

Something I argued for years was that I'd much rather have more cells than more coverage, but I think I've fallen headlong into the coverage race along with everyone else.... this paper is a solid smack in the face because they did A LOT with a few hundred proteins per cell. 

They say they get 800 on average in most of the cells. I'm spot checking in DIA-NN and before bioinformagic, I'm getting 450 or so. Probably by the time you match between runs and stuff you probably can double that. I ain't reprocessing 1,500 files, so I have a clear sample bias. 

Edit after this post blew up - I kept forgetting to mention the size of the cells, which is a big deal. They think some of these are like 50 picograms of protein! These are like 1/3 the size of the cells we use as our control cell line in my lab. This is a big deal. 

And - this is going to sound critical - and I don't mean it to be that way, because this is just a stunning work - but mass spec proteomics people may really just care far too much about quantitative accuracy. This isn't the first time one of our key tenets of proteomics has been really challenged. A Slavov lab study made the heretical decision of not fully resolving TMT reporter ions at baseline. Something that has been unthinkable for a decade or more. It still totally worked. We may try it ourselves sometime here. 

This study did around 2,000 single cells, about 1,500 of them from "brain cell types" by cranking their resolution and ion injection time to the moon on an Orbitrap Fusion III (Eclipse) with 40SPD "Whisper" on an EvoSep (the 100nL/min one)

I'm not looking at the paper now, but my notes say that it was DIA with 50Da windows and 250(!!) milliseconds of fill time at 120,000 resolution per MS/MS event. With 12(!!) windows.

12 x .25s x 1 MS1 (which might have been 240,000 resolution) so 3.25 SECONDS? per cycle. Someone somewhere in Seattle was shown this line I just typed - and threw up. All over the place. 

But hear me out. For real, what if your quan doesn't have to be good? 

Whew! Files finally downloaded so I could look at some of them and --- yeah -- you're going to get a lot of peptides, maybe most of them, with 3 scans/peak...

One of a pile of peptides I've pulled out, but look, I'm a blogger and I have a lecture due today for a class I'm teaching next week and 1-2 feet of snow between me and work, download 'em yourself here if you don't believe me - 

ftp://ftp.pride.ebi.ac.uk/pride/data/archive/2025/12/PXD071075

Here is the point to stick output of the peptide above. It looks like a triangle, but it isn't actually as good as a triangle would be. 

Is the area under the curve of this peak a reasonable approximation of the signal of the peptide? Who knows? Not me, and not these authors. But is it probably reflective of whether one of the 800 proteins in this cell is higher than the 800 proteins in another cell? Probably! At this depth you're going to be doing a lot of presence/absence stuff. And in this model that is probably a lot of power! 

OMG, and I have laughed for real multiple times about S.Table 2. Man, did they throw some shade at just about every label free single cell study that did fewer cells than this one did! Wow. I would like to thank these authors for not citing me, therefore I did not appear on their Table of Shame. They wouldn't want my stuff there because with SCoPE-MS/SCoPE2 this is actually a very normal number of cells analyzed, but the authors made it very clear that label free quan, regardless of how poor, is the superior option. They might be right.

Okay, but the take aways here should be 

1) You can do a lot with a lot of cells 

Even if!

2) You only get a few hundred proteins per cell

3) And the proteins you detect aren't all that well quantified! 

A good experimental design and cool samples and solid informatics can push you through to an amazing study. 

Quick math, btw, at 40SPD these 2,300 runs or whatever ran - with no blanks, no QCs, and not stopping to calibrate and no failed cells (there are always failed cells) a little more than 2 months on a system that is a couple generations back. That's...not bad....

And they used FACs so the cell prep was inexpensive. I don't have our calculator in front of me, but I'm going to go with this being in the $20/cell range in total costs/cell before any labor. Possibly less. 

Kid's up, gotta run. Super super super cool paper you should check out! 

MALDI imaging on a super cheap little benchtop TOF?

 

I want to close this tab on my desktop so I can see other tabs. 

Direct link to the PDF. 

For real, I think this box is cheap. Like I've seen it second hand for less than an HPLC. Even if you needed to buy some expensive reagents, it could be a legit way to get yourself into the world of MS imaging...

Draft - Bunny proteomics or how NOT to write an LCMS methods section

Not for posting - maybe keep it to write a tutorial on writing a mass spec method section. 

I should probably start with a HOW TO write your proteomics methods paper's method section. But I'm just all around annoyed with this manuscript as well as whoever peer reviewed it. And the editor, and maybe whatever this journal is. 

I'm also probably annoyed with the premise of injurying bunnies with a balloon. And maybe about the entire state of the world. 

So here we go! 

First, let's fractionate peptides with an Orbitrap Exploris 480. I only briefly had unlimited access prior to the pandemic to a 480 and only have a little data from it. I easily have 800GB of other people's Orbitrap Exploris data (mostly drug data from Tubingen). I was not aware that you could fractionate peptides with one. I'll go out on a limb and say that...you can't.... but that didn't stop these authors! 

(But...for real...the hardware described can not do this. You fractionate peptides on an HPLC, which is probably what these authors did, but we'd generally require the column length, diameter, flow rate, model of HPLC, gradient, column particle size. You'll note we get none of these things here.)

Was the data acquired with DDA or DIA? High flow LC or microflow or nanoflow LC? We'll never know.

Let's get to the results! Oh, cool. They used TMT! So they probably used DDA. But who knows? 

Wait! Let's go back to the methods! 

Or not. Wow. The sample prep is...unique...let's go with innovative. Hey! When there are 50 established ways of doing something you might as well get out molar concentrations of urea and add it and then remove it and add it again, right? I suspect this was not actually how the proteins were prepared based on the descriptions of other procedures, but it might be. Just an aside, TMT doesn't label well in high concentrations of urea. I think you need it below 2M or something. 

Please note above that proteins were considered diffentially expressed if they were 1.2-fold differential. Yeah! Arbitrary hard cutoffs! Is that including unique peptides only? Or are razor peptides included? If unique, how was "uniqueness" determined? There are some absurd defaults out there in the world for the latter which could have drastic consequences for a well annoated canonical protein database. There is no way bunnies have 65,000 genes (probably? I'm no expert) so...the process of selecting uniquenes should be a point of specific focus for this analysis. 

The point of writing your methods is so someone else can reproduce your results. At this point I don't know the starting amount of protein, the intermediate amounts of protein, the HPLC, the mass spec method type, even broadly, the search engine used, the FDR calculations or how the quantification was performed. This silliness should never have gotten off the editor's desk. 

The metabolomics has the exact same problems - with the added fun of a clear fun and strange type for the metabolomics. A google search should have allowed this to be corrected by anyone, at any stage of the submission process. 

Here we do get a gradient! We do not, however, know what the column used was, or the flow rate. Was this DDA untargeted metabolomics? DIA? What resolution? What cycle time (which is a bigger deal in metabolomics than proteomics)? 

Aside here - An S-lens RF of 60 on an HF (no ion funnel) is probably still not ideal for metabolites. You are asking for a lot of in-source decay of these things. Bet you $10 you don't see inosine in these data. Just two very nice hypoxanthine peaks (the former decays to the latter when the RF is too high). 

The good news here is that THERE IS A DATA AVAILABILITY STATEMENT. 

This weekend I wrote several different authors to request data that "is available upon reasonable request", so this is good to see while I wait to find out if my requests count as reasonable. 

Let's break this down, though for reviewers without proteomics experience. And maybe I should just post this part.

This is how I review a paper (or edit it for Proteomes). 

1) Is the data publicly deposited? 

2) Are the methods described adequately to allow me to give this to someone to reproduce in entirety? 

3) Do the methods reflect previously described methods? If they differ from these, are they justified and/or well optimized?

4) Do the results make sense? Are they well described? How many controls/experimental exist?

5) Conclusions - this is where I'm the weakest. I often include in my letter to the editor that I'm insufficiently trained or versed in this biology to interpret these conclusions and I defer to (hopefully) a reviewer who was selected for expertise in INJURING BUNNY RABBITS WITH BALLOONS. Or whatever. 

Yeah, definitely don't post this one either. I've had a rough

Single cell SDS-PAGE!

 

Wait. SDS-PAGE has enough sensitivity for single cell proteomics?  Hmmmm.... It probably does....at least for high concentration band recognition...and it would provide some level of proteoform resolution. 

I'll be honest, I first thought "...that sounds slow and silly..." but the more I think about it the more I like having this around as a concept - or even a first pass.

They use the migration patterns for their output and then use statistics that can tell different single cancer cells apart by those patterns....

I don't know about the 3D imaging part, and it is a preprint, so grain of salt over your shoulder or whatever, but I'm definitely going to think about this on my commute. 

Single arabidopsis cell proteomics!

 

The preprint is also on this blog somewhere, but it's cool to see it out! 

Proteomic (and transcriptomic) map of 28 primary cell types!

 

Great new dataset alert! 

28 different primary cell types! What a treasure trove (is that a word? I feel like it's a word. Like valuable stuff you'd need to sort through?) 

Primary, in most cases means something like "we didn't get this from a cancer patient in 1958 and somehow it is still growing and mutating a century later". It can mean different things in different contexts, though. Sometimes it's cells that won't divide, but they will stick to plates and divide for a little while. Just bringing this up so you're cautious about use of the term around biologists and pathologists.

It does give the feel of maybe a pre-pandemic study that finally got prioritized for writing. Orbitrap Fusion 2 system, DDA, SCX or SAX fractionation involved. That doesn't mean it's bad, by any means. It means it's high resolution fractionated DDA data that took way more time to generate than if we ran it today on one of the fast DIA boxes. In fact, it means a dataset that could yield new findings in the future, you'll just have a lot more (and smaller) files to keep organized. 

PTM analysis was done using BOLT! Yeah! First paper I've seen with this cloud based search engine (that I'm very biased about due to like 5 papers I'm on about it, including the very first one) for a while. If you're not into advanced PTM analysis and worried about that, the data was also analyzed with MaxQuant and the results summaries are available in the Supplemental and on PRIDE as PXD062642. 

Global proteomic structural analysis IN LIVING CELLS!

 

Dropping this here so I can get back to it! 

The omics molecule extractor! What a fun and easy way to visualize aptamer data!

 

All, this is legitimately a very nice and very easy to use tool.

You can load metabolomics data or transcriptomics or even proteomics data into it! The test data is some previously published aptamer data, and it's really cool to look at. 

You should check it out. 

You can go right to the online tool here and start pushing buttons. 

I haven't analyzed a load of SomaScan data before, but I've got a couple datasets and the Omics extractor can simplify my pipelines for looking at it down to a single push button. 

Check this out! Lots of proteins detected at just about exactly the same amount in every single sample! In this case it is people with or without arthritis! Look at that precision!  If I didn't know better I'd think that aptamers bind to and offload from proteins but do not do so in a quantitative manner outside of an extremely narrow dynamic range. 

Cricket enriched pasta proteomics!

 

Google said I could use this image, and it's amazing. 

Seriously, though, this is new study is also really cool. 

Look, I have no idea if the deoxidation potential of crickets as a food additive has any scientific merit. I can't possibly know this, and legitimately have no interest. I am, however, aware that the global population is still expanding and the climate is collapsing and no one is going to do anything to stop either thing. Other food supplies are going to be necessary possibly within my lifetime? Definitely within my child's. And we're going to need to think about the allergenic implications of doing things like introducing 20% ground crickets into our spaghetti.

This paper focuses on the benefits of adding cricket protein, but - wow - do they do some cool stuff with some endogenous peptides in ultra-complex matrices. Just about what you'd do to look for allergenic peptides. The LCMS was an EvoSep and a TIMSTOF Flex. Maybe the inflammatory stuff is real as well.