As the mass specs get increasingly fasterly it becoming increasingly temptingly to run shorterly chromatographly. (I need a coffee, this was too funly... )
Which makes a lot of very boring things really important. If you are running really fast (here 5 minutes) nanoflow chromatography even the volume of your emitter can cause big changes in your results.
THE iSCMS/BMSS Proteomics Show is live! Link here! Wait. Did the Spotify AI do something to our faces? I think it smoothed our faces. You get what you pay for, I guess.
While unjetlagging myself I felt silly about things I forgot about iSCMS, especially some thank you!
Thank you Proteomes! for the editor travel support. First time I've used that perk of being an editor. Like I said on stage, the impact factor of all the core proteomics journals suck. So if you don't have enough biology to submit to a journal people read and cite why pay 3x as much for page and OA fees? (That's a joke. Or 3 jokes in one.)
Thank you Jarrod Sandow of IonOpticks for possibly being the very first person to ever take me up on attending a metal show at a conference. Definitely the first person ever?
Ultimately - I think it is just going to be absolutely essential we understand the artifacts caused by both and make sure that for one study we do everything the exact same way, but the results from this new study (about 140 cells) add more information to the discussion.
Do you need a phosphopeptide spectral library? Holy shit, this group decided to just do phosphopeptide enrichment on everything with 2.5 Da narrow window DIA.
They even went slow on the Astral (30SPD, so 44 minutes?) I don't have time to check how easy it is to parse the data out. Like, am I looking for every phosphopeptide that can appear in every cancer (or some IPSC) cell line? Or do I want to search my liver stuff against the liver library? I legitimately don't know the answer to that from either the biological side or from the ion/false discovery statistics side. Either way, this looks like a fantastic new resource.
These meetings exceeded them all.
Important part: If you are developing some single cell technology that you KNOW is cool. That you KNOW from painstaking empirical measurements (and also in your heart, I guess) that it works and you think it has applications if you can get some skeptics off your back (skeptics who are whispering "...why don't you just poorly measure the transcript abundance of 4 BILLION CELLS? People have been spending billions of dollars on transcriptomics for 20 years and it has solved every disease and has led to unprecedented levels of new diagnostics and drugs [clarification. my sarcasm is underappreciated. it has NOT]..." iSCMS is where people question everything. iSCMS is where you see multi-multi-multi-omics and meet people who have coupled 2 things together that you've riddled over. Maybe it's backward. It's probably backward.
There was a lot of unpublished data. And a lot of great people. So I'm going to be very careful here. On the proteomics side it was heavy hitters all the way through. Ryan Kelly showed off the 5 minute proteomics stuff (paper on the blog about 3 weeks ago?) and Erwin Schoof went over his recent Science paper and the smartest use of an antibody you've ever seen. He also inspired me to apply for permission to get the API access on my TIMSTOF again, because he's doing stuff I really want to be able to do.
There was a lot of discussion around...um...my last blog post....not that...actually... the paper it was about.... but it was very positive. Single cell proteomics is growing fast enough to have cool new stuff to talk about almost every week. I think the last time I saw so much conversation around a paper and so many people with the downloaded .raw files sitting around discussing it was 2014. I can't find a post, but that's when we discovered with the Human Proteome Draft maps that our false discovery rate tools were vastly underpowered when given millions of mass spectra. That discussion transformed LCMS proteomics and made it more powerful and made us more confident in our findings and I think we've hit one of those moments that is going to be very good for science.
But it's not all proteomics here. There is single cell lipidomics and metabolomics. Work from the conference chair and host Melanie Bailey has been featured on this blog more than once. Wow. Blast from the past, I guess I was really skeptical about the first paper. I clearly warmed up to it a little while later, and I'm 100% converted. The number of controls they put in to make sure they're measure lipids and metabolites in single cells is AMAZING.
On...that...note....on the proteomics side, Alejandro Brenes (I'm jumping to BMSS) gave a plenary lecture that made me legitimately nauseous. Puke emoji. Take away - do a master mix blank! Don't do a water blank on single cells. And...maybe do all your prep in a hood. Which is going to suck. But...we're totally going to do it. I may very seriously need to start working on an erratum regarding one of my papers. It is possible that one of my findings is just single cell specific contaminants I've never heard of until this week. We'll look at it when I get back to fast wifi.
Breaking up all this text with a picture from the next meeting The British Mass Spec Society! Was there single cell proteomics? Sure! Was there a custom PARTICLE ACCELERATOR for bombardment level mass spec that hits a surface with an impact at 10% of the speed of light? It's only just now operational but what would you do with mass spec imaging if you could hit your tissue hard enough for proteins to ionize off of it AND X-RAYS? Well, Catia Costa was like "I'll do that first and then do immunohistochemistry afterward!" which sounds...absurd at first, but with sub micron spatial resolution... there's material left for those boring old low resolution antibodies. [Sarcasm check, generally antibodies are considered far higher resolution than any technology with mass spec].
London has been slandered, btw, I was expecting flat beer and gross food. And I've had neither of those things. Actually, someone at my hotel thought I was American or drunk or both and gave me scratch off tickets and I won 50% off dinner at some very American restaurant. It was mediocre food, but at a super great price.
Honestly this might be one of my most professionally successful conferences. Do I now know a guy who can sample live cells for metabolomics? Did I talk him into sending me cells for something? I hope so.
Did I convince a company or two to let me send them part of our rapidly growing cohort of carefully curated human liver samples/cells/tissues to help me get un-stuck where I'm currently very very stuck? Again, I hope so.
I finally saw a single cell robot that I really truly think I need and now I need demo data to go try and find money for it. Oh and Bogdan popped in to show off single cell spatial (optical) labeling and then showed off way better SCP data on his 7600 than what I was able to get on mine. (He has the Ion Opticks nanospray source that wasn't available before the flood destroyed mine. It probably isn't just that he's a better mass spectrometrist than me.)
I also got to see posters and flash talks from this generation of scientists who are doing their graduate work in the post-pandemic environment where single cell mass spectrometry is just - a thing their mentor taught them how to do. That looks silly as I type it, but - sure - there is still lots of method development we can do. And probably have to do. But you don't have to spend every minute of every day trying to get single cell or spatial to work. I saw 20 great posters where students are APPLYING this stuff. From basic biological discovery (super cool award winning work on single plant cells!) through glioblastoma drugs and how they effect the sub-cellular single cell metabolome (another award winning poster).
Not every poster or student talk could win an award. But I swear EVERY SINGLE ONE was something that I'd consider science fiction or silicon valley con artist level bullshit just a few years ago. I'm up to my neck in single cell data and not everyone is. There are real skeptics out there that I don't really have to face as much anymore, single cell proteomics is nearly established. This was the place to discuss how to get the evidence necessary to convince those critics that what you're doing in single cells is valid (and important). I hope students were inspired by the numbers of rejection letters I proudly talk about rather than just concerned about ever graduating if it happens to them.
I should probably stop typing and go see another famous bridge or clock or torture chamber or get a beer that requires a lot of physical force to squeeze out of a wooden...barrel...?...it looks funny and it's slow, but it's also novel and delicious. I don't have a lot of hours left in this beautiful city.
Thanks for the organizers for letting me show off my lab's research in liver diseases in women that don't have any effective diagnostics and talk about what collaborations are going well which ones are challenging with our SCP tools and hardware. AND there IS a live proteomics show podcast that was recorded here! I'll get the audio when some of the organizers get a chance to sleep (this was clearly SO MUCH WORK). Thank you to King's College, Dr. Bailey, SEISMIC, iSCMS, and BMSS and everyone I forgot.
(For real, ask a sales rep, 7600+ systems are an amazing bargain for what they can do!) And....getting good quan on E.coli spiked into human plasma? Both in numbers of IDs and quantitative accuracy? Worth thinking about, yo.
The paper itself is really pretty. I don't know how they generated such high resolution plots and images and convinced this specific journal to not run it through their 1988 Xerox filter.
The results are even more impressive. In one example, this team identifies a protein that a single HeLa cell growing in a lab in central Iowa in 2008 produced in a total of 4 copies - in one of their single HeLa cells. Not only did they identify the first ever case of single-cell proteomic quantum entanglement but they also identified 18 new post-translational modifications on that protein! I know what you're thinking, this is obviously some super secret new hardware none of us will see until Houston (puke emoji, no I won't be there). You'd be wrong again! They used exactly the same instruments that lots and lots of people have (I don't know how, no one in my city can afford one. I've heard they're less expensive elsewhere? They'd have to be. We can buy 3 nice TIMSTOFs for one of these things). This lab is just way better at everything than everyone else.
Of particular importance, the abstract clearly states 4,000 proteins in Human PBMCs! This is super important because these tiny cells are incredibly tough to work with. You know who has tried this and not gotten anywhere near this number?
Me (no publication yet, because I suck at mass spectrometry AND I'm a slow writer) but also
PNNL/Genentech (bums)
City of Hope (dummies with ....weird...the same hardware as this paper...)
NorthEastern/Parallel Squared (geez...remember when they were leaders in SCP research...?)
How did they get these amazing and undeniably field leading results? By doing weird stuff!
Let's start here on page 10 where things seem very normal.
On these tiny ass cells (6-8 microns most of the time) they get results that are inline with what we get here, and PNNL/Genentech, the City of Hope, and Slavov collaborative preprints demonstrate.
However, if your real goal for a proteomics study is TYPING THE BIGGEST NUMBER POSSIBLE INTO YOUR MANUSCRIPT AND GETTING AWAY WITH IT this team provides the secret trick for doing this with PBMCs. (I believe this is Figure 8b, the figure legends don't fit well on the pages in the PDF)
In these PBMC populations there are a very very small population (is that a straight line? so ...is there one cell at the very end?? is that how one of these plots work? I think it might be) of PBMCs that are bigger than a lot of the cancer cells people worth with today.
If you don't...I dunno....sort those cells away because they're ....not...normal....PBMCs..... hell, I'll even open the question that if those are even PBMCs at all. (The term used in the paper is "minimally manipulated" or something). To be nice let's call them GODZILLA PBMCs!
If you analyze GODZILLA PBMCs you can get a number of precursors or proteins especially if you use match between runs (don't forget to use software that doesn't do match between runs FDR.....puke emoji....angry puke emoji....) and then put this number in your abstract!
You win! Now you get all the collaborations!
You probably haven't noticed, but I'm annoyed about this. My biased opinion is that the people in my lab are getting very good at single-cell proteomics. It's what they do every single day. And they recently put a lot of time into cells like these and our collaborators were and are disappointed. We were getting data in line with these new preprints which made me want to give them a call and forward things over. "See? These are hard, we don't actually suck at this and you can learn things from these data."
And I'd bet you $10 that sometime soon they're going to see this new Nature Comms paper and they're going to think that good mass spectrometrists can get 4,000 to maybe even 7,000 proteins per PBMC, since figures 1-7 are all HeLa. And I'm not the only person this is going to happen to.
Your PBMC grant application might face similar scrutiny based on your preliminary data. Losers. These things are generally not helpful in any way to the field. They muddy the water, complicate things, set stupid and unattainable expectations, and will ultimately backfire on even the authors when they can't deliver on these numbers on the next study.
Still, beautiful paper. Despite the Astral and Astral Zoom looking about the same in the plasma proteomics data we've posted recently, the Zoom looks better for SCP. FAIMS also appears to help in both cases.
Whoops - Forgot one last criticism. The PRIDE repositories are weird. There are three and on the surface they look like they're just the same HeLa dilutions and single cell files repeatedly posted. I assume the PBMC data is there somewhere but they're not easy to find from the SDRFs if they are there. I'll assume the reviewers (of which, I was not one, which is probably a very good thing for this pretty paper) verified that there are actual PBMC raw files publicly available, and it's all fine.
But....it is an IMS as well. And in this paper, the -now - Dr. Patrick Garrett and et al., said "wait a minute, peptides and their isotopes seem to make a very normal pattern in the ion mobility space. What if we just kept those?"
You can find the preprint here.
Ultimately, what you find is that, wait. Can I just copy and paste this summary image from Biorxiv?
Do you lose some IDs? Yes, in this iteration it looks like you lose a few. But....can you now process those 500 x 4GB .d files? It sure is a lot more likely of completing before I retire! All the code is up on Github and the files come out compatible with all the downstream stuff, just smaller (of course the originals are maintained)
I'm just taking screenshots of this stunning paper to make sure you read it before I get a chance to. It's not mass spec multiplexing. It's image multiplexing, but THEN deep visual proteomics!
Wow. What a blast from the past. A new preprint just dropped that describes the triumphant return of what I've long thought was one of the smartest plasma proteomics methods of all time!
Its called NCTPro, but it is very similar to the old method Promis-Quan, and it gets very similar (amazing!) protein coverage!
This is the original Promis-Quan post (paper here) and here is the new preprint!
Hey you! Have you drugged or poisoned or mutated a mouse or rat so that it induces Parkinson's Disease (PD) or schizophrenia (SC) symptoms in? Do you want to fix it?
Do I ever have a veterinary drug target for you! Check out this new paper!
There have been a lot of recent advances to try and take advantage of all these crazy fast instruments.
I'll start with the dumbest analysis possible. That's probably all I have time for anyway.
I reprocessed a small population of large single human cells (TIMSTOF Ultra2 with EvoSep One 40SPD "Whisper Zoom") 41 cells that are EXTREMELY difficult to extract intact out of a cadaver.
No filters, no libraries, just the same library free approach, same FASTA database. Both came back with a little over 7,000 protein groups.
Big difference? On the same PC (an i9 desktop tower purchased last year with a ton of ram put in it before the prices went to the fucking moon) diaTracer wrapped up in about 4 hours from beginning to end. SpectroNaut was close to double that.
The reason this analysis is exciting and not depressing is how the respective softwares name protein groups and genes. SpectroNaut gives you your output as a "Protein Accession; Another protein accession" where multiple proteins could fall in that group. The way I have FragPipe 24 set up I just get a single entry.
I strongly suspect the overlap is better than this.
Besides being fast, I went back and reprocessed both of these datasets looking for PTMs in these big single cells. And diaTracer found over 650 phosphorylation sites! SpectroNaut found ...fewer.... Are they real? I dunno! It would be super cool if they are!
Obviously this is only cool if you're an academic who can use FragPipe, but - hot dog - I'm super excited that FragPipe can 1) process my diaPASEF single cell data and 2) it looks like it's at least very in line with software I've used to process thousands of files and have grown to trust over the years!
Hey you! You have so many options today for doing spatial measurements. You really do. There are at least 10 different options for measuring gene products in different cells. I was at a couple of recent meetins where biologists were talking about how much these things cost, and it's impressive.
Direct quote from a core director "Each slide costs me $7,000 in reagents" for her favorite spatial transcriptomics technology. She owns all the hardware!
I pulled some prices for internal at my University and - wow -
There are now ways to do up to 1,200 antibody probes for proteins in addition to single cell transcriptomics. This appears very new (Bruker's new acquisitions) and I've heard it is in line with the spatial transcriptomics.
Let's break this down for deep visual proteomics (spatial proteomics by microdissection) again this is internal at my University.
For a 10x Visium 2 tissue 11 mm capture for $10,900 you get 50um pixels with 100um center-to-center (according to their website) and adding on proteins is an extra $3,900. Let's call it $15k for 2 images with 100 micron resolution.
This is whole transcript sequencing in most cases so they expect 18,000 gene products theoretically measured across the slide and it looks like 35 protein maximum right now per panel if you add that on.
For $11,000 if you cut out 100um spots by LCMS we could (internal) do between 200 and 400 of those spots with proteomics (you'd have to find someone with an LCM to do it - I know a guy) and unless I'm doing this wrong, 220 100 micron spots (center to center) would be more than one 11 mm capture.
A 50 micron pixel would be somewhere in the 8 cell range so we'd expect a proteomic depth of at least 7,000 proteins per "pixel" not "over the total study" or hypothetical. That's solid depth and could easily be mined for PTMs, etc.,
And....we'd deliver a fully processed visual report in a GUI so the data could be fully mined by the end user.....
Compare that to the Xenium at 5,000 genes and $23,500! Holy shit. Xenium might be far higher resolution, I'm not sure, but if there is a takeaway here -
Spatial proteomics (or deep visual proteomics, if you prefer) is WAY WAY less expensive than spatial transcriptomics.
And....at the end of a deep visual proteomics experiment you don't have to do a western blot to see if any of those gene products actually....you know.....are real at the protein level. Which, depending in how you count the cutoffs, about half of them are actually real.
Maybe this is a rant, but holy shit, I do love spatial transcriptomics and their pretty slides. But when I see 4 slides in a talk now I know they spent $60k - $100k on their n=1 data. And then they did IHC or western blots after. It seems like a misuse of resources.
I've been generally confused about the whole "FragPipe + DIA" thing for a while. What's the DIA-NN node doing there....? Why wouldn't I just run DIA-NN...? (for examples.)
When a new way to process DIA data showed up last year, I had my hands full and just didn't get around to it. I've got SpectroNaut, BPS and automatic command line DIA-NN (academic version). Why would I need anything else?
Need THE crash course in doing shotgun proteomics statistics? Enrollment is open for the upcoming May Institute (I think that is what it is called. Not the month.)
Do you (or your reviewers!) need more than a text file to prove you did some proteomics?
There are some nice DIA-NN tools out there now that make looking at these data at depth easier, and this is seems like a nice new entry.
I have to admit I'm only just now starting to understand the differences between all these plasma proteomics thingamabobs.
Since we've got our hands full with more single cells than we can possibly run, I even missed the absolutely hilariously weird stuff going on with the owners at SEER.
Importantly: I desperately hope all of this summer's drama over ownership hasn't had too terrible of an impact on the great scientists at SEER who are doing great and important stuff. And if it has, and you see a CV from a SEER proteomics scientist, you should probably hire them immediately. There are some amazing people there.
Not familiar? Look it up, I'm a little scared to give you my summary of all the stuff I've read. Lawyers and accountants are expensive. I just deleted it, but you don't need my summary of investors said, CEO said. Though there are some pretty funny plots about how the company stock has performed vs the CEO's salary...the latter wouldn't be considered bad for an NBA player.
But here is the thing that threw me off even though I think people have mentioned it.
Mag-Net appears to get big numbers because it's an EV (extracellular vesicles) enrichment technology. Which is cool and everything. If you're an EV researcher you should be stoked. This stuff looks super smart. But....I'm not an EV researcher.....so...like...do I care?
Which...honestly...I guess is sorta the question about anyt of these technologies. If Aim 4 of basically every grant I'm a high part of is "and then we port this marker to the extremely limited list of FDA/CLIA LCMS instruments for making a LDT or IVD. Can I afford to put a nanoparticle on the front end of it? My hope/guess/approach is that the problem is we can't do discovery, right? But if we go to the completely over the top pain in the butt that is validating an LCMS QQQ assay, then it will see stuff. Right?
I really like the paper above, but it let some things click into my brain so I understand these different technologies a bit better.
Oh. I did actually post this. Since it's been up for a couple of days I guess I'll leave it.
And if you are going to do this EV stuff, this automated procedure from EvoSep looks extremely straight-forward.
https://www.evosep.com/wp-content/uploads/2023/09/Poster_Auto-Mag-Net-final_HUPO2023_web.pdf
Oh shit. That's a snake in the picture!
What if you just wanted to sample out the endogenous peptides before you did your intact protein stuff - or digestion for shotgun proteomics?
They stage tipped out of the solution to enrich the small peptides, and then precipitated all the big stuff to do the rest. It's probably a solid assumption that most of the big proteins won't either go into the little stage tip, and the ones that do won't elute out under the same conditions that the endogenous peptides will!
Smart, right? And then they can do all the venomomics!
My wife has a conference that she chairs or is president of or something important sounding very soon and I'm very concerned about the heat index. Holy fuck it has been hot in this mountain city. Kindergarten on zoom is rough, y'all. There is far too much dancing for this to occur in my office space around a bunch of serious looking virologists and pharmacologists.
Emergency backup ideas, like this one?
I liked this paper from the very first sentence...
Prey rely on chemical cues to detect predators, especially in coastal ecosystems where danger is concealed by turbid waters. Juvenile oysters (Crassostrea virginica) respond to blue crab (Callinectus sapidus) chemical cues by growing stronger shells, while mud crabs (Panopeus herbstii) reduce foraging to avoid detection.
...because...what...?
Did you know that? No, I did not. This is the paper, and this isn't what I expected to learn about at lunch today. I expected to be at a liver center seminar but it's so hot in Pittsburgh (a small city in the Appalachian mountains) to safely have school. I tell you what, the climate collapsing isn't going to be boring. Also, kindergarten remote learning is a lot of dancing.
Wait. Okay, how do you set up an experimental design for something like this?
Oysters were settled onto 4.5 cm × 4.5 cm marble tiles and allowed to grow for 1 week in 1250 L mesocosms with flow-through seawater prior to experimentation. Oyster spat were then transferred to aquaria and repeatedly exposed to 1 mL mixtures of blue crab urine from crabs that subsisted on either an oyster or mud crab diet (n = 3 mixtures in block 1 and n = 9 mixtures in block 2 per predator diet), settled seawater (negative control; n = 3 mixtures in block 1 and n = 9 mixtures in block 2), or a positive control of predator water
Ummm....okay, that makes sense, but it's sorta funny, right?
Oh. Okay, I had all sorts of dumb ideas about how they'd ever get the molecules in the sea water. What they do instead is see what oysters make thicker cells in response to crab A or crab B's urine and then they save back some of the urine that they analyze. Then they can track down the molecule that the oysters respond to. They use NMR for the metabolomics. So - no proteomics here - but it is in Journal of Proteome Research so it counts.
Imagine that you're trying to climb Everest and your goal is to have mice carry all your stuff. Maybe you can't afford a human to carry the stuff? Maybe you're just really mean to animals.
Everything is going great, until about 1/3 of the way up you discover that these mice are getting really lazy. Do you have an oxygen tank for a mouse? NO? I think you're just doing this because you don't like animals.
Okay, but I wouldn't type all of this because I don't have a solution, right? What if it's a solution you can get at a gas station?!? Check this out.
Nicotinamide mononucleotide (NMN) and sugar! To prove that this made the mice better at carrying stuff, they chopped a bunch of them up and then did a bunch of multiomics on the resulting tissue.
Metabolomics and lipidomics were performed on an Exploris 120 and the Proteomics of just the tendons and ligaments were performed on an Orbitrap Eclipse.
Something interesting is that they used a specific kit for extracting proteins from the ligaments and tendons. I don't know if it's any good, but it is interesting to me that such a thing (and a company I've never heard of) exists. You can find it here.
The end findings are extended to humans as the mechanism and how to reduce hypobaric hypoxia in us as well. Given the ENORMOUS differences in how humans and mice respond to these conditions...
(for real, mice drop their body temperatures at high elevations, on purpose. Humans generally never change their body temperatures.... human do a funny thing with a word I can't remember where we jack our body pressure way up to compensate for extremely high altitudes, which is part of the reason we need time to compensate and recover at high altitudes. Here's a good link to a whole lot of reasons we have to be cautious interpreting rodent and humans in these kinds of experiments)
... this could be a stretch, but I like a good over the counter solution to problems, so if anyone ever talks me into running a race in Colorado again I'll try pumping myself more of more NMN and sugar than I normally do just to find out, I guess.
