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. 

Super fast targeted proteomics on an ion trap for IBD monitoring!

 

Sometimes you already know your targets, but porting them to a targeted assay is BORING, and typically requires one of those crappy triple quads. One transition per peptide? Repetitive and boring and you basically get a true/false for each transition per scan. Is it real? Is it coeluting in this one patient? Good luck sorting that out! 

Could you use a super fast, crazy sensitive, but absolutely overpriced (come on, y'all, you've got the only ion trap, you could claim targeted proteomics market share) ion trap to get PRM (multiple transitions per target!) to biomarker studies? 

Maybe! Wait. Maybe if someone proved it could work to the QQQ people then you could maybe then try to corner the targeted market? 

This group went up to 300 SPD with almost 1,000 samples! 300SPD wasn't everything they hoped and dreamed, but the quan at 140 SPD looks great! That's still a 1,000 cohort study in a couple of days. Okay, it's several days, but still. I'm impressed. Now...if only the clinics could afford it..... 

Deplete out 99% of the dead cells to clean up your proteomic data!

 

Even if you're not doing single cell type studies, chances are you've got some percentage of your data that is being biased by nonviable / dead cells that may not represent what you're trying to study. We kept trying to do single cell on toxicity models and - if you're doing an IC50 study...50% of your cells are dead. So....do you care about the dead cells? And do the ones that are not dead, or possibly more resistant, represent your phenotype accurately?

What if you could easily deplete out the dead ones? Would that clean everything up? 

Sure looks like it in this case! Over a 55% increase in signal for the proteins this group cares about. AND they got a list of high abundance proteins that seem to drop off when they remove dead cells. New markers for excessive cell death? Sounds like that to me! 

How does COVID affect hamsters? Proteomics answers the big questions!

 

I had a short day last Friday because one of the kids that plays Paw Patrol with my kid had a positive Covid test. Wait. Is that still a thing? Yes it is. So the very first thing I wondered was "I wonder what the virus does to hamsters???"

Proteomics to the rescue! 

If you're not good at reading or a conservative or both, the two seem to correspond, I will clarify that I'm being facetious here. 

I legitimately think that some friends and I were among the first people in the US to have Covid. There was an international trip and one person had just flown in a very long way east from a meeting and we were so so so sick. But there weren't tests then, and who knows?  And who knows what other people fall in that category of "possibly had the virus, but we'll never actually know?" How do you do "changed by virus" studies on people who may have had it? Or may not? 

Apparently these poor little rodents express all the correct proteins to make them a good model of something that can get the virus, but probably hasn't. So the animal testing is justified by the fact it's probably impossible to build a good human cohort that hasn't been exposed. I hate animals models. Abhor. Loathe. But I also can't CRISPR the most abundant protein in a human's hippocampus either. 

Animal model...is...I'll grudgingly admit, the only way to do some things. 

Proteomics was done by DIA on an Orbitrap III (Eclipse), pooled samples were used to make a spectral library then DIA was used for quan. Phosphoproteomics was also performed and a pile of IHC and validation was also performed. Solid looking study. Easy to write a silly headline. A+. 

Proteomics of butterfly metamorphosis!

 

A little disappointed by this picture, but nothing else in this great new study! 

Fixed it! Wrong butterfly, but I don't care and neither do you! This is the whole genomics vs proteomics argument - 

same genome - different proteome! Where are the wing proteins in the early life cycle stages, Mr.Genome person? Checkmate. 

Let's go! 

The data was acquired on a Q Exactive Classic using nanoLC and DDA. Data was searched in Proteome Discoverer 1.4 (I guess they could have cited something for the software. I wonder where they'd find a reference for that?) and a RefSeq genome with 19k protein sequences (whoa!) was used for analysis. Okay, peptide mass tolerance is too high by about 20x for this instrument, but maybe they did that to increase the number of bad hits for the FDR. The method for estimating FDR isn't disclosed, so it might be anyone's guess. 

Oh. Weird. I think they spectral counted. In 2025 2026 on a Q Exactive which has onboard electronics to limit repetitive peptide sampling...? Interesting.... And they used a microarray normalization R package for differential quan....which...is.... whatever. 

They did pull data from a previous paper where they did transcriptomics on another pile of these butterflies, which is cool, and demonstrates that basically transcript abundance is a pretty uselessthing to do with your time. 

The data is up on ProteomeXchange if you wanted to do a reanalysis with a less eccentric data analysis pipeline as well!