First ASMS instrument drop? A new Tribrid?? Check out ApeX!

 

Official site link here! 

Highlights? 75 Hz in a Tribrid? That's rocket fast. Probably rocking the Excedion Pro's lower resolution Orbitrap scan rates? Unclear, but that would be the safe assumption. 

It looks like there will be 4 versions of ApeX aimed at different markets? 

There is a long held tradition for this vendor to take a big 'ol dump on the systems that you currently have.....wow, they rolled out a lot of details on these systems.... but there it is....

...a giant jump forward from the venerable Assend....

If this is the one that is boring enough to release several days before the conference, it should be an eventful week! 

Wait. There is another one hidden in here as well - The Excedion not Pro. Amateur? Excedion Rec League! 

....my brain is off. Obviously it's the 

The upgrade video is a fun watch - the first thing they do is CHANGE THE STICKER! At 1:37 seconds, but then you get to see the differences at the very end. 

PAQu - Integrate transcript and proteomics data to get protein isoform quantification!

 

I find it more helpful these days to simply point out the failure rate of transcript level measurements (because just about every wet bench scientist out there has ran into it), it is relatively cheap and easy to get those transcript measurements. (However, I've still never been offered a $100 genome. Have you? I hear it's a thing, but it still seems like $100s plural). 

What if it still had some value (besides finding point mutations in variant call files, of course!)? 

These authors suggest straight out heresy and suggest implying that you could integrate these data to group those peptide IDs into actual protein data better. Proteoform data thanks to RNA? 

All you need to do is - 

Okay, but since my hiragana is not good nor has it ever been good, I have to just skip over this in every paper. It's just impressive to see this much written out in one block (I didn't even capture it all). AND you can get all the code at this Github. 

What matters is that they demonstrate that peptide level TMT data integrated in this approach improves their analysis. It also lends support to a hypothesis that 2 proteins are very differential in this disease. Two proteins that are so close together in sequence homology that your standard proteomics pipelines probably just lump them into a single group! 

MSstatsResponse - Make sense of Chemoproteomic data!

 

Just leaving this here so I can get back to it later in case we have some drug response data with a lot of variables! 

Super smart and some of the most honest writing. "Yes, in this dataset this other tool actually proves more sensitive"! I love it. The authors use both simulated and real datasets they generated using DIA and TMT and compared them. Refreshing and clever, even if you can't follow the maths. 

Bridging simplicity and depth in single cell proteomics.- some neat observations!

 I do like it when a new group gets on the the single cell proteomics train and starts optimizing/reoptimizing things. Despite the 300 reviews, 50 method optimization papers and 20 biological studies that have been published, each new one brings a new perspectice and observations.

While I don't love every aspect of this paper (some insight on what LC gradients were used when seems to be entirely absent from the main manuscript, which makes me question the title which seems to describe a single workflow) there is some gold in here! 

In my lab we don't reduce and alkylate the single cell derived proteins. I do this because I'm lazy. And also because I spend a lot of time studying drugs that modify cysteines. 

A really nice evaluation of different reduction and alkylation conditions in this study finds optimal conditions and reagents for reducing and alkylating. However, the %CV decreases when doing so under most conditions, probably due to the extra manipulation steps. This section is really well done.

This group also looks at how to digest single cell derived proteins at 37C without those teeny tiny droplets of trypsin evaluating and comes up with a method that works well. They also describe an easy way to get to 100% humidity. 

Since they're using a nanoelute they can go between 96 and 384 well plates. Simply moving from the 96 - 384 well plates is enough to give them 300 protein groups! 

Multiple cell lines were used here. A549 cancer cells, primary astrocytes grown on poly-lysine, etc., it and the system in use is a TIMSTOF SCP and data was processed in DIA-NN 1.8.2 (?) Even if you ran your first single cells (eek. 8...or 9...? years? ago?) There is probably something to learn or re-learn here. I'm certaily adding it to my lab's Slack channel for literature now!

From peaks to power - scans/peak still really truly matters!

 

If you've been on this blog much recently, I am sorry.

Also, you have probably seen me in some level of outrage about some recent studies where people have gotten anywhere from 1-4 measurements of the peptides they are looking at. Is it better than Illumina ProteinCrap? Absolutely. But is it good for mass spectrometry data? No. 

Why is it bad? Because some blogging academic says so? 

This new preprint looks at the problem in depth and finds that for high abundance proteins in blood, the 1-4 measurements per peak is actually not all that bad. Unfortunately....the cancer biomarker you are looking for is probably not albumin, transferring, or immunoglobulins. For low abundance proteins, getting fewer scans per peak means you miss any changes between healthy and cancer patient blood. So....honestly... what's the fucking point of doing the study anyway? 

They say it nicer than this here! 

Nanopores are coming with 150,000 peptide libraries!

There is some replication is flattery quote, right? I forget what it is.

You might need a free account to read this, though. And the stuff from the article that I found most interesting was a link to another GenomeWeb article. Not sure what the rules are for taking screenshots from it.... But the point is that the Oxford people have taken a page out of the ProteomeTools project and have 150,000 peptides multiplexed labeled that they're currently running through nanopores! Smart, right?

Which seems similar to what this group recently published on here, except they aren't working from synthetic peptides, rather LysC digested proteins. 

Nanosplit the transcriptome and proteome from single cells (without the hard part!)

 

When I first saw this I thought - okay, so someone copied the nanosplits paper but they had an Asstral.

And it's almost what this is - 

...but nanosplits requires a technically tough step where you split the droplet containing your mostly lysed single cells. This protocol gets around that step. They still use the same silly robot to isolate the cells, but you absolutely don't need it here (where you basically do need it for nanosplits, it's tough to print that droplet array in a FACs core), and that's a huge win for anyone who doesn't have the slow silly robot. 

Library biases still remain in proteomics hardware particularly for low input TIMSTOF data!

 

I was first going to start with something like this - 

When I read this title 

But I realized that 

1) That's sorta mean.

2) I bet a lot of people thought that all the work that has been done to adjust spectral libraries and deep learning algorithms has been successful

3) Not everyone is doing loads of weird cell types by single cell proteomics on TIMSTOFs and probably doesn't run into this every single day that their TIMSTOF happens to be working.

4) The giant red light on the whole front of my instrument is bumming me out. 

Here is the thing. The Orbitraps had a HUGE head start on data on public repositories. And in the libraries we used to train deep learning algorithms. And every other data type is just different. Especially when you're going down to low load. Even there, we know the Orbitraps struggle against high load libraries. I should put a link in but I can't find it. 

We absolutely find that having reference libraries in single cells helps a lot. On an Ultra2 we like a 25, 50 or even a 100 (for very small cells) cell pool that we run a couple of times and include that in our data analysis workflows. For big studies I've had luck making the library with those 100 cell pools and then just searching the single cells against those new libraries. Now...you'll probably miss that rare cell type and what makes it special, but you might not care about that in every experiment. 

Anyway - this group has some really smart tips for how to build these libraries and the observations in different software. Ultimately they report a 90%(!!!!!) improvement in low load peptide ID rates, so...that's absolutely worth looking at!

S100P levels are linked to recurrence in cholangiocarcinoma

 

It might be easier to make a list of things S100 proteins don't appear involved in at this point.

This paper is going to be posted here because I'm personally interested in it and I wish my lab had access to these samples. 

The samples were digested with some amount of trypsin. You'll never find out how much, but I bet it is fine. They were also labeled with some kind of TMT reagents. The TMT labeled (and, presumably, pooled) samples were analyzed with a Q Exactive of some kind, probably, despite the Agilent high flow coupled Fusion system in the diagram above. The files are on ProteomeXchange if you cared to look. A secret length and flow rate of a gradient of some length you could extract from the .raw files if you wanted, was used for what was most likely a very reasonable DDA method. They couldn't share the resolution of the MS/MS because that might tip you off to what TMT reagents were used. And if they said they used a 1.4Da isolation window someone would complain about it, as would another group if they used a 0.4 Da isolation window. The authors avoid all that controversy by not sharing any of the steps necessary to repeat this analysis of these same tissues.

That being said - the files are publicly available. It could be one of those things where a core ran the samples and the group never paid them, and the core subsequently couldn't find the hours to contribute meaningful corrections to the paper. Also, the downstream analysis seems compelling and it looks like they really thought about their stats in this little cohort. We can probably assume that the mass spec stuff was done right. We can also assume that the reviewers and editors had a lot on their plates when this one slid through peer review. And that happens, we're all busy.

Taxonomy source identification from proteomics of hair!

 

Are you an investigator who was assigned a bank heist? 

Do you suspect a certain goat, recently out of the pen, with an alibi that seems a little too good to be true? 

If you can find just one hair at the scene of the crime, this is the study and  these are the resources you need! 

Is that goat still baaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaad, or has 20 years on the streets stripped you completely of your idealism about the system and it's ability to reform animals? 

Find out with proteomics!