Friday, August 7, 2026

Bruker is boosting customer support and hiring.



Today I had the privilege of meeting with some fancy people from all over the place to discuss some concerns I have voiced and/or amplified about Bruker Daltronics support system. My concerns largely centered on what looked like a model I've seen before where an LCMS business is the cash cow (wtf is a cash cow?) for a big company to buy a lot of dumb shit. Who cares if a company uses their profits to buy other little companies? Not me, as long as that doesn't mean you lose all your field applications scientists or field service engineers for the stuff that you've already sold. 

It is possible that I over-reacted. It would absolutely be the first time that has ever happened, however. As anyone who has ever worked with me can attest to 😇. But it's also possible that the mass spectrometry at the company isn't in charge of the whole thing and that sometimes there are corporate level shakeups where you have to do what you are told and deal with the fall-out. 

Shortly after my ASMS possible over-reaction we had one of their top engineers on site followed by the amazing Dr. Josh Beri that most of us know who made sure our Ultra2 was back to pumping out world class data. To be fair I should have probably updated a really negative post yet again and I'll link it back to this one. 

More importantly, I have independently verified that what I heard from said nice fancy people that they are actively building up the LCMS support network. At least one person I got used to emailing when we were stuck will be back in a couple of weeks and there are a pile of open field service jobs at least around the US. I've screenshotted some of those above. Which, given, the fact the economy in the US is rooooouuugh right now, sounds both very positive and hard to do. As an aside, the actual Bruker career page is a silly mess. Support jobs are distributed between the categories of Technical support and sales/support and you have to actually look at "ALL" to find the open applications jobs by manually going through. Interestingly it maxes out at 3 pages. So you might have to go through several iterations to find that there is a job posting on their website. I've seen worse. 

I'm not sure I've seen any openings for field applications scientists while digging around, but there was a specific TIMSOmni Apps job on Linkedin that appears to be closed and possibly filled so I'm optimistic.

New people I've met from Bruker now have been legit as well. There are some serious key hires who are accomplished mass spectrometrists who are currently adjusting to their roles and willing to get on planes to talk to customers even if they're weird bloggers in small cities who were kindof a lot mean to them right when they started their jobs. 

Summary - science business stuff is hard. My TIMSTOF is back up and generating really good data and I am a lot more confident and happy with my customer experience right now than I was a couple of months ago. 

If you are wondering if there are people standing in my office making me type thiiiiiiiii HELP sdlkjdsfkjldasfljk980909098890098fjljdsflkjdsflksfjkfdaslkas;dlkjsdf lkhjasdfljksdf 

That's a joke. I wrote this because I felt like it even though it was very very boring to do. 

Thursday, August 6, 2026

Defining quality standards for single cell proteomics OR Astral vs TIMSTOF Ultra2

 


Okay. So what if you sent the same single cells out to a whole bunch of labs that agreed to send the data back?

Obviously no one would want to play unless they had the very best stuff, right? Or maybe you had to have the stuff to play? I don't know but this is super cool. 


Now....I am a little confused about the HPLC setup. It looks like they used a bunch of instruments but there is exactly one NanoLC method in the paper. I know people who like the Neo enough that they have their LC on one computer and their MS on another. So maybe that's what happened here? 

It's a bunch of labs so you do get to see lab to lab variability on the same instruments as well. 

Wednesday, August 5, 2026

NanoLC dead volume calculator since I can't find the MSBioworks one!

 

nanoLC System Volume — Flow Path Calculator
This is a nanoLC volume calculator inspired by the AMAZING (but dead?) MSBioWorks phone application I miss a lot and made with the frustrating assistance of some AI thing my university lets me use. It's honestly pretty good at color palettes, even if it's bad at math and confident/arrogant about it.

nanoLC System Volume

To use this, I've assumed some things about your flow path. You'll need to select how many columns and tubing sections. Let me know if I'm not considering modern nanoLC options, yo. It'll autofill some normalish stuff, so you'll need to fill in the inner diameter and length for each. It sums everything into total system volume live.

00 Set Up Your Flow Path

Sections 02 and 03 below build themselves to match — enter the inner diameter and length for each capillary as it appears.

Line weight and column shading scale with each segment's volume — thicker lines and denser fill hold more mobile phase.You'll have to believe me because I can't figure out why these fonts are OUT OF CONTROL
Total system volume
0.0 nL
01 Injection Loop 0.0 nL
02 Tubing Sections 0.0 nL total
03 Columns 0.0 nL total
04 Additional Dead Volume 0.0 nL total
Breakdown
Internal Dead Volume Injection loop + pre-valve tubing
0.0 nL
0.0% of total
Operational Dead Volume (Probably the one you care about) Valve → columns → MS inlet
0.0 nL
0.0% of total
ComponentZoneVolumeShare%
How this is calculated (shoutout to Cheryl Reger for somehow being a legit geometry and algebra2 teacher in one of the single worst school districts in the United States) It's all middle school geometry, yo!
Cylindrical volume — tubing, loop, and columns are treated as cylinders:
V = Ï€ × (ID / 2)² × L
Column void volume — the mobile-phase-accessible fraction of a packed bed:
Vvoid = Vgeometric × Îµt   (εt ≈ 0.6–0.7 for fully porous particles)
Total system volume — sum of every contributor along the flow path:
Vsystem = Vloop + ΣVtubing + ΣVcolumn + ΣVfittings
This is the volume relevant to gradient dwell / delay volume and extra-column band broadening — figures depend heavily on real fitting and frit geometry, so treat this as an estimate to refine against a manufacturer's spec sheet. Also...there isn't really a "Zero Dead Volume" union. There has to be a little gap.

Monday, August 3, 2026

AI in proteomics - from protein identification to virtual cells!

 


Man, I need to start being more collaborative so I can get on these gigantic papers that are going to get a billion lazy citations (that's where you use the title and abstract to make a citation without ever reading the paper).

This new one is definitely going to get cited like crazy and since there are people out there who judge you by how many citations you have, each of these people are going to get supercharged CVs! 


You don't have to read it, you can just guess what it's about and when you need a citation number 68 for deep learning you can just pop this one in. 

There are SO MANY obvious uses of AI out there! Check out this month's cover of the Journal of Proteome Research (real, not edited) - it's killing me. 

Wait. So....WHAT IS IN THE "S-TRAP?" It's a lava lamp shaped like a 1980s Bosch automobile fuel filter! AND the EvoTip loading is a syringe that drips Play-Doh(TM) onto the top of an oRsBiTraP? 

I posted this on the socials somewhere else, but it's similar to this used TIMMYTOOFY SCeePea system from Booker Caltronics. 

Objectively, $350k is pretty legit, but you do have to wonder why the decision was made to like not...take a picture of the system.... 



Sunday, August 2, 2026

Proteome Discoverer 3.3 has a DIA-NN (commercial license required) node!

 


I was trying to help someone out who, due to the fact the US government is working great, has instruments but can't pay their annual license fee to use the DIA cloud nodes. I was like - how hard could it be to link PD through the node maker things to DIA-NN and then integrate the whole thing.

Up until the .parquet thing appears it looked pretty straight-forward if you're smart, but I'm not and it all looked like it was going to work, but wasn't so I decided to blame it on 1) whatever I was actually supposed to be doing 2) the parquet thing and 3) and the fact that after a certain version, DIA-NN isn't entirely open access. It's free but there are permissions to consider. Yeah, that's totally what it was. One or all of those. 

Turns out there is already a DIA-NN node released in 2026. That was last year? Nope! This year! It's still 2026!

You can watch a webinar about it here by the amazing Michaela Scigelova and that DIA-NN guy. 

Now...obviously...it only accepts .raw files. But why would you run anything else in Thermo's solution for Protein Informatics? Weirdo. 

Tuesday, July 28, 2026

O-Link identifies ALS phenoconversion markers!

 


There is a LOT to be excited about in this new study. 

This statement at the end of it, is....maybe not one of them.... but they specifically limited access to these data in the IRB documentation, so even if you wonder what personal information could possibly be contained within a targeted antibody panel, it's against the rules to make this publicly available. 


However - here we are with what might be the first win in ALS diagnostics with a proteomics technology in....ever....? 


Surprisingly, it's actually a small cohort. It's less than 70 individuals? But with a pile of blood / plasma draws so it works out to ~550 samples. So....also evidence that O-Link Explore targeted panels can provide statistically valid data without an n>1000? 

The end point isn't a new ALS diagnostic marker. It is, however, a panel of proteins that may allow earlier diagnosis. And maybe if it is caught earlier then there are ways to treat it and prolong lifespan and (as/more imporant) healthspan in this fucked up awful disease. 

Monday, July 27, 2026

Proteomics takes on the brain decay paradox! The what?!?

 


So....ummm...this is a thing....


...a gross thing....

I know a lot of people who worry about stuff like "post-mortem degradation" which is where you might not want to do proteomics on someone who has been dead and puppeted around for a month in the hot sun. 


However - and this is where it gets weird - sometimes the most fragile and easy to homogenize part of the body (the brain) ends up around IN ANCIENT BODIES. What? Gross? YES! 

I'm not sure I'm going to be able to eat after this. Maybe I'll stop. 

Meh. According to these people over 4,000 ANCIENT BRAINS have been recovered from the past. Some as old as 12,000 years??? Which sounds impossible. My postdoc advisor would time me getting brains out of mice because she was sure we wouldn't care about the proteins in the cells in the subventricular thingamabob if we didn't do it fast. 

It turns out that if you have the right conditions the brain doesn't just break down. Why? When? Proteomics and a very icky experimental design to the rescue! 

I can't spend a lot more time on this, but you should check it out. There is some DDA proteomics and some DIA and two different instruments AND there are proteins that last a LONG TIME and these authors work out the biochemical reasons for it! Super cool stuff. 


Saturday, July 18, 2026

Air conditioner!

 


Edit: (Had to make a QR code for an emotional 5 year old at 6 AM. I can post anything here in like 10 seconds). 

Thursday, July 16, 2026

Preprinted data featured at SCP2026 day 2!


Whoa. Day 1 kicked off with "you should get another espresso, Ben" this is some heavy stuff. Preprinted here, though! 


For you mass spec nerds the method is worth thinking about. What if you spiked each single cell with the same SILAC heavy bulk digest? It looks like it works really well, but what he cares about is intrinsic vs extrinsic noise in -omics datasets. 

Damn, what a cool work. They also do metabolic labeling and then single cell proteomics. And I honestly think the preprint contains every detail necessary to reproduce this method and analysis. I wouldn't even know where to start for processing SILAC diaPASEF data in DIA-NN, but - boom - they're all there! 

Oh. 

Ummmmm.......okay....so...this ISN'T preprinted yet, but I didn't beat 3 other people to basically the same question. So...maybe just watch out for an upcoming Parallel Squared preprint from Dr. Megan Elcheikhali where they succeeded in something that an extremely skilled former postdoc in my lab (and many other great scientists in other labs) have failed to do. I'm dying to know what the trick is. (Likely very very many tricks.) 

This happens to me all the time: 

Someone kicks in my office door and yells  "HEY NERD! I'm here to do single cell proteomics! I've got a bunch of brains or livers in this Yeti cooler in the trunk of my car!" And I have to say "Wait. What? Where do you get all these organs? And why did you freeze the whole thing?? If you froze it, the cell membranes definitely ruptured, so you can't sort them. And if they can't be sorted with an intact membrane, it doesn't work. 

So....if what she said is real then....about 9 out of ever 10 collaboration requests I turn down might actually be ....viable.... so.... this was a ridiculously important talk. 


Man, I really thought I was going to bug out and work on a proposal I really think is going to define the rest of my career, but - damn this panel talk was lit. 

Topics covered -

-Most biologists still don't know single cell proteomics is a thing. But the point was brought up that most biologists don't really know that proteomics is a thing. Not in the way we currently do it. 

-Slavov shared some tips for how he's finding all these amazing collaborations. Cutting out as much useless jargon as possible and engaging everyone he can in other fields. The 100 Nature family papers probably doesn't hurt. 

-Definite concerns about costs discussed by everyone. No real solution yet. 1,000 single cells from some weird cell line is still going to be expensive. 

I had to do some ...work...ugh...and I only half paid attention to the next couple of talks. 

This seems like the most recent paper out of one of them. Though you'll find these authors are prolific right now. 


I had an alarm set for Ronnie Cutler's talk and it didn't disappoint. I'm not sure what I can share from that one yet. Maybe, what if you really did look at integrating single cell transcriptomics - not the read count data, but the other data you get from it. At a single cell level. With PTMs you can target at those same levels, maybe you can find a depressing (for old people like me) story in it? 

It's after 3am when I'm posting whatever I typed in this orange box, because I did get to doing the work ...ugh... stuff I needed to. I did finally get to see a talk on how the AIP works from a guy with an amazing last name. If he was at Thermo, new people would infer that he was critically involved in the development of their long running series of flagships. And Bernard Delanghe found out that no one there wanted to look at mass spectra. I feel like there was another preprint I had in hand, but I forget. 

It was yet another amazing meeting in a long string of them. 

Wednesday, July 15, 2026

Preprinted data featured in Day 1 at SCP2026!

 


I've got to start a new page of notes for day 2 or OneNote will definitely crash. Obviously there is a lot of unpublished data here I don't know if I can share or not, but some of this is out or preprinted. 

If you very very carefully controlled pH and reaction kinetics could you label all of one side of a peptide with TMT and all of the other side with dimethyl labels? Apparently, yes.

Nikolai beat me to the question of "wait. better than 95% labeling efficiency ON BOTH TERMINI?"

The only link I can find to this publicly so far is a dissertation that is public (and really really well written) you can find here.


Now...the stochastic sampling appears to be a major issue because you have 3-plexes of each TMT tag. So you drop to a very low overlap from cell to cell. I'm personally about 90% sure that it's due to MIPs. That's a lot of complexity that is far outside of the model of averagine within a very tight m/z window, but it does still allow for separating very different cell types apart. They have tried semi-targeting and other intelligent acquisitions, but - this ain't a terrible start for huge throughput potential. Especially if they've worked out the double labeling kinetics so you and I don't have to. 


And there are always people at this meeting doing crazy stuff out there you never heard of, right? Like, where does Parallel Squared find these people? This was the first one of the meeting and it was preprinted a few months ago. 



Crazy and amazingly ambitious. So...what if you coupled expansion microscopy (where you put the diaper gel stuff on your tissue and stretch it out so you can image it? But you did that to de-crowd the spatial peptide domain enough that you could essentially do Edman single molecule degradation in a spatial context? You might immediately have objections about depth, complexity, and orders of magnitude or 6 that would be tough to reach, but - damn....I would have never ever thought of this, nor have I ever heard anything even remotely similar to this idea. Really really cool. 

One more. I don't understand this one, but it's another off-the-wall technology and I really liked the speaker and his animations for BALLISTIC MICROSCOPY!



MORE EMERIL! 


Day 2 is starting now! Let's go!