Lee Cantrell

Yeah just run a sample every 2 minutes for 5 years with no overhead or down time and a 2M instrument costs 1.5 per sample…

Pretend 15 min with 75% up time and it’s 15/sample. Adjust cost relative to purchase price.

Isomerization? Especially in deamidation prone peptides with NQ. This does split peaks in LC. Eye lens work has seen this for decades. Doesn’t mean deamidation necessarily. Can generate 4plet or more if including modification. Tris in prep?

Or on high mass? I haven’t seen much for >100ng from the 8600.

Not that I’m aware of - though I’d also be nervous about charge capacity on transmission through the tims device. I’d rather see the 7600 or 8600.

It’s unclear that SLIMS has the reproducibility or resolution to achieve this. Marketing figures aside, peptides don’t always make nice Gaussians. Co-resolving charge states likely isn’t an issue.

What I want to see is an honest effort on DIA vs PAMAF with a good DIA TOF instrument.

Rationale seems to be more ions = better data. Astral transmits ~1/200th of ions within a target search space at a time (less the overhead and MS1 times).

A 400ms SLIMS separation could reasonably replace half the selectivity of the quad and 4x throughput of qTOF.

When I check in every month or so, it’s dry. Much drier than bsky

Improving proteomic dynamic range with Multiple Accumulation Precursor Mass Spectrometry (MAP-MS) www.biorxiv.org/cont...

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#proteomics #prot-preprint

For the right biological question, low IDs may even be preferential if they are subcomponent oriented. I'd argue that any method has its biases, both towards abundance and structural subcomponent.

However, the readout really shows that the sample was not rich in the target subpopulation for that method.

Toolset comparisons should really focus on the same plasma preparation protocol. Ideally there would be robust discussion to the tradeoffs of protocol to implemented toolset comparisons.

Implementation of any toolset should be done consistently within a project, but comparing study a to study b with different clinical protocols is a bit challenging.

Plenty of techniques now do well for EV proteins. But a double spun sample will typically be low EV. Readout is bad IDs.

There's huge dependency on the sample and its consistency in preparation for observables... Double spun plasma is very distinct from single-spun. Storage conditions and a myriad of other variables also impact readouts.

Plasma proteomics is ultimately a tool for clinical sciences to implement.

Not fully following. But you’ll have MS data files and search result files depending on parameters. There are wrong ways to search, but not a single right way. Upload ideally includes both data and search files.

A 500 sample study can easily exceed TB data size on most any MS.

Loading 1k astral files searched by Diann in a R interface is relatively painful in my opinion. There’s probably more necessary innovation to search, process and share data from mega cohorts that are now arriving.

Unfortunately, MS data is massive. Many observations in a vector multiplexed by LC. New instruments have been “worse” about this.

OLink is quite smart with data presentation being a neat and contained parquet.

I’m very pro data sharing. I’ve benefited a number of times from it and been limited by it a number of times. Even available upon request or application data may as well be not shared.

Maybe a challenge is upload. I doubt MOMI 46k astral bio samples will be shared. Well over 1 PB…

Disagree. There can be legal or IP reasons to not share. Probably why a lot of expensive OLink data wasn’t shared, before. IP can be mined by anyone once out there.

Also human data sharing issues etc.

I wish at least control files would be shared at minimum to validate functionality, though.

“chemical and physical biology; structural biology track”

Realistically I was housed in biochemistry and chemistry. Vanderbilt fortunately has two dedicated MS courses, one specifically in proteomics. Another class for using SIMION with John McLean too.

In grad school - 2018

Collisional cross-section prediction for multiconformational peptide ions with IM2Deep Peptide collisional cross-section (CCS) prediction is complicated by the tendency of peptide ions to exhibit multiple conformations in the gas phase. This adds further complexity to downstream analysi...

🚀 New preprint alert! We've improved IM2Deep for accurate peptide collisional cross-section (CCS) prediction, even for peptides exhibiting multiple conformations in the gas phase! 🎯

Check it out here:

www.biorxiv.org/content/10.1...

The MMCC data gets to this point. There is cross species reactivity. Not provider-given data, but reasonably matches results in DE experiment so the ratio of reactivity seems reasonable. Just not definite.

I’d be surprised if the instrument is this low of cv on reasonably complex samples on repetitive reinjection.

Agreed it’s likely log2 transform

Plasma proteome variation and its genetic determinants in children and adolescents - Nature Genetics This mass spectrometry-based proteomic study profiles the plasma proteome in 2,147 children and adolescents and reveals its association with age, sex, puberty, body mass index and genetics.

Out today in Nature Genetics: Using MS-based proteomics, we mapped 1,200+ plasma proteins in 2,100+ children, showing how genetics & development shape blood protein levels during childhood.
nature.com/articles/s41588-025-02089-2
#PediatricProteomics #pQTL
First author @liliniu.bsky.social explains ⬇️

Cloud-Enabled Scalable Analysis of Large Proteomics Cohorts Rapid advances in depth and throughput of untargeted mass-spectrometry-based proteomic technologies enable large-scale cohort proteomic and proteogenomic analyses. As such, the data infrastructure and search engines required to process data must also scale. This challenge is amplified in search engines that rely on library-free match between runs (MBR) search, which enable enhanced depth-per-sample and data completeness. However, to date, no MBR-based search could scale to process cohorts of thousands or more individuals. Here, we present a strategy to deploy search engines in a distributed cloud environment without source code modification, thereby enhancing resource scalability and throughput. Additionally, we present an algorithm, Scalable MBR, that replicates the MBR procedure of popular DIA-NN software for scalability to thousands of samples. We demonstrate that Scalable MBR can search thousands of MS raw files in a few hours compared to days required for the original DIA-NN MBR procedure and demonstrate that the results are almost indistinguishable to those of DIA-NN native MBR. We additionally show that empirical spectra generated by Scalable MBR better approximates DIA-NN native MBR compared to semiempirical alternatives such as ID-RT-IM MBR, preserving user choice to use empirical libraries in large cohort analysis. The method has been tested to scale to over 15,000 injections and is available for use in the Proteograph Analysis Suite.

pubs.acs.org/doi/10.1021/...

Exciting to get this paper into press! It's a game changer to be able to search thousands of files from Astral on the order of hours.

Very nice work. We had studied deamidation from Tris on eye lens material a while ago.

Deamidation sure is a challenging PTM to rigorously detect, especially if there is endogenous isomerization.

US Government Places Export Controls on LC-MS Instruments for Top-Down Proteomics www.genomeweb.com/po...

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#proteomics #prot-article

In a chicken:human MMCC in the paper, about 500 assays responded quantitatively to chicken.

Another small subset couldn’t be distinguished between human and chicken.

Interesting observation - has this been published in other PCA papers? Also is there a DIA implementation of this?

Curious about RT training models and the added value of these sample-prep PTM inclusions!

The De-Modifier: a tool for screening modification-induced alternate peptide taxonomy in palaeoproteomics www.biorxiv.org/cont...

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#proteomics #prot-preprint

A Technical Evaluation of Plasma Proteomics Technologies www.biorxiv.org/cont...

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#proteomics #prot-preprint