The ceiling is not going to be reached, but cohort size is correlated.
Also think goal of search. Fragpipe and spectrum centric searches will find PTMs easier. Peptide centric searches are much faster but have more narrow scope
In effect, your FDR has upper ceiling of n files * first pass threshold (e.g., 1%). Without decoy management in second pass MBR you inflate FDR significantly.
A massive limitation of some algs is lack of decoy reporting. This undermines confidence of match and especially in MBR based search, inflates FDR. These aren’t detected at high risk in n=4 studies. But for n=100-100000 there are profound consequences.
Best software is super subjective. Higher count algorithms tend to be less conservative for FDR. Lower count tend to be overly conservative. The folks at MSAID have good thoughts on this as do MSFragger/Fragmatics.
These are also pretty conservative loading masses. Comparison of 4 ng to 4000 ng may understandably be quite different. I’d imagine AGC is being used frequently throughout the study.
This is a nice study. I’d anticipate conclusions are a bit biosample specific with respect to reasonable comparable range. (LoD/LoQ dist related).
An advantage of protein norm is reduction of a variable in case of other systematic, independent variable. Protein norm has its issues too though.
Not to be lost - this is an assessment for discovery experiments to max. biological yield. Targeted will still benefit from added DPPP in many cases
I think the reality is that the model in 2020 paper is an ideal scenario for uniform high SNR peptides. In reality, ideal isn't probable for all acquisition. Greater divergence decreases DPPP : accuracy dependency in model. Low SNR is still very much dependent on DPPP to enable LOQ assessment.
Yep! Can recreate the plot, but added detector characteristic, SNR, and peak asymmetry. Esp. SNR impacts the quant. If I drop SNR from the model it looks basically identical to the 2020 MCP paper.
Check out my US HUPO poster and soon to come pre-print :)
Good chromatography and high SNR peaks will be fine with few DPPP. Low abundance features suffer with few DPPP.
Discord chat room?
I'm excited to share new results at #HUPO2025!
I’ll be presenting our latest work on a next-generation DIA search and FDR pipeline that enables sensitive, accurate and scalable proteomic analysis — in just a fraction of the time required by current algorithms.
📍 Poster PV.01.009 — Monday
To clarify, (not speaking for employer), this is the Seer XT nanoparticle product. Customers like Chiara Guerrera at Necker Proteomics have independently evaluated multiple technologies speaking to this. www.linkedin.com/posts/chiara...
Clinical protocols are very tricky to standardize across sites.
Pre-analytical drivers of bias in bead-enriched plasma proteomics | EMBO Molecular Medicine www.embopress.org/do...
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#proteomics #prot-paper
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.
