Lee Cantrell

@leecantrell.bsky.social

Biological Mass Spectrometry Proteomics at Scale Scientist at Seer Opinions independent of employer

DIA proteomics needs more than powerful search — it needs transparent, reproducible, and scalable software. I’m excited to share that Radiant DIA™ and the Fulcrum Scalable Pipeline™ are now released with publicly available source code.

I'm sure @leecantrell.bsky.social will do a real tweetorial, but my favorite: "Reduced peak completeness minimally affected detectability (LOD) but substantially degraded quantifiability (LOQ), indicating that identification-centric readouts can remain stable while quantitative utility declines."

bioRxiv Bioinfo@biorxiv-bioinfo.bsky.social · 3mo ago

From Peaks to Power: Systematic Evaluation of Chromatographic Sampling Reveals Determinants of Quantification and Biological Discovery in DIA Proteomics https://www.biorxiv.org/content/10.64898/2026.05.13.724964v1

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

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.

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