bioRxiv Science⌕ Search

Biology subjects

Brennsteiner, V.

Publications and source records attributed to Brennsteiner, V..

2 recordsLinked to original sources

A simplified perchloric acid workflow with neutralization (PCA-N) for democratizing deep plasma proteomics at population scale

Large scale plasma proteomics studies offer tremendous potential for biomarker discovery but face significant challenges in balancing analytical depth, throughput and cost-effectiveness. We present an optimized perchloric acid-based workflow with neutralization - PCA-N - that addresses these limitations. By introducing a neutralization step following protein precipitation, PCA-N enables direct enzymatic digestion without additional purification steps, reducing sample volume requirements to only 5 {micro}L of plasma while maintaining deep plasma proteome coverage. The streamlined protocol allows preparation of over 10,000 samples per day using 384-well formats at costs comparable to undepleted plasma analysis (NEAT). Rigorous validation according to the recently introduced CLSI C64 guideline demonstrated that despite somewhat higher technical variability compared to NEAT, PCA-N maintained excellent biological resolution and reproducibility. We confirmed the workflows exceptional stability through analysis of 1,500 quality control samples systematically interspersed among 36,000 plasma samples measured continuously over 311 days. Technical performance remained consistent across multiple instruments, sample preparation batches and nearly a year of measurements. Compared to NEAT plasma proteomics, PCA-N doubled the proteomic depth while maintaining comparable reagent costs and throughput. The minimal sample reequipments, operational simplicity while using only common laboratory chemical and exceptional scalability positions PCA-N as an attractive approach for population-level plasma proteomics, democratizing access to deep plasma proteome analysis. HighlightsO_LIPerchloric acid with neutralization (PCA-N) allows drastic upscaling of plasma proteomics C_LIO_LIMinimal sample input (5 {micro}L) in 384-well format enables thousands of samples processed daily C_LIO_LIPCA-N democratizes deep plasma proteomics using only common chemicals C_LIO_LIValidation per CLSI C64 demonstrates excellent repeatability and reproducibility C_LIO_LIPCA-N showed exceptional stability across 311 days of continuous MS acquisition C_LI Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=106 HEIGHT=200 SRC="FIGDIR/small/645089v1_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@11eb315org.highwire.dtl.DTLVardef@7f4f41org.highwire.dtl.DTLVardef@2c41feorg.highwire.dtl.DTLVardef@1a09925_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

AlphaDIA enables End-to-End Transfer Learning for Feature-Free Proteomics

Mass spectrometry (MS)-based proteomics continues to evolve rapidly, opening more and more application areas. The scale of data generated on novel instrumentation and acquisition strategies pose a challenge to bioinformatic analysis. Search engines need to make optimal use of the data for biological discoveries while remaining statistically rigorous, transparent and performant. Here we present alphaDIA, a modular open-source search framework for data independent acquisition (DIA) proteomics. We developed a feature-free identification algorithm particularly suited for detecting patterns in data produced by sensitive time-of-flight instruments. It naturally adapts to novel, more eTicient scan modes that are not yet accessible to previous algorithms. Rigorous benchmarking demonstrates competitive identification and quantification performance. While supporting empirical spectral libraries, we propose a new search strategy named end-to-end transfer learning using fully predicted libraries. This entails continuously optimizing a deep neural network for predicting machine and experiment specific properties, enabling the generic DIA analysis of any post-translational modification (PTM). AlphaDIA provides a high performance and accessible framework running locally or in the cloud, opening DIA analysis to the community.

bioinformatics↗