Poster Presentation 31st Annual Lorne Proteomics Symposium 2026

Technical nuances of plasma proteomic workflows in clinical and preclinical contexts  (#34)

Samantha Emery-Corbin 1 , Joel R Steele 1 , Iresha Hanchapola 1 , Komagal K Sivaraman 1 , Han Lee 1 , Dylan H Multari 1 , Scott A Blundell 1 , Erwin Tanuwidjaya 1 2 , Pouya Faridi 1 2 3 , Terry O'Brien 4 5 6 , Idrish Ali 6 , Ralf B Schittenhelm 1
  1. Monash Proteomics and Metabolomics Platform, Monash University, Melbourne
  2. Centre for Cancer Research, Hudson Institute of Medical Research, Clayton, Victoria, Australia
  3. Department of Medicine, School of Clinical Science, Monash University, Clayton, Victoria, Australia
  4. Department of Neurology, The Alfred Hospital, Melbourne, Victoria, Australia
  5. Department of Neurology, The Royal Melbourne Hospital, Melbourne, Victoria, Australia
  6. Department of Neuroscience, School of Translational Medicine, Monash University, Clayton, Victoria, Australia

Plasma proteomics underpins much of today’s biomarker discovery pipeline, yet biological variation and extreme dynamic range continue to challenge depth, reproducibility, and throughput. Recent advances in mass-spectrometry (MS) instrumentation have enabled deeper proteome coverage, but systematic comparisons across biofluids, species, and modern sample-preparation strategies remain limited.

Here, we benchmarked eight sample-preparation workflows spanning neat approaches (SP3, STrap), depletion (perchloric acid, PerCA), corona-enrichment (Enrich-iST, ProteoNano) and extracellular vesicle (EV)-enrichment (MagNet HILIC/SAX). We have also analysed performance across human plasma, human serum and rat plasma, evaluating if methods are biofluid- and species-agnostic. All samples were analysed on Orbitrap Astral (Thermo) using two plasma-optimised data-independent (DIA) methods: one discovery-maximised and one throughput-maximised. We identified 2,726 human and 3,767 rat proteins across workflows and methods, including ~1,000 from neat plasma. As expected, the higher-throughput method reduced proteome depth by ~20–30%, but with workflow- and species-specific effects. Enrichment, via corona-formation or EV-enrichment, strongly shifted the detectable proteome, and redistributed the dynamic range. Resultant subproteomes of circulating EVs achieved excellent depth, but revealed a distinct sub-proteome that was not accessible by neat, depleted, or secreted-protein-enriched approaches. Several workflows performed substantially better in rat plasma, highlighting the need to consider species-specific sensitivity in preclinical studies.

Importantly, each workflow produced a unique distribution of tissue-, organ-, and immune-associated proteins. Depletion and enrichment strongly shifted the detectable proteome, emphasising that workflow choice determines not only depth but biological bias. More than 90% of detected proteins were differentially abundant across workflows, with corona-enrichment revealing the largest methodological effects. Collectively, these results map a rapidly expanding plasma-proteomics landscape and demonstrate that no single workflow is universally optimal.