bioRxivNeurosciencePreprint
Novel native serum peptidomics workflow enables the discovery of circulating subtype-specific peptide biomarkers in acute ischemic and haemorrhagic stroke
Sachin KoteiD, Jakub FaktoriD, Marc MülleriD, Artur PirógiD, Paulina CzaplewskaiD, Bartosz KaraszewskiiD, Ted Hupp, Natalia-Marek Trzonkowska
Abstract
Novel serum peptidomics offers a direct insight into proteolytic activity, tissue injury, and systemic signaling. Nevertheless, existing workflows suffer from low peptide yields, low throughput, and limited recovery of low-abundance species. Here we present a native serum peptidomics protocol that integrates mild acid treatment, solid-phase extraction with molecular weight cutoff filtration and data-independent acquisition mass spectrometry (DIA-MS). The protocol requires less than 100 {micro}l of serum or plasma, is completed within hours, time-cost-effective and compatible with 96-well formats without specialized equipment. Applied to a proof-of-concept cohort of patients with acute ischemic stroke (AIS), intracranial haemorrhage (ICH), and healthy controls, the workflow identified over 12,000 peptides, exceeding the three-fold threshold of existing peptidomics approaches. DIA-MS analysis across independent batches demonstrated 78-83% peptide overlap and consistent fold-change directionality. We further introduce peptide locus analysis, which aggregates overlapping peptides within defined protein regions. This approach revealed bidirectional regulation within individual precursor proteins such as the fibrinogen alpha chain (FIBA), resolving intraprotein proteolytic dynamics. Three candidate peptides from TYB4, CO4B, and ITIH4 proteins accurately distinguished stroke subtypes and controls, while characteristic shifts in peptide physicochemical properties were observed across strokes. This workflow substantially advances the sensitivity, throughput, and biological resolution of serum peptidomics for quantitative multi-biomarker discovery, validation and its output promises effective implementation of AI/ML models aiming for new dimensions in diagnostics, prognostics, prediction and monitoring.
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