EPCO-03. COMPARATIVE ANALYSIS OF CSF PROTEOMIC TECHNIQUES FOR BRAIN TUMOR RESEARCH

Abstract INTRODUCTION Cerebrospinal fluid (CSF) proteomics offers transformative potential for brain tumor management, yet low sample volumes and diverse enrichment techniques complicate its application. A systematic comparison of proteomic methods is essential to optimize peptide yield, reproducibility, and cost-effectiveness. This study evaluates multiple analytical approaches to guide their use in CSF proteomics, aiming to enhance biomarker discovery and clinical decision-making for brain tumor patients. METHODS Retrospective CSF samples from 39 patients with CNS lymphoma were analyzed. Extracellular vesicle (EV) isolation was tested across 1.5-9 mL volumes, with reproducibility assessed using 6 mL pooled replicates. Five methods were compared for peptide profiles: MStern (50 µL), Seer (250 µL), N-glycoproteomics (N-Gp, 200 µL), P150, and P20 (both EV-based, 6 mL). Evaluations included peptide counts, reproducibility, biophysical characteristics, cellular compartment enrichment, and pathway profiles, quantified via mass spectrometry. RESULTS EV isolation required a minimum of 6 mL. P20 surpassed P150 in protein yield and particle density. Seer led with 17,000 unique peptides and top reproducibility, followed by P20 (9,000), MStern (5,500), P150 (5,000), and N-Gp (1,000). N-Gp favored acidic glycopeptides and larger hydrophilic peptides. Enrichment patterns in terms of cellular location differed: P20 (mitochondrial), N-Gp (lysosomal), Seer (nuclear). Pathway analyses revealed complementary functional insights across methods, with no single approach capturing all molecular signatures. CONCLUSIONS CSF proteomic methods are not one-size-fits-all, each offering unique strengths. Seer excels in peptide yield but is costly. CSF EV analytic volumes can be pushed as low as 6 mL. MStern, requiring only 50 µL, balances yield, cost, and scalability, suiting high-throughput studies. These results inform tailored method selection for brain tumor proteomics research.