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Abstract Background Due to its proximity to cells of the central nervous system, cerebrospinal fluid (CSF) is an important source of novel biomarkers for neurological diseases. Membrane-bound extracellular vesicles (EVs) are enriched for proteins of intracellular and membrane origin, implicated in the pathogenesis of some neurological diseases, and secreted into CSF. Proteomic profiling of CSF-EVs, however, is limited by the large volumes required for typical EV isolation protocols. Methods We appraised the performance of tetraspanin (CD81, CD63, CD9)-based immunocapture for EV isolation from 200 to 1000 µL CSF sample and compared to size-exclusion chromatography (SEC). EVs were profiled by library-free data independent-acquisition (DIA) mass spectrometry to assess protein depth and abundance of specific EV markers and known co-isolates. Abundance and precursor peptide locations for potential neuronal-specific immunocapture targets described in the literature were also assessed. Results Immunocapture was effective using CSF volumes as low as 200 µL, consistently detecting core EV markers and reducing relative levels of non-vesicular proteins such as Apolipoprotein B (APOB) and galectin 3 binding protein (LGALS3BP) compared with size-exclusion chromatography (SEC). Proteomic depth reached 811 ± 14 protein groups in EVs from 200 µL CSF, increasing to 1285 ± 224 when using feature alignment across runs with up to 1000 µL starting volume. These included eleven candidate biomarkers of neurological diseases that were detected in all preparation methods, with additional candidates detected by immunocapture only. Increased depth was observed for both transmembrane and secreted proteins using immunocapture compared with SEC, with proportional enrichment of transmembrane proteins. Conclusions This work demonstrates the effectiveness of tetraspanin immunocapture for proteomic profiling of EVs in small volumes of CSF that can be adapted to use with cell-type-specific markers of choice.

More information Original publication

DOI

10.1186/s12014-025-09579-9

Type

Journal article

Publisher

Springer Science and Business Media LLC

Publication Date

2026-12-01T00:00:00+00:00

Volume

23