Protein fingerprints of brain-derived extracellular vesicles predict types of tau pathology

Abstract

Background: Tauopathies are a heterogeneous group of neurodegenerative disorders characterized by the brain-regional aggregation of three-repeat (3R) or four-repeat (4R) tau isoforms. Current fluid and imaging biomarkers rarely discriminate these isoforms, hampering early, pathology‑specific diagnosis.

Objective: To determine whether proteomic fingerprints of brain‑derived extracellular vesicles (BD‑EVs) isolated from the prefrontal cortex can (i) distinguish 3R from 4R tauopathies and (ii) mirror the histopathological burden of phosphorylated tau.

Methods: BD‑EVs were purified from post‑mortem prefrontal cortex interstitial fluid of Pick’s disease (PiD; 3R), progressive supranuclear palsy (PSP; 4R), and control cases (CTRL). Nanoparticle tracking analysis quantified the concentration and size of vesicles. Label‑free LC–MS/MS profiled BD‑EV proteomes, followed by differential expression, gene set enrichment (GSEA), weighted gene co‑expression network analysis (WGCNA), and machine‑learning classification. AT8 immunohistochemistry quantified cortical tau pathology, enabling protein–pathology correlations.

Results: Tau pathology did not alter overall BD‑EV yield but shifted vesicle size distribution in PiD (higher small/large EV ratio). Proteomic analysis identified two discriminant modules: an astrocyte-derived mitochondrial cluster enriched in PiD and a neuron-derived microtubule cluster depleted in PiD relative to PSP and control groups. Combined glial protein abundance (e.g., GFAP, AQP4, S100β, GLAST, ANXA1) classified PiD, PSP, and controls with perfect accuracy (F1 = 1.0). Several BD‑EV proteins—including CAMKV, TMEM30A, NMT1, AK1 (PiD‑specific), and CALB2 (PSP‑specific)—correlated strongly with regional AT8 burden (|ρ| ≥ 0.70, FDR < 0.05).

Conclusions: BD‑EV proteomic fingerprints robustly differentiate 3R and 4R tauopathies and track disease severity, unveiling astrocytic mitochondrial proteins as candidate biomarkers. Overall, our results indicate that BD-EV profiling may complement existing approaches for distinguishing tau isoforms and, pending further validation, could ultimately be adapted for use in more accessible biofluids.

Supplementary Information: The online version contains supplementary material available at 10.1186/s13195-025-01865-w.

Keywords: BD-EV; Brain secretome; Glia, prefrontal cortex; Tau isoform; Tauopathy.

Fig. 1

Consequences of tau pathology on concentration, size and quality of BD-EVs. (A) Illustration of the procedure to investigate how BD-EVs may reflect brain tau pathology. Highlighted is the MISEV annotation of proteins in BD-EV samples as EV-associated or non-EV-associated. (B) Violin plot depicting the concentration of BD-EVs detected by NTA by patient group. (C) Area graph depicting the size and number of EVs in each patient group. Particle concentration has been normalized by tissue weight prior to EV extraction. (D) Stacked bar graph showing the ratio of small (< 150 nm) to large (> 150 nm) EVs by patient group. (E) Venn diagram showing the number of proteins present after threshold filtration in the BD-EVs of CTRL, PiD and PSP patients. (F) Bar graph quantifying the abundance of protein associated with EVs vs. potentially contaminating proteins of EV isolation according to MISEV2023 guideline categories. (G) Performance matrix depicting the predictive capability of a classification model based on BD-EV quality control features

Fig. 2

Differential proteomic signatures of BD-EVs in PiD and PSP compared to controls. (A) Schematic overview of the experimental workflow: BD-EVs were isolated from post-mortem prefrontal cortex samples, analyzed by mass spectrometry, and subjected to differential expression and pathway enrichment analysis. (B) Venn diagrams showing the number of shared and unique BD-EV proteins detected in control (CTRL; n = 4) versus Pick’s disease (PiD; n = 5; left) and PSP (n = 10; right) cases. (C–D) Volcano plots depicting differentially abundant BD-EV proteins in PiD (C) and PSP (D) relative to controls. Each dot represents a protein; red indicates significantly upregulated, blue downregulated (FDR < 0.05, fold change > 1 or < 1). Selected proteins of interest are annotated. (E–F) Gene set enrichment analysis (GSEA) of BD-EV proteins differentially abundant in PiD (E) and PSP (F). Dot plots summarize significantly enriched biological processes (Reactome, GO, KEGG, Wikipathways), with dot size representing gene set size, color representing Average normalized enrichment score (Average NES), and fill (transparency) indicating significance level

Fig. 3

Distinct protein signatures of BD-EVs during 3R and 4R tauopathies. (A) Illustration of the procedure to investigate how BD-EVs may reflect brain tau pathology. Highlighted is the Weighted Gene Co-expression Network Analysis (WGCNA) of the BD-EV proteomic dataset. (B) Weighted gene co-expression network analysis emphasizing four protein modules which account for the majority of variance between patient groups: Endoplasmic Reticulum, Mitochondrion, Microtubule, and Trivalent Inorganic Cation Transport. (C) Violin plots of the cumulative abundance of the proteins contained within each of the four highlighted modules, in the form of eigenegene values (y axis) calculated in WGCNA. (D) Performance matrix depicting the predictive capability of a classification model based on abundance of WGCNA module proteins. (E) Barplots representing brain cell type enrichment for corresponding WGCNA modules. We used -log10(p value) for FET values. Each barplot has a threshold with a single line for significant p value. (F) Protein-Protein Interaction Network of prospective biomarker proteins. Node colors are represents brain cell specificity: Blue: Neuron, Red: Astrocyte, Orange: Oligodendrocyte, Yellow: Microglia, Lightgrey: Unspecified; Node border color are representing the associated WGCNA module: Black: Microtubule module, Turquoise: Mitochondria Module, Pink: Cation Transport Module, Green: Endoplasmic Reticulum. Hexagonal node: Protein Complex, Rectangular Node: Phenotype. Edge Color represents type of regulation: Blue: Positive regulation, Red: Negative Regulation. Edge type represents mechanism: Open Circle: Phosphorylation and Dephosphorylation, Closed Square: Binding, Dashed line: Undirect interaction

Fig. 4

Neuronal and glial composition of BD-EV during 3R and 4R tauopathies. (A) Illustration of the procedure to investigate how BD-EVs may reflect brain tau pathology. Highlighted is the Human Protein Atlas annotation of the BD-EV proteomic dataset according to brain cell type specificity. (B) Bar graph showing the percentage of brain-specific proteins which can be categorized as neuronal, astrocytic, oligodendrocytic, or microglial per patient group. (C) Bar graph depicting the overall relative abundance of neuron-specific and glia-specific material by patient group. (D) Performance matrix depicting the performance of a classification model based on abundance of glial and neuronal proteins. (E-N) Violin plots depicting the relative abundance of five neuronal (E-I) and astrocytic (J-N) proteins across patient groups. (O) Performance matrix depicting the predictive capability of a classification model based on abundance of specific neuronal and astrocytic proteins depicted in Fig. 3E-N

Fig. 5

BD-EV protein signatures reflect tau pathology severity in the prefrontal cortex. (A) Experimental workflow: BD-EVs were isolated from post-mortem prefrontal cortex, analyzed by mass spectrometry, and correlated with AT8 tau pathology quantified by automated image analysis. (B) Representative AT8 immunostaining images from control (CTRL), Pick’s disease (PiD, 3R), and PSP (4R) cases. (C) Quantification of AT8 inclusions (per mm²) by diagnostic group. Each dot = one patient; bars: mean ± SEM. (D–G) For each comparison (D: all diagnostic groups; E: CTRL + PSP; F: CTRL + PiD; G: PiD + PSP), the left panel shows the top 10 BD-EV proteins most significantly correlated with AT8 histological burden, based on Spearman correlation. The tables indicate the correlation coefficient (R), p-value, number of subjects (N), and direction (positive or negative). The right panel in each case displays a scatter plot showing the relationship between a composite protein score (C-score) and AT8 pathology burden per subject. These composite scores reveal both common and disease-specific BD-EV proteomic signatures associated with tau accumulation in the brain