Steric hindrance-mediated extracellular vesicle size fractionation for rapid prehospital diagnosis of intracerebral hemorrhage

Domain: neuroscience
Published: 2026-04-09
Editorial outcome: major_revision

Abstract
Diagnosing intracerebral hemorrhage (ICH) in prehospital settings remains challenging due to unavailability of immediate neuroimaging, clinical overlap with ischemic stroke, and absence of validated circulating biomarkers for time-critical settings. Extracellular vesicles (EVs), subcellular structures capable of transporting biomolecular payloads (e.g., proteins, nucleic acids) across the blood-brain barrier, have emerged as compelling diagnostic candidates for ICH. Nevertheless, their clinical translation has been impeded by inherent biophysical heterogeneity, particularly polydisperse size distributions. To address this limitation, we engineer a steric hindrance-mediated EV analysis and size fractionation (SHEAF) platform, integrating steric hindrance-based size fractionation with membrane protein profiling to stratify EVs into three size subtypes within a 45-min workflow. Systematic evaluation using the SHEAF platform reveals that the 90 - 180 nm EV subtype exhibits superior discriminative capacity in differentiating ICH from ischemic stroke plasma specimens. This technology not only advances rapid prehospital ICH diagnostics but also establishes a method for elucidating size-dependent EV functionalities across neurological pathologies.

Introduction
Intracerebral hemorrhage (ICH) and ischemic stroke (IS), the two primary stroke subtypes, differ fundamentally in pathophysiology and clinical management. ICH involves acute bleeding into the brain parenchyma, often leading to neurological deterioration due to mass effect and elevated intracranial pressure; management focuses on hemorrhage control, intensive neurocritical care, and potential surgical evacuation1,2. In contrast, IS results from cerebral artery occlusion, causing rapid neuronal death from ischemia, with treatment centered on timely reperfusion via intravenous thrombolysis or mechanical thrombectomy3,4. Critically, these therapeutic approaches are mutually exclusive: thrombolytic agents, essential in IS, are strictly contraindicated in ICH due to the risk of exacerbating hemorrhage. Moreover, each minute of untreated ischemia leads to the loss of approximately 1.9 million neurons5, while delays in ICH intervention worsen mass effect and outcomes6. Therefore, rapid and accurate differentiation between ICH and IS from symptom onset is crucial. Although neuroimaging modalities such as computed tomography (CT) and magnetic resonance imaging (MRI) serve as the diagnostic gold standard7-9, their limited availability to hospital settings hinders prehospital diagnosis and delays subtype identification. This diagnostic gap underscores the urgent need for biomarker-based diagnostic strategies. However, many candidate biomarkers, such as glial fibrillary acidic protein10,11, may lack sufficient specificity and accuracy alone, highlighting the necessity of identifying more reliable molecular markers or panels to improve diagnostic precision.

Extracellular vesicles (EVs) are bi-layered nanoparticles that encapsulate diverse molecular cargo—including nucleic acids, proteins, and saccharides. The surrounding lipid bilayer shields this cargo from degradation, which better preserves biomarker integrity compared to free-floating molecules12. This protective feature makes EVs highly sensitive and reliable circulating biomarkers for various diseases, particularly in early stages13-16. Furthermore, EVs can cross the blood-brain barrier via transcytosis, enabling non-invasive detection of brain-derived pathological signals in peripheral blood17,18. This capability is especially relevant in conditions like ICH, where elevated levels of EV biomarkers have been consistently observed19,20. However, a significant challenge in EV-based diagnostics is their high heterogeneity in native biofluids, evident in size, origin, and cargo composition. While this heterogeneity complicates analysis, it also unveils a layer of biological specificity: different EV subpopulations are now understood to carry distinct biomarker profiles and functions. For instance, emerging evidence confirms that specific size-defined EV subtypes are selectively enriched with disease-specific molecular signatures, thereby enhancing diagnostic accuracy for cancer, neurodegenerative, and cardiovascular disease21-25. Nevertheless, existing technologies for EV size subtyping remain constrained by high costs, low throughput, and risks of damaging EV integrity during isolation26-29. Addressing these limitations is essential to unlocking the full clinical diagnostic potential of EVs.

Steric hindrance at nano-interfaces reduces the activity of ligands immobilized on nanoparticle surfaces. This effect intensifies as nanoparticle size increases. For example, reduced binding efficiencies were observed for duramycin, cinnamycin, α-synuclein, and β-amyloid when interacting with phospholipids on larger EV membranes30-32. Moreover, ligand activity on nanoparticle surfaces can be modulated by tuning the grafting density and the length of inert protective chains33. Building upon these insights, we developed a steric hindrance-mediated EV analysis and size fractionation (SHEAF) platform, which integrated size-selective separation with EV membrane protein profiling into a microfluidic system. This platform revealed that the distinct protein signatures in 90 - 180 nm EVs can effectively differentiate ICH and IS plasma samples. Our work established a rapid, accurate, and user-friendly system for EV size fractionation and molecular characterization, underscoring the diagnostic significance of specific EV size subtypes in ICH assessment.

Results
Design of SHEAF platform
To accomplish size-selective EV isolation through precisely controlled surface interactions, aptamers specifically targeting the EV transmembrane protein CD63 (APT ) were chemically co-immobilized with polyethylene glycol (PEG) polymer chains on magnetic nanoparticle (MNP) surface. Within this dual-functional interface, the APT ligands served as molecular recognition ligands for CD63-mediated EV binding, while the PEG created an adjustable steric barrier through their flexible polymer chains to spatially regulate ligand accessibility (Fig. 1a). Through iterative experimental optimization of two critical surface parameters - the APT grafting ratio and the PEG molecular weight (ranging from 2 kDa to 30 kDa) - three distinct MNP formulations were developed for capturing EVs with diameters below 90 nm diameter, below 180 nm, and exceeding 180 nm.

A three-stage fractionation protocol was subsequently established using these engineered MNPs in serial processing steps: first applying <90 nm-capture MNPs to remove the smallest EVs from biofluids, followed by <180 nm-capture MNPs to isolate medium-sized EVs, and concluding with >180 nm-capture MNPs to harvest large EVs, thereby achieving precise EV size fractionation (Fig. 1b). Each isolated EV subpopulation then underwent quantitative membrane protein analysis using a horseradish peroxidase (HRP)-based enzymatic signal amplification system, where HRP generated chemiluminescent (CL) signals proportional to specific biomarker on EV surfaces (Fig. 1c). The complete workflow was subsequently miniaturized into an integrated microfluidic system combining three essential components: (1) programmable peristaltic pumps for automated fluid handling, (2) microchannels implementing the sequential MNP processing steps, and (3) smartphone-based optical detectors for biomarker quantification, collectively enabling point-of-care clinical plasma analysis with a total processing time under 45 min (Fig. 1d). When applied to ICH diagnostics, this platform effectively discriminated size-subtype-specific EV protein signatures between ICH and IS patients through machine learning-assisted pattern recognition, demonstrating superior diagnostic performance compared to conventional approaches (Fig. 1e).

Steric hindrance-mediated size selective capture
Quantitative assessment of APT binding efficiency was performed through analysis of colorimetric ELISA signal variations under different experimental conditions (Fig. 2a). To minimize interference from MNP size heterogeneity, monodisperse MNPs with a controlled diameter of 37.8 ± 1.7 nm were employed throughout the study (Supplementary Fig. 1). APT and PEG were anchored at MNP surface through sequential aza-Michael addition (Supplementary Fig. 2) to give MNP decorated solely with APT (MNP-APT), solely with PEG (MNP-PEG), or with both APT and PEG (MNP-APT-PEG). The APT grafting ratio and PEG molecular weight of MNP-APT-PEG were designated as MNP-APT20%-PEG20K, where 20% denoted the APT grafting ratio and 20K denoted the PEG molecular weight (Mw = 20 kDa). Mesoporous silica nanoparticle (MSN), characterized by tunable nanoarchitectures, narrow size distributions, and low buoyant density34,35, served as synthetic EV analogues. Considering the established biological significance of 30 - 180 nm EV subpopulations in disease biomarker transport36,37, six distinct MSN size variants (37.5 nm, 61.1 nm, 93.1 nm, 124.1 nm, 152.7 nm, 182.9 nm) were functionalized with complementary sequence of APT (cAPT) and HRP (MSN-cAPT-HRP, Supplementary Fig. 3 and Fig. 4). The hybridization efficiency between surface-immobilized APT and cAPT served as the primary metric for binding efficiency quantification, as both DNA duplex formation and aptamer-protein recognition require complete spatial accessibility of aptamer sequences. PEG's inert chain effect was validated through statistically indistinguishable ELISA signals observed in MSN-cAPT-HRP solutions treated with PBS, MNP-PEG8K, MNP-PEG14K, and MNP-PEG20K (Supplementary Fig. 5a). Kinetic analysis demonstrated complete MSN capture within 30 min, as shown by time-dependent attenuation of ELISA signal difference before and after incubation with MNP-APT20%-PEG20K and MNP-APT40%-PEG20K (Supplementary Fig. 5b). High-resolution X-ray photoelectron spectroscope (XPS) analyses of C 1s, N 1s, and P 2p enabled precise calculation of APT grafting ratio (Supplementary Fig. 6). Fig. 2b demonstrated the APT grafting ratios increased from approximately 15% to 70% as the feeding ratios of APT were increased from 20% to 80%, confirming controllable aptamer surface functionalization

APT binding efficiency exhibited progressive reduction with decreasing aptamer grafting ratios, demonstrating pronounced attenuation at 40% and 20% grafting ratios (Fig. 2c). Under fixed 20% APT grafting ratio, MNPs functionalized with PEG having a molecular weight of 8 kDa, 10 kDa, and 12 kDa displayed marginal binding efficiency reductions compared to MNP-APT (PEG0K). Given APT 's molecular weight of 8,259.4 g/mol, this observation indicate subcritical steric hindrance when PEG chain length approximated or slightly exceeded the aptamer. A marked efficiency decline occurred in PEG14K, with progressive attenuation continuing until PEG chains molecular weight surpassed 20 kDa, beyond which binding efficiency plateaued at baseline levels (Fig. 2d). These data established that PEG polymers exceeding 14 kDa could generate sufficient steric barrier to effectively block APT -cAPT hybridization.

To establish MNP-APT-PEG systems with defined upper diameter thresholds for MSN-cAPT-HRP capture, systematic binding efficiency analyses were conducted using low APT grafting ratio (20% and 40%) and long PEG chain (Mw = 14 - 20 kDa) co-functionalized MNPs. Progressive reductions in binding efficiency were observed against MSN-cAPT-HRP with increasing diameters (Fig. 2e and 2f), confirming enhanced steric hindrance from cAPT at larger MSN surface. At 40% grafting ratio, APT retained measurable binding efficiencies (≥ 4%) across all MSN-cAPT-HRP diameters and PEG molecular weights (Fig. 2e). This residual activity stemmed from APT -saturated microdomains on MNP surface that preserved spatially confined hybridization zones. In contrast, MNPs with 20% APT grafting ratio exhibited <0.5% binding efficiency against 182.9 nm MSN-cAPT-HRP across all PEG variants, while MNP-APT20%-PEG20K showed equivalent suppression against MSNs exceeding 93.1 nm (Fig. 2f). Notably, MNP-APT20%-PEG20K retained strong binding against sub-93.1 nm MSNs, and MNP-APT20%-PEG14K demonstrated comparable performance for sub-182.9 nm MSNs, establishing capturable upper diameter limits of 93.1 nm and 182.9 nm, respectively. Consequently, MNP-APT20%-PEG20K and MNP-APT20%-PEG14K were employed to sequentially isolate MSNs with diameters <93.1 nm and those between 93.1 - 182.9 nm. To validate this separation capability, differentially sized MSN-cAPT-HRP particles (61.1 nm, 124.1 nm, and 182.9 nm) were fluorescently tagged with 7-Amino-4-methyl-3-coumarinylacetic acid N-succinimidyl ester (AMCA-NHS), fluorescein 5-isothiocyanate (FITC), and rhodamine B isothiocyanate (RITC), respectively. The labeled particles were mixed and subjected to sequential treatment with MNP-APT20%-PEG20K, followed by MNP-APT20%-PEG14K, and finally MNP-APT. This procedure resulted in the progressive disappearance of AMCA, FITC, and RITC fluorescence peaks from the residual mixture, accompanied by the emergence of corresponding fluorescence peaks with matching intensities in the isolated MSN fractions (Fig. 2g). The ordered elimination of AMCA, FITC, and RITC signals conclusively demonstrated the sequential isolation of 61.1 nm, 124.1 nm, and 182.9 nm MSNs by MNP-APT20%-PEG20K, MNP-APT20%-PEG14K, and MNP-APT, respectively.

Steric hindrance-mediated EV size fractionation
For EV size fractionation, MNP-APT20%-PEG20K, MNP-APT20%-PEG14K, and MNP-APT were sequentially applied to process EV containing solution. The concentration and purity of ultracentrifugation-isolated EVs were verified through nanoparticle tracking analysis (NTA), transmission electron microscope (TEM), and western blotting (Supplementary Fig. 7). Prior to dynamic light scattering (DLS) and TEM characterization, EVs isolated via magnetic separation were chemically detached from MNPs using 10 mM dithiothreitol38. DLS measurements revealed mean hydrodynamic diameters of 54.4 ± 11.9 nm for EV subtype 1 and 127.9 ± 17.1 nm for EV subtype 2 (Fig. 3a), with corresponding size distributions spanning 23.8 - 86.5 nm (subtype 1) and 87.9 - 186.4 nm (subtype 2). These results confirmed that MNP-APT20%-PEG20K and MNP-APT20%-PEG14K maintained consistent size-selective performance for both synthetic MSNs and biological EVs. Residual nanostructures in EV subtype 3 (isolated by MNP-APT) exhibited a mean diameter of 220.2 ± 26.3 nm with a size distribution of 180.0 - 280.5 nm, demonstrating effective removal of sub-182.9 nm nanostructures during preceding fractionation steps. TEM analysis of the original EV solution revealed characteristic nano-vesicular structures exhibiting high-contrast peripheral membranes and electron-lucent compartments, with a mean diameter of 114.8 ± 38.0 nm (Fig. 3b, Input). Analogous vesicular morphologies were preserved across all fractionated EV subtypes: EV subtype 1 (56.8 ± 15.4 nm), EV subtype 2 (112.6 ± 6.4 nm), and EV subtype 3 (200.4 ± 20.2 nm), as investigated through systematic TEM measurements. This size-stratified conservation of structural features conclusively validated successful EV subtypes isolation via the steric hindrance-mediated methodology. Western blotting further confirmed subtype purity, with bands for EV biomarkers: CD63, heat shock proteins 70 (HSP70), ALG-2-interacting protein X (ALIX) detected in all size subtypes, while absent signals for apolipoprotein B (APOB) excluded plasma protein contamination across all subtypes (Fig. 3c). Note that the successful fractionation of the three EV subtypes was also validated by analyzing the TEM images of MNP-EV hybrid obtained through magnetic separation (Supplementary Fig. 8)

Three distinct EV size subtypes with diameters <90 nm, 90 - 180 nm, and >180 nm were successfully isolated from HUVEC, HMC3, SK-N-BE(2), and THP-1 cell culture mediums using steric hindrance-mediated fractionation (Supplementary Fig. 9), validating the method's efficacy across diverse cellular origins. A strong linear correlation (R2 = 0.9079) was observed between EV protein concentrations from steric hindrance-isolated subtypes and NTA-quantified particle concentrations (Fig. 3d and Supplementary Fig. 10), confirming high size-fractionation accuracy and specificity. Notably, EV subtype 2 (90–180 nm) showed higher abundance compared to the other two subtypes across all four cell lines, consistent with published data and attributable to inherent EV biogenesis characteristics. The 90–180 nm range encompasses fractions of two major EV subpopulations: large EVs (100–1000 nm) generated by plasma membrane budding, and exosomes (< 200 nm) derived from the endosomal pathway39,40. Total EV recovery rates exceeded 95% at EV concentrations ≤1.0×1012 particle/mL, with <5% inter-subtype variability across solutions of varying EV concentrations (Fig. 3e). However, at 5×1012 particle/mL, a significant recovery rate elevation occurred specifically in subtype 3, revealing incomplete isolation of sub-180 nm EVs by MNP-APT20%-PEG20K and MNP-APT20%-PEG14K. This delineated the method's operational upper limit at 1×1012 particle/mL, consistent with clinically relevant EV concentrations (sub-1×1012 particle/mL) observed in plasma from stroke or cancer patients41,42.

The SHEAF platform offers significant advantages over established EV fractionation methods, such as size exclusion chromatography (SEC), density gradient ultracentrifugation (DGUC) and asymmetric-flow field-flow fractionation (AF4). Using a self-made SEC column, EVs in native biofluids were separated into three size fractions (<50 nm, 110 nm, and 210 nm) within 1 h (Fig. 3f upper)27,43-45. However, the SEC method had limited EV recovery (65.53%) and substantial protein contamination in the <50 nm fraction (Fig. 3g left and 3 h left). Per established protocols, DGUC requires 12-hour ultracentrifugation to accelerate particle movement through density gradient flotation, plus pre- and post-fractionation ultracentrifugation to remove protein contaminants46-48. Our results confirmed DGUC exhibited poor size resolution due to overlapping EV densities (Fig. 3f lower)46 and APOB contamination issues in FS7 (Fig. 3g right). Additionally, DGUC demonstrated a low EV recovery rate of only 71.33% (Fig. 3h right), attributed in part to EV membrane rupture during the high-speed ultracentrifugation48,49. AF4, a recently developed microfluidic EV fractionation method, was reported to provide high size resolution but require pre-processing with ultracentrifugation (2 h) to remove protein aggregations, limiting its clinical utility25,50. Overall, SHEAF can separate EV in complex biofluids into three high-purity EV size subtypes in 1 h without pre-processing, achieving near-complete recovery (>95%) and minimal protein contamination (Supplementary Table 1).

Molecular profiling of EV size subtypes
The SHEAF platform was employed to analyze EV membrane proteins across three EV size subtypes secreted by HUVEC and HMC3. Cells were stimulated with tumor necrosis factor alpha (TNF-α) or oxidized low-density lipoprotein (OX-LDL) to establish inflammatory models prior to medium collection (Supplementary Fig. 11). Six inflammation-related proteins, including vascular endothelial growth factor (VEGF), transforming growth factor beta (TGF-β), programmed cell death ligand 1 (PD-L1), C-reactive protein (CRP), TNF-α, and intercellular adhesion molecule 1 (ICAM-1), were selected for analysis based on their known associations with EV membrane and elevated circulatory levels in stroke patients51-56. EV membrane proteins were first labeled via double-stranded DNA comprising an aptamer sequence against target protein and an HRP-conjugated transducing sequence (APT -TR -HRP). Activating sequence (ACT ) subsequently displaced TR -HRP from EV surfaces within 30 min, initiating the CL reaction through interaction with ELISA substrate. This HRP-catalyzed system demonstrated target-specific membrane protein recognition, evidenced by substantially amplified CL signals in EV solutions treated with APT -TR -HRP plus ACT (Fig. 4a). By contrast, negligible signal enhancement occurred when APT -TR -HRP was replaced with its scrambled sequence counterpart or when EV solutions were treated solely with APT -TR -HPR or ACT (PBS). The steric hindrance-mediated EV size fractionation technique and membrane protein analysis workflow were subsequently incorporated into a microfluidic platform, which achieved a VEGF detection limit of 0.380 × 107 particle/mL during titration experiments on VEGF associated to HMC3 EV membrane (Fig. 4b).

Expression levels of the six proteins in the whole EV population were quantified using both the SHEAF platform and sandwich ELISA for comparative analysis. SHEAF-derived measurements showed strong linear correlation with ELISA results (R2 = 0.9682, Fig. 4c), confirming method reliability. Subsequent analysis revealed distinct protein expression patterns across all three EV subtypes in various inflammation models. Specifically, elevated signals were observed in EV subtype 1 and EV subtype 2 compared to whole EV population and EV subtype 3 (Fig. 4d), demonstrating inflammation-related protein enrichment in these subtypes. Pairwise comparisons of EV membrane protein compositions were further used to evaluate the correlations between the three EV size subtypes and whole EV population. Positive correlations were detected between nearly all EV size subtypes and the whole EV population, given their shared common cellular origins (Fig. 4e). However, weak correlations or negative correlations (r ≤ 0.1) were identified between EV subtype 1 and EV subtype 2 derived from HMC3 cells, along with EV subtype 2 and EV subtype 3 isolated from TNF-α stimulated HUVEC cultures. Furthermore, moderate correlations (0.1 <r ≤0.5) were observed between EV subtype 3 and two other EV subtypes in OX-LDL-stimulated HUVECs, and with EV subtype 2 in TNF-α-stimulated HMC3 cells. These weak inter-subtype correlations revealed substantial biomolecular and biophysical heterogeneity intrinsic to EVs, thereby emphasizing the indispensable requirement for sophisticated EV size subtype methodologies in elucidating EV-associated biological mechanisms. Proteomic analysis further delineated distinct protein expression profiles among the three EV subtypes (Supplementary Fig. 12a,b). Analysis of the proteomic dataset for subcellular localization indicated that the proteins significantly enriched in EV Subtype 2 were derived from diverse intracellular compartments (Supplementary Fig. 12c,d), suggesting this subtype is likely produced mainly through the endosomal pathway. This origin underpinned its enhanced potential as a source of diagnostic biomarkers. Notably, proteins detected by western blotting were also identified within the proteomic dataset.

Clinical sample analysis
To assess the diagnostic efficacy of the SHEAF platform for stroke classification, plasma samples were collected from 25 ICH patients and 25 IS patients. In addition, plasma samples from 20 healthy donors were collected through age- and sex-matched cohort enrollment as control group (Supplementary Table 2). Employing the SHEAF platform, EVs in patient plasma samples were categorized into 3 size subtypes (Supplementary Fig. 13), followed by membrane protein profiling for both fractionated EVs and whole EV population. Substantial disparities in expression levels were detected among the six target proteins when comparing the three EV size subtypes with whole EV population (Fig. 5a). This finding was consistent with the cellular analysis, thereby reinforcing the functional specialization of EV subtypes in inflammatory pathway regulation.

Two-dimensional projections of EV protein compositions across three size subtypes and the whole EV population were generated through principal component analysis (PCA). Significant spatial overlap was identified between ICH, IS, and healthy cohorts in the protein profiles of EV subtype 1, subtype 3, and the unfractionated EV pool (Fig. 5b). Conversely, EV subtype 2 demonstrated minimal inter-group overlap in protein composition. This differential clustering pattern suggested superior diagnostic capability of EV subtype 2 protein signatures for distinguishing stroke from healthy control and ICH from IS. Consistent with PCA findings, EV subtypes 1, subtype 3, and whole EVs demonstrated limited ability to differentiate between stroke and healthy plasma samples, and between ICH and IS samples (Supplementary Fig. 14). The mixed area under the curve (AUC) scores for these populations were 0.807 (subtype 1), 0.695 (subtype 3), and 0.792 (whole EVs) in distinguishing stroke from healthy controls, with most individual protein AUC values below 0.70. For differentiating ICH from IS, the mixed AUCs were 0.728 (subtype 1), 0.680 (subtype 3), and 0.736 (whole EVs), again with most individual protein AUCs below 0.60. In contrast, proteins from EV subtype 2 showed markedly improved diagnostic performance in both comparisons (Fig. 5c, upper). Specifically, TGF-β and PD-L1 achieved AUC values above 0.80 for distinguishing stroke from healthy controls, while a combined six-protein panel in subtype 2 yielded an exceptional AUC of 0.951 in stroke diagnosis. Similarly, in differentiating ICH from IS, all individual proteins in subtype 2 had AUC values exceeding 0.60, with VEGF, CRP, and ICAM-1 surpassing 0.70 (Fig. 5c, lower). The combined model based on EV subtype 2 reached an AUC of 0.958 for classifying ICH versus IS. The cut-points for EV subtype 2 showed a sensitivity of 0.88 and a specificity of 0.92 in differentiating ICH from IS, as well as a sensitivity of 0.84 and a specificity of 0.95 in distinguishing stroke from healthy controls (Supplementary Table 3).

Methods
Materials
DNA oligos were purchased from Sangon Biotech (Shanghai) Co., Ltd. and were stored at -20 oC before use. Aptamer sequences against CD63, VEGF, TGF-β, PD-L1, CRP, TNF-α, and ICAM-1 were established according to previous publications (Supplementary Table 5)60-65. Primary amine capped MNP (MNP-NH ) and OptiPrep™ density gradient medium (60%, w/v, iodixanol) were obtained from Merck Ltd. TNF-α, OX-LDL, SuperSignal™ ELISA Femto substrate, HRP-conjugated goat anti-mouse IgG secondary antibody (Invitrogen, catalog no. A16066), and primary antibodies against the following proteins - CD63 (Invitrogen, clone Mo34, catalog no. MA5-47655), HSP70 (Invitrogen, clone 2A4, catalog no. MA3-008), ALIX (Invitrogen, clone 3A9, catalog no. MA1-83977), APOB (NeoBiotechnologies, clone APOB/4335, catalog no. 338-MSM5-P1) - were purchased from Thermo Fisher Scientific Inc. Diacrylate PEG (DAPEG, Mw = 1,000), methoxypolyethylene glycol amine (mPEG-NH , Mw = 8,000, 10,000, 20,000 and 30,000), PEG bis(amine) (NH -PEG-NH , Mw = 2,000, 4,000, 6,000 and 8,000), PEG methyl ether acrylate (PEG-MA, Mw = 10,000 and 20,000), triethylamine, and N,N-methylenediacrylamide were purchased from Beijing InnoChem Science & Technology Co., Ltd. AMCA-NHS, FITC, RITC, tetraethyl orthosilicate (TEOS), (3-aminopropyl)triethoxysilane (APTES), hexadecyltrimethylammonium bromide (CTAB), and polyetherimide (PEI, Mw = 600) were purchased from Shanghai Aladdin BioChem Technology Co., Ltd, and were used as received. Sepharose® CL-2B agarose gel, ELISA for IL-1β and MCP-1, bicinchoninic acid assay (BCA), radioimmunoprecipitation assay (RIPA) containing protease inhibitors, HRP streptavidin, TMB single-component substrate solution, and ELISA stopping solution were purchased from Shanghai Acmec Biochemical Co., Ltd. EV-depleted fetal bovine serum were prepared according to standard protocol49.

Cell culture and EV collection
HUVEC (catalog no. GPC0114) cell line was purchased from the China Center for Type Culture Collection (Wuhan, China). HMC3 (catalog no. SCSP-540), SK-N-BE(2) (catalog no. TCHu200), and THP-1 (catalog no. SCSP-567) cell lines were purchased from the National Collection of Authenticated Cell Cultures (Shanghai, China). All cell lines were cultured in Dulbecco`s modified Eagle`s medium (DMEM, Gibco) containing 10% EV-depleted fetal bovine serum and 1% penicillin-streptomycin (Gibco). After being cultured for 48 h, the cell culture mediums were centrifuged at 1,000 g for 20 min and treated by a 0.22 μm filter. The obtained supernatant was sequentially centrifuged at 10,000 g for 10 min and 100,000 g for 90 min to isolate EV. The EV pellet was washed by resuspending in PBS and centrifugation at 100,000 g for 90 min.
For analyzing EV secreted by cells under inflammatory states, HUVEC and HMC3 cells were exposed to TNF-α or OX-LDL according to previous publications66-68. In a typical procedure, 1.0  106 HMC3 cells were incubated with 100 ng/mL TNF-α or 50 μg/mL OX-LDL for 24 h before collecting cell culture mediums. Cellular inflammatory models were validated by measuring inflammatory cytokines (IL-1β and MCP-1) levels in cell culture mediums via ELISA per the manufacturer’s protocol. The harvested EVs were investigated by western blotting, NTA, and TEM to confirm their protein composition, concentration, and structure, respectively. To preserve EV integrity, disaggregation was not performed during the EV isolation step.

Surface modification of MNP-NH
In a typical procedure, 0.5 mg MNP-NH and 10 mg DAPEG were incubated together in 1.0 mL PBS for 24 h at 37 oC. The obtained MNP-DAPEG was further incubated with APT (2.0 μM, primary amine at 5` end) and mPEG-NH (8.0 μM, Mw = 20,000) for 24 h to obtain MNP with 20% APT grafting ratio (MNP-APT20%-PEG20K). The APT grafting ratio of MNP was adjusted by varying the dosage of APT and mPEG-NH . After each step, the MNP was dialyzed (MWCO = 50,000) against PBS for 24 h to remove residuals. The MNP surface modification process was illustrated in Supplementary Fig. 2.
mPEG-NH with molecular weight of 8,000, 10,000, 20,000, and 30,000 were commercially available and directly used in MNP surface modification. mPEG-NH with molecular weight of 12,000, 14,000, 16,000, 18,000 and 24,000 were synthesized through aza-Michael reaction. Typically, 1.0 mg PEG-MA (Mw = 10,000) and 10 mg NH -PEG-NH (Mw = 2,000) were added into 2.0 mL PBS and reacted at 37 oC for 24 h. Then, the mixture was treated by a dialysis bag (MWCO = 5,000) against PBS for 48 h, followed by lyophilization to give mPEG-NH with a Mw of 12,000.

Preparation and surface modification of MSN
Primary amine capped MSN (MSN-NH ) were prepared according to previous publications34,35. Briefly, 0.4 g APTES and 1.6 g TEOS were mixed and slowly added into 140 mL CTAB solution (4.0 mM). Then, 0.5 mL NH3•H2O (30%) was added into the mixture under stirring, followed by reacting at 25 oC for 5 h. Subsequently, the obtained solid was collected by centrifugation and washed with ethanol. To remove residual CTAB, the obtained MSN-NH was treated by 100 mL HCl solution (3.7%) for 12 h at 60 oC. To control the size of MSN-NH , the dosage of silane and NH3•H2O were adjusted.
For labeling MSN with HRP and cAPT (primary amine at 5` end), 1.0 mg MSN-NH and 10.0 mg N,N-methylenediacrylamide were incubated in 1.0 mL PBS buffer at 37 oC for 24 h. After being washed by centrifugation and re-dispersion, the solution was further incubated with 100 μL mixture solution containing HRP (1.0 mg/mL) and cAPT (100 μM, primary amine at 5` end) for 24 h at 37 oC. The obtained MSN-cAPT-HRP was purified by centrifugation and re-dispersing in PBS. In a typical procedure for labeling MSN with fluorophores, 1.0 mL MSN-cAPT-HRP (1.0 mg/mL) was reacted with 0.5 mg FITC for 12 h at 25 oC in dark. Then, the FITC labeled MSN was purified by centrifugation and re-dispersion in PBS. The MSN surface modification process was illustrated in Supplementary Fig. 4.

APT binding efficiency investigation
The binding efficiency of APT at MNP surface was investigated by analyzing the amount of MSN-cAPT-HRP remained in the solution after being treated by MNP-APT-PEG. In a typical procedure, 50 μL MSN-cAPT-HRP solution (diameter = 124.1 nm, 0.01 mg/mL) was incubated with 50 μL MNP-APT20%-PEG20K solution (0.1 mg/mL) for 30 min at 25 oC. After incubation, the mixture was treated by a magnetic bar for 3 min to remove MNP and MNP-MSN hybrid. Then, the supernatant was sequentially treated by TMB substrate solution and ELISA stopping solution under manufacturer’s guidance, followed by analyzing solution absorbance at 450 nm. MNP-APT or MNP-PEG20K were used to treat MSN-cAPT-HRP following the same procedure as positive and negative controls, respectively. The APT binding efficiency of MNP-APT20%-PEG20K was calculated as follow:
binding efficiency = (ABS – ABS ) / (ABS - ABS ) (1)
apt positive negative positive
where ABS , ABS , and ABS were the absorbance (450 nm) of MSN-cAPT-HRP solution under the treatment of MNP-APT20%-PEG20K, MNP-APT, and MNP-PEG20K, respectively.

Steric hindrance mediated EV size fractionation
In the first round of EV size fractionation process, 10 μL MNP-APT20%-PEG20K (0.5 mg/mL) solution was incubated with 40 μL EV containing medium for 30 min at 25 oC. Then, the mixture was treated by a magnetic bar for 3 min to collect EV subtype 1 (EV diameter <90 nm). The EV containing supernatant was further treated by 10 μL MNP-APT20%-PEG14K (0.5 mg/mL) and a magnetic bar under the same procedure to collect EV subtype 2 (90 nm <EV diameter <180 nm). Finally, the EVs remained in the supernatant were treated by 10 μL MNP-APT (0.5 mg/mL) and a magnetic bar to harvest EV subtype 3 (180 nm <EV diameter). Prior to size fractionating, all EV containing mediums were treated by 0.22 μm filters. For analyzing the morphology and size of each EV subtype, cystamine was conjugated to the 5` end of APT before it was decorated to MNP surface. After fractionating, EV and MNP mixtures were treated by 10 mM dithiothreitol for 4 h at 37 oC to release EV from MNP surface for measuring hydrodynamic size by a DLS instrument38.

EV membrane protein profiling
The expression levels of EV membrane associated proteins, including VEGF, TGF-β, PD-L1, CRP, TNF-α, and ICAM-1, were analyzed by an HRP catalyzed CL reaction. Typically, APT was hybridized with TR -HRP to obtain APT -TR -HRP. Then, 5 μL APT -TR -HRP (1.0 μg/mL) solution and 10 μL MNP-APT20%-PEG20K (0.5 mg/mL) solution were incubated together with 40 μL EV containing medium for 30 min, followed by the same EV size fractionation processes described in Section “Steric hindrance mediated EV size fractionation”. For each subtype, the MNP-EV hybrid was treated by ACT (30μL, 0.1 μM) for 30 min at 25 oC to detach TR -HRP from EV surface. Subsequently, the released TR -HRP were transferred and treated by SuperSignal™ ELISA Femto substrate to measure CL signal at 425 nm. The scrambled structure of APT -TR -HRP was used to treat the EV containing medium with the same procedure as negative control. The signals for protein measurement was defined as ∆CL and was calculated as:
∆CL = (CL - CL ) / CL (2)
APT SCR SCR
where CL and CL were the CL signals of samples treated by aptamer sequence and scrambled sequence, respectively. The TR -HRP were prepared by incubating TR (biotin at 5` end) with HRP streptavidin at 25 oC for 4 h, followed by removing residual TR using a Zeba™ spin desalting column (Thermo, MWCO = 40,000).

Microfluidic chip fabrication
The microfluidic chip comprised three layers. The cover layer was fabricated from a PMMA sheet using a CO laser engraver (Atomstack K60). The microchannel layer was produced through SU-8 photolithography. Specifically, SU-8 photoresist was spin-coated onto a silicon wafer and UV-exposed to pattern a casting mold. A PDMS/crosslinker mixture (10:1) was poured onto this mold, cured at 60°C overnight, and then peeled off. Holes for the inlet and outlet ports were punched into the PDMS layer using a hole puncher. To covalently bond the layers together, the cover and substrate layers were surface-activated with oxygen plasma, followed by immersion in APTES solution (5% v/v) for 20 min. The microchannel layer was then surface-activated with oxygen plasma and attached to the cover and substrate layers overnight at 70°C. The fabrication processes and operational procedure of microfluidic chip were illustrated in Supplementary Fig. 15 and Fig. 16.

Device integration and manipulation
The pumping system consisted of four components: a peristaltic pump (Kamoer), a manifold with nine solenoid valves (Dehnker), a control board, and six electromagnets (JX-P10/10). This pumping system, along with three microfluidic chips and an optical detector, was assembled into a 3D-printed enclosure (Bambu Lab X1) to form the SHEAF platform, which was connected to a smartphone interface via Bluetooth. For the work-flow of the integrated SHEAF device, 10 μL plasma sample and 2.5 μL solution containing MNP-APT20%-PEG20K (0.5 mg/mL) and APT -TR -HRP (0.5 μg/mL) were introduced through inlet 1 and inlet 2, respectively. The mixture solution was directed through the serpentine channel into the incubation chamber, followed by incubating for 10 min and electromagnetic separation to isolate EV subtype1. The supernatant was transferred to the second microfluidic chip for isolating EV subtype 2 and the third microfluidic chip for isolating EV subtype 3 under the same protocol in a sequential manner. The obtained MNP-EV hybrid was mixed with 7.5 μL ACT (0.1 μM) solution introduced via inlet 3 and was incubated together in the next incubation chamber for 10 min to release TR -HRP. Then, the electromagnet was activated to sediment MNP-EV hybrid , while the TR -HRP was further mixed with 2.0 μL ELISA substrate introduced via inlet 4. The CL reaction was carried out for 120 s with the CL intensity being recorded by a smartphone. Plasma samples treated by the scrambled structure of APT -TR -HRP were used as negative control. The system configuration and operational logic of the SHEAF device were illustrated in Supplementary Fig. 17. The optimization and reproducibility of SHEAF device was illustrated in Supplementary Fig. 18.

Clinical samples
Clinical analyses were conducted in accordance with the ethical standards of the Helsinki Declaration and received approval from the Ethics Committee of Guizhou Medical University (Approval No. 2024-126). Informed consent was secured from each participant before blood samples collection. Clinical samples of stroke patients were collected at the Department of emergency of The Affiliated Hospital of Guizhou Medical University, while clinical samples of healthy controls were collected at the Department of Cardiovascular Medicine. The initial cohort of samples was collected from January to September 2024, while the additional samples incorporated during the revision phase were obtained between August and October 2025. Clinical diagnoses were performed by medical staff from the Department of Emergency Medicine and the Department of Cardiovascular Medicine. Subsequent analyses based on SHEAF platform were conducted by researchers from the School of Pharmacy at Guizhou Medical University and School of Mechanical Engineering at Northwestern Polytechnical University. All sample-related assays were performed in a blinded manner. For stroke patients, blood was collected prior to any treatment intervention. Diagnosis of either ICH or IS was confirmed within one hour after blood sampling. Prior to the study, none of the patients had a history of stroke or myocardial infarction, nor had they received any form of treatment for stroke. The baseline characteristics of patients were summarized in Supplementary Table 2.
Blood samples were collected in EDTA tubes, followed by immediate centrifugation at 1,000 g for 10 min to harvest upper plasma layer. The collected plasma was further centrifuged at 10,000 g for 10 min and was filtered through a 0.22 μm filter to remove large aggregations. Then, the plasma samples were stored at - 80 oC before use. A total of 70 samples were collected, including 25 patients with ICH, 25 patients with IS, and 20 healthy donors. The clinical diagnoses were made by neurologists following a series of clinical tests for each patient. These tests included physical and neurological examinations, blood tests, MRI, and computed CT scans. The clinical samples were analyzed by the integrated SHEAF platform according to work-flow described in Section “Device integration and manipulation”.

Western blotting
EVs were lysed by RIPA buffer and were quantified using BCA assay. Subsequently, the EV proteins were resolved by sodium dodecyl sulfate-polyacrylamide gel electrophoresis and transferred onto a poly(vinylidene fluoride) membrane. After being washed by Tris buffered saline with Tween-20 and blocked with 5% skimmed milk, the blots were further incubated with primary antibodies against the following proteins: CD63, ALIX, HSP70, and APOB at 4 °C for 12 h. After incubation, the blots were washed and treated with an HRP-conjugated goat anti-mouse IgG secondary antibody for 2 h. Finally, the CL of blot was analyzed using a BIORAD ChemiDOC™ XRS imaging system.

Proteomic analysis of EV size subtypes
EV size subtypes were fractionated using the SHEAF platform from the cell culture mediums of HUVEC and HMC3. Each EV size subtype was re-dispersed in PBS and treated with 1.0% Triton X-100 for 30 min at 37 oC, followed by magnetic separation to remove MNP. The obtained sample solution was further treated with lysis buffer (4% sodium dodecyl sulfate, 100 mM Tris-HCl, pH 7.6) directly. Subsequently, dithiothreitol was added to each sample to reduce disulfide bonds by incubating at 37 oC for 1.5 h, followed by incubation with iodoacetamide for 30 min at 25 oC to block cysteine residues. Trypsin was added into sample solution at a trypsin-to-protein ratio of 1:50 (w/w), and the mixture was incubated at 37°C overnight. The digested peptides were desalted using an MCX desalting column (Omicsolution, OS-MCX-1ML), concentrated by vacuum centrifugation, and reconstituted in 20 μL formic acid solution (0.1%). The peptide concentration was estimated by measuring the UV absorbance at 280 nm. The obtained sample solution was mixed with iRT peptides (Biognosys) according to manufacturer`s guidance for data-independent acquisition (DIA) experiments.
Peptides from each sample were analyzed using an OrbitrapTM AstralTM mass spectrometer (Thermo Scientific) coupled with a Vanquish Neo UHPLC system (Thermo Scientific) in DIA mode. MS1 spectra (intact form) were acquired over a mass range of 380–980 m/z with a resolution of 240,000 at 200 m/z, a normalized automatic gain control target of 500%, and a maximum injection time of 5 ms. MS2 (fragmented form) analysis was performed with 299 windows, an isolation window of 2 m/z, and an HCD collision energy of 25 eV, using a normalized automatic gain control target of 500% and a maximum injection time of 3 ms. The acquired data were processed with DIA-NN (version 1.8.1) using the following parameters: enzyme set to trypsin, maximum missed cleavages set to 1, fixed modification set to carbamidomethylation, and dynamic modifications set to oxidation and N-terminal acetylation. Subcellular localization of proteins was predicted using CELLO (http://cello.life.nctu.edu.tw/), a multi-class support vector machine-based classification system. All reported identifications were based on 99% confidence at the protein level, corresponding to a false discovery rate ≤ 1%.

EV size fractionation by SEC and DGUC
The SEC-based EV size fractionation was performed according to previous publications27,43. Sepharose® CL-2B agarose gel was mixed 3:1 (v/v) with PBS buffer and stacked into plastic columns (inner diameter 5 mm) to a height of 20 cm. The columns were equilibrated with 10 mL of PBS buffer for 30 min prior to the loading of 500 μL EV containing medium. The eluate collection was initiated immediately using graduated 2.0 mL glass tubes, yielding 300 μL per fraction. The presence and size distribution of EVs in each eluate were assessed by DLS analysis.
For DGUC-based EV size fraction, OptiPrep™ density gradient medium (60%, w/v, iodixanol) were diluted in cold PBS to give solutions with 40%, 20%, 10%, and 5% iodixanol46,47. Then, 2 mL of those iodixanol solutions each was gentle layered in an ultracentrifugation tube in descending order of concentration to generate a discontinuous density gradient. Crude EV pellet isolated by ultracentrifugation was placed at the bottom of the tube. The bottom-loaded 5% - 40% gradient was centrifuged at 100,000 g for 12 h at 4 oC. After ultracentrifugation, 8 fractions (1 mL each) were collected from the top of the gradient, followed by resuspension in PBS and ultracentrifugation at 100,000 g for 90 min at oC. The obtained pellet was resuspended in PBS for further analysis.

Instrument
The solution absorbance, fluorescence, and CL measurements were performed on a plate reader (Thermo Fisher Scientific) at 25 oC. The hydration diameters (number average) and surface zeta potentials of EVs were analyzed by a DLS instrument (NanoBrook 173Plus, Brookhaven) at 25 oC. The concentration of the isolated EV was determined using an NTA system (ZetaView, Particle Metrix) in PBS buffer at 25 oC. For each NTA analysis, 11 positions were recorded using a maximum area of 1000, a minimum area of 10, and a minimum brightness of 30. TEM images were acquired using a TalosTM L120C G2 (Thermo) instrument operated at an accelerating voltage of 120 kV. EV size subtypes were visualized without staining, while EV-MNP hybrids were visualized after staining with phosphotungstic acid to enhance the contrast of EVs. The morphology and size of MSN-NH were analyzed by a scanning electron microscope (CLARA S-3400N, TESCAN). MSN-NH was dispersed on a silica wafer and subjected to 60 s of gold sputter-coating prior to SEM analysis. XPS analysis was performed on an ESXCALAB Xi+ instrument (Thermo), and data was analyzed by Thermo Scientific™ Avantage software. The ultracentrifugation was performed by an Optima XPN (Beckman) at 4 oC. A SW 41 Ti rotor (Beckman, k factor = 124) and Thinwall Polyallomer tubes (Beckman) were employed for this procedure.

Statistics
All experiments were conducted in triplicate unless otherwise specified, and the results were presented as the mean ± standard deviation (SD). Experiments were repeated thrice independently with similar results for Figs. 3b, c, g. P <0.05 was considered the threshold for statistical significance. Pairwise correlation analysis (Pearson) was employed to generate correlation matrices for EV membrane proteins. False discovery rate was used to assess the proportion of false positives in multiple hypothesis testing. Sample size calculation were performed with α = 0.05 and power (1 – β) = 0.95. The calculated false discovery rate and sample size were summarized in Supplementary Table 6. The EV membrane protein expression levels of clinical samples were visualized in a two-dimensional space using PCA. Multiple linear regression scoring models for stroke diagnosis were constructed using leave-one-out cross-validation. The multiple regression score was calculated as follows:
6
Score(n)=∑α ×V+β (3)
i i
i=1
where α is the regression coefficient, V is the measured value, and β is the intercept. A summary of the regression coefficients and intercept was included in Supplementary Table 7. Receiver operating characteristic curves were plotted for both single protein and multi-protein analyses, based on the expression level of protein and the regression scores, respectively. The cut-off points of receiver operating characteristic curves were determined using the closest-to-(0,1) criterion based on Euclidean distance. The 95% confidence intervals for the area under curve value were calculated using DeLong's nonparametric method, which accounts for the correlation between the two samples. Statistical analyses were conducted using GraphPad Prism 10.3.0 and RStudio 2024.12.1 with the following packages: broom, dplyr, ggplot2, pROC, reshape2, and tidyr.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "In this manuscript, Zhao et al. describe a microfluidic platform (termed SHEAF) that integrates steric-hindrance-mediated magnetic sorting of plasma extracellular vesicles (EVs) with chemiluminescent membrane-protein profiling to achieve rapid, pre-hospital discrimination of intracerebral hemorrhage (ICH) from ischemic stroke (IS). The authors report that the 90–180 nm EV subtype (subtype 2) exhibits superior discriminative capacity in differentiating ICH from IS within a 45-min workflow. Although the technical ingenuity and potential clinical impact are compelling, several concerns need to be addressed before the work can be recommended for publication:\n1. The authors use 90 nm and 180 nm as cut-offs, yet the rationale is lacking (Fig. 2f). Please clarify why these diameters are biologically meaningful for stroke-related EVs. It seems that the abundance of the 90–180 nm subtype 2 is much higher than subtype 1 and 3.\n2. How the PEG layer generates a steric barrier: relate PEG length (14–20 kDa) to the resulting exclusion distance. Will PEG steric hindrance compromise the recognition ability of aptamers? Why use aptamers instead of antibodies?\n3. Provide large-area TEM micrographs that show intact EVs bound to MNPs for all three size fractions.\n4. Please include NTA. TEM, and Western-blot data for the three size subtypes isolated from plasma samples.\n5. Clarify why VEGF, TGF-β, PD-L1, CRP, TNF-α, and ICAM-1 were chosen.\n6. Explain mechanistically why subtype 2 (90–180 nm) is diagnostically superior.\n7. Please add more clinical samples and include healthy controls.", "reviewer_2": "Noteworthy and significance:\nThe new method presented in this paper is of scientific merit and clearly based upon well established ideas for nanoparticle and filtration based techniques. The authors propose a rapid method for the detection of three EV subtypes based upon their preliminary research.\nThe technique offers a high purity EV recovery (95%), this will be particularly interesting to the EV community. Integration into a microfluidic device offers a unique, dedicated apparatus for EV isolation. The use of smart phone detection is innovative but should be treated cautiously.\nConclusions:\nThe authors offer significant support in the manuscript to support their ideas. They validated using multiple gold standard techniques and provided tangible evidence (TEM, NTP and Western blotting). Many of their ideas are supported with the literature such as EVs carrying proinflammatory markers.\nAdditional evidence and flaws:\nThe author have not stated the properties of these EV subtypes other than size or vesicle diameter, however there are distinct subtypes of EV that are produced with distinctive sizes that are formed through different biogenic pathways.\nThese have characteristic biomarkers, such as sMVs or exosomes typically express CD63 on their outer membrane and are 30 - 100nm (there abouts) and are as a result of the endocytic pathway, however, microvesicles (s/EVs) are larger 100nmm - 1000nm, produced by blebbing at the cell surface and express little or no CD63. From what I can ascertain, there are two populations of microvesicles, that should express negligible CD63 levels.\nIs it possible that the fractions containing larger EVs are 'contaminated' with smaller, aggregated EVs expressing CD63? I saw no comment about disaggregation steps in the protocols.\nConceptually the work is interesting and if correct will offer an advantage to clinical laboratories.\nI would urge the authors to address this in their manuscript, either highlighting this as a flaw, altering their emphasis and conclusions or addressing this issue with the necessary supportive supplementary information, i.e., that they have removed aggregated exosomes, that the cells themselves express CD63 on their surface or that all EV subtypes are enriched for CD63 - this is highly unlikely though.\nThe clinical sample size is relatively small. Although a larger sample size is probably difficult to find participants the authors should acknowledge this as a limitation or propose a larger study.\nMethods:\nThis are sounds - except what has been previously stated. Gold standard tools are used.\nThe methods should be reproducible based upon protocol written, including aptimer sequences and analytical techniques and so on.\nHowever, the manufacture and calibration of the microfluidic device requires more detail.\nOverall, the work is exciting and conducted well. It has the potential to offer a clear clinical application as well as offering a rapid techniques for research labs too.\nAddressing my comments will strengthen the overall impact of the manuscript.", "reviewer_3": "In this manuscript entitled “Steric Hindrance-Mediated Extracellular Vesicle Size Fractionation for Rapid Prehospital Diagnosis of Intracerebral Hemorrhage”, the authors present a novel SHEAF platform designed to enable rapid EV size-fractionation and protein profiling. They report that EVs within the 90–180 nm range demonstrate potential as highly discriminative biomarkers for ICH, with possible applications for rapid and precise stroke diagnostics in the prehospital setting.\nThe paper is clearly written, and the investigators provide interesting and timely data. Nonetheless, several important aspects require clarification and further elaboration. My detailed comments are outlined below.\n- The introduction is overly superficial. Relevant issues such as cause-specific management of acute stroke patients in the prehospital setting (e.g., blood pressure control and the need for ICH patients to be admitted to specialized units) are not addressed. A more comprehensive rationale for the study design is required, particularly concerning the potential added value of including EVs. Additionally, the cited literature is somewhat outdated.\n- The methodological section is insufficiently detailed. Critical clinical information is missing, such as patient recruitment details (e.g., location, timeframe), laboratory where analyses were performed, and whether assays were conducted in a blinded manner. The timing of sample collection—fundamental to the claimed prehospital application—is not reported. The statistical methods are inadequately described, with no mention of sample size calculation, limited descriptive statistics, and no adjustment for multiple comparisons.\n- The absence of a healthy control group significantly weakens the interpretability of the findings.\n- The claim that the platform could serve as a robust prehospital diagnostic system is unrealistic. The requirement for plasma processing and the reported 45-minute workflow make its use in ambulances unfeasible.\n- The statement that the platform outperforms “conventional approaches” is vague, as no clear comparator methods are identified.\n- Several claims lack statistical support. For example, differences in protein expression levels across EV subtypes (Fig. 5a) are not substantiated by statistical testing. Results of the multiple linear regression models for stroke diagnosis are not reported. ROC analyses raise concerns given the small sample size, and in subtype 2, the majority of proteins demonstrate only poor discriminatory power (AUC 0.6–0.7). Furthermore, 95% confidence intervals—essential to quantify uncertainty—are not provided. The statement that subtype 2 achieved “near-ideal classification accuracy” (AUC 0.933) is overstated, especially since no cut-offs, sensitivity, or specificity are provided.\n- The discussion does not sufficiently address the biological or pathophysiological rationale underlying the superior performance of subtype 2 EVs. Moreover, there is no consideration of the optimal diagnostic time window for the proposed approach.\n- The limitations of the study are not adequately acknowledged, including small sample size, lack of control for confounders (e.g., age, sex, comorbidities), absence of healthy controls, and missing sampling time points—all of which preclude meaningful conclusions about prehospital applicability."}, "author_response": "Point-by-point response\nReviewer #1:\nIn this manuscript, Zhao et al. describe a microfluidic platform (termed SHEAF) that\nintegrates steric-hindrance-mediated magnetic sorting of plasma extracellular vesicles\n(EVs) with chemiluminescent membrane-protein profiling to achieve rapid, pre-hospital\ndiscrimination of intracerebral hemorrhage (ICH) from ischemic stroke (IS). The authors\nreport that the 90–180 nm EV subtype (subtype 2) exhibits superior discriminative capacity\nin differentiating ICH from IS within a 45-min workflow. Although the technical ingenuity and\npotential clinical impact are compelling, several concerns need to be addressed before the\nwork can be recommended for publication:\n1. The authors use 90 nm and 180 nm as cut-offs, yet the rationale is lacking (Fig. 2f).\nPlease clarify why these diameters are biologically meaningful for stroke-related EVs. It\nseems that the abundance of the 90–180 nm subtype 2 is much higher than subtype 1 and\n3.\nOur response: We thank the reviewer for this insightful comment. Extracellular vesicles\n(EVs) below approximately 200 nm are known to cross the blood-brain barrier (BBB),\nmaking them ideal candidates for detecting brain-specific changes in the early phases of\nstroke1-3. Furthermore, EVs smaller than 90 nm possess limited membrane surface area\nfor adequately displaying stroke-specific biomarkers4, while those exceeding 200 nm may\ninclude heterogeneous apoptotic bodies. Thus, the 90–180 nm range represents an\noptimal balance: it enriches for brain-derived EVs with sufficient surface area to carry\ndisease-relevant proteins, while minimizing inclusion of larger heterogenous particles.\nSupporting this, our proteomic analysis revealed that EV subtype 2 (90–180 nm) contains\na significantly greater number of proteins of subcellular origin compared to subtype 1\n(<90 nm) and subtype 3 (>180 nm), further underscoring the critical importance of\nsubtype 2 as a diagnostically relevant EV fraction enriched in intracellular, and potentially\nbrain-specific, biomarkers (new Supplementary Figure 12).\nAlthough biologically informed, the exact thresholds of 90 nm and 180 nm were determined\nempirically based on the binding behavior of aptamer- and PEG-functionalized magnetic\nnanoparticles (MNPs). Through systematical investigation (Figure 2), we observed two\nMNP formulations selectively bound nanoparticles below approximately 182.9 nm and\n93.1 nm, respectively, while effectively shielding larger particles. These two MNP\nformulations were subsequently applied in EV size fractionation for selectively capture EV\nbelow 90 nm and 180 nm, respectively.\nOur experimental data, derived from both bicinchoninic acid (BCA) assay and nanoparticle\ntracking analyzer (NTA), indicate much higher abundance of EVs within the 90–180 nm\nsize range (subtype 2). This observation aligns with previously published reports5,6, and\ncan be attributed to the inherent characteristics of EV biogenesis. Specifically, EVs within\n90–180 nm range are generated through two major EV biogenesis pathways: EVs (100–\n1000 nm) formed through direct plasma membrane budding and exosomes (<200 nm)\noriginating through the endosomal pathway7-9. In contrast, EV subtype 1 (<90 nm) and EV\nsubtype 3 (>180 nm) were primarily generated through only one of the two major pathways.\nConsequently, the pronounced abundance of EV subtype 2 can be attributed to the\ncombined output of these two major EV populations. We have further elaborated on the\nreasons underlying the abundance variation across the three EV size subtypes in \"Steric\nhindrance-mediated EV size fractionation\".\nSupplementary Figure 12. Proteomic analysis of EV size subtypes generated by the\nSHEAF platform.\n(a, b) Venn diagrams illustrate the number of proteins identified in EV subtype 1, subtype\n2, and subtype 3 isolated from HMC3 (a) and HUVEC (b) cell culture mediums using the\nSHEAF platform. (c, d) Subcellular localization of differentially expressed proteins between\npaired EV subtype comparisons derived from HMC3 (c) and HUVEC (d) cell culture\nmediums. The numbers indicated the count of proteins with significantly different\nabundance (P < 0.05) and the presence or absence of a protein in each comparison. #\ndenoted the control group. ER, endoplasmic reticulum.\n2. How the PEG layer generates a steric barrier: relate PEG length (14–20 kDa) to the\nresulting exclusion distance. Will PEG steric hindrance compromise the recognition ability\nof aptamers? Why use aptamers instead of antibodies?\nOur response: We gratefully acknowledge the reviewer for their insightful comments\nconcerning the mechanism of PEG-mediated steric shielding and the rationale for aptamer\nselection. As supported by established literature, hydrophilic and flexible PEG chains\ngrafted onto nanoparticle surfaces extend into the surrounding medium and form hydration\nlayers through interactions with water molecules, thereby creating a robust steric barrier\nthat effectively impedes the approach of biomolecules10,11. The extent of this barrier—\ncommonly referred to as the exclusion distance—is well documented to depend on both\nthe PEG grafting ratio and molecular weight12,13. Increasing either parameter leads to a\ngreater exclusion distance and enhanced steric hindrance. Our experimental findings are\nfully consistent with this mechanistic (Figure 2 c-f), demonstrating that the binding activity\nof surface-conjugated aptamers is precisely regulated by PEG chain length and grafting\nratio. Notably, when PEGs with a molecular weight above 14 kDa were applied at a grafting\nratio of 80% (correspondingly reducing the aptamer grafting ratio to 20%), the resulting\nsteric exclusion distance was sufficient to effectively reduce aptamer binding affinity by\nshielding the aptamer from interacting with CD63 on EV surface. Through systematic\noptimization, two distinct MNP formulations were identified that achieved complete steric\nshielding of CD63 on EVs larger than 182.9 nm and 93.1 nm in diameter, respectively.\nThese MNPs were subsequently used to capture EV subpopulations below these size\nthresholds, enabling size fractionation of EVs.\nRegarding the concern about potential compromise of aptamer function, we would like to\nclarify that the steric hindrance introduced by PEG is non-specific and physical in nature.\nIt does not alter the intrinsic structure of the conjugated aptamers, which retain their\nconformation, high-affinity binding sites, and recognition specificity. Instead, the PEG layer\nprimarily modulates aptamer binding behavior by controlling the rate and probability of\nencounters between the target protein and the aptamer. While the PEG chain may reduce\nthe frequency of collisions between the aptamer and its target protein due to steric shielding,\nonce contact occurs, the recognition specificity remains unchanged. It is precisely this\nmechanism - controlled binding behavior through steric hindrance at the interface - that\nenables efficient and selective EV size fractionation. We have clarified this point in the\nDiscussion section.\nWe thank the reviewer for raising the point regarding our choice of aptamers over\nantibodies. This decision was guided by three main considerations: precise control over\nsteric hindrance on MNP surfaces, consistent batch-to-batch performance, and the\npotential for point-of-care application of the SHEAF platform. Owing to their low molecular\nweight and chemically defined synthesis, aptamers enable accurate control of grafting sites\nand ratios—factors critical for reliable steric hindrance analysis. In contrast, antibodies\noften undergo random conjugation due to their structural complexity14, which increases\ninactivation risks and complicates data interpretation. Furthermore, aptamers exhibit\nexcellent ambient stability and minimal batch variation as they are chemically synthesized,\nunlike antibodies, which typically require frozen storage and may exhibit batch-to-batch\nvariation. These attributes make aptamers not only suitable for controlled experimental\nstudies but also practical for future translation into robust, point-of-care EV analytical tools.\nWe have explicitly clarified the rationale for choosing aptamers in the Discussion section.\n3. Provide large-area TEM micrographs that show intact EVs bound to MNPs for all three\nsize fractions.\nOur response: We thank the reviewer for emphasizing the value of including transmission\nelectron microscope (TEM) images showing intact EVs bound to MNPs. Due to the\ninherent and significant contrast differences between EVs and MNPs, we employed\nstandard phosphotungstic acid staining during TEM analysis. While staining successfully\nimproved the visibility of the EVs, it also resulted in the deposition of phosphotungstic acid\non and around the MNPs. This common issue in TEM analysis made it difficult to\ndifferentiate between the MNPs and the stain deposits in the images. Furthermore,\nconstrained by the concentration limit of EVs processable by our SHEAF platform, the\ncaptured TEM images displayed a low density of EVs per field of view (2.1 μm  2.1 μm).\nDespite these limitations, we were able to identify three representative EV-MNP hybrid\nsubtypes with EV diameters of <90 nm, 90–180 nm, and >180 nm in the new\nSupplementary Figure 8. These observations, together with the DLS and NTA results,\ncorroborate the successful size-based fractionation of EVs achieved through our steric\nhindrance-mediated method.\nSupplementary Figure 8. Characterization of EV-MNP hybrids by TEM.\nThe TEM images of EV-MNP hybrids formed by using MNP-APT20%-PEG20K (subtype\n1), MNP-APT20%-PEG14K (subtype 2), and MNP-APT (subtype 3) to treat EV solution in\na sequential manner. Scale bar = 500 nm. Red arrow indicated MNP, and blue arrow\nindicated EV.\n4. Please include NTA. TEM, and Western-blot data for the three size subtypes isolated\nfrom plasma samples.\nSupplementary Figure 13. Characterization of EV size subtype derived from patient\nplasma.\n(a) The Western blotting bands of patient plasma EV subtypes derived by SHEAF platform.\nEV marker: CD63, ALIX, and HSP70. Contaminant marker: APOB. (b) The TEM images of\npatient plasma EV subtypes isolated by SHEAF platform. Scale bar = 100 nm. (c) The NTA\nanalysis results of patient plasma EVs subtypes.\nOur response: We thank the reviewer for highlighting the importance of characterizing the\nthree EV size subtypes isolated from patient plasma via NTA, TEM, and western blot\nanalysis. We fully agree that comprehensive validation strengthens the reliability of our\nisolation platform. However, due to the limited plasma volume available from each patient\n(typically 300-400 µL), it was not feasible to perform all three assays for every individual\nsample, as each technique generally requires 50–100 µL of plasma per experiment\n(depending on EV concentration). To address this point pragmatically, we selected one\npatient sample with sufficient volume and conducted complete NTA, TEM, and western blot\ncharacterization on the three size-based subtypes isolated from that sample (new\nSupplementary Figure 13). Western blot analysis confirmed that all three subtypes\nexhibited clear EV biomarker bands (CD63, ALIX, and HSP70), while no contaminant\nAPOB bands were detected, indicating high purity of the isolates (new Supplementary\nFigure 13a). According to NTA and TEM results, the SHEAF platform successfully isolated\nEVs with diameters of <90 nm, 90–180 nm, and >180 nm from plasma (new\nSupplementary Figure 13b,c). We have incorporated these findings into the revised\nmanuscript to better address the reviewer’s concern.\n5. Clarify why VEGF, TGF-β, PD-L1, CRP, TNF-α, and ICAM-1 were chosen.\nOur response: We sincerely thank the reviewer for raising this important question\nregarding our selection of biomarkers. The proteins VEGF, TGF-β, PD-L1, CRP, TNF-α,\nand ICAM-1 were carefully chosen based on two main considerations: (1) their well-\nestablished and complementary roles in vascular dysfunction and inflammatory pathways\nrelevant to stroke pathophysiology; (2) substantiated evidence that these proteins can be\nassociated on EV membrane. Briefly, VEGF was included for its critical function in\nmodulating angiogenesis and vascular permeability15; TGF-β for its involvement in immune\nregulation, fibrosis, and vascular remodeling16; PD-L1 as a key immune checkpoint\nmolecule reflecting inflammatory suppression17; CRP as a systemic marker of inflammation\nand acute-phase response18; TNF-α for its pro-inflammatory role in BBB disruption and\nneuroinflammation19; and ICAM-1 for mediating leukocyte adhesion and endothelial\nactivation during cerebral ischemia20. Importantly, existing literature confirms that these\nproteins have been identified on EV surfaces, supporting their relevance for evaluating EV\nbiomarker cargo21-23. Together, these markers provide broad coverage of crucial pathways\nassociated with cerebrovascular and inflammatory injury in stroke.\n6. Explain mechanistically why subtype 2 (90–180 nm) is diagnostically superior.\nOur response: We sincerely thank the reviewer for raising this insightful question\nregarding the mechanistic basis for the diagnostic superiority of EV subtype 2. We would\nlike to clarify that this advantage primarily stems from the subtype’s optimal size distribution,\nwhich effectively balances two critical factors: efficient translocation across the BBB and\nsufficient membrane surface area for robust biomarker display. Specifically, EVs within the\n90–180 nm range (characteristic of subtype 2) demonstrate enhanced efficiency for\ncrossing the BBB into peripheral circulation1-3. Concurrently, this specific size range\nprovides an ideal platform for biomarker presentation. EVs smaller than 90 nm often lack\nadequate membrane space for displaying a substantial number of specific proteins4,\nwhereas those larger than 200 nm may frequently incorporate heterogeneous components,\nsuch as apoptotic bodies. Therefore, EV subtype 2 represents a unique population with\nboth high abundance in blood and ample surface real estate for carrying cell-type-specific,\ndiagnostically informative markers. Our proteomic analysis confirmed the superior capacity\nof EV subtype 2 for carrying a wide range of proteins from various subcellular structures,\nincluding the cytoplasm, cytoskeleton, mitochondria, and nucleus (new Supplementary\nFigure 12). In summary, the diagnostic superiority of this EV subpopulation arises from its\nbalanced capacity for efficient BBB crossing and substantial biomarker-carrying capability.\nWe appreciate the opportunity to clarify this point and have revised the manuscript\naccordingly.\n7. Please add more clinical samples and include healthy controls.\nOur response: We sincerely thank the reviewer for highlighting the critical importance of\nsample size and the inclusion of healthy controls in our clinical validation. In direct response,\nwe have significantly expanded our clinical cohort to enhance the statistical robustness of\nour findings. Briefly, we collected an additional 20 plasma samples from stroke patients,\nincluding 10 with ischemic stroke (IS) and 10 with intracerebral hemorrhage (ICH), along\nwith 20 plasma samples from healthy donors as a control group.\nFigure 5. SHEAF-based clinical analysis.\n(b) Two-dimensional PCA projection of plasma sample clustering patterns. Input features\ncomprised SHEAF-quantified expression profiles of six target proteins from each EV size\nsubtype and the whole EV population. (c) The receiver operating characteristic curves\nevaluating SHEAF-derived protein composition data of EV size subtype 2 for discriminating\nstroke patients (ICH and IS) from healthy controls, and ICH from IS. Numbers denoted\narea under the curve values with 95% confidence intervals.\nAll plasma samples were processed using the integrated SHEAF platform to perform size-\nbased fractionation of EVs into three subtypes, followed by membrane protein profiling of\nsix stroke-related biomarkers. The results consistently demonstrated the superior\ndiscriminatory power of EV subtype 2. Principal component analysis (PCA) of protein\nexpression profiles revealed minimal inter-group overlap for subtype 2, whereas significant\noverlap was observed for other subtypes and the whole EV population, indicating its\nenhanced classification capability (revised Figure 5b). Receiver operating characteristic\n(ROC) curves further validated this finding. For distinguishing stroke patients (ICH and IS\ncombined) from healthy controls, the six-protein panel in EV subtype 2 achieved an area\nunder the curve (AUC) of 0.951 (revised Figure 5c). In differentiating ICH from IS, the\ncombined AUC for subtype 2 reached 0.958, significantly outperforming other subtypes\nand the unfractionated EV population (new Supplementary Figure 14).\nWhile the expanded cohort strengthens our conclusions, we acknowledge that further\nmulti-center validation is needed to confirm prehospital applicability. We have revised the\nmanuscript to include these findings and limitations in the Discussion section.\nSupplementary Figure 14. Receiver operator characteristic curves of EV subtype 1.\nEV subtype 3, and whole EV population.\nReceiver operator characteristic curves of whole EV, EV subtype 1, and EV subtype 3 in\ndifferentiating stroke from healthy controls (upper), and ICH from IS (lower). Numbers\ndenoted area under the curve values with 95% confidence intervals.\nReviewer #2\nNoteworthy and significance:\nThe new method presented in this paper is of scientific merit and clearly based upon well\nestablished ideas for nanoparticle and filtration based techniques. The authors propose a\nrapid method for the detection of three EV subtypes based upon their preliminary research.\nThe technique offers a high purity EV recovery (95%), this will be particularly interesting to\nthe EV community. Integration into a microfluidic device offers a unique, dedicated\napparatus for EV isolation. The use of smart phone detection is innovative but should be\ntreated cautiously.\nConclusions:\nThe authors offer significant support in the manuscript to support their ideas. They\nvalidated using multiple gold standard techniques and provided tangible evidence (TEM,\nNTP and Western blotting). Many of their ideas are supported with the literature such as\nEVs carrying proinflammatory markers.\nAdditional evidence and flaws:\n1. The author have not stated the properties of these EV subtypes other than size or vesicle\ndiameter, however there are distinct subtypes of EV that are produced with distinctive sizes\nthat are formed through different biogenic pathways.\nOur response: We thank the reviewer for this insightful comment. In addition to the size\ncharacterization of EV subtypes, we have conducted a series of experiments to evaluate\ntheir physicochemical and biochemical properties. Briefly, dynamic light scattering (DLS)\nwas used to determine particle size and surface potential, western blot was employed to\ndetect typical EV protein markers, and BCA assay was utilized to quantify protein content\n(summarized in Figure 3c–e and Supplementary Figure 9).\nFigure 3. Steric hindrance-mediated EV size fractionation.\n(c) Western blotting validation of EV markers (CD63, ALIX, and HSP70) and contaminant\ncontrol (APOB) across subtypes. (d) Linear regression analysis demonstrating correlation\nbetween EV protein concentrations quantified through BCA assay and EV particle\nconcentrations measured via NTA, with protein values determined using EV subtypes 1 -\n3 isolated by steric hindrance-mediated fractionation from HUVEC, HMC3, SK-N-BE(2),\nand THP-1 cell culture mediums. EV particle concentrations were obtained from NTA\nmeasurements of unfractionated culture mediums. (e) Recovery rates of the three EV\nsubtypes fractionated from solutions with known EV concentrations, calculated relative to\nmaximal protein recovery controls (EVs isolated using 1.0 mg/mL MNP-APT), with\nstatistical significance determined through inter-group comparisons of identical subtypes\nisolated from solutions with varying EV concentrations. * indicated significant difference, P\n<0.05.\nSupplementary Figure 9. DLS analysis of EV subtypes fractionated by SHEAF.\nThe hydration diameters (a) and surface zeta potentials (b) of EV subtype 1, subtype 2,\nand subtype 3 fractionated from the culture mediums of HUVEC, HMC3, SK-N-BE(2) and\nTHP-1 through the SHEAF platform.\nSupplementary Figure 12. Proteomic analysis of EV size subtypes generated by the\nSHEAF platform.\n(a, b) Venn diagrams illustrate the number of proteins identified in EV subtype 1, subtype\n2, and subtype 3 isolated from HMC3 (a) and HUVEC (b) cell culture mediums using the\nSHEAF platform. (c, d) Subcellular localization of differentially expressed proteins between\npaired EV subtype comparisons derived from HMC3 (c) and HUVEC (d) cell culture\nmediums. The numbers indicated the count of proteins with significantly different\nabundance (P < 0.05) and the presence or absence of a protein in each comparison. #\ndenoted the control group. ER, endoplasmic reticulum.\nTo further explore the biogenic pathways associated with EV subtypes originating from\nHMC3 and HUVEC cell culture mediums, we performed proteomic analyses on these EVs.\nOur results demonstrate that EV Subtype 2 exhibits the highest diversity of protein content\nin both HMC3 and HUVEC cells (new Supplementary Figure 12a,b). Importantly,\ndifferential analysis across subtypes, particularly as visualized in new Supplementary\nFigure 12c,d, reveals that proteins significantly enriched in EV Subtype 2 are derived from\ndiverse intracellular compartments, including the cytoplasm, cytoskeleton, mitochondria,\nand nucleus. In contrast, the set of differentially expressed proteins defining subtype 1\nexhibits markedly less diverse subcellular origins. Similarly, while subtype 3 shares certain\nprotein components with subtype 2, its distinct differential expression profile implies a\ndifferent and possibly more restricted intracellular sourcing mechanism. Thus, the\ndifferential protein distribution patterns confirm that the distinct cargo profile of subtype 2,\ncharacterized by its wide-ranging intracellular protein composition, supports its role as a\nmultifaceted vehicle in transducing intercellular communication.\n2.These have characteristic biomarkers, such as sMVs or exosomes typically express\nCD63 on their outer membrane and are 30 - 100nm (there abouts) and are as a result of\nthe endocytic pathway, however, microvesicles (s/EVs) are larger 100nmm - 1000nm,\nproduced by blebbing at the cell surface and express little or no CD63. From what I can\nascertain, there are two populations of microvesicles, that should express negligible CD63\nlevels.\nOur response: We thank the reviewer for this important comment regarding the differential\nexpression of CD63 across EV subpopulations. We agree that exosomes, which are\ngenerated via the endocytic pathway and are typically under 200 nm24, are\ncharacteristically enriched in CD63. In contrast, large EVs (200-1000 nm), which\npredominantly originate from plasma membrane budding, often demonstrate negligible\nCD63 expression. However, it should be noted that CD63 is expressed on the membranes\nof various cell types, including HUVEC and HMC3, as supported by previous studies25,26.\nConsequently, a subset of large EVs may retain elevated levels of CD63. We acknowledge\nthat our CD63-based isolation method may under-represent large EV populations with\nminimal CD63 expression. This limitation, along with its potential implications for data\ninterpretation, has been explicitly discussed in the revised Discussion.\nFurthermore, we selected CD63 as a capture target because CD63-positive EVs are widely\nrecognized to carry biologically informative molecular cargo that reflects the pathological\nstate of parental cells24, making them highly relevant for our stroke diagnostic objectives.\n3.Is it possible that the fractions containing larger EVs are 'contaminated' with smaller,\naggregated EVs expressing CD63? I saw no comment about disaggregation steps in the\nprotocols. Conceptually the work is interesting and if correct will offer an advantage to\nclinical laboratories.\nOur response: We thank the reviewer for raising this important point regarding the\npotential aggregation of smaller EVs in larger size fractions. In our study, specific\ndisaggregation steps (such as ultrasonication or surfactant treatment) were omitted based\non two primary considerations. First, we aimed to preserve the native state of EVs.\nDisaggregation treatments are well documented to compromise the integrity of the EV lipid\nbilayer and alter their biological properties27,28. Second, EVs are generally stable and\nremain well dispersed under physiological conditions in both culture media and plasma.\nThis observation aligns with established EV fractionation techniques—such as size-\nexclusion chromatography (SEC)29, density gradient ultracentrifugation (DGUC)30, and\nasymmetric field-flow fractionation (AF4)31—which routinely proceed without\ndisaggregation steps and yield well-dispersed, functionally intact EV subpopulations.\nAlthough we cannot entirely rule out the possibility of some degree of aggregation, all size-\nbased separation and characterization steps in this study were conducted under optimized\nbuffer conditions to minimize such artifacts. We have clarified in the “Cell culture and EV\ncollection” section that no disaggregation step was included during SHEAF\nmeasurements.\n4. I would urge the authors to address this in their manuscript, either highlighting this as a\nflaw, altering their emphasis and conclusions or addressing this issue with the necessary\nsupportive supplementary information, i.e., that they have removed aggregated exosomes,\nthat the cells themselves express CD63 on their surface or that all EV subtypes are\nenriched for CD63 - this is highly unlikely though.\nOur response: We thank the reviewer for this insight. As indicated in our earlier response,\nthe disaggregation step was omitted to preserve EV membrane integrity. Moreover, since\nEVs are naturally well-dispersed in biofluids, additional disaggregation was considered\nunnecessary—an approach aligned with several established EV fractionation studies29-31.\nWe are therefore confident that our protocol remains appropriate and have clarified this\npoint in the revised manuscript.\nRegarding the use of CD63 as a marker, we thank the reviewer for this comment. We\nselected CD63 because it is widely recognized as a common marker protein for EVs,\nparticularly those originating from the endosomal pathway. Furthermore, a variety of cell\ntypes relevant to brain stroke, such as HUVECs and HMC3 cells, express CD63 on their\nmembranes. This suggests that EVs generated by membrane budding from these cells\nmay exhibit elevated CD63 expression levels. Although not all EV subtypes uniformly\nexpress CD63, focusing on the CD63-positive EV subpopulation is a strategic approach to\nidentify the EV size subtype most directly relevant to stroke diagnosis. We acknowledge\nthat using CD63 as the capture target may omit bio-signals from EV subtypes with minimal\nCD63 expression. We have revised the Discussion section to better articulate this\nstrategic choice.\n5. The clinical sample size is relatively small. Although a larger sample size is probably\ndifficult to find participants the authors should acknowledge this as a limitation or propose\na larger study.\nOur response: We thank the reviewer for highlighting the importance of sample size in\nclinical studies. We agree that a larger cohort would further strengthen the statistical power\nof our findings. In response to this comment, we have collected additional samples,\nexpanding our cohort to include 20 more stroke patients and 20 healthy controls as a\nnegative reference group.\nAnalysis using the SHEAF platform on the expanded cohort revealed that EV subtype 2\n(90-180 nm) exhibited distinct separation between groups in the PCA analysis, showing\nminimal inter-group overlap. This was in contrast to the significant overlap observed for\nother EV subtypes and the whole EV population (revised Figure 5b). This superior\ncapability of EV subtype 2 was further confirmed by its exceptional diagnostic performance.\nThe six-protein panel within this subtype yielded an AUC of 0.951 for distinguishing stroke\npatients from healthy controls. Furthermore, for the critical differentiation between ICH and\nIS, the combined AUC for EV subtype 2 reached 0.958. Individual proteins in the panel,\nsuch as VEGF, CRP, and ICAM-1, each achieved AUC values exceeding 0.70 (revised\nFigure 5c). The diagnostic performance of the other EV subtypes is presented in new\nSupplementary Figure 14.\nNevertheless, we fully acknowledge that the sample size remains relatively limited, and we\nhave explicitly stated the need for larger, multi-center validation in the Discussion section\nto confirm and extend our findings.\nFigure 5. SHEAF-based clinical analysis.\n(b) Two-dimensional PCA projection of plasma sample clustering patterns. Input features\ncomprised SHEAF-quantified expression profiles of six target proteins from each EV size\nsubtype and the whole EV population. (c) The receiver operating characteristic curves\nevaluating SHEAF-derived protein composition data of EV size subtype 2 for\ndiscriminating stroke patients (ICH and IS) from healthy controls, and ICH from IS.\nNumbers denoted area under the curve values with 95% confidence intervals.\nSupplementary Figure 14. Receiver operator characteristic curves of EV subtype 1.\nEV subtype 3, and whole EV population.\nReceiver operator characteristic curves of whole EV, EV subtype 1, and EV subtype 3 in\ndifferentiating stroke from healthy controls (upper), and ICH from IS (lower). Numbers\ndenoted area under the curve values with 95% confidence intervals.\nMethods:\n6. This are sounds - except what has been previously stated. Gold standard tools are used.\nOur response: We thank the reviewer for this valuable comment. SEC and DGUC are\nwell-established as gold-standard methods for EV size-based fractionation. In this study,\nthey were therefore employed as benchmark techniques to evaluate the performance of\nour SHEAF platform. Experimental results confirmed that SHEAF outperformed both SEC\nand DGUC in terms of purity, recovery rate, and processing time (Figure 3f-h). Detailed\nprotocols for SEC and DGUC have been provided in the “EV size fractionation by SEC\nand DGUC” section.\nFigure 3. Steric hindrance-mediated EV size fractionation.\n(f) Hydrodynamic diameters of nanostructures in SEC-isolated fractions (upper) and\nDGUC-isolated fractions (lower). In the SEC method, 10 fractions (F1 to F10, where F1\ndenoted Fraction 1) were collected with an elution time of 1135 s. In the DGUC method, 8\nfractions (1 mL each) were sequentially collected from the top of the density gradient.\nWestern blot bands (g) and recovery rates (h) of EV size fractions isolated by SEC (left)\nand DGUC (right). Fractions with hydrodynamic diameters >20 nm (SEC: F5 - F7; DGUC:\nF4 - F7) were considered vesicular nanostructures and analyzed by western blotting and\nrecovery rate assays. EV recovery rate was calculated as: (protein concentration in the EV\nfraction / protein concentration of total EVs) × 100%.\n7. The methods should be reproducible based upon protocol written, including aptimer\nsequences and analytical techniques and so on.\nOur response: We thank the reviewer for emphasizing the importance of methodological\nreproducibility. In response, detailed fabrication procedures of the microfluidic chip are now\nillustrated in new Supplementary Figure 15, while the system configuration and\noperational logic of the SHEAF device are described in detail in new Supplementary\nFigure 17. The optimization process and assessment of the device's reproducibility are\nfurther presented in new Supplementary Figure 18. For the Instrument section,\nadditional experimental details regarding instrument parameter settings have been\nincluded. For the clinical analysis, baseline characteristics of the patient cohort have been\nprovided in new Supplementary Table 2. Additionally, all aptamers used in this study were\nselected based on previously published works, which have well-documented their\nspecificity and affinity. Detailed sequences were provided in the revised Supplementary\nTable 5.\nSupplementary Table 2. Baseline characteristics of patients.\nICH patients IS patients Healthy donors\n62.8 63.3 61.1\nAge (year)\n(IQR* 57.0 – 72.0) (IQR 59.0 – 66.0) (IQR 52.8 – 66.8)\nMale 17 16 14\nFemale 8 9 6\nAlcohol users 13 12 9\nTobacco\n10 7 6\nsmokers\nHypertension 16 17 20\nDiabetes 3 4 4\nSolid tumor\n0 0 0\nand/or leukemia\n* IQR denoted interquartile range.\nSupplementary Table 5. The DNA sequences used in this research.\nSequences\nTR GAA CCT GAA CCT GAA CCT\nPD-L1\nTAC AGG TTC TGG GGG GTG GGT GGG GAA CCT GTT AGG TTC AGG\nAPT\nPD-L1 TTC AGG TTC CCC AAA ATG (Kd = 90.8)32\nACT CAT TTT GGG GAA CCT GAA CCT GAA CCT\nPD-L1\nTR GTT CCA GTT CCA GTT CCA\nCRP\nCGA AGG GGA TTC GAG GGG TGA TTG CGT GCT CCA TTT GGT GTG\nAPT\nCRP GAA CTG GAA CTG GAA CTT TCC CCG A (Kd = 22.1 nM)33\nACT TCG GGG AAA GTT CCA GTT CCA GTT CCA\nCRP\nTR CTG GTT TCA ACT GCA CGA\nVEGF\nTGT GGG GGT GGA CTG GGT GGG TAC CTC GTG CAG TTG AAA CCA\nAPT\nVEGF GTT CCG AGA C (Kd = 0.30 nM)34\nACT GTC TCG GAA CTG GTT TCA ACT GCA CGA\nVEGF\nTR GCA ACC TTG GGG AAT TCC\nTGF\nTGT CGT TGT GTC CTG TAC CCG CCT TGA CCA GGA ATT CCC CAA\nAPT\nTGF GGT TGC TAG CTA GCT (Kd = 90.0 nM)35\nACT AGC TAG CTA GCA ACC TTG GGG AAT TCC\nTGF\nTR GCG CGC GCG CAT ATA TAT\nTNF\nGCG CCA CTA CAG GGG AGC TGC CAT TCG AAT AGG TGG GCC GCA\nAPT\nTNF TAT ATA TGC GCG CGC GCA TGG CCT TA (Kd = 8.0 nM)36\nACT TAA GGC CAT GCG CGC GCG CAT ATA TAT\nTNF\nTR GTA GTC TGG TCC AAA GCT\nICAM\nACA CCA TAG TAG CAA TGG GCA CCA CTA GTA CAC AGT AGC ACA\nAPT CCA GTA GTC AGC TTT GGA CCA GAC TAC GTG ATC CGA (Kd = 151.6\nICAM\nnM)37\nACT TCG GAT CAC GTA GTC TGG TCC AAA GCT\nICAM\nAPT CAC CCC ACC TCG CTC CCG TGA CAC TAA TGC TA (Kd = 17.1 nM)32\nCD63\n8. However, the manufacture and calibration of the microfluidic device requires more detail.\nOur response: We thank the reviewer for this comment regarding the preparation and\ncalibration of the microfluidic device. In response, we have provided a detailed description\nof the chip manufacturing process, covering cover layer fabrication, substrate preparation,\nmicrochannel formation, and final assembly (new Supplementary Figure 15). Additionally,\nwe have included the system configuration and operational logic of the automated device,\nfeaturing an isometric view of the setup, the control architecture, and a time-sequence\ndiagram of component states during operation (new Supplementary Figure 17). For\ncalibration, optimization experiments were conducted on key operational parameters,\nincluding peristaltic pump driving voltage versus chamber filling times, filling time\nreproducibility across five independent chips, electromagnet driving voltage versus\nseparation efficiency, and processing duration versus separation efficiency (new\nSupplementary Figure 18).\nSupplementary Figure 15. Fabrication\nprocesses of the microfluidic chip.\n(a) Cover layer fabrication: The cover layer\nwas patterned using a laser engraver,\nfollowed by threading of access holes. (b)\nSubstrate layer preparation: PDMS was\nmolded on a silicon wafer. After curing, the\nPDMS sheet was demolded, and through-\nholes were punched. (c) Microchannel\nlayer formation: An SU-8 photoresist was\nspin-coated onto a silicon wafer and\nexposed to UV light through a photomask\nto create a casting mold. A 10:1 mixture of\nPDMS and crosslinker was poured onto the\nmold, cured at 60 °C overnight, and demolded. Inlet and outlet ports were punched using\na hole puncher. (d) Microfluidic chip assembly: The cover and substrate layers were\nactivated with oxygen plasma, treated with 5% (v/v) APTES solution for 20 min, and rinsed.\nThe microchannel layer was activated with oxygen plasma, aligned and assembled with\nthe cover and substrate layers, and bonded at 70 °C overnight.\nSupplementary Figure 17. System\nconfiguration and operational logic of\nthe automated microfluidic device.\n(a) Isometric view of the physical setup. (b)\nSystem control architecture and signal\nflow. (c) Time-sequence diagram of the\ncomponent states (pump: red, valves: blue,\nelectromagnet: green) during a protocol\nfeaturing incubation and separation steps.\nSupplementary Figure 18. Parameter\noptimization for the operation of the\ntriple-chamber microfluidic chip.\n(a) Filling times of the three chambers over\na range of peristaltic pump driving voltages.\nAn optimal voltage of 5 V was identified,\nproviding the shortest filling time with\nminimal variability; higher voltages did not\nfurther reduce the time. (b) Filling time\nreproducibility for each chamber tested\nacross five independent chips,\ndemonstrating high operational stability. (c) Influence of electromagnet driving voltage on\nthe separation efficiency of MNPs in chamber A and chamber B, quantified by absorbance\nchange at 450 nm. Peak efficiency was achieved at 6 V, with no improvement observed\nbeyond this voltage. (d) Effect of electromagnet processing duration on MNP separation\nefficiency in chamber A and chamber B. Efficiency reached a plateau after 60 s of\nprocessing.\nReviewer #3\nIn this manuscript entitled “Steric Hindrance-Mediated Extracellular Vesicle Size\nFractionation for Rapid Prehospital Diagnosis of Intracerebral Hemorrhage”, the authors\npresent a novel SHEAF platform designed to enable rapid EV size-fractionation and protein\nprofiling. They report that EVs within the 90–180 nm range demonstrate potential as highly\ndiscriminative biomarkers for ICH, with possible applications for rapid and precise stroke\ndiagnostics in the prehospital setting.\nThe paper is clearly written, and the investigators provide interesting and timely data.\nNonetheless, several important aspects require clarification and further elaboration. My\ndetailed comments are outlined below.\n1. The introduction is overly superficial. Relevant issues such as cause-specific\nmanagement of acute stroke patients in the prehospital setting (e.g., blood pressure control\nand the need for ICH patients to be admitted to specialized units) are not addressed. A\nmore comprehensive rationale for the study design is required, particularly concerning the\npotential added value of including EVs. Additionally, the cited literature is somewhat\noutdated.\nOur response: We sincerely thank the reviewer for these insightful comments. We have\nthoroughly revised the Introduction to address these concerns. Briefly, we have expanded\nthe explanation of cause‑specific prehospital management in acute stroke, now clearly\ndifferentiating the early protocols for ICH and IS. These include the emphasis on rapid\nblood‑pressure control and direct triage to specialized neurocritical care units for ICH\npatients, as opposed to time‑sensitive reperfusion therapies for IS patients. This\nenhancement strengthens the clinical rationale for rapid prehospital differentiation of stroke\nsubtypes, a central theme of our study.\nIn response to the reviewer’s comment, we have refined the rationale to highlight the added\nvalue of EV in Introduction section. The SHEAF platform addresses the critical unmet\nneed for prehospital diagnosis when timely neuroimaging is unavailable. EVs serve as\nideal circulating biomarkers in this context because they cross the BBB and transport brain-\nspecific biomarkers (e.g., VEGF, TNF-α) into circulation. Furthermore, EVs can protect\nencapsulated biomarkers from degradation, thereby offering enhanced specificity and\nstability compared to free biomarkers. However, EV heterogeneity—especially\npolydisperse sizes—reduces diagnostic accuracy. To overcome this, our SHEAF platform\nemploys steric hindrance-mediated size fractionation to selectively isolate EV subtypes\n(e.g., 90–180 nm) that optimally balance barrier penetration and biomarker load. This\nenriches disease-specific signals while reducing background noise. By integrating this\nunique biological property of EVs with a sub-45-min workflow, the SHEAF platform enables\naccurate ICH/IS discrimination without imaging, providing a rapid biomarker-based tool to\nimprove prehospital stroke triage.\nFinally, we have updated the cited literature with references from the last five years. At the\nsame time, references on aptamer development (reporting Kd values) and curvature-\ndependent binding of phospholipids at the EV surface have been retained as originally\ncited.\n2. The methodological section is insufficiently detailed. Critical clinical information is\nmissing, such as patient recruitment details (e.g., location, timeframe), laboratory where\nanalyses were performed, and whether assays were conducted in a blinded manner. The\ntiming of sample collection—fundamental to the claimed prehospital application—is not\nreported. The statistical methods are inadequately described, with no mention of sample\nsize calculation, limited descriptive statistics, and no adjustment for multiple comparisons.\nOur response: We thank the reviewer for pointing out the missing of clinical information.\nThat critical information has been provided in the revised manuscript. Specifically, clinical\nsamples of stroke patients were collected at the Department of emergency of The Affiliated\nHospital of Guizhou Medical University, while clinical samples of healthy controls were\ncollected at the Department of Cardiovascular Medicine. The initial cohort of samples was\ncollected from January to September 2024, while the additional samples incorporated\nduring the revision phase were obtained between August and October 2025. Clinical\ndiagnoses were performed by medical staff from the Department of Emergency Medicine\nand the Department of Cardiovascular Medicine. Subsequent analyses based on SHEAF\nplatform were conducted by researchers from the School of Pharmacy at Guizhou Medical\nUniversity and School of Mechanical Engineering at Northwestern Polytechnical University.\nAll sample-related assays were performed in a blinded manner. For stroke patients, blood\nwas collected prior to any treatment intervention. Diagnosis of either ICH or IS was\nconfirmed within one hour after blood sampling. For healthy donors, blood samples were\nobtained from individuals with no history of stroke or myocardial infarction. For clarification,\nwe have revised the “Clinical samples” section and added a baseline characteristics table\nas new Supplementary Table 2.\nWe are grateful to the reviewer for the valuable suggestion to enhance the statistical rigor\nof our study. Accordingly, we have revised the Statistics section to include a sample size\ncalculation for the clinical analyses and have incorporated false discovery rate adjustments\nfor multiple comparisons. The detailed results of these analyses are now provided in new\nSupplementary Table 6.\nSupplementary Table 2. Baseline characteristics of patients.\nICH patients IS patients Healthy donors\n62.8 63.3 61.1\nAge (year)\n(IQR* 57.0 – 72.0) (IQR 59.0 – 66.0) (IQR 52.8 – 66.8)\nMale 17 16 14\nFemale 8 9 6\nAlcohol users 13 12 9\nTobacco\n10 7 6\nsmokers\nHypertension 16 17 20\nDiabetes 3 4 4\nSolid tumor\n0 0 0\nand/or leukemia\n* IQR denoted interquartile range.\nSupplementary Table 6. Statistical analysis of clinical samples.\nICH vs. IS Stroke vs. Healthy donors\nFalse False Sample size\nSample size\nP value discovery P value discovery (stroke /\n(ICH / IS)\nrate rate healthy)\nVEGF 0.1510 0.1812 125 / 125 0.6659 0.6659 2183 / 873\nTGF-β 0.1933 0.1933 152 / 152 0.3133 0.3760 574 / 230\nWhole PD-L1 0.0369 0.0738 58 / 58 0.0019 0.0063 49 / 19\nEV CRP 0.1481 0.1812 123 / 123 0.0021 0.0063 44 / 18\nTNF-α 0.0241 0.0738 50 / 50 0.0889 0.1334 190 / 76\nICAM-1 0.0369 0.0738 58 / 58 0.0179 0.0358 86 / 34\nVEGF 0.0794 0.1191 83 / 83 0.0095 0.0119 66 / 26\nTGF-β 0.2798 0.2798 221 / 221 0.0119 0.0119 64 / 26\nSubtype PD-L1 0.0125 0.0250 41 / 41 0.0061 0.0119 54 / 22\nTNF-α 0.0061 0.0207 33 / 33 0.0002 0.0012 36 / 14\nICAM-1 0.1345 0.1614 115 / 115 0.0079 0.0119 64 / 26\nVEGF 0.0007 0.0022 22 / 22 0.0059 0.0071 87 / 35\nTGF-β 0.0522 0.0522 68 / 68 0.0001 0.0003 19 / 7\nSubtype PD-L1 0.0035 0.0053 29 / 29 0.0001 0.0003 26 / 10\nTNF-α 0.0011 0.0022 23 / 23 0.0253 0.0253 121 / 49\nICAM-1 0.0008 0.0022 22 / 22 0.0010 0.0015 54 / 22\nVEGF 0.1529 0.2616 126 / 126 0.1055 0.1583 201 / 81\nTGF-β 0.4334 0.4334 422 / 422 0.3500 0.3500 534 / 214\nSubtype PD-L1 0.1744 0.2616 140 / 140 0.0795 0.1583 151 / 61\nTNF-α 0.0162 0.0969 44 / 44 0.3028 0.3500 420 / 168\nICAM-1 0.3520 0.4224 299 / 299 0.0126 0.0378 91 / 37\n3. The absence of a healthy control group significantly weakens the interpretability of the\nfindings.\nOur response: In direct response to the reviewer's insightful comment, we expanded our\nclinical cohort by collecting plasma samples from 20 age- and sex-matched healthy donors,\nalong with additional 20 plasma samples from stroke patients (10 with IS and 10 with ICH).\nThose samples were subsequently analyzed alongside the original patient samples using\nthe integrated SHEAF platform. The updated results decisively demonstrated that the\nprotein signatures of EV size subtype 2 (90-180 nm) not only achieved an exceptional AUC\nvalue of 0.951 for distinguishing stroke patients (ICH and IS combined) from healthy\ncontrols but also maintained a superior discriminatory power for differentiating ICH from IS,\nwith a combined AUC value of 0.958 (revised Figure 5c and new Supplementary Figure\n14). This analysis, supported by PCA projections that demonstrate a clear separation\nspecifically for subtype 2 (revised Figure 5b), confirms the SHEAF platform as a highly\naccurate tool for rapid prehospital diagnosis.\nFigure 5. SHEAF-based clinical analysis.\n(b) Two-dimensional PCA projection of plasma sample clustering patterns. Input features\ncomprised SHEAF-quantified expression profiles of six target proteins from each EV size\nsubtype and the whole EV population. (c) The receiver operating characteristic curves\nevaluating SHEAF-derived protein composition data of EV size subtype 2 for\ndiscriminating stroke patients (ICH and IS) from healthy controls, and ICH from IS.\nNumbers denoted area under the curve values with 95% confidence intervals.\nSupplementary Figure 14. Receiver operator characteristic curves of EV subtype 1.\nEV subtype 3, and whole EV population.\nReceiver operator characteristic curves of whole EV, EV subtype 1, and EV subtype 3 in\ndifferentiating stroke from healthy controls (upper), and ICH from IS (lower). Numbers\ndenoted area under the curve values with 95% confidence intervals.\nunrealistic. The requirement for plasma processing and the reported 45-minute workflow\nmake its use in ambulances unfeasible.\nOur response: We sincerely thank the reviewer for this insightful comment. We agree that\nthe current requirement for plasma processing, together with an ~45-min workflow, poses\npractical challenges for immediate deployment and routine operation in an ambulance\nsetting. Nevertheless, SHEAF was conceived as a compact platform that operates without\nlarge, fixed instruments, which provides a realistic pathway toward future prehospital use\nunder resource- and space-constrained conditions. In practice, plasma preparation could\nbe enabled by pairing SHEAF with a portable centrifuge, allowing sample processing\ndirectly after collection within the ambulance compartment. Importantly, initiating the assay\nduring patient transport shifts testing earlier in the care pathway; thus, even with the current\n~45-min workflow, results could be available either en route or shortly after hospital arrival,\nthereby shortening the time to clinical decision-making. We emphasize, however, that\nfurther workflow optimization (e.g., reducing processing time and simplifying sample\nhandling) and validation in real-world prehospital settings will be required before routine\nclinical implementation. We have clarified these aspects in the revised Discussion.\n5. The statement that the platform outperforms “conventional approaches” is vague, as no\nclear comparator methods are identified.\nOur response: We thank the reviewer for this insightful comment. We fully acknowledge\nthat computed tomography (CT) and magnetic resonance imaging (MRI) represent the\ncurrent gold standard for stroke diagnosis, delivering exceptional diagnostic accuracy and\ndetailed anatomical information essential for guiding treatment decisions. The key\nstrengths of our platform lie in its markedly shorter end-to-end time compared to the\nlogistical delays of CT/MRI acquisition, as well as its potential in resource-limited settings\nthat lack immediate access to advanced neuroimaging (new Supplementary Table 4). To\nclarify this distinction, we have revised the Discussion to explicitly state that the SHEAF\nplatform is intended as a rapid supplementary tool—not a replacement—for established\nimaging modalities, facilitating earlier triage and intervention during the prehospital phase\nof stroke care.\nSupplementary Table 4. Comparison of SHEAF, MRI, and CT technologies.\nMRI CT SHEAF\nEV size fractionation\nWorking Reconstructed images Cross-sectional images\nand membrane protein\nPrinciple from RF signals from X-ray attenuation\nprofiling\nEnd-to-end 30-60 min + logistical 5-15 min + logistical\n~ 45 min\ntime delay delay\nPortability Low Low to moderate High\nCost per test High (~ $600) High (~ $400) Low (~ $10)\nSpecialized Microfluidic device with\nMRI scanner CT scanner\nEquipment smartphone detector\nNeeded\nPrehospital and\nAccessibility Hospital settings Hospital settings\nresource-limited settings\nHigh\nDiagnostic High High\n(AUC = 0.958 for ICH vs\nAccuracy (Gold standard for IS) (Gold standard for ICH)\nIS)\nSample Blood plasma\nN.A. N.A.\nRequired (< 1.0 mL)\nContraindicated for Radiation exposure and\nBlood sampling and\nLimitations patients with metal limited soft tissue\nplasma separation\nimplants contrast\nlevels across EV subtypes (Fig. 5a) are not substantiated by statistical testing. Results of\nthe multiple linear regression models for stroke diagnosis are not reported. ROC analyses\nraise concerns given the small sample size, and in subtype 2, the majority of proteins\ndemonstrate only poor discriminatory power (AUC 0.6–0.7). Furthermore, 95% confidence\nintervals—essential to quantify uncertainty—are not provided. The statement that subtype\n2 achieved “near-ideal classification accuracy” (AUC 0.933) is overstated, especially since\nno cut-offs, sensitivity, or specificity are provided.\nOur response: We thank the reviewer for raising these important statistical points. In\nresponse, we have performed two-tailed t-tests to evaluate protein expression differences\nacross EV subtypes (new Supplementary Table 6). The multiple regression score was\ncalculated as follows:\ni i\ni=1\nwhere α is the regression coefficient, V is the measured value, and β is the intercept. A\ni i\nsummary of the regression coefficients and intercept is included in new Supplementary\nTable 7.\nWe also acknowledge the concern about the reliability of ROC analyses given the initial\nsample size. In our study, we used the leave-one-out method—a technique appropriate for\nsmall clinical datasets—to generate the ROC curves. Furthermore, during the revision\nperiod, we collected additional patient samples to enhance the statistical robustness of our\nconclusions.\nThe reviewer noted that most individual proteins showed limited discriminatory power (AUC\n0.6–0.7), which we agree is consistent with the multifactorial nature of stroke. It is widely\nrecognized that a single biomarker may be insufficient to capture the complexity of such\ndiseases, whereas a combination of biomarkers can reflect complementary pathways38,\nthereby improving overall classification performance—as demonstrated by the higher AUC\nachieved by the six-protein panel.\nIn accordance with the reviewer's suggestion, we have removed the phrase \"near-ideal\nclassification accuracy\" to ensure a more precise description of the diagnostic performance.\nThe previously missing metrics—including cut-off values, sensitivity, and specificity—have\nbeen added to the new Supplementary Table 3. Specifically, for subtype 2 EVs, the\nsensitivity and specificity were 0.88 and 0.92, respectively, in differentiating ICH from IS,\nand 0.84 and 0.95, respectively, in distinguishing stroke from healthy controls. We believe\nthese revisions have significantly enhanced the statistical rigor and clarity of the manuscript.\nSupplementary Table 3. Cut-off point of receiver operator characteristic curves.\nStroke vs. Healthy donors ICH vs. IS\nCut-point Cut-point\nSensitivity Specificity Sensitivity Specificity\n(a.u.) (a.u.)\nMix 0.670 0.64 0.85 0.569 0.68 0.68\nVEGF 0.713 0.64 0.90 0.452 0.72 0.60\nTGF-β 0.705 0.46 0.70 0.448 0.84 0.36\nWhole EV PD-L1 0.727 0.52 0.75 0.473 0.6 0.56\nCRP 0.709 0.50 0.80 0.47 0.6 0.56\nTNF-α 0.634 0.84 0.40 0.487 0.72 0.68\nICAM-1 0.710 0.60 0.70 0.532 0.52 0.68\nMix 0.593 0.76 0.75 0.458 0.76 0.68\nVEGF 0.672 0.70 0.70 0.478 0.60 0.64\nTGF-β 0.706 0.56 0.75 0.488 0.60 0.6\nSubtype 1 PD-L1 0.674 0.60 0.75 0.496 0.60 0.56\nCRP 0.713 0.60 0.75 0.545 0.48 0.84\nTNF-α 0.698 0.70 0.75 0.531 0.56 0.80\nICAM-1 0.723 0.60 0.75 0.521 0.64 0.52\nMix 0.641 0.84 0.95 0.512 0.88 0.92\nVEGF 0.709 0.68 0.65 0.474 0.84 0.68\nTGF-β 0.522 0.90 0.70 0.447 0.80 0.44\nSubtype 2 PD-L1 0.640 0.78 0.80 0.463 0.68 0.64\nCRP 0.603 0.80 0.60 0.530 0.76 0.72\nTNF-α 0.702 0.66 0.60 0.507 0.80 0.64\nICAM-1 0.669 0.74 0.60 0.503 0.72 0.64\nMix 0.681 0.68 0.75 0.553 0.64 0.72\nVEGF 0.719 0.54 0.80 0.470 0.68 0.52\nTGF-β 0.714 0.56 0.60 0.491 0.60 0.60\nSubtype 3 PD-L1 0.695 0.66 0.55 0.469 0.76 0.44\nCRP 0.712 0.54 0.80 0.468 0.72 0.60\nTNF-α 0.719 0.54 0.60 0.451 0.72 0.52\nICAM-1 0.660 0.68 0.60 0.503 0.60 0.56\nEuclidean distance was applied to calculate the cut-off point.\nSupplementary Table 7. Regression coefficients of multiple regression models\ngenerated from clinical sample analysis results.\nICH vs. IS Stroke vs. Healthy donors\nα (P value) β (P value) α (P value) β (P value)\nVEGF 0.4182 (0.0782) 0.1228 (0.5438)\nTGF-β 0.3738 (0.2177) 0.1273 (0.5410)\nWhole PD-L1 0.6526 (0.0344) 0.7662 (0.0045)\n-0.6490 (0.0169) -0.1452 (0.4400)\nEV CRP 0.4598 (0.0963) 0.7070 (0.0040)\nTNF-α 0.3845 (0.1346) 0.0363 (0.8451)\nICAM-1 0.6463 (0.0292) 0.4961 (0.0264)\nVEGF 0.2474 (0.4226) 0.3174 (0.1918)\nTGF-β 0.3378 (0.3960) 0.5899 (0.0740)\nSubtype PD-L1 0.6471 (0.0543) 0.4000 (0.1466)\n-0.5824 (0.0324) -0.1492 (0.3731)\nTNF-α 0.5950 (0.1278) 0.6226 (0.0296)\nICAM-1 0.1928 (0.5252) 0.2029 (0.4052)\nVEGF 0.5846 (0.0025) 0.2783 (0.1355)\nTGF-β 0.6761 (0.0025) 0.6945 (0.0001)\nSubtype PD-L1 0.8092 (0.0002) 0.7177 (0.0002)\n1.5165 (<0.0001) -0.5371 (0.0007)\nTNF-α 0.6616 (0.0018) 0.2800 (0.0735)\nICAM-1 0.5394 (0.0031) 0.1737 (0.2801)\nVEGF 0.3591 (0.2206) 0.2499 (0.2712)\nTGF-β 0.1557 (0.5911) 0.0846 (0.7227)\nSubtype PD-L1 0.5419 (0.0834) 0.3794 (0.1298)\n-0.4726 (0.0965) -0.0813 (0.7111)\nTNF-α 0.7769 (0.0125) 0.2481 (0.3215)\nICAM-1 -0.0770 (0.7825) 0.3078 (0.1958)\nunderlying the superior performance of subtype 2 EVs. Moreover, there is no consideration\nof the optimal diagnostic time window for the proposed approach.\nOur response: We sincerely thank the reviewer for their insightful comment regarding the\nbiological rationale for the superior performance of subtype 2 EVs. The enhanced\ndiagnostic capability of this subset stems from its optimal size, which uniquely balances\nefficient translocation across BBB with substantial biomarker-carrying capacity. Specifically,\nEVs within the 90–180 nm range are sufficiently small to traverse the BBB effectively1-3,\nfacilitating their release into circulation after stroke, while also providing greater membrane\nsurface area for robust presentation of cell-type-specific markers compared to smaller EVs\n(<90 nm)4. Moreover, EVs larger than 180 nm often represent apoptotic bodies and exhibit\ngreater heterogeneity. In support of this, our proteomic analysis revealed that subtype 2\ncontains a significantly higher number of proteins of subcellular origin compared to both\nsubtype 1 (<90 nm) and subtype 3 (>180 nm) (new Supplementary Figure 12).\nRegarding the optimal diagnostic time window, we agree on its critical importance for\nclinical translation. In alignment with stroke management guidelines that emphasize\nintervention as early as possible to prevent irreversible brain injury, we propose that blood\nsampling for the SHEAF platform should be performed immediately upon patient\npresentation. Notably, unlike conventional diagnostic approaches that can only be\nconducted after hospital arrival, our method holds potential for prehospital use—such as\nduring ambulance transport—enabling significantly earlier diagnosis. This shift forward in\nthe diagnostic timeline allows crucial treatment decisions (e.g., thrombolysis or surgery) to\nbe made sooner upon arrival, thereby preserving the “time is brain” principle and improving\npatient outcomes. We have revised the Discussion to highlight this advantage of early,\ntime-sensitive application of our approach.\nSupplementary Figure 12. Proteomic analysis of EV size subtypes generated by the\nSHEAF platform.\n(a, b) Venn diagrams illustrate the number of proteins identified in EV subtype 1, subtype\n2, and subtype 3 isolated from HMC3 (a) and HUVEC (b) cell culture mediums using the\nSHEAF platform. (c, d) Subcellular localization of differentially expressed proteins between\npaired EV subtype comparisons derived from HMC3 (c) and HUVEC (d) cell culture\nmediums. The numbers indicated the count of proteins with significantly different\nabundance (P < 0.05) and the presence or absence of a protein in each comparison. #\ndenoted the control group. ER, endoplasmic reticulum.\nsize, lack of control for confounders (e.g., age, sex, comorbidities), absence of healthy\ncontrols, and missing sampling time points—all of which preclude meaningful conclusions\nabout prehospital applicability.\nOur response: We sincerely thank the reviewer for highlighting these important limitations\nregarding our initial cohort. In direct response to the comments, we have expanded the\nclinical cohort to include additional samples—specifically, 10 ICH, 10 IS, and 20 healthy\ndonors as a control group—which has strengthened the statistical reliability of our findings.\nTo improve transparency and allow for a better assessment of potential confounders, we\nhave also included a new patient information table (new Supplementary Table 2)\nsummarizing age, sex, and comorbidities. We clarify that for all stroke patients, blood was\ncollected prior to any treatment intervention, and the diagnosis of either ICH or IS was\nconfirmed within one hour after blood sampling.\nWhile the expanded sample size represents a meaningful improvement, we acknowledge\nthat further scaling through larger, multicenter studies will be important to support the\nbroader applicability of our findings. As such, we have explicitly discussed the current\ncohort size in the Discussion section, framing it as a focused foundation for future\nvalidation. We also emphasize that this work provides a robust methodological platform\nand preliminary clinical evidence, paving the way for subsequent multi-institutional\ncollaborations aimed at prehospital translation.\nReferences:\n1. Lino, M. M. et al. Engineered extracellular vesicles as brain therapeutics. J. Control.\nRelease 338, 472-485 (2021).\n2. Vandendriessche, C., Kapogiannis, D. & Vandenbroucke, R. E. 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Chem. 85, 1132–1137 (2012).\n35. Kang, J., Myung Soog Lee, Copland, J. A., Luxon, B. A. & Gorenstein, D. G.\nCombinatorial selection of a single stranded DNA thioaptamer targeting TGF-β1\nprotein. Bioorg. Med. Chem. Lett. 18, 1835–1839 (2008).\n36. Lai, W.-Y., Wang, J.-W., Huang, B.-T., Lin, E. P.-Y. & Yang, P.-C. A novel TNF-α-\ntargeting aptamer for TNF-α-mediated acute lung injury and acute liver failure.\nTheranostics 9, 1741–1751 (2019).\n37. Dursun, A. D. et al. Surface plasmon resonance aptasensor for soluble ICAM-1 protein\nin blood samples. Analyst 147, 1663–1668 (2022).\n38. Montaner, J. et al. Multilevel omics for the discovery of biomarkers and therapeutic\ntargets for stroke. Nat. Rev. Neurol. 16, 247-264 (2020).\nPoint-by-point response\nReviewer #2:\nThe rebuttal and comments, extra clarifications and experiments are sufficient to make\nthese claims with caveats that are discussed. There is some important information in this\narticle. However I will recommend it for publishing, I would like a clearer distinction of EV\nsubtypes, it is simple acceptable to say sEVs.\nOur response: Thank you for your suggestion. In response, we have revised Figure 1 to\nexplicitly indicate the size distribution of each EV subtype, as well as the specific step in\nthe fractionation process at which each subtype is obtained. We hope this modification\nhelps to better convey the size characteristics of the different EV subtypes."}, {"round": 2, "reviewer_comments": {"reviewer_1": "In the revised version the authors have addressed my comments satisfactorily. I recommend the paper for publication.", "reviewer_2": "The rebuttal and comments, extra clarifications and experiments are sufficient to make these claims with caveats that are discussed. There is some important information in this article. However I will recommend it for publishing, I would like a clearer distinction of EV subtypes, it is simple acceptable to say sEVs.", "reviewer_3": "I have no further criticisms of the manuscript. Previous questions have been adequately addressed."}, "author_response": ""}]

Decision letter