Brain-Derived Extracellular Vesicle Subpopulations: from Bulk Measurements to Single-Entity Assays

Neurodegenerative diseases (NDs), such as Alzheimer’s disease (AD) and Parkinson’s disease (PD), pose a significant global health challenge, currently affecting over 40 million individuals and placing a heavy burden on healthcare systems. Existing diagnostic methods, such as neuroimaging and cognitive assessments, often lack sufficient sensitivity and specificity, especially in the early stages of disease. This underscores the need for novel biomarkers. Extracellular vesicles (EVs), particularly those derived from the brain, i.e., brain-derived extracellular vesicles (BDEVs), hold great potential as noninvasive diagnostic tools due to their ability to reflect the physiological and pathological states of their cells of origin. However, isolation and detection of such EV subpopulations from accessible body fluids such as blood remain a technical challenge due to their low abundance and overlapping physical properties compared to other EVs. This review discusses rationally designed isolation and detection technologies for EVs from major brain cell subpopulations and their integration with emerging fields like AI and big data analysis. We specifically contrast traditional ensemble-averaged bulk measurements with emerging single-entity assays, highlighting how the latter bypass biological noise to resolve rare BDEV subpopulations. It highlights the potential of these EV subpopulations as biomarkers, addresses EV isolation challenges and proposes standardized methodologies, and emphasizes the need for comprehensive profiling of EV markers at single-entity level, and point-of-care testing (POCT) development.

Authors

Institutions

Publication Details

Journal
JACS Au
Published
2026-05-10
DOI
https://doi.org/10.1021/jacsau.6c00300
Primary Topic
Extracellular vesicles in disease
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Brain-Derived Extracellular Vesicle Subpopulations: from Bulk Measurements to Single-Entity Assays

Lianqun Zhou, Weini Xin, Le Chen, Wei Zhang et al.
JACS Au
Extracellular vesicles in disease
article

Brain-Derived Extracellular Vesicle Subpopulations: from Bulk Measurements to Single-Entity Assays

Lianqun Zhou, Weini Xin, Le Chen, Wei Zhang, Mingyuan Li, Shiyao Bai, Chenzhong Li, Han Liu, Cheng Jiang, Xiaowen Huang
article en

Abstract

Neurodegenerative diseases (NDs), such as Alzheimer’s disease (AD) and Parkinson’s disease (PD), pose a significant global health challenge, currently affecting over 40 million individuals and placing a heavy burden on healthcare systems. Existing diagnostic methods, such as neuroimaging and cognitive assessments, often lack sufficient sensitivity and specificity, especially in the early stages of disease. This underscores the need for novel biomarkers. Extracellular vesicles (EVs), particularly those derived from the brain, i.e., brain-derived extracellular vesicles (BDEVs), hold great potential as noninvasive diagnostic tools due to their ability to reflect the physiological and pathological states of their cells of origin. However, isolation and detection of such EV subpopulations from accessible body fluids such as blood remain a technical challenge due to their low abundance and overlapping physical properties compared to other EVs. This review discusses rationally designed isolation and detection technologies for EVs from major brain cell subpopulations and their integration with emerging fields like AI and big data analysis. We specifically contrast traditional ensemble-averaged bulk measurements with emerging single-entity assays, highlighting how the latter bypass biological noise to resolve rare BDEV subpopulations. It highlights the potential of these EV subpopulations as biomarkers, addresses EV isolation challenges and proposes standardized methodologies, and emphasizes the need for comprehensive profiling of EV markers at single-entity level, and point-of-care testing (POCT) development.

JACS Au
Shanghai University (CN), Academy of Medical Sciences (GB), Chinese University of Hong Kong (HK), Chinese Academy of Sciences (CN), Shantou University (CN), Suzhou Institute of Biomedical Engineering and Technology (CN), Institute for Basic Science (KR), Laboratoire d’Imagerie Biomédicale (FR), Shantou University Medical College (CN), Stomatology Hospital (CN), Shandong First Medical University (CN), Shanghai Center for Brain Science and Brain-Inspired Technology (CN)
Openalex Percentile: Top 8%
Extracellular vesicles in disease
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.