Evidence mapPaperPMID 41606293Full record

ArticleNature biomedical engineering2026

Automated disc device for multiplexed extracellular vesicle isolation and labelling from liquid biopsies in cancer diagnostics.

Hyun-Kyung Woo, Changhyun Kim, Yoonjeong Choi, Young Kwan Cho, Luu-Ngoc Do, Hyunho Kim, Dae-Han Jung, Matt Allen, Jueun Jeon, Seok Chung and 5 more

Abstract read
In one paragraph

Article in Nature biomedical engineering, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors.

Hyun-Kyung Woo *Center for Systems Biology, Massachusetts General Hospital Research Institute, Boston, MA, USA.ORCID http://orcid.org/0000-0003-1332-6029
Changhyun Kim *Department of Surgery, Chonnam National University Hwasun Hospital, Chonnam National University Medical School, Hwasun, Republic of Korea.
Yoonjeong ChoiCenter for Systems Biology, Massachusetts General Hospital Research Institute, Boston, MA, USA.ORCID http://orcid.org/0000-0002-4422-604X
Young Kwan ChoCenter for Systems Biology, Massachusetts General Hospital Research Institute, Boston, MA, USA.
Luu-Ngoc DoDepartment of Radiology, Chonnam National University Medical School and Hospital, Gwangju, Republic of Korea.
Hyunho KimCenter for Systems Biology, Massachusetts General Hospital Research Institute, Boston, MA, USA.
Dae-Han JungCenter for Systems Biology, Massachusetts General Hospital Research Institute, Boston, MA, USA.
Matt AllenCenter for Systems Biology, Massachusetts General Hospital Research Institute, Boston, MA, USA.
Jueun JeonCenter for Systems Biology, Massachusetts General Hospital Research Institute, Boston, MA, USA.ORCID http://orcid.org/0000-0003-1888-1216
Seok ChungSchool of Mechanical Engineering, Korea University, Seoul, Republic of Korea.
Soo Yeun ParkColorectal Cancer Center, Kyungpook National University Chilgok Hospital, School of Medicine, Kyungpook National University, Daegu, Republic of Korea.
Ilwoo ParkDepartment of Radiology, Chonnam National University Medical School and Hospital, Gwangju, Republic of Korea.
Cesar M CastroCenter for Systems Biology, Massachusetts General Hospital Research Institute, Boston, MA, USA.ORCID http://orcid.org/0000-0002-1159-5658
Jun Seok ParkColorectal Cancer Center, Kyungpook National University Chilgok Hospital, School of Medicine, Kyungpook National University, Daegu, Republic of Korea. parkjs0802@knu.ac.kr.ORCID http://orcid.org/0000-0001-5443-6748
Hakho LeeCenter for Systems Biology, Massachusetts General Hospital Research Institute, Boston, MA, USA. hlee@mgh.harvard.edu.ORCID http://orcid.org/0000-0002-0087-0909

Funding

Expanding early cancer detection with high throughput OCEANA - Ovarian Cancer Exosome Analysis with Nanoplasmonic ArrayU01CA284982 · MASSACHUSETTS GENERAL HOSPITAL · 2025 to 2025
$941k
High-throughput Phenotyping of iPSC-derived Airway Epithelium by Multiscale Machine Learning MicroscopyR01HL163513 · BOSTON CHILDREN'S HOSPITAL · 2025 to 2025
$780k
Composing CODAs to cervical cancer screening through an integrated CRISPR and fluorescent nucleic acid approachU01CA279858 · MASSACHUSETTS GENERAL HOSPITAL · 2025 to 2025
$521k
Streamlining sample preparation with high throughput SpinEx (Separation processing integration for Extracellular vesicles)R61CA297878 · MASSACHUSETTS GENERAL HOSPITAL · 2025 to 2025
$231k
NCI NIH HHS R01 CA237500NCI NIH HHS R01 CA239078NCI NIH HHS R01 CA264363NCI NIH HHS R21 CA267222NCI NIH HHS R61 CA297878NCI NIH HHS U01 CA279858NCI NIH HHS U01 CA284982NHLBI NIH HHS R01 HL163513NIDA NIH HHS R21 DA049577U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI) R01CA229777U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI) U01CA284982
6 · The paper itself

Abstract

Circulating extracellular vesicles can be used for tumour diagnostics. However, current isolation methods are time consuming, require manual handling and are prone to contamination. Here we report on SpinEx (separation-processing integration for extracellular vesicles), a compact disc device for automatic isolation and multiplex immunolabelling of whole-blood samples. SpinEx integrates on-disc chromatography, centripetal liquid transfer and bead-based vesicle capture with antibody labelling. The system processes 150 µl of whole blood, enriching and labelling vesicles for 16 protein targets in under 75 minutes. Detection is performed by measuring dual fluorescence signals from labelled extracellular vesicles captured on microbeads. In a pilot clinical study, SpinEx was used to process 221 plasma samples for multiplex profiling of 30 vesicle-associated proteins. Using fluorescence flow cytometry to analyse cancer-specific biomarker expression, we found that vesicles processed by SpinEx distinguished cancer from non-cancer samples with 90% accuracy and 97% specificity, and classified 5 tumour types with 96% accuracy. SpinEx enables automated and multiplex processing of extracellular vesicles from blood, which may support the development of clinically viable assays for cancer detection and classification.

Indexed as

Extracellular VesiclesNeoplasmsAutomationBiomarkers, TumorFlow CytometryHumansLiquid BiopsyPilot ProjectsBiomarkers, Tumor

Identifiers

PMID41606293
PMCPMC13396894

What Socratic holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.