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

Nature Biomedical Engineering (2026)Cite this article

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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.

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Fig. 1: Overview of the SpinEx approach.
Fig. 2: SpinEx operation for EV enrichment from whole blood.
Fig. 3: Characterization of EV enrichment by SpinEx.
Fig. 4: Design of EV-capture and labelling module.
Fig. 5: Validation of EV capture and labelling.
Fig. 6: EV protein profiling.
Fig. 7: Clinical application of SpinEx for cancer detection and classification.

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Data availability

The raw patient datasets generated and analysed during the study are available from the corresponding authors, subject to approval from the Institutional Review Boards of MGH, Kyungpook National University Medical Center and Chonnam National University Hwasun Hospital. Source data are provided with this paper.

Code availability

Source codes for the machine learning are available at https://github.com/kylie0914/SpinEx.

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Acknowledgements

We thank D. G. You (MGH) and H. Lee (MGH) for their assistance in bead imaging. This work was supported in part by NIH 1U01CA279858 (C.M.C. and H.L.), U01CA284982 (H.L. and C.M.C.), R01CA229777 (H.L.), R01CA239078 (H.L.), R01HL163513 (H.L.), R01CA237500 (H.L.), R21CA267222 (H.L.), R01CA264363 (C.M.C. and H.L.) and R61CA297878 (H.L.); the Korea Health Industry Development Institute grant HR22C1832 (J.S.P.); Samsung Research Funding and Incubation Center of Samsung Electronics SRFC-IT1901-51 (S.C.); and a National Research Foundation grant funded by the Korean government 2022M3A9B6018217 (S.C.).

Author information

Author notes

  1. These authors contributed equally: Hyun-Kyung Woo, Changhyun Kim.

Authors and Affiliations

  1. Center for Systems Biology, Massachusetts General Hospital Research Institute, Boston, MA, USA

    Hyun-Kyung Woo, Yoonjeong Choi, Young Kwan Cho, Hyunho Kim, Dae-Han Jung, Matt Allen, Jueun Jeon, Cesar M. Castro & Hakho Lee

  2. Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA

    Hyun-Kyung Woo, Yoonjeong Choi, Hyunho Kim, Jueun Jeon & Hakho Lee

  3. Department of Surgery, Chonnam National University Hwasun Hospital, Chonnam National University Medical School, Hwasun, Republic of Korea

    Changhyun Kim

  4. Department of Radiology, Chonnam National University Medical School and Hospital, Gwangju, Republic of Korea

    Luu-Ngoc Do & Ilwoo Park

  5. School of Mechanical Engineering, Korea University, Seoul, Republic of Korea

    Hyunho Kim & Seok Chung

  6. KU-KIST Graduate School of Converging Science and Technology, Korea University, Seoul, Republic of Korea

    Seok Chung

  7. Center for Brain Technology, Brain Science Institute, Korea Institute of Science and Technology (KIST), Seoul, Republic of Korea

    Seok Chung

  8. Colorectal Cancer Center, Kyungpook National University Chilgok Hospital, School of Medicine, Kyungpook National University, Daegu, Republic of Korea

    Soo Yeun Park & Jun Seok Park

  9. Department of Artificial Intelligence Convergence, Chonnam National University, Gwangju, Republic of Korea

    Ilwoo Park

  10. Cancer Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA

    Cesar M. Castro

  11. Department of Surgery, School of Medicine, Kyungpook National University Hospital, Kyungpook National University, Daegu, Republic of Korea

    Jun Seok Park

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Contributions

H.-K.W., C.K., C.M.C., J.S.P. and H.L. designed the study, prepared the figures and wrote the paper. H.-K.W., Y.K.C. and H.K. conducted the experiments. J.J. conducted a Western blot. H.-K.W. designed the device, and H.K. and S.C. assisted in its implementation. H.K. and D.-H.J. performed the fluidic simulation. H.-K.W., Y.C., L.-N.D., M.A., I.P. and H.L. developed the machine learning models. C.K., S.Y.P., C.M.C. and J.S.P. analysed clinical data. C.K., S.Y.P. and J.S.P. acquired clinical samples. All authors contributed to writing the paper.

Corresponding authors

Correspondence to Jun Seok Park or Hakho Lee.

Ethics declarations

Competing interests

The authors (H.-K.W., Y.C. and H.L.) are inventors on an invention disclosure related to components of the SpinEx technology, which was filed and assigned to MGH. This intellectual property is related to the methods and devices described in this paper. The other authors declare no competing interests.

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Nature Biomedical Engineering thanks Lingxin Chen, Mei He and Takao Yasui for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 SpinEx operation steps.

1 A whole-blood (150 µL) sample is loaded. 2 Plasma is separated from the whole blood. 3 Plasma is transferred to an on-disc chromatography column, and an elution buffer is introduced. 4 A dense oil pushes the enriched EVs toward the inner side for further processing. 5 EVs are transferred to an assay chamber containing polystyrene beads. EVs bind to the bead surface via physisorption. 6 EV-bead complexes are washed by introducing a buffer. 7 TFP-biotin reagent is introduced to the assay chamber, and EVs are biotinylated. TFP, 2,3,5,6-tetrafluorophenyl. 8 Excess biotinylation reagent is removed via washing. 9 A block solution (10% Superblock) is introduced to the assay chamber. 10 The blocking solution is removed with another washing step. 11 EV-bead complexes are dispensed into eight labeling compartments, each containing a primary antibody (1° Ab) against a target protein. 12 Dye-conjugated streptavidin and secondary antibody (2° Ab) are introduced to the compartments. The labelled EV-bead complexes are ready for fluorescent measurements (for example, microscopy, flow cytometry).

Extended Data Fig. 2 Workflow for machine learning analyses.

(a) Constructing a model for cancer diagnosis. The LASSO regression determined the most informative marker sets from the training set (n = 154; left). The selected markers were used to train an SVM model for binary classification (cancer versus non-cancer). This trained model was evaluated using an independent test set (n = 67; right). (b) Model for five-cancer classification. Data from all cancer patients (n = 157) were used. The model performed a one-versus-one classification to differentiate between five tumor types.

Extended Data Table 1 SpinEx operation parameters
Full size table
Extended Data Table 2 Information on clinical samples
Full size table

Supplementary information

Supplementary Information (download PDF )

Supplementary Methods, Figs. 1–25 and Tables 1–3.

Reporting Summary (download PDF )

Supplementary Video 1 (download MP4 )

SpinEx disc fluidic workflow. This video illustrates the overall sequence of assay steps performed within a single device, including on-disc plasma separation, dual-mode chromatography–based EV enrichment, bead-based EV capture and biotinylation, and multiplexed fluorescent labeling across eight downstream chambers.

Supplementary Video 2 (download MP4 )

Density-based centripetal fluid transport. This video shows the simulation of density-driven fluidic exchange during the centripetal transfer step. The high-density oil phase displaces the aqueous EV solution toward the disc center under rotation (1800 rpm), enabling rapid (<2 s) transfer between adjacent assay chambers.

Supplementary Video 3 (download MP4 )

Fluidic simulations of a cascading elution. This video shows the simulated flow pattern of the on-disc chromatography module during disc rotation. The eluate first fills the chamber closest to the column outlet (F3) and then sequentially transfers to the adjacent chambers (F2 and F1), producing an inverted elution order compared with conventional column devices. This cascading liquid transfer explains why early fractions are collected in F1 and late fractions, including potential (V)LDL-rich eluates, are collected in F3, enabling optimized selection of the EV-enriched fraction in F2.

Source data

Source Data Fig. 2 (download XLSX )

Raw numerical data used to generate the plots in Fig. 2.

Source Data Fig. 3 (download ZIP )

Uncropped transmission electron microscopy micrograph and uncropped single-EV fluorescence microscopy images used in Fig. 3.

Source Data Fig. 3 (download XLSX )

Raw numerical data used to generate the plots in Fig. 3.

Source Data Fig. 4 (download XLSX )

Raw numerical data used to generate the plots in Fig. 4.

Source Data Fig. 5 (download ZIP )

Uncropped scanning electron microscopy micrograph and uncropped bead fluorescence microscopy images used in Fig. 5.

Source Data Fig. 5 (download XLSX )

Raw numerical data used to generate the plots in Fig. 5.

Source Data Fig. 6 (download XLSX )

Raw numerical data used to generate the plots in Fig. 6.

Source Data Fig. 7 (download XLSX )

Raw numerical data used to generate the plots in Fig. 7.

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Woo, HK., Kim, C., Choi, Y. et al. Automated disc device for multiplexed extracellular vesicle isolation and labelling from liquid biopsies in cancer diagnostics. Nat. Biomed. Eng (2026). https://doi.org/10.1038/s41551-025-01601-7

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