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    Cellular and Molecular Bioengineering

    Highly-sensitive microwell digital assay for the detection of extracellular vesicle molecules

    (E-186) Double digital assay for single extracellular vesicle and single molecule detection

    Thursday, October 12, 2023
    9:30 AM – 10:30 AM PDT
    Location: Exhibit Hall - Row E - Poster # 186

      Presenting Author(s)

    • David Reynolds (he/him/his)

      PhD Student
      University of Pennsylvania
      Philadelphia, Pennsylvania, United States

      • Primary Investigator(s)

    • Jina Ko, PhD

      Assistant Professor
      University of Pennsylvania, United States

      • Co-Author(s)

    • MP

      Menghan Pan

      Master's Student
      University of Pennsylvania, United States

    • GG

      George Galanis

      Master's Student
      University of Pennsylvania, United States

    • YR

      Yoon Ho Roh

      Post-doc
      University of Pennsylvania, United States

    • RM

      Renee Morales

      Master's Student
      University of Pennsylvania, United States

    • JY

      Jingbo Yang

      Post-doc
      University of Pennsylvania, United States

    • XX

      Xiaowei Xu

      Associate Professor
      University of Pennsylvania, United States

    • WG

      Wei Guo

      Associate Professor
      University of Pennsylvania, United States

    Introduction:: Extracellular vesicles (EVs) have emerged as a promising source of biomarkers for disease diagnosis. However, current diagnostic methods for EVs present formidable challenges, given the low expression levels of biomarkers carried by EV samples, as well as their complex physical and biological properties. Herein, we develop a highly sensitive digital assay that allows for the compartmentalization of single EVs and quantification of their protein abundance. Because the relative abundance of proteins is low for a single EV, we integrate tyramide signal amplification (TSA) to increase the fluorescent signal readout for quantification.


    Materials and Methods::





    The use of digital microwell assays has proven to be a highly effective method for compartmentalizing and profiling individual cells, allowing for genomic, transcriptomic, and proteinic profiling. These assays are typically fabricated on microfluidic devices and can be customized to accommodate various cell types. However, despite their tunable properties, they have not yet been utilized for single EV profiling. To address this issue, we propose a highly sensitive digital microfluidic assay that can partition and quantify the protein abundance of individual EVs. The microwell array was fabricated with photo and soft lithography. The assay involves loading beads coated with capture-targeting antibodies and spacer beads into microwells, followed by the addition of EV samples and lysis buffer. The TSA workflow is then implemented for signal amplification, which allows for a localized signal to be generated that can be easily recorded.















    Results, Conclusions, and Discussions:: Herein, we present a droplet-free platform that utilizes antibody-based on-bead microwell arrays and tyramide signal amplification (TSA) to achieve a highly sensitive digital ELISA. The innovative technique involves the use of microwells to compartmentalize antibody-coated and uncoated beads evenly and form a monolayer for subsequent digital ELISA. By quantifying the fluorescent signals within the microwells, this platform can precisely present the expression level of certain biomarkers, improving our understanding of the relative abundance of proteins between each EV. The working principle of the technology was proven by characterizing epidermal growth factor receptor (EGFR) protein from an A431 cell line. After validation, we subsequently used our method to assess the relative abundance of the programmed death-ligand 1 (PD-L1) protein in a melanoma cell line (624-mel), proving the system's utility in detecting significant prognostic and diagnostic cancer markers. Together, we believe the presented technology integrates the unmet needs of a highly sensitive, high-throughput, and translatable system for improving EV biomarker characterization and discovery.


    Acknowledgements (Optional): :

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