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Advanced Science
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Advanced Science
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Article . 2023
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Double Digital Assay for Single Extracellular Vesicle and Single Molecule Detection

Authors: David E. Reynolds; Menghan Pan; Jingbo Yang; George Galanis; Yoon Ho Roh; Renee‐Tyler T. Morales; Shailesh Senthil Kumar; +4 Authors

Double Digital Assay for Single Extracellular Vesicle and Single Molecule Detection

Abstract

AbstractExtracellular 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, a highly sensitive double digital assay is developed that allows for the absolute quantification of individual molecules from a single EV. Because the relative abundance of proteins is low for a single EV, tyramide signal amplification (TSA) is integrated to increase the fluorescent signal readout for evaluation. With the integrative microfluidic technology, the technology's ability to compartmentalize single EVs is successfully demonstrated, proving the technology's digital partitioning capacity. Then the device is applied to detect single PD‐L1 proteins from single EVs derived from a melanoma cell line and it is discovered that there are ≈2.7 molecules expressed per EV, demonstrating the applicability of the system for profiling important prognostic and diagnostic cancer biomarkers for therapy response, metastatic status, and tumor progression. The ability to accurately quantify protein molecules of rare abundance from individual EVs will shed light on the understanding of EV heterogeneity and discovery of EV subtypes as new biomarkers.

Keywords

tyramide signal amplification, Science, Q, Microfluidics, Proteins, Extracellular Vesicles, double digital microfluidics, Cell Line, Tumor, biomarker discovery, Biomarkers, Tumor, microwells, extracellular vesicle, Research Articles

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    selected citations
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    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    36
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 1%
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
36
Top 10%
Top 10%
Top 1%
Green
gold