journal article·2024-09-18

Highly Sensitive Detection of Tumor Cell-Derived Exosomes Using Solid-State Nanopores Assisted with a Slight Salt Gradient

Libo Zhu, Zhengyuan Xu, Yanfeng Gao, Na Sun, Lei Qiu, Jinsong Zhao

ACS Applied Materials & Interfaces· Volume 16· Issue 37
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Abstract

As an important biomarker, tumor cell-derived exosomes have substantial application prospects in early cancer screening and diagnosis. However, the unsatisfactory sensitivity and complicated sample pretreatment processes of conventional detection approaches have limited their use in clinical diagnosis. Nanopore sensors, as a highly sensitive, label-free, single-molecule technology, are widely utilized in molecule and bioparticle detection. Nevertheless, the exosome capture rate through nanopores is extremely low due to the low surface charge densities of exosomes and the effects of electrolyte concentration on their structural stability, thereby reducing the detection throughput. Here, we report an approach to improve the capture rate of exosome translocations using silicon nitride (SiN x ) nanopores assisted by a slight salt electrolyte gradient. Improvements in exosome translocation event frequency are assessed in electrolyte solutions with different concentration gradients. In the case of asymmetric electrolytes ( cis 1× PBS and trans 0.2 M NaCl, 1× PBS ), the event frequency of tumor cell (HepG2)-derived exosome translocations is enhanced by nearly 2 orders of magnitude while maintaining vesicle structure stability. Furthermore, benefiting from the salt gradient effect, tumor cell (AsPC-1 and HCT116)-derived exosome translocations could be discriminated from those of HepG2 cell-derived exosomes. The developed highly sensitive detection method for tumor cell-derived exosomes at the single-particle level provides an approach for early cancer diagnosis.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Nanopore and Nanochannel Transport Studies · Ion-surface interactions and analysis · Extracellular vesicles in disease

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

School of Medical Imaging, Wannan Medical College, Wuhu 241002, China

CAS Key Laboratory of Nano-Bio Interface, Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences, Suzhou 215123, China

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