DOI 10.1021/acs.analchem.5c07668
DNA Logic Gate-Integrated Peptide Nucleic Acid-Optical Fiber Sensor for Ultrasensitive Breast Cancer Exosome Profiling.
Аннотация
Tumor-derived exosomes (TDEs) have emerged as promising biomarkers for cancer liquid biopsy. However, their phenotypic heterogeneity and trace-level concentrations pose considerable analytical challenges, hampering the advancement of highly sensitive detection platforms. Here, we present a novel biosensing platform that integrates a DNA logic gate nanomachine (DLg) with a peptide nucleic acid (PNA)-modified optical fiber sensor (POFS) for highly specific and sensitive detection of breast cancer (BC) exosomes. An AND logic gate strategy targeting epithelial cell adhesion molecule (EpCAM) and human epidermal growth factor receptor 2 (HER2) ensures exceptional specificity while minimizing nonspecific binding. Signal amplification is achieved through coordination-driven self-assembly of carboxyl-rich hematin micelles on exosomal membranes via phosphate-Zr4+-carboxylate and carboxyl-Zr4+-carboxylate coordination, which utilizes the abundant phosphate groups for direct labeling without additional enzymatic amplification steps. The DLg-POFS platform demonstrates exceptional analytical performance, exhibiting an ultralow detection limit of 0.57 particles/μL across a broad linear range (1-106 particles/μL), with robust anti-interference capability, high reproducibility, excellent storage stability, and reusability. Clinical validation with serum samples demonstrated 100% accuracy in distinguishing BC patients from healthy controls and 93% concordance with immunohistochemistry for HER2-positive subtyping, outperforming conventional assays. This innovation merges molecular computing logic with nanoscale signal amplification and PNA-enhanced biosensing, offering a scalable solution for early cancer diagnosis and precision subtyping. Its compatibility with portable optical fiber technology supports potential point-of-care applications, addressing critical needs in clinical analytics.
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