Sequence-specific nanoparticle barcode strategy for multiplex human enterovirus typing

Zecheng Zhong, Xiaosong Su, Kunyu Yang, Weida Huang, Jin Wang, Zhihao Zhuo, Jiyu Xiang, Lesi Lin, Shuizhen He, Tingdong Li, Jun Zhang, Shengxiang Ge (), Shiyin Zhang () and Ningshao Xia ()
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Zecheng Zhong: Xiamen University
Xiaosong Su: Zhongshan Hospital Fudan University Xiamen Branch
Kunyu Yang: Xiamen University
Weida Huang: Xiamen University
Jin Wang: Xiamen University
Zhihao Zhuo: Xiamen University
Jiyu Xiang: Xiamen University
Lesi Lin: Xiamen University
Shuizhen He: Xiamen Haicang Hospital
Tingdong Li: Xiamen University
Jun Zhang: Xiamen University
Shengxiang Ge: Xiamen University
Shiyin Zhang: Xiamen University
Ningshao Xia: Xiamen University

Nature Communications, 2024, vol. 15, issue 1, 1-9

Abstract: Abstract Human enteroviruses (HEV) can cause a range of diseases from mild to potentially life-threatening. Identification and genotyping of HEV are crucial for disease management. Existing typing methods, however, have inherent limitations. Developing alternative methods to detect HEV with more virus types, high accuracy, and sensitivity in an accessible manner presents a technological and analytical challenge. Here, a sequence-specific nanoparticle barcode (SSNB) method is presented for simultaneous detection of 10 HEV types. This method significantly increases sensitivity, enhancing detection by 10-106 times over the traditional multiplex hybrid genotyping (MHG) method, by resolving cross-interference between the multiple primer sets. Furthermore, the SSNB method demonstrates a 100% specificity in accurately distinguishing between 10 different HEV types and other prevalent clinical viruses. In an analysis of 70 clinical throat swab samples, the SSNB method shows slightly higher detection rate for positive samples (50%) compared to the RT-PCR method (48.6%). Additionally, further assessment of the typing accuracy for samples identified as positive by SSNB using sequencing method reveals a concordance rate of 100%. The combined high sensitivity and specificity level of the methodology, together with the capability for multiple type analysis and compatibility with clinical workflow, make this approach a promising tool for clinical settings.

Date: 2024
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DOI: 10.1038/s41467-024-50921-w

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