The accurate, sensitive, and rapid detection of analytes in complex samples using point-of-care testing (POCT) can be useful, e.g., in the diagnosis of diseases. Lateral flow immunoassays (LFIAs) can be useful in this context due to their simplicity, short analysis time, and low costs. However, the detection accuracy and sensitivity can be compromised by the low fluorescence quantum efficiency of the near-infrared (NIR) fluorescent probes that are used as signal labels.

Xiaolin Huang, Nanchang University, China, Rui Chen, Chongqing Medical University, China, Peizeng Yang, The First Affiliated Hospital of Chongqing Medical University, China, and colleagues have developed ultrabright NIR AIEgen nanoparticles (PS@AIE₈₃₀NPs, pictured schematically above) made from polystyrene (PS) nanoparticles and an NIR aggregation-induced emission luminogen (AIEgen) with a maximum emission at 830 nm (AIE₈₃₀, pictured above). This probe can be used for the accurate and sensitive detection of targets by LFIAs.

The team first designed AIE₈₃₀ using benzobisthiadiazole as an acceptor and methoxyl-tetraphenylethene units as electron donors. Fluorescent nanoparticles (PS@AIE₈₃₀NPs) were then prepared by encapsulating AIE₈₃₀ into PS nanoparticles with sizes around 300 nm using an organic solvent swelling method.

The team found that the relative quantum yield (QY) of the PS@AIE₈₃₀NPs is ca. 14.8 %, an improvement compared with that of, e.g., existing indocyanine green (ICG)-based NIR nanoparticles (4.1 %). The PS@AIE₈₃₀NPs eliminate the background interference in complex samples, and they allow sensitive detection without any pre-treatment steps. According to the researchers, the developed ultrabright NIR AIEgen nanoparticles could serve as universally applicable signal probes for NIR-LFIA diagnostics.

• Ultrabright NIR AIEgen nanoparticles‐enhanced lateral flow immunoassay platform for accurate diagnostics of complex samples,
  Jia Shu, Yujian Li, Huan Cai, Qing Fu, Chunyang Li, Jianbo Yuan, Yan Zhao, Changjin Liu, Haiping Wu, Doudou Ling, Zhangluxi Liu, Guannan Su, Qingfeng Cao, Xiaolin Huang, Rui Chen, Peizeng Yang,
  Aggregate 2024.
  https://doi.org/10.1002/agt2.551