Computer Vision-Based Artificial Intelligence-Mediated Encoding-Decoding for Multiplexed Microfluidic Digital Immunoassay.

Journal: ACS nano
PMID: 37458511

Abstract

Digital immunoassays with multiplexed capacity, ultrahigh sensitivity, and broad affordability are urgently required in clinical diagnosis, food safety, and environmental monitoring. In this work, a multidimensional digital immunoassay has been developed through microparticle-based encoding and artificial intelligence-based decoding, enabling multiplexed detection with high sensitivity and convenient operation. The information encoded in the features of microspheres, including their size, number, and color, allows for the simultaneous identification and accurate quantification of multiple targets. Computer vision-based artificial intelligence can analyze the microscopy images for information decoding and output identification results visually. Moreover, the optical microscopy imaging can be well integrated with the microfluidic platform, allowing for encoding-decoding through the computer vision-based artificial intelligence. This microfluidic digital immunoassay can simultaneously analyze multiple inflammatory markers and antibiotics within 30 min with high sensitivity and a broad detection range from pg/mL to μg/mL, which holds great promise as an intelligent bioassay for next-generation multiplexed biosensing.

Authors

  • Weiqi Zhao
    College of Food Science and Technology, Huazhong Agricultural University, Wuhan 430070, Hubei, China.
  • Yang Zhou
    State Environmental Protection Key Laboratory of Environmental Pollution Health Risk Assessment, South China Institute of Environmental Sciences, Ministry of Environmental Protection, Guangzhou, China.
  • Yao-Ze Feng
    College of Engineering, Huazhong Agricultural University, Wuhan, Hubei, China; Key Laboratory of Agricultural Equipment in Mid-lower Yangtze River, Ministry of Agriculture, Wuhan, Hubei, China. Electronic address: yaoze.feng@mail.hzau.edu.cn.
  • Xiaohu Niu
    College of Engineering, Huazhong Agricultural University, Wuhan 430070, Hubei, China.
  • Yongkun Zhao
    College of Food Science and Technology, Huazhong Agricultural University, Wuhan, 430070, Hubei China.
  • Junpeng Zhao
    College of Food Science and Technology, Huazhong Agricultural University, Wuhan, 430070, Hubei China.
  • Yongzhen Dong
    College of Food Science and Technology, Huazhong Agricultural University, Wuhan 430070, Hubei, China.
  • Mingqian Tan
    Academy of Food Interdisciplinary Science, School of Food Science and Technology, Dalian Polytechnic University, Dalian, 116034, Liaoning China.
  • Yunlei Xianyu
    College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058, Zhejiang China.
  • Yiping Chen
    Beijing Engineering Research Center for BioNanotechnology & CAS Key Laboratory for Biological Effects of Nanomaterials and Nanosafety, National Center for Nanoscience and Technology, Beijing 100190, PR China. Electronic address: chenyp@nanoctr.cn.

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