Scholarlink

<p>Hepatitis B virus (HBV) infection can lead to hepatocellular carcinoma, and HBV integration into the host genome is regularly observed in the liver of chronic HBV carriers and is speculated to trigger carcinogenesis. To detect HBV integration, PCR-based methods are sensitive but may not be effective depending on the HBV integration site. High-throughput sequencing reveals not only HBV integration but also the site. However, it is still expensive for clinical applications. In situ hybridization is efficient and allows detection of DNA and RNA in single cells and has been applied to study HBV infection and dynamics. The technique, however, has not been applied to detect HBV integration. The task is challenging because of the small copy number of integrated HBV genomes, which results in a small signal-to-noise ratio. Here, we developed a fluorescence in situ hybridization approach for examining HBV integration in each liver cell. The obtained images of cells were analyzed using a deep learning model. Using several hepatoma cell lines with and without integrated HBV DNA, we showed that our trained neural network achieved an over 90% accuracy for majority of positive and negative control cells. This is the first proof-of-concept study showing that fluorescence imaging and deep learning are useful in detecting HBV integration at the single-cell level.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A Fluorescence Imaging- and Deep Learning-Based Approach for Detecting Hepatitis B Virus Integration into Host Genomes

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月18日

查看全文
  • Tzu-Hsien Yang,
  • Wen-Tai Chiu,
  • Yun-Hsuan Chu,
  • Han-Ting Hong,
  • Cheng-Wei Liu,
  • Lian-Yun Chang,
  • Hsu-Chin Hung,
  • Wei-Cheng Dai,
  • Yi-Lin Chen,
  • Sern Yan Lim,
  • Yu-Ching Chuang,
  • Wenya Huang,
  • Tsunglin Liu

摘要

Hepatitis B virus (HBV) infection can lead to hepatocellular carcinoma, and HBV integration into the host genome is regularly observed in the liver of chronic HBV carriers and is speculated to trigger carcinogenesis. To detect HBV integration, PCR-based methods are sensitive but may not be effective depending on the HBV integration site. High-throughput sequencing reveals not only HBV integration but also the site. However, it is still expensive for clinical applications. In situ hybridization is efficient and allows detection of DNA and RNA in single cells and has been applied to study HBV infection and dynamics. The technique, however, has not been applied to detect HBV integration. The task is challenging because of the small copy number of integrated HBV genomes, which results in a small signal-to-noise ratio. Here, we developed a fluorescence in situ hybridization approach for examining HBV integration in each liver cell. The obtained images of cells were analyzed using a deep learning model. Using several hepatoma cell lines with and without integrated HBV DNA, we showed that our trained neural network achieved an over 90% accuracy for majority of positive and negative control cells. This is the first proof-of-concept study showing that fluorescence imaging and deep learning are useful in detecting HBV integration at the single-cell level.