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RNA-seq03:21

RNA-seq

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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
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Updated: Jan 10, 2026

Detection of Low Copy Number Integrated Viral DNA Formed by In Vitro Hepatitis B Infection
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Protocol for quantifying hepatitis B virus transcript abundance and genomic segment distribution from single-cell

Lina Liu1, Liyu Chen2, Lingyun Zhou2

  • 1Center of Infectious Diseases, West China Hospital of Sichuan University, Chengdu, China; Laboratory for Pathogen Research, West China Hospital, Sichuan University, Chengdu, China.

STAR Protocols
|November 28, 2025
PubMed
Summary

This study introduces a new protocol for analyzing single cells from Hepatitis B virus (HBV)-infected liver tissue. It allows detailed examination of viral gene expression and host interactions at the cellular level.

Keywords:
BioinformaticsCell biologyGene expressionHealth sciencesMolecular biologySequence analysisSingle cell

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Area of Science:

  • Hepatology
  • Virology
  • Molecular Biology

Background:

  • Hepatitis B virus (HBV) infection is a major cause of chronic liver disease and hepatocellular carcinoma.
  • Understanding HBV-host interactions at a cellular level is crucial for developing effective therapies.

Purpose of the Study:

  • To present a comprehensive protocol for single-cell analysis of HBV-infected liver tissue.
  • To enable quantitative assessment of HBV transcript abundance and genome-wide read distribution in single cells.

Main Methods:

  • Tissue dissociation and purification of cells from HBV-infected liver tissue.
  • Single-cell RNA sequencing (scRNA-seq) library preparation, sequencing, and data processing.
  • Analysis of HBV expression patterns and viral genome distribution within host cells.

Main Results:

  • The protocol yields quantitative per-cell HBV transcript abundance data.
  • A genome-wide map of read distribution provides insights into viral integration and expression.
  • Detailed analysis of viral expression patterns and HBV-host interactions at single-cell resolution is achieved.

Conclusions:

  • This protocol offers a powerful tool for dissecting the complexities of HBV infection at the single-cell level.
  • It facilitates a deeper understanding of viral pathogenesis and host responses in HBV-infected livers.
  • The method supports the identification of novel therapeutic targets and strategies for managing HBV-related liver disease.