Detection of Hepatitis C Virus Infection from Patient Sera in Cell Culture Using Semi-Automated Image Analysis

Area of Science:

• Virology
• Cell Biology
• Hepatology

Background:

• Hepatitis C virus (HCV) replication studies traditionally rely on adapted viral clones.
• Wild-type (WT) HCV isolates require specific cellular factors (e.g., SEC14L2) or inhibitors for cell culture adaptation.
• Efficient cell culture models for direct HCV infection from patient sera are needed.

Purpose of the Study:

• To optimize cell culture models for direct infection with HCV from patient sera.
• To develop a reliable method for quantifying HCV infection from clinical samples.
• To improve upon existing methods for studying wild-type HCV replication in vitro.

Main Methods:

• Utilized Huh7-Lunet cells engineered to express SEC14L2, CD81, and a GFP reporter.
• Implemented a drug-based regimen to stimulate non-modified wild-type HCV isolates.
• Established an automated image analysis pipeline with machine learning for GFP localization readout.
• Validated findings through visual inspection and comparison with RT-qPCR.

Main Results:

• The automated image analysis pipeline eliminated background noise in treated samples.
• Successfully detected HCV infection in 15 out of 34 high-titer genotype 1b (gt1b) patient sera.
• Demonstrated a significant correlation between serum viral titer and successful cell culture infection.
• Transfection of viral RNA from sera showed limited but viable infection efficiency.

Conclusions:

• Developed a robust and semi-automated serum infection assay for gt1b HCV isolates.
• The new assay is superior to conventional RT-qPCR for quantifying viral genomes in patient sera.
• This model facilitates the study of wild-type HCV replication directly from clinical specimens.