Spatial transcriptional profiling of CHB liver biopsies reveals an undetected population of zonally biased HBV-integrated cells

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Background & Aims HBV covalently closed circular DNA (cccDNA) and HBV-integrated DNA (iDNA) are features of chronic HBV (CHB). Elimination of both cell populations is likely to be required for functional cure. RNA-based spatial methods, chromogenic in situ hybridization (CISH), and multiplex immunofluorescence (mIF) combined with machine learning methods were used to evaluate archival liver biopsy samples from patients with CHB. Methods Modified Visium Spatial Transcriptomics (ST) and Single Nuclei RNA sequencing (snRNA-Seq) were performed on four commercial HBV + biopsies, and six liver biopsies from the clinical trial GS-US-174-0149. mIF for HBV viral proteins and CISH for HBV RNA and DNA were also performed. Machine learning was used to annotate ST data for the presence of cccDNA vs . iDNA. A deep learning (DL) algorithm was used to perform classification based on HBsAg mIF stain patterns and integrated into the ST analytical framework. Results The experimental workflow detected HBV RNA and DNA, alongside the human transcriptome. We identified an undescribed population of iDNA cells, which are HBsAg mIF - but produce comparable levels of viral RNA to iDNA cells that are HBsAg mIF + . These cells exhibit a periportal zonal preference in their spatial location within the liver. Analysis of a subset of CHB biopsies by snRNA-Seq confirmed a higher burden (approximately three-to seven-fold) of HBs RNA+ nuclei compared with HBsAg mIF + cells. Conclusions Our findings indicate the presence of a previously undescribed population of zonally enriched HBV-integrated hepatocytes that are not detectable by protein-based methods. Intrahepatic viral burden estimates by RNA-based methods were consistently higher than protein-based estimates in all CHB samples profiled. Impact and implications This study addresses crucial gaps in understanding HBV infection heterogeneity, zonal distribution, and the interplay between viral RNA, protein expression, and HBV integration events. Protein-based measures of intrahepatic HBV have been recognized as incomplete indicators of HBV burden. Accurate characterization of the viral landscape is essential for developing safe and efficacious therapies aimed at achieving a functional cure. Using RNA-based approaches, we uncovered a hidden HBV burden, which was consistently higher compared with protein-based estimates and was predominantly driven by HBV iDNA. Moreover, we identified two spatially and phenotypically distinct iDNA hepatocyte populations, raising new questions about mutational load and hepatocellular carcinoma risk in regions previously overlooked. Access to RNA-based methods that capture the full extent of HBV burden within the liver is vital for advancing fundamental research, improving disease models, and guiding curative therapies. Clinical Trials registration The study is registered under GS-US-174-0149.

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