Precision-cut liver slices as a model for evaluating therapeutic agents against hepatitis B and delta viruses

Precision-cut liver slices (PCLS) were prepared from human liver resections (n = 3) and treated ex vivo with selgantolimod (SLGN, 1 µM, 48 h) or control (DMSO). Following ex vivo culture, PCLS were snap-frozen in liquid nitrogen and stored at −80°C until tissue processing. In order to obtain 25 mg of tissue, five liver slices per condition were pooled, fixed with 4% paraformaldehyde for 20 h (BP531-25, Thermo Fisher Scientific), dissociated using 1 mg/mL Liberase TH for 20 min (5401135001, Merck KGaA) followed by mechanical dissociation with spring scissors, and preserved according to the Tissue Fixation & Dissociation for Chromium Fixed RNA Profiling protocol CG000553 (10x Genomics, Pleasanton, CA, US). Cell number was determined to obtain an expected recovery of 10000 cells/channel, and samples were run on a Chromium Controller system (10x Genomics) according to manufacturer’s instructions. Single-cell (sc)RNA-seq libraries were generated with the Chromium Fixed RNA Profiling Kit for Multiplexed Samples (10x Genomics, Human Transcriptome Probe Set v1.0.1), and sequenced on a NovaSeq 6000 platform (Illumina, San Diego, CA, US) to obtain 50000 reads/cell. Data analysis was performed as described in Van Renne et al., JHEP Rep. 2026.1 Briefly, matrices were loaded into Seurat (v5.0.2)2 using the Read10X function. Cells were filtered based on mitochondrial content (<25%), total number of transcripts (>1500, <99999), and total number of genes (>200). Data were normalized using the NormalizeData function to log-transform the counts. Variable features were identified with the FindVariableFeatures function, selecting the top 2000 most variable genes. Data were then scaled using the ScaleData function. Principal component analysis (PCA) was performed using RunPCA on the first 30 dimensions. Harmony integration was performed to correct for donor effects.3 The RunUMAP function was used for dimensionality reduction using the first 30 dimensions. To identify cell clusters, the FindNeighbors function was employed, followed by FindClusters with a resolution of 3. Clusters were annotated using canonical cell type markers.4 Gene signatures were estimated using gene set variation analysis (GSVA, v2.4.9),5 Hallmark gene sets from the MSigDB (v2026.1),6 and the SLGN-UP and SLGN-DOWN signatures described in Roca Suarez, Plissonnier et al., Gut 2024.7 Graphs were generated using the DimPlot, FeaturePlot, VlnPlot, and DotPlot functions of Seurat. References 1. Van Renne, N. et al. A liver gene signature links liver cancer risk in chronic viral hepatitis to intrahepatic IgA plasma cells. JHEP Rep 8, 101714 (2026). 2. Hao, Y. et al. Integrated analysis of multimodal single-cell data. Cell 184, 3573-3587.e29 (2021). 3. Korsunsky, I. et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat Methods 16, 1289–1296 (2019). 4. Guilliams, M. et al. Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches. Cell 185, 379-396.e38 (2022). 5. Hänzelmann, S., Castelo, R. & Guinney, J. GSVA: gene set variation analysis for microarray and RNA-Seq data. BMC Bioinformatics 14, 7 (2013). 6. Liberzon, A. et al. The Molecular Signatures Database (MSigDB) hallmark gene set collection. Cell Syst 1, 417–425 (2015). 7. Roca Suarez, A. A. et al. TLR8 agonist selgantolimod regulates Kupffer cell differentiation status and impairs HBV entry into hepatocytes via an IL-6-dependent mechanism. Gut 73, 2012–2022 (2024).