Precision-cut liver slices as a model for the evaluation of host-targeting agents against hepatitis B and delta viruses.

Single-cell RNA sequencing Human precision-cut liver slices (PCLS) were treated with selgantolimod (SLGN, 1 µM, 48 h, n=3) or control (DMSO, n=3). 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 during 20 h (Thermo Fisher Scientific), dissociated using 1 mg/mL Liberase TH during 20 min (Merck KGaA, Darmstadt, DE) followed by mechanical dissociation with spring scissors, and preserved according to the Tissue Fixation & Dissociation for GEM-X Flex Gene Expression protocol CG000783 (10x Genomics, Pleasanton, CA, US). Cell number was determined in order 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. ScRNA-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 number of genes (>200). Data were normalized using the NormalizeData function to log-transform and scale 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 and Hallmark gene sets from the MSigDB (v2026.1).6 Graphs were generated using the DimPlot, FeaturePlot, VlnPlot, and DotPlot functions of Seurat. References: 1. Van Renne N, Ballet F, Van Hees S, et al. A liver gene signature links liver cancer risk in chronic viral hepatitis to intrahepatic IgA plasma cells. JHEP Rep. 2026;8(4):101714. doi:10.1016/j.jhepr.2025.101714 2. Hao Y, Hao S, Andersen-Nissen E, et al. Integrated analysis of multimodal single-cell data. Cell. 2021;184(13):3573-3587.e29. doi:10.1016/j.cell.2021.04.048 3. Korsunsky I, Millard N, Fan J, et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat Methods. 2019;16(12):12. doi:10.1038/s41592-019-0619-0 4. Guilliams M, Bonnardel J, Haest B, et al. Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches. Cell. 2022;185(2):379-396.e38. doi:10.1016/j.cell.2021.12.018 5. Hänzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for microarray and RNA-Seq data. BMC Bioinformatics. 2013;14(1):7. doi:10.1186/1471-2105-14-7 6. Liberzon A, Subramanian A, Pinchback R, Thorvaldsdóttir H, Tamayo P, Mesirov JP. Molecular signatures database (MSigDB) 3.0. Bioinformatics. 2011;27(12):1739-1740. doi:10.1093/bioinformatics/btr260