Single-Cell Atlas of Spleen Remodeling Reveals Macrophage Subset-Driven ASFV Pathogenesis

Figures

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  Figure 1. Single-cell transcriptomic landscape of spleen cells across four time points following ASFV infection. (a) UMAP visualization of spleen cells collected at four time points post-challenge (Day 0, 3, 5, and 7), colored by cell type. Distinct immune populations were identified based on canonical marker genes and unsupervised clustering. (b) Volcano plots showing differentially expressed genes (DEGs) for each major cell type upon ASFV infection. For each comparison, DEGs were identified using the Wilcoxon rank-sum test. Genes with an adjusted p-value < 0.05 and logfc.threshold = 0.25. (c) Stacked line-bar plots depicting dynamic changes in the abundance of each cell type over the infection course. Relative frequencies (shown as percentages of total spleen cells) are presented for each time point. (d) Heatmap displaying the expression patterns of representative marker genes across identified cell clusters. The scale represents normalized expression levels (Z-scores), allowing comparison across genes and cell types. (e) Time-course visualization of spleen cells.

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  Figure 2. Viral dynamics and cell-type-specific ASFV expression across different time points post-challenge. (a) UMAP visualization showing the distribution of spleen cells at different time points (Day 0, 3, 5, and 7) post-challenge. (b) UMAP plot colored by infection status (infected vs. ininfected). Cells are clustered by gene expression profile. (c) UMAP plot showing the distribution of spleen cells colored by ASFV viral gene expression levels. (d) Heatmap of ASFV infection rates across various cell types at different time points. Infection rates were calculated as the percentage of infected cells within each cell type, with values scaled from 0 to 100. (e) Line plot showing the infection rates of each cell type over time. (f) Line plot displaying the mean viral load in infected cells of each cell type over the course of ASFV infection. (g) Bar plot of the top 10 ASFV genes expressed in each cell type, with circle size representing the percentage of expressing cells and color intensity representing the average expression level of each gene. (h) Correlation analysis between host gene expression and viral load in infected cells. Interferon_Response (MX1, ISG15, IFIT1, IFIT3, OAS1, STAT1,…

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  Figure 3. Macrophage subpopulation dynamics during ASFV infection. (a) UMAP visualization of spleen macrophages isolated from the integrated single-cell RNA-seq dataset, clustered into four transcriptionally distinct subpopulations: Remodeling Macrophages (RemodelMac, crimson), Susceptible Macrophages (SusceptibleMac, medium purple), Repairing Macrophages (RepairMac, orange), and Antiviral Macrophages (AntiviralMac, cadet Blue). Subcluster identities were determined based on differential gene expression profiles and functional annotation. (b) UMAP plots depicting the temporal distribution of macrophage subtypes across the four time points (Day 0, 3, 5, and 7 post-infection). (c) UMAP visualization of macrophage subtypes colored by infection status (ASFV-infected vs. uninfected). (d) UMAP plot colored by viral load (ASFV gene expression level) among macrophage subtypes. (e) UMAP plots depicting the temporal distribution of macrophage subtypes across the four time points (Day 0, 3, 5, and 7 post-infection), respectively. (f) Donut charts illustrating the relative abundance of each macrophage subtype at different time points. (g) Heatmap of signature gene expression and enriched biological…

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  Figure 4. Viral dynamics in sub-macrophages cell types across different time points post-challenge. (a) Heatmap of ASFV infection rates across sub-macrophage cell types at different time points. Infection rates were calculated as the percentage of infected cells within each cell type, with values scaled from 0 to 100%. (b,c) Line plot showing the infection rates of each sub-macrophage cell type over time. (d) Correlation analysis between host gene expression and viral load in infected cells. (e) Heatmap of the expression patterns of the top ASFV genes across different cell types at different time points. (f) Correlation analysis between host gene module expression and viral load in infected cells. Interferon_Response (MX1, ISG15, IFIT1, IFIT3, OAS1, STAT1, IRF7, IRF9), Inflammatory (TNF, IL6, IL1B, IL18, CCL2, CXCL8, CXCL10), Anti-inflammatory (IL10, TGFB1, SOCS1, SOCS3), Antigen_Presentation (CD80, CD86, CD40, HLA-DRA, HLA-DRB1), Tcell_Markers (CD3D, CD3E, CD4, CD8A, CD8B, FOXP3, GZMB), PRR, Pattern Recognition Receptor (TLR3, TLR7, TLR8, DDX58, IFIH1, NLRP3). (g) Co-expression analysis of ASFV genes.

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  Figure 5. Pseudotemporal analysis of macrophage subtypes. (a) Overall trajectory of macrophage subtypes in pseudotime. Black circles with numbers represent trajectory nodes inferred by Monocle2 pseudotime analysis, where higher values indicate later developmental stages. (b) Trajectory plot showing the distribution of macrophage subtypes across pseudotime intervals, highlighting the temporal shifts in infection patterns. (c) Detailed trajectory plots for each macrophage subtype before and after ASFV infection, revealing subtype-specific changes in infection dynamics over time. (d) Comparison of macrophage trajectories before and after ASFV infection, showing the overall impact of infection on the progression of each subtype. (e) Expressing changes in marker genes during ASFV infection. (f) Distribution of different cell states across the pseudotemporal trajectory. States 1–3 represent macrophage populations at early infection stages. State 4 specifically marks the SusceptibleMac subset unique to early infection. States 5–6 correspond to late-stage infected macrophages. (g) Marker genes and enriched pathways for different cell states identified through pseudotemporal analysis.

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  Figure 6. Transcriptional regulation and cellular crosstalk in macrophage subsets during ASFV infection. (a) Heatmap showing transcription factors (TFs) significantly enriched in infected versus uninfected cells across macrophage subsets and time points. (b) Ranked regulon activity of key TFs in AntiviralMac and SusceptibleMac during early infection. (c) Cell–cell communication network specific to AntiviralMac, highlighting ligand–receptor interactions. (d) Global communication patterns among all macrophage subsets, with edge weights indicating interaction strength. (e) Heatmap of Netrin signaling pathway interactions between macrophage subsets. (f) Circos plot visualizing Netrin-mediated crosstalk between macrophage subsets. (g) Expression profiles of Netrin pathway genes in infected versus uninfected cells across subtypes. (h) Comparative GO term enrichment analysis of Netrin signaling genes and ASFV infection-associated genes, revealing shared and distinct biological processes.