Comprehensive single-cell analysis deciphered the immunoregulatory mechanism of TPPU in alleviating sepsis-related acute liver injury

Abstract

Introduction: Sepsis-related acute liver injury involves complex immune dysfunctions. Epoxyeicosatrienoic acids (EETs), bioactive molecules derived from arachidonic acid (AA) via cytochrome P450 (CYP450) and rapidly hydrolyzed by soluble epoxide hydrolase (sEH), possess anti-inflammatory properties. Nevertheless, the impact of the sEH inhibitor TPPU on sepsis-related acute liver injury remains uncertain.

Objectives: This study utilized comprehensive single-cell analysis to investigate the immunoregulatory mechanism of TPPU in alleviating sepsis-related acute liver injury.

Methods: Hepatic bulk RNA sequencing and proteomics analyses were employed to investigate the mechanisms underlying sepsis-related acute liver injury induced by cecal ligation and puncture in mice. Cytometry by time-of-flight and single-cell RNA sequencing were conducted to thoroughly examine the immunoregulatory role of TPPU at single-cell resolution.

Results: Downregulation of AA metabolism and the CYP450 pathway was observed during sepsis-related acute liver injury, and TPPU treatment reduced inflammatory cytokine production and mitigated sepsis-related hepatic inflammatory injury. Comprehensive single-cell analysis revealed that TPPU promotes the expansion of anti-inflammatory CD206⁺CD73⁺ M2-like macrophages and PDL1^-CD39^-CCR2⁺ neutrophils, reprogramming liver neutrophils to an anti-inflammatory CAMP⁺NGP⁺CD177⁺ phenotype. Additionally, TPPU inhibits the CCL6-CCR1 signaling mediated by M2-like macrophages and CAMP⁺NGP⁺CD177⁺ neutrophils, altering intercellular communication within the septic liver immune microenvironment.

Conclusion: This study demonstrated TPPU's protective efficacy against sepsis-related acute liver injury, underscoring its vital role in modulating liver macrophages and neutrophils and enhancing prospects for personalized immunomodulatory therapy.

Keywords: Arachidonic acid metabolism; Cytochrome P450; Macrophages; Neutrophils; Sepsis-related acute liver injury; TPPU.

Graphical abstract

Fig. 1

Downregulation of AA metabolism and CYP450 pathway in CLP mice and the ameliorative effect of sEH inhibitor TPPU on hepatic inflammation in CLP-induced sepsis mice. (A) Serum levels of ALT and AST (n = 12 per group). (B) Representative H&E stained liver sections. Scale bar: 500 μm or 100 μm. (C) Modified liver histological activity index (n = 12 per group). (D) Genome circle diagram of differentially expressed genes (DEGs) (n = 12 per group). (E) KEGG pathway analysis of significant DEGs. (F) Volcano plot displaying the number and distribution of differentially expressed proteins (DEPs) (n = 12 per group). (G) KEGG pathway analysis of significant DEPs. (H) Venn diagram showing the overlap of identified mRNA and proteins, as well as DEGs and DEPs. (I) Correlation analysis of all quantitative proteins and their associated genes. (J) KEGG pathway analysis of associated DEGs and DEPs. (K) Relative abundance of proteins in the AA metabolism pathway. (L) Relative abundance of proteins in the CYP450 pathway. (M) GSEA analysis of gene sets related to AA metabolism. (N) GSEA analysis of gene sets related to the CYP450 pathway. (O) Serum levels of ALT and AST (n = 12 per group). (P) Representative H&E stained liver sections. Scale bar: 500 μm or 100 μm. (Q) Modified liver histological activity index (n = 12 per group). (R) Heatmap representing serum cytokine levels among groups (n = 6 per group).

Fig. 2

CyTOF analysis revealing the immunomodulatory function of TPPU in sepsis-related acute liver injury. (A) Manual gating of CD4⁺ T cells, CD8⁺ T cells, B cells, NK cells, neutrophils, ILCs, macrophages, Kupffer cells, DCs, and monocytes based on cell surface marker expression. (B) t-SNE dimensional reduction applied to the nine major cell lineages. (C) t-SNE plots displaying the expression levels of ten key markers, with color mapping from blue (low expression) to red (high expression). (D) Heatmap showing the normalized expression of 42 markers across 22 immune cell clusters. (E) t-SNE dimensional reduction applied to the 22 immune cell clusters. (F) Bar plots depicting the proportions of nine major cell lineages in each group (n = 6 per group). (G) Expression of markers CCR2, CX3CR1, and CD73 on neutrophils. (H) Expression of markers CCR2, CX3CR1, CD73, and CD206 on macrophages. (I) Representative contour plots showing gating strategy of M2-macrophage. (J) Frequency of M2-macrophages in the control and TPPU groups.

Fig. 3

High-dimensional analyses of macrophages and neutrophils. (A) t-SNE dimensional reduction applied to the 18 macrophage subsets. (B) t-SNE plots displaying the expression of four key markers: CD206, CD73, MHC II, and CD11c. (C) Boxplots comparing the frequency of seven macrophage subsets in the control and TPPU groups (n = 6 per group). (D) t-SNE dimensional reduction applied to the eight neutrophil subsets. (E) Heatmap showing the normalized expression of 24 markers across the eight neutrophil subsets. (F) Frequency of PDL1^-CD39⁺ neutrophils and PDL1^-CD39^-CCR2⁺ neutrophils in the control and TPPU groups. (G) Expression of markers CD73, CD172a, CD206, and MHC II on PDL1^-CD39⁺ neutrophils and PDL1^-CD39^-CCR2⁺ neutrophils. (H) Density of markers CD73, CD172a, CD206, and MHC II on PDL1^-CD39⁺ neutrophils and PDL1^-CD39^-CCR2⁺ neutrophils.

Fig. 4

Single-cell RNA profiling of the liver landscape following sepsis-related acute liver injury and TPPU treatment. (A) Heatmap displaying the normalized expression of top 5 marker genes across eleven cell populations. (B) UMAP plot showing the expression of representative marker genes. (C) UMAP plot illustrating the eleven identified immune cell types. (D) UMAP plot illustrating the eleven cell populations in the control and TPPU groups (n = 6 per group). (E) Bar plots depicting the proportions of the eleven cell populations in each group. (F) Violin plot showing expression of marker genes in macrophages from both groups. (G) Violin plot showing expression of marker genes in neutrophils from both groups.

Fig. 5

TPPU-promoted expansion of anti-inflammatory M2-like macrophages. (A) UMAP plot depicting the four identified macrophage subtypes. (B) Dot plot showing the normalized expression of characteristic marker genes in the four macrophage subtypes. (C) Developmental trajectory of macrophages inferred by Monocle, colored by pseudotime, cell subtypes, and state. (D) Heatmap illustrating gene expression dynamics along pseudotime. (E) Scatter plot showing the relative expression of IL1a, IL6, TNF, CCL3, and CCL9 during pseudotime, colored by cell subtypes. (F) Evaluation of potential biological processes of the four macrophage subtypes by ssGSEA. (G) Density plot (top) and boxplot (bottom) displaying the inflammatory response activity score in the four macrophage subtypes, with p values calculated using the Kruskal-Wallis test. (H) UMAP plot depicting the four identified macrophage subtypes in the control (left) and TPPU (right) groups (n = 6 per group). (I) Percentage of the four macrophage subtypes in each group. (J) Violin plot showing the relative gene expression of IL1RN, IL1a, IL1b, TNF, CCL3, and CCL6 based on scRNA-seq data. (K-L) Significant KEGG pathway enriched by up-regulated DEGs in TPPU group in (K) M1-like macrophages and (L) M2-like macrophages.

Fig. 6

Reprogramming of liver neutrophils by TPPU to suppress hepatic inflammation. (A) UMAP plot depicting the four identified neutrophil subtypes. (B) Dot plot showing the normalized expression of characteristic marker genes in the four neutrophil subtypes. (C) Developmental trajectory of neutrophils inferred by Monocle, colored by pseudotime, cell subtypes, and state. (D) Heatmap illustrating gene expression dynamics along pseudotime. (E) Scatter plot showing the relative expression of IL1a, IL1b, and TNF during pseudotime, colored by cell subtypes. (F) Evaluation of potential biological processes of the four neutrophil subtypes by ssGSEA. (G) Density plot (top) and boxplot (bottom) displaying the inflammatory response activity score in the four neutrophil subtypes, with p values calculated using the Kruskal-Wallis test. (H) Volcano plot showing differentially expressed genes between TNF⁺CCL3⁺ neutrophils and CAMP⁺NGP⁺CD177⁺ neutrophils. (I) Differential pathway enrichment in TNF⁺CCL3⁺ neutrophils and CAMP⁺NGP⁺CD177⁺ neutrophils by GSVA, using a two-sided unpaired limma-moderated t-test. (J) Percentage of the four neutrophil subtypes in the control and TPPU groups (n = 6 per group). (K) Violin plot showing the relative gene expression of IL1RN, IL1a, IL1b, TNF, CCL3, and CCL6 based on scRNA-seq data. (L-M) Significant KEGG pathway enriched by up-regulated DEGs in TPPU group in (L) TNF⁺CCL3⁺ neutrophils and (M) CAMP⁺NGP⁺CD177⁺ neutrophils.

Fig. 7

TPPU-rewired intercellular communication between macrophages and neutrophils in the septic liver microenvironment. (A) Circle plots depicting the number of ligand-receptor pairs between different neutrophil and macrophage subsets in the control and TPPU groups, respectively (n = 6 per group). (B) Inferred CCL signaling pathway networks in the control and TPPU groups. (C) Heatmap showing the differential number and weight of intercellular interactions in the TPPU group compared to the control group. (D) Bubble plots illustrating significant ligand-receptor pairs sent from CAMP⁺NGP⁺CD177⁺ neutrophils to other cell clusters. (E) Inferred TNF signaling pathway networks in the control and TPPU groups. (F) Circle plots showing the number of ligand-receptor pairs between M2-like macrophages and other cell clusters in the control and TPPU groups. (G) Dot plot displaying the outgoing communication patterns from M2-like macrophages to other cell clusters.