Transcriptomic profiling of immune cells in murine polymicrobial sepsis

Abstract

Introduction: Various immune cell types play critical roles in sepsis with numerous distinct subsets exhibiting unique phenotypes even within the same cell population. Single-cell RNA sequencing (scRNA-seq) enables comprehensive transcriptome profiling and unbiased cell classification. In this study, we have unveiled the transcriptomic landscape of immune cells in sepsis through scRNA-seq analysis.

Methods: We induced sepsis in mice by cecal ligation and puncture. 20 h after the surgery, the spleen and peritoneal lavage were collected. Single-cell suspensions were processed using a 10× Genomics pipeline and sequenced on an Illumina platform. Count matrices were generated using the Cell Ranger pipeline, which maps reads to the mouse reference transcriptome, GRCm38/mm10. Subsequent scRNA-seq analysis was performed using the R package Seurat.

Results: After quality control, we subjected the entire data set to unsupervised classification. Four major clusters were identified as neutrophils, macrophages, B cells, and T cells according to their putative markers. Based on the differentially expressed genes, we identified activated pathways in sepsis for each cell type. In neutrophils, pathways related to inflammatory signaling, such as NF-κB and responses to pathogen-associated molecular patterns (PAMPs), cytokines, and hypoxia were activated. In macrophages, activated pathways were the ones related to cell aging, inflammatory signaling, and responses to PAMPs. In B cells, pathways related to endoplasmic reticulum stress were activated. In T cells, activated pathways were the ones related to inflammatory signaling, responses to PAMPs, and acute lung injury. Next, we further classified each cell type into subsets. Neutrophils consisted of four clusters. Some subsets were activated in inflammatory signaling or cell metabolism, whereas others possessed immunoregulatory or aging properties. Macrophages consisted of four clusters, namely, the ones with enhanced aging, lymphocyte activation, extracellular matrix organization, or cytokine activity. B cells consisted of four clusters, including the ones possessing the phenotype of cell maturation or aging. T cells consisted of six clusters, whose phenotypes include molecular translocation or cell activation.

Conclusions: Transcriptomic analysis by scRNA-seq has unveiled a comprehensive spectrum of immune cell responses and distinct subsets in the context of sepsis. These findings are poised to enhance our understanding of sepsis pathophysiology, offering avenues for targeting novel molecules, cells, and pathways to combat infectious diseases.

Keywords: lymphocyte; macrophage; neutrophil; sepsis; single-cell RNA sequencing.

Figure 1

Identification of neutrophils, macrophages, B cells, and T cells. (A) UMAP plots showing the results of post-filtering unsupervised random forest classification of all groups combined. (B) Violin plots of key markers used for identifying cell types. (C) UMAP plots showing the post-filtering unsupervised random forest classification of each group (peritoneal cavity of sham and CLP, spleen of sham and CLP).

Figure 2

Transcriptomic heterogeneity caused by sepsis. Volcano plots showing the differentially expressed genes of CLP vs. sham in (A) neutrophils, (B) macrophages, (C) B cells, and (D) T cells.

Figure 3

Distribution of neutrophil subsets. UMAP plots showing the subsets of neutrophils in (A) all groups combined and (B) each group.

Figure 4

Distribution of macrophage subsets. UMAP plots showing the subsets of macrophages in (A) all groups combined and (B) each group.

Figure 5

Distribution of B cell subsets. UMAP plots showing the subsets of B cells in (A) all groups combined and (B) each group.

Figure 6

Distribution of T-cell subsets. UMAP plots showing the subsets of T cells in (A) all groups combined and (B) each group.