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. 2022 Nov 2:13:981784.
doi: 10.3389/fimmu.2022.981784. eCollection 2022.

Integrating bulk and single-cell sequencing reveals the phenotype-associated cell subpopulations in sepsis-induced acute lung injury

Fuquan Wang  1   2 , Ming Chen  1   2 , Jiamin Ma  1   2 , Chenchen Wang  1   2 , Jingxu Wang  1   2 , Haifa Xia  1   2 , Dingyu Zhang  1   2   3 , Shanglong Yao  1   2
Affiliations

Integrating bulk and single-cell sequencing reveals the phenotype-associated cell subpopulations in sepsis-induced acute lung injury

Fuquan Wang et al. Front Immunol. .

Abstract

The dysfunctional immune response and multiple organ injury in sepsis is a recurrent theme impacting prognosis and mortality, while the lung is the first organ invaded by sepsis. To systematically elucidate the transcriptomic changes in the main constituent cells of sepsis-injured lung tissue, we applied single-cell RNA sequencing to the lung tissue samples from septic and control mice and created a comprehensive cellular landscape with 25044 cells, including 11317 immune and 13727 non-immune cells. Sepsis alters the composition of all cellular compartments, particularly neutrophils, monocytes, T cells, endothelial, and fibroblasts populations. Our study firstly provides a single-cell view of cellular changes in septic lung injury. Furthermore, by integrating bulk sequencing data and single-cell data with the Scissors-method, we identified the cell subpopulations that are most associated with septic lung injury phenotype. The phenotypic-related cell subpopulations identified by Scissors-method were consistent with the cell subpopulations with significant composition changes. The function analysis of the differentially expressed genes (DEGs) and the cell-cell interaction analysis further reveal the important role of these phenotype-related subpopulations in septic lung injury. Our research provides a rich resource for understanding cellular changes and provides insights into the contributions of specific cell types to the biological processes that take place during sepsis-induced lung injury.

Keywords: Scissors-method; cellular landscape; lung injury; sepsis; single-cell sequencing.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Identifying the infiltrated cell types in septic and non-septic lung tissue. (A) The main workflow of the study. (B) The He staining results of the lung tissue (magnification 200×). (C) The UMAP plot of all scRNA-seq data, shows a total of 14 distinct cell types that were identified. (D) UMAP plot of sequenced cells grouped by color by immune and non-immune cells. (E) Dot plot for cell-type-specific signature genes. (F) The number of immune cells and non-immune cells in each group. (G) The proportion of various types of cells in different groups of immune cells. (H) The proportion of various types of cells in different groups of non-immune cells.
Figure 2
Figure 2
Cellular composition of monocytes in normal and septic lung tissue. (A) a total of five clusters of monocytes were identified in lung tissue. Cell subpopulations are colored as shown in the legend. (B) Dot plot for principal identifiers of different subclusters. (C) The proportions of different monocyte subclusters. (D) The UMAP visualization of the Scissor-medthod selected cells. The red dots are cells associated with the sepsis phenotypes. (E) The prediction of monocyte differentiation trajectories with Monocle. (F) Heatmap of DEGs of the Mono1 subcluster. (G) Enriched GO functions of upregulated genes in Mono1.
Figure 3
Figure 3
Cellular composition of neutrophils in normal and septic lung tissue. (A) a total of four subclusters of neutrophils were identified in lung tissue. Cell subpopulations are colored as shown in the legend. (B) Dot plot for principal identifiers of different subclusters. (C) The proportions of different monocyte subclusters. (D) The prediction of neutrophils differentiation trajectories with Monocle. (E) The UMAP visualization of the Scissor-method selected cells. The red dots are cells associated with the sepsis phenotype. (F) Violin plots of DEGs for Neutrophil3-Cxcl3. (G) Enriched GO functions of upregulated genes in Neutrophil3-Cxcl3. (H) Dot plot show ligand-receptor pairs of cytokines between Neutrophil3-Cxcl3 and other immune cell groups.
Figure 4
Figure 4
Cellular composition of lymphocytes in normal and septic lung tissue. (A) a total of five subclusters of lymphocytes were identified in lung tissue. Cell subpopulations are colored as shown in the legend. (B) Dot plot for principal identifiers of different subclusters. (C) The proportions of different lymphocyte subclusters. (D) The UMAP visualization of the Scissor-method selected cells. The red and blue dots are cells associated with sepsis and normal phenotypes (E) Heatmap of DEGs of the ILC2 subcluster. (F) Enriched GO functions of upregulated genes in ILC2. (G) Violin plot of the expression levels of Areg in different lymphocyte subclusters.
Figure 5
Figure 5
Cellular composition of endothelial cells in normal and septic lung tissue (A) a total of six subclusters of endothelial cells were identified in lung tissue. Cell subpopulations are colored as shown in the legend. (B) Dot plot for principal identifiers of different subclusters. (C) The proportions of different lymphocyte subclusters. (D) The UMAP visualization of the Scissor-method selected cells. The red and blue dots are cells associated with sepsis and normal phenotypes. (E) Enriched GO functions upregulated genes of Capillary ECs-Sema3c and Capillary Ecs in the Sham group. (F) Enriched GO functions upregulated genes of lymphatic ECs in the sepsis group. (G) Heatmap of DEGs of Capillary ECs-Sema3c and Capillary ECs subpopulations in the Sham group. (H) Heatmap of DEGs of lymphatic ECs in the sepsis group.
Figure 6
Figure 6
Cellular composition of fibroblasts in normal and septic lung tissue. (A) a total of seven subclusters of fibroblasts were identified in lung tissue. Cell subpopulations are colored as shown in the legend. (B) Dot plot for principal identifiers of different subclusters. (C) The proportions of different fibroblasts subclusters. (D) The UMAP visualization of the Scissor-method selected cells. The red and blue dots are cells associated with sepsis and normal phenotypes. (E) The prediction of fibroblast differentiation trajectories with Monocle. (F) Heatmap of top10 DEGs of different groups of fibroblasts. (G) Dot plot show ligand-receptor pairs of cytokines between fibroblasts and other immune cell groups in the CLP group.
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