High-dimensional analysis reveals an immune atlas and novel neutrophil clusters in the lungs of model animals with Actinobacillus pleuropneumoniae-induced pneumonia

Abstract

Due to the increase in bacterial resistance, improving the anti-infectious immunity of the host is rapidly becoming a new strategy for the prevention and treatment of bacterial pneumonia. However, the specific lung immune responses and key immune cell subsets involved in bacterial infection are obscure. Actinobacillus pleuropneumoniae (APP) can cause porcine pleuropneumonia, a highly contagious respiratory disease that has caused severe economic losses in the swine industry. Here, using high-dimensional mass cytometry, the major immune cell repertoire in the lungs of mice with APP infection was profiled. Various phenotypically distinct neutrophil subsets and Ly-6C⁺ inflammatory monocytes/macrophages accumulated post-infection. Moreover, a linear differentiation trajectory from inactivated to activated to apoptotic neutrophils corresponded with the stages of uninfected, onset, and recovery of APP infection. CD14⁺ neutrophils, which mainly increased in number during the recovery stage of infection, were revealed to have a stronger ability to produce cytokines, especially IL-10 and IL-21, than their CD14^- counterparts. Importantly, MHC-II⁺ neutrophils with antigen-presenting cell features were identified, and their numbers increased in the lung after APP infection. Similar results were further confirmed in the lungs of piglets infected with APP and Klebsiella pneumoniae infection by using a single-cell RNA-seq technique. Additionally, a correlation analysis between cluster composition and the infection process yielded a dynamic and temporally associated immune landscape where key immune clusters, including previously unrecognized ones, marked various stages of infection. Thus, these results reveal the characteristics of key neutrophil clusters and provide a detailed understanding of the immune response to bacterial pneumonia.

Keywords: Actinobacillus pleuropneumoniae; bacterial pneumonia; immune response; mass cytometry; neutrophils; piglet; subset.

Figure 1

Identification of major immune lineages in APP-infected lungs. A, B Changes in lung weight change rate, lung weight increase rate, lung index (A), and pathological changes (B) at 6, 12, 24, and 48 h after APP infection compared to no infection (0 h). C, D Changes in spleen weight increase rate, spleen index (C), and pathological changes (D) at the indicated time points. For the lung analysis: 0 h: N = 6; 6 h: N = 5; 12 h: N = 5; 24 h: N = 5; 48 h: N = 2; for the spleen analysis: 0 h: N = 6; 6 h: N = 5; 12 h: N = 6; 24 h: N = 5; 48 h: N = 2. E HSNE embeddings of 219,967 immune cells derived from murine lung (N = 14) at the overview level. Each dot represents a landmark, and the size of the landmark is proportional to the number of cells it represents. Colours indicate the ArcSinh5-transformed expression value of each indicated marker. F HSNE plots show the cell density. G HSNE plots show the major immune lineage cluster partitions in different colours. H Cell frequencies of each major immune lineage in CD45⁺ cells during APP infection. All mass cytometric results were generated from 14 samples (0 h: N = 3; 6 h: N = 3; 12 h: N = 3; 24 h: N = 3; 48 h: N = 2) in one mass cytometry experiment.

Figure 2

Cluster identification in the myeloid cell compartment in the lung. A An HSNE embedding of 219 967 immune cells derived from lungs (N = 14). Colours represent different immune lineages. B t-SNE embeddings of 148 820 myeloid cells show the ArcSinh5-transformed expression value of each indicated marker. C A density map shows the local probability density of the embedded cells. D A t-SNE plot shows cluster partitions. E A heatmap displays the median marker expression value and hierarchical clustering of the markers for 27 clusters identified in panel D. F Vertical bar graph depicting the composition of the myeloid cell compartment in each murine lung at the indicated time points. The coloured segment lengths represent the proportion of cells as a percentage of total myeloid cells in each sample. Colours as in panel E. G–N Cell frequencies of the indicated cell types in myeloid cells.

Figure 3

Formation of a linear differentiation trajectory from inactivated to activated to apoptotic PMNs during APP infection. A A t-SNE embedding of PMNs derived from lungs (N = 14). Colours represent distinct PMN clusters, as shown in Figure 2E. B t-SNE embeddings of PMNs show the ArcSinh5-transformed expression value of each indicated marker. C A density map shows the local probability density of the embedded PMN. Red encirclement indicates the cells in the linear trajectory. D Wanderlust graph showing the relative expression of the indicated markers along the differentiation trajectory, as indicated in panel C by the arrow. E t-SNE embeddings of PMNs derived from lungs (N = 14). Colours represent different time points of infection (upper row) and local density features of t-SNE-embedded cells (bottom row) at the indicated time points. F Cell frequencies of the indicated cell types in myeloid cells. G Overlay histograms show the indicated marker expression by five types of PMNs.

Figure 4

CD14⁺ PMNs in the lung produce more cytokines than their CD14⁻ counterparts. A, B Biaxial plots (A) from one representative experiment and bar charts (B) show the production of IL-17A, TNF-α, IFN-γ, IL-10, and IL-21 in CD14⁻ and CD14⁺ PMNs after stimulation with PMA and ionomycin. N = 6–12 samples in three independent experiments. Error bars indicate the mean ± SD. C Violin plots showing the RNA expression (log-normalized) of the indicated genes by CD14⁺ and CD14⁻ PMNs in the piglet lung after APP infection. D Bar plots show the frequencies of CD14⁺ and CD14⁻ PMNs in the piglet lung after APP infection. E GO and KEGG analyses show the main functions and signalling pathways of differentially expressed genes in CD14⁺ compared with CD14⁻ PMNs. All RNA-seq data were generated from one experiment with 3 samples per group. F The frequencies of CD14⁺ PMNs in the lungs after the indicated infection. N = 6 samples in three independent experiments.

Figure 5

Identification of APC-like PMNs in the lungs of mice and piglets after APP infection. A Frequencies of MHC-II⁺ PMNs in the murine lung post APP infection. B, C Biaxial plots (B) from one representative experiment and bar charts (C) show the percentage of CD14⁺MHC-II⁺CD86⁺ or CD14⁺MHC-II⁺CD86⁺ cells within Ly-6G⁺ PMNs and the frequencies of APP⁺ cells within their parent cells. N = 5 samples in three independent experiments. Error bars indicate the mean ± SD. D Bar plots show the frequencies of SLA-DQB1⁺ and SLA-DQB1⁻ PMNs in the piglet lung after APP infection. E Violin plots show the RNA expression (log-normalized) of indicted genes by SLA-DQB1⁺ and SLA-DQB1⁻ PMNs in the piglet lung after APP infection. F GO and KEGG analyses show the main functions and signalling pathways enriched in the differentially expressed genes between SLA-DQB1⁺ PMNs and SLA-DQB1⁻ PMNs.

Figure 6

Infection-associated immune clusters are identified in the lung. A A t-SNE embedding of 14 lung samples, where the t-SNE was computed based on the cell frequencies of 48 immune clusters (% of CD45⁺ cells). One dot represents one sample. B t-SNE embeddings of 48 immune clusters from 14 samples. One dot represents one cluster. The size of the dot is proportional to the cell frequency value, which is more similar across tissues, and the clusters are closer. C A table depicts the top 6 clusters contributing to the infection-time-specific t-SNE signatures. D A heatmap shows the correlation among 48 immune clusters based on the cell frequencies of total CD45⁺ cells in each sample and hierarchical clustering. The top 6 clusters and the clusters that are significantly differentially enriched at different infection times are highlighted in different colours. Green: 0 h, yellow: 6 h, blue: 12 h, purple: 24 h, and red: 48 h.