A Large-Scale Single-Cell Atlas Reveals the Peripheral Immune Panorama of Bacterial Pneumonia

Abstract

Rationale: Bacterial pneumonia poses a substantial global health burden; yet, the immunological mechanisms driving disease pathogenesis and resolution are incompletely understood. Methods: We generated a large-scale single-cell transcriptomic atlas of peripheral blood immune cells from 100 individuals: 39 with severe bacterial pneumonia, 31 with mild disease, and 30 healthy control subjects. Objectives: Integrating single-cell RNA sequencing with clinical and molecular data revealed profound remodeling of the peripheral immune landscape across disease severities. Measurements and Main Results: Severe pneumonia was characterized by lymphopenia and monocytosis, accompanied by distinct shifts in T cell, B cell, and myeloid cell subset composition. Classical monocytes emerged as central orchestrators of the cytokine storm observed in severe cases, displaying elevated expression of proinflammatory genes (e.g., S100A8/9/12) and enhanced TLR4-MYD88 signaling. Exhaustion of innate-like CD8⁺ T cells, marked by upregulation of canonical inhibitory receptors, was a hallmark of severe disease. In contrast, mild pneumonia exhibited robust CD8⁺ T effector and helper memory cell activation, together with effective humoral immunity, evidenced by plasma cell expansion and coordinated T follicular helper cell-B cell interactions. B cells in mild cases showed enhanced antigen recognition, BCR signaling, and costimulatory gene expression, whereas those in severe cases displayed signs of dysfunction. Myeloid cell alterations in severe pneumonia included increased monocytic myeloid-derived suppressor cells and nonclassical monocytes, contributing to immunosuppression and complement overactivation, respectively. Conclusions: This high-resolution atlas of peripheral immune responses in bacterial pneumonia identifies key cellular and molecular drivers of disease severity, providing potential therapeutic targets for immunomodulation and improved outcomes.

Keywords: T cell exhaustion; bacterial pneumonia; cytokine storm; peripheral immune response; single-cell RNA sequencing.

Figure 1.

An overview of the study design and results for peripheral blood mononuclear cell single-cell transcriptomic study. (A) Diagram outlining the overall study design, which included 100 individuals, including 70 patients (31 patients with mild disease and 39 patients with severe disease) and 30 healthy donors. (B) The clustering result (left row) of 47 cell subtypes (right row) from 100 samples. Each point represents a single cell, colored according to cell type. (C) Uniform manifold approximation and projection of the healthy donors and those with mild and severe disease. (D) Disease preference of major cell clusters as estimated using the ratio of observed to expected cell counts.

Figure 2.

Monocytes are the primary contributors to the production of proinflammatory cytokines in patients with severe bacterial pneumonia. (A) Uniform manifold approximation and projections of peripheral blood mononuclear cells. Colored on the basis of 11 major cell types (top left), 7 hyperinflammatory cell subtypes (top right), cytokine score (middle), and inflammatory score (bottom). (B) Pie charts depicting the relative contribution of each inflammatory cell subtype to the cytokine and inflammatory scores in patients with severe bacterial pneumonia. (C) Heatmap depicting the expression of cytokines within each hyperinflammatory cell subtype identified. (D) Bar chart depicting the relative contribution of the top 10 cytokines in patients with severe disease. (E) Boxplots of S100A8/A9/A12 expression based on single-cell RNA-sequencing profiling for healthy control subjects, patients with mild disease, and patients with severe disease. Significance was evaluated using the Kruskal-Wallis test with Bonferroni correction (*P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001, ^nsP > 0.05). (F) Heatmap plots of the sum of significant interaction among the seven hyperinflammatory cell subtypes. (G) Dot plot illustrating the strength and significance of ligand–receptor interactions between major inflammatory subtype (Mono_01_Classical_CD24) and other inflammatory subtypes. Dot size corresponds to the −log₁₀(P value), and dot color indicates the mean expression level of the interacting pair. Displayed interactions were filtered for nominal significance (P < 0.05).

Figure 3.

Immunological features of CD8⁺ T cell subsets. (A) The clustering result (left row) of 9 CD8⁺ T cell types (right row) from 100 samples. Each point represents a single cell, colored according to cell type. (B) Box plots showing the exhausted scores in innate-like and effector CD8⁺ T cell subsets across disease conditions. Significance was evaluated using the Kruskal-Wallis test with Bonferroni correction (*P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001, ^nsP > 0.05). (C) Box plots showing the cell exhaustion–related markers in innate-like CD8⁺ T cells across disease conditions. Significance was evaluated using the Kruskal-Wallis test with Bonferroni correction (*P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001, ^nsP > 0.05). (D) Uniform manifold approximation and projections illustrating IFN-I response and unhelped signature scores for CD8⁺ T cells across disease conditions. (E) Box plots showing the cytotoxic score in effector CD8⁺ T cells across disease conditions. Significance was evaluated using the Kruskal-Wallis test with Bonferroni correction (*P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001, ^nsP > 0.05). (F) Dot plots showing the cytotoxicity-related genes in effector CD8⁺ T cell subsets across disease conditions. (G) Bar plots showing the enrichment Gene Ontology terms in effector CD8⁺ T cell subsets from patients with mild and severe bacterial pneumonia.

Figure 4.

Immunological features of CD4⁺ T cell subsets. (A) The clustering result (left row) of 11 CD4⁺T cell types (right row) from 100 samples. (B) Box plots showing the percentage of T regulatory (Treg) cells and CD4_05_Treg across disease conditions. Significance was evaluated using the Kruskal-Wallis test with Bonferroni correction (*P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001, ^nsP > 0.05). (C) Heatmap showing the expression of selected immunomodulatory molecules in CD4T_11_Treg_FOXP3_IL2RA subsets across disease conditions. (D) Partition-based graph abstraction analysis of CD4⁺ T pseudotime: The associated cell type and the corresponding status are listed. (E) Dot plot of the interactions among CD4T_11_Treg_FOXP3_IL2RA with exhausted innate CD8⁺ T cells in patients with severe bacterial pneumonia. P values are indicated by the circle sizes, as shown in the scale on the right. (F) Dot plots showing the selected genes in helper memory T cells across disease conditions. (G) Box plots showing the IL1RA and CD69 cores in helper memory T cells across disease conditions. Significance was evaluated using the Kruskal-Wallis test with Bonferroni correction (*P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001, ^nsP > 0.05). (H) Bar plots showing the enrichment Gene Ontology terms in helper memory T cells in patients with mild and severe disease.

Figure 5.

Immunological features of B cell subsets. (A) The clustering result (left row) of 7 B cell types (right row) from 100 samples. (B) Box plots showing the percentage of B_05_Plasma_IGHA1, B_06_Plasmablast_MKI67, and B_07_Plasma_IGHA1_IGHG1across disease conditions. Significance was evaluated using the Kruskal-Wallis test with Bonferroni correction (*P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001, ^nsP > 0.05). (C) Classes of heavy chains for plasma cells and plasmablasts. (D) Heatmap plots showing the expression of selected genes in plasma cells across disease conditions. (E) Correlation between plasma with T follicular helper cells in patients with mild disease. (F) Dot plots showing the selected genes in plasmablasts and plasma cells across disease conditions. (G) Dot plots showing the selected genes in B cells across disease conditions. (H) Venn diagram illustrating the number of upregulated genes in B cells.

Figure 6.

Immunological features of myeloid cells. (A) The clustering result (left row) of 13 myeloid subtypes (right row) from 100 samples. Each point represents a single cell, colored according to myeloid subtype. (B) Bar plots showing the expression of S100A8/A9 and HLA-DRA/B5/B1 in monocytic myeloid-derived suppressor cells across disease groups. (C) Heatmap plots of the selected genes in monocytic myeloid-derived suppressor cells across disease groups. (D) Heatmap plots of selected genes (C1QA/B/C) in nonclassical monocyte subset (Mono_06_Non-classical_CD16_C1QA) across disease conditions. (E) Partition-based graph abstraction analysis of monocyte pseudotime in patients with severe (left) and mild (right) disease: The associated cell type and the corresponding status are listed. (F) Venn diagram illustrating the number of upregulated genes in classical monocytes. (G) Bar plots showing cytokine and inflammatory scores in classical monocytes across disease groups. (H) Dot plots showing the selected genes in classical monocytes across disease conditions.