A single-cell atlas of the peripheral immune response in patients with influenza A virus infection

Abstract

Influenza A virus (IAV) remains a pressing global health concern, yet our understanding of the specific nature and functional roles of certain circulating cell subsets in relation to this viral infection remains unclear. We performed single-cell RNA sequencing (scRNA-seq) on single-cell whole-blood (scWB) isolated from various populations using the Singleron Matrix platform. Our investigation showed a significant upregulation of the IFN-stimulated gene, IFN-α-inducible protein 27 (IFI27), in patients affected by IAV infection and further found that the heightened expression of IFI27 was primarily concentrated in specific immune cell populations, including monocytes and conventional dendritic cells (cDCs). Notably, we identified a specific subset of neutrophils, neutrophil_ISG15, which implicates interferon (IFN) signaling in IAV infection. Our findings provide a comprehensive understanding of the cellular subtypes and molecular characteristics of scWB across different populations with IAV infection.

Keywords: Immunology; Transcriptomics; Virology.

Graphical abstract

Figure 1

Single-cell transcriptional profiling of single-cell whole-blood (scWB) from HDs and patients with IAV (A) A schematic showing the overall study design. Schematic illustrating the process of identifying cell populations within scWB obtained from children, adults, and pregnant women with IAV infection, as well as healthy individuals, using single-cell RNA sequencing (scRNA-seq). (B) Cellular populations identified. The UMAP projection of 103,561 single cells from HC (n = 2), IAV-children (IC, n = 2), IAV-adults (IA, n = 2), Pregnant HC (PHC, n = 2) and Pregnant IAV (PI, n = 3) samples, showing the formation of 9 clusters with the respective labels. Each dot corresponds to a single cell, colored according to cell type. (C) Average proportion of each cell type derived from HC (n = 2), IA (n = 2), IC (n = 2), PHC (n = 3) and PI (n = 2) samples.

Figure 2

Assessment of changes in T/NK cells in transcriptional profiles between IAV patients and healthy controls (A) UMAP visualization of T/NK cell subsets. We identified 12 T/NK cell clusters across 24,032 cells. (B) Bar plots showing the relative percentage of T/NK cell subtypes for each group. (C) Violin plots showing marker genes across T/NK cell subtypes. (D) The gene set variation analysis (GSVA) showing expression levels of important immune-related pathways in T/NK clusters.

Figure 3

Boxplots of the exhaustion scores across different clusters and conditions p < 0.05 was considered significant. ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001, ∗∗∗∗p < 0.0001; ns, not significant.

Figure 4

Transcriptome heterogeneity of B cell subsets (A) UMAP plot of B cell subsets in samples. (B) Cell proportions of B cell subtypes among five groups. (C) Dot plot of top three marker genes between different B cell clusters. (D) GSVA showing the pathways with significantly different activation in B cell clusters. Different colors represent different activation scores. (E) Enrichment analysis of DEGs from naive B cell subtype between IA group and HC group. (F) GO pathway enrichment analysis of identified naive B cell subtype DEGs between IA group and HC group. (G) Bar plots showing the enriched upregulated KEGG pathways in naive B cell subtype between IA group and HC group. (H) Bar plots showing the enriched upregulated KEGG pathways in naive B cell subtype between PI group and PHC group.

Figure 5

Assessment of changes in neutrophils in transcriptional profiles between IAV patients and healthy controls (A) UMAP visualization of neutrophil subsets. We identified 6 neutrophil clusters across 58,508 cells. (B) Bar plots showing the relative percentage of neutrophil subtypes for each group. (C) Dot plot shows the top 3 marker genes by six cell subsets in the neutrophils. (D) The gene set variation analysis (GSVA) showing expression levels of important immune-related pathways in neutrophil clusters.

Figure 6

Monocle pseudotime trajectory analysis of neutrophil subpopulations (A) Trajectory order of the neutrophil populations by pseudotime value. (B) Distribution of neutrophils on the developmental tree by clusters. (C) Heatmap of differentially expressed genes, ordered based on their common kinetics through pseudotime using Monocle. (D) The expression of the most variable genes involved in the neutrophil state transition is shown. (E) Heatmap of the AUC scores of expression regulation by transcription factors (regulon activity).

Figure 7

Features of monocyte subtypes of IAV patients compared with the healthy controls (A) UMAP plot of mononuclear phagocyte subtypes (MPs) in samples, colored by fine labels. (B) UMAP plot of monocyte subtypes in samples, colored by fine labels. (C) Heatmap of top ten differentially expressed genes between different monocyte clusters. (D) Cell proportions of monocyte subtypes among five groups, colored either by subtypes. (E) The gene set variation analysis (GSVA) showing expression levels of important pathways in monocyte clusters.

Figure 8

The key transcription factors of the JAK-STAT signaling pathway in patients with IAV infection (A) Heatmap of the AUC scores of expression regulation by transcription factors in nonclassical monocytes. (B) The boxplots show the expression of IRF1, IRF2, IRF7, and STAT1 in monocyte subsets from IAV patients and healthy controls. (C) The dot plot shows the expression of ISG15, CXCL10 and IFI27 in monocyte subpopulations from IAV patients and healthy controls. (D) The dot plot shows the expression of IRF7 in monocyte subpopulations from IAV patients and healthy controls.

Figure 9

Violin plot showing the expression levels of IFI27 in each immune cell subtype