Welcome to our interactive web interface which allows you to access the single-cell mRNA Seq data from Single-cell RNA-seq ties macrophage polarization to growth rate of intracellular Salmonella by Antoine-Emmanuel Saliba et al. (Nature Microbiology, 2016)

Intracellular bacterial pathogens can exhibit large heterogeneity in growth rate inside host cells, with major consequences for the infection outcome. If and how the host responds to this heterogeneity remains poorly understood. Here, we combined a fluorescent reporter of bacterial cell division with single-cell RNA-sequencing analysis to study the macrophage response to different intracellular states of the model pathogen Salmonella enterica serovar Typhimurium. The transcriptomes of individual infected macrophages revealed a spectrum of functional host response states to growing and non-growing bacteria. Intriguingly, macrophages harbouring non-growing Salmonella display hallmarks of the proinflammatory M1 polarization state and differ little from bystander cells, suggesting that non-growing bacteria evade recognition by intracellular immune receptors. By contrast, macrophages containing growing bacteria have turned into an anti-inflammatory, M2-like state, as if fast-growing intracellular Salmonella overcome host defence by reprogramming macrophage polarization. Additionally, our clustering approach reveals intermediate host functional states between these extremes. Altogether, our data suggest that gene expression variability in infected host cells shapes different cellular environments, some of which may favour a growth arrest of Salmonella facilitating immune evasion and the establishment of a long-term niche, while others allow Salmonella to escape intracellular antimicrobial activity and proliferate.

Figure 2 | Single-cell RNA-seq profiling reveals specific transcriptional signatures associated with MΦs containing non-growing or growing Salmonella. a, Gene expression correlation between individual cells from distinct sorting fractions. The heatmap displays Pearson’s r values clustered on the y axis by the euclidian method (dendrogram). This matrix revealed the existence of two major groups of cells corresponding to naive and challenged cells. Colours indicate cellular identities as inferred from the original FC gates. b, PCA allowed for a refinement of the group of challenged cells into groups II and III (group I: naive MΦs). Each dot represents a single cell (60 cells in total). Colours indicate cellular identities as inferred from the original FACS gates. c, Transcriptional profiles specific to individual cell groups (60 cells in total). A total of 1,073 genes were differentially expressed, as identified by SCDE (P < 0.01; Supplementary Fig. 7) among the three cell groups identified in b, and were grouped into individual gene clusters (A–D) and plotted as a heatmap. Clusters A and B are derived by comparing naive cells to challenged cells, labelled respectively cell groups I and II plus III on the PCA map. Clusters C and D are derived by comparing cell groups II and III (Supplementary Fig. 7 and Supplementary Table 2b–d).

Contact:
Antoine-Emmanuel Saliba: emmanuel.saliba@helmholtz-hiri.de
Jörg Vogel: joerg.vogel@helmholtz-hiri.de

Saliba lab - Single Cell Analysis
Helmholtz Institute for RNA-based Infection Research
Josef Schneider Str. 2/ D15, D-97080 Würzburg