Single-cell RNA-seq ties macrophage polarization to growth rate of intracellular Salmonella

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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.

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Figure 1: Experimental strategy to sort single MΦs with different bacterial content.
Figure 2: Single-cell RNA-seq profiling reveals specific transcriptional signatures associated with MΦs containing non-growing or growing Salmonella.
Figure 3: Different bacterial loads correlate with divergent macrophage polarization transcription programs.
Figure 4: Bacterial growth triggers a different MΦ polarization pattern.

Change history

  • 14 July 2017

    In the PDF version of this article previously published, the year of publication provided in the footer of each page and in the 'How to cite' section was erroneously given as 2017, it should have been 2016. This error has now been corrected. The HTML version of the article was not affected.


  1. 1

    Bumann, D. Heterogeneous host–pathogen encounters: act locally, think globally. Cell Host Microbe 17, 13–19 (2015).

    CAS  Article  Google Scholar 

  2. 2

    Price, J. V. & Vance, R. E. The macrophage paradox. Immunity 41, 685–693 (2014).

    CAS  Article  Google Scholar 

  3. 3

    Avraham, R. et al. Pathogen cell-to-cell variability drives heterogeneity in host immune responses. Cell 162, 1309–1321 (2015).

    CAS  Article  Google Scholar 

  4. 4

    Monack, D. M., Mueller, A. & Falkow, S. Persistent bacterial infections: the interface of the pathogen and the host immune system. Nat. Rev. Microbiol. 2, 747–765 (2004).

    CAS  Article  Google Scholar 

  5. 5

    Claudi, B. et al. Phenotypic variation of salmonella in host tissues delays eradication by antimicrobial chemotherapy. Cell 158, 722–733 (2014).

    CAS  Article  Google Scholar 

  6. 6

    Helaine, S. et al. Internalization of Salmonella by macrophages induces formation of nonreplicating persisters. Science 343, 204–208 (2014).

    CAS  Article  Google Scholar 

  7. 7

    Helaine, S. et al. Dynamics of intracellular bacterial replication at the single cell level. Proc. Natl Acad. Sci. USA 107, 3746–3751 (2010).

    CAS  Article  Google Scholar 

  8. 8

    Jenner, R. G. & Young, R. A. Insights into host responses against pathogens from transcriptional profiling. Nat. Rev. Microbiol. 3, 281–294 (2005).

    CAS  Article  Google Scholar 

  9. 9

    Saliba, A. E., Westermann, A. J., Gorski, S. A. & Vogel, J. Single-cell RNA-seq: advances and future challenges. Nucleic Acids Res. 42, 8845–8860 (2014).

    CAS  Article  Google Scholar 

  10. 10

    Shalek, A. K. et al. Single-cell RNA-seq reveals dynamic paracrine control of cellular variation. Nature 510, 363–369 (2014).

    CAS  Article  Google Scholar 

  11. 11

    Keestra-Gounder, A. M., Tsolis, R. M. & Bäumler, A. J. Now you see me, now you don't: the interaction of Salmonella with innate immune receptors. Nat. Rev. Microbiol. 13, 206–216 (2015).

    CAS  Article  Google Scholar 

  12. 12

    Eisele, N. A. et al. Salmonella require the fatty acid regulator PPARδ for the establishment of a metabolic environment essential for long-term persistence. Cell Host Microbe 14, 171–182 (2013).

    CAS  Article  Google Scholar 

  13. 13

    Kreibich, S. & Hardt, W. D. Experimental approaches to phenotypic diversity in infection. Curr. Opin. Microbiol. 27, 25–36 (2015).

    Article  Google Scholar 

  14. 14

    Picelli, S. et al. Smart-seq2 for sensitive full-length transcriptome profiling in single cells. Nat. Methods 10, 1096–1098 (2013).

    CAS  Article  Google Scholar 

  15. 15

    Eisenreich, W., Heesemann, J., Rudel, T. & Goebel, W. Metabolic host responses to infection by intracellular bacterial pathogens. Front. Cell Infect. Microbiol. 3, 24 (2013).

    Article  Google Scholar 

  16. 16

    Lawrence, T. & Natoli, G. Transcriptional regulation of macrophage polarization: enabling diversity with identity. Nat. Rev. Immunol. 11, 750–761 (2011).

    CAS  Article  Google Scholar 

  17. 17

    Kohyama, M. et al. Role for Spi-C in the development of red pulp macrophages and splenic iron homeostasis. Nature 457, 318–321 (2009).

    CAS  Article  Google Scholar 

  18. 18

    Nix, R. N., Altschuler, S. E., Henson, P. M. & Detweiler, C. S. Hemophagocytic macrophages harbor Salmonella enterica during persistent infection. PLoS Pathog. 3, e193 (2007).

    Article  Google Scholar 

  19. 19

    Guilliams, M., Bruhns, P., Saeys, Y., Hammad, H. & Lambrecht, B. N. The function of Fcγ receptors in dendritic cells and macrophages. Nat. Rev. Immunol. 14, 94–108 (2014).

    CAS  Article  Google Scholar 

  20. 20

    Martinez, F. O., Helming, L. & Gordon, S. Alternative activation of macrophages: an immunologic functional perspective. Annu. Rev. Immunol. 27, 451–483 (2009).

    CAS  Article  Google Scholar 

  21. 21

    Bronte, V. & Zanovello, P. Regulation of immune responses by l-arginine metabolism. Nat. Rev. Immunol. 5, 641–654 (2005).

    CAS  Article  Google Scholar 

  22. 22

    De Jong, H. K. et al. Expression and function of S100A8/A9 (calprotectin) in human typhoid fever and the murine Salmonella model. PLOS Negl. Trop. Dis. 9, e0003663 (2015).

    Article  Google Scholar 

  23. 23

    Ashkar, S. et al. Eta-1 (osteopontin): an early component of type-1 (cell-mediated) immunity. Science 287, 860–864 (2000).

    CAS  Article  Google Scholar 

  24. 24

    Benoit, M., Desnues, B. & Mege, J. L. Macrophage polarization in bacterial infections. J. Immunol. 181, 3733–3739 (2008).

    CAS  Article  Google Scholar 

  25. 25

    McCoy, M. W., Moreland, S. M. & Detweiler, C. S. Hemophagocytic macrophages in murine typhoid fever have an anti-inflammatory phenotype. Infect. Immun. 80, 3642–3649 (2012).

    CAS  Article  Google Scholar 

  26. 26

    Vazquez-Torres, A. et al. Salmonella pathogenicity island 2-dependent evasion of the phagocyte NADPH oxidase. Science 287, 1655–1658 (2000).

    CAS  Article  Google Scholar 

  27. 27

    Burton, N. A. et al. Disparate impact of oxidative host defenses determines the fate of Salmonella during systemic infection in mice. Cell Host Microbe 15, 72–83 (2014).

    CAS  Article  Google Scholar 

  28. 28

    Tahoun, A. et al. Salmonella transforms follicle-associated epithelial cells into M cells to promote intestinal invasion. Cell Host Microbe 12, 645–656 (2012).

    CAS  Article  Google Scholar 

  29. 29

    Masaki, T. et al. Reprogramming adult Schwann cells to stem cell-like cells by leprosy bacilli promotes dissemination of infection. Cell 152, 51–67 (2013).

    CAS  Article  Google Scholar 

  30. 30

    Westermann, A. J. et al. Dual RNA-seq unveils noncoding RNA functions in host–pathogen interactions. Nature 529, 496–501 (2016).

    CAS  Article  Google Scholar 

  31. 31

    Figueira, R., Watson, K. G., Holden, D. W. & Helaine, S. Identification of Salmonella pathogenicity Island-2 type III secretion system effectors involved in intramacrophage replication of S. enterica serovar typhimurium: implications for rational vaccine design. mBio 4, e00065-13 (2013).

    Article  Google Scholar 

  32. 32

    Picelli, S. et al. Full-length RNA-seq from single cells using smart-seq2. Nat. Protoc. 9, 171–181 (2014).

    CAS  Article  Google Scholar 

  33. 33

    Kapteyn, J., He, R., McDowell, E. T. & Gang, D. R. Incorporation of non-natural nucleotides into template-switching oligonucleotides reduces background and improves cDNA synthesis from very small RNA samples. BMC Genomics 11, 413 (2010).

    Article  Google Scholar 

  34. 34

    Patel, A. P. et al. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science 344, 1396–1401 (2014).

    CAS  Article  Google Scholar 

  35. 35

    Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal 17, 10–12 (2011).

    Article  Google Scholar 

  36. 36

    Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with bowtie 2. Nat. Methods 9, 357–359 (2012).

    CAS  Article  Google Scholar 

  37. 37

    Trapnell, C., Pachter, L. & Salzberg, S. L. TopHat: discovering splice junctions with RNA-seq. Bioinformatics 25, 1105–1111 (2009).

    CAS  Article  Google Scholar 

  38. 38

    Anders, S., Pyl, P. T. & Huber, W. HTSeq—a python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169 (2015).

    CAS  Article  Google Scholar 

  39. 39

    Trapnell, C. et al. Differential gene and transcript expression analysis of RNA-seq experiments with topHat and cufflinks. Nat. Protoc. 7, 562–578 (2012).

    CAS  Article  Google Scholar 

  40. 40

    Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

    Article  Google Scholar 

  41. 41

    Brennecke, P. et al. Accounting for technical noise in single-cell RNA-seq experiments. Nat. Methods 10, 1093–1095 (2013).

    CAS  Article  Google Scholar 

  42. 42

    Lê, S., Josse, J. & Husson, F. Factominer: an R package for multivariate analysis. J. Stat. Soft. 25, 1–18 (2008).

    Article  Google Scholar 

  43. 43

    Kharchenko, P. V., Silberstein, L. & Scadden, D. T. Bayesian approach to single-cell differential expression analysis. Nat. Methods 11, 740–742 (2014).

    CAS  Article  Google Scholar 

  44. 44

    Fan, J. et al. Characterizing transcriptional heterogeneity through pathway and gene set overdispersion analysis. Nat. Methods 13, 241–244 (2016).

    CAS  Article  Google Scholar 

  45. 45

    Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).

    CAS  Article  Google Scholar 

  46. 46

    Grün, D. et al. Single-cell messenger RNA sequencing reveals rare intestinal cell types. Nature 525, 251–255 (2015).

    Article  Google Scholar 

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The authors thank T. Achmedov, V. McParland, H. Merkert and B. Plaschke for technical support. A.-E.S. was supported by the PostDoc Plus program of the University of Würzburg. A.J.W. was the recipient of an Elite Advancement PhD stipend from Universität Bayern e.V. S.H. was supported by an MRC Career Development Award (MR/M009629/1). D.A.C.S. was the recipient of an EMBO postdoctoral fellowship (ALTF 441-2015).

Author information




A.-E.S., A.J.W. and J.V. designed and A.-E.S. performed the experiments. L.L. and S.A. performed bioinformatics analysis. S.H. and D.A.C.S. performed flow cytometry experiments. A.J.W., S.H. and L.N.S. provided reagents. A.-E.S., A.J.W., S.H. and J.V. wrote the manuscript.

Corresponding author

Correspondence to Jörg Vogel.

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The authors declare no competing financial interests.

Supplementary information

Supplementary information

Supplementary Figures 1–12, Supplementary References (PDF 1900 kb)

Supplementary Table 1

Sequencing results summary (XLSX 15 kb)

Supplementary Table 2

Cell clustering and differentially expressed genes between subpopulations (XLSX 1371 kb)

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Saliba, AE., Li, L., Westermann, A. et al. Single-cell RNA-seq ties macrophage polarization to growth rate of intracellular Salmonella. Nat Microbiol 2, 16206 (2017).

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