Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

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

Nature Microbiology volume 2, Article number: 16206 (2017) Cite this article

Subjects

This article has been updated

Abstract

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.

Bacterial infections constitute some of the most complex inter-organismic interactions, and it is increasingly recognized that both the pathogen and the invaded host cell can exhibit a large phenotypic heterogeneity during the infection process1,2. Examples of phenotypic diversity include variations in bacterial lipopolysaccharide (LPS) composition or intracellular growth status and in host defence mechanisms. This heterogeneity can be a major determinant of disease outcome and the success of antimicrobial treatment1,3. In vivo studies have shown that bacterial pathogens can exist in different subpopulations, from highly proliferative to non-replicative individuals, a subset of which can ‘persist’ up to years in the host4–6. Heterogeneity in pathogen behaviour is also observed in ex vivo infection of monolayers of seemingly homogeneous cultured mammalian cells7. Whether and how individual host cells respond to the diverse states of intracellular pathogens is largely unknown.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Learn more

Prices may be subject to local taxes which are calculated during checkout

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.

References

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  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).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  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).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  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).

    Article  CAS  Google Scholar 

  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).

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  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).

    Article  CAS  Google Scholar 

  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. 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).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  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).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  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).

    Article  CAS  Google Scholar 

  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).

    Article  CAS  Google Scholar 

  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).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  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).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  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).

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

Download references

Acknowledgements

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

Authors and Affiliations

  1. RNA Biology Group, Institute for Molecular Infection Biology, University of Würzburg, Josef-Schneider-Straße 2, D-97080 Würzburg, Germany

    Antoine-Emmanuel Saliba, Alexander J. Westermann, Leon N. Schulte & Jörg Vogel

  2. Core Unit SysMed, University of Würzburg, Josef-Schneider-Straße 2, D-97080 Würzburg, Germany

    Antoine-Emmanuel Saliba, Lei Li & Silke Appenzeller

  3. Section of Microbiology, Medical Research Council (MRC) Centre for Molecular Bacteriology and Infection, Imperial College London, Armstrong Road, London, SW7 2AZ, UK

    Daphne A. C. Stapels & Sophie Helaine

Authors
  1. Antoine-Emmanuel Saliba
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lei Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Alexander J. Westermann
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Silke Appenzeller
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Daphne A. C. Stapels
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Leon N. Schulte
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Sophie Helaine
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Jörg Vogel
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

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.

Ethics declarations

Competing interests

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)

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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). https://doi.org/10.1038/nmicrobiol.2016.206

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1038/nmicrobiol.2016.206

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing