Population context determines cell-to-cell variability in endocytosis and virus infection (original) (raw)

Nature volume 461, pages 520–523 (2009)Cite this article

Abstract

Single-cell heterogeneity in cell populations arises from a combination of intrinsic and extrinsic factors1,2,3. This heterogeneity has been measured for gene transcription, phosphorylation, cell morphology and drug perturbations, and used to explain various aspects of cellular physiology4,5,6. In all cases, however, the causes of heterogeneity were not studied. Here we analyse, for the first time, the heterogeneous patterns of related cellular activities, namely virus infection, endocytosis and membrane lipid composition in adherent human cells. We reveal correlations with specific cellular states that are defined by the population context of a cell, and we derive probabilistic models that can explain and predict most cellular heterogeneity of these activities, solely on the basis of each cell’s population context. We find that accounting for population-determined heterogeneity is essential for interpreting differences between the activity levels of cell populations. Finally, we reveal that synergy between two molecular components, focal adhesion kinase and the sphingolipid GM1, enhances the population-determined pattern of simian virus 40 (SV40) infection. Our findings provide an explanation for the origin of heterogeneity patterns of cellular activities in adherent cell populations.

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

Access options

Subscribe to this journal

Receive 51 print issues and online access

$199.00 per year

only $3.90 per issue

Buy this article

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

Additional access options:

Similar content being viewed by others

References

  1. Elowitz, M. B., Levine, A. J., Siggia, E. D. & Swain, P. S. Stochastic gene expression in a single cell. Science 297, 1183–1186 (2002)
    Article ADS CAS Google Scholar
  2. Maheshri, N. & O’Shea, E. K. Living with noisy genes: how cells function reliably with inherent variability in gene expression. Annu. Rev. Biophys. Biomol. Struct. 36, 413–434 (2007)
    Article CAS Google Scholar
  3. Raj, A. & van Oudenaarden, A. Nature, nurture, or chance: stochastic gene expression and its consequences. Cell 135, 216–226 (2008)
    Article CAS Google Scholar
  4. Sigal, A. et al. Variability and memory of protein levels in human cells. Nature 444, 643–646 (2006)
    Article ADS CAS Google Scholar
  5. Sachs, K., Perez, O., Pe’er, D., Lauffenburger, D. A. & Nolan, G. P. Causal protein-signaling networks derived from multiparameter single-cell data. Science 308, 523–529 (2005)
    Article ADS CAS Google Scholar
  6. Keren, K. et al. Mechanism of shape determination in motile cells. Nature 453, 475–480 (2008)
    Article ADS CAS Google Scholar
  7. Liberali, P., Ramo, P. & Pelkmans, L. Protein kinases: starting a molecular systems view of endocytosis. Annu. Rev. Cell Dev. Biol. 24, 501–523 (2008)
    Article CAS Google Scholar
  8. Pelkmans, L. et al. Genome-wide analysis of human kinases in clathrin- and caveolae/raft-mediated endocytosis. Nature 436, 78–86 (2005)
    Article ADS CAS Google Scholar
  9. Wilkinson, D. J. Stochastic modelling for quantitative description of heterogeneous biological systems. Nature Rev. Genet. 10, 122–133 (2009)
    Article CAS Google Scholar
  10. Eifart, P. et al. Role of endocytosis and low pH in murine hepatitis virus strain A59 cell entry. J. Virol. 81, 10758–10768 (2007)
    Article CAS Google Scholar
  11. Neu, U., Woellner, K., Gauglitz, G. & Stehle, T. Structural basis of GM1 ganglioside recognition by simian virus 40. Proc. Natl Acad. Sci. USA 105, 5219–5224 (2008)
    Article ADS CAS Google Scholar
  12. Pelkmans, L. Secrets of caveolae- and lipid raft-mediated endocytosis revealed by mammalian viruses. Biochim. Biophys. Acta 1746, 295–304 (2005)
    Article CAS Google Scholar
  13. Conner, S. D. & Schmid, S. L. Regulated portals of entry into the cell. Nature 422, 37–44 (2003)
    Article ADS CAS Google Scholar
  14. Mayor, S. & Pagano, R. E. Pathways of clathrin-independent endocytosis. Nature Rev. Mol. Cell Biol. 8, 603–612 (2007)
    Article CAS Google Scholar
  15. Holmgren, J., Lonnroth, I. & Svennerholm, L. Tissue receptor for cholera exotoxin: postulated structure from studies with GM1 ganglioside and related glycolipids. Infect. Immun. 8, 208–214 (1973)
    CAS PubMed PubMed Central Google Scholar
  16. Sacher, R., Stergiou, L. & Pelkmans, L. Lessons from genetics: interpreting complex phenotypes in RNAi screens. Curr. Opin. Cell Biol. 20, 483–489 (2008)
    Article CAS Google Scholar
  17. Eagle, H. & Levine, E. M. Growth regulatory effects of cellular interaction. Nature 213, 1102–1106 (1967)
    Article ADS CAS Google Scholar
  18. Castor, L. N. Flattening, movement and control of division of epithelial-like cells. J. Cell. Physiol. 75, 57–64 (1970)
    Article CAS Google Scholar
  19. Slack, M. D., Martinez, E. D., Wu, L. F. & Altschuler, S. J. Characterizing heterogeneous cellular responses to perturbations. Proc. Natl Acad. Sci. USA 105, 19306–19311 (2008)
    Article ADS CAS Google Scholar
  20. Nachman, I., Regev, A. & Ramanathan, S. Dissecting timing variability in yeast meiosis. Cell 131, 544–556 (2007)
    Article CAS Google Scholar
  21. St-Pierre, F. & Endy, D. Determination of cell fate selection during phage lambda infection. Proc. Natl Acad. Sci. USA 105, 20705–20710 (2008)
    Article ADS CAS Google Scholar
  22. Ben-Jacob, E., Cohen, I. & Gutnick, D. L. Cooperative organization of bacterial colonies: from genotype to morphotype. Annu. Rev. Microbiol. 52, 779–806 (1998)
    Article CAS Google Scholar
  23. Lopez, S. & Arias, C. F. Multistep entry of rotavirus into cells: a Versaillesque dance. Trends Microbiol. 12, 271–278 (2004)
    Article CAS Google Scholar
  24. Geiger, B., Spatz, J. P. & Bershadsky, A. D. Environmental sensing through focal adhesions. Nature Rev. Mol. Cell Biol. 10, 21–33 (2009)
    Article CAS Google Scholar
  25. Neumann, A. K., Thompson, N. L. & Jacobson, K. Distribution and lateral mobility of DC-SIGN on immature dendritic cells–implications for pathogen uptake. J. Cell Sci. 121, 634–643 (2008)
    Article CAS Google Scholar
  26. Iwabuchi, K. et al. Reconstitution of membranes simulating “glycosignaling domain” and their susceptibility to Lyso-GM3. J. Biol. Chem. 275, 15174–15181 (2000)
    Article CAS Google Scholar
  27. Palazzo, A. F., Eng, C. H., Schlaepfer, D. D., Marcantonio, E. E. & Gundersen, G. G. Localized stabilization of microtubules by integrin- and FAK-facilitated Rho signaling. Science 303, 836–839 (2004)
    Article ADS CAS Google Scholar
  28. Newman, J. R. & Weissman, J. S. Systems biology: many things from one. Nature 444, 561–562 (2006)
    Article ADS CAS Google Scholar
  29. Carpenter, A. E. et al. CellProfiler: image analysis software for identifying and quantifying cell phenotypes. Genome Biol. 7, R100 (2006)
    Article Google Scholar

Download references

Acknowledgements

We acknowledge H. Verheije and L. Burleigh for providing images of MHV and dengue virus infection, G. Jurisic for providing primary cells and help with experiments, and all members of the laboratory for comments on the manuscript. P.R. is supported by the European Molecular Biology Organisation and the Human Frontiers Science Program, E.-M.D. by Oncosuisse and P.L. by the Federation of European Biochemical Societies. L.P. is supported by the ETH Zürich, SystemsX.ch, the Swiss National Science Foundation and the European Union.

Author Contributions L.P. supervised and conceived the project. R.S., B.S., E.-M.D. and P.L. performed experiments, B.S. and P.R. developed computational image analysis methods, B.S. performed all computational image analysis, B.S. and P.R. conceived the statistical analysis methods, B.S. performed all statistical analysis, L.P. and B.S. wrote the manuscript.

Author information

Authors and Affiliations

  1. Institute of Molecular Systems Biology, ETH Zurich (Swiss Federal Institute of Technology), Wolfgang Pauli-Strasse 16, CH-8093 Zurich, Switzerland ,
    Berend Snijder, Raphael Sacher, Pauli Rämö, Eva-Maria Damm, Prisca Liberali & Lucas Pelkmans
  2. Zurich PhD Program in Molecular Life Sciences, Zurich, Switzerland
    Berend Snijder & Raphael Sacher

Authors

  1. Berend Snijder
    You can also search for this author inPubMed Google Scholar
  2. Raphael Sacher
    You can also search for this author inPubMed Google Scholar
  3. Pauli Rämö
    You can also search for this author inPubMed Google Scholar
  4. Eva-Maria Damm
    You can also search for this author inPubMed Google Scholar
  5. Prisca Liberali
    You can also search for this author inPubMed Google Scholar
  6. Lucas Pelkmans
    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence toLucas Pelkmans.

Supplementary information

Supplementary Information

This file contains Supplementary Figures 1-10 with Legends, Supplementary Movie 1 Legend, Supplementary Methods, Supplementary Data, Supplementary Table 1 and Supplementary References. (PDF 6784 kb)

Supplementary Movie 1

This movie file shows that population properties are determined during growth of adherent human cells - see file s1 for full Legend. (MOV 9511 kb)

PowerPoint slides

Rights and permissions

About this article

Cite this article

Snijder, B., Sacher, R., Rämö, P. et al. Population context determines cell-to-cell variability in endocytosis and virus infection.Nature 461, 520–523 (2009). https://doi.org/10.1038/nature08282

Download citation

This article is cited by

Editorial Summary

Cells with a difference

Susceptibility to drug treatment or viral infection can vary from one cell to another even in a population of genetically identical cells cultured together. Such heterogeneity has largely been attributed to intrinsic noise such as variability in gene expression or fluctuations in levels of signalling molecules. Now Snijder et al. have looked quantitatively at large populations of co-cultured cells and they find deterministic links between fundamental cellular features (for example, membrane lipid composition or infectivity by some but not other viruses) and a cell's population context (whether localized at the centre or at the periphery of an island of adhering cells, for instance). The computer-assisted methods used to assess cell populations in this work may also find application in drug screens.