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Measurement of single-cell dynamics

Author

Listed:
  • David G. Spiller

    (Centre for Cell Imaging, School of Biological Sciences)

  • Christopher D. Wood

    (Instituto de Biotecnología, Universidad Nacional Autónoma de México)

  • David A. Rand

    (Warwick Systems Biology and Mathematics Institute, Coventry House, University of Warwick)

  • Michael R. H. White

    (Centre for Cell Imaging, School of Biological Sciences)

Abstract

Populations of cells are almost always heterogeneous in function and fate. To understand the plasticity of cells, it is vital to measure quantitatively and dynamically the molecular processes that underlie cell-fate decisions in single cells. Early events in cell signalling often occur within seconds of the stimulus, whereas intracellular signalling processes and transcriptional changes can take minutes or hours. By contrast, cell-fate decisions, such as whether a cell divides, differentiates or dies, can take many hours or days. Multiparameter experimental and computational methods that integrate quantitative measurement and mathematical simulation of these noisy and complex processes are required to understand the highly dynamic mechanisms that control cell plasticity and fate.

Suggested Citation

  • David G. Spiller & Christopher D. Wood & David A. Rand & Michael R. H. White, 2010. "Measurement of single-cell dynamics," Nature, Nature, vol. 465(7299), pages 736-745, June.
    • Handle: RePEc:nat:nature:v:465:y:2010:i:7299:d:10.1038_nature09232
    DOI: 10.1038/nature09232
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    Cited by:

    1. Juliane Liepe & Sarah Filippi & Michał Komorowski & Michael P H Stumpf, 2013. "Maximizing the Information Content of Experiments in Systems Biology," PLOS Computational Biology, Public Library of Science, vol. 9(1), pages 1-13, January.
    2. Artémis Llamosi & Andres M Gonzalez-Vargas & Cristian Versari & Eugenio Cinquemani & Giancarlo Ferrari-Trecate & Pascal Hersen & Gregory Batt, 2016. "What Population Reveals about Individual Cell Identity: Single-Cell Parameter Estimation of Models of Gene Expression in Yeast," PLOS Computational Biology, Public Library of Science, vol. 12(2), pages 1-18, February.

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