Letter | Published:

Lineage correlations of single cell division time as a probe of cell-cycle dynamics

Nature 519, 468–471 (26 March 2015) | Download Citation

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Abstract

Stochastic processes in cells are associated with fluctuations in mRNA1, protein production and degradation2,3, noisy partition of cellular components at division4, and other cell processes. Variability within a clonal population of cells originates from such stochastic processes, which may be amplified or reduced by deterministic factors5. Cell-to-cell variability, such as that seen in the heterogeneous response of bacteria to antibiotics, or of cancer cells to treatment, is understood as the inevitable consequence of stochasticity. Variability in cell-cycle duration was observed long ago; however, its sources are still unknown. A central question is whether the variance of the observed distribution originates from stochastic processes, or whether it arises mostly from a deterministic process that only appears to be random. A surprising feature of cell-cycle-duration inheritance is that it seems to be lost within one generation but to be still present in the next generation, generating poor correlation between mother and daughter cells but high correlation between cousin cells6. This observation suggests the existence of underlying deterministic factors that determine the main part of cell-to-cell variability. We developed an experimental system that precisely measures the cell-cycle duration of thousands of mammalian cells along several generations and a mathematical framework that allows discrimination between stochastic and deterministic processes in lineages of cells. We show that the inter- and intra-generation correlations reveal complex inheritance of the cell-cycle duration. Finally, we build a deterministic nonlinear toy model for cell-cycle inheritance that reproduces the main features of our data. Our approach constitutes a general method to identify deterministic variability in lineages of cells or organisms, which may help to predict and, eventually, reduce cell-to-cell heterogeneity in various systems, such as cancer cells under treatment.

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Acknowledgements

We thank H. Miyoshi at the Riken Tsukuba for the Fucci markers, N. Barkai, N. Shoresh, J. Theiler, S. Kadener, L. Glass, G. Asher, A. W. Murray and J. Paulsson for discussions, and M. Gorfine and R. Heller for advice on statistical analysis. We thank Q. Yang and A. van Oudenaarden for the Cyanobacteria data sets, and the authors of ref. 24 for making their published data available online. This work was supported by the ISF (grants no. 592/10 (N.Q.B.); no. 567/10(I.S.); and no. 9/09, 302/14 (O.A.)) and the ERC Starting Grant no. 281306 (I.S.), no. 260871 (N.Q.B.), the Chief Scientist Office of the Israel Ministry of Health and the Weinkselbaum family medical research fund (I.S.). I.S. thanks the USAID’s ASHA Program for the upgrading of the FACS laboratory. S.P.M. is supported by the Clore Foundation.

Author information

    • Oded Sandler
    •  & Sivan Pearl Mizrahi

    These authors contributed equally to this work.

    • Itamar Simon
    •  & Nathalie Q. Balaban

    These authors jointly supervised this work.

Affiliations

  1. Department of Microbiology and Molecular Genetics, IMRIC, The Hebrew University Hadassah Medical School, Jerusalem 91120, Israel

    • Oded Sandler
    • , Sivan Pearl Mizrahi
    •  & Itamar Simon

  2. Racah Institute of Physics, Edmond J. Safra Campus, The Hebrew University, Jerusalem 91904, Israel

    • Sivan Pearl Mizrahi
    • , Noga Weiss
    • , Oded Agam
    •  & Nathalie Q. Balaban

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Contributions

O.S. constructed the Fucci cell lines; S.P.M. performed the time-lapse experiments; O.S. and S.P.M. wrote the image analysis codes; N.Q.B., I.S., S.P.M. and O.S. designed the experiments; N.Q.B., O.A. and S.P.M. developed the model and analysis; N.W. analysed the Cyanobacteria data sets; N.Q.B. and S.P.M. wrote the manuscript

Competing interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to Oded Agam or Itamar Simon or Nathalie Q. Balaban.

Extended data

Extended data figures

  1. 1.

    Mean cell-cycle duration during long-term time-lapse microscopy.

  2. 2.

    Lineage correlations in different strains and clones.

  3. 3.

    No spatial effect detected on the cell-cycle duration.

  4. 4.

    Correlation dimension.

  5. 5.

    Correlation-dimension analysis.

  6. 6.

    Inter-generation correlation.

  7. 7.

    Total cell-cycle duration (Ttot) measurements and circadian phase at birth in Cyanobacteria.

  8. 8.

    Simulation results of the kicked cell cycle model of G1 and G2 durations fit the experimental observations.

Extended data tables

  1. 1.

    Parameters used for the simulations presented in Fig. 3 (equations 1–4)

  2. 2.

    A literature compilation of correlation coefficients for mother–daughter cells and sister cells

Supplementary information

PDF files

  1. 1.

    Supplementary Information

    This file contains Supplementary Text and Data, which relates to the Grassberger-Procaccia algorithm and theoretical model.

Videos

  1. 1.

    Time-lapse imaging of dividing L1210 Fucci cells.

    The video shows time lapse microscopy of dividing L1210 cells and their expression of the Fucci markers (clone 2). Cells were grown in PDMS chambers under the microscope and imaged with x20 magnification. Here a composite of phase-contrast, red fluorescence and green fluorescence is shown with time interval of 23 min. Phase contrast images were acquired at a faster rate.

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DOI

https://doi.org/10.1038/nature14318

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