1. Computational and Systems Biology
Download icon

Hidden long-range memories of growth and cycle speed correlate cell cycles in lineage trees

  1. Erika E Kuchen
  2. Nils B Becker
  3. Nina Claudino
  4. Thomas Höfer  Is a corresponding author
  1. German Cancer Research Center (DKFZ), Germany
Research Article
  • Cited 0
  • Views 558
  • Annotations
Cite this article as: eLife 2020;9:e51002 doi: 10.7554/eLife.51002

Abstract

Cell heterogeneity may be caused by stochastic or deterministic effects. The inheritance of regulators through cell division is a key deterministic force, but identifying inheritance effects in a systematic manner has been challenging. Here we measure and analyze cell cycles in deep lineage trees of human cancer cells and mouse embryonic stem cells and develop a statistical framework to infer underlying rules of inheritance. The observed long-range intra-generational correlations in cell-cycle duration, up to second cousins, seem paradoxical because ancestral correlations decay rapidly. However, this correlation pattern is naturally explained by the inheritance of both cell size and cell-cycle speed over several generations, provided that cell growth and division are coupled through a minimum-size checkpoint. This model correctly predicts the effects of inhibiting cell growth or cycle progression. In sum, we show how fluctuations of cell cycles across lineage trees help understand the coordination of cell growth and division.

Article and author information

Author details

  1. Erika E Kuchen

    Division of Theoretical Systems Biology, German Cancer Research Center (DKFZ), Heidelberg, Germany
    Competing interests
    The authors declare that no competing interests exist.
  2. Nils B Becker

    Division of Theoretical Systems Biology, German Cancer Research Center (DKFZ), Heidelberg, Germany
    Competing interests
    The authors declare that no competing interests exist.
  3. Nina Claudino

    Division of Theoretical Systems Biology, German Cancer Research Center (DKFZ), Heidelberg, Germany
    Competing interests
    The authors declare that no competing interests exist.
  4. Thomas Höfer

    Division of Theoretical Systems Biology, German Cancer Research Center (DKFZ), Heidelberg, Germany
    For correspondence
    t.hoefer@dkfz.de
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3560-8780

Funding

Bundesministerium für Bildung und Forschung (0316076A)

  • Thomas Höfer

Bundesministerium für Bildung und Forschung (01ZX1307)

  • Thomas Höfer

Bundesministerium für Bildung und Forschung (031L0087A)

  • Thomas Höfer

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. Naama Barkai, Weizmann Institute of Science, Israel

Publication history

  1. Received: August 10, 2019
  2. Accepted: January 22, 2020
  3. Accepted Manuscript published: January 23, 2020 (version 1)
  4. Version of Record published: February 13, 2020 (version 2)

Copyright

© 2020, Kuchen et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 558
    Page views
  • 107
    Downloads
  • 0
    Citations

Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Download citations (links to download the citations from this article in formats compatible with various reference manager tools)

Open citations (links to open the citations from this article in various online reference manager services)

Further reading

    1. Cell Biology
    2. Computational and Systems Biology
    Christopher Edelmaier et al.
    Research Article
    1. Computational and Systems Biology
    Dana M King et al.
    Research Article