Automatic analysis of dividing cells in live cell movies to detect mitotic delays and correlate phenotypes in time

  1. Nathalie Harder1,3,
  2. Felipe Mora-Bermúdez2,3,4,
  3. William J. Godinez1,
  4. Annelie Wünsche2,
  5. Roland Eils1,5,
  6. Jan Ellenberg2 and
  7. Karl Rohr1
  1. 1 University of Heidelberg, IPMB, BIOQUANT, and DKFZ Heidelberg, Department of Bioinformatics and Functional Genomics, Biomedical Computer Vision Group, D-69120 Heidelberg, Germany;
  2. 2 European Molecular Biology Laboratory (EMBL), Gene Expression and Cell Biology/Biophysics Programmes, D-69117 Heidelberg, Germany

    • ↵4 Present address: Max Planck Institute of Molecular Cell Biology and Genetics, (MPI-CBG), Pfotenhauerstrasse 108, D-01307 Dresden, Germany.

    1. ↵3 These authors contributed equally to this work.

    Abstract

    Live-cell imaging allows detailed dynamic cellular phenotyping for cell biology and, in combination with small molecule or drug libraries, for high-content screening. Fully automated analysis of live cell movies has been hampered by the lack of computational approaches that allow tracking and recognition of individual cell fates over time in a precise manner. Here, we present a fully automated approach to analyze time-lapse movies of dividing cells. Our method dynamically categorizes cells into seven phases of the cell cycle and five aberrant morphological phenotypes over time. It reliably tracks cells and their progeny and can thus measure the length of mitotic phases and detect cause and effect if mitosis goes awry. We applied our computational scheme to annotate mitotic phenotypes induced by RNAi gene knockdown of CKAP5 (also known as ch-TOG) or by treatment with the drug nocodazole. Our approach can be readily applied to comparable assays aiming at uncovering the dynamic cause of cell division phenotypes.

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    1. Published in Advance October 1, 2009, doi: 10.1101/gr.092494.109 Genome Res. 19: 2113-2124 Copyright © 2009 by Cold Spring Harbor Laboratory Press

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