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Updated: Apr 18, 2026
Live-cell Imaging of Migrating Cells Expressing Fluorescently-tagged Proteins in a Three-dimensional Matrix
Published on: December 22, 2011
Factor graph analysis of live cell-imaging data reveals mechanisms of cell fate decisions
Theresa Niederberger1, Henrik Failmezger2, Diana Uskat1
1Gene Center, Department of Chemistry and Biochemistry, Ludwig-Maximilians-University München, Germany, Max-Planck-Institute for Plant Breeding Research, Cologne, Germany Department of Biology, Albertus-Magnus University, Cologne, Germany, Institute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, TU Dresden, Germany, Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany and Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.
Motivation:
Cell fate decisions have a strong stochastic component. The identification of the underlying mechanisms therefore requires a rigorous statistical analysis of large ensembles of single cells that were tracked and phenotyped over time.
Results:
We introduce a probabilistic framework for testing elementary hypotheses on dynamic cell behavior using time-lapse cell-imaging data. Factor graphs, probabilistic graphical models, are used to properly account for cell lineage and cell phenotype information. Our model is applied to time-lapse movies of murine granulocyte-macrophage progenitor (GMP) cells. It decides between competing hypotheses on the mechanisms of their differentiation. Our results theoretically substantiate previous experimental observations that lineage instruction, not selection is the cause for the differentiation of GMP cells into mature monocytes or neutrophil granulocytes.
Availability And Implementation:
The Matlab source code is available at http://treschgroup.de/Genealogies.html.
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