Factor graph analysis of live cell-imaging data reveals mechanisms of cell fate decisions

Theresa Niederberger; Henrik Failmezger; Diana Uskat; Don Poron; Ingmar Glauche; Nico Scherf; Ingo Roeder; Timm Schroeder; Achim Tresch

doi:10.1093/bioinformatics/btv040

Factor graph analysis of live cell-imaging data reveals mechanisms of cell fate decisions

Bioinformatics. 2015 Jun 1;31(11):1816-23. doi: 10.1093/bioinformatics/btv040. Epub 2015 Jan 31.

Authors

Theresa Niederberger¹, Henrik Failmezger², Diana Uskat¹, Don Poron¹, Ingmar Glauche¹, Nico Scherf², Ingo Roeder¹, Timm Schroeder¹, Achim Tresch³

Affiliations

¹ Gene 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.
² Gene 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 Gene 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.
³ Gene 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 Gene 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 Gene 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.

PMID: 25638814
DOI: 10.1093/bioinformatics/btv040

Abstract

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.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Algorithms
Animals
Cell Differentiation*
Cell Lineage
Granulocyte-Macrophage Progenitor Cells / cytology
Mice
Models, Statistical*
Monocytes / cytology
Neutrophils / cytology
Single-Cell Analysis
Time-Lapse Imaging*