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Mol Biol Cell. 2015 Nov 5;26(22):4046-56. doi: 10.1091/mbc.E15-06-0370. Epub 2015 Sep 9.

Joint modeling of cell and nuclear shape variation.

Author information

1
Computational Biology Department and Center for Bioimage Informatics, Carnegie Mellon University, Pittsburgh, PA 15213.
2
Computational Biology Department and Center for Bioimage Informatics, Carnegie Mellon University, Pittsburgh, PA 15213 Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213.
3
Computational Biology Department and Center for Bioimage Informatics, Carnegie Mellon University, Pittsburgh, PA 15213 Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213 Departments of Biological Sciences and Machine Learning, Carnegie Mellon University, Pittsburgh, PA 15213 Freiburg Institute for Advanced Studies and Faculty of Biology, Albert Ludwig University of Freiburg, 79104 Freiburg im Breisgau, Germany murphy@cmu.edu.

Abstract

Modeling cell shape variation is critical to our understanding of cell biology. Previous work has demonstrated the utility of nonrigid image registration methods for the construction of nonparametric nuclear shape models in which pairwise deformation distances are measured between all shapes and are embedded into a low-dimensional shape space. Using these methods, we explore the relationship between cell shape and nuclear shape. We find that these are frequently dependent on each other and use this as the motivation for the development of combined cell and nuclear shape space models, extending nonparametric cell representations to multiple-component three-dimensional cellular shapes and identifying modes of joint shape variation. We learn a first-order dynamics model to predict cell and nuclear shapes, given shapes at a previous time point. We use this to determine the effects of endogenous protein tags or drugs on the shape dynamics of cell lines and show that tagged C1QBP reduces the correlation between cell and nuclear shape. To reduce the computational cost of learning these models, we demonstrate the ability to reconstruct shape spaces using a fraction of computed pairwise distances. The open-source tools provide a powerful basis for future studies of the molecular basis of cell organization.

PMID:
26354424
PMCID:
PMC4710235
DOI:
10.1091/mbc.E15-06-0370
[Indexed for MEDLINE]
Free PMC Article

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