Image-derived models of cell organization changes during differentiation and drug treatments

PC12 cells are a popular model system to study changes driving and accompanying neuronal differentiation. While attention has been paid to changes in transcriptional regulation and protein signaling, much less is known about the changes in organization that accompany PC12 differentiation. Fluorescen...

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Bibliographic Details
Published in:Molecular biology of the cell 2020-03, Vol.31 (7), p.655-666
Main Authors: Ruan, Xiongtao, Johnson, Gregory R, Bierschenk, Iris, Nitschke, Roland, Boerries, Melanie, Busch, Hauke, Murphy, Robert F
Format: Article
Language:English
Subjects:
Animals
Cell Differentiation
Cell Nucleus Shape
Cell Shape
Cell Size
Imaging, Three-Dimensional
Induced Pluripotent Stem Cells - cytology
Induced Pluripotent Stem Cells - drug effects
Kinetics
Mitochondria - metabolism
Models, Biological
Nerve Growth Factor - pharmacology
PC12 Cells
Pharmaceutical Preparations - metabolism
Probability
Rats
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Summary:PC12 cells are a popular model system to study changes driving and accompanying neuronal differentiation. While attention has been paid to changes in transcriptional regulation and protein signaling, much less is known about the changes in organization that accompany PC12 differentiation. Fluorescence microscopy can provide extensive information about these changes, although it is difficult to continuously observe changes over many days of differentiation. We describe a generative model of differentiation-associated changes in cell and nuclear shape and their relationship to mitochondrial distribution constructed from images of different cells at discrete time points. We show that the model accurately represents complex cell and nuclear shapes and learn a regression model that relates cell and nuclear shape to mitochondrial distribution; the predictive accuracy of the model increases during differentiation. Most importantly, we propose a method, based on cell matching and interpolation, to produce realistic simulations of the dynamics of cell differentiation from only static images. We also found that the distribution of cell shapes is hollow: most shapes are very different from the average shape. Finally, we show how the method can be used to model nuclear shape changes of human-induced pluripotent stem cells resulting from drug treatments.
ISSN:1059-1524
1939-4586
DOI:10.1091/mbc.E19-02-0080