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

Ruan, Xiongtao, Carnegie Mellon University
Johnson, Gregory R., Carnegie Mellon University
Bierschenk, Iris, Centar za Promociju Nauke
Nitschke, Roland, Centar za Promociju Nauke
Boerries, Melanie, Institut de Pharmacologie Moléculaire et Cellulaire
Busch, Hauke, University of Lübeck, https://orcid.org/0000-0003-4763-4521
Murphy, Robert F., Carnegie Mellon University
ruan.xiongtao@gmail.com, gregjohnso@gmail.com, iris.bierschenk@biologie.uni-freiburg.de, roland.nitschke@biologie.uni-freiburg.de, melanie.boerries@uniklinik-freiburg.de, hauke.busch@uni-luebeck.de, murphy@cmu.edu
Published Jan 13, 2020 on Dryad. https://doi.org/10.5061/dryad.hc8037v

Cite this dataset

Ruan, Xiongtao et al. (2020). Image-derived models of cell organization changes during differentiation and drug treatments [Dataset]. Dryad. https://doi.org/10.5061/dryad.hc8037v

Abstract

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. [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text]

Usage notes

PC12_images_exp48A

PC12_images_exp48B

PC12_images_exp96

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